def kaitphot(refimage, inlist, sncoo, aperture=10.0, dx=20, dy=20):
infiles = iraffiles(inlist)
# Find location of SN
[[snra, sndec]] = impix2wcs(refimage, sncoo[0], sncoo[1])
sn=[Star(name="New_SN",xval=sncoo[0],yval=sncoo[1])]
snstars=Starlist(stars=sn)
print "Coordinates of new SN: %s %s (J2000.0)" % (snra, sndec)
# Grab reference rootname
refroot,null = refimage.split('.')
# Big loop
for image in infiles:
# Image root
root,null = image.split('.')
# Grab zeropt and zeropt error from image header
zp,zpu = get_head(image,[ZPTKEY,ZPUKEY])
# Detect objects in difference image
iraf.iqobjs('%s.sub.fits' % root,SIGMA,SATVAL,skyval="0.0",
zeropt=float(zp),masksfx=MASKSFX,wtimage="",fwhm=FWHM,
pix=PIXSCALE,aperture=2*FWHM/PIXSCALE,gain=FL_GAIN,
minlim=no,clobber=yes,verbose=no)
# See if object was detected
stars=Starlist('%s.sub.fits.stars' % root)
match,snmatch=stars.match(snstars,useflags=yes)
# If so, use SN magnitude and statistical error
if len(match):
snmag = match[0].mag
snerr = match[0].magu
# Otherwise calculate upper limit
else:
statsec = '[%i:%i,%i:%i]' % (int(sncoo[0]-dx/2), int(sncoo[0]+dx/2),
int(sncoo[1]-dy/2), int(sncoo[1]+dy/2))
iraf.iterstat('%s.sub%s' % (root, statsec), nsigrej=5, maxiter=5,
prin=no,verbose=no)
sigma=float(iraf.iterstat.sigma)
snmag = - 2.5 * log10(3 * sigma * sqrt(pi * power(aperture,2))) + \
float(zp)
snerr = 99.999
# Want UT of Observation
[dateobs,ut] = get_head(image,['DATE-OBS','UT'])
tobs=DateTime(int(dateobs[6:]),int(dateobs[3:5]),int(dateobs[:2]),
int(ut[:2]),int(ut[3:5]),int(ut[6:]))
# Return SN Mag / UL
print '%s\t%s %.3f %10.3f%10.3f%10.3f%10.3f' % (root,tobs.Format("%b"),
(((tobs.second/3600.0)+tobs.minute)/60.0+tobs.hour)/24.0+
tobs.day,snmag,snerr,float(zp),float(zpu))
print 'Exiting Successfully'
return
def lmi_detrend(
imlist,
otype="OBJECT",
ppre="p",
bkey="BIASSEC",
tkey="TRIMSEC",
bpre="b",
bfile="Bias.fits",
fkey="FILTER",
fpre="f",
flatpre="Flat",
skybkg="SKYBKG",
skysub="SKYSUB",
skysig="SKYSIG",
fpfx="F",
clobber=globclob,
verbose=globver,
):
"""Identify science frames, pre-process, subtract bias, divide by flat,
and correct for non-linearity."""
images = glob.glob(imlist)
images.sort()
# Loop through all images
for im in images:
hdr = pyfits.getheader(im)
if hdr["OBSTYPE"] == otype:
# Preprocess
preproc(im, fkey=fkey, ppre=ppre, bkey=bkey, tkey=tkey, clobber=clobber, verbose=verbose)
# Bias subtraction
lmi_debias("%s%s" % (ppre, im), bpre=bpre, bfile=bfile)
# Flat field
lmi_flat("%s%s%s" % (bpre, ppre, im), fpre=fpre, fkey=fkey, flatpre="Flat")
# Linearity correction
lmi_lincor("%s%s%s%s" % (fpre, bpre, ppre, im))
# Write sky values to header
iraf.iterstat("%s%s%s%s" % (fpre, bpre, ppre, im), verbose=no, prin=no)
fimg = pyfits.open("%s%s%s%s" % (fpre, bpre, ppre, im), mode="update")
fimg[0].header[skybkg] = iraf.iterstat.median
fimg[0].header[skysig] = iraf.iterstat.sigma
fimg[0].header[skysub] = 0
fimg.flush()
fimg.close()
# Final processed image
shutil.copy("%s%s%s%s" % (fpre, bpre, ppre, im), "%s%s" % (fpfx, im))
return
def nirc_sky(image, bpm):
usebpm = 0
if len(bpm) > 0:
re1 = re.search("^\!(\w+)", bpm)
if re1:
bpmkey = re1.group(1)
bpmfile = get_head(image, bpmkey)
else:
bpmfile = bpm
if len(bpmfile) > 0 and os.path.exists(bpmfile):
usebpm = 1
if usebpm:
mimstat = iraf.mimstatistics(
image,
imasks='!BPM',
omasks="",
fields='image,npix,mean,stddev,min,max,mode',
lower='INDEF',
upper='INDEF',
nclip=4,
lsigma=5.0,
usigma=5.0,
binwidth=0.1,
format=no,
Stdout=1,
Stderr=1)
mimels = mimstat[0].split()
skybkg = None
if len(mimels) == 6:
try:
skybkg = float(mimels[5])
except:
pass
if not skybkg:
usebpm = 0
if not usebpm:
iraf.iterstat(image, nsigrej=5, maxiter=10, prin=no, verbose=no)
skybkg = float(iraf.iterstat.median)
return skybkg
def wirc_sky(image,bpm):
usebpm=0
if len(bpm)>0:
re1=re.search("^\!(\w+)",bpm)
if re1:
bpmkey=re1.group(1)
bpmfile=get_head(image,bpmkey)
else:
bpmfile=bpm
if len(bpmfile)>0 and os.path.exists(bpmfile):
usebpm=1
if usebpm:
mimstat=iraf.mimstatistics(image,imasks='!BPM',omasks="",
fields='image,npix,mean,stddev,min,max,mode',
lower='INDEF',upper='INDEF',nclip=4,
lsigma=5.0,usigma=5.0,binwidth=0.1,
format=no,Stdout=1,Stderr=1)
mimels=mimstat[0].split()
skybkg=None
if len(mimels)==6:
try:
skybkg=float(mimels[5])
except:
pass
if not skybkg:
usebpm=0
if not usebpm:
iraf.iterstat(image,nsigrej=5,maxiter=10,
prin=no,verbose=no)
skybkg=float(iraf.iterstat.median)
return skybkg
def iq_ratir(chip="C1", filt="i", reflist="sdss.reg"):
'''Process RATIR data for given chip and filter'''
# Check and see if bias frame exists. If not, create one
if not os.path.exists("Bias-%s.fits" % chip):
imlist = glob.glob("[0-9]*T[0-9]*%sb.fits" % chip)
hdr = pyfits.getheader(imlist[0])
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = 'median'
zerocombine.reject = 'avsigclip'
zerocombine.ccdtype = ''
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = 'none'
zerocombine.statsec = '*'
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = 0.0
zerocombine.gain = hdr['SOFTGAIN']
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(imlist)
zerocombine(input=bstr, output="Bias-%s.fits" % chip)
# Bias subtract flat frames
imlist = glob.glob("[0-9]*T[0-9]*%sf.fits" % chip)
flis = []
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("b%s" % im)):
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = no
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="b%s" % im)
flis.append("b%s" % im)
elif (hdr["FILTER"] == filt):
flis.append("b%s" % im)
# Create flat field
if not os.path.exists("Flat-%s-%s.fits" % (chip, filt)):
glis = []
for im in flis:
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat(im)
if (iraf.iterstat.median <
RATIRSAT[chip]) and (iraf.iterstat.median > 1000.0):
glis.append(im)
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = 'median'
flatcombine.reject = 'avsigclip'
flatcombine.ccdtype = ''
flatcombine.scale = 'median'
flatcombine.statsec = ''
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = 0.0
flatcombine.gain = hdr["SOFTGAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
# Run flatcombine
fstr = ",".join(glis[:10])
flatcombine(fstr, output="Flat-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("Flat-%s-%s.fits" % (chip, filt))
iraf.imarith("Flat-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Flat-%s-%s.fits" % (chip, filt))
# Bias subtract and flat-field science images
imlist = glob.glob("[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("fb%s" % im)):
hdr = pyfits.getheader(im)
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = yes
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.flat = "Flat-%s-%s.fits" % (chip, filt)
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="fb%s" % im)
# Create sky frame (including dark current!)
if not os.path.exists("Sky-%s-%s.fits" % (chip, filt)):
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
hdr = pyfits.getheader(imlist[0])
slis = []
for im in imlist:
if (hdr["FILTER"] == filt):
slis.append(im)
# Set up combine
imcombine = iraf.immatch.imcombine
imcombine.headers = ""
imcombine.bpmasks = ""
imcombine.rejmasks = ""
imcombine.nrejmasks = ""
imcombine.expmasks = ""
imcombine.sigmas = ""
imcombine.combine = "median"
imcombine.reject = "avsigclip"
imcombine.project = no
imcombine.outtype = "real"
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "median"
imcombine.zero = "none"
imcombine.weight = "none"
imcombine.statsec = ""
imcombine.lsigma = 3.0
imcombine.hsigma = 3.0
imcombine.rdnoise = 0.0
imcombine.gain = hdr["SOFTGAIN"]
sstr = ",".join(slis[:10])
imcombine(sstr, "Sky-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat("Sky-%s-%s.fits" % (chip, filt))
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Sky-%s-%s.fits" % (chip, filt))
