def apply_picker(picker):
# create copy of hand that we'll remove cards from as cards are selected for playing
pick_hand = list(hand)
picked_cards = []
first_data = [
TractorMetadata(tractor.rank, tractor.length)
for tractor in first_tractors
]
while first_data:
# compute the minimum rank/length tractor we must play
suit_tractors = state.get_suit_tractors_from_cards(
pick_hand, trick_suit)
hand_data = [
TractorMetadata(tractor.rank, tractor.length)
for tractor in suit_tractors
]
if len(hand_data) == 0:
break
hand_idx = find_matching_data_index(hand_data, first_data[0])
min_data = get_min_data(first_data[0], hand_data[hand_idx])
# get tractors that exactly match the minimum, and tractors that are larger in rank/length
exact = []
larger = []
for tractor in suit_tractors:
if tractor.rank == min_data.rank and tractor.length == min_data.length:
exact.append(tractor)
elif tractor.rank >= min_data.rank and tractor.length >= min_data.length:
larger.append(tractor)
# run the picker
cur_picked = picker(min_data, exact, larger)
for card in cur_picked:
picked_cards.append(card)
pick_hand.remove(card)
first_data = update_data_array(first_data, min_data)
# at this point there should be no cards in suit matching first_play
# so we provide single cards to the picker
while len(picked_cards) < len(first_play):
exact = []
for card in pick_hand:
suit_type = card_to_suit_type(card, trick_card.suit,
state.trump_card)
tractor = Tractor(1, 1, card.suit_power(state.trump_card),
suit_type)
tractor.orig_cards = [[card]]
exact.append(tractor)
cur_picked = picker(TractorMetadata(1, 1), exact, [])
for card in cur_picked:
picked_cards.append(card)
pick_hand.remove(card)
return picked_cards
def get_splits(large, small):
# first split the length, then for each split, cut the rank
splits = []
for i in range(large.length - small.length + 1):
tractor = Tractor(large.rank, small.length, large.power + i,
large.suit_type)
tractor.orig_cards = large.orig_cards[i:i + small.length]
splits.append(tractor)
for split in splits:
split.rank = small.rank
split.orig_cards = [t[:small.rank] for t in split.orig_cards]
return splits
def _unwise_mod(tim, use_cat, use_srcs, margin=10):
# Select sources in play.
wisewcs = tim.wcs.wcs
timH, timW = tim.shape
ok, x, y = wisewcs.radec2pixelxy(use_cat.ra, use_cat.dec)
x = (x - 1.).astype(np.float32)
y = (y - 1.).astype(np.float32)
I = np.flatnonzero((x >= -margin) * (x < timW + margin) *
(y >= -margin) * (y < timH + margin))
#print('Found {} sources within the image + margin = {} pixels'.format(len(I), margin))
subcat = [use_srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
return mod
def init(cont):
if cont.owner["is3P"]:
if cont.owner["isUser"]:
import tractor.settings.estractor as settings
else:
import tractor.settings.estractor_sentient as settings
else:
import tractor.settings.estractorfp as settings
cont.activate("set_hud")
trac = Tractor(cont.owner)
trac.setup(settings)
cont.owner.controller = cont
cont.owner["initialized"] = True
def run_forced_phot(cat,
tim,
ceres=True,
derivs=False,
agn=False,
do_forced=True,
do_apphot=True,
get_model=False,
ps=None,
timing=False,
fixed_also=False,
ceres_threads=1):
'''
fixed_also: if derivs=True, also run without derivatives and report
that flux too?
'''
if timing:
tlast = Time()
if ps is not None:
import pylab as plt
opti = None
forced_kwargs = {}
if ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
try:
opti = CeresOptimizer(BW=B, BH=B, threads=ceres_threads)
except:
if ceres_threads > 1:
raise RuntimeError(
'ceres_threads requested but not supported by tractor.ceres version'
)
opti = CeresOptimizer(BW=B, BH=B)
#forced_kwargs.update(verbose=True)
# nsize = 0
for src in cat:
# Limit sizes of huge models
# from tractor.galaxy import ProfileGalaxy
# if isinstance(src, ProfileGalaxy):
# px,py = tim.wcs.positionToPixel(src.getPosition())
# h = src._getUnitFluxPatchSize(tim, px, py, tim.modelMinval)
# MAXHALF = 128
# if h > MAXHALF:
# #print('halfsize', h,'for',src,'-> setting to',MAXHALF)
# nsize += 1
# src.halfsize = MAXHALF
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
#print('Limited the size of', nsize, 'large galaxy models')
if derivs:
realsrcs = []
derivsrcs = []
Iderivs = []
for i, src in enumerate(cat):
from tractor import PointSource
realsrcs.append(src)
if not isinstance(src, PointSource):
continue
Iderivs.append(i)
brightness_dra = src.getBrightness().copy()
brightness_ddec = src.getBrightness().copy()
brightness_dra.setParams(np.zeros(brightness_dra.numberOfParams()))
brightness_ddec.setParams(
np.zeros(brightness_ddec.numberOfParams()))
brightness_dra.freezeAllBut(tim.band)
brightness_ddec.freezeAllBut(tim.band)
dsrc = SourceDerivatives(src, [brightness_dra, brightness_ddec],
tim, ps)
derivsrcs.append(dsrc)
Iderivs = np.array(Iderivs)
if fixed_also:
pass
else:
# For convenience, put all the real sources at the front of
# the list, so we can pull the IVs off the front of the list.
cat = realsrcs + derivsrcs
if agn:
from tractor.galaxy import ExpGalaxy, DevGalaxy, FixedCompositeGalaxy
from tractor import PointSource
from legacypipe.survey import SimpleGalaxy, RexGalaxy
realsrcs = []
agnsrcs = []
iagn = []
for i, src in enumerate(cat):
realsrcs.append(src)
