def lmi_defringe(imlist, filts=["SDSS-Z"], fkey="FILTER", skybkg="SKYBKG", skysub="SKYSUB", fpfx="F"):
"""Create and apply fringe frame."""
ims = glob.glob(imlist)
ims.sort()
for filt in filts:
# Identify all the images in a given filter
fims = []
for im in ims:
hdr = pyfits.open(im)
if hdr[0].header[fkey] == filt:
fims.append(im)
if len(fims) == 0:
continue
fimlist = ""
for fim in fims:
# Run iqobjs to get mask
iqpkg.iqobjs(fim, 2.0, 50000.0, wtimage="", verbose=no)
fimlist += "%s," % fim
iqpkg.iqfringe(fimlist[:-1], "Fringe-%s.fits" % filt, verbose=no)
iqpkg.iqdefringe(fimlist[:-1], "Fringe-%s.fits" % filt, outpfx="f", verbose=no)
for fim in fims:
shutil.move("f%s" % fim, "%s%s" % (fpfx, fim[-13:]))
def lmi_stats(imlist, outf):
'''Basic image statistics.'''
ims = glob.glob(imlist)
ims.sort()
outfile = open(outf, "w")
for im in ims:
hdr = pyfits.open(im)
ra = hdr[0].header["RA"]
dec = hdr[0].header["DEC"]
obj = hdr[0].header["OBJECT"]
filt = hdr[0].header["FILTER"]
exptime = hdr[0].header["EXPTIME"]
tobs = hdr[0].header["DATE-OBS"]
skybkg = hdr[0].header["SKYBKG"]
skysig = hdr[0].header["SKYSIG"]
iqpkg.iqobjs(im, 3.0, 50000.0, wtimage="", verbose=no)
os.system("getsdss.pl -r 10.0 -f %s.reg -p %s %s %s.txt" %
(obj, ra, dec, obj))
stars = iqutils.Starlist("%s.stars" % im)
refstars = iqutils.Starlist("%s.reg" % obj)
if len(refstars) == 0:
continue
refstars.wcs2pix(im)
if not FDICT.has_key(filt):
continue
refstars.set_mag(FDICT[filt])
a, b = stars.match(refstars, maxnum=1000)
if len(a) == 0:
continue
fwhm = np.median(a.fwhms()) * LMIPIXSCALE
zp, zpu = stars.zeropt(refstars, method="mean", rejout=1)
if (zp == 0.0) or (zpu > 0.20):
tzp = 99.0
lmag1 = 99.0
lmag2 = 99.0
else:
tzp = 25.0 + zp - 2.5 * np.log10(float(exptime))
area = np.pi * np.power(1.2 * fwhm / LMIPIXSCALE, 2)
lmag1 = -2.5 * np.log10(3 * np.sqrt(area) * skysig / exptime) + tzp
lmag2 = -2.5 * np.log10(
3 * np.sqrt(area) * skysig / np.sqrt(100.0 * exptime)) + tzp
outfile.write(
"%s%25s%15s%8s%8.2f%10.2f%10.2f%8.2f%10.3f%10.3f%10.3f%10.3f\n" %
(im, tobs, obj, filt, exptime, skybkg, skysig, fwhm, tzp, zpu,
lmag1, lmag2))
outfile.close()
return
def lmi_stats(imlist, outf):
"""Basic image statistics."""
