def make_coadds(tims,
bands,
targetwcs,
mods=None,
xy=None,
apertures=None,
apxy=None,
ngood=False,
detmaps=False,
psfsize=False,
callback=None,
callback_args=[],
plots=False,
ps=None,
lanczos=True,
mp=None):
from astrometry.util.ttime import Time
t0 = Time()
class Duck(object):
pass
C = Duck()
W = int(targetwcs.get_width())
H = int(targetwcs.get_height())
# always, for patching SATUR, etc pixels?
unweighted = True
if not xy:
psfsize = False
C.coimgs = []
if detmaps:
C.galdetivs = []
C.detivs = []
if mods is not None:
C.comods = []
C.coresids = []
if apertures is not None:
unweighted = True
C.AP = fits_table()
if xy:
ix, iy = xy
C.T = fits_table()
C.T.nobs = np.zeros((len(ix), len(bands)), np.uint8)
C.T.anymask = np.zeros((len(ix), len(bands)), np.int16)
C.T.allmask = np.zeros((len(ix), len(bands)), np.int16)
if psfsize:
C.T.psfsize = np.zeros((len(ix), len(bands)), np.float32)
if detmaps:
C.T.depth = np.zeros((len(ix), len(bands)), np.float32)
C.T.galdepth = np.zeros((len(ix), len(bands)), np.float32)
if lanczos:
print('Doing Lanczos resampling')
for tim in tims:
# surface-brightness correction
tim.sbscale = (targetwcs.pixel_scale() / tim.subwcs.pixel_scale())**2
# We create one iterator per band to do the tim resampling. These all run in
# parallel when multi-processing.
imaps = []
for band in bands:
args = []
for itim, tim in enumerate(tims):
if tim.band != band:
continue
if mods is None:
mo = None
else:
mo = mods[itim]
args.append((itim, tim, mo, lanczos, targetwcs))
if mp is not None:
imaps.append(mp.imap_unordered(_resample_one, args))
else:
import itertools
imaps.append(itertools.imap(_resample_one, args))
# Args for aperture photometry
apargs = []
tinyw = 1e-30
for iband, (band, timiter) in enumerate(zip(bands, imaps)):
print('Computing coadd for band', band)
# coadded weight map (moo)
cow = np.zeros((H, W), np.float32)
# coadded weighted image map
cowimg = np.zeros((H, W), np.float32)
kwargs = dict(cowimg=cowimg, cow=cow)
if detmaps:
# detection map inverse-variance (depth map)
detiv = np.zeros((H, W), np.float32)
C.detivs.append(detiv)
kwargs.update(detiv=detiv)
# galaxy detection map inverse-variance (galdepth map)
galdetiv = np.zeros((H, W), np.float32)
C.galdetivs.append(galdetiv)
kwargs.update(galdetiv=galdetiv)
if mods is not None:
# model image
cowmod = np.zeros((H, W), np.float32)
# chi-squared image
cochi2 = np.zeros((H, W), np.float32)
kwargs.update(cowmod=cowmod, cochi2=cochi2)
if unweighted:
# unweighted image
coimg = np.zeros((H, W), np.float32)
if mods is not None:
# unweighted model
comod = np.zeros((H, W), np.float32)
# number of exposures
con = np.zeros((H, W), np.uint8)
# inverse-variance
coiv = np.zeros((H, W), np.float32)
kwargs.update(coimg=coimg, coiv=coiv)
# Note that we have 'congood' as well as 'nobs':
# * 'congood' is used for the 'nexp' *image*.
# * 'nobs' is used for the per-source measurements
#
# (you want to know the number of observations within the
# source footprint, not just the peak pixel which may be
# saturated, etc.)
if ngood:
congood = np.zeros((H, W), np.uint8)
kwargs.update(congood=congood)
if xy:
# These match the type of the "DQ" images.
# "any" mask
ormask = np.zeros((H, W), np.int16)
# "all" mask
andmask = np.empty((H, W), np.int16)
allbits = reduce(np.bitwise_or, CP_DQ_BITS.values())
andmask[:, :] = allbits
# number of observations
nobs = np.zeros((H, W), np.uint8)
kwargs.update(ormask=ormask, andmask=andmask, nobs=nobs)
if psfsize:
psfsizemap = np.zeros((H, W), np.float32)
for R in timiter:
if R is None:
continue
itim, Yo, Xo, iv, im, mo, dq = R
#print('timiter Yo,Xo,im.shape=',Yo,Xo,im.shape)
tim = tims[itim]
# invvar-weighted image
cowimg[Yo, Xo] += iv * im
cow[Yo, Xo] += iv
if unweighted:
if dq is None:
goodpix = 1
else:
# include BLEED, SATUR, INTERP pixels if no other
# pixels exists (do this by eliminating all other CP
# flags)
badbits = 0
for bitname in ['badpix', 'cr', 'trans', 'edge', 'edge2']:
badbits |= CP_DQ_BITS[bitname]
goodpix = ((dq & badbits) == 0)
coimg[Yo, Xo] += goodpix * im
con[Yo, Xo] += goodpix
coiv[Yo, Xo] += goodpix * 1. / (tim.sig1 *
tim.sbscale)**2 # ...ish
if xy:
if dq is not None:
ormask[Yo, Xo] |= dq
andmask[Yo, Xo] &= dq
# raw exposure count
nobs[Yo, Xo] += 1
if psfsize:
# psfnorm is in units of 1/pixels.
# (eg, psfnorm for a gaussian is ~ 1/psf_sigma)
# Neff is in pixels**2
neff = 1. / tim.psfnorm**2
# Narcsec is in arcsec**2
narcsec = neff * tim.wcs.pixel_scale()**2
psfsizemap[Yo, Xo] += iv * (1. / narcsec)
if detmaps:
# point-source depth
detsig1 = tim.sig1 / tim.psfnorm
detiv[Yo, Xo] += (iv > 0) * (1. / detsig1**2)
# Galaxy detection map
gdetsig1 = tim.sig1 / tim.galnorm
galdetiv[Yo, Xo] += (iv > 0) * (1. / gdetsig1**2)
if ngood:
congood[Yo, Xo] += (iv > 0)
if mods is not None:
# straight-up
comod[Yo, Xo] += goodpix * mo
# invvar-weighted
cowmod[Yo, Xo] += iv * mo
# chi-squared
cochi2[Yo, Xo] += iv * (im - mo)**2
del mo
del goodpix
del Yo, Xo, im, iv
# END of loop over tims
# Per-band:
cowimg /= np.maximum(cow, tinyw)
C.coimgs.append(cowimg)
if mods is not None:
cowmod /= np.maximum(cow, tinyw)
C.comods.append(cowmod)
coresid = cowimg - cowmod
coresid[cow == 0] = 0.
C.coresids.append(coresid)
if unweighted:
coimg /= np.maximum(con, 1)
del con
cowimg[cow == 0] = coimg[cow == 0]
if mods is not None:
cowmod[cow == 0] = comod[cow == 0]
if xy:
C.T.nobs[:, iband] = nobs[iy, ix]
C.T.anymask[:, iband] = ormask[iy, ix]
C.T.allmask[:, iband] = andmask[iy, ix]
# unless there were no images there...
C.T.allmask[nobs[iy, ix] == 0, iband] = 0
if detmaps:
C.T.depth[:, iband] = detiv[iy, ix]
C.T.galdepth[:, iband] = galdetiv[iy, ix]
if psfsize:
wt = cow[iy, ix]
# psfsizemap is in units of iv * (1 / arcsec**2)
sz = psfsizemap[iy, ix]
sz /= np.maximum(wt, tinyw)
sz[wt == 0] = 0.
# Back to units of linear arcsec.
sz = 1. / np.sqrt(sz)
sz[wt == 0] = 0.
# Correction factor to get back to equivalent of Gaussian sigma
sz /= (2. * np.sqrt(np.pi))
# Conversion factor to FWHM (2.35)
sz *= 2. * np.sqrt(2. * np.log(2.))