## ??
if isinstance(src, (SimpleGalaxy, RexGalaxy)):
#print('Skipping SIMP or REX:', src)
continue
if isinstance(src, (ExpGalaxy, DevGalaxy, FixedCompositeGalaxy)):
iagn.append(i)
bright = src.getBrightness().copy()
bright.setParams(np.zeros(bright.numberOfParams()))
bright.freezeAllBut(tim.band)
agn = PointSource(src.pos, bright)
agn.freezeAllBut('brightness')
#print('Adding "agn"', agn, 'to', src)
#print('agn params:', agn.getParamNames())
agnsrcs.append(src)
iagn = np.array(iagn)
cat = realsrcs + agnsrcs
print('Added AGN to', len(iagn), 'galaxies')
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
disable_galaxy_cache()
F = fits_table()
if do_forced:
if timing and (derivs or agn):
t = Time()
print('Setting up:', t - tlast)
tlast = t
if derivs:
if fixed_also:
print('Forced photom with fixed positions:')
R = tr.optimize_forced_photometry(variance=True,
fitstats=False,
shared_params=False,
priors=False,
**forced_kwargs)
F.flux_fixed = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
N = len(cat)
F.flux_fixed_ivar = R.IV[:N].astype(np.float32)
if timing:
t = Time()
print('Forced photom with fixed positions finished:',
t - tlast)
tlast = t
cat = realsrcs + derivsrcs
tr.setCatalog(Catalog(*cat))
print('Forced photom with position derivatives:')
if ps is None and not get_model:
forced_kwargs.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True,
fitstats=True,
shared_params=False,
priors=False,
**forced_kwargs)
if ps is not None or get_model:
(data, mod, ie, chi, roi) = R.ims1[0]
if ps is not None:
ima = dict(vmin=-2. * tim.sig1,
vmax=5. * tim.sig1,
interpolation='nearest',
origin='lower',
cmap='gray')
imchi = dict(interpolation='nearest',
origin='lower',
vmin=-5,
vmax=5,
cmap='RdBu')
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
if derivs:
trx = Tractor([tim], realsrcs)
trx.freezeParam('images')
modx = trx.getModelImage(0)
chix = (data - modx) * tim.getInvError()
plt.clf()
plt.imshow(modx, **ima)
plt.title('Model without derivatives: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chix, **imchi)
plt.title('Chi without derivatives: %s' % tim.name)
ps.savefig()
if derivs or agn:
cat = realsrcs
N = len(cat)
F.flux = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
F.flux_ivar = R.IV[:N].astype(np.float32)
F.fracflux = R.fitstats.profracflux[:N].astype(np.float32)
F.rchisq = R.fitstats.prochi2[:N].astype(np.float32)
try:
F.fracmasked = R.fitstats.promasked[:N].astype(np.float32)
except:
print(
'No "fracmasked" available (only in recent Tractor versions)')
if derivs:
F.flux_dra = np.zeros(len(F), np.float32)
F.flux_ddec = np.zeros(len(F), np.float32)
F.flux_dra[Iderivs] = np.array(
[src.getParams()[0] for src in derivsrcs]).astype(np.float32)
F.flux_ddec[Iderivs] = np.array(
[src.getParams()[1] for src in derivsrcs]).astype(np.float32)
F.flux_dra_ivar = np.zeros(len(F), np.float32)
F.flux_ddec_ivar = np.zeros(len(F), np.float32)
F.flux_dra_ivar[Iderivs] = R.IV[N::2].astype(np.float32)
F.flux_ddec_ivar[Iderivs] = R.IV[N + 1::2].astype(np.float32)
if agn:
F.flux_agn = np.zeros(len(F), np.float32)
F.flux_agn_ivar = np.zeros(len(F), np.float32)
F.flux_agn[iagn] = np.array(
[src.getParams()[0] for src in agnsrcs])
F.flux_agn_ivar[iagn] = R.IV[N:].astype(np.float32)
if timing:
t = Time()
print('Forced photom:', t - tlast)
tlast = t
if do_apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack(
[tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
#print('Apertures:', apertures, 'pixels')
#print('apxy shape', apxy.shape) # --> (2,N)
# The aperture photometry routine doesn't like pixel positions outside the image
H, W = img.shape
Iap = np.flatnonzero((apxy[0, :] >= 0) * (apxy[1, :] >= 0) *
(apxy[0, :] <= W - 1) * (apxy[1, :] <= H - 1))
print('Aperture photometry for', len(Iap), 'of', len(apxy[0, :]),
'sources within image bounds')
for rad in apertures:
aper = photutils.CircularAperture(apxy[:, Iap], rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = np.zeros((len(F), len(apertures)), np.float32)
F.apflux[Iap, :] = ap.astype(np.float32)
apimgerr = np.vstack(apimgerr).T
apiv = np.zeros(apimgerr.shape, np.float32)
apiv[apimgerr != 0] = 1. / apimgerr[apimgerr != 0]**2
F.apflux_ivar = np.zeros((len(F), len(apertures)), np.float32)
F.apflux_ivar[Iap, :] = apiv
if timing:
print('Aperture photom:', Time() - tlast)
if get_model:
return F, mod
return F
def main(survey=None, opt=None):
'''Driver function for forced photometry of individual Legacy
Survey images.
'''
if opt is None:
parser = get_parser()
opt = parser.parse_args()
Time.add_measurement(MemMeas)
t0 = Time()
if os.path.exists(opt.outfn):
print('Ouput file exists:', opt.outfn)
sys.exit(0)
if opt.derivs and opt.agn:
print('Sorry, can\'t do --derivs AND --agn')
sys.exit(0)
if not opt.forced:
opt.apphot = True
zoomslice = None
if opt.zoom is not None:
(x0, x1, y0, y1) = opt.zoom
zoomslice = (slice(y0, y1), slice(x0, x1))
ps = None
if opt.plots is not None:
import pylab as plt
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence(opt.plots)
# Try parsing filename as exposure number.
try:
expnum = int(opt.expnum)
filename = None
except:
# make this 'None' for survey.find_ccds()
expnum = None
filename = opt.expnum
# Try parsing HDU number
try:
hdu = int(opt.ccdname)
ccdname = None
except:
hdu = -1
ccdname = opt.ccdname
if survey is None:
survey = LegacySurveyData()
catsurvey = survey
if opt.catalog_dir is not None:
catsurvey = LegacySurveyData(survey_dir=opt.catalog_dir)
if filename is not None and hdu >= 0:
# FIXME -- try looking up in CCDs file?
# Read metadata from file
print('Warning: faking metadata from file contents')
T = exposure_metadata([filename], hdus=[hdu])
print('Metadata:')
T.about()
if not 'ccdzpt' in T.columns():
phdr = fitsio.read_header(filename)
T.ccdzpt = np.array([phdr['MAGZERO']])
print('WARNING: using header MAGZERO')
T.ccdraoff = np.array([0.])
