def _get_photometry(self):
""" Get the photometry for the target.
If the target is a standard star, aperture photometry will be performed. For the moment nothing is done with
the others, but in due time (TODO) photometry.py will be included here. """
basename = "standards"
fd, coords_file = tempfile.mkstemp(prefix=basename, suffix=".coords")
os.write(fd, "{0} {1} \n".format(self.RA, self.DEC))
os.close(fd)
if self.objtype == "standard":
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
seeing = self.header.hdr[self.header.seeingk]
photfile_name = self.header.im_name + ".mag.1"
utilities.if_exists_remove(photfile_name)
kwargs = dict(output=photfile_name, coords=coords_file,
wcsin='world', fwhm=seeing, gain=self.header.gaink, exposure=self.header.exptimek,
airmass=self.header.airmassk, annulus=6*seeing, dannulus=3*seeing,
apert=2*seeing, verbose="no", verify="no", interac="no")
iraf.phot(self.header.im_name, **kwargs)
[counts] = iraf.module.txdump(photfile_name, 'FLUX', 'yes', Stdout=subprocess.PIPE)
utilities.if_exists_remove(coords_file)
return float(counts)
def align_combine(fitsdir, myfilter, examine=True):
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
os.chdir(fitsdir)
listfiles = glob.glob(myfilter)
listfiles.sort()
if (examine):
print "Opening ",listfiles[0]," to examine."
iraf.imexamine(input=listfiles[0], \
logfile="coords.dat", \
keeplog="yes")
with open("align.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Aligning with reference:",listfiles[0]
iraf.imalign( input = "@align.list", referenc= listfiles[0], coords = "coords.dat", output = "*****@*****.**")
listfiles = glob.glob("a_"+myfilter)
listfiles.sort()
with open("comb.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Combining"
iraf.imcombine(input = "@comb.list",\
output = "out.fits",\
combine= "median")
def getSkyMeanSDinAnnulus(ann,delta=5):
iraf.noao()
iraf.digiphot()
iraf.apphot()
iraf.photpars.setParam('apertures','1')
iraf.phot.setParam('interactive','no')
iraf.phot.setParam('image',fitsDir+fitsFile)
iraf.phot.setParam('coords',fitsDir+fitsFile+".coo")
outmag=".maglim"
try:
os.remove(fitsDir+fitsFile+outmag)
except:
print "File does not exist BEFORE running phot, so no need to delete."
iraf.phot.setParam('output',fitsDir+fitsFile+outmag)
iraf.phot.setParam('interac','no')
iraf.fitskypars.setParam('annulus',str(ann))
iraf.fitskypars.setParam('dannulus',str(delta))
## NO SIGMA CLIPPING! JUST TO BE SAFE: (6/2013)
iraf.fitskypars.setParam('sloclip',"0")
iraf.fitskypars.setParam('shiclip',"0")
iraf.phot(fitsDir+fitsFile,Stdin=cr)
aa, nn, xx, ss = ao.readPhotMagFile(fitsDir,fitsFile,outmag)
try:
os.remove(fitsDir+fitsFile+outmag)
except:
print "File not found to delete AFTER running phot; that's odd."
return xx
def daophot(inp, fwhm):
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.datapars.fwhmpsf = float(fwhm)
iraf.datapars.datamin = 10
iraf.datapars.datamax = 60000
iraf.datapars.sigma = 50
iraf.datapars.readnoise = 2.3
iraf.datapars.epadu = 0.8
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 6
iraf.fitskypars.annulus = 20
iraf.fitskypars.dannulus = 10
app = str(fwhm) + ":" + str(4 * float(fwhm)) + ":" + "1"
iraf.photpars.apertures = app
iraf.photpars.zmag = 25
iraf.phot(image=inp,
output='default',
verify='No',
verbose='No',
coords='default')
def align_combine(fitsdir, myfilter, examine=True):
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
os.chdir(fitsdir)
listfiles = glob.glob(myfilter)
listfiles.sort()
if (examine):
print "Opening ",listfiles[0]," to examine."
iraf.imexamine(input=listfiles[0], \
logfile="coords.dat", \
keeplog="yes")
with open("align.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Aligning with reference:",listfiles[0]
iraf.imalign( input = "@align.list", referenc= listfiles[0], coords = "coords.dat", output = "*****@*****.**")
listfiles = glob.glob("a_"+myfilter)
listfiles.sort()
with open("comb.list",'w') as f:
for i in listfiles:
f.write(i+"\n")
print "Combining"
iraf.imcombine(input = "@comb.list",\
output = "out.fits",\
combine= "median")
def doiraf(ximage, yimage, xmax, ymax):
(xpos, ypos) = getpositions(ximage, yimage, xmax, ymax)
coords = open("noisecoords.dat", 'w')
for i in range(len(xpos)):
coords.write("%8.1f %8.1f \n" % (xpos[i], ypos[i]))
coords.close()
sky = open("sky", 'w')
for i in range(npoints):
sky.write("0.0 \n")
sky.close()
aps = open("apertures", 'w')
aps.write("1,1.5,2,2.6,3,3.5,4,4.5,5,5.5")
aps.close()
#runiraf()
iraf.digiphot()
iraf.daophot()
##iraf.apphot()
image = 'mosaic_minus_median_extract.fits'
#print image
os.system("rm noise.dat")
iraf.digiphot.daophot.phot(image,
coords="noisecoords.dat",
output="noise.dat",
calgorithm='none',
skyfile="sky",
salgori="file",
aperture="apertures",
interactive="no",
verify='no',
verbose='no')
def identify_objects(fnlist,skysiglist,fwhmlist,suffix=".coo"):
"""Runs the IRAF routine 'daofind' to locate objects in a series of images,
creating coordinate files.
Inputs:
fnlist -> List of strings, each the path to a fits image.
skysiglist -> List of floats, each the sky background sigma for an image.
fwhmlist -> List of floats, each the FWHM of objects in an image.
suffix -> Suffix for the coordinate files. '.coo' by default.
Outputs:
coolist -> List of strings, each the path to the coordinate files created.
"""
print "Identifying objects in images..."
coolist = []
#Open IRAF packages
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
for i in range(len(fnlist)):
coolist.append(fnlist[i]+suffix)
iraf.daofind(image=fnlist[i],
output=fnlist[i]+suffix,
fwhmpsf=fwhmlist[i],
sigma=skysiglist[i],
threshold=4.0,
datamin='INDEF',
datamax='INDEF',
verify='N')
return coolist
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile, calculate the magnitudes of strs in the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.photpars.zmag = zmag
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
iraf.delete("_als2,_psf.grp,_psf.nrj", verify=False)
iraf.group(img + '[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_als2', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als2", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def psffit2(img, fwhm, psfstars, hdr, _datamax, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile, calculate the magnitudes of strs in the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.photpars.zmag = zmag
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
iraf.delete("_als2,_psf.grp,_psf.nrj", verify=False)
iraf.group(img + '[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_als2', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als2", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def set_default(base):
# Loading necessary IRAF packages
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.reset(min_lenuserarea='200000')
iraf.datapars.setParam('datamin','INDEF')
iraf.datapars.setParam('datamax','60000')
iraf.datapars.setParam('exposure','EXPTIME')
iraf.datapars.setParam('airmass', 'AIRMASS')
iraf.datapars.setParam('filter', 'FILTER')
iraf.findpars.setParam('threshold', 3.0)
iraf.findpars.setParam('sharphi', 1)
iraf.findpars.setParam('roundhi', 1.3)
iraf.findpars.setParam('roundlo', -1.3)
iraf.daofind.setParam('verify','no')
iraf.daofind.setParam('interactive','no')
iraf.photpars.setParam('zmag',25.0)
iraf.photpars.setParam('weighti','constant')
iraf.photpars.setParam('apertur',3.0)
iraf.phot.setParam('output',base+'default')
iraf.phot.setParam('coords',base+'default')
iraf.phot.setParam('verify','no')
iraf.phot.setParam('interactive','no')
iraf.fitpsf.setParam('box',10.0)
iraf.fitpsf.setParam('verify','no')
iraf.fitpsf.setParam('interactive','no')
iraf.centerpars.setParam('calgori','none')
iraf.fitskypars.setParam('salgorithm','mode')
iraf.daopars.setParam('functio','moffat15')
iraf.daopars.setParam('varorde','0')
iraf.daopars.setParam('nclean','0')
iraf.daopars.setParam('saturat','no')
iraf.daopars.setParam('fitsky','yes')
iraf.daopars.setParam('recenter','yes')
iraf.daopars.setParam('groupsk','yes')
iraf.daopars.setParam('maxnsta','40000')
iraf.psf.setParam('photfile',base+'default')
iraf.psf.setParam('pstfile',base+'default')
iraf.psf.setParam('psfimage',base+'default')
iraf.psf.setParam('opstfile',base+'default')
iraf.psf.setParam('groupfil',base+'default')
iraf.psf.setParam('interac','no')
iraf.psf.setParam('matchby','yes')
iraf.psf.setParam('verify','no')
iraf.psf.setParam('showplo','no')
iraf.allstar.setParam('verify','no')
def get_fake_centroid(filename,x,y,instrument,filt):
"""
Locate the center of a fake psf
INPUTS: The fake-SN psf image in filename, the expected x,y position
of the center of the psf, the instrument and filter being modeled.
RETURNS: xcentroid, ycentroid, fluxcorr
"""
from pyraf import iraf
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.centerpars)
#Use the centroid algorithm right now as it seems more robust to geometric distortion.
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 5.0
iraf.unlearn(iraf.fitskypars)
iraf.unlearn(iraf.photpars)
photparams = {
'interac':False,
'radplot':False,
}
iraf.datapars.readnoise = 0.0
iraf.datapars.itime = 1.0
iraf.datapars.epadu = 1.0
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'constant'
iraf.fitskypars.skyvalue = 0.0
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = 20 # TODO : set this more intelligently !
iraf.photpars.zmag = 25
iraf.photpars.mkapert = False
#Write the coordinate file starting as position x and y
coxyfile = 'centroid.xycoo'
coxy = open(coxyfile, 'w')
print >> coxy, "%10.2f %10.2f" % (x,y)
coxy.close()
if os.path.exists('centroid.mag'): os.remove('centroid.mag')
iraf.phot(image=filename, skyfile='', coords=coxyfile, output='centroid.mag',
verify=False, verbose=True, Stdout=1, **photparams)
f = open('centroid.mag', 'r')
maglines = f.readlines()
f.close()
xcentroid = float(maglines[76].split()[0])
ycentroid = float(maglines[76].split()[1])
return xcentroid,ycentroid
def findStars(frame,fwhm,sigma,thresh,extra="",overwrite=True,interactive=True):
if overwrite:
if os.path.isfile(frame+".fullcoo"):
os.system("rm "+frame+".fullcoo")
iraf.noao()
iraf.digiphot()
iraf.daophot()
iraf.datapars.setParam("fwhmpsf",fwhm)
iraf.datapars.setParam("sigma",sigma)
iraf.findpars.setParam("thresh",thresh)
foo = iraf.daofind(image=frame+extra,output=frame+".fullcoo",Stdin=cr,Stdout=1)
def psf(args):
""" Calculate the PSF of an image.
"""
# Load iraf packages: phot, pstselect, psf will be needed
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.module.daophot(_doprint=0)
# Read the seeing and sigma of the sky from the header
seeing, sigma = utils.get_from_header(args.input, args.FWHM_key, args.sigma)
# Do photometry on the image
#print "photometry: \n"
photfile_name = args.input + ".mag.1"
utils.if_exists_remove(photfile_name)
iraf.module.phot(args.input, output=photfile_name, coords=args.stars,
wcsin=args.coords, fwhm=seeing,
sigma=sigma, datamax=args.maxval, datamin=args.minval,
ccdread=args.ron_key, gain=args.gain_key, exposure=args.expt_key,
airmass=args.airm_key, annulus=6*seeing, dannulus=3*seeing,
apert=2*seeing, verbose="no", verify="no", interac="no")
# Select stars on the image
#print "pstselect: \n"
pstfile_name = args.input + ".pst.1"
utils.if_exists_remove(pstfile_name)
iraf.module.pstselect(args.input, photfile=photfile_name, pstfile=pstfile_name,
maxnpsf=20, fwhm=seeing, sigma=sigma,
datamax=args.maxval, ccdread=args.ron_key, gain=args.gain_key,
exposure=args.expt_key, function="auto", nclean=1,
psfrad=2*seeing, fitrad=seeing, maxnstar=20, verbose="no",
verify="no")
# Build psf of the stars
#print "psf: \n"
psffile_table = args.input + ".psf.1.fits" # iraf keeps adding the .fits :(
psgfile_name = args.input + ".psg.1"
pstfile_name2 = args.input + ".pst.2"
utils.if_exists_remove(psffile_table,psgfile_name, pstfile_name2)
iraf.module.psf( args.input, photfile=photfile_name, pstfile=pstfile_name,
groupfile=psgfile_name, opstfile=pstfile_name2,
psfimage=psffile_table,fwhm=seeing, sigma=sigma, datamax=args.maxval,
datamin=args.minval, ccdread=args.ron_key, gain=args.gain_key,
exposure=args.expt_key, function="moffat25", nclean=1,
psfrad=12, fitrad=seeing, maxnstar=20, interactive="no",
varorder=-1, verbose="no",verify="no")
# Use seepsf to build the image of the psf
psffile_name = args.input + ".psf.fits"
utils.if_exists_remove(psffile_name)
iraf.module.seepsf(psffile_table, psffile_name)
return psffile_name
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
import agnkey
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = agnkey.util.readhdr(img+'.fits')
_pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
_center='no'
iraf.fitskypars.annulus = a4
iraf.fitskypars.dannulus = a4
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.wsannul = int(a4)
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
# iraf.photpars.apertures = '%d,%d,%d'%(a2,a3,a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = agnkey.util.readkey3(hdr, 'ron')
iraf.datapars.epadu = agnkey.util.readkey3(hdr, 'gain')
iraf.datapars.exposure = 'exptime' #agnkey.util.readkey3(hdr,'exptime')
iraf.datapars.airmass = 'airmass'
iraf.datapars.filter = 'filter2'
iraf.centerpars.calgori = 'gauss'
iraf.centerpars.cbox = 1
iraf.daopars.recenter = _center
iraf.photpars.zmag = zmag
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = int(a4)
iraf.daopars.wsannul = int(a4)
iraf.daopars.recenter = _center
iraf.daopars.varorder = varord
iraf.delete("_als,_psf.grp,_psf.nrj", verify=False)
iraf.group(img+'[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img+'[0]', '_psf.grp', img + '.psf', '_als', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
import agnkey
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = agnkey.util.readhdr(img+'.fits')
_pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
_center='no'
iraf.fitskypars.annulus = a4
iraf.fitskypars.dannulus = a4
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.wsannul = int(a4)
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
# iraf.photpars.apertures = '%d,%d,%d'%(a2,a3,a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = agnkey.util.readkey3(hdr, 'ron')
iraf.datapars.epadu = agnkey.util.readkey3(hdr, 'gain')
iraf.datapars.exposure = 'exptime' #agnkey.util.readkey3(hdr,'exptime')
iraf.datapars.airmass = 'airmass'
iraf.datapars.filter = 'filter2'
iraf.centerpars.calgori = 'gauss'
iraf.centerpars.cbox = 1
iraf.daopars.recenter = _center
iraf.photpars.zmag = zmag
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = int(a4)
iraf.daopars.wsannul = int(a4)
iraf.daopars.recenter = _center
iraf.daopars.varorder = varord
iraf.delete("_als,_psf.grp,_psf.nrj", verify=False)
iraf.group(img+'[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img+'[0]', '_psf.grp', img + '.psf', '_als', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def aperture_photometry(image,photfilesuffix,threshold=4.,wcs='logical',mode='small', telescope='Pairitel'):
'''perform aperture photometry on a given image
This procedure perfrom IRAF/DAOPHOT aperture phtometry on (all extensions of) a fits image.