ims = glob.glob(imlist)
ims.sort()
outfile = open(outf, "w")
for im in ims:
hdr = pyfits.open(im)
ra = hdr[0].header["RA"]
dec = hdr[0].header["DEC"]
obj = hdr[0].header["OBJECT"]
filt = hdr[0].header["FILTER"]
exptime = hdr[0].header["EXPTIME"]
tobs = hdr[0].header["DATE-OBS"]
skybkg = hdr[0].header["SKYBKG"]
skysig = hdr[0].header["SKYSIG"]
iqpkg.iqobjs(im, 3.0, 50000.0, wtimage="", verbose=no)
os.system("getsdss.pl -r 10.0 -f %s.reg -p %s %s %s.txt" % (obj, ra, dec, obj))
stars = iqutils.Starlist("%s.stars" % im)
refstars = iqutils.Starlist("%s.reg" % obj)
if len(refstars) == 0:
continue
refstars.wcs2pix(im)
if not FDICT.has_key(filt):
continue
refstars.set_mag(FDICT[filt])
a, b = stars.match(refstars, maxnum=1000)
if len(a) == 0:
continue
fwhm = np.median(a.fwhms()) * LMIPIXSCALE
zp, zpu = stars.zeropt(refstars, method="mean", rejout=1)
if (zp == 0.0) or (zpu > 0.20):
tzp = 99.0
lmag1 = 99.0
lmag2 = 99.0
else:
tzp = 25.0 + zp - 2.5 * np.log10(float(exptime))
area = np.pi * np.power(1.2 * fwhm / LMIPIXSCALE, 2)
lmag1 = -2.5 * np.log10(3 * np.sqrt(area) * skysig / exptime) + tzp
lmag2 = -2.5 * np.log10(3 * np.sqrt(area) * skysig / np.sqrt(100.0 * exptime)) + tzp
outfile.write(
"%s%25s%15s%8s%8.2f%10.2f%10.2f%8.2f%10.3f%10.3f%10.3f%10.3f\n"
% (im, tobs, obj, filt, exptime, skybkg, skysig, fwhm, tzp, zpu, lmag1, lmag2)
)
outfile.close()
return
def p48coaddphot(inlis, refstars, ot, filter, outfile):
outf = open(outfile, "w")
for image in inlis:
root = image.split(".")[0]
# Get the seeing in the image
iqpkg.iqobjs(image, SIGMA, get_head(image, "SATURATE"), skyval="0.0")
seepix = get_head(image, "SEEPIX")
# Calculate zeropoint
refstars.wcs2pix(image)
refstars.set_mag(filter)
xyfile = open("%s.xy" % root, "w")
for star in refstars:
xyfile.write("%10.3f%10.3f\n" % (star.xval, star.yval))
xyfile.close()
iraf.phot(image, coords="%s.xy" % root, output="%s.mag" % root,
aperture=1.2*float(seepix), interac=no)
stars = Starlist("%s.mag" % root)
zp, zpu = stars.zeropt(refstars,method="mean",rejout=0)
# Measure source
ot.wcs2pix(image)
coofile = open("%s.coo" % root, "w")
coofile.write("%10.3f%10.3f\n" % (ot[0].xval, ot[0].yval))
coofile.close()
iraf.phot(image, coords="%s.coo" % root, output="%s.ot" % root,
aperture=1.2*float(seepix), calgorithm="none", interac=no)
stars = Starlist("%s.ot" % root)
# Just return 99 for non-detection
if len(stars)==1:
smag = stars[0].mag
smagu = stars[0].magu
mag = smag + zp
else:
mag = 99.0
smagu = 99.0
mjdobs = DateTimeFrom(get_head(image, OBSDATEKEY)).mjd
exptime = get_head(image, EXPTIMEKEY)
outf.write("%15.3f%10.1f%10.3f%10.3f%10.3f\n" % (mjdobs, exptime, mag, smagu, zpu))
outf.close()
def lmi_defringe(imlist,
filts=["SDSS-Z"],
fkey="FILTER",
skybkg="SKYBKG",
skysub="SKYSUB",
fpfx="F"):
'''Create and apply fringe frame.'''
ims = glob.glob(imlist)
ims.sort()
for filt in filts:
# Identify all the images in a given filter
fims = []
for im in ims:
hdr = pyfits.open(im)
if hdr[0].header[fkey] == filt:
fims.append(im)
if len(fims) == 0:
continue
fimlist = ""
for fim in fims:
# Run iqobjs to get mask
iqpkg.iqobjs(fim, 2.0, 50000.0, wtimage="", verbose=no)
fimlist += "%s," % fim
iqpkg.iqfringe(fimlist[:-1], "Fringe-%s.fits" % filt, verbose=no)
iqpkg.iqdefringe(fimlist[:-1],
"Fringe-%s.fits" % filt,
outpfx="f",
verbose=no)
for fim in fims:
shutil.move("f%s" % fim, "%s%s" % (fpfx, fim[-13:]))
def id_ot(inlis,
refstars,
tmass,
ra,
dec,
radius,
output="Cands.reg",
sigma=3.0,
satval=60000.0,
wtroot="/Users/scenko/CALIB/p48/mask_C",
pixtol=5.0):
fchips = {} # Stores field / chip dictionary
center = astrocoords(ra, dec) # Center of error region
newcands = [] # New candidate OTs
for image in inlis:
# Identify all field and chip combinations
[field, chip] = get_head(image, ["PTFFIELD", "CCDID"])