T.ccddecoff = np.array([0.])
print('WARNING: setting CCDRAOFF, CCDDECOFF to zero.')
else:
# Read metadata from survey-ccds.fits table
T = survey.find_ccds(expnum=expnum, ccdname=ccdname)
print(len(T), 'with expnum', expnum, 'and CCDname', ccdname)
if hdu >= 0:
T.cut(T.image_hdu == hdu)
print(len(T), 'with HDU', hdu)
if filename is not None:
T.cut(np.array([f.strip() == filename for f in T.image_filename]))
print(len(T), 'with filename', filename)
if opt.camera is not None:
T.cut(T.camera == opt.camera)
print(len(T), 'with camera', opt.camera)
assert (len(T) == 1)
ccd = T[0]
im = survey.get_image_object(ccd)
if opt.do_calib:
#from legacypipe.survey import run_calibs
#kwa = dict(splinesky=True)
#run_calibs((im, kwa))
im.run_calibs(splinesky=True)
tim = im.get_tractor_image(slc=zoomslice,
pixPsf=True,
splinesky=True,
constant_invvar=opt.constant_invvar,
hybridPsf=opt.hybrid_psf,
normalizePsf=opt.normalize_psf)
print('Got tim:', tim)
print('Read image:', Time() - t0)
if opt.catfn in ['DR1', 'DR2', 'DR3', 'DR5', 'DR']:
margin = 20
TT = []
chipwcs = tim.subwcs
bricks = bricks_touching_wcs(chipwcs, survey=catsurvey)
for b in bricks:
# there is some overlap with this brick... read the catalog.
fn = catsurvey.find_file('tractor', brick=b.brickname)
if not os.path.exists(fn):
print('WARNING: catalog', fn, 'does not exist. Skipping!')
continue
print('Reading', fn)
T = fits_table(fn)
ok, xx, yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W, H = chipwcs.get_width(), chipwcs.get_height()
I = np.flatnonzero((xx >= -margin) * (xx <= (W + margin)) *
(yy >= -margin) * (yy <= (H + margin)))
T.cut(I)
print('Cut to', len(T), 'sources within image + margin')
# print('Brick_primary:', np.unique(T.brick_primary))
T.cut(T.brick_primary)
print('Cut to', len(T), 'on brick_primary')
for col in ['out_of_bounds', 'left_blob']:
if col in T.get_columns():
T.cut(T.get(col) == False)
print('Cut to', len(T), 'on', col)
if len(T):
TT.append(T)
if len(TT) == 0:
print('No sources to photometer.')
return 0
T = merge_tables(TT, columns='fillzero')
T._header = TT[0]._header
del TT
print('Total of', len(T), 'catalog sources')
# Fix up various failure modes:
# FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0)
# -> convert to EXP
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapedev_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to EXP')
for i in I:
T.type[i] = 'EXP'
# Same thing with the exp component.
# -> convert to DEV
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapeexp_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to DEV')
for i in I:
T.type[i] = 'DEV'
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
else:
T = fits_table(opt.catfn)
surveydir = survey.get_survey_dir()
del survey
kwargs = {}
cols = T.get_columns()
if 'flux_r' in cols and not 'decam_flux_r' in cols:
kwargs.update(fluxPrefix='')
cat = read_fits_catalog(T, **kwargs)
# Replace the brightness (which will be a NanoMaggies with g,r,z)
# with a NanoMaggies with this image's band only.
for src in cat:
src.brightness = NanoMaggies(**{tim.band: 1.})
print('Read catalog:', Time() - t0)
print('Forced photom...')
F = run_forced_phot(cat,
tim,
ceres=opt.ceres,
derivs=opt.derivs,
fixed_also=True,
agn=opt.agn,
do_forced=opt.forced,
do_apphot=opt.apphot,
ps=ps)
t0 = Time()
F.release = T.release
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.camera = np.array([ccd.camera] * len(F))
F.expnum = np.array([im.expnum] * len(F)).astype(np.int32)
F.ccdname = np.array([im.ccdname] * len(F))
# "Denormalizing"
F.filter = np.array([tim.band] * len(T))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T)).astype(np.float32)
F.ra = T.ra
F.dec = T.dec
ok, x, y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x - 1).astype(np.float32)
F.y = (y - 1).astype(np.float32)
h, w = tim.shape
F.mask = tim.dq[np.clip(np.round(F.y).astype(int), 0, h - 1),
np.clip(np.round(F.x).astype(int), 0, w - 1)]
program_name = sys.argv[0]
version_hdr = get_version_header(program_name, surveydir)
filename = getattr(ccd, 'image_filename')
if filename is None:
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn)
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
filename = os.path.join(d2, d1, fname)
print('Trimmed filename to', filename)
version_hdr.add_record(
dict(name='CPFILE', value=filename, comment='CP file'))
version_hdr.add_record(dict(name='CPHDU', value=im.hdu, comment='CP ext'))
version_hdr.add_record(
dict(name='CAMERA', value=ccd.camera, comment='Camera'))
version_hdr.add_record(
dict(name='EXPNUM', value=im.expnum, comment='Exposure num'))
version_hdr.add_record(
dict(name='CCDNAME', value=im.ccdname, comment='CCD name'))
version_hdr.add_record(
dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(
dict(name='EXPOSURE',
value='%s-%s-%s' % (ccd.camera, im.expnum, im.ccdname),
comment='Name of this image'))
keys = [
'TELESCOP', 'OBSERVAT', 'OBS-LAT', 'OBS-LONG', 'OBS-ELEV', 'INSTRUME'
]
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
hdr = fitsio.FITSHDR()
units = {
'exptime': 'sec',
'flux': 'nanomaggy',
'flux_ivar': '1/nanomaggy^2'
}
columns = F.get_columns()
for i, col in enumerate(columns):
if col in units:
hdr.add_record(dict(name='TUNIT%i' % (i + 1), value=units[col]))
outdir = os.path.dirname(opt.outfn)
if len(outdir):
trymakedirs(outdir)
fitsio.write(opt.outfn, None, header=version_hdr, clobber=True)
F.writeto(opt.outfn, header=hdr, append=True)
print('Wrote', opt.outfn)
if opt.save_model or opt.save_data:
hdr = fitsio.FITSHDR()
tim.getWcs().wcs.add_to_header(hdr)
if opt.save_model:
print('Getting model image...')
tr = Tractor([tim], cat)
mod = tr.getModelImage(tim)
fitsio.write(opt.save_model, mod, header=hdr, clobber=True)
print('Wrote', opt.save_model)
if opt.save_data:
fitsio.write(opt.save_data, tim.getImage(), header=hdr, clobber=True)
print('Wrote', opt.save_data)
print('Finished forced phot:', Time() - t0)
return 0
def wise_cutouts(ra, dec, radius, ps, pixscale=2.75, tractor_base=".", unwise_dir="unwise-coadds"):
"""
radius in arcsec.
pixscale: WISE pixel scale in arcsec/pixel;
make this smaller than 2.75 to oversample.