It sets some global daophot parameters, then perfroms daophot.daofind and daophot.phot
The output .mag file is filtered for valid magnitudes and a mag.sex file computed, where
all coordinates are transformed to sexagesimal coordinates.
input: image: filename
keywords: threshold=4 :daofind detection threshold in sigma_sky
wcs='logical' :working wcs system, can be 'logical','physical' or 'tv'
in any case an additional sexagesimal output file will be computed
mode='small' : 'standard' = 'large' or 'psf' = 'small' choses a pre-defined aperture size for
aperture photometry of standards or just as starting point for psf photometry
'''
print 'Performing aperture photometry on '+image
if telescope == 'Pairitel':
set_Pairitel_params()
elif telescope == 'FLWO':
set_FLWO_params()
else:
print 'No parameters found for this telescope!'
iraf.mscred(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.daophot.verify = False
iraf.daophot.wcsin = wcs
iraf.daophot.wcsout = wcs
iraf.daophot.wcspsf = wcs
iraf.centerpars.cbox=2.*iraf.datapars.fwhmpsf
iraf.fitskypars.annulus=20 # usually one would use 5.*iraf.datapars.fwhmpsf
#annulus must be wide enough to have good sky statistics
iraf.fitskypars.dannulus=10. # this is standard
if 'standard'.find(mode.lower()) != -1 or 'large'.find(mode.lower()) != -1:
iraf.photpars.aperture = 4.*iraf.datapars.fwhmpsf
iraf.centerpars.calgorithm = 'centroid'
elif 'small'.find(mode.lower()) != -1 or 'psf'.find(mode.lower()) != -1:
iraf.photpars.aperture = 4. # close in for crowded regions
iraf.centerpars.calgorithm = 'none'
else:
print '''use: aperture_photometry(image,threshold=4.,wcs="logical",mode="small")
The following mode keywords are implemented: standard, large, psf, small'''
raise NotImplementedError
#for imagebi in fits_ext(image):
sky,skydev=get_sky(image)
iraf.datapars.datamin=sky-5*skydev
iraf.datapars.sigma=skydev
#this could be changed to use Stdin and Stdout so that fewer files are written (saves space and time), but for debug purposes better keep all that
iraf.daofind(image, output='default', verbose=False, verify=False, threshold=threshold)
iraf.daophot.phot(image, coords='default', output=image+photfilesuffix, verbose=False, verify=False, interactive=False, wcsout=wcs)
def set_FLWO_params(): '''Set some daophot parameters, which are typical for my FLWO images ''' iraf.digiphot(_doprint=0) iraf.daophot(_doprint=0) iraf.datapars.fwhmpsf = 4.5 iraf.datapars.datamax=40000. iraf.datapars.ccdread = "rdnoise" iraf.datapars.gain = "gain" iraf.datapars.exposure = "exptime" iraf.datapars.airmass = "air" iraf.datapars.filter = "filter" iraf.datapars.obstime="date-obs"
def run_fitparams(weight=True):
if weight:
weighting = 'photometric'
else:
weighting = 'uniform'
ir.delete('standards.transParams')
ir.digiphot()
ir.photcal()
ir.unlearn('fitparams')
ir.fitparams('standards.instMag', 'photcal_ukirt_faint.dat',
'standards.config', 'standards.transParams',
weighting=weighting)
def _dao_setup(fwhmpsf, threshold, sigma):
iraf.digiphot(_doprint=0)
iraf.digiphot.apphot(_doprint=0)
datapars = iraf.noao.digiphot.apphot.datapars
findpars = iraf.noao.digiphot.apphot.findpars
datapars.scale = 1.0
datapars.sigma = sigma * (3.0 / 4.0)
datapars.fwhmpsf = fwhmpsf
# not important for source finding
datapars.readnoise = 0.0
datapars.epadu = 1.0
findpars.threshold = threshold # only care about brightest
def marksn2(img,fitstab,frame=1,fitstab2='',verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr=lsc.util.readhdr(fitstab)
_filter=lsc.util.readkey3(hdr,'filter')
column=lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if fitstab2:
hdr=lsc.util.readhdr(fitstab2)
_filter=lsc.util.readkey3(hdr,'filter')
_exptime=lsc.util.readkey3(hdr,'exptime')
column=lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2=array(column['ra0'],float)
decsex2=array(column['dec0'],float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]',frame,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0,len(column['ra0'])):
print xy[i],column['ra0'][i],column['dec0'][i],column['magp3'][i],column['magp4'][i],column['smagf'][i],column['magp2'][i]
if fitstab2:
vector2=[]
for i in range(0,len(rasex2)):
vector2.append(str(rasex2[i])+' '+str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy1),mark="cross",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=205,txsize=2)
def run_fitparams(weight=True):
if weight:
weighting = 'photometric'
else:
weighting = 'uniform'
ir.delete('standards.transParams')
ir.digiphot()
ir.photcal()
ir.unlearn('fitparams')
ir.fitparams('standards.instMag',
'photcal_ukirt_faint.dat',
'standards.config',
'standards.transParams',
weighting=weighting)
def detect_stars(f,params):
print 'Detecting stars in',f.name
print 'Current directory is', os.getcwd()
fp = params.loc_output+os.path.sep
fn = f.fullname
iraf.digiphot()
iraf.daophot()
print 'FWHM = ',f.fw
nstars = 0
thresh = 100
while (nstars < 2*params.nstamps) and (thresh > 1.5):
print 'thresh = ',thresh
for d in ['temp.stars','temp.phot']:
if os.path.exists(fp+d):
os.system('/bin/rm '+fp+d)
iraf.daofind(image=fn,output=fp+'temp.stars',interactive='no',verify='no',
threshold=thresh,sigma=30,fwhmpsf=f.fw,
datamin=params.pixel_min,datamax=params.pixel_max,
epadu=params.gain,readnoise=params.readnoise,
noise='poisson')
iraf.phot(image=fn,output=fp+'temp.phot',coords=fp+'temp.stars',interactive='no',
verify='no',
sigma=30,fwhmpsf=f.fw,datamin=params.pixel_min,
datamax=params.pixel_max,epadu=params.gain,
readnoise=params.readnoise,noise='poisson',Stdout='/dev/null')
nstars = 0
if os.path.exists(fp+'temp.phot'):
iraf.psort(infiles=fp+'temp.phot',field='MAG')
iraf.prenumber(infile=fp+'temp.phot')
s = iraf.pdump(infiles=fp+'temp.phot',Stdout=1,fields='ID,XCENTER,YCENTER,MAG',
expr='yes')
stars = np.zeros([len(s),3])
i = 0
for line in s:
mag = line.split()[3]
if not(mag == 'INDEF'):
stars[i,:] = np.array(map(float,line.split()[1:4]))
i += 1
nstars = i
thresh = thresh*0.5
if nstars == 0:
print 'Error: could not detect stars in',fn
return None
stars = stars[:i,:].copy()
sys.old_stdout = sys.stdout
return stars
def set_Pairitel_params(): '''Set some daophot parameters, which are typical for my Pairitel images ''' iraf.digiphot(_doprint=0) iraf.daophot(_doprint=0) iraf.apphot(_doprint=0) iraf.datapars.fwhmpsf = 4.5 iraf.datapars.datamax=15000. iraf.centerpars.cbox = 9.0 iraf.centerpars.calgorithm = "none" iraf.datapars.datamin = "INDEF" iraf.datapars.ccdread="RDNOISE" # 30. in 2MASS? iraf.datapars.gain = "GAIN" iraf.datapars.exposure = "EXPTIME" iraf.datapars.airmass = "AIRMASS" iraf.datapars.filter = "FILTER" iraf.datapars.obstime="DATE-OBS"
def createMagFile(inputDir, inputFile, useCoordFile, outputFile, fullyAutomatic=False, aperturesString='5,10,15,20', bestSkyAperture='100'):
cr=['\n']
iraf.cd(inputDir)
iraf.noao()
iraf.digiphot()
iraf.apphot()
iraf.phot.setParam('interactive','no')
iraf.centerpars.setParam('cbox','5')
iraf.photpars.setParam('apertures',aperturesString)
iraf.fitskypars.setParam('annulus',bestSkyAperture)
iraf.fitskypars.setParam('dannulus','10')
iraf.phot.setParam('image',inputFile)
iraf.phot.setParam('coords',useCoordFile)
iraf.phot.setParam('output',outputFile)
if fullyAutomatic: ## If you use this option, it will not prompt you for any of the aperture/sky settings
iraf.phot(mode='h',Stdin=cr)
else: # Otherwise, you can change them at will, and get to press enter a bunch
iraf.phot(mode='h')
def make_iraf_psf(fname):
with fits.open(fname) as stp: # Read in image info placed in header previously
header = stp[0].header
ra = header["RA"]
dec = header["DEC"]
for ext in range(len(stp)): # Look in each header section
try:
header = stp[ext].header
expnum = header["EXPNUM"]
except:
continue
stamp = fits.open(fname)
for ext in range(
len(stamp)
): # Establish size of image and location of source in image (source should be centered in cutout)
try:
world_coords = wcs.WCS(stamp[ext].header)
image_coords = world_coords.sky2xy(float(ra), float(dec))
image_size = stamp[ext].data.shape
except:
continue
# Some cutouts are split over multiple CCDs, meaning the source may not actually be in some of these small image 'pieces'
if 0 < image_coords[0] < image_size[0] and 0 < image_coords[1] < image_size[1]:
ccdnum = stamp[ext].header.get("EXTNO", None)
psf_file = storage.get_file(expnum, ccd=ccdnum, ext="psf.fits") # Get psf file for image from storage
iraf.noao()
iraf.digiphot()
iraf.daophot(_doprint=0)
psf_image = psf_file.replace("psf", "seepsf")
# Seepsf creates psf_image
iraf.seepsf(psf_file, psf_image, xpsf=image_coords[0], ypsf=image_coords[1])
os.unlink(psf_file)
return psf_image
else:
return None
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint = 0)
iraf.apphot(_doprint = 0)
iraf.images(_doprint = 0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.images(_doprint=0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def doPhot():
print 'Running doPhot'
try:
mapFile = open('map.dat')
except:
print 'map.dat not found. Exiting.'
sys.exit()
try:
changeFile = open('change.dat')
except:
print 'change.dat not found. Exiting.'
sys.exit()
coordList = list()
for line in mapFile:
coords = map(float,line.split())
coordList.append(coords)
coordList = np.array(coordList)
iraf.noao()
iraf.digiphot()
iraf.daophot()
iraf.ptools()
iraf.set(clobber='yes')
photFile = open('phot.dat', 'w')
for line in changeFile.readlines():
elms = line.split()
imageName = elms[0]
changeCoords = np.array([float(elms[1]),float(elms[2])])
newCoords = coordList + changeCoords
print 'Image: '+imageName
coordFile = makeCoordFile(newCoords)
iraf.noao.digiphot.daophot.phot(image=imagesDir+imageName, coords=coordFile, output='.temp-phot', skyfile='', verify='no', fwhmpsf=fwhmpsf, sigma=sigma, readnoise=readnoise, epadu=epadu, exposure=exposureHeader, obstime=obstimeHeader, calgorithm=calgorithm, cbox=cbox, apertures=apertures, annulus=annulus, dannulus=dannulus)
result = iraf.noao.digiphot.ptools.txdump(Stdout=1, textfiles='.temp-phot', fields='mag, merr, otime', expr='yes')
writeString = result[0].split()[-1] +' '+ ' '.join([' '.join(x.split()[:2]) for x in result])
photFile.write(writeString+"\n")
photFile.close()
raw_input('doPhot Done. Hit return key to continue.')
def doPhotometryACS(self, file, inputcoords, apertures, zeropoint, bgsigma, skyann, skydann): ''' This function can be used to do photometry from a given image. Input parameters can be varied, however, the task assumes that photometry is done from an ACS image and the exposure time is found from the header of the file. Object recentering is done with centroid algorithm and shifts up to 6 pixels are possible. For skyfitting algorithm mode is adopted. ''' #load packages, should supress the output I.digiphot() I.apphot() #setting up for ACS I.datapar(sigma = bgsigma, exposure = 'exptime', gain = 'ccdgain') I.centerpars(calgorithm = 'centroid', cbox = 10., maxshift = 6.) I.fitskypars(salgorithm = 'mode', annulus = skyann, \ dannulus = skydann, skyvalue = 0.) I.photpars(apertures = apertures, zmag = zeropoint) I.phot(file, coords=inputcoords, verify='no', verbose = 'no')
def doPhotometryACS(self, file, inputcoords, apertures, zeropoint, bgsigma,
skyann, skydann):
'''
This function can be used to do photometry from a given image. Input parameters
can be varied, however, the task assumes that photometry is done from an ACS
image and the exposure time is found from the header of the file.