C.T.psfsize[:, iband] = sz
del psfsizemap
if apertures is not None:
# Aperture photometry, using the unweighted "coimg" and
# "coiv" arrays.
with np.errstate(divide='ignore'):
imsigma = 1.0 / np.sqrt(coiv)
imsigma[coiv == 0] = 0
for irad, rad in enumerate(apertures):
apargs.append((irad, band, rad, coimg, imsigma, True, apxy))
if mods is not None:
apargs.append(
(irad, band, rad, coresid, None, False, apxy))
if callback is not None:
callback(band, *callback_args, **kwargs)
# END of loop over bands
t2 = Time()
print('coadds: images:', t2 - t0)
if apertures is not None:
# Aperture phot, in parallel
if mp is not None:
apresults = mp.map(_apphot_one, apargs)
else:
apresults = map(_apphot_one, apargs)
del apargs
apresults = iter(apresults)
for iband, band in enumerate(bands):
apimg = []
apimgerr = []
if mods is not None:
apres = []
for irad, rad in enumerate(apertures):
(airad, aband, isimg, ap_img, ap_err) = apresults.next()
assert (airad == irad)
assert (aband == band)
assert (isimg)
apimg.append(ap_img)
apimgerr.append(ap_err)
if mods is not None:
(airad, aband, isimg, ap_img, ap_err) = apresults.next()
assert (airad == irad)
assert (aband == band)
assert (not isimg)
apres.append(ap_img)
assert (ap_err is None)
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_img_%s' % band, ap)
ap = 1. / (np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_img_ivar_%s' % band, ap)
if mods is not None:
ap = np.vstack(apres).T
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_resid_%s' % band, ap)
t3 = Time()
print('coadds apphot:', t3 - t2)
return C
def edr_dr2_vs_dr3():
#plotrange = ((240.9, 242.1), (4.8, 5.9))
plotrange = ((240, 245), (4.5, 12.0))
#fns = glob('dr2-tractor/241/tractor-*p05*.fits')
fns = glob('dr2-tractor/24[01234]/tractor-*.fits')
fns.sort()
TT2, TT3 = [], []
for fn in fns:
fn3 = fn.replace('dr2-tractor', 'dr3-tractor')
if not os.path.exists(fn3):
print('Does not exist:', fn3)
continue
cols = 'ra dec brick_primary brickname decam_anymask'.split()
T2 = fits_table(fn, columns=cols)
T3 = fits_table(fn3, columns=cols)
print('Reading', fn, '->', len(T2), 'DR2,', len(T3), 'DR3')
TT2.append(T2)
TT3.append(T3)
T2 = merge_tables(TT2, columns='fillzero')
T3 = merge_tables(TT3, columns='fillzero')
del TT2, TT3
plt.clf()
plothist(T2.ra, T2.dec, nbins=200, range=plotrange)
plt.title('DR2')
ps.savefig()
plt.clf()
plothist(T3.ra, T3.dec, nbins=200, range=plotrange)
plt.title('DR3')
ps.savefig()
plt.clf()
I = np.flatnonzero(T2.brick_primary)
print('DR2:', len(I), 'brick_primary')
H2, xe, ye = plothist(T2.ra[I], T2.dec[I], nbins=200, range=plotrange)
mx = H2.max()
plt.title('DR2 brick_primary')
ps.savefig()
plt.clf()
I = np.flatnonzero(T3.brick_primary)
print('DR3:', len(I), 'brick_primary')
H3, xe, ye = plothist(T3.ra[I],
T3.dec[I],
nbins=200,
imshowargs=dict(vmax=mx),
range=plotrange)
plt.title('DR3 brick_primary')
ps.savefig()
plt.clf()
plt.imshow((H3 - H2).T,
interpolation='nearest',
origin='lower',
cmap='hot',
extent=(min(xe), max(xe), min(ye), max(ye)),
aspect='auto')
plt.colorbar()
plt.title('DR3 - DR2 brick_primary')
ps.savefig()
for name, bit in CP_DQ_BITS.items():
plt.clf()
anymask = reduce(np.bitwise_or,
(T3.decam_anymask[:, 1], T3.decam_anymask[:, 2],