"""
npix = int(np.ceil(radius / pixscale))
print("Image size:", npix)
W = H = npix
pix = pixscale / 3600.0
wcs = Tan(ra, dec, (W + 1) / 2.0, (H + 1) / 2.0, -pix, 0.0, 0.0, pix, float(W), float(H))
# Find DECaLS bricks overlapping
decals = Decals()
B = bricks_touching_wcs(wcs, decals=decals)
print("Found", len(B), "bricks overlapping")
TT = []
for b in B.brickname:
fn = os.path.join(tractor_base, "tractor", b[:3], "tractor-%s.fits" % b)
T = fits_table(fn)
print("Read", len(T), "from", b)
primhdr = fitsio.read_header(fn)
TT.append(T)
T = merge_tables(TT)
print("Total of", len(T), "sources")
T.cut(T.brick_primary)
print(len(T), "primary")
margin = 20
ok, xx, yy = wcs.radec2pixelxy(T.ra, T.dec)
I = np.flatnonzero((xx > -margin) * (yy > -margin) * (xx < W + margin) * (yy < H + margin))
T.cut(I)
print(len(T), "within ROI")
# Pull out DECaLS coadds (image, model, resid).
dwcs = wcs.scale(2.0 * pixscale / 0.262)
dh, dw = dwcs.shape
print("DECaLS resampled shape:", dh, dw)
tags = ["image", "model", "resid"]
coimgs = [np.zeros((dh, dw, 3), np.uint8) for t in tags]
for b in B.brickname:
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-image-r.fits" % b)
bwcs = Tan(fn)
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(dwcs, bwcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from", b)
xl, xh, yl, yh = Xi.min(), Xi.max(), Yi.min(), Yi.max()
print(
"python legacypipe/runbrick.py -b %s --zoom %i %i %i %i --outdir cluster --pixpsf --splinesky --pipe --no-early-coadds"
% (b, xl - 5, xh + 5, yl - 5, yh + 5)
+ " -P 'pickles/cluster-%(brick)s-%%(stage)s.pickle'"
)
for i, tag in enumerate(tags):
fn = os.path.join(tractor_base, "coadd", b[:3], b, "decals-%s-%s.jpg" % (b, tag))
img = plt.imread(fn)
img = np.flipud(img)
coimgs[i][Yo, Xo, :] = img[Yi, Xi, :]
tt = dict(image="Image", model="Model", resid="Resid")
for img, tag in zip(coimgs, tags):
plt.clf()
dimshow(img, ticks=False)
plt.title("DECaLS grz %s" % tt[tag])
ps.savefig()
# Find unWISE tiles overlapping
tiles = unwise_tiles_touching_wcs(wcs)
print("Cut to", len(tiles), "unWISE tiles")
# Here we assume the targetwcs is axis-aligned and that the
# edge midpoints yield the RA,Dec limits (true for TAN).
r, d = wcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]), np.array([H / 2, H / 2, 1, H]))
# the way the roiradec box is used, the min/max order doesn't matter
roiradec = [r[0], r[1], d[2], d[3]]
ra, dec = T.ra, T.dec
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
srcs = read_fits_catalog(T, ellipseClass=EllipseE)
wbands = [1, 2]
wanyband = "w"
for band in wbands:
T.wise_flux[:, band - 1] *= 10.0 ** (primhdr["WISEAB%i" % band] / 2.5)
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
print("Photometering WISE band", band)
wband = "w%i" % band
for i, src in enumerate(srcs):
# print('Source', src, 'brightness', src.getBrightness(), 'params', src.getBrightness().getParams())
# src.getBrightness().setParams([T.wise_flux[i, band-1]])
src.setBrightness(NanoMaggies(**{wanyband: T.wise_flux[i, band - 1]}))
# print('Set source brightness:', src.getBrightness())
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
for tile in tiles:
print("Reading tile", tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir, tile.coadd_id, band, bandname=wanyband, roiradecbox=roiradec)
if tim is None:
print("Actually, no overlap with tile", tile.coadd_id)
continue
print("Read image with shape", tim.shape)
# Select sources in play.
wisewcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wisewcs.radec2pixelxy(ra, dec)
x = (x - 1.0).astype(np.float32)
y = (y - 1.0).astype(np.float32)
margin = 10.0
I = np.flatnonzero((x >= -margin) * (x < W + margin) * (y >= -margin) * (y < H + margin))
print(len(I), "within the image + margin")
subcat = [srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
# plt.clf()
# dimshow(tim.getImage(), ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
# plt.clf()
# dimshow(mod, ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print("Resampling", len(Yo), "pixels from WISE", tile.coadd_id, band)
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
con[iband][Yo, Xo] += 1
for img, mod, n in zip(coimgs, comods, con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
for band, img, mod in zip(wbands, coimgs, comods):
lo, hi = np.percentile(img, [25, 99])
plt.clf()
dimshow(img, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Data" % band)
ps.savefig()
plt.clf()
dimshow(mod, vmin=lo, vmax=hi, ticks=False)
plt.title("WISE W%i Model" % band)
ps.savefig()
resid = img - mod
mx = np.abs(resid).max()
plt.clf()
dimshow(resid, vmin=-mx, vmax=mx, ticks=False)
plt.title("WISE W%i Resid" % band)
ps.savefig()
# kwa = dict(mn=-0.1, mx=2., arcsinh = 1.)
kwa = dict(mn=-0.1, mx=2.0, arcsinh=None)
rgb = _unwise_to_rgb(coimgs, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Data")
ps.savefig()
rgb = _unwise_to_rgb(comods, **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Model")
ps.savefig()
kwa = dict(mn=-1, mx=1, arcsinh=None)
rgb = _unwise_to_rgb([img - mod for img, mod in zip(coimgs, comods)], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title("WISE W1/W2 Resid")
ps.savefig()
def psf_residuals(expnum,
ccdname,
stampsize=35,
nstar=30,
magrange=(13, 17),
verbose=0,
splinesky=False):
# Set the debugging level.
if verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum, ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0, r=1, i=2, z=3, Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = decals.get_image_object(ccd)
iminfo = im.get_image_info()
H, W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band, magrange=magrange)
rand = np.random.RandomState(seed=expnum * ccd.ccdnum)
these = rand.choice(len(cat) - 1, nstar, replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:, ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
get_tim_kwargs = dict(const2psf=True, splinesky=splinesky)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(**get_tim_kwargs)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5, 10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.format(
band, expnum, ccdname, ccd.airmass, ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,
ypos,
'{:2d}'.format(istar + 1),
color='red',
horizontalalignment='left')
circ = plt.Circle((xpos, ypos), radius=30, color='g', fill=False, lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix + '-ccd.png', bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar / ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp * 3 * ncols, inchperstamp * nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix, iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy - stampsize, 0), min(iy + stampsize + 1, H)),
slice(max(ix - stampsize, 0), min(ix + stampsize + 1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, **get_tim_kwargs)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra, dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0 * tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0 * tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp - model_psfex)[ivarstamp > 0])
mn, mx = [-2.0 * sig, 5 * sig]
# Generate a QAplot.
if (istar > 0) and (istar % (ncols) == 0):
irow = irow + 1
icol = 3 * istar - 3 * ncols * irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol),
aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax1.text(0.1,
0.9,
'{:2d}'.format(istar + 1),
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 1),
aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp - model_mog,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax2.text(0.1,
0.9,
'MoG',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,
0.08,
'{:.3f}'.format(mag_mog),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 2),
aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp - model_psfex,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax3.text(0.1,
0.9,
'PSFEx',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,
0.08,
'{:.3f}'.format(mag_psfex),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
if istar == (nstar - 1):
break
fig.savefig(pngprefix + '-stargrid.png', bbox_inches='tight')
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--catalog', help='Catalog to render')
parser.add_argument('--brick-coadd', help='Produce a coadd of the images overlapping the given brickname.')