# Subtract sky frame (and dark!)
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("s%s" % im):
iraf.iterstat(im)
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "*",
iraf.iterstat.median, "temp.fits")
iraf.imarith(im, "-", "temp.fits", "s%s" % im)
os.remove("temp.fits")
fimg = pyfits.open("s%s" % im, "update")
fimg[0].header["SKYMED"] = iraf.iterstat.median
fimg[0].header["SKYSIG"] = iraf.iterstat.sigma
fimg[0].header["SKYSUB"] = 1
fimg.flush()
# Remove cosmic rays
imlist = glob.glob("sfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("c%s" % im):
fimg = pyfits.open(im)
iraf.lacos_im(im,
"c%s" % im,
"c%s.mask.fits" % im[:-5],
gain=hdr['SOFTGAIN'],
readn=0.0,
statsec="*,*",
skyval=fimg[0].header["SKYMED"],
sigclip=4.5,
sigfrac=0.5,
objlim=1.0,
niter=3,
verbose=no)
# Add WCS keywords
clis = glob.glob("csfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in clis:
fimg = pyfits.open(im, mode='update')
if fimg[0].header.get("RA") == None:
ra0 = fimg[0].header["STRCURA"]
dec0 = fimg[0].header["STRCUDE"]
nax = [fimg[0].header["NAXIS1"], fimg[0].header["NAXIS2"]]
fimg[0].header["RA"] = ra0
fimg[0].header["DEC"] = dec0
fimg[0].header["PIXSCALE"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL1"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL2"] = RATIRPIXSCALE[chip]
fimg[0].header["CTYPE1"] = "RA---TAN"
fimg[0].header["CTYPE2"] = "DEC--TAN"
fimg[0].header["WCSDIM"] = 2
fimg[0].header["WAT0_001"] = "system=image"
fimg[0].header["WAT1_001"] = "wtype=tan axtype=ra"
fimg[0].header["WAT2_001"] = "wtype=tan axtype=dec"
fimg[0].header["LTM1_1"] = 1.0
fimg[0].header["LTM2_2"] = 1.0
fimg[0].header["CRPIX1"] = nax[0] / 2.0
fimg[0].header["CRPIX2"] = nax[1] / 2.0
fimg[0].header["CRVAL1"] = ra0
fimg[0].header["CRVAL2"] = dec0
fimg[0].header["CD1_1"] = -RATIRPIXSCALE[chip] / 3600.0
fimg[0].header["CD1_2"] = 0.0
fimg[0].header["CD2_1"] = 0.0
fimg[0].header["CD2_2"] = RATIRPIXSCALE[chip] / 3600.0
fimg.flush()
# Crude astrometry
for im in clis:
if not os.path.exists("a%s" % im):
os.system("python %s %s" % (PYASTROM, im))
# Refine Astrometry
o1 = open("daofind.param", "w")
o1.write(
"NUMBER\nXWIN_IMAGE\nYWIN_IMAGE\nMAG_AUTO\nFLAGS\nA_IMAGE\nB_IMAGE\n")
o1.write("ELONGATION\nFWHM_IMAGE\nXWIN_WORLD\nYWIN_WORLD\n")
o1.write(
"ERRAWIN_IMAGE\nERRBWIN_IMAGE\nERRTHETAWIN_IMAGE\nERRAWIN_WORLD\n")
o1.write(
"ERRBWIN_WORLD\nERRTHETAWIN_WORLD\nFLUX_AUTO\nFLUX_RADIUS\nFLUXERR_AUTO"
)
o1.close()
o2 = open("default.conv", "w")
o2.write(
"CONV NORM\n# 5x5 convolution mask of a gaussian PSF with FWHM = 3.0 pixels.\n0.092163 0.221178 0.296069 0.221178 0.092163\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.296069 0.710525 0.951108 0.710525 0.296069\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.092163 0.221178 0.296069 0.221178 0.092163"
)
o2.close()
alis = glob.glob("acsfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in alis:
# Detect sources
os.system(
"sex -CATALOG_NAME %s.cat -CATALOG_TYPE FITS_LDAC -PARAMETERS_NAME daofind.param -DETECT_THRESH 2.0 -ANALYSIS_THRESH 2.0 -GAIN_KEY SOFTGAIN -PIXEL_SCALE 0 %s"
% (im[:-5], im))
# Run scamp for alignment
imlist = " ".join(alis)
os.system(
"scamp -ASTREF_CATALOG SDSS-R7 -DISTORTDEG 1 -SOLVE_PHOTOM N -SN_THRESHOLDS 3.0,10.0 -CHECKPLOT_DEV NULL %s"
% imlist.replace(".fits", ".cat"))
# Update header
for im in alis:
os.system("missfits %s" % im)
os.system("rm %s.head %s.back" % (im[:-5], im))
# Find good images for coadd
slis = []
for im in alis:
hdr = pyfits.getheader(im)
rms1 = hdr["ASTRRMS1"]
rms2 = hdr["ASTRRMS2"]
if (rms1 < 1.0e-4) and (rms1 > 5.0e-6) and (rms2 < 1.0e-4) and (
rms2 > 5.0e-6):
slis.append(im)
# Initial (unweighted) coadd
sstr = " ".join(slis)
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Identify sources in the coadded frame
hdr = pyfits.getheader("coadd.fits")
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=20.0,
wtimage="coadd.weight.fits")
hdr = pyfits.getheader("coadd.fits")
cpsfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=cpsfdiam,
wtimage="coadd.weight.fits")
refstars1 = Starlist("coadd.fits.stars")
refstars1.pix2wcs("coadd.fits")
truemags = np.array(refstars1.mags())
maglist = []
errlist = []
# (Relative) Zeropoints for individual images
for im in slis:
hdr = pyfits.getheader(im)
iqpkg.iqobjs(im,
3.0,
RATIRSAT[chip],
skyval="!SKYMED",
pix=RATIRPIXSCALE[chip],
gain=hdr["SOFTGAIN"],
aperture=20.0)
hdr = pyfits.getheader(im)
psfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs(im,
3.0,
RATIRSAT[chip],
skyval="!SKYMED",
pix=RATIRPIXSCALE[chip],
gain=hdr["SOFTGAIN"],
aperture=psfdiam)
stars = Starlist("%s.stars" % im)
refstars1.wcs2pix(im)
a, b = stars.match(refstars1, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars1))
newwts = np.zeros(len(refstars1))
for i in range(len(refstars1)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01), 2)
continue
maglist.append(newmags)
errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
# Update headers
medzp = np.median(zpts)
for i in range(len(slis)):
im = slis[i]
fimg = pyfits.open(im, mode="update")
fimg[0].header["RELZPT"] = zpts[i]
fimg[0].header["RELZPTSC"] = scatt[i]
fimg[0].header["RELZPRMS"] = rms[i]
fimg[0].header["FLXSCALE"] = 1.0 / np.power(10,
(zpts[i] - medzp) / 2.5)
fimg.flush()
# Final coadd
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Calibration
iqpkg.iqobjs("coadd.fits",
10.0,
RATIRSAT[chip],
skyval="0.0",
pix=RATIRPIXSCALE[chip],
gain=hdr["GAIN"],
aperture=cpsfdiam,
wtimage="coadd.weight.fits")
stars = Starlist("coadd.fits.stars")
refstars = Starlist(reflist)
refstars.wcs2pix("coadd.fits")
refstars.set_mag("%sMAG" % filt.upper())
truemags = np.array(refstars.mags())
maglist = []
errlist = []
a, b = stars.match(refstars, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars))
newwts = np.zeros(len(refstars))
for i in range(len(refstars)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01), 2)
continue
maglist.append(newmags)
errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
fimg = pyfits.open("coadd.fits", mode="update")
fimg[0].header["ABSZPT"] = zpts[0] + 25
fimg[0].header["ABSZPTSC"] = scatt[0]
fimg[0].header["ABZPRMS"] = rms[0]
print "Zeropoint for coadded image: %.3f" % (25.0 + zpts[0])
print "Robust scatter for coadded image: %.3f" % scatt[0]
print "RMS for coadded image: %.3f" % rms[0]
def lmi_cals(
imlist,
dobias=yes,
dobpm=yes,
doflats=yes,
btype="BIAS",
ftype="SKY FLAT",
fkey="FILTER",
ppre="p",
bkey="BIASSEC",
tkey="TRIMSEC",
bfile="Bias.fits",
bpmfile="BPM.pl",
bpre="b",
flatpre="Flat",
clobber=globclob,
verbose=globver,
):
"""Process bias and twilight flats, creating relevant calibration files for
nightly processing of LMI data."""
images = glob.glob(imlist)
blist = []
flist = []
# Preprocess all the relevant images
for im in images:
hdr = pyfits.getheader(im)
if hdr["OBSTYPE"] == btype:
blist.append("%s%s" % (ppre, im))
preproc(im, fkey=fkey, ppre=ppre, bkey=bkey, tkey=tkey, clobber=clobber, verbose=verbose)
elif hdr["OBSTYPE"] == ftype:
flist.append("%s%s" % (ppre, im))
preproc(im, fkey=fkey, ppre=ppre, bkey=bkey, tkey=tkey, clobber=clobber, verbose=verbose)
if dobias:
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = "median"
zerocombine.reject = "avsigclip"
zerocombine.ccdtype = ""
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = "none"
zerocombine.statsec = "*"
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = hdr["RDNOISE"]
zerocombine.gain = hdr["GAIN"]
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(blist)
if os.path.exists(bfile):
os.remove(bfile)
zerocombine(input=bstr, output=bfile)
if dobpm:
# Setup imcombine
imcombine = iraf.immatch.imcombine
imcombine.sigmas = "bsigma.fits"
imcombine.combine = "median"
imcombine.reject = "none"
imcombine.project = no
imcombine.outtype = "real"
imcombine.outlimits = ""
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "none"
imcombine.zero = "none"
imcombine.weight = "none"
# Run imcombine
bstr = ",".join(blist)
if os.path.exists("junk.fits"):
os.remove("junk.fits")
if os.path.exists("bsigma.fits"):
os.remove("bsigma.fits")
imcombine(input=bstr, output="junk.fits")
# Run ccdmask to create BPM file
if os.path.exists(bpmfile):
os.remove(bpmfile)
iraf.ccdred.ccdmask(
"bsigma.fits",
bpmfile,
ncmed=7,
nlmed=7,
ncsig=15,
nlsig=15,
lsigma=10,
hsigma=10,
ngood=1,
linterp=1,
cinterp=1,
eqinterp=1,
)
os.remove("bsigma.fits")
os.remove("junk.fits")
if doflats:
# Subtract bias images from all flats
for im in flist:
lmi_debias(im, bfile=bfile)
# Loop over filters
for filt in LMIFILTS:
# ID files in appropriate filter
flis = []
for im in flist:
hdr = pyfits.getheader(im)
if hdr[fkey] == filt:
flis.append("%s%s" % (bpre, im))
if flis == []:
continue
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = "median"
flatcombine.reject = "avsigclip"
flatcombine.ccdtype = ""
flatcombine.process = no
flatcombine.scale = "median"
flatcombine.statsec = ""
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = hdr["RDNOISE"]
flatcombine.gain = hdr["GAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
flatcombine.blank = 1.0
# Run flatcombine
fstr = ",".join(flis)
if os.path.exists("%s-%s.fits" % (flatpre, filt)):
os.remove("%s-%s.fits" % (flatpre, filt))
flatcombine(input=fstr, output="%s-%s.fits" % (flatpre, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("%s-%s.fits" % (flatpre, filt), verbose=no, prin=no)
iraf.imarith("%s-%s.fits" % (flatpre, filt), "/", iraf.iterstat.median, "%s-%s.fits" % (flatpre, filt))
return
def spitzer_sub(new,
newunc,
newcov,
ref,
refunc,
refcov,
out,
stamps=None,
tmass=None):
# Remove nan from mosaics
x = pyfits.open(new)
y = np.nan_to_num(x[0].data)
x[0].data = y
new2 = "n%s" % new
x.writeto(new2)
# Find stars in new image
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (new2, newcov))
# Find stars in reference
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (ref, refcov))
# Create file for geotran
stars = Starlist("%s.stars" % new2)
refstars = Starlist("%s.stars" % ref)
refstars.pix2wcs(ref)
refstars.wcs2pix(new2)
a, b = stars.match(refstars, maxnum=1000)
# Create file for geotran
b.pix2wcs(new2)
b.wcs2pix(ref)
refroot = ref.split(".")[0]
outf = open("%s.match" % refroot, "w")
for i in range(len(a)):
outf.write("%10.3f%10.3f%10.3f%10.3f\n" %
(a[i].xval, a[i].yval, b[i].xval, b[i].yval))
outf.close()
# Geomap and geotran
[naxis1, naxis2] = get_head(new, ["NAXIS1", "NAXIS2"])
iraf.geomap("%s.match" % refroot,
"%s.db" % refroot,
1,
naxis1,
1,
naxis2,
fitgeometry="rotate",
interactive=no)
iraf.geotran(ref, "t%s" % ref, "%s.db" % refroot, "%s.match" % refroot)
iraf.geotran(refunc, "t%s" % refunc, "%s.db" % refroot,
"%s.match" % refroot)
# Get stars for PSF matching
if stamps != None:
psfstars = Starlist(stamps)
psfstars.pix2wcs(ref)
psfstars.wcs2pix(new2)
outf = open("stamps.lis", "w")
for star in psfstars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
# Appropriate parameters
iraf.iterstat(ref)
update_head(ref, ["MEDSKY", "SKYSIG"],
[iraf.iterstat.median, iraf.iterstat.sigma])
[refskybkg, refskysig] = get_head(ref, ["MEDSKY", "SKYSIG"])
tl = refskybkg - 10 * refskysig
tu = 30000.0
iraf.iterstat(new)
update_head(new, ["MEDSKY", "SKYSIG"],
[iraf.iterstat.median, iraf.iterstat.sigma])
[newskybkg, newskysig] = get_head(new, ["MEDSKY", "SKYSIG"])
il = newskybkg - 10 * newskysig
iu = 30000.0
# Run hotpants
hpcmd = "hotpants -inim %s -tmplim t%s -outim %s -tni t%s -ini %s -nsx 3 -nsy 3 -savexy %s.xy -ko 0 -bgo 0 -oni u%s -n t -tl %.2f -tu %.2f -il %.2f -iu %.2f -r 7.5 -rss 18.0" % (
new2, ref, out, refunc, newunc, new2, out, tl, tu, il, iu)
if stamps != None:
hpcmd += " -ssf stamps.lis -afssc 0"
os.system(hpcmd)
#iraf.imarith(new2, "-", "t%s" % ref, out)
# Create appropriate weight image
#iraf.imexpr("sqrt(a**2 + b**2)", "u%s" % out, a=newunc, b=refunc)
iraf.imarith(1, "/", "u%s" % out, "temp1.fits")
iraf.imarith("temp1.fits", "/", "u%s" % out, "w%s" % out)
os.remove("temp1.fits")
# Pick up candidates
os.system("$REDUCTION/runsex.pl %s 2.0 -weight w%s" % (out, out))
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (
nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (
nax2 - EDGETOL) and refu[0].data[
star.yval, star.
xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
# Filter out bright stars
if tmass == None:
scands.write("cands.reg")
else:
stmass = Starlist(tmass)
stmass.wcs2pix(ref)
s2cands = scands.nomatch(stmass)
s2cands.write("cands.reg")
# More comprehensive list
os.system("$REDUCTION/runsex.pl %s 1.5" % out)
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (
nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (
nax2 - EDGETOL) and refu[0].data[
star.yval, star.
xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
scands.write("cands_all.reg")
return
def saofocus(inpat,prepfile=yes,endhow="never",endwhen="",
clobber=globclob,verbose=globver):
""" derive best focus from an SAOFOCUS run on P60 """
# Defaults
twait=30
reduced={}
bpmname="BPM.pl"
saokey="IMGTYPE"
saore="SAOFOCUS"
fseqkey="SAOFVALS"
sigma=2.0
satval=50000.0
masksfx="mask"
pix=0.3787 # arcsec per pixel
mindiff=2 # minimum tolerable diff b/ best and worst seeing (pix)
# Parse end-condition "time"
if endhow=="time":
re1=re.search("^(\d+):(\d+)",endwhen)
if re1:
tnow=time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime=time.mktime([tnow[0],tnow[1],tnow[2],
int(re1.group(1)),int(re1.group(2)),0,
tnow[6],tnow[7],0]) - time.timezone
if reftime<time.time():
reftime+=86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow="never"
##################################################
# Big Loop
done=no
while not done:
# Parse inputs
allfiles=glob.glob(inpat)
newfiles=[]
for image in allfiles:
if not reduced.has_key(image):
# Interested only in SAOFOCUS images
saoval=get_head(image,saokey)
if re.search(saore,saoval,re.I):
reduced[image]=no
newfiles.append(image)
else:
# Skip
reduced[image]=yes
for image in newfiles:
if verbose:
print "Processing new SAOFOCUS image %s" % image
# Only attempt reduction once for each image
reduced[image]=yes
# Demosaic
ccdproc=iraf.ccdproc
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
iraf.iqmosaic(image,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,verbose=verbose)
# Fix the WCS that IRAF has now clobbered
iraf.add_wcs(image,instrument="p60new")
# Get some important keywords
[raoff,decoff,saonseq,saoshift] = \
get_head(image,['RAOFF','DECOFF','NUMFOC',
'SAOSHIFT'])
# Type conversions
saonseq=int(saonseq)
saoshift=float(saoshift)
# No bias-subtraction or flat-fielding
# Subtract stair-step background if requested
if prepfile:
qtmp=iraf.mktemp("saofoc")+".fits"
iraf.imcopy(image,qtmp,verbose=no)
for iamp in [-1,1]:
for istep in range(saonseq):
qsec=iraf.mktemp("saofsc")+".fits"
y1=1024+iamp*(1+istep*saoshift)+max(0,iamp)
y2=y1+iamp*(saoshift-1)
if istep==0:
y1-=iamp
elif istep==saonseq-1:
y2=1+max(0,iamp)*2047
ymin=min(y1,y2)
ymax=max(y1,y2)
statsec='[*,%d:%d]' % (ymin,ymax)
iraf.iterstat(image+statsec,nsigrej=5,
maxiter=10,prin=no,verbose=no)
medreg=float(iraf.iterstat.median)
iraf.imarith(image+statsec,'-',medreg,
qsec,verbose=no,noact=no)
iraf.imcopy(qsec,qtmp+statsec,verbose=no)
iraf.imdel(qsec,verify=no,go_ahead=yes)
iraf.imdel(image,verify=no,go_ahead=yes)
iraf.imrename(qtmp,image,verbose=no)
# No renaming of the file
# No Bad Pixel Masking or rudimentary WCS
# Object-detection
doobj=1
if check_head(image,'STARFILE'):
if os.path.exists(get_head(image,'STARFILE')):
doobj=0
if doobj:
iraf.iqobjs(image,sigma,satval,skyval="0.0",
masksfx=masksfx,wtimage="none",minlim=no,
clobber=yes,verbose=verbose)
# Establish our "region of interest" for pixels
pixrough=800
pixminx=1024-float(decoff)/pix-pixrough/2
pixmaxx=pixminx+pixrough
pixmaxy=1023
pixminy=pixmaxy-float(raoff)/pix-(1+saonseq)*saoshift-pixrough/2
# The array of focus positions (from low Y to high Y)
farrkeys=[]
for i in range(saonseq):
farrkeys.append("SAOFOC%02d" % (i+1))
farr=get_head(image,farrkeys)
# Starlist from Sextractor
starfile=get_head(image,'STARFILE')
stars=Starlist(starfile)
stars.mag_sort()
# Find some of the stars in the sequence
xvals=[]
yvals=[]
good1=[]
for star in stars:
# We're going in order from bright to faint
if star.xval>pixminx and star.xval<pixmaxx and \
star.yval<pixmaxy and star.yval>pixminy and \
star.elong<3:
good1.append(star)
xvals.append(star.xval)
yvals.append(star.yval)
if len(good1)>=1.2*saonseq:
break
# Sanity checking
if len(xvals)<1:
print "No stars found in search region"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Guess for x is median of the xvals of the goodstars
xctr=median(xvals,pick=1)
# First guess for y is median of the goodstars that fall
# within +/- (pixfine) pix of this X value
pixfine=8
yval2=[]
good2=[]
for star in good1:
if abs(star.xval-xctr)<pixfine:
good2.append(star)
yval2.append(star.yval)
# Sanity checking
if len(yval2)<1:
print "No stars found in refined search region"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
yctr=median(yval2,pick=1)
# This will be our reference star
usestar=good2[yval2.index(yctr)]
[refx,refy]=[usestar.xval,usestar.yval]
# Identify additional stars, working out from the
# reference star
usestars=[usestar]
# Search increasing & decreasing in Y
# (we use a bigger box in Y, and correct for the last star
# position, because of potential for tracking errors)
for isgn in [+1,-1]:
shifty=refy
for i in range(1,saonseq):
shifty += isgn*saoshift
tgtstars=stars.starsinbox([[refx-pixfine,refx+pixfine],
[shifty-1.5*pixfine,
shifty+1.5*pixfine]])
if len(tgtstars)>0:
minmag=99.99
# Pick the brightest in the box
for tstar in tgtstars:
if tstar.mag<minmag:
svstar=tstar
minmag=tstar.mag
usestars.append(svstar)
shifty=svstar.yval
else:
# Quit searching when there are no stars in box
break
# Exclude faint stars if we have too many
# (could also drop stars until we reach the number, but
# then we would want to check that all the y-values are in
# strict succession...)
magrough=2.0
if len(usestars)>saonseq:
usemags=[]
for star in usestars:
usemags.append(star.mag)
minmag=min(usemags)
use2=[]
for star in usestars:
if star.mag<minmag+magrough:
use2.append(star)
if len(use2)==saonseq:
usestars=use2
# Check for the right number
if len(usestars)!=saonseq:
print "Failed to get right number of target stars"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Construct the array of x- and y-values
xval3=[]
yval3=[]
for star in usestars:
xval3.append(star.xval)
yval3.append(star.yval)
# Choose a single x-value for the run
xctr2="%.3f" % median(xval3)
# Increasing y-value means increasing index in farr
yval3.sort()
ylis=','.join(map(str,yval3))
flis=','.join(map(str,farr))
# Run iqfocus
iraf.iqfocus(image,xctr2,ylis,flis,method="acorr",
boxsize=saoshift,focuskey="BESTFOC",
seekey="SEEING",fseekey="SAOSEE",
update=yes,clobber=yes,verbose=verbose)
# Check for successful iqfocus run
if not check_head(image,'BESTFOC'):
print "iqfocus run failed to find good focus"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Grab focus positions and seeing values
bestfoc,saosee=get_head(image,['BESTFOC','SAOSEE'])
# Some information
if verbose:
print "Focus settings: "+flis
print "Seeing values: "+saosee
print "Best focus: %.3f" % bestfoc
# Check for best-focus "near edge" condition
for i in range(saonseq):
if ("%.3f" % bestfoc)==("%.3f" % float(farr[i])):
ibest=i
break
if (saonseq>3 and (ibest==0 or ibest==saonseq-1)) or \
(saonseq>5 and (ibest==1 or ibest==saonseq-2)):
update_head(image,'SAOEDGE',1,
"Best seeing is near edge of focus run")
# Check for insufficient variation across the focus run
maxsee=0
minsee=0.5*saoshift
saoseevals=eval(saosee)
for seeval in saoseevals:
minsee=min(minsee,seeval)
maxsee=max(maxsee,seeval)
if (maxsee-minsee) < mindiff:
print "Failed to observe significant variation "+\
"across focus run"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Convert seeing value to arcsec
seepix=get_head(image,'SEEPIX')
update_head(image,'BESTSEE',seepix,
"Best seeing in pixels for this series")
try:
seeasec=float(seepix)*pix
update_head(image,'SEEING',seeasec,
"Best seeing in arcsec for this series")
except:
print "Failed to convert seeing to arcsec for %s" % image
# Successful processing
update_head(image,'SAOFOCUS',1,
"saofocus processing succeeded for this image")
##############################
# Test end conditions
if endhow=="never":
done=no
elif endhow=="once":
done=yes
elif endhow=="time":
if time.time()>reftime:
done=yes
# Wait a little while
if not done:
time.sleep(twait)
def fastfocus(inpat,
prepfile=yes,
endhow="never",
endwhen="",
clobber=globclob,
verbose=globver):
""" derive best focus from an SAOFOCUS run on P60 """
# Defaults
twait = 30
reduced = {}
bpmname = "BPM.pl"
saokey = "IMGTYPE"
saore = "SAOFOCUS"
fseqkey = "SAOFVALS"
sigma = 10.0
satval = 50000.0
masksfx = "mask"
pix = 0.3787 # arcsec per pixel
mindiff = 2 # minimum tolerable diff b/ best and worst seeing (pix)
# Parse end-condition "time"
if endhow == "time":
re1 = re.search("^(\d+):(\d+)", endwhen)
if re1:
tnow = time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime = time.mktime([
tnow[0], tnow[1], tnow[2],
int(re1.group(1)),
int(re1.group(2)), 0, tnow[6], tnow[7], 0
]) - time.timezone
if reftime < time.time():
reftime += 86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow = "never"
# Open fake OCS
fakeocs = ocs_ooriented(TEST=1)
fakeocs.read_foctable()
##################################################
# Big Loop
done = no
while not done:
# Parse inputs
allfiles = glob.glob(inpat)
newfiles = []
for image in allfiles:
if not reduced.has_key(image):
# Interested only in SAOFOCUS images
saoval = get_head(image, saokey)
if re.search(saore, saoval, re.I):
reduced[image] = no
newfiles.append(image)
else:
# Skip
reduced[image] = yes
for image in newfiles:
if verbose:
print "Processing new SAOFOCUS image %s" % image
# Only attempt reduction once for each image
reduced[image] = yes
# Demosaic
ccdproc = iraf.ccdproc
ccdproc.overscan = yes
ccdproc.trim = yes
ccdproc.zerocor = no
iraf.iqmosaic(image,
outpfx="",
biassec="!BIASSEC",
trimsec="!TRIMSEC",
joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,
verbose=verbose)
# Fix the WCS that IRAF has now clobbered
iraf.add_wcs(image, instrument="p60new")
# Get some important keywords
[raoff,decoff,saonseq,saoshift] = \
get_head(image,['RAOFF','DECOFF','NUMFOC',
'SAOSHIFT'])
# Type conversions
saonseq = int(saonseq)
saoshift = float(saoshift)
# No bias-subtraction or flat-fielding
# Subtract stair-step background if requested
if prepfile:
qtmp = iraf.mktemp("saofoc") + ".fits"
iraf.imcopy(image, qtmp, verbose=no)
for iamp in [-1, 1]:
for istep in range(saonseq):
qsec = iraf.mktemp("saofsc") + ".fits"
y1 = 1024 + iamp * (1 + istep * saoshift) + max(
0, iamp)
y2 = y1 + iamp * (saoshift - 1)
if istep == 0:
y1 -= iamp
elif istep == saonseq - 1:
y2 = 1 + max(0, iamp) * 2047
ymin = min(y1, y2)
ymax = max(y1, y2)
statsec = '[*,%d:%d]' % (ymin, ymax)
iraf.iterstat(image + statsec,
nsigrej=5,
maxiter=10,
prin=no,
verbose=no)
medreg = float(iraf.iterstat.median)
iraf.imarith(image + statsec,
'-',
medreg,
qsec,
verbose=no,
noact=no)
iraf.imcopy(qsec, qtmp + statsec, verbose=no)
iraf.imdel(qsec, verify=no, go_ahead=yes)
iraf.imdel(image, verify=no, go_ahead=yes)
iraf.imrename(qtmp, image, verbose=no)
# No renaming of the file
# No Bad Pixel Masking or rudimentary WCS
# Object-detection
doobj = 1
if check_head(image, 'STARFILE'):
if os.path.exists(get_head(image, 'STARFILE')):
doobj = 0
if doobj:
iraf.iqobjs(image,
sigma,
satval,
skyval="0.0",
masksfx=masksfx,
wtimage="none",
minlim=no,
clobber=yes,
verbose=verbose)
# Establish our "region of interest" for pixels
pixrough = 800
pixminx = 1024 - float(decoff) / pix - pixrough / 2
pixmaxx = pixminx + pixrough
pixmaxy = 1023
pixminy = pixmaxy - float(raoff) / pix - (
1 + saonseq) * saoshift - pixrough / 2
# The array of focus positions (from low Y to high Y)
farrkeys = []
for i in range(saonseq):
farrkeys.append("SAOFOC%02d" % (i + 1))
farr = get_head(image, farrkeys)
# Starlist from Sextractor
starfile = get_head(image, 'STARFILE')
stars = Starlist(starfile)
stars.mag_sort()
# Find some of the stars in the sequence
xvals = []
yvals = []
good1 = []
for star in stars:
# We're going in order from bright to faint
if star.xval>pixminx and star.xval<pixmaxx and \
star.yval<pixmaxy and star.yval>pixminy and \
star.elong<3:
good1.append(star)
xvals.append(star.xval)
yvals.append(star.yval)
if len(good1) >= 1.2 * saonseq:
break
# Sanity checking
if len(xvals) < 1:
print "No stars found in search region"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Guess for x is median of the xvals of the goodstars
xctr = median(xvals, pick=1)
# First guess for y is median of the goodstars that fall
# within +/- (pixfine) pix of this X value
#pixfine=10
pixfine = 20
yval2 = []
good2 = []
for star in good1:
if abs(star.xval - xctr) < pixfine:
good2.append(star)
yval2.append(star.yval)
# Sanity checking
if len(yval2) < 1:
print "No stars found in refined search region"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
yctr = median(yval2, pick=1)
# This will be our reference star
usestar = good2[yval2.index(yctr)]
[refx, refy] = [usestar.xval, usestar.yval]
# Identify additional stars, working out from the
# reference star
usestars = [usestar]
# Search increasing & decreasing in Y
# (we use a bigger box in Y, and correct for the last star
# position, because of potential for tracking errors)
for isgn in [+1, -1]:
shifty = refy
for i in range(1, saonseq):
shifty += isgn * saoshift
tgtstars = stars.starsinbox(
[[refx - pixfine, refx + pixfine],
[shifty - pixfine, shifty + pixfine]])
#[shifty-1.5*pixfine,
#shifty+1.5*pixfine]])
if len(tgtstars) > 0:
minmag = 99.99
# Pick the brightest in the box
for tstar in tgtstars:
if tstar.mag < minmag:
svstar = tstar
minmag = tstar.mag
usestars.append(svstar)
shifty = svstar.yval
else:
# Quit searching when there are no stars in box
break
# Exclude faint stars if we have too many
# (could also drop stars until we reach the number, but
# then we would want to check that all the y-values are in
# strict succession...)
magrough = 2.0
if len(usestars) > saonseq:
usemags = []
for star in usestars:
usemags.append(star.mag)
minmag = min(usemags)
use2 = []
for star in usestars:
if star.mag < minmag + magrough:
use2.append(star)
if len(use2) == saonseq:
usestars = use2
# Check for the right number
if len(usestars) != saonseq:
print "Failed to get right number of target stars"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Construct the array of x- and y-values
xval3 = []
yval3 = []
for star in usestars:
xval3.append(star.xval)
yval3.append(star.yval)
# Choose a single x-value for the run
xctr2 = "%.3f" % median(xval3)
# Increasing y-value means increasing index in farr
yval3.sort()
ylis = ','.join(map(str, yval3))
flis = ','.join(map(str, farr))
# Run iqfocus
iraf.iqfocus(image,
xctr2,
ylis,
flis,
method="acorr",
boxsize=saoshift,
focuskey="BESTFOC",
seekey="SEEPIX",
fseekey="SAOSEE",
update=yes,
clobber=yes,
verbose=verbose)
# Check for successful iqfocus run
if not check_head(image, 'BESTFOC'):
print "iqfocus run failed to find good focus"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Grab focus positions and seeing values
bestfoc, saosee = get_head(image, ['BESTFOC', 'SAOSEE'])
# Some information
if verbose:
print "Focus settings: " + flis
print "Seeing values: " + saosee
print "Best focus: %.3f" % bestfoc
# Check for best-focus "near edge" condition
for i in range(saonseq):
if ("%.3f" % bestfoc) == ("%.3f" % float(farr[i])):
ibest = i
break
if (saonseq>3 and (ibest==0 or ibest==saonseq-1)) or \
(saonseq>5 and (ibest==1 or ibest==saonseq-2)):
update_head(image, 'SAOEDGE', 1,
"Best seeing is near edge of focus run")
# Check for insufficient variation across the focus run
maxsee = 0
minsee = 0.5 * saoshift
saoseevals = eval(saosee)
for seeval in saoseevals:
minsee = min(minsee, seeval)
maxsee = max(maxsee, seeval)
if (maxsee - minsee) < mindiff:
print "Failed to observe significant variation "+\