Object recentering is done with centroid algorithm and shifts up to 6 pixels are
possible. For skyfitting algorithm mode is adopted.
'''
#load packages, should supress the output
I.digiphot()
I.apphot()
#setting up for ACS
I.datapar(sigma=bgsigma, exposure='exptime', gain='ccdgain')
I.centerpars(calgorithm='centroid', cbox=10., maxshift=6.)
I.fitskypars(salgorithm = 'mode', annulus = skyann, \
dannulus = skydann, skyvalue = 0.)
I.photpars(apertures=apertures, zmag=zeropoint)
I.phot(file, coords=inputcoords, verify='no', verbose='no')
def do_phot(fnlist,coofile,fwhmlist,skysiglist):
"""Runs the IRAF 'phot' routine on a series of images to determine their
centroid positions and magnitudes. It is very likely you don't want to call
this routine on its own, but rather have align_norm do it for you.
Inputs:
fnlist -> List of strings, each the path to a fits image
coofile -> Path to the file containing the coordinate list of stars
fwhmlist -> List of the PSF FWHM's for each image
skysiglist -> List of the sky background sigmas for each image
Outputs:
photlist -> List of strings, the paths to the photometry outputs
"""
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
photlist = []
for i in range(len(fnlist)):
print "Performing photometry on the stars in image "+fnlist[i]
photlist.append(fnlist[i]+".mag")
iraf.phot(image=fnlist[i],
skyfile="",
coords=coofile,
output=photlist[i],
interactive="N",
sigma=skysiglist[i],
datamin='INDEF',
datamax='INDEF',
calgorithm='centroid',
cbox=2*fwhmlist[i],
salgorithm='centroid',
annulus=4*fwhmlist[i],
dannulus=6*fwhmlist[i],
apertures=4*fwhmlist[i],
verify='N')
return photlist
def calcFluxMulti(imageName, coords, rMin, rMax, step=1, centerBox=5.0):
nCoords = coords.shape[0]
results = []
coordsFN = tempfile.NamedTemporaryFile(delete=False)
for xx, yy in coords: print >>coordsFN, xx, yy
coordsFN.close()
digiphot = iraf.digiphot(_doprint=0)
apphot = iraf.apphot(_doprint=0)
qphot = iraf.qphot
pdump = iraf.pdump
qphot.coords = coordsFN.name
#if (iraf.hedit(image=imageName,fields='EXPTIME',value='.')):
#^this line prints the value of EXPTIME which is not needed
#need to check for EXPTIME or qphot will raise an error
try:
pyfits.open(imageName)[0].header['EXPTIME']
qphot.exposure = 'EXPTIME'
except:
pass
qphot.airmass = ''
qphot.interactive = False
qphot.radplots = False
qphot.verbose = False
apertures = ','.join([str(nn) for nn in range(int(rMin), int(rMax+1))])
annulus = rMax
dannulus = rMax + 1
qphot.output = 'qPhotOutput.tmp'
if os.path.exists(qphot.output): os.remove(qphot.output)
qphot(imageName, centerBox, annulus, dannulus, apertures, Stdout=1)
for nn in range(nCoords):
areas = np.array(map(float, pdump(qphot.output, 'AREA', 'yes', Stdout=1)[nn].split()))
fluxes = np.array(map(float, pdump(qphot.output, 'SUM', 'yes', Stdout=1)[nn].split()))
newXX = np.array(map(float, pdump(qphot.output, 'XCENTER', 'yes', Stdout=1)[nn].split()))[0]
newYY = np.array(map(float, pdump(qphot.output, 'YCENTER', 'yes', Stdout=1)[nn].split()))[0]
results.append([nn, coords[nn][0], coords[nn][1], newXX, newYY, areas, fluxes])
#if os.path.exists(qphot.output): os.remove(qphot.output)
return results
def getnoise(image, catalog): #sky-subtracted image
(ximage, yimage, isoarea, fluxerriso) = readcatalog(catalog)
(xpos, ypos) = getpositions(ximage, yimage, isoarea, image)
measurephot(xpos, ypos, image)
iraf.digiphot()
iraf.daophot()
iraf.phot('check.fits',
coords='noisecoords.dat',
output='noise.dat',
skyfile='sky',
salgori='file',
aperture='apertures',
interactive='no',
calgorith='none',
verify='no')
(aveap, aveaperr, avearea, aveareaerr) = calcavesky()
(a, b) = solveforab(aveaperr, avearea)
print "a,b", a, b
s = str(image)
(file, post) = s.split('.')
#plotnoisepylab(aveap,aveaperr,avearea,aveareaerr,a,b,file,isoarea,fluxerriso)
return a, b
def get_star_data(asteroid_id, mag, expnum, header):
"""
From ossos psf fitted image, calculate mean of the flux of each row of the rotated PSF
"""
# calculate mean psf
uri = storage.get_uri(expnum.strip('p'), header[_CCD].split('d')[1])
ossos_psf = '{}.psf.fits'.format(uri.strip('.fits'))
local_psf = '{}{}.psf.fits'.format(expnum, header[_CCD].split('d')[1])
local_file_path = '{}/{}'.format(_STAMPS_DIR, local_psf)
storage.copy(ossos_psf, local_file_path)
# pvwcs = wcs.WCS(header)
# x, y = pvwcs.sky2xy(asteroid_id['ra'].values, asteroid_id['dec'].values)
x = asteroid_id[_XMID_HEADER].values[0]
y = asteroid_id[_YMID_HEADER].values[0]
# run seepsf on the mean psf image
iraf.set(uparm="./")
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.seepsf(local_file_path, local_psf, xpsf=x, ypsf=y, magnitude=mag)
with fits.open(local_psf) as hdulist:
data = hdulist[0].data
th = math.degrees(asteroid_id[_THETA_HEADER].values[0])
data_rot = rotate(data, th)
data_rot = np.ma.masked_where(data_rot == 0, data_rot)
data_mean = np.ma.mean(data_rot, axis=1)
os.unlink(local_psf)
os.unlink(local_file_path)
return data_mean[np.nonzero(np.ma.fix_invalid(data_mean, fill_value=0))[0]]
def measurephot(xpos, ypos, image):
coords = open("noisecoords.dat", 'w')
for i in range(len(xpos)):
coords.write("%8.1f %8.1f \n" % (xpos[i], ypos[i]))
sky = open("sky", 'w')
for i in range(npoints):
sky.write("0.0 \n")
sky.close()
aps = open("apertures", 'w')
aps.write("1,2,3,4,5,6,7,8,9,10,11,12,13,14,15")
aps.close()
os.system("rm noise.dat")
iraf.digiphot()
iraf.daophot()
print image
image = "j-sky.fits"
image = "n-sky.fits"
iraf.digiphot.daophot.phot(image,
coords="noisecoords.dat",
output="noise.dat",
skyfile="sky",
salgori="file",
aperture="apertures",
interactive="no",
wcsin='logical')
iraf.apphot()
iraf.phot('check.fits',
coords='noisecoords.dat',
output='noise.dat',
skyfile='sky',
salgori='file',
aperture='apertures',
interactive='no')
test = raw_input("enter anything")
def init(iraf_dir=DEFAULT_IRAF_DIR):
'''Initialize IRAF for use in photometry
@param iraf_dir The home directory for iraf, i.e. where login.cl
and uparm are located, slightly annoying that we need this, but not
much we can do about it'''
global _initialized
if _initialized:
#already initialized no need to run again
return
global iraf
global yes
global no
with workingDirectory(iraf_dir):
from pyraf import iraf
yes = iraf.yes
no = iraf.no
#now load the packages we need
iraf.noao()
iraf.digiphot()
iraf.daophot()
iraf.obsutil()
_initialized = True
return
def calcFlux(imageName, xx, yy, rr):
coordsFN = tempfile.NamedTemporaryFile(delete=False)
print >>coordsFN, xx, yy
coordsFN.close()
digiphot = iraf.digiphot(_doprint=0)
apphot = iraf.apphot(_doprint=0)
qphot = iraf.qphot
pdump = iraf.pdump
qphot.coords = coordsFN.name
#if (iraf.hedit(image=imageName,fields='EXPTIME',value='.')):
#^this line prints the value of EXPTIME which is not needed
#need to check for EXPTIME or qphot will raise an error
try:
pyfits.open(imageName)[0].header['EXPTIME']
qphot.exposure = 'EXPTIME'
except:
pass
qphot.airmass = ''
qphot.interactive = False
qphot.radplots = False
qphot.verbose = False
annulus = rr
dannulus = 1.0
qphot.output = 'qPhotOutput.tmp'
if os.path.exists(qphot.output): os.remove(qphot.output)
qphot(imageName, 1.0, annulus, dannulus, annulus, Stdout=1)
areas = np.array(map(float, pdump(qphot.output, 'AREA', 'yes', Stdout=1)[0].split()))
fluxes = np.array(map(float, pdump(qphot.output, 'SUM', 'yes', Stdout=1)[0].split()))
if os.path.exists(qphot.output): os.remove(qphot.output)
return [np.float64(areas[0]), np.float64(fluxes[0])]
def phot(fits_filename,
x_in,
y_in,
aperture=15,
sky=20,
swidth=10,
apcor=0.3,
maxcount=30000.0,
exptime=1.0,
zmag=None,
extno=0,
centroid=True):
"""
Compute the centroids and magnitudes of a bunch sources on fits image.
:rtype : astropy.table.Table
:param fits_filename: Name of fits image to measure source photometry on.
:type fits_filename: str
:param x_in: x location of source to measure
:type x_in: float, numpy.array
:param y_in: y location of source to measure
:type y_in: float, numpy.array
:param aperture: radius of circular aperture to use.
:type aperture: float
:param sky: radius of inner sky annulus
:type sky: float
:param swidth: width of the sky annulus
:type swidth: float
:param apcor: Aperture correction to take aperture flux to full flux.
:type apcor: float
:param maxcount: maximum linearity in the image.
:type maxcount: float
:param exptime: exposure time, relative to zmag supplied
:type exptime: float
:param zmag: zeropoint magnitude
:param extno: extension of fits_filename the x/y location refers to.
"""
if not hasattr(x_in, '__iter__'):
x_in = [
x_in,
]
if not hasattr(y_in, '__iter__'):
y_in = [
y_in,
]
if (not os.path.exists(fits_filename)
and not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
# get the filter for this image
filter_name = input_hdulist[extno].header.get('FILTER', 'DEFAULT')
# Some nominal CFHT zeropoints that might be useful
zeropoints = {
"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 32.0,
'r.MP9601': 31.9,
'gri.MP9603': 33.520
}
if zmag is None:
logger.warning(
"No zmag supplied to daophot, looking for header or default values."
)
zmag = input_hdulist[extno].header.get('PHOTZP',
zeropoints[filter_name])
logger.warning("Setting zmag to: {}".format(zmag))
# check for magic 'zeropoint.used' files
for zpu_file in [
"{}.zeropoint.used".format(os.path.splitext(fits_filename)[0]),
"zeropoint.used"
]:
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
logger.warning("Using file {} to set zmag to: {}".format(
zpu_file, zmag))
break
photzp = input_hdulist[extno].header.get(
'PHOTZP', zeropoints.get(filter_name, zeropoints["DEFAULT"]))
if zmag != photzp:
logger.warning(
("zmag sent to daophot: ({}) "
"doesn't match PHOTZP value in image header: ({})".format(
zmag, photzp)))
# setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
if centroid:
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
else:
iraf.centerpars.calgori = "none"
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
for i in range(len(x_in)):
coofile.write("%f %f \n" % (x_in[i], y_in[i]))
coofile.flush()
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the target_name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename + "[{}]".format(extno), coofile.name, magfile.name)
pdump_out = ascii.read(magfile.name, format='daophot')
logging.debug("PHOT FILE:\n" + str(pdump_out))
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
# apply the aperture correction
pdump_out['MAG'] -= apcor
# if pdump_out['PIER'][0] != 0 or pdump_out['SIER'][0] != 0 or pdump_out['CIER'][0] != 0:
# raise ValueError("Photometry failed:\n {}".format(pdump_out))
# Clean up temporary files generated by IRAF
os.remove(coofile.name)
os.remove(magfile.name)
logger.debug("Computed aperture photometry on {} objects in {}".format(
len(pdump_out), fits_filename))
del input_hdulist
return pdump_out
def run(imfile,
coordlist,
WCS=False,
outroot=None,
poisson=True,
verbose=True,
debug=False,
abmags=False,
decliner=False,
smallskyann=False,
calgorithm='gauss',
upperlim=None,
snanadat=False,
cbox=5.0):
"""
Use iraf.digiphot.apphot to collect aperture photometry in Vega magnitudes.