parser.add_argument('--ccds', help='Use this table of CCDs')
parser.add_argument('--brick-wcs', help='File containing a WCS header describing the coadd WCS to render.')
parser.add_argument('--brick-wcs-ext', type=int, help='FITS file extension containing a WCS header describing the coadd WCS to render.')
parser.add_argument('--outlier-mask-brick', help='Comma-separated list of bricknames from which outlier masks should be read.')
parser.add_argument('--out', help='Filename pattern ("BAND" will be replaced by band name) of output images.')
parser.add_argument('--resid', help='Filename pattern ("BAND" will be replaced by band name) of residual images.')
parser.add_argument('--jpeg', help='Write RGB image to this filename')
parser.add_argument('--resid-jpeg', help='Write RGB residual image to this filename')
opt = parser.parse_args()
if opt.catalog is None:
print('Need catalog!')
return -1
cat = fits_table(opt.catalog)
if opt.ccds is None:
if opt.brick_coadd is None:
print('Need brick catalog!')
return -1
brickname = opt.brick_coadd
survey = LegacySurveyData()
if opt.brick_wcs is None:
print('FIXME')
return -1
else:
wcs = Tan(opt.brick_wcs, opt.brick_wcs_ext)
tcat = read_fits_catalog(cat)
if opt.ccds:
ccdfn = opt.ccds
else:
ccdfn = survey.find_file('ccds-table', brick=brickname)
print('Reading', ccdfn)
ccds = fits_table(ccdfn)
H,W = wcs.shape
targetrd = np.array([wcs.pixelxy2radec(x,y) for x,y in
[(1,1),(W,1),(W,H),(1,H),(1,1)]])
tims = []
for ccd in ccds:
im = survey.get_image_object(ccd)
#slc = slice(ccd.ccd_y0, ccd.ccd_y1), slice(ccd.ccd_x0, ccd.ccd_x1)
#tim = im.get_tractor_image(slc=slc)
tim = im.get_tractor_image(radecpoly=targetrd)
print('Read', tim)
tims.append(tim)
if opt.outlier_mask_brick is not None:
bricks = opt.outlier_mask_brick.split(',')
for b in bricks:
print('Reading outlier mask for brick', b,
':', survey.find_file('outliers_mask', brick=b, output=False))
ok = read_outlier_mask_file(survey, tims, b,
subimage=True, output=False)
tr = Tractor(tims, tcat)
mods = list(tr.getModelImages())
bands = 'grz'
def write_model(band, cowimg=None, cowmod=None, **kwargs):
if cowmod is None:
print('No model for', band)
return
outfn = opt.out.replace('BAND', band)
fitsio.write(outfn, cowmod, clobber=True)
print('Wrote model for', band, 'to', outfn)
if opt.resid:
outfn = opt.resid.replace('BAND', band)
fitsio.write(outfn, cowimg - cowmod, clobber=True)
print('Wrote resid for', band, 'to', outfn)
C = make_coadds(tims, bands, wcs, mods=mods,
callback=write_model)
if opt.jpeg:
from legacypipe.survey import get_rgb
import pylab as plt
plt.imsave(opt.jpeg, get_rgb(C.comods, bands), origin='lower')
if opt.resid_jpeg:
from legacypipe.survey import get_rgb
import pylab as plt
plt.imsave(opt.resid_jpeg,
get_rgb([im-mod for im,mod in zip(C.coimgs, C.comods)], bands),
origin='lower')
def psf_residuals(expnum,ccdname,stampsize=35,nstar=30,
magrange=(13,17),verbose=0):
# Set the debugging level.
if verbose==0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum,ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0,r=1,i=2,z=3,Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = DecamImage(decals,ccd)
iminfo = im.get_image_info()
H,W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band,magrange=magrange)
rand = np.random.RandomState(seed=expnum*ccd.ccdnum)
these = rand.choice(len(cat)-1,nstar,replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:,ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(const2psf=True)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5,10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.
format(band,expnum,ccdname,ccd.airmass,ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,ypos,'{:2d}'.format(istar+1),color='red',
horizontalalignment='left')
circ = plt.Circle((xpos,ypos),radius=30,color='g',fill=False,lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix+'-ccd.png',bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar/ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp*3*ncols,inchperstamp*nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix,iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy-stampsize, 0), min(iy+stampsize+1, H)),
slice(max(ix-stampsize, 0), min(ix+stampsize+1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, const2psf=True)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra,dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0*tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0*tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp-model_psfex)[ivarstamp>0])
mn, mx = [-2.0*sig,5*sig]
# Generate a QAplot.
if (istar>0) and (istar%(ncols)==0):
irow = irow+1
icol = 3*istar - 3*ncols*irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows,3*ncols), (irow,icol), aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax1.text(0.1,0.9,'{:2d}'.format(istar+1),color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows,3*ncols), (irow,icol+1), aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp-model_mog,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax2.text(0.1,0.9,'MoG',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,0.08,'{:.3f}'.format(mag_mog),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows,3*ncols), (irow,icol+2), aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp-model_psfex,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax3.text(0.1,0.9,'PSFEx',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,0.08,'{:.3f}'.format(mag_psfex),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax3.transAxes)
if istar==(nstar-1):
break
fig.savefig(pngprefix+'-stargrid.png',bbox_inches='tight')
def main(decals=None, opt=None):
'''Driver function for forced photometry of individual DECam images.
'''
if opt is None:
parser = get_parser()
opt = parser.parse_args()
Time.add_measurement(MemMeas)
t0 = Time()
if os.path.exists(opt.outfn):
print('Ouput file exists:', opt.outfn)
sys.exit(0)
if not opt.forced:
opt.apphot = True
zoomslice = None
if opt.zoom is not None:
(x0,x1,y0,y1) = opt.zoom
zoomslice = (slice(y0,y1), slice(x0,x1))
ps = None
if opt.plots is not None:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence(opt.plots)
# Try parsing filename as exposure number.
try:
expnum = int(opt.filename)
opt.filename = None
except:
# make this 'None' for decals.find_ccds()
expnum = None
# Try parsing HDU number
try:
opt.hdu = int(opt.hdu)
ccdname = None
except:
ccdname = opt.hdu
opt.hdu = -1
if decals is None:
decals = Decals()
if opt.filename is not None and opt.hdu >= 0:
# Read metadata from file
T = exposure_metadata([opt.filename], hdus=[opt.hdu])
print('Metadata:')
T.about()
else:
# Read metadata from decals-ccds.fits table
T = decals.find_ccds(expnum=expnum, ccdname=ccdname)
print(len(T), 'with expnum', expnum, 'and CCDname', ccdname)
if opt.hdu >= 0:
T.cut(T.image_hdu == opt.hdu)
print(len(T), 'with HDU', opt.hdu)
if opt.filename is not None:
T.cut(np.array([f.strip() == opt.filename for f in T.image_filename]))
print(len(T), 'with filename', opt.filename)
assert(len(T) == 1)
im = decals.get_image_object(T[0])
tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True)
print('Got tim:', tim)
if opt.catfn in ['DR1', 'DR2']:
if opt.catalog_path is None:
opt.catalog_path = opt.catfn.lower()
margin = 20
TT = []
chipwcs = tim.subwcs
bricks = bricks_touching_wcs(chipwcs, decals=decals)
for b in bricks:
# there is some overlap with this brick... read the catalog.
fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3],
'tractor-%s.fits' % b.brickname)
if not os.path.exists(fn):
print('WARNING: catalog', fn, 'does not exist. Skipping!')