"across focus run"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Convert seeing value to arcsec
seepix = get_head(image, 'SEEPIX')
update_head(image, 'BESTSEE', seepix,
"Best seeing in pixels for this series")
try:
seeasec = float(seepix) * pix
update_head(image, 'SEEING', seeasec,
"Best seeing in arcsec for this series")
except:
print "Failed to convert seeing to arcsec for %s" % image
# Successful processing
update_head(image, 'SAOFOCUS', 1,
"saofocus processing succeeded for this image")
# Get other necessary keywords
[filter, btubtemp, airmass] = get_head(image, \
['FILTER','BTUBTEMP','AIRMASS'])
# Update focus file
fakeocs.focus = [filter, bestfoc, float(btubtemp), float(airmass)]
fakeocs.write_foctable()
##############################
# Test end conditions
if endhow == "never":
done = no
elif endhow == "once":
done = yes
elif endhow == "time":
if time.time() > reftime:
done = yes
# Wait a little while
if not done:
time.sleep(twait)
def psfphot(image,
coofile,
ot,
wtimage="",
varorder=1,
clobber=globclob,
verbose=globver,
pixtol=3.0,
maxnpsf=25):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult = 5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall = 3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
iraf.digiphot()
iraf.daophot()
# Detect stars
iqobjs("%s.sub.fits" % image[:-5],
1.5,
12000.0,
wtimage=wtimage,
skyval="0.0")
root = image[:-5]
[gain, rnoise, fwhm] = get_head(image, ["GAIN", "READN", "SEEPIX"])
fwhm = float(fwhm)
rnoise = float(rnoise)
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 0.50 * saturate
iraf.datapars.readnoise = rnoise
iraf.datapars.epadu = gain
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
iraf.daopars.varorder = varorder
# Reference stars file
stars = Starlist(coofile)
stars.wcs2pix(image)
outf = open("%s.coo.1" % image[:-5], "w")
for star in stars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
#Aperture photometry
iraf.daophot.phot(root,
'default',
'default',
apertures=fwhm,
verify=no,
interac=no,
verbose=verbose)
iraf.datapars.datamax = 0.50 * saturate
iraf.pstselect(root,
'default',
'default',
maxnpsf,
interactive=no,
verify=no,
verbose=verbose)
iraf.psf(root,
'default',
'default',
'default',
'default',
'default',
interactive=no,
showplots=no,
verify=no,
verbose=verbose)
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
# Prep for subtracted image
iraf.iterstat("%s.sub.fits" % root)
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = saturate
# Look for source at OT location
substars = Starlist("%s.sub.fits.stars" % image[:-5])
otstars = Starlist(ot)
otstars.wcs2pix("%s.sub.fits" % image[:-5])
smatch, omatch = substars.match(otstars, tol=pixtol, useflags=no)
# Generate coo file
otcoo = open("%s.sub.coo.1" % image[:-5], "w")
if len(smatch) == 0:
otcoo.write("%10.3f%10.3f\n" % (otstars[0].xval, otstars[0].yval))
else:
otcoo.write("%10.3f%10.3f\n" % (smatch[0].xval, smatch[0].yval))
otcoo.close()
iraf.daophot.phot("%s.sub.fits" % root,
"%s.sub.coo.1" % image[:-5],
'default',
'default',
apertures=fwhm,
calgorithm="none",
interac=no,
verify=no,
verbose=verbose)
if len(smatch) == 0:
print "No match in subtracted image: %s.sub.fits" % root
else:
iraf.allstar("%s.sub.fits" % root,
'default',
"%s.psf.1.fits" % root,
'default',
'default',
'default',
verify=no,
verbose=no)
return
def iq_ratir(chip="C1", filt="i", reflist="sdss.reg"):
'''Process RATIR data for given chip and filter'''
# Check and see if bias frame exists. If not, create one
if not os.path.exists("Bias-%s.fits" % chip):
imlist = glob.glob("[0-9]*T[0-9]*%sb.fits" % chip)
hdr = pyfits.getheader(imlist[0])
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = 'median'
zerocombine.reject = 'avsigclip'
zerocombine.ccdtype = ''
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = 'none'
zerocombine.statsec = '*'
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = 0.0
zerocombine.gain = hdr['SOFTGAIN']
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(imlist)
zerocombine(input=bstr, output="Bias-%s.fits" % chip)
# Bias subtract flat frames
imlist = glob.glob("[0-9]*T[0-9]*%sf.fits" % chip)
flis = []
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("b%s" % im)):
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = no
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="b%s" % im)
flis.append("b%s" % im)
elif (hdr["FILTER"] == filt):
flis.append("b%s" % im)
# Create flat field
if not os.path.exists("Flat-%s-%s.fits" % (chip, filt)):
glis = []
for im in flis:
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat(im)
if (iraf.iterstat.median < RATIRSAT[chip]) and (iraf.iterstat.median > 1000.0):
glis.append(im)
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = 'median'
flatcombine.reject = 'avsigclip'
flatcombine.ccdtype = ''
flatcombine.scale = 'median'
flatcombine.statsec = ''
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = 0.0
flatcombine.gain = hdr["SOFTGAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
# Run flatcombine
fstr = ",".join(glis[:10])
flatcombine(fstr, output="Flat-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("Flat-%s-%s.fits" % (chip, filt))
iraf.imarith("Flat-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Flat-%s-%s.fits" % (chip, filt))
# Bias subtract and flat-field science images
imlist = glob.glob("[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
hdr = pyfits.getheader(im)
if (hdr["FILTER"] == filt) and (not os.path.exists("fb%s" % im)):
hdr = pyfits.getheader(im)
# Subtract bias frame
ccdproc = iraf.ccdred.ccdproc
ccdproc.ccdtype = ""
ccdproc.noproc = no
ccdproc.fixpix = no
ccdproc.overscan = no
ccdproc.trim = no
ccdproc.zerocor = yes
ccdproc.darkcor = no
ccdproc.flatcor = yes
ccdproc.illumcor = no
ccdproc.fringecor = no
ccdproc.readcor = no
ccdproc.scancor = no
ccdproc.readaxis = 'line'
ccdproc.fixfile = ""
ccdproc.zero = "Bias-%s.fits" % chip
ccdproc.flat = "Flat-%s-%s.fits" % (chip, filt)
ccdproc.interactive = no
ccdproc.function = "legendre"
ccdproc.order = 1
ccdproc.sample = "*"
ccdproc.naverage = 1
ccdproc.niterate = 1
ccdproc.low_reject = 3.0
ccdproc.high_reject = 3.0
ccdproc.grow = 0
ccdproc(images=im, output="fb%s" % im)
# Create sky frame (including dark current!)
if not os.path.exists("Sky-%s-%s.fits" % (chip, filt)):
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
hdr = pyfits.getheader(imlist[0])
slis = []
for im in imlist:
if (hdr["FILTER"] == filt):
slis.append(im)
# Set up combine
imcombine = iraf.immatch.imcombine
imcombine.headers = ""
imcombine.bpmasks = ""
imcombine.rejmasks = ""
imcombine.nrejmasks = ""
imcombine.expmasks = ""
imcombine.sigmas = ""
imcombine.combine = "median"
imcombine.reject = "avsigclip"
imcombine.project = no
imcombine.outtype = "real"
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "median"
imcombine.zero = "none"
imcombine.weight = "none"
imcombine.statsec = ""
imcombine.lsigma = 3.0
imcombine.hsigma = 3.0
imcombine.rdnoise = 0.0
imcombine.gain = hdr["SOFTGAIN"]
sstr = ",".join(slis[:10])
imcombine(sstr, "Sky-%s-%s.fits" % (chip, filt))
# Normalize
iraf.iterstat("Sky-%s-%s.fits" % (chip, filt))
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "/",
iraf.iterstat.median, "Sky-%s-%s.fits" % (chip, filt))
# Subtract sky frame (and dark!)
imlist = glob.glob("fb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("s%s" % im):
iraf.iterstat(im)
iraf.imarith("Sky-%s-%s.fits" % (chip, filt), "*", iraf.iterstat.median,
"temp.fits")
iraf.imarith(im, "-", "temp.fits", "s%s" % im)
os.remove("temp.fits")
fimg = pyfits.open("s%s" % im, "update")
fimg[0].header["SKYMED"] = iraf.iterstat.median
fimg[0].header["SKYSIG"] = iraf.iterstat.sigma
fimg[0].header["SKYSUB"] = 1
fimg.flush()
# Remove cosmic rays
imlist = glob.glob("sfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in imlist:
if not os.path.exists("c%s" % im):
fimg = pyfits.open(im)
iraf.lacos_im(im, "c%s" % im, "c%s.mask.fits" % im[:-5],
gain=hdr['SOFTGAIN'], readn=0.0, statsec="*,*",
skyval=fimg[0].header["SKYMED"], sigclip=4.5,
sigfrac=0.5, objlim=1.0, niter=3, verbose=no)
# Add WCS keywords
clis = glob.glob("csfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in clis:
fimg = pyfits.open(im, mode='update')
if fimg[0].header.get("RA")==None:
ra0 = fimg[0].header["STRCURA"]; dec0 = fimg[0].header["STRCUDE"]
nax = [fimg[0].header["NAXIS1"], fimg[0].header["NAXIS2"]]
fimg[0].header["RA"] = ra0
fimg[0].header["DEC"] = dec0
fimg[0].header["PIXSCALE"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL1"] = RATIRPIXSCALE[chip]
fimg[0].header["PIXSCAL2"] = RATIRPIXSCALE[chip]
fimg[0].header["CTYPE1"] = "RA---TAN"
fimg[0].header["CTYPE2"] = "DEC--TAN"
fimg[0].header["WCSDIM"] = 2
fimg[0].header["WAT0_001"] = "system=image"
fimg[0].header["WAT1_001"] = "wtype=tan axtype=ra"
fimg[0].header["WAT2_001"] = "wtype=tan axtype=dec"
fimg[0].header["LTM1_1"] = 1.0
fimg[0].header["LTM2_2"] = 1.0
fimg[0].header["CRPIX1"] = nax[0] / 2.0
fimg[0].header["CRPIX2"] = nax[1] / 2.0
fimg[0].header["CRVAL1"] = ra0
fimg[0].header["CRVAL2"] = dec0
fimg[0].header["CD1_1"] = -RATIRPIXSCALE[chip] / 3600.0
fimg[0].header["CD1_2"] = 0.0
fimg[0].header["CD2_1"] = 0.0
fimg[0].header["CD2_2"] = RATIRPIXSCALE[chip] / 3600.0
fimg.flush()
# Crude astrometry
for im in clis:
if not os.path.exists("a%s" % im):
os.system("python %s %s" % (PYASTROM, im))
# Refine Astrometry
o1 = open("daofind.param", "w")
o1.write("NUMBER\nXWIN_IMAGE\nYWIN_IMAGE\nMAG_AUTO\nFLAGS\nA_IMAGE\nB_IMAGE\n")
o1.write("ELONGATION\nFWHM_IMAGE\nXWIN_WORLD\nYWIN_WORLD\n")
o1.write("ERRAWIN_IMAGE\nERRBWIN_IMAGE\nERRTHETAWIN_IMAGE\nERRAWIN_WORLD\n")
o1.write("ERRBWIN_WORLD\nERRTHETAWIN_WORLD\nFLUX_AUTO\nFLUX_RADIUS\nFLUXERR_AUTO")
o1.close()
o2 = open("default.conv", "w")
o2.write("CONV NORM\n# 5x5 convolution mask of a gaussian PSF with FWHM = 3.0 pixels.\n0.092163 0.221178 0.296069 0.221178 0.092163\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.296069 0.710525 0.951108 0.710525 0.296069\n0.221178 0.530797 0.710525 0.530797 0.221178\n0.092163 0.221178 0.296069 0.221178 0.092163")
o2.close()
alis = glob.glob("acsfb[0-9]*T[0-9]*%so_img_1.fits" % chip)
for im in alis:
# Detect sources
os.system("sex -CATALOG_NAME %s.cat -CATALOG_TYPE FITS_LDAC -PARAMETERS_NAME daofind.param -DETECT_THRESH 2.0 -ANALYSIS_THRESH 2.0 -GAIN_KEY SOFTGAIN -PIXEL_SCALE 0 %s" % (im[:-5], im))