T3.decam_anymask[:, 4]))
I = np.flatnonzero((anymask & bit) > 0)
#plt.subplot(1,2,1)
plothist(T3.ra[I],
T3.dec[I],
nbins=200,
imshowargs=dict(vmax=mx),
doclf=False,
range=plotrange)
plt.title('DR3, %s any' % name)
#plt.suptitle('DR3')
ps.savefig()
def edr_dr2_vs_dr3():
#plotrange = ((240.9, 242.1), (4.8, 5.9))
plotrange = ((240, 245), (4.5, 12.0))
#fns = glob('dr2-tractor/241/tractor-*p05*.fits')
fns = glob('dr2-tractor/24[01234]/tractor-*.fits')
fns.sort()
TT2,TT3 = [],[]
for fn in fns:
fn3 = fn.replace('dr2-tractor', 'dr3-tractor')
if not os.path.exists(fn3):
print('Does not exist:', fn3)
continue
cols = 'ra dec brick_primary brickname decam_anymask'.split()
T2 = fits_table(fn, columns=cols)
T3 = fits_table(fn3, columns=cols)
print('Reading', fn, '->', len(T2), 'DR2,', len(T3), 'DR3')
TT2.append(T2)
TT3.append(T3)
T2 = merge_tables(TT2, columns='fillzero')
T3 = merge_tables(TT3, columns='fillzero')
del TT2, TT3
plt.clf()
plothist(T2.ra, T2.dec, nbins=200, range=plotrange)
plt.title('DR2')
ps.savefig()
plt.clf()
plothist(T3.ra, T3.dec, nbins=200, range=plotrange)
plt.title('DR3')
ps.savefig()
plt.clf()
I = np.flatnonzero(T2.brick_primary)
print('DR2:', len(I), 'brick_primary')
H2,xe,ye = plothist(T2.ra[I], T2.dec[I], nbins=200, range=plotrange)
mx = H2.max()
plt.title('DR2 brick_primary')
ps.savefig()
plt.clf()
I = np.flatnonzero(T3.brick_primary)
print('DR3:', len(I), 'brick_primary')
H3,xe,ye = plothist(T3.ra[I], T3.dec[I], nbins=200, imshowargs=dict(vmax=mx), range=plotrange)
plt.title('DR3 brick_primary')
ps.savefig()
plt.clf()
plt.imshow((H3 - H2).T, interpolation='nearest', origin='lower', cmap='hot',
extent=(min(xe), max(xe), min(ye), max(ye)),
aspect='auto')
plt.colorbar()
plt.title('DR3 - DR2 brick_primary')
ps.savefig()
for name,bit in CP_DQ_BITS.items():
plt.clf()
anymask = reduce(np.bitwise_or, (T3.decam_anymask[:,1],
T3.decam_anymask[:,2],
T3.decam_anymask[:,4]))
I = np.flatnonzero((anymask & bit) > 0)
#plt.subplot(1,2,1)
plothist(T3.ra[I], T3.dec[I], nbins=200, imshowargs=dict(vmax=mx),
doclf=False, range=plotrange)
plt.title('DR3, %s any' % name)
#plt.suptitle('DR3')
ps.savefig()
def make_coadds(tims, bands, targetwcs,
mods=None, xy=None, apertures=None, apxy=None,
ngood=False, detmaps=False, psfsize=False,
callback=None, callback_args=[],
plots=False, ps=None,
lanczos=True, mp=None):
from astrometry.util.ttime import Time
t0 = Time()
class Duck(object):
pass
C = Duck()
W = int(targetwcs.get_width())
H = int(targetwcs.get_height())
# always, for patching SATUR, etc pixels?
unweighted=True
if not xy:
psfsize = False
C.coimgs = []
if detmaps:
C.galdetivs = []
C.detivs = []
if mods is not None:
C.comods = []
C.coresids = []
if apertures is not None:
unweighted = True
C.AP = fits_table()
if xy:
ix,iy = xy
C.T = fits_table()
C.T.nobs = np.zeros((len(ix), len(bands)), np.uint8)
C.T.anymask = np.zeros((len(ix), len(bands)), np.int16)
C.T.allmask = np.zeros((len(ix), len(bands)), np.int16)
if psfsize:
C.T.psfsize = np.zeros((len(ix), len(bands)), np.float32)
if detmaps:
C.T.depth = np.zeros((len(ix), len(bands)), np.float32)
C.T.galdepth = np.zeros((len(ix), len(bands)), np.float32)
if lanczos:
print('Doing Lanczos resampling')
for tim in tims:
# surface-brightness correction
tim.sbscale = (targetwcs.pixel_scale() / tim.subwcs.pixel_scale())**2
# We create one iterator per band to do the tim resampling. These all run in
# parallel when multi-processing.
imaps = []
for band in bands:
args = []
for itim,tim in enumerate(tims):
if tim.band != band:
continue
if mods is None:
mo = None
else:
mo = mods[itim]
args.append((itim,tim,mo,lanczos,targetwcs))
if mp is not None:
imaps.append(mp.imap_unordered(_resample_one, args))
else:
import itertools
imaps.append(itertools.imap(_resample_one, args))
# Args for aperture photometry
apargs = []
tinyw = 1e-30
for iband,(band,timiter) in enumerate(zip(bands, imaps)):
print('Computing coadd for band', band)
# coadded weight map (moo)
cow = np.zeros((H,W), np.float32)
# coadded weighted image map
cowimg = np.zeros((H,W), np.float32)
kwargs = dict(cowimg=cowimg, cow=cow)
if detmaps:
# detection map inverse-variance (depth map)
detiv = np.zeros((H,W), np.float32)
C.detivs.append(detiv)
kwargs.update(detiv=detiv)
# galaxy detection map inverse-variance (galdepth map)
galdetiv = np.zeros((H,W), np.float32)
C.galdetivs.append(galdetiv)
kwargs.update(galdetiv=galdetiv)
if mods is not None:
# model image
cowmod = np.zeros((H,W), np.float32)
# chi-squared image
cochi2 = np.zeros((H,W), np.float32)
kwargs.update(cowmod=cowmod, cochi2=cochi2)
if unweighted:
# unweighted image
coimg = np.zeros((H,W), np.float32)
if mods is not None:
# unweighted model
comod = np.zeros((H,W), np.float32)
# number of exposures
con = np.zeros((H,W), np.uint8)
# inverse-variance
coiv = np.zeros((H,W), np.float32)
kwargs.update(coimg=coimg, coiv=coiv)
# Note that we have 'congood' as well as 'nobs':
# * 'congood' is used for the 'nexp' *image*.
# * 'nobs' is used for the per-source measurements
#
# (you want to know the number of observations within the
# source footprint, not just the peak pixel which may be
# saturated, etc.)
if ngood:
congood = np.zeros((H,W), np.uint8)
kwargs.update(congood=congood)
if xy:
# These match the type of the "DQ" images.
# "any" mask
ormask = np.zeros((H,W), np.int16)
# "all" mask
andmask = np.empty((H,W), np.int16)
allbits = reduce(np.bitwise_or, CP_DQ_BITS.values())
andmask[:,:] = allbits
# number of observations
nobs = np.zeros((H,W), np.uint8)
kwargs.update(ormask=ormask, andmask=andmask, nobs=nobs)
if psfsize:
psfsizemap = np.zeros((H,W), np.float32)
for R in timiter:
if R is None:
continue
itim,Yo,Xo,iv,im,mo,dq = R
#print('timiter Yo,Xo,im.shape=',Yo,Xo,im.shape)
tim = tims[itim]
# invvar-weighted image
cowimg[Yo,Xo] += iv * im
cow [Yo,Xo] += iv
if unweighted:
if dq is None:
goodpix = 1
else:
# include BLEED, SATUR, INTERP pixels if no other
# pixels exists (do this by eliminating all other CP
# flags)
badbits = 0
for bitname in ['badpix', 'cr', 'trans', 'edge', 'edge2']:
badbits |= CP_DQ_BITS[bitname]
goodpix = ((dq & badbits) == 0)
coimg[Yo,Xo] += goodpix * im
con [Yo,Xo] += goodpix
coiv [Yo,Xo] += goodpix * 1./(tim.sig1 * tim.sbscale)**2 # ...ish
if xy:
if dq is not None:
ormask [Yo,Xo] |= dq
andmask[Yo,Xo] &= dq
# raw exposure count
nobs[Yo,Xo] += 1
if psfsize:
# psfnorm is in units of 1/pixels.
# (eg, psfnorm for a gaussian is ~ 1/psf_sigma)
# Neff is in pixels**2
neff = 1./tim.psfnorm**2
# Narcsec is in arcsec**2
narcsec = neff * tim.wcs.pixel_scale()**2
psfsizemap[Yo,Xo] += iv * (1. / narcsec)
if detmaps:
# point-source depth
detsig1 = tim.sig1 / tim.psfnorm
detiv[Yo,Xo] += (iv > 0) * (1. / detsig1**2)
# Galaxy detection map
gdetsig1 = tim.sig1 / tim.galnorm
galdetiv[Yo,Xo] += (iv > 0) * (1. / gdetsig1**2)
if ngood:
congood[Yo,Xo] += (iv > 0)
if mods is not None:
# straight-up
comod[Yo,Xo] += goodpix * mo
# invvar-weighted
cowmod[Yo,Xo] += iv * mo
# chi-squared
cochi2[Yo,Xo] += iv * (im - mo)**2
del mo
del goodpix
del Yo,Xo,im,iv
# END of loop over tims
# Per-band:
cowimg /= np.maximum(cow, tinyw)
C.coimgs.append(cowimg)
if mods is not None:
cowmod /= np.maximum(cow, tinyw)
C.comods.append(cowmod)
coresid = cowimg - cowmod
coresid[cow == 0] = 0.
C.coresids.append(coresid)
if unweighted:
coimg /= np.maximum(con, 1)
del con
cowimg[cow == 0] = coimg[cow == 0]
if mods is not None:
cowmod[cow == 0] = comod[cow == 0]
if xy:
C.T.nobs [:,iband] = nobs[iy,ix]
C.T.anymask[:,iband] = ormask [iy,ix]
C.T.allmask[:,iband] = andmask[iy,ix]
# unless there were no images there...
C.T.allmask[nobs[iy,ix] == 0, iband] = 0
if detmaps:
C.T.depth [:,iband] = detiv[iy, ix]
C.T.galdepth[:,iband] = galdetiv[iy, ix]
if psfsize:
wt = cow[iy,ix]
# psfsizemap is in units of iv * (1 / arcsec**2)
sz = psfsizemap[iy,ix]
sz /= np.maximum(wt, tinyw)
sz[wt == 0] = 0.
# Back to units of linear arcsec.
sz = 1. / np.sqrt(sz)
sz[wt == 0] = 0.
# Correction factor to get back to equivalent of Gaussian sigma
sz /= (2. * np.sqrt(np.pi))
# Conversion factor to FWHM (2.35)
sz *= 2. * np.sqrt(2. * np.log(2.))
C.T.psfsize[:,iband] = sz
del psfsizemap
if apertures is not None:
# Aperture photometry, using the unweighted "coimg" and
# "coiv" arrays.
with np.errstate(divide='ignore'):
imsigma = 1.0/np.sqrt(coiv)
imsigma[coiv == 0] = 0
for irad,rad in enumerate(apertures):
apargs.append((irad, band, rad, coimg, imsigma, True, apxy))
if mods is not None:
apargs.append((irad, band, rad, coresid, None, False, apxy))
if callback is not None:
callback(band, *callback_args, **kwargs)
# END of loop over bands
t2 = Time()
print('coadds: images:', t2-t0)
if apertures is not None:
# Aperture phot, in parallel
if mp is not None:
apresults = mp.map(_apphot_one, apargs)
else:
apresults = map(_apphot_one, apargs)
del apargs
apresults = iter(apresults)
for iband,band in enumerate(bands):
apimg = []
apimgerr = []
if mods is not None:
apres = []
for irad,rad in enumerate(apertures):
(airad, aband, isimg, ap_img, ap_err) = apresults.next()
assert(airad == irad)
assert(aband == band)
assert(isimg)
apimg.append(ap_img)
apimgerr.append(ap_err)
if mods is not None:
(airad, aband, isimg, ap_img, ap_err) = apresults.next()
assert(airad == irad)
assert(aband == band)
assert(not isimg)
apres.append(ap_img)
assert(ap_err is None)
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_img_%s' % band, ap)
ap = 1./(np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_img_ivar_%s' % band, ap)
if mods is not None:
ap = np.vstack(apres).T
ap[np.logical_not(np.isfinite(ap))] = 0.
C.AP.set('apflux_resid_%s' % band, ap)
t3 = Time()
print('coadds apphot:', t3-t2)
return C