continue
print('Reading', fn)
T = fits_table(fn)
ok,xx,yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W,H = chipwcs.get_width(), chipwcs.get_height()
I = np.flatnonzero((xx >= -margin) * (xx <= (W+margin)) *
(yy >= -margin) * (yy <= (H+margin)))
T.cut(I)
print('Cut to', len(T), 'sources within image + margin')
# print('Brick_primary:', np.unique(T.brick_primary))
T.cut(T.brick_primary)
print('Cut to', len(T), 'on brick_primary')
T.cut((T.out_of_bounds == False) * (T.left_blob == False))
print('Cut to', len(T), 'on out_of_bounds and left_blob')
TT.append(T)
T = merge_tables(TT)
T._header = TT[0]._header
del TT
# Fix up various failure modes:
# FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0)
# -> convert to EXP
I = np.flatnonzero(np.array([((t.type == 'COMP') and
(not np.isfinite(t.shapedev_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to EXP')
for i in I:
T.type[i] = 'EXP'
# Same thing with the exp component.
# -> convert to DEV
I = np.flatnonzero(np.array([((t.type == 'COMP') and
(not np.isfinite(t.shapeexp_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to DEV')
for i in I:
T.type[i] = 'DEV'
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
else:
T = fits_table(opt.catfn)
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE)
# print('Got cat:', cat)
print('Forced photom...')
opti = None
if opt.ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
for src in cat:
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
F = fits_table()
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.filter = np.array([tim.band] * len(T))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T))
ok,x,y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x-1).astype(np.float32)
F.y = (y-1).astype(np.float32)
if opt.apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack([tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
print('Apertures:', apertures, 'pixels')
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = ap
ap = 1./(np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux_ivar = ap
if opt.forced:
kwa = {}
if opt.plots is None:
kwa.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True, fitstats=True,
shared_params=False, **kwa)
if opt.plots:
(data,mod,ie,chi,roi) = R.ims1[0]
ima = tim.ima
imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=5)
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
F.flux = np.array([src.getBrightness().getFlux(tim.band)
for src in cat]).astype(np.float32)
F.flux_ivar = R.IV.astype(np.float32)
F.fracflux = R.fitstats.profracflux.astype(np.float32)
F.rchi2 = R.fitstats.prochi2 .astype(np.float32)
program_name = sys.argv[0]
version_hdr = get_version_header(program_name, decals.decals_dir)
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn)
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
fname = os.path.join(d2, d1, fname)
print('Trimmed filename to', fname)
#version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file'))
version_hdr.add_record(dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file'))
version_hdr.add_record(dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext'))
version_hdr.add_record(dict(name='CAMERA', value='DECam', comment='Dark Energy Camera'))
version_hdr.add_record(dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num'))
version_hdr.add_record(dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name'))
version_hdr.add_record(dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(dict(name='EXPOSURE', value='decam-%s-%s' % (im.expnum, im.ccdname), comment='Name of this image'))
keys = ['TELESCOP','OBSERVAT','OBS-LAT','OBS-LONG','OBS-ELEV',
'INSTRUME']
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
hdr = fitsio.FITSHDR()
units = {'mjd':'sec', 'exptime':'sec', 'flux':'nanomaggy',
'flux_ivar':'1/nanomaggy^2'}
columns = F.get_columns()
for i,col in enumerate(columns):
if col in units:
hdr.add_record(dict(name='TUNIT%i' % (i+1), value=units[col]))
outdir = os.path.dirname(opt.outfn)
if len(outdir):
trymakedirs(outdir)
fitsio.write(opt.outfn, None, header=version_hdr, clobber=True)
F.writeto(opt.outfn, header=hdr, append=True)
print('Wrote', opt.outfn)
print('Finished forced phot:', Time()-t0)
return 0
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra, dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0, x1, y0, y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
r0, r1, d0, d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [
brick_catalog_for_radec_box(ra, rb, d0 - margin, d1 + margin,
decals, catpattern)
for (ra, rb) in [(0, r1 + margin), (r0 - margin, 360.)]
]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0 - margin, r1 + margin, d0 - margin,
d1 + margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0, y1), slice(x0, x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok, x, y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat, T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
def galex_forcedphot(galex_dir, cat, tiles, band, roiradecbox,
pixelized_psf=False, ps=None):
'''
Given a list of tractor sources *cat*
and a list of GALEX tiles *tiles* (a fits_table with RA,Dec,tilename)
runs forced photometry, returning a FITS table the same length as *cat*.
'''
from tractor import Tractor
from astrometry.util.ttime import Time
if False:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence('wise-forced-w%i' % band)
plots = (ps is not None)
if plots:
import pylab as plt
use_ceres = True
wantims = True
get_models = True
gband = 'galex'
phot = fits_table()
tims = []
for tile in tiles:
info('Reading GALEX tile', tile.visitname.strip(), 'band', band)
tim = galex_tractor_image(tile, band, galex_dir, roiradecbox, gband)
if tim is None:
debug('Actually, no overlap with tile', tile.tilename)
continue
# if plots:
# sig1 = tim.sig1
# plt.clf()
# plt.imshow(tim.getImage(), interpolation='nearest', origin='lower',
# cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
# plt.colorbar()
# tag = '%s W%i' % (tile.tilename, band)
# plt.title('%s: tim data' % tag)
# ps.savefig()
if pixelized_psf:
psfimg = galex_psf(band, galex_dir)
tim.psf = PixelizedPSF(psfimg)