Required Arguments:
imfile (str): fits image file name (cannot be fpacked)
coord : the coordinate list for photometry
as a str, this is the name of a text file in two columns giving x,y coordinates.
as a python list or numpy array, this gives coordinate pairs for each target
e.g. coord=[ [1024,440], [502,680] ]
Optional arguments:
WCS : If input coordinates are in RA and DEC (degrees), user must set WCS=True.
outroot : root of the output file names (outroot.mag, outroot.full, outroot.out)
if unspecified, the root of imfile is used.
poisson: Use a poisson noise model or not. If set to false, a constant noise model is
used. Default is True
calgorithm: Centering algorithm to pass to iraf.apphot. Choices are "gauss","centroid",
"none", or "ofilter". Default is "gauss"
cbox: Size of centering box to pass to iraf.apphot. Default is 5.0
upperlim: None = when flux<3-sigma then report 3-sig upper limit
True = force computation of 3-sigma upper limit
False = disallow upper limits (i.e. always report measured flux)
snanadat: True = Report the mjd, flux and mag in a SNANA-style OBS: line
False = report the filename, mjd, source position, mag and errors.
verbose: Default is True.
debug: Start pdb. Default is False.
Output products : phot.out, phot.mag, phot.full
phot.out : the raw output from apphot
phot.full : a detailed photometry file
phot.mag : header+one line summary output file :
#image name, filter, xpos, ypos, magnitude, and errors
Requires : numpy, pyfits (astLib is required if WCS=True)
"""
if debug:
import pdb
pdb.set_trace()
from math import sqrt
from numpy import nan, log10
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
if outroot == None: outroot = os.path.splitext(os.path.basename(imfile))[0]
# Get info from header
hdr = pyfits.getheader(imfile)
exptime = hdr['EXPTIME']
if 'EXPSTART' in hdr:
mjdobs = "%.1f" % hdr['EXPSTART']
else:
mjdobs = '0.0'
if 'DATE-OBS' in hdr:
dateobs = hdr['DATE-OBS']
elif 'DATE' in hdr:
dateobs = hdr['DATE']
else:
dateobs = 'unknown'
if 'D001OUUN' in hdr:
if hdr['D001OUUN'].upper() == 'CPS':
gain = exptime
exptime = 1.0
elif hdr['D001OUUN'].upper() == 'COUNTS':
gain = 1.0
elif hdr['BUNIT'].upper() == 'ELECTRONS/S':
gain = exptime
exptime = 1.0
elif hdr['BUNIT'].upper() == 'ELECTRONS':
gain = 1.0
else:
print 'Problem determining units, check image'
return
if 'FILTER' in hdr:
filt = hdr['FILTER']
elif 'FILTER1' in hdr:
filt = hdr['FILTER1']
if filt.startswith('CLEAR'):
filt = hdr['FILTER2']
instrument = hdr['INSTRUME']
if instrument == 'WFC3':
instrument = instrument + '_' + hdr['DETECTOR']
if abmags:
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_AB[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_AB[filt]
elif instrument == 'ACS':
ZPT = ZPT_ACS_AB[filt]
else:
print "Can't handle instrument: %s; check the fits header." % instrument
#Give up and die
return
else:
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_VEGA[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_VEGA[filt]
elif instrument == 'ACS':
ZPT = ZPT_ACS_VEGA[filt]
else:
print "Can't handle instrument: %s; check the fits header." % instrument
#Give up and die
return
# format for coord list:
# coordlist= [ [x0,y0], [x1,y1], ... ]
# or a two-column text file
if type(coordlist) == str:
# user provided name of a coordinate file
fin = open(coordlist, 'r')
coordlines = fin.readlines()
fin.close()
coordvals = array([cline.split() for cline in coordlines], dtype=float)
elif len(shape(coordlist)) == 1:
# user provided something like coord=[x,y]
coordvals = array([coordlist])
else:
# user provided something like coord= [[x1,y1],[x2,y2],[x3,y3]]
coordvals = array(coordlist)
# how many objects do we have ?
numcoo = len(coordvals)
if WCS:
# If coords in wcs instead of x,y,
# get wcs information from imfile for converting to xy.
from astLib import astWCS
wcsfits = astWCS.WCS(imfile)
# (re)write a list of x,y positions
coxyfile = '%s.xycoo' % outroot
coxy = open(coxyfile, 'w')
for coord in coordvals:
if WCS:
#Convert from RA and Dec to xy
# NOTE: wcsfits returns values based on a 0,0 origin, but the iraf phot
# packages expect a 1,1 origin. So we add 1 to each value
xy = wcsfits.wcs2pix(coord[0], coord[1])
xy[0] += 1
xy[1] += 1
else:
xy = coord
print >> coxy, "%10.2f %10.2f" % (float(xy[0]), float(xy[1]))
coxy.close()
if verbose > 1: print("XY coords written to %s" % coxyfile)
""" iraf.digiphot.apphot.datapars :
2013.09.06 SR: updated to use Poisson noise model (which includes sky noise) as
the default, instead of 'constant' """
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.datapars.scale = 1.0
iraf.datapars.fwhmpsf = psffwhm[instrument]
iraf.datapars.emission = not decliner
iraf.datapars.sigma = 'INDEF'
iraf.datapars.datamin = 'INDEF'
iraf.datapars.datamax = 'INDEF'
if poisson:
iraf.datapars.noise = 'poisson'
else:
iraf.datapars.noise = 'constant'
iraf.datapars.ccdread = ''
#iraf.datapars.gain = ''
iraf.datapars.readnoise = 0.0
#iraf.datapars.exposure = ' '
#iraf.datapars.airmass = ''
#iraf.datapars.obstime = ''
iraf.datapars.itime = exptime
iraf.datapars.epadu = gain
iraf.datapars.xairmass = 'INDEF'
iraf.datapars.ifilter = 'INDEF'
iraf.datapars.otime = 'INDEF'
# iraf.digiphot.apphot.centerpars :
iraf.unlearn(iraf.centerpars)
iraf.centerpars.calgorithm = calgorithm
iraf.centerpars.cbox = cbox
iraf.centerpars.cthreshold = 0.0
iraf.centerpars.minsnratio = 1.0
iraf.centerpars.cmaxiter = 10.0
iraf.centerpars.maxshift = 1.0
iraf.centerpars.clean = False
iraf.centerpars.rclean = 1.0
iraf.centerpars.rclip = 2.0
iraf.centerpars.kclean = 3.0
iraf.centerpars.mkcenter = False
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'median'
if smallskyann:
iraf.fitskypars.annulus = 8.0
iraf.fitskypars.dannulus = 12.0
else:
iraf.fitskypars.annulus = 25.0
iraf.fitskypars.dannulus = 40.0
iraf.fitskypars.skyvalue = 0.0
iraf.fitskypars.smaxiter = 10.0
iraf.fitskypars.sloclip = 0.0
iraf.fitskypars.shiclip = 0.0
iraf.fitskypars.snreject = 50.0
iraf.fitskypars.sloreject = 3.0
iraf.fitskypars.shireject = 3.0
iraf.fitskypars.khist = 3.0
iraf.fitskypars.binsize = 0.1
iraf.fitskypars.smooth = False
iraf.fitskypars.rgrow = 0.0
iraf.fitskypars.mksky = False
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = APLIST
iraf.photpars.zmag = ZPT
iraf.photpars.mkapert = False
photparams = {
'interac': False,
'radplot': False,
}
magfile_out = outroot + '.out'
magfile_full = outroot + '.full'
magfile_phot = outroot + '.mag'
if os.path.exists(magfile_out):
os.remove(magfile_out)
# run photometry using the newly created coxyfile for providing input coordinates
try:
iraf.phot(image=imfile,
skyfile='',
coords=coxyfile,
output=magfile_out,
verify=False,
verbose=True,
Stdout=1,
**photparams)
except iraf.IrafError, e:
print("phot failed on %s with IRAF error :\n%s" % (imfile, e))
def phot(fits_filename, x_in, y_in, aperture=15, sky=20, swidth=10, apcor=0.3,
maxcount=30000.0, exptime=1.0):
"""
Compute the centroids and magnitudes of a bunch sources detected on
CFHT-MEGAPRIME images.
Args:
fits_filename: str
The name of the file containing the image to be processed.
Returns a MOPfiles data structure.
"""
if (not os.path.exists(fits_filename) and
not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
raise TaskError("Failed to open input image: %s" % err.message)
## get the filter for this image
filter = input_hdulist[0].header.get('FILTER', 'DEFAULT')
### Some CFHT zeropoints that might be useful
zeropoints = {"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 26.4
}
### load the
if not filter in zeropoints:
filter = "DEFAULT"
zmag = input_hdulist[0].header.get('PHOTZP', zeropoints[filter])
### setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
### check for the magical 'zeropoint.used' file
zpu_file = "zeropoint.used"
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
iraf.photpars.apertures = int(aperture)
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
#iraf.datapars.exposur="EXPTIME"
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
coofile.write("%f %f \n" % (x_in, y_in))
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename, coofile.name, magfile.name)
pdump_out = iraf.pdump(magfile.name, "XCENTER,YCENTER,MAG,MERR,ID,XSHIFT,YSHIFT,LID",
"MERR < 0.4 && MERR != INDEF && MAG != INDEF && PIER==0", header='no', parameters='yes',
Stdout=1)
os.remove(coofile.name)
os.remove(magfile.name)
### setup the mop output file structure
hdu = {}
hdu['header'] = {'image': input_hdulist,
'aper': aperture,
's_aper': sky,
'd_s_aper': swidth,
'aper_cor': apcor,
'zeropoint': zmag}
hdu['order'] = ['X', 'Y', 'MAG', 'MERR', 'ID', 'XSHIFT', 'YSHIFT', 'LID']
hdu['format'] = {'X': '%10.2f',
'Y': '%10.2f',
'MAG': '%10.2f',
'MERR': '%10.2f',
'ID': '%8d',
'XSHIFT': '%10.2f',
'YSHIFT': '%10.2f',
'LID': '%8d'}
hdu['data'] = {}
for col in hdu['order']:
hdu['data'][col] = []
for line in pdump_out:
values = line.split()
for col in hdu['order']:
if re.match('\%.*f', hdu['format'][col]):
if col == 'MAG':
values[0] = float(values[0]) - float(apcor)
hdu['data'][col].append(float(values.pop(0)))
elif re.match('\%.*d', hdu['format'][col]):
hdu['data'][col].append(int(values.pop(0)))
else:
hdu['data'][col].append(values.pop(0))
# Clean up temporary files generated by IRAF
os.remove("datistabe.par")
os.remove("datpdump.par")
return hdu
def psffit(img,
fwhm,
psfstars,
hdr,
interactive,
_datamin,
_datamax,
psffun='gauss',
fixaperture=False):
'''
giving an image, a psffile compute the psf using the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning gain not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img + '.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(
5. / _pixelscale), float(8. / _pixelscale), float(10. /
_pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 +
0.5), int(fwhm * 3 +
0.5), int(fwhm * 4 +
0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = _datamin
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.photpars.zmag = zmag
psfout = img.replace('.zogypsf', '') + '.psf.fits'
iraf.delete('_psf.ma*,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?,' + psfout,
verify=False)
iraf.phot(img + '[0]',
'_psf.coo',
'_psf.mag',
interac=False,
verify=False,
verbose=False)
# removes saturated stars from the list (IRAF just issues a warning)
with open('_psf.mag') as f:
text = f.read()
text = re.sub('(.*\n){6}.*BadPixels\* \n', '', text)
with open('_psf.mag', 'w') as f:
f.write(text)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
if interactive: # not possible to run pstselect or psf interactively on 64-bit linux (Error 851)
os.system('cp _psf.mag _psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img + '[0]',
'_psf.mag',
'_psf.pst',
psfstars,
interac=False,
verify=False)
iraf.psf(img + '[0]',
'_psf.mag',
'_psf.pst',
psfout,
'_psf.psto',
'_psf.psg',
interac=interactive,
verify=False,
verbose=False)
iraf.group(img + '[0]',
'_psf.mag',
psfout,
'_psf.grp',
verify=False,
verbose=False)
iraf.nstar(img + '[0]',
'_psf.grp',
psfout,
'_psf.nst',
'_psf.nrj',
verify=False,
verbose=False)
photmag = iraf.txdump("_psf.mag",
'xcenter,ycenter,id,mag,merr',
expr='yes',
Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst",
'xcenter,ycenter,id,mag,merr',
expr='yes',
Stdout=1)
return photmag, pst, fitmag
def main(argv):
home_root = os.environ['HOME']
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
for file_ in os.listdir("./"):
if file_.endswith("_i.fits"):
fits_file_i = file_
title_string = file_[0:9]
if file_.endswith("_g.fits"):
fits_file_g = file_
fits_h_i = fits.open(fits_file_i)
fits_h_g = fits.open(fits_file_g)
fwhm_i = fits_h_i[0].header['FWHMPSF']
fwhm_g = fits_h_g[0].header['FWHMPSF']
print 'Image FWHM :: g = {0:5.3f} : i = {1:5.3f}'.format(fwhm_g,fwhm_i)
# if not os.path.isdir('psf'): # make a psf subfolder if it doesn't exist
# os.mkdir('psf')
# # and then copy in the data files if they don't already exist as files
# if not os.path.isfile('psf/'+fits_file_i):
# iraf.images.imcopy(fits_file_i,'psf/'+fits_file_i,verbose="yes")
#
# if not os.path.isfile('psf/'+fits_file_g) :
# iraf.images.imcopy(fits_file_g,'psf/'+fits_file_g,verbose="yes")
#
# # also copy in the apcor star lists
# if not (os.path.isfile('psf/apcor_stars_i.txt') or os.path.islink('psf/apcor_stars_i.txt')) :
# shutil.copyfile('apcor_stars_i.txt','psf/apcor_stars_i.txt')
#
# if not (os.path.isfile('psf/apcor_stars_g.txt') or os.path.islink('psf/apcor_stars_g.txt')) :
# shutil.copyfile('apcor_stars_g.txt','psf/apcor_stars_g.txt')
#
# # and change to the psf folder
# os.chdir('psf')
iraf.unlearn(iraf.phot,iraf.datapars,iraf.photpars,iraf.centerpars,iraf.fitskypars)
iraf.apphot.phot.setParam('interactive',"no")
iraf.apphot.phot.setParam('verify',"no")
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.photpars.setParam('zmag',0.)