# Run scamp for alignment
imlist = " ".join(alis)
os.system("scamp -ASTREF_CATALOG SDSS-R7 -DISTORTDEG 1 -SOLVE_PHOTOM N -SN_THRESHOLDS 3.0,10.0 -CHECKPLOT_DEV NULL %s" % imlist.replace(".fits", ".cat"))
# Update header
for im in alis:
os.system("missfits %s" % im)
os.system("rm %s.head %s.back" % (im[:-5], im))
# Find good images for coadd
slis = []
for im in alis:
hdr = pyfits.getheader(im)
rms1 = hdr["ASTRRMS1"]; rms2 = hdr["ASTRRMS2"]
if (rms1 < 1.0e-4) and (rms1 > 5.0e-6) and (rms2 < 1.0e-4) and (rms2 > 5.0e-6):
slis.append(im)
# Initial (unweighted) coadd
sstr = " ".join(slis)
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Identify sources in the coadded frame
hdr = pyfits.getheader("coadd.fits")
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=20.0,
wtimage="coadd.weight.fits")
hdr = pyfits.getheader("coadd.fits")
cpsfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=cpsfdiam,
wtimage="coadd.weight.fits")
refstars1 = Starlist("coadd.fits.stars")
refstars1.pix2wcs("coadd.fits")
truemags = np.array(refstars1.mags())
maglist = []; errlist = []
# (Relative) Zeropoints for individual images
for im in slis:
hdr = pyfits.getheader(im)
iqpkg.iqobjs(im, 3.0, RATIRSAT[chip], skyval="!SKYMED",
pix=RATIRPIXSCALE[chip], gain=hdr["SOFTGAIN"], aperture=20.0)
hdr = pyfits.getheader(im)
psfdiam = 1.34 * float(hdr["SEEPIX"])
iqpkg.iqobjs(im, 3.0, RATIRSAT[chip], skyval="!SKYMED",
pix=RATIRPIXSCALE[chip], gain=hdr["SOFTGAIN"], aperture=psfdiam)
stars = Starlist("%s.stars" % im)
refstars1.wcs2pix(im)
a,b = stars.match(refstars1,tol=10.0,maxnum=1000)
newmags = np.zeros(len(refstars1)); newwts = np.zeros(len(refstars1))
for i in range(len(refstars1)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01),2)
continue
maglist.append(newmags); errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
# Update headers
medzp = np.median(zpts)
for i in range(len(slis)):
im = slis[i]
fimg = pyfits.open(im, mode="update")
fimg[0].header["RELZPT"] = zpts[i]
fimg[0].header["RELZPTSC"] = scatt[i]
fimg[0].header["RELZPRMS"] = rms[i]
fimg[0].header["FLXSCALE"] = 1.0 / np.power(10, (zpts[i] - medzp) / 2.5)
fimg.flush()
# Final coadd
os.system("swarp -GAIN_KEYWORD SOFTGAIN %s" % sstr)
# Calibration
iqpkg.iqobjs("coadd.fits", 10.0, RATIRSAT[chip], skyval="0.0",
pix=RATIRPIXSCALE[chip], gain=hdr["GAIN"], aperture=cpsfdiam,
wtimage="coadd.weight.fits")
stars = Starlist("coadd.fits.stars")
refstars = Starlist(reflist)
refstars.wcs2pix("coadd.fits")
refstars.set_mag("%sMAG" % filt.upper())
truemags = np.array(refstars.mags())
maglist = []; errlist = []
a,b = stars.match(refstars, tol=10.0, maxnum=1000)
newmags = np.zeros(len(refstars)); newwts = np.zeros(len(refstars))
for i in range(len(refstars)):
for j in range(len(a)):
if b[j].mag == truemags[i]:
newmags[i] = a[j].mag
newwts[i] = 1.0 / np.power(np.maximum(a[j].magu, 0.01),2)
continue
maglist.append(newmags); errlist.append(newwts)
obsmags = np.array(maglist)
wts = np.array(errlist)
[zpts, scatt, rms] = calc_zpts(truemags, obsmags, wts, sigma=3.0)
fimg = pyfits.open("coadd.fits", mode="update")
fimg[0].header["ABSZPT"] = zpts[0] + 25
fimg[0].header["ABSZPTSC"] = scatt[0]
fimg[0].header["ABZPRMS"] = rms[0]
print "Zeropoint for coadded image: %.3f" % (25.0+zpts[0])
print "Robust scatter for coadded image: %.3f" % scatt[0]
print "RMS for coadded image: %.3f" % rms[0]
def psfphot(image,
clobber=globclob,
verbose=globver,
pixtol=3.0,
maxnpsf=5,
interact=yes):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult = 5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall = 3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Detect stars
iqpkg.iqobjs(image, 3.0, 50000.0, wtimage="", skyval="!MEDSKY")
root = image[:-5]
[gain, rnoise, fwhm] = get_head(image, ["GAIN", "READNOI", "SEEPIX"])
fwhm = float(fwhm)
rnoise = float(rnoise)
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 70000.0
iraf.datapars.readnoise = rnoise
iraf.datapars.epadu = gain
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
# coo file
stars = Starlist("%s.stars" % image)
outf = open("%s.coo.1" % image[:-5], "w")
for star in stars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
#initial photometry
iraf.daophot.phot(root,
'default',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
iraf.datapars.datamax = 30000.0
iraf.pstselect(root,
'default',
'default',
maxnpsf,
interactive=yes,
verify=no,
verbose=verbose)
iraf.psf(root,
'default',
'default',
'default',
'default',
'default',
interactive=interact,
verify=no,
verbose=verbose)
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
iraf.iterstat("%s.sub.fits" % root)
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 70000.0
iraf.daophot.phot("%s.sub.fits" % root,
"SN.coo",
'default',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
iraf.datapars.datamax = 30000.0
iraf.daopars.fitrad = fwhm * 2.0
iraf.allstar("%s.sub.fits" % root,
'default',
"%s.psf.1.fits" % root,
'default',
'default',
'default',
verify=no,
verbose=no)
def psfphot(inlist,
ra,
dec,
reffilt,
interact,
fwhm,
readnoise,
gain,
threshold,
refimage=None,
starfile=None,
maxnpsf=5,
clobber=globclob,
verbose=globver,
skykey='SKYBKG',
filtkey='FILTER',
pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult = 5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall = 3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles = iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage == None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot = 's' + refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0, refimage)
#setup for keywords
if gain == "!GAIN":
try:
gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try:
readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image, "r")
# Grab image root name
root = image.split('.')[0]
# Map image to reference image
if not (image == refimage):
[nx, ny] = get_head(image, ['NAXIS1', 'NAXIS2'])
stars = Starlist(get_head(image, 'STARFILE'))
refstars = Starlist(get_head(refimage, 'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match, refmatch = stars.match(refstars, useflags=yes, tol=10.0)
nstars = len(match)
if not (nstars > 2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile = open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval, refmatch[i].yval,
match[i].xval, match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,
'%s.geodb' % root,
1.0,
nx,
1.0,
ny,
verbose=no,
interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,
's%s' % root,
'%s.geodb' % root,
'%s.match' % root,
geometry="geometric",
boundary="constant",
verbose=no)
else:
iraf.imcopy(image, 's%s' % root)
root = 's%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf = fwhm
iraf.datapars.sigma = iraf.iterstat.sigma
iraf.datapars.datamin = iraf.iterstat.median - 10 * iraf.iterstat.sigma
iraf.datapars.datamax = 0.90 * saturate
iraf.datapars.readnoise = readval
iraf.datapars.epadu = gainval
iraf.datapars.filter = filtkey
iraf.daopars.psfrad = psfmult * fwhm
iraf.daopars.fitrad = fwhm
iraf.daopars.function = "gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image == refimage and starfile == None:
iraf.daophot.daofind(root,
'refimage.coo.1',
threshold=threshold,
verify=no,
verbose=verbose)
elif image == refimage:
shutil.copy(starfile, 'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=fwhm,
verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,
'default',
refstarsfile,
maxnpsf,
interactive=yes,
verify=no,
verbose=verbose)
#fit the psf
iraf.psf(root,
'default',
refstarsfile,
'default',
'default',
'default',
interactive=interact,
verify=no,
verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root + ".psg.1"
iraf.nstar(root,
groupingfile,
'default',
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,
'default',
refstarsfile,
'default',
'default',
psfrad=psfmultsmall * fwhm,
verify=no,
verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root + ".sub.1"
iraf.psf(subtractedimage,
root + ".nst.1",
refstarsfile,
'%s.psf.2' % root,
'%s.pst.2' % root,
'%s.psg.2' % root,
interactive=interact,
verify=no,
verbose=verbose)
#Need to make sure SN was detected by daofind
stars = Starlist('%s.mag.1' % root)
SN = Star(name='SN', radeg=ra, dcdeg=dec, fwhm=2.0, fwhmw=2.0)
SNlis = Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0]) == 0):
#No match - need to add to daofind file
print "No match!"
coofile = open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' %
(SNlis[0].xval, SNlis[0].yval, 99.999, 0.500, 0.000,
0.000, 999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,
'refimage.coo.1',
'default',
aperture=psfmult * fwhm,
verify=no,
verbose=verbose)
# allstar run
iraf.allstar(root,
'default',
'default',
'default',
'default',
'default',
verify=no,
verbose=verbose)
def lmi_detrend(imlist,
otype="OBJECT",
ppre="p",
bkey="BIASSEC",
tkey="TRIMSEC",
bpre="b",
bfile="Bias.fits",
fkey="FILTER",
fpre="f",
flatpre="Flat",
skybkg="SKYBKG",
skysub="SKYSUB",
skysig="SKYSIG",
fpfx="F",
clobber=globclob,
verbose=globver):
'''Identify science frames, pre-process, subtract bias, divide by flat,
and correct for non-linearity.'''
images = glob.glob(imlist)
images.sort()
# Loop through all images
for im in images:
hdr = pyfits.getheader(im)
if hdr['OBSTYPE'] == otype:
# Preprocess
preproc(im,
fkey=fkey,
ppre=ppre,
bkey=bkey,
tkey=tkey,
clobber=clobber,
verbose=verbose)
# Bias subtraction
lmi_debias("%s%s" % (ppre, im), bpre=bpre, bfile=bfile)
# Flat field
lmi_flat("%s%s%s" % (bpre, ppre, im),
fpre=fpre,
fkey=fkey,
flatpre="Flat")
# Linearity correction
lmi_lincor("%s%s%s%s" % (fpre, bpre, ppre, im))
# Write sky values to header
iraf.iterstat("%s%s%s%s" % (fpre, bpre, ppre, im),
verbose=no,
prin=no)
fimg = pyfits.open("%s%s%s%s" % (fpre, bpre, ppre, im),
mode="update")
fimg[0].header[skybkg] = iraf.iterstat.median
fimg[0].header[skysig] = iraf.iterstat.sigma
fimg[0].header[skysub] = 0
fimg.flush()
fimg.close()
# Final processed image
shutil.copy("%s%s%s%s" % (fpre, bpre, ppre, im),
"%s%s" % (fpfx, im))
return
def lmi_cals(imlist,
dobias=yes,
dobpm=yes,
doflats=yes,
btype="BIAS",
ftype="SKY FLAT",
fkey="FILTER",
ppre="p",
bkey="BIASSEC",
tkey="TRIMSEC",
bfile="Bias.fits",
bpmfile="BPM.pl",
bpre="b",
flatpre="Flat",
clobber=globclob,
verbose=globver):
'''Process bias and twilight flats, creating relevant calibration files for
nightly processing of LMI data.'''