# if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
# mjd[I] = (tim.mjdmin + tim.mjdmax) / 2.
# # PSF norm for depth
# psf = tim.getPsf()
# h,w = tim.shape
# patch = psf.getPointSourcePatch(h//2, w//2).patch
# psfnorm = np.sqrt(np.sum(patch**2))
# # To handle zero-depth, we return 1/nanomaggies^2 units rather than mags.
# psfdepth = 1. / (tim.sig1 / psfnorm)**2
# phot.get(wband + '_psfdepth')[I] = psfdepth
tim.tile = tile
tims.append(tim)
tractor = Tractor(tims, cat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
ceres_block = 8
tractor.optimizer = CeresOptimizer(BW=ceres_block, BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(gband)
t0 = Time()
R = tractor.optimize_forced_photometry(
fitstats=True, variance=True, shared_params=False,
wantims=wantims)
info('GALEX forced photometry took', Time() - t0)
#info('Result:', R)
if use_ceres:
term = R.ceres_status['termination']
# Running out of memory can cause failure to converge and term
# status = 2. Fail completely in this case.
if term != 0:
info('Ceres termination status:', term)
raise RuntimeError('Ceres terminated with status %i' % term)
if wantims:
ims1 = R.ims1
flux_invvars = R.IV
# if plots:
# # Create models for just the brightest sources
# bright_cat = [src for src in cat
# if src.getBrightness().getBand(wanyband) > 1000]
# debug('Bright soures:', len(bright_cat))
# btr = Tractor(tims, bright_cat)
# for tim in tims:
# mod = btr.getModelImage(tim)
# tile = tim.tile
# tag = '%s W%i' % (tile.tilename, band)
# sig1 = tim.sig1
# plt.clf()
# plt.imshow(mod, interpolation='nearest', origin='lower',
# cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
# plt.colorbar()
# plt.title('%s: bright-star models' % tag)
# ps.savefig()
if get_models:
models = []
for i,tim in enumerate(tims):
tile = tim.tile
(dat, mod, ie, _, _) = ims1[i]
models.append((tile.visitname, band, tim.wcs.wcs, dat, mod, ie))
if plots:
for i,tim in enumerate(tims):
tile = tim.tile
tag = '%s %s' % (tile.tilename, band)
(dat, mod, _, chi, _) = ims1[i]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
cmap='gray', vmin=-5, vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
nm = np.array([src.getBrightness().getBand(gband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their default
# (1-sigma or whatever) initial fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
niceband = band + 'uv'
phot.set('flux_' + niceband, nm.astype(np.float32))
phot.set('flux_ivar_' + niceband, nm_ivar.astype(np.float32))
#phot.set(band + '_mjd', mjd)
rtn = gphotduck()
rtn.phot = phot
rtn.models = None
if get_models:
rtn.models = models
return rtn
def scan_dchisq(seeing, target_dchisq, ps, e1=0.):
pixscale = 0.262
psfsigma = seeing / pixscale / 2.35
print('PSF sigma:', psfsigma, 'pixels')
psf = GaussianMixturePSF(1., 0., 0., psfsigma**2, psfsigma**2, 0.)
sig1 = 0.01
psfnorm = 1./(2. * np.sqrt(np.pi) * psfsigma)
detsig1 = sig1 / psfnorm
sz = 50
cd = pixscale / 3600.
wcs = Tan(0., 0., float(sz/2), float(sz/2), -cd, 0., 0., cd,
float(sz), float(sz))
band = 'r'
tim = Image(data=np.zeros((sz,sz)), inverr=np.ones((sz,sz)) / sig1,
psf=psf,
wcs = ConstantFitsWcs(wcs),
photocal = LinearPhotoCal(1., band=band))
re_vals = np.logspace(-1., 0., 50)
all_runs = []
mods = []
for i,re in enumerate(re_vals):
true_src = ExpGalaxy(RaDecPos(0., 0.),
NanoMaggies(**{band: 1.}),
EllipseE(re, e1, 0.))
print('True source:', true_src)
tr = Tractor([tim], [true_src])
tr.freezeParams('images')
true_mod = tr.getModelImage(0)
dchisq_none = np.sum((true_mod * tim.inverr)**2)
scale = np.sqrt(target_dchisq / dchisq_none)
true_src.brightness.setParams([scale])
true_mod = tr.getModelImage(0)
dchisq_none = np.sum((true_mod * tim.inverr)**2)
mods.append(true_mod)
tim.data = true_mod
exp_src = true_src.copy()
psf_src = PointSource(true_src.pos.copy(), true_src.brightness.copy())
simp_src = SimpleGalaxy(true_src.pos.copy(), true_src.brightness.copy())
dchisqs = []
#for src in [psf_src, simp_src, exp_src]:
for src in [psf_src, simp_src]:
src.freezeParam('pos')
#print('Fitting source:', src)
#src.printThawedParams()
tr.catalog[0] = src
tr.optimize_loop()
#print('Fitted:', src)
mod = tr.getModelImage(0)
dchisqs.append(dchisq_none - np.sum(((true_mod - mod) * tim.inverr)**2))
#print('dchisq:', dchisqs[-1])
dchisqs.append(dchisq_none)
all_runs.append([re,] + dchisqs)
all_runs = np.array(all_runs)
re = all_runs[:,0]
dchi_psf = all_runs[:,1]
dchi_simp = all_runs[:,2]
dchi_exp = all_runs[:,3]
dchi_ps = np.maximum(dchi_psf, dchi_simp)
dchi_cut1 = dchi_ps + 3+9
dchi_cut2 = dchi_ps + dchi_psf * 0.02
dchi_cut3 = dchi_ps + dchi_psf * 0.008
plt.clf()
plt.plot(re, dchi_psf, 'k-', label='PSF')
plt.plot(re, dchi_simp, 'b-', label='SIMP')
plt.plot(re, dchi_exp, 'r-', label='EXP')
plt.plot(re, dchi_cut2, 'm--', alpha=0.5, lw=2, label='Cut: 2%')
plt.plot(re, dchi_cut3, 'm:', alpha=0.5, lw=2, label='Cut: 0.08%')
plt.plot(re, dchi_cut1, 'm-', alpha=0.5, lw=2, label='Cut: 12')
plt.xlabel('True r_e (arcsec)')
plt.ylabel('dchisq')
#plt.legend(loc='lower left')
plt.legend(loc='upper right')
tt = 'Seeing = %g arcsec, S/N ~ %i' % (seeing, int(np.round(np.sqrt(target_dchisq))))
if e1 != 0.:
tt += ', Ellipticity %g' % e1
plt.title(tt)
plt.ylim(0.90 * target_dchisq, 1.05 * target_dchisq)
# aspect = 1.2
# ax = plt.axis()
# dre = (ax[1]-ax[0]) / 20 / aspect
# dchi = (ax[3]-ax[2]) / 20
# I = np.linspace(0, len(re_vals)-1, 8).astype(int)
# for mod,re in [(mods[i], re_vals[i]) for i in I]:
# print('extent:', [re-dre, re+dre, ax[2], ax[2]+dchi])
# plt.imshow(mod, interpolation='nearest', origin='lower', aspect='auto',
# extent=[re-dre, re+dre, ax[2], ax[2]+dchi], cmap='gray')
# plt.axis(ax)
ps.savefig()
psf=psf,
wcs = ConstantFitsWcs(wcs),
photocal = LinearPhotoCal(1., band=band))
for i,sn in enumerate(sn_vals):
for j,re in enumerate(re_vals):
## HACK -- this is the flux required for a PSF to be
## detected at target S/N... adjust for galaxy?
flux = sn * detsig1
# Create round EXP galaxy
#PixPos(sz/2, sz/2),
true_src = ExpGalaxy(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}),
EllipseE(re, 0., 0.))