iraf.centerpars.setParam('cbox',9.)
iraf.centerpars.setParam('maxshift',3.)
iraf.fitskypars.setParam('salgorithm',"median")
iraf.fitskypars.setParam('dannulus',10.)
iraf.datapars.setParam('fwhmpsf',fwhm_g)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_g))
iraf.fitskypars.setParam('annulus',(6.*fwhm_g))
iraf.apphot.phot(image=fits_file_g, coords='apcor_stars_g.txt', output="g_psfstars.mag.1")
iraf.datapars.setParam('fwhmpsf',fwhm_i)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_i))
iraf.fitskypars.setParam('annulus',(6.*fwhm_i))
iraf.apphot.phot(image=fits_file_i, coords='apcor_stars_i.txt', output="i_psfstars.mag.1")
with open("apcor_stars_i.txt") as foo:
lines = len(foo.readlines())
iraf.daophot(_doprint=0)
iraf.pstselect.setParam('image',fits_file_i)
iraf.pstselect.setParam('photfile',"i_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect(verify='no', mode='h')
with open("apcor_stars_g.txt") as foo:
lines = len(foo.readlines())
iraf.pstselect.setParam('image',fits_file_g)
iraf.pstselect.setParam('photfile',"g_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect()
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.daopars.setParam('psfrad',iraf.nint(3.*fwhm_i))
iraf.daopars.setParam('fitrad',fwhm_i)
iraf.psf.setParam('image',fits_file_i)
iraf.psf.setParam('photfile',"i_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_i+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
iraf.psf.setParam('image',fits_file_g)
iraf.psf.setParam('photfile',"g_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_g+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
def psffit(img, fwhm, psfstars, hdr, interactive, _datamax=45000, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile compute the psf using the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.photpars.zmag = zmag
iraf.delete('_psf.ma*,' + img + '.psf.fit?,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?', verify=False)
iraf.phot(img+'[0]', '_psf.coo', '_psf.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
if interactive:
shutil.copyfile('_psf.mag', '_psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img+'[0]', '_psf.mag', '_psf.pst', psfstars, interac=False, verify=False)
iraf.psf(img + '[0]', '_psf.mag', '_psf.pst', img + '.psf', '_psf.psto', '_psf.psg', interac=interactive,
verify=False, verbose=False)
iraf.group(img + '[0]', '_psf.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_psf.nst', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, pst, fitmag
else:
_show = True
if option.redo == False:
redo = False
else:
redo = True
arterr = ''
if _interactive:
_show = True
print _show, _interactive
# ####################### SET IRAF PARAMETERS #######################
from pyraf import iraf
iraf.astcat(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0
iraf.digiphot.daophot.photpars.zmag = zmag
################################# CHECK HEADER ######################################
warn = '##################################\n'
for imglong in imglist:
if imglong:
if imglong[-5:] == '.fits':
img = imglong[:-5]
else:
img = imglong
######
try:
print string.split(img, '/')[-1] + '.fits'
agnkey.agnsqldef.updatevalue(
def get_fake_centroid_and_fluxcorr(filename, x, y, instrument, filt):
"""
Locate the center of a fake psf and determine the flux correction
needed to bring it into alignment with the hstsnphot zero points.
INPUTS: The fake-SN psf image in filename, the expected x,y position
of the center of the psf, the instrument and filter being modeled.
RETURNS: xcentroid, ycentroid, fluxcorr
"""
from pyraf import iraf
if instrument == 'WFC3_IR':
ZPT = ZPT_WFC3_IR_VEGA[filt]
elif instrument == 'WFC3_UVIS':
ZPT = ZPT_WFC3_UVIS_VEGA[filt]
elif instrument.startswith('ACS'):
ZPT = ZPT_ACS_VEGA[filt]
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn(iraf.apphot.phot)
iraf.unlearn(iraf.datapars)
iraf.unlearn(iraf.centerpars)
#Use the centroid algorithm right now as it seems more robust to geometric distortion.
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 5.0
iraf.unlearn(iraf.fitskypars)
iraf.unlearn(iraf.photpars)
photparams = {
'interac': False,
'radplot': False,
}
iraf.datapars.readnoise = 0.0
iraf.datapars.itime = 1.0
iraf.datapars.epadu = 1.0
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn(iraf.fitskypars)
iraf.fitskypars.salgorithm = 'constant'
iraf.fitskypars.skyvalue = 0.0
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = APDEFAULTSIZE[instrument] * PIXSIZE[
instrument] / NATIVE_PIXSIZE[instrument]
iraf.photpars.zmag = ZPT
iraf.photpars.mkapert = False
#Write the coordinate file starting as position x and y
coxyfile = 'centroid.xycoo'
coxy = open(coxyfile, 'w')
print >> coxy, "%10.2f %10.2f" % (x, y)
coxy.close()
if os.path.exists('centroid.mag'): os.remove('centroid.mag')
iraf.phot(image=filename,
skyfile='',
coords=coxyfile,
output='centroid.mag',
verify=False,
verbose=True,
Stdout=1,
**photparams)
f = open('centroid.mag', 'r')
maglines = f.readlines()
f.close()
xcentroid = float(maglines[76].split()[0])
ycentroid = float(maglines[76].split()[1])
#Calculate the aperture correction necessary to match the observed aperture correction.
meas_flux_frac = float(maglines[79].split()[1])
expected_flux_frac = 10.0**(
-0.4 * APCOR[filt][APSIZE.index('%1i' % APDEFAULTSIZE[instrument])])
flux_corr = expected_flux_frac / meas_flux_frac
return xcentroid, ycentroid, flux_corr
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
import copy, os, shutil, glob, sys, string, re, math, operator, time
import pyfits
from types import *
from mx.DateTime import *
from iqpkg import *
import ephem
# Necessary packages
iraf.images()
iraf.immatch()
iraf.imfilter()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.digiphot()
iraf.apphot()
yes=iraf.yes
no=iraf.no
INDEF=iraf.INDEF
hedit=iraf.hedit
imgets=iraf.imgets
imcombine=iraf.imcombine
pyrafdir="python/pyraf/"
pyrafdir_key='PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir=os.environ[pyrafdir_key]
else:
def get_app_phot_target(image, ra=None, dec=None, plot=True, store=True, wcsin="logical", fwhm=None, box=15, arcsecpix=0.394, app=2):
'''
coords: files:
wcsin: can be "world", "logic"
fwhm: in arcsec
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
print "Assuming ra=%.5f, dec=%.5f"%(ra, dec)
pra, pdec = get_xy_coords(image, ra, dec)
else:
if("logic" in wcsin):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as wcs module does not seem to be working well.
try:
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#print "Retrieved the pixel number"
pra, pdec = get_xy_coords(image, ra, dec)
except IndexError:
print "Error with astrometry.net. trying the rudimentary method."
pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
if (not fwhm is None):
fwhm_value = fwhm
elif (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
else:
#Put some default value for Palomar
fwhm_value=1.5
if (wcsin == 'logical'):
fwhm_value = fwhm_value / arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
if (not fitsutils.has_par(image, "EXPTIME")):
if (fitsutils.has_par(image, "ITIME") and fitsutils.has_par(image, "COADDS")):
exptime = fitsutils.get_par(image, "ITIME")*fitsutils.get_par(image, "COADDS")
fitsutils.update_par(image, "EXPTIME", exptime)
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*app) # Set aperture radius to two times the PSF radius
sky_rad= math.ceil(aperture_rad*app*2)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), \
("stdev","<f4"), ("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT") and fitsutils.has_par(image, "ZEROPTU")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
else:
mag = 0
magerr = 0
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
#zmin, zmax = zscale.zscale(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50])
#zmin, zmax = zscale.zscale(impf[0].data)
#im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
impf[0].data[np.isnan(impf[0].data)] = np.nanmedian(impf[0].data)
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
im = plt.imshow(impf[0].data, vmin=np.percentile(impf[0].data, 5), vmax=np.percentile(impf[0].data, 95), origin="bottom")
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"), dpi=200)
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
def organizeFiles():
# Create files that are usually generated by mkimset.
# We have more information about how to properly group our
# files together.
imsetsFilename = 'standards.imageSets'
obsparFilename = 'standards.obsParams'
shiftsFilename = 'standards.shifts'
iMagniFilename = 'standards.instMag'
imsets = open(imsetsFilename, 'w')
obspar = open(obsparFilename, 'w')
shifts = open(shiftsFilename, 'w')
for star in stars:
roots = []
for filter in filters:
dir = star.lower() + '_' + filter.lower()
if filter == 'Lp':
dir += '2'
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = []
for ii in images:
fileRoot = ii.split('/')[-1]
imageRoots.append(fileRoot.replace('.fits', ''))
roots.append(imageRoots)
print star, filter, len(imageRoots)
longCount = np.array([len(r) for r in roots]).max()
print 'Long Count for %s = %d' % (star, longCount)
for ii in range(longCount):
imsets.write('%5s : ' % star)
for ff in range(len(roots)):
if ii < len(roots[ff]):
# Write obsParams
ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,IFILTER,ITIME,XAIRMASS,OTIME',
'yes',
Stdout=obspar)
# Write shifts
posInfo = ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,XCENTER,YCENTER',
'yes',
Stdout=1)
posFields = posInfo[0].split()
shiftX = -1.0 * float(posFields[1])
shiftY = -1.0 * float(posFields[2])
shifts.write('%-10s %7.2f %7.2f\n' %
(posFields[0], shiftX, shiftY))
# Write imageSet
imsets.write('%-10s ' % (roots[ff][ii] + '.fits'))
else:
imsets.write('%-10s ' % ('INDEF'))
imsets.write('\n')
imsets.close()
obspar.close()
shifts.close()
# Now run mknobsfile
ir.delete(iMagniFilename)
ir.digiphot()
ir.photcal()
ir.unlearn('mknobsfile')
ir.mknobsfile.obsparams = obsparFilename
ir.mknobsfile.shifts = shiftsFilename
ir.mknobsfile('*.phot.mag',
','.join(filters),
imsetsFilename,
iMagniFilename,
verbose='no',
wrap='no')
# Run mkconfig
ir.delete('standards.config')
ir.unlearn('mkconfig')
ir.mkconfig('standards.config',
'fphotcal_ukirt_faint.dat',
'fstandards.instMag.dat',
'tphotcal_ukirt_faint.dat',
check='no',
edit='no')
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2.5, plotdir=None, box=15, arcsecpix=0.394):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
from pyraf import iraf
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
if (plotdir is None):
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
print "Will create output files", out_name, clean_name
# Read values from .ec file
fwhm_value = fwhm/arcsecpix
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')/arcsecpix
elif (fwhm is None):
fwhm_value=3.5/arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
noise = fitsutils.get_par(image, 'RDNOISE')
print "FWHM: %.1f pixels, %.1f arcsec"%(fwhm_value, fwhm_value*arcsecpix)
aperture_rad = math.ceil(float(fwhm_value)*1.5) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*4
if (not plot_only):
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime",\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
#fwhm = fwhm_value,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), ("stdev","<f4"),\
("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(np.floor(np.sqrt(len(m))))
dimY = int(np.ceil(len(m)*1./dimX))
outerrad = sky_rad+10
cutrad = outerrad + 15
print "Cutrad %.1f"%cutrad
plt.suptitle("FWHM=%.2f arcsec. %d stars"%(fwhm_value*arcsecpix, len(m)))
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( i*dimY + j < len(m)):
k = i*dimY + j
#print dimX, dimY, i, j, k
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
y1 = np.maximum(y1, 0); y2=np.maximum(y2, 0); x1=np.maximum(x1, 0); x2 = np.maximum(x2, 0)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except ValueError:
print y1, y2, x1, x2
print img[x1:x2,y1:y2]
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=aperture_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+20)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
plt.tight_layout()
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
def findStars(self):