images = glob.glob(imlist)
blist = []
flist = []
# Preprocess all the relevant images
for im in images:
hdr = pyfits.getheader(im)
if hdr['OBSTYPE'] == btype:
blist.append("%s%s" % (ppre, im))
preproc(im,
fkey=fkey,
ppre=ppre,
bkey=bkey,
tkey=tkey,
clobber=clobber,
verbose=verbose)
elif hdr['OBSTYPE'] == ftype:
flist.append("%s%s" % (ppre, im))
preproc(im,
fkey=fkey,
ppre=ppre,
bkey=bkey,
tkey=tkey,
clobber=clobber,
verbose=verbose)
if dobias:
# Setup zerocombine
zerocombine = iraf.ccdred.zerocombine
zerocombine.combine = 'median'
zerocombine.reject = 'avsigclip'
zerocombine.ccdtype = ''
zerocombine.process = no
zerocombine.delete = no
zerocombine.clobber = no
zerocombine.scale = 'none'
zerocombine.statsec = '*'
zerocombine.nlow = 0
zerocombine.nhigh = 1
zerocombine.nkeep = 1
zerocombine.mclip = yes
zerocombine.lsigma = 3.0
zerocombine.hsigma = 3.0
zerocombine.rdnoise = hdr['RDNOISE']
zerocombine.gain = hdr['GAIN']
zerocombine.snoise = 0
zerocombine.pclip = -0.5
zerocombine.blank = 0.0
# Run zerocombine
bstr = ",".join(blist)
if os.path.exists(bfile):
os.remove(bfile)
zerocombine(input=bstr, output=bfile)
if dobpm:
# Setup imcombine
imcombine = iraf.immatch.imcombine
imcombine.sigmas = "bsigma.fits"
imcombine.combine = "median"
imcombine.reject = "none"
imcombine.project = no
imcombine.outtype = "real"
imcombine.outlimits = ""
imcombine.offsets = "none"
imcombine.masktype = "none"
imcombine.scale = "none"
imcombine.zero = "none"
imcombine.weight = "none"
# Run imcombine
bstr = ",".join(blist)
if os.path.exists("junk.fits"):
os.remove("junk.fits")
if os.path.exists("bsigma.fits"):
os.remove("bsigma.fits")
imcombine(input=bstr, output="junk.fits")
# Run ccdmask to create BPM file
if os.path.exists(bpmfile):
os.remove(bpmfile)
iraf.ccdred.ccdmask("bsigma.fits",
bpmfile,
ncmed=7,
nlmed=7,
ncsig=15,
nlsig=15,
lsigma=10,
hsigma=10,
ngood=1,
linterp=1,
cinterp=1,
eqinterp=1)
os.remove("bsigma.fits")
os.remove("junk.fits")
if doflats:
# Subtract bias images from all flats
for im in flist:
lmi_debias(im, bfile=bfile)
# Loop over filters
for filt in LMIFILTS:
# ID files in appropriate filter
flis = []
for im in flist:
hdr = pyfits.getheader(im)
if hdr[fkey] == filt:
flis.append("%s%s" % (bpre, im))
if flis == []:
continue
# Set up flatcombine
flatcombine = iraf.ccdred.flatcombine
flatcombine.combine = 'median'
flatcombine.reject = 'avsigclip'
flatcombine.ccdtype = ''
flatcombine.process = no
flatcombine.scale = 'median'
flatcombine.statsec = ''
flatcombine.nlow = 1
flatcombine.nhigh = 1
flatcombine.nkeep = 1
flatcombine.mclip = yes
flatcombine.lsigma = 3.0
flatcombine.hsigma = 3.0
flatcombine.rdnoise = hdr["RDNOISE"]
flatcombine.gain = hdr["GAIN"]
flatcombine.snoise = 0.0
flatcombine.pclip = -0.5
flatcombine.blank = 1.0
# Run flatcombine
fstr = ",".join(flis)
if os.path.exists("%s-%s.fits" % (flatpre, filt)):
os.remove("%s-%s.fits" % (flatpre, filt))
flatcombine(input=fstr, output="%s-%s.fits" % (flatpre, filt))
# Normalize
iraf.iterstat.nsigrej = 5.0
iraf.iterstat.maxiter = 10
iraf.iterstat.verbose = globver
iraf.iterstat.lower = INDEF
iraf.iterstat.upper = INDEF
iraf.iterstat("%s-%s.fits" % (flatpre, filt), verbose=no, prin=no)
iraf.imarith("%s-%s.fits" % (flatpre, filt), "/",
iraf.iterstat.median, "%s-%s.fits" % (flatpre, filt))
return
def psfphot(inlist, ra, dec, reffilt, interact, fwhm, readnoise, gain,
threshold,refimage=None,starfile=None,maxnpsf=5,
clobber=globclob,verbose=globver,skykey='SKYBKG',
filtkey='FILTER',pixtol=3.0):
""" perform PSF-based photometry on a single target star (SN?) at RA, Dec and
also on a set of comparison stars, using daophot. simultaneously
perform aperture photometry on all the comparison stars (after
subtracting off contributions from neighbors) to enable absolute
photometry by comparison to aperture photometry of standard stars
observed in other fields """
# Defaults / constants
psfmult=5.0 #standard factor (multiplied by fwhm to get psfradius)
psfmultsmall=3.0 #similar to psfmult, adjusted for nstar and substar
# Necessary package
iraf.imutil()
# Parse inputs
infiles=iraffiles(inlist)
# Which file is reffilt? call it refimage
if refimage==None:
for image in infiles:
if check_head(image, filtkey):
try:
imgfilt = get_head(image, filtkey)
if imgfilt == reffilt:
refimage = image
break
except:
pass
if not refimage:
print "BAD USER! No image corresponds to the filter: %s" % reffilt
return
else:
refroot='s'+refimage.split('.')[0]
#first make sure to add back in background of sky
iraf.iqsubsky(inlist, sub=no, skykey=skykey)
#put reference image first on list
infiles.remove(refimage)
infiles.insert(0,refimage)
#setup for keywords
if gain == "!GAIN":
try: gainval = float(get_head(image, gain))
except:
print "Bad header keyword for gain."
else:
gainval = float(gain)
if readnoise == "!READNOISE":
try: readval = float(get_head(image, readnoise))
except:
print "Bad header keyword for readnoise."
else:
readval = float(readnoise)
# Process each file in turn
for image in infiles:
# Check that the image is there
check_exist(image,"r")
# Grab image root name
root=image.split('.')[0]
# Map image to reference image
if not (image==refimage):
[nx,ny]=get_head(image,['NAXIS1','NAXIS2'])
stars=Starlist(get_head(image,'STARFILE'))
refstars=Starlist(get_head(refimage,'STARFILE'))
refstars.pix2wcs(refimage)
refstars.wcs2pix(image)
match,refmatch=stars.match(refstars,useflags=yes,tol=10.0)
nstars=len(match)
if not (nstars>2):
print 'Could not find star matches between reference and %s' % image
infiles.remove(image)
continue
refmatch.pix2wcs(image)
refmatch.wcs2pix(refimage)
matchfile=open('%s.match' % root, 'w')
for i in range(len(match)):
matchfile.write('%10.3f%10.3f%10.3f%10.3f\n' %
(refmatch[i].xval,refmatch[i].yval,
match[i].xval,match[i].yval))
matchfile.close()
check_exist('%s.geodb' % root, 'w', clobber=clobber)
iraf.geomap('%s.match' % root,'%s.geodb' % root,1.0,nx,1.0,ny,
verbose=no,interactive=no)
check_exist('s%s.fits' % root, 'w', clobber=clobber)
iraf.geotran(image,'s%s' % root,'%s.geodb' % root,
'%s.match' % root,geometry="geometric",
boundary="constant",verbose=no)
else:
iraf.imcopy(image,'s%s' % root)
root='s%s' % root
#get sky level and calculate sigma
#if check_head(image, skykey):
# try:
# sky=float(get_head(image, skykey))
# except:
# print "No sky levels in header."
#sigma= (((sky * gainval) + readval**2)**.5) / gainval
iraf.iterstat(image)
# Saturation level
if not check_head(image, "SATURATE"):
saturate = 60000.0
else:
saturate = get_head(image, "SATURATE")
# Update datapars and daopars
iraf.datapars.fwhmpsf=fwhm
iraf.datapars.sigma=iraf.iterstat.sigma
iraf.datapars.datamin=iraf.iterstat.median-10*iraf.iterstat.sigma
iraf.datapars.datamax=0.90*saturate
iraf.datapars.readnoise=readval
iraf.datapars.epadu=gainval
iraf.datapars.filter=filtkey
iraf.daopars.psfrad=psfmult*fwhm
iraf.daopars.fitrad=fwhm
iraf.daopars.function="gauss,moffat15,moffat25,lorentz,penny1"
#find stars in image unless a starlist is given
if image==refimage and starfile==None:
iraf.daophot.daofind(root,'refimage.coo.1',threshold=threshold,verify=no,
verbose=verbose)
elif image==refimage:
shutil.copy(starfile,'refimage.coo.1')
#initial photometry
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=fwhm,verify=no,
verbose=verbose)
#select stars for psf the first time
refstarsfile = "refimage.pst.1"
if image == refimage:
iraf.pstselect(root,'default',refstarsfile,maxnpsf,
interactive=yes,verify=no,verbose=verbose)
#fit the psf
iraf.psf(root,'default',refstarsfile,'default','default','default',
interactive=interact,verify=no,verbose=verbose)
#identify neighboring/interfering stars to selected stars
groupingfile = root+".psg.1"
iraf.nstar(root,groupingfile,'default','default','default',
psfrad= psfmultsmall * fwhm,verify=no,verbose=verbose)
#subtract out neighboring stars from image
iraf.substar(root,'default',refstarsfile,'default','default',
psfrad=psfmultsmall*fwhm,verify=no,verbose=verbose)
#repeat psf to get better psf model
#IRAF's interactive version usually crashes
subtractedimage = root+".sub.1"
iraf.psf(subtractedimage,root+".nst.1",refstarsfile,'%s.psf.2' % root,
'%s.pst.2' % root,'%s.psg.2' % root,interactive=interact,
verify=no,verbose=verbose)
#Need to make sure SN was detected by daofind
stars=Starlist('%s.mag.1' % root)
SN=Star(name='SN',radeg=ra,dcdeg=dec,fwhm=2.0,fwhmw=2.0)
SNlis=Starlist(stars=[SN])
SNlis.wcs2pix(image)
if (len(stars.match(SNlis)[0])==0):
#No match - need to add to daofind file
print "No match!"