if not fchips.has_key(field):
fchips[field] = [chip]
else:
try:
ind = fchips[field].index(chip)
except ValueError:
fchips[field].append(chip)
# Also grab objects
iqpkg.iqobjs(image,
sigma,
satval,
skyval="0.0",
wtimage="%s%02i.fits" % (wtroot, int(chip)),
wtcut=0.1,
fwhm=1.5,
pix=1.0)
for (field, chips) in fchips.iteritems():
for chip in chips:
nonmatches = []
for image in inlis:
# Only look at images for the given field/chip pair
[nfield, nchip] = get_head(image, ["PTFFIELD", "CCDID"])
if (nfield == field) and (nchip == chip):
# Grab all the objects from this image that don't appear in
# reference star list
stars = Starlist(get_head(image, "STARFILE"))
refstars.wcs2pix(image)
new = stars.nomatch(refstars, tol=pixtol)
# Make sure the new objects fall inside error circle
new.pix2wcs(image)
new_incircle = []
for star in new:
temp = astrocoords(star.radeg(), star.dcdeg())
dist = temp.diff(center, degree=1)
if sqrt(pow(dist[0], 2) + pow(dist[1], 2)) < radius:
new_incircle.append(star)
# If first image from field/chip pair, add everything to
# nonmatches. Otherwise, only add objects if they have shown
# up already
if len(nonmatches) == 0:
nonmatches = new_incircle
else:
s1 = Starlist(stars=new_incircle)
sr = Starlist(stars=nonmatches)
s1.wcs2pix(image)
sr.wcs2pix(image)
temp, junk = sr.match(s1, maxnum=10000, useflags=no)
nonmatches = temp.stars
# When done with a field/chip pair, add candidates to list
for star in nonmatches:
newcands.append(star)
# Final comparison with 2MASS
temp = Starlist(stars=newcands)
temp.wcs2pix(image)
tmass.wcs2pix(image)
result = temp.nomatch(tmass, tol=pixtol)
result.write(output)
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 id_ot(inlis, refstars, tmass, ra, dec, radius, output="Cands.reg",
sigma=3.0, satval=60000.0, wtroot="/Users/scenko/CALIB/p48/mask_C",
pixtol=5.0):
fchips = {} # Stores field / chip dictionary
center = astrocoords(ra, dec) # Center of error region
newcands = [] # New candidate OTs
for image in inlis:
# Identify all field and chip combinations
[field, chip] = get_head(image, ["PTFFIELD", "CCDID"])
if not fchips.has_key(field):
fchips[field] = [chip]
else:
try:
ind = fchips[field].index(chip)
except ValueError:
fchips[field].append(chip)
# Also grab objects
iqpkg.iqobjs(image, sigma, satval, skyval="0.0", wtimage="%s%02i.fits" %
(wtroot, int(chip)), wtcut=0.1, fwhm=1.5, pix=1.0)
for (field, chips) in fchips.iteritems():
for chip in chips:
nonmatches = []
for image in inlis:
# Only look at images for the given field/chip pair
[nfield, nchip] = get_head(image, ["PTFFIELD", "CCDID"])
if (nfield == field) and (nchip == chip):
# Grab all the objects from this image that don't appear in
# reference star list
stars = Starlist(get_head(image, "STARFILE"))
refstars.wcs2pix(image)
new = stars.nomatch(refstars, tol=pixtol)
# Make sure the new objects fall inside error circle
new.pix2wcs(image)
new_incircle = []
for star in new:
temp = astrocoords(star.radeg(), star.dcdeg())
dist = temp.diff(center, degree=1)
if sqrt(pow(dist[0],2) + pow(dist[1],2)) < radius:
new_incircle.append(star)
# If first image from field/chip pair, add everything to
# nonmatches. Otherwise, only add objects if they have shown
# up already
if len(nonmatches) == 0:
nonmatches = new_incircle
else:
s1 = Starlist(stars=new_incircle)
sr = Starlist(stars=nonmatches)
s1.wcs2pix(image)
sr.wcs2pix(image)
temp, junk = sr.match(s1, maxnum=10000, useflags=no)
nonmatches = temp.stars
# When done with a field/chip pair, add candidates to list
for star in nonmatches:
newcands.append(star)
# Final comparison with 2MASS
temp = Starlist(stars=newcands)
temp.wcs2pix(image)
tmass.wcs2pix(image)
result = temp.nomatch(tmass, tol=pixtol)
result.write(output)
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]