# To look for the stars that exist in the image specified.
# It will:
# Utilise the IRAF package daofind
# be used for this image subtraction package
# Gives ouput of co-ords, "#imagename_daofind.coo"
output = self._Name.replace(".fits", "_daofind.coo")
try:
self.cleanOutputFiles(output)
except:
print "Error cleaning output files"
print sys.exc_info()[0]
# Load as in IRAF and neglect output:
# NOAO
# DIGIPHOT
# APPHOT
# DAOFIND
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
# Set the parameter list
# image: input image
# output: output co-ordinates
# verify: don't know
# threshold: sigma above which stars are determined (fixed in this script)
# datapar: point to a file with the user input
#
# ccdread: CCD readout noise (image header keyword)
# gain: CCD gain (image header keyword)
# readnoi: CCD readout noise in e
# epadu: gain in e- per count
#
# findpar: point to a file with the user input
#
# threshold: in units of sigma
#
# centerpars:
#
# Save the parameter list
# Pull information from the header of the FITS image
#self.loadHeader()
#datapars = iraf.datapars.getParList()
#findpars = iraf.findpars.getParList()
#ceneterpars = iraf.centerpars.getParList()
#for par in datapars:
#print par
## Print to user
#print "Information on images:"
#print "Gain: %s" % self.getHeader('RON')
#print "RON: %s" % self.getHeader('RON')
# datapar:
iraf.datapars.setParam('ccdread', 'RON')
iraf.datapars.setParam('gain', 'GAIN')
iraf.datapars.setParam('exposur','EXPTIME')
iraf.datapars.setParam('airmass','AIRMASS')
iraf.datapars.setParam('filter','SUBSET')
iraf.datapars.setParam('obstime','HIERARCH ESO OBS START')
iraf.datapars.setParam('datamin', '-100')
iraf.datapars.setParam('datamax', '60000')
# iraf.datapars.saveParList(filename="%s/uparm/datapars.par" % os.getcwd())
# findpar:
#threshold = 4.0
#iraf.findpars.setParam('threshold', str(threshold))
#iraf.findpars.saveParList(filename="uparm/findpars.par")
#print glob.glob(output)
# daofind:
iraf.daofind(image=self._Name,
output=output,
verify=0,
verbose=1,
sigma=self._skySTDEV,
nsigma=1,
scale=1,
fwhmpsf=self._MEDFWHM,
sharplo='0.0',
sharphi='1.0',
roundlo='-0.9',
roundhi='0.9',
thresho='6.0',
wcsout="logical",
Stdout=1,
mode='h'
)
# 1. Open the .coo file
# 2. Pasre the input
# 1. take input file
filename = "%s" % (self._Name.replace(".fits", "_daofind.coo"))
coords = open(filename)
coord = coords.readlines()
coords.close()
# 2. parse out the lines beginning with "#"
filename = "%s" % (self._Name.replace(".fits", "_daofind.reg"))
coordregfile = open(filename, "w")
coobject = []
for co in coord:
if co[0] != "#":
# 2b. parse each element
newCoordObject = imObject()
coitem = [i for i in co.replace("\n","").split(" ") if i!= ""]
newCoordObject._id = self.giveObjectID()
newCoordObject._pixelx = float(coitem[0])
newCoordObject._pixely = float(coitem[1])
newCoordObject._mag = float(coitem[2])
#newCoordObject.printInfo()
coordregfile.write("image; circle(%f,%f,4) # color = red\n" % (newCoordObject._pixelx, newCoordObject._pixely))
coobject.append(newCoordObject)
coordregfile.close()
self._ObjectList = coobject
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 runApperturePhotometryOnObject(self, photObject, write=False):
# Aperture sizes
#fap = 1.0
#fdan = 2.0
#fan = 3.0
# To take photometry of a given object using DAOPHOT/APPHOT.PHOT
# Check we have an object list
coordfilename = self._Name.replace(".fits", "objectID_%s.coo" % photObject._id)
coordfilename2 = self._Name.replace(".fits", "_apphot_%s.reg" % photObject._Name.replace(" ",""))
try:
regionOut = open(coordfilename2, "w")
regionOut.write("image; circle(%s, %s, %s) # color = red\n"% (photObject._pixelx, photObject._pixely, photObject._fap))
regionOut.write("image; circle(%s, %s, %s) # color = blue\n" % (photObject._pixelx, photObject._pixely, photObject._fdan))
regionOut.write("image; circle(%s, %s, %s) # color = green\n" % (photObject._pixelx, photObject._pixely, photObject._fan))
regionOut.close()
except:
print "ERROR opening region file to write"
print sys.exc_info()[0]
try:
objectList = open(coordfilename,"r")
objectList.close()
except:
objectList = open(coordfilename,"w")
objectList.write("%s %s" % (photObject._pixelx, photObject._pixely))
objectList.close()
# Load as in IRAF and neglect output:
# NOAO
# DIGIPHOT
# APPHOT
# DAOFIND
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
# DAOPHOT/APPHOT - PHOT Package
# Parameters:
#
# image
# skyfile???
# coords - input file
# output - output file
# datapars
# centerpars
# fitskypars
# photpars
cwd = os.getcwd()
# datapars:
datapars = "%s/uparm/datapars.par" % cwd
#datapars = "uparm/datapars.par"
#iraf.datapars.saveParList(filename=datapars)
# centerpars:
centerpars = "%s/uparm/centerpars.par" % cwd
#centerpars = "uparm/centerpars.par"
# iraf.centerpars.saveParList(filename=centerpars)
# fitskypars:
fitskypars = "%s/uparm/fitskypars.par" % cwd
#fitskypars = "uparm/fitskypars.par"
# iraf.fitskypars.saveParList(filename=fitskypars)
# photpars:
photpars = "%s/uparm/photpars.par" % cwd
#photpars = "uparm/photpars.par"
# iraf.photpar.saveParList(filename=photpars)
#iraf.datapar(sigma=1.5, exposure='IMGEXP', gain='GAIN', ccdread='RON')
# standard
#iraf.phot.setParam('image', self._Name)
#iraf.phot.setParam("output", self._Name.replace(".fits", "_phot.mag"))
#iraf.phot.setParam("datapar", datapars)
#iraf.phot.setParam("centerp", centerpars)
#iraf.phot.setParam("fitskyp", fitskypars)
#iraf.phot.setParam("photpar", photpars)
#iraf.phot.setParam("interac", "no")
#iraf.phot.setParam("verify", "yes")
# co-ordinate file
iraf.phot.setParam("coords", coordfilename)
# run phot
apphotpar = "%s/uparm/phot.par" % cwd
# iraf.phot.saveParList(filename=apphotpar)
output = self._Name.replace(".fits", "_objectID%s_phot.mag" % photObject._id)
try:
self.cleanOutputFiles(output)
except:
print "Error cleaning failed"
print sys.exc_info()[0]
#apsizes= (.4*fap*self._MEDFWHM,.5*fap*self._MEDFWHM,.6*fap*self._MEDFWHM,.8*fap*self._MEDFWHM,
# 1.*fap*self._MEDFWHM,1.2*fap*self._MEDFWHM, 1.5*fap*self._MEDFWHM,2.*fap*self._MEDFWHM,
# 2.5*fap*self._MEDFWHM,3.*fap*self._MEDFWHM
# )
#irafapsizes = '%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f' % apsizes
#irafapsizes = '%.2f' % (5*apsizes[4])
self.loadHeader()
if self._Band in ["J", "H", "K"]:
ifilter = self.getHeader("FILTER")
else:
ifilter = self.getHeader("SUBSET")
print "FILTER: %s" % ifilter
print "RON: %s" % self.getHeader("RON")
print "GAIN: %s" % self.getHeader("GAIN")
print "EXPTIME: %s" % self.getHeader("EXPTIME")
print "AIRMASS: %s" % self.getHeader("AIRMASS")
print "DATE: %s" % self.getHeader("DATE-OBS")
print "ZP: %s" % self._ZPDICT[self.getHeader("FILTER")]
try:
EPADU = (2/3)*self.getHeader("NIMGS")*self.getHeader("GAIN")
print "Keyword used: NIMGS"
except:
EPADU = (2/3)**self.getHeader("NCOMBINE")*self.getHeader("GAIN")
print "Keyword uparmsed: NCOMBINE"
iraf.phot(image=self._Name,
coords=coordfilename,
output=output,
verify=0,
verbose=1,
interac="no",
scale=1,
fwhmpsf=self._MEDFWHM,
sigma=self._skySTDEV,
wcsin="logical",
wcsout="logical",
datamin=-100,
datamax="INDEF",
zmag=self._ZPDICT[self.getHeader("FILTER")], # to do: make function to calculate
annulus=photObject._fan,
dannulus=photObject._fdan,
calgorithm="none",
aperture=photObject._fap,
cbox=1.5*self._MEDFWHM,
maxshift=15,
mode="ql",
Stdout=1,
readnoi=self.getHeader("RON"),
epadu=EPADU,
itime=self.getHeader("EXPTIME"),
xairmass=self.getHeader("AIRMASS"),
ifilter=ifilter,
otime=self.getHeader("DATE-OBS"),
salgori="mode",
#skyvalu=0,
smaxite=1
)
# Parse the output
photout = open(output, "r")
photoObjectProperties = photout.readlines()
photout.close()
propList = []
for Properties in photoObjectProperties:
if Properties[0] != "#":
Property = [i for i in Properties.replace("\n","").replace("\\","").split(" ") if i != ""]
propList.append(Property)
try:
photObject._skyFlux = float(propList[3][0])
except:
photObject._skyFlux = (propList[3][0])
try:
photObject._flux = float(propList[4][1])
except:
photObject._flux = (propList[4][1])
try:
photObject._appMag = float(propList[4][4])
except:
photObject._appMag = (propList[4][4])
try:
photObject._appMagErr = float(propList[4][5])
except:
photObject._appMagErr = (propList[4][5])
photObject._midMJD, photObject._midMJDErr = self.getMidMJD()#seconds=True,zero=55822.89371528)
def escut(image, pos_file, fwhm, peak):
# input image file name, file name with matched source positions, **np.array of fwhm measurements for each source
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pyraf import iraf
# import sewpy
import os
from matplotlib.path import Path
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.unlearn(iraf.phot, iraf.datapars, iraf.photpars, iraf.centerpars,
iraf.fitskypars)
iraf.apphot.phot.setParam('interactive', "no")
iraf.apphot.phot.setParam('verify', "no")
iraf.datapars.setParam('datamax', 50000.)
iraf.datapars.setParam('gain', "gain")
iraf.datapars.setParam('ccdread', "rdnoise")
iraf.datapars.setParam('exposure', "exptime")
iraf.datapars.setParam('airmass', "airmass")
iraf.datapars.setParam('filter', "filter")
iraf.datapars.setParam('obstime', "time-obs")
# iraf.datapars.setParam('obstime',"date-obs")
iraf.datapars.setParam('sigma', "INDEF")
iraf.photpars.setParam('zmag', 0.)
iraf.centerpars.setParam('cbox', 9.)
iraf.centerpars.setParam('maxshift', 3.)
iraf.fitskypars.setParam('salgorithm', "median")
iraf.fitskypars.setParam('dannulus', 10.)
# clean up the indefs so we can actually do stats, but reassign them to 99999 so we don't lose track of things
# keep a separate list without them to do the median (we need floats)
indefs = np.where(fwhm == 'INDEF')
good = np.where(fwhm != 'INDEF')
fwhm[indefs] = 99.999
fwhm = fwhm.astype(float)
fwhm_good = fwhm[good].astype(float)
indefs = np.where(peak == 'INDEF')
peak[indefs] = -999.999
peak = peak.astype(float)
peak_good = peak[good].astype(float)
if not os.path.isfile(image[0:-5] + '.txdump'):