coofile=open('refimage.coo.1', 'a+')
coofile.write('%10.3f%10.3f%9.3f%8.3f%13.3f%12.3f%8i\n' % (SNlis[0].xval, SNlis[0].yval,99.999,0.500,0.000,0.000,999))
coofile.close()
#repeat aperture photometry to get good comparisons to standard fields
iraf.daophot.phot(root,'refimage.coo.1','default',aperture=psfmult*fwhm,
verify=no,verbose=verbose)
# allstar run
iraf.allstar(root,'default','default','default','default','default',
verify=no,verbose=verbose)
def spitzer_sub(new, newunc, newcov, ref, refunc, refcov, out,
stamps=None, tmass=None):
# Remove nan from mosaics
x = pyfits.open(new)
y = np.nan_to_num(x[0].data)
x[0].data = y
new2 = "n%s" % new
x.writeto(new2)
# Find stars in new image
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (new2, newcov))
# Find stars in reference
os.system("$REDUCTION/runsex.pl %s 5.0 -weight %s" % (ref, refcov))
# Create file for geotran
stars = Starlist("%s.stars" % new2)
refstars = Starlist("%s.stars" % ref)
refstars.pix2wcs(ref)
refstars.wcs2pix(new2)
a,b = stars.match(refstars, maxnum=1000)
# Create file for geotran
b.pix2wcs(new2)
b.wcs2pix(ref)
refroot = ref.split(".")[0]
outf = open("%s.match" % refroot, "w")
for i in range(len(a)):
outf.write("%10.3f%10.3f%10.3f%10.3f\n" % (a[i].xval, a[i].yval,
b[i].xval, b[i].yval))
outf.close()
# Geomap and geotran
[naxis1, naxis2] = get_head(new, ["NAXIS1", "NAXIS2"])
iraf.geomap("%s.match" % refroot, "%s.db" % refroot, 1, naxis1, 1, naxis2,
fitgeometry="rotate", interactive=no)
iraf.geotran(ref, "t%s" % ref, "%s.db" % refroot, "%s.match" % refroot)
iraf.geotran(refunc, "t%s" % refunc, "%s.db" % refroot,
"%s.match" % refroot)
# Get stars for PSF matching
if stamps != None:
psfstars = Starlist(stamps)
psfstars.pix2wcs(ref)
psfstars.wcs2pix(new2)
outf = open("stamps.lis", "w")
for star in psfstars:
outf.write("%10.3f%10.3f\n" % (star.xval, star.yval))
outf.close()
# Appropriate parameters
iraf.iterstat(ref)
update_head(ref, ["MEDSKY", "SKYSIG"], [iraf.iterstat.median,
iraf.iterstat.sigma])
[refskybkg, refskysig] = get_head(ref, ["MEDSKY", "SKYSIG"])
tl = refskybkg - 10 * refskysig; tu = 30000.0
iraf.iterstat(new)
update_head(new, ["MEDSKY", "SKYSIG"], [iraf.iterstat.median,
iraf.iterstat.sigma])
[newskybkg, newskysig] = get_head(new, ["MEDSKY", "SKYSIG"])
il = newskybkg - 10 * newskysig; iu = 30000.0
# Run hotpants
hpcmd = "hotpants -inim %s -tmplim t%s -outim %s -tni t%s -ini %s -nsx 3 -nsy 3 -savexy %s.xy -ko 0 -bgo 0 -oni u%s -n t -tl %.2f -tu %.2f -il %.2f -iu %.2f -r 7.5 -rss 18.0" % (new2, ref, out, refunc, newunc, new2, out, tl, tu, il, iu)
if stamps != None:
hpcmd += " -ssf stamps.lis -afssc 0"
os.system(hpcmd)
#iraf.imarith(new2, "-", "t%s" % ref, out)
# Create appropriate weight image
#iraf.imexpr("sqrt(a**2 + b**2)", "u%s" % out, a=newunc, b=refunc)
iraf.imarith(1, "/", "u%s" % out, "temp1.fits")
iraf.imarith("temp1.fits", "/", "u%s" % out, "w%s" % out)
os.remove("temp1.fits")
# Pick up candidates
os.system("$REDUCTION/runsex.pl %s 2.0 -weight w%s" % (out, out))
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (nax2 - EDGETOL) and refu[0].data[star.yval,star.xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
# Filter out bright stars
if tmass==None:
scands.write("cands.reg")
else:
stmass = Starlist(tmass)
stmass.wcs2pix(ref)
s2cands = scands.nomatch(stmass)
s2cands.write("cands.reg")
# More comprehensive list
os.system("$REDUCTION/runsex.pl %s 1.5" % out)
stars = Starlist("%s.stars" % out)
cands = []
[nax1, nax2] = get_head(ref, ["NAXIS1", "NAXIS2"])
stars.pix2wcs(new)
stars.wcs2pix(ref)
refu = pyfits.open(refunc)
for star in stars:
if star.xval > EDGETOL and star.xval < (nax1 - EDGETOL) and star.yval > EDGETOL and star.yval < (nax2 - EDGETOL) and refu[0].data[star.yval,star.xval] != 0 and star.fwhmw > 0.5 and star.fwhmw < 10.0:
cands.append(star)
scands = Starlist(stars=cands)
scands.write("cands_all.reg")
return
def p60sdsssub(inlis,
refimage,
ot,
distortdeg=1,
scthresh1=3.0,
scthresh2=10.0,
tu=50000,
iu=50000,
ig=2.3,
tg=1.0,
stamps=None,
nsx=4,
nsy=4,
ko=0,
bgo=0,
radius=10,
tlow=None,
ilow=None,
sthresh=5.0,
ng=None,
aperture=10.0):
'''P60 Subtraction using SDSS image as reference'''
images = iraffiles(inlis)
images.sort()
# Get WCS center, pixel scale, and pixel extent of reference
[n1, n2] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
[[ractr, dcctr]] = impix2wcs(refimage, n1 / 2.0, n2 / 2.0)
if not (check_head(refimage, 'PIXSCALE')):
print 'Error: Please add PIXSCALE keyword to reference image'
return 0
else:
pix = get_head(refimage, 'PIXSCALE')
# Create stamps file
if not (stamps == None):
refstars = Starlist(stamps)
refstars.wcs2pix(refimage)
outf = open('ref.stamps', 'w')
for star in refstars:
outf.write('%.1f\t%.1f\n' % (star.xval, star.yval))
outf.close()
# Create OT file
refstars = Starlist(ot)
refstars.wcs2pix(refimage)
outf = open('ref.coo', 'w')
outf.write('%.2f\t%.2f\n' % (refstars[0].xval, refstars[0].yval))
outf.close()
# First get good distortion correction (if succifient number of images)
# if len(images)>5:
# p60scampall(images, 't20', 'dist', distortdeg=3, scthresh1=3.0,
# scthresh2=10.0)
# else:
# for image in images:
# iraf.imcopy(image, '%s.dist.fits' % image.split('.')[0])
for image in images:
root = image.split('.')[0]
# Subtract sky background if necessary
[iskysub, iskybkg] = get_head(image, ['SKYSUB', 'SKYBKG'])
if (iskysub == 1):
iraf.imarith(image, '+', iskybkg, image)
update_head(image, 'SKYSUB', 0)
# First use pre-determined distortion file
#shutil.copy(distfile, '%s.head' % root)
#p60swarp(image, '%s.dist.fits' % root, backsub=no)
# Run scamp
#p60scamp('%s.dist.fits' % root, refimage=refimage,
p60scamp('%s.fits' % root,
refimage=refimage,
distortdeg=distortdeg,
scthresh1=scthresh1,
scthresh2=scthresh2,
rms=True,
mask=True)
# Run Swarp
#p60swarp('%s.dist.fits' % root, '%s.shift.fits' % root, ractr=ractr,
p60swarp('%s.fits' % root,
'%s.shift.fits' % root,
backsub=no,
refimage=refimage,
rms=True,
mask=True)
# Subtract
if (ilow == None):
iraf.iterstat(image)
ilow = iraf.iterstat.median - 10.0 * iraf.iterstat.sigma
if (tlow == None):
iraf.iterstat(refimage)
tlow = iraf.iterstat.median - 10.0 * iraf.iterstat.sigma
tu = get_head(refimage, "SATURATE") * 0.90
iu = get_head(image, "SATURATE") * 0.90
fwhm = get_head(image, "SEEPIX")
if fwhm > 15.0:
radius = 15
p60hotpants('%s.shift.fits' % root,
refimage,
'%s.sub.fits' % root,
tu=tu,
iu=iu,
ko=ko,
bgo=bgo,
nsx=nsx,
nsy=nsy,
radius=radius,
tlow=tlow,
ilow=ilow,
sthresh=sthresh,
ng=ng,
stamps="ref.stamps",
scimage=no,
rms=True)
# Photometer subtracted image
#iraf.phot('%s.sub.fits' % root, coords='ref.coo', output='%s.mag' %
#root, epadu=ig, exposure='', calgorithm='none',
#salgorithm='median', annulus=30.0, dannulus=10.0,
#weighting='constant', apertures=aperture, zmag=25.0,
#interactive=no)
print "Exiting successfully"
return
def kaitphot(refimage, inlist, sncoo, aperture=10.0, dx=20, dy=20):
infiles = iraffiles(inlist)
# Find location of SN
[[snra, sndec]] = impix2wcs(refimage, sncoo[0], sncoo[1])
sn = [Star(name="New_SN", xval=sncoo[0], yval=sncoo[1])]
snstars = Starlist(stars=sn)
print "Coordinates of new SN: %s %s (J2000.0)" % (snra, sndec)
# Grab reference rootname
refroot, null = refimage.split('.')
# Big loop
for image in infiles:
# Image root
root, null = image.split('.')
# Grab zeropt and zeropt error from image header
zp, zpu = get_head(image, [ZPTKEY, ZPUKEY])
# Detect objects in difference image
iraf.iqobjs('%s.sub.fits' % root,
SIGMA,
SATVAL,
skyval="0.0",
zeropt=float(zp),
masksfx=MASKSFX,
wtimage="",
fwhm=FWHM,
pix=PIXSCALE,
aperture=2 * FWHM / PIXSCALE,
gain=FL_GAIN,
minlim=no,
clobber=yes,
verbose=no)
# See if object was detected
stars = Starlist('%s.sub.fits.stars' % root)
match, snmatch = stars.match(snstars, useflags=yes)
# If so, use SN magnitude and statistical error
if len(match):
snmag = match[0].mag
snerr = match[0].magu
# Otherwise calculate upper limit
else:
statsec = '[%i:%i,%i:%i]' % (
int(sncoo[0] - dx / 2), int(sncoo[0] + dx / 2),
int(sncoo[1] - dy / 2), int(sncoo[1] + dy / 2))
iraf.iterstat('%s.sub%s' % (root, statsec),
nsigrej=5,
maxiter=5,
prin=no,
verbose=no)
sigma = float(iraf.iterstat.sigma)
snmag = - 2.5 * log10(3 * sigma * sqrt(pi * power(aperture,2))) + \
float(zp)
snerr = 99.999
# Want UT of Observation
[dateobs, ut] = get_head(image, ['DATE-OBS', 'UT'])
tobs = DateTime(int(dateobs[6:]), int(dateobs[3:5]), int(dateobs[:2]),
int(ut[:2]), int(ut[3:5]), int(ut[6:]))
# Return SN Mag / UL
print '%s\t%s %.3f %10.3f%10.3f%10.3f%10.3f' % (
root, tobs.Format("%b"),
(((tobs.second / 3600.0) + tobs.minute) / 60.0 + tobs.hour) / 24.0
+ tobs.day, snmag, snerr, float(zp), float(zpu))
print 'Exiting Successfully'
return