# findavgfwhm = sewpy.SEW(
# params = ["X_IMAGE", "Y_IMAGE", "FWHM_IMAGE", "FLAGS"],
# config = {"DETECT_THRESH":200.0},
# sexpath = "sex"
# )
#
# out = findavgfwhm(image)["table"]
#
# fwhms = out['FWHM_IMAGE'] # This is an astropy table.
# flags = out['FLAGS']
# get a really rough estimate of the stellar FWHM in the image to set apertures
# use the input fwhm measurement
# ap1x = fwhm_est
# xpos = datatable['X_IMAGE']
# ypos = datatable['Y_IMAGE']
# fwhm = datatable['FWHM_IMAGE']
# flags = datatable['FLAGS']
# idno = datatable['NUMBER']
ap1x = np.median(
fwhm_good
) # only use isolated detections of stars, this is the 1x aperture
# print ap1x
ap2x = 2.0 * ap1x
# these = [ i for i,id in enumerate(idno) if (flags[i] == 0)]
# with open(image[0:-5]+'.escut.pos','w+') as f:
# for j in range(len(xpos)):
# print >> f, xpos[j], ypos[j], fwhm[j], idno[j]
iraf.datapars.setParam('fwhmpsf', ap1x)
iraf.photpars.setParam('apertures', repr(ap1x) + ', ' + repr(ap2x))
iraf.fitskypars.setParam('annulus', 4. * ap1x)
iraf.apphot.phot(image=image,
coords=pos_file,
output=image[0:-5] + '.phot')
with open(image[0:-5] + '.txdump', 'w+') as txdump_out:
iraf.ptools.txdump(
textfiles=image[0:-5] + '.phot',
fields=
"id,mag,merr,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image",
expr=
'MAG[1] != INDEF && MERR[1] != INDEF && MAG[2] != INDEF && MERR[2] != INDEF',
headers='no',
Stdout=txdump_out)
mag1x, mag2x = np.loadtxt(image[0:-5] + '.txdump',
usecols=(1, 2),
unpack=True)
iraf_id = np.loadtxt(image[0:-5] + '.txdump',
usecols=(0, ),
dtype=int,
unpack=True)
# idno = np.loadtxt(image[0:-5]+'.escut.pos', usecols=(3,), dtype=int, unpack=True)
xpos, ypos = np.loadtxt(pos_file, usecols=(0, 1), unpack=True)
keepIndex = iraf_id - 1
xpos, ypos, fwhm, peak = xpos[keepIndex], ypos[keepIndex], fwhm[
keepIndex], peak[keepIndex]
# print idno.size, iraf_id.size, xpos.size
diff = mag2x - mag1x
diffCut = diff
magCut = mag2x
xCut = xpos #[good]
yCut = ypos #[good]
idCut = iraf_id
fwhmCut = fwhm #_good
peakCut = peak
print(peakCut.size, magCut.size, diffCut.size)
print(diffCut.size, 0, np.median(diffCut), diffCut.std())
nRemoved = 1
# plt.clf()
# plt.scatter(peakCut, magCut, edgecolor='none')
# plt.savefig('peaktest.pdf')
plt.clf()
# plt.hlines(bin_edges, -2, 1, colors='red', linestyle='dashed')
plt.scatter(diff, mag2x, edgecolor='none', facecolor='black', s=4)
# plt.scatter(diffCut, magCut, edgecolor='none', facecolor='blue', s=4)
magdiff = list(
zip(magCut.tolist(), diffCut.tolist(), peakCut.tolist(),
idCut.tolist()))
dtype = [('mag', float), ('diff', float), ('peak', float), ('id', int)]
magdiff = np.array(magdiff, dtype=dtype)
magSort = np.sort(magdiff, order='peak')
peakRange = (magSort['peak'] > 20000.0) & (magSort['peak'] < 40000.0)
peakVal = np.median((magSort['diff'])[np.where(peakRange)])
# peakVal = np.median(diffCut)
print(peakVal)
plt.scatter((magSort['diff'])[np.where(peakRange)],
(magSort['mag'])[np.where(peakRange)],
edgecolor='none',
facecolor='blue',
s=4)
while nRemoved != 0:
nBefore = diffCut.size
diffCheck = np.where(
abs(peakVal - diffCut) < 2.0 *
diffCut.std()) #[i for i,d in enumerate(diff) if (-0.5 < d < 0.0)]
#
diffCut = diffCut[diffCheck]
nRemoved = nBefore - diffCut.size
magCut = magCut[diffCheck]
xCut = xCut[diffCheck]
yCut = yCut[diffCheck]
idCut = idCut[diffCheck]
fwhmCut = fwhmCut[diffCheck]
print(diffCut.size, nRemoved, np.median(diffCut), diffCut.std())
if 0.05 < diffCut.std() < 0.06:
nRemoved = 0
# plt.fill_betweenx(bin_centers, bin_meds+3.0*bin_stds, bin_meds-3.0*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
# with open('escutSTD_i.pos','w+') as f:
# for i,blah in enumerate(xCut):
# print >> f, xCut[i], yCut[i], diffCut[i]
bin_meds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic='median',
bins=24,
range=(-12, 0))
bin_stds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic=np.std,
bins=24,
range=(-12, 0))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width / 2
# print bin_meds, bin_stds
bin_hw = np.zeros_like(bin_stds)
for i, bin_std in enumerate(bin_stds):
if bin_std > 0.025:
bin_hw[i] = 3.0 * bin_std
else:
bin_hw[i] = 0.075
# print len(binnumber)
# for i,bin_hwi in enumerate(bin_hw):
left_edge = np.array(list(zip(peakVal - bin_hw, bin_centers)))
right_edge = np.flipud(np.array(list(zip(peakVal + bin_hw, bin_centers))))
# print left_edge, right_edge
verts = np.vstack((left_edge, right_edge))
# print verts
# verts = np.delete(verts, np.array([0,1,2,22,23,24,25,45,46,47]), axis=0)
# DON'T USE A PATH BECAUSE APPARENTLY IT CAN SELECT THE INVERSE SET!! WTF
# print verts
esRegion = Path(verts)
sources = esRegion.contains_points(list(zip(diff, mag2x)))
# print sources
with open('escutREG_i.pos', 'w+') as f:
for i, blah in enumerate(xpos[sources]):
print((xpos[sources])[i], (ypos[sources])[i], (diff[sources])[i],
file=f)
magCut2 = mag2x[sources]
magCut1 = mag1x[sources]
fwhmCut = fwhm[sources]
xCut = xpos[sources]
yCut = ypos[sources]
diffCut = diff[sources]
# find the sources that are in the std method but not the region method
# print idCut, idno[sources]
# extrasSTD = np.setdiff1d(idno[sources], idCut)
# print extrasSTD.size
# print extrasSTD
# with open('escutUNIQUE.pos','w+') as f:
# for i,blah in enumerate(extrasSTD):
# print >> f, xpos[blah-1], ypos[blah-1]
# fwhmcheck = np.loadtxt('testfwhmREG.log', usecols=(10,), unpack=True)
fwhmchk2 = np.where((magCut2 < -4) & (fwhmCut < 90.0))
print(np.median(fwhmCut[fwhmchk2]), np.std(fwhmCut[fwhmchk2]))
fwchk = np.where(
np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 *
np.std(fwhmCut[fwhmchk2]))
drop = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 * np.std(
fwhmCut[fwhmchk2])
keep = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) <= 10.0 * np.std(
fwhmCut[fwhmchk2])
with open('escutVBAD_i.pos', 'w+') as f:
for i, blah in enumerate(xCut[fwchk]):
print((xCut[fwchk])[i], (yCut[fwchk])[i], file=f)
with open('escut_i.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
with open('escut_g.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
plt.fill_betweenx(bin_centers,
peakVal + bin_hw,
peakVal - bin_hw,
facecolor='red',
edgecolor='none',
alpha=0.4,
label='2x RMS sigma clipping region')
plt.scatter(diffCut[fwchk],
magCut2[fwchk],
edgecolor='none',
facecolor='red',
s=4)
plt.ylim(0, -12)
plt.xlabel('$m_{2x} - m_{1x}$')
plt.ylabel('$m_{2x}$')
plt.xlim(-2, 1)
plt.savefig('testmagiraf.pdf')
plt.clf()
plt.scatter(magCut2, fwhmCut, edgecolor='none', facecolor='black')
plt.scatter(magCut2[fwchk],
fwhmCut[fwchk],
edgecolor='none',
facecolor='red')
plt.hlines([np.median(fwhmCut)], -12, 0, colors='red', linestyle='dashed')
plt.hlines([
np.median(fwhmCut) + fwhmCut.std(),
np.median(fwhmCut) - fwhmCut.std()
],
-12,
0,
colors='red',
linestyle='dotted')
plt.ylim(0, 20)
plt.xlim(-12, 0)
plt.ylabel('fwhm')
plt.xlabel('$m_{2x}$')
plt.savefig('fwhmcheck.pdf')
return fwhmCut[keep]
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 marksn2(img, fitstab, frame=1, fitstab2='', verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr = lsc.util.readhdr(fitstab)
_filter = lsc.util.readkey3(hdr, 'filter')
column = lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex = array(column['ra0'], float)
decsex = array(column['dec0'], float)
if fitstab2:
hdr = lsc.util.readhdr(fitstab2)
_filter = lsc.util.readkey3(hdr, 'filter')
_exptime = lsc.util.readkey3(hdr, 'exptime')
column = lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2 = array(column['ra0'], float)
decsex2 = array(column['dec0'], float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]', frame, fill=True, Stdout=1)
vector = []
for i in range(0, len(rasex)):
vector.append(str(rasex[i]) + ' ' + str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy),
mark="circle",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=207,
txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0, len(column['ra0'])):
print xy[i], column['ra0'][i], column['dec0'][i], column['magp3'][
i], column['magp4'][i], column['smagf'][i], column['magp2'][i]
if fitstab2:
vector2 = []
for i in range(0, len(rasex2)):
vector2.append(str(rasex2[i]) + ' ' + str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy1),
mark="cross",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=205,
txsize=2)
iraf.imarith('Ha_final', "-", imageName + "_scaledplus",
"Hacs_finalplus")
iraf.hedit("Hacs_finalplus", 'Rscale', avgScaleFactor + stDev)
#minus one stdev
iraf.imarith('R_final', "*", avgScaleFactor - stDev,
imageName + "_scaledminus")
iraf.imarith('Ha_final', "-", imageName + "_scaledminus",
"Hacs_finalminus")
iraf.hedit("Hacs_finalminus", 'Rscale', avgScaleFactor - stDev)
#clear up superfluous images
for f in (imageName + "_scaledminus.fits", imageName + "_scaledplus.fits",
imageName + "_scaled.fits", 'rIn.fits', 'haIn.fits'):
silentDelete(f)
print(
'The continuum subtracted images are Hacs_final.fits, Hacs_finalminus.fits, and Hacs_finalplus.fits'
)
#This bit just takes the arguments from the command line amd runs the above
#two functions on them.
filename_R = sys.argv[1]
filename_Ha = sys.argv[2]
iraf.digiphot()
print('Aligning images...')
alignImages(filename_R, filename_Ha)
print('Subtracting continuum...')
continuumReduce(filename_R)
from scipy import interpolate
from scipy.optimize import leastsq
from astropy import wcs
from astropy.coordinates import ICRS, Galactic
from astropy import units
from astropy.io import fits,ascii
from astropy.modeling import models, fitting
from astropy.table import Table, Column
from astLib import astCoords
import matplotlib.pyplot as plt
import pyfits
import psycopg2
from pyraf import iraf
iraf.obsutil(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.digiphot.daophot(_doprint=0)
import traceback
if ndmode in ['d','D','database','Database']:
conn_string = "host='10.36.2.113' dbname='postgres' user='******' password='******'"
print "Connecting to database\n ->%s" % (conn_string)
conn = psycopg2.connect(conn_string)
cursor = conn.cursor()
errlogname = 'ErrorPhot_'+time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime())+'.log'
logerror = open(errlogname,'wa')
def absphot(img,_field,_catalogue,_fix,_color,rejection,_interactive,_type='fit',redo=False,show=False,cutmag=-1,database='dataredulco',_calib='sloan'):
from astropy.io import fits
import lsc
import math
import sys,re,string,os
from lsc.util import readkey3, readhdr
from numpy import array, compress, zeros, median, std, asarray, isfinite,mean
from pyraf import iraf
if show:
from pylab import ion,plot,draw,clf
import time
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
t = fits.open(img)
tbdata = t[1].data
hdr2=t[1].header
hdr=lsc.util.readhdr(img)
_cat=readkey3(hdr,'catalog')
_telescope=lsc.util.readkey3(hdr,'telescop')
_instrume=lsc.util.readkey3(hdr,'instrume')
_filter=lsc.util.readkey3(hdr,'filter')
_airmass=lsc.util.readkey3(hdr,'airmass')
_exptime=lsc.util.readkey3(hdr,'exptime')
_date=lsc.util.readkey3(hdr,'date-obs')
_object=lsc.util.readkey3(hdr,'object')
_ra=lsc.util.readkey3(hdr,'RA')
_dec=lsc.util.readkey3(hdr,'DEC')
print _filter
if _telescope in ['lsc','1m0-04','1m0-05','1m0-09']: kk=lsc.sites.extintion('ctio')
elif _telescope in ['elp','1m0-08']: kk=lsc.sites.extintion('mcdonald')
elif _telescope in ['cpt','1m0-12','1m0-10','1m0-13']: kk=lsc.sites.extintion('southafrica')
elif _telescope in ['ftn','Faulkes Telescope North']: kk=lsc.sites.extintion('mauna')
elif _telescope in ['1m0-03','1m0-11','coj','fts','Faulkes Telescope South']: kk=lsc.sites.extintion('siding')
if _calib not in ['sloan','sloanprime','natural','apass','']: colorefisso=lsc.sites.colfix(_instrume)
else: colorefisso=lsc.sites.colfix(_instrume,_calib)
print redo
print _cat
if _cat and not redo:
print 'already calibrated'
else:
try:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
column=makecatalogue([img])[_filter][img]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if _type=='fit':
magsex=array(column['smagf'],float)
magerrsex=array(column['smagerrf'],float)
elif _type=='ph':
magsex=array(column['magp3'],float)
magerrsex=array(column['merrp3'],float)
else: sys.exit(_type+' not valid (ph or fit)')
print len(rasex)
if not cutmag: cutmag=99
#else: cutmag= cutmag-2.5*math.log10(float(_exptime))
if len(compress( array(magsex) < float(cutmag) , magsex)) < 5 : cutmag=99 # not cut if only few object
rasex = compress(array(magsex,float)<=cutmag,rasex)
decsex = compress(array(magsex,float)<=cutmag,decsex)
magerrsex = compress(array(magsex,float)<=cutmag,magerrsex)
magsex = compress(array(magsex,float)<=cutmag,array(magsex))
print len(rasex)
if _interactive:
iraf.set(stdimage='imt1024')
iraf.display(re.sub('.sn2','',img),1,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img,inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(1,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
# raw_input('here')
if _catalogue:
######## use external catalogue
if _catalogue[0]=='/': stdcooC=lsc.lscastrodef.readtxt(_catalogue)
else: stdcooC=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/'+_catalogue)
rastdC,decstdL=array(stdcooC['ra'],float),array(stdcooC['dec'],float)
lsc.util.delete('tmp.stdL.pix')
colonne=str(stdcooC['rapos'])+' '+str(stdcooC['decpos'])
if _catalogue[0]=='/':
iraf.wcsctran(_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
else:
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/'+_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
standardpixC=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
xstdC=standardpixC['ra']
ystdC=standardpixC['dec']
idstdC=standardpixC['id']
xstdC=compress((array(xstdC,float)<readkey3(hdr,'XDIM'))&(array(xstdC,float)>0)&(array(ystdC,float)>0)&(array(ystdC,float)<readkey3(hdr,'YDIM')),xstdC)
xstdL=xstdLL=xstdS=xstdC
standardpixL=standardpixLL=standardpixS=standardpixC
stdcooL=stdcooLL=stdcooS=stdcooC
else:
######## check if it is landolt field
stdcooL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/landolt.cat')
rastdL,decstdL=array(stdcooL['ra'],float),array(stdcooL['dec'],float)
lsc.util.delete('tmp.stdL.pix')
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/landolt.cat','tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixL=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'yelow circles sextractor'
xstdL=standardpixL['ra']
ystdL=standardpixL['dec']
idstdL=standardpixL['id']
xstdL=compress((array(xstdL,float)<readkey3(hdr,'XDIM'))&(array(xstdL,float)>0)&(array(ystdL,float)>0)&(array(ystdL,float)<readkey3(hdr,'YDIM')),xstdL)
######## check if it is Stetson field
stdcooLL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/StetsonCat.dat')
ww=asarray([i for i in range(len(stdcooLL['ra'])) if ( abs(float(stdcooLL['ra'][i])-float(_ra))<.2 and abs(float(stdcooLL['dec'][i])-_dec)<.2 )])
if len(ww)>0:
for hh in stdcooLL.keys():
if type(stdcooLL[hh])!=int:
if hh not in ['id','ra','dec']:
stdcooLL[hh]=array(array(stdcooLL[hh])[ww],float)
else:
stdcooLL[hh]=array(stdcooLL[hh])[ww]
lll=[]
for i in range(0,len(stdcooLL['ra'])): lll.append(stdcooLL['ra'][i]+' '+stdcooLL['dec'][i])
rastdLL,decstdLL=array(stdcooLL['ra'],float),array(stdcooLL['dec'],float)
lsc.util.delete('tmp.stdLL.pix')
iraf.wcsctran('STDIN','tmp.stdLL.pix',img,inwcs='world',Stdin=lll,units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
if _interactive:
iraf.tvmark(1,'tmp.stdLL.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'red crosses Stetson'
standardpixLL={}
for ii in stdcooLL.keys(): standardpixLL[ii]=stdcooLL[ii]
standardpixLL['ra']=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
standardpixLL['dec']=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
xstdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
ystdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
idstdLL=standardpixLL['id']
xstdLL=compress((array(xstdLL,float)<readkey3(hdr,'XDIM'))&(array(xstdLL,float)>0)&(array(ystdLL,float)>0)&(array(ystdLL,float)<readkey3(hdr,'YDIM')),xstdLL)
######## check if it is sloan field
magsel0,magsel1=12,18
_ids=lsc.lscastrodef.sloan2file(_ra,_dec,20,float(magsel0),float(magsel1),'_tmpsloan.cat')
ascifile='_tmpsloan.cat'
stdcooS=lsc.lscastrodef.readtxt(ascifile)
rastdS,decstdS=array(stdcooS['ra'],float),array(stdcooS['dec'],float)
lsc.util.delete('tmp.stdS.pix')
iraf.wcsctran(ascifile,'tmp.stdS.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixS=lsc.lscastrodef.readtxt('tmp.stdS.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdS.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=205,txsize=2)
print 'green cross sloan'
xstdS=standardpixS['ra']
ystdS=standardpixS['dec']
idstdS=standardpixS['id']
xstdS=compress((array(xstdS,float)<readkey3(hdr,'XDIM'))&(array(xstdS,float)>0)&(array(ystdS,float)>0)&(array(ystdS,float)<readkey3(hdr,'YDIM')),xstdS)
##############################################3
if not _catalogue and len(xstdLL)>0:
xstdL=xstdLL
standardpixL=standardpixLL
stdcooL=stdcooLL
if _filter in ['U', 'B', 'V', 'R','I','Bessell-B','Bessell-V','Bessell-R','Bessell-I']:
filters={'U':'U', 'B':'B', 'V':'V', 'R':'R', 'I':'I','Bessell-B':'B','Bessell-V':'V','Bessell-R':'R','Bessell-I':'I'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
else:
colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
if _field=='sloan':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='landolt'
if len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
if not _catalogue:
if len(xstdLL)>0: _catalogue='StetsonCat.dat'
else: _catalogue='landolt.dat'
elif len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
stdcoo=lsc.lscastrodef.transformsloanlandolt(stdcoo)
if not _catalogue: _catalogue='sdss8'
print '\n### transform sloan in landolt'
else:
print 'landolt, but catalogue not found'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
elif _filter in ['up','gp','rp','ip','zs','SDSS-G','SDSS-R','SDSS-I','Pan-Starrs-Z']:
filters={'up':'u','ip':'i','gp':'g','rp':'r','zs':'z','SDSS-G':'g','SDSS-R':'r','SDSS-I':'i','Pan-Starrs-Z':'z'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
else:
colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
if _field=='landolt':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='sloan'
if len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
elif len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
stdcoo=lsc.lscastrodef.transformlandoltsloan(stdcoo)
if not _catalogue: _catalogue='landolt.dat'
print '\n### transform landolt to sloan'
else:
print 'sloan, but not in the sdss footprint'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
xstd=standardpix['ra']
ystd=standardpix['dec']
idstd=standardpix['id']
rastd,decstd=array(stdcoo['ra'],float),array(stdcoo['dec'],float)
xstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)&(array(ystd,float)<readkey3(hdr,'YDIM')),xstd)
if len(xstd0)>1: ######## go only if standard stars are in the field ##########
magstd0={}
airmass0={}
result={}
fileph={}
print '\n### standard field: '+str(_field)
ystd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),ystd)
rastd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),rastd)
decstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),decstd)
idstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),idstd)
stdcoo0={}
for key in stdcoo.keys():
if key in 'ugrizUBVRI':
stdcoo0[key]=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),stdcoo[key])
###############################################################
# pos0 = standard pos1 = sextractor
distvec,pos0,pos1=lsc.lscastrodef.crossmatch(array(rastd0),array(decstd0),array(rasex),array(decsex),10)
for key in stdcoo0.keys(): stdcoo0[key]=stdcoo0[key][pos0]
rastd0=rastd0[pos0]
decstd0=decstd0[pos0]
idstd0=idstd0[pos0]
rasex=rasex[pos1]
decsex=decsex[pos1]
# after change in may 2013 mag in sn2.fits file are already at 1s
magsex=magsex[pos1]-kk[filters[_filter]]*float(_airmass) # - K x airmass
# magsex=magsex[pos1]+2.5*math.log10(float(_exptime))-kk[filters[_filter]]*float(_airmass) # mag exptime - K x airmass
#################################################################################
if _field=='landolt':
print '\n### landolt system'
for _filtlandolt in 'UBVRI':
if _filtlandolt==filters[_filter]: airmass0[_filtlandolt]= 0 #_airmass
else: airmass0[_filtlandolt]= 0
magstd0[_filtlandolt]=stdcoo0[_filtlandolt]
fileph['mU']=zeros(len(rastd0))+999
fileph['mB']=zeros(len(rastd0))+999
fileph['mV']=zeros(len(rastd0))+999
fileph['mR']=zeros(len(rastd0))+999
fileph['mI']=zeros(len(rastd0))+999
fileph['V']=magstd0['V']
fileph['BV']=array(array(magstd0['B'],float)-array(magstd0['V'],float),str)
fileph['UB']=array(array(magstd0['U'],float)-array(magstd0['B'],float),str)
fileph['VR']=array(array(magstd0['V'],float)-array(magstd0['R'],float),str)
fileph['RI']=array(array(magstd0['R'],float)-array(magstd0['I'],float),str)
elif _field=='sloan':
for _filtsloan in 'ugriz':
if _filtsloan==filters[_filter]: airmass0[_filtsloan]= 0 # _airmass
else: airmass0[_filtsloan]=0
magstd0[_filtsloan]=stdcoo0[_filtsloan]
fileph['mu']=zeros(len(rastd0))+999
fileph['mg']=zeros(len(rastd0))+999
fileph['mr']=zeros(len(rastd0))+999
fileph['mi']=zeros(len(rastd0))+999
fileph['mz']=zeros(len(rastd0))+999
fileph['r']=magstd0['r']
fileph['gr']=array(array(magstd0['g'],float)-array(magstd0['r'],float),str)
fileph['ri']=array(array(magstd0['r'],float)-array(magstd0['i'],float),str)
fileph['ug']=array(array(magstd0['u'],float)-array(magstd0['g'],float),str)
fileph['iz']=array(array(magstd0['i'],float)-array(magstd0['z'],float),str)
########################################################################################
zero=[]
magcor=[]
fil = open(re.sub('.fits','.ph',img),'w')
fil.write(str(_instrume)+' '+str(_date)+'\n')
fil.write('*** '+_object+' '+str(len(magsex))+'\n')
if _field=='landolt':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['U']),str(airmass0['B']),str(airmass0['V']),str(airmass0['R']),str(airmass0['I'])))
elif _field=='sloan':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['u']),str(airmass0['g']),str(airmass0['r']),str(airmass0['i']),str(airmass0['z'])))
for i in range(0,len(magsex)):
fileph['m'+filters[_filter]][i]=magsex[i] # instrumental mangitude of std in pos0[i]
if _field=='landolt':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['V'][i],fileph['BV'][i],fileph['UB'][i],\
fileph['VR'][i],fileph['RI'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mU'][i]),str(fileph['mB'][i]),str(fileph['mV'][i]),str(fileph['mR'][i]),str(fileph['mI'][i]),str(stringastandard)))
elif _field=='sloan':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['r'][i],fileph['gr'][i],fileph['ug'][i],\
fileph['ri'][i],fileph['iz'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mu'][i]),str(fileph['mg'][i]),str(fileph['mr'][i]),str(fileph['mi'][i]),str(fileph['mz'][i]),str(stringastandard)))
zero.append(float(float(magstd0[filters[_filter]][i]))-float(magsex[i]))
magcor.append(magsex[i])
fil.close()
if show:
import time
from pylab import ion,plot,draw
ion()
aa=mean(compress(abs(array(zero))<99,zero))
xxx=compress((abs(array(magcor))<99)&(abs(array(zero))<99),magcor)
yyy=compress((abs(array(zero))<99)&(abs(array(magcor))<99),zero)
plot(xxx,yyy,'or')
plot([min(compress(abs(array(magcor))<99,magcor)),max(compress(abs(array(magcor))<99,magcor))],[aa,aa],'-b')
draw()
print std(compress(abs(array(zero))<99,zero))
time.sleep(5)
colorvec=colors[filters[_filter]]
for col in colorvec:
col0=magstd0[col[0]]
col1=magstd0[col[1]]
colstd0=array(col0,float)-array(col1,float)
################## sex ######################
colore=[]
for i in range(0,len(pos1)): colore.append(colstd0[i])
colore1=compress(abs(array(zero))<50,array(colore))
zero1=compress(abs(array(zero))<50,array(zero))
zero2=compress(abs(array(colore1))<2,array(zero1))
colore2=compress(abs(array(colore1))<2,array(colore1))
if _fix: fisso=colorefisso[filters[_filter]+col]
else: fisso=''
if len(colore2)==0:
print 'no calibration, '+_filter+' '+_field
b,a,sa,sb=9999,9999,0,0
else:
if _interactive: a,sa,b,sb=fitcol(colore2,zero2,_filter,col,fisso)
else: a,sa,b,sb=fitcol2(colore2,zero2,_filter,col,fisso,show,rejection)
print a,sa,b,sb
result[filters[_filter]+col]=[a,sa,b,sb]
if result:
print '\n### zeropoint ..... done at airmass 0'
if _catalogue:
lsc.util.updateheader(img,0,{'CATALOG':[str(string.split(_catalogue,'/')[-1]),'catalogue source']})
stringa=''
for ll in result:
for kk in range(0,len(result[ll])):
if not isfinite(result[ll][kk]): result[ll][kk]=0.0
valore='%3.3s %6.6s %6.6s %6.6s %6.6s' % (str(ll),str(result[ll][0]),str(result[ll][2]),str(result[ll][1]),str(result[ll][3]))
print '### ',valore
lsc.util.updateheader(img,0,{'zp'+ll:[str(valore),'a b sa sb in y=a+bx']})
if ll[0]==ll[2]: num=2
elif ll[0]==ll[1]: num=1
else: sys.exit('somthing wrong with color '+ll)
print ll,num
try:
print 'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1]
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
if result[ll][0]!=9999:
print _catalogue
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
else:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
def plant_kbos(filename, psf, kbos, shifts, prefix):
"""
Add KBOs to an image
:param filename: name of the image to add KBOs to
:param psf: Point Spread Function in IRAF/DAOPHOT format, used by ADDSTAR
:param kbos: list of KBOs to add, has format as returned by KBOGenerator
:param shifts: dictionary to transform coordinates to reference frame.
:param prefix: an estimate FWHM of the image, used to determine trailing.
:return: None
"""
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.images()
if shifts['nmag'] < 4:
logging.warning("Mag shift based on fewer than 4 common stars.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift based on fewer than 4 common stars.")
fd.close()
if shifts['emag'] > 0.05:
logging.warning("Mag shift has large uncertainty.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift hsa large uncertainty.")
fd.close()
addstar = tempfile.NamedTemporaryFile(suffix=".add", mode='w')
# transform KBO locations to this frame using the shifts provided.
w = get_wcs(shifts)
header = fits.open(filename)[0].header
# set the rate of motion in units of pixels/hour instead of ''/hour
scale = header['PIXSCAL1']
rate = kbos['sky_rate'] / scale
# compute the location of the KBOs in the current frame.
# offset magnitudes from the reference frame to the current one.
mag = kbos['mag'] - shifts['dmag']
angle = radians(kbos['angle'])
# Move the x/y locations to account for the sky motion of the source.
x = kbos['x'] - rate * 24.0 * shifts['dmjd'] * cos(angle)
y = kbos['y'] - rate * 24.0 * shifts['dmjd'] * sin(angle)
x, y = w.wcs_world2pix(x, y, 1)
# Each source will be added as a series of PSFs so that a new PSF is
# added for each pixel the source moves.
itime = float(header['EXPTIME']) / 3600.0
npsf = fabs(rint(rate * itime)) + 1
mag += 2.5 * log10(npsf)
dt_per_psf = itime / npsf
# Build an addstar file to be used in the planting of source.
idx = 0
for record in transpose([x, y, mag, npsf, dt_per_psf, rate, angle]):
x = record[0]
y = record[1]
mag = record[2]
npsf = record[3]
dt = record[4]
rate = record[5]
angle = record[6]
for i in range(int(npsf)):
idx += 1
x += dt * rate * math.cos(angle)
y += dt * rate * math.sin(angle)
addstar.write("{} {} {} {}\n".format(x, y, mag, idx))
addstar.flush()
fk_image = prefix + filename
try:
os.unlink(fk_image)
except OSError as err:
if err.errno == errno.ENOENT:
pass
else:
raise
# add the sources to the image.
if os.access(f'{fk_image}.art', os.R_OK):
os.unlink(f'{fk_image}.art')
iraf.daophot.addstar(filename,
addstar.name,
psf,
fk_image,
simple=True,
verify=False,
verbose=False)
# convert the image to short integers.
iraf.images.chpix(fk_image, fk_image, 'ushort')