tr = Tractor([tim], [true_src])
tr.freezeParams('images')
true_mod = tr.getModelImage(0)
ima = dict(interpolation='nearest', origin='lower',
vmin=-2.*sig1, vmax=5.*sig1, cmap='hot')
this_dchisqs = []
flux_sns = []
for k in range(Nper):
noise = np.random.normal(scale=sig1, size=true_mod.shape)
tim.data = true_mod + noise
if k == 0 and False:
from tractor import Image, GaussianMixturePSF, LinearPhotoCal
from tractor import PixPos, Flux, EllipseE, Tractor, ModelMask
import pylab as plt
# Create a Tractor Image that works in pixel space (WCS not specified).
tim = Image(data=np.zeros((h,w)), inverr=np.ones((h,w)),
psf=GaussianMixturePSF(1., 0., 0., 3., 3., 0.),
photocal=LinearPhotoCal(1.))
# Create a plain Exp galaxy to generate a postage stamp that we'll try to fit with
# the MogGalaxy model.
gal = ExpGalaxy(PixPos(w//2, h//2), Flux(1000.),
EllipseE(10., 0.5, 0.3))
# Get the model
tractor = Tractor([tim], [gal])
mod = tractor.getModelImage(0)
#mog = gal._getAffineProfile(tim, w//2, h//2)
#print('Exp galaxy profile:', str(mog))
# Plot the model
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.savefig('mod.png')
# Set the tractor Image to the Exp model postage stamp -- this is what we'll try to fit.
tim.data = mod
# Initialize the MoG components
amp = np.array([0.4, 0.3, 0.3])
def main(decals=None, opt=None):
'''Driver function for forced photometry of individual DECam images.
'''
if opt is None:
parser = get_parser()
opt = parser.parse_args()
Time.add_measurement(MemMeas)
t0 = Time()
if os.path.exists(opt.outfn):
print('Ouput file exists:', opt.outfn)
sys.exit(0)
if not opt.forced:
opt.apphot = True
zoomslice = None
if opt.zoom is not None:
(x0, x1, y0, y1) = opt.zoom
zoomslice = (slice(y0, y1), slice(x0, x1))
ps = None
if opt.plots is not None:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence(opt.plots)
# Try parsing filename as exposure number.
try:
expnum = int(opt.filename)
opt.filename = None
except:
# make this 'None' for decals.find_ccds()
expnum = None
# Try parsing HDU number
try:
opt.hdu = int(opt.hdu)
ccdname = None
except:
ccdname = opt.hdu
opt.hdu = -1
if decals is None:
decals = Decals()
if opt.filename is not None and opt.hdu >= 0:
# Read metadata from file
T = exposure_metadata([opt.filename], hdus=[opt.hdu])
print('Metadata:')
T.about()
else:
# Read metadata from decals-ccds.fits table
T = decals.find_ccds(expnum=expnum, ccdname=ccdname)
print(len(T), 'with expnum', expnum, 'and CCDname', ccdname)
if opt.hdu >= 0:
T.cut(T.image_hdu == opt.hdu)
print(len(T), 'with HDU', opt.hdu)
if opt.filename is not None:
T.cut(
np.array([f.strip() == opt.filename
for f in T.image_filename]))
print(len(T), 'with filename', opt.filename)
assert (len(T) == 1)
im = decals.get_image_object(T[0])
tim = im.get_tractor_image(slc=zoomslice, pixPsf=True, splinesky=True)
print('Got tim:', tim)
if opt.catfn in ['DR1', 'DR2']:
if opt.catalog_path is None:
opt.catalog_path = opt.catfn.lower()
margin = 20
TT = []
chipwcs = tim.subwcs
bricks = bricks_touching_wcs(chipwcs, decals=decals)
for b in bricks:
# there is some overlap with this brick... read the catalog.
fn = os.path.join(opt.catalog_path, 'tractor', b.brickname[:3],
'tractor-%s.fits' % b.brickname)
if not os.path.exists(fn):
print('WARNING: catalog', fn, 'does not exist. Skipping!')
continue
print('Reading', fn)
T = fits_table(fn)
ok, xx, yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W, H = chipwcs.get_width(), chipwcs.get_height()
I = np.flatnonzero((xx >= -margin) * (xx <= (W + margin)) *
(yy >= -margin) * (yy <= (H + margin)))
T.cut(I)
print('Cut to', len(T), 'sources within image + margin')
# print('Brick_primary:', np.unique(T.brick_primary))
T.cut(T.brick_primary)
print('Cut to', len(T), 'on brick_primary')
T.cut((T.out_of_bounds == False) * (T.left_blob == False))
print('Cut to', len(T), 'on out_of_bounds and left_blob')
TT.append(T)
T = merge_tables(TT)
T._header = TT[0]._header
del TT
# Fix up various failure modes:
# FixedCompositeGalaxy(pos=RaDecPos[240.51147402832561, 10.385488075518923], brightness=NanoMaggies: g=(flux -2.87), r=(flux -5.26), z=(flux -7.65), fracDev=FracDev(0.60177207), shapeExp=re=3.78351e-44, e1=9.30367e-13, e2=1.24392e-16, shapeDev=re=inf, e1=-0, e2=-0)
# -> convert to EXP
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapedev_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to EXP')
for i in I:
T.type[i] = 'EXP'
# Same thing with the exp component.
# -> convert to DEV
I = np.flatnonzero(
np.array([((t.type == 'COMP') and (not np.isfinite(t.shapeexp_r)))
for t in T]))
if len(I):
print('Converting', len(I), 'bogus COMP galaxies to DEV')
for i in I:
T.type[i] = 'DEV'
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
else:
T = fits_table(opt.catfn)
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE)
# print('Got cat:', cat)
print('Forced photom...')
opti = None
if opt.ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
for src in cat:
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
F = fits_table()
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.filter = np.array([tim.band] * len(T))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(T))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(T))
ok, x, y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x - 1).astype(np.float32)
F.y = (y - 1).astype(np.float32)
if opt.apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack(
[tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
print('Apertures:', apertures, 'pixels')
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = ap
ap = 1. / (np.vstack(apimgerr).T)**2
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux_ivar = ap
if opt.forced:
kwa = {}
if opt.plots is None:
kwa.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True,
fitstats=True,
shared_params=False,
**kwa)
if opt.plots:
(data, mod, ie, chi, roi) = R.ims1[0]
ima = tim.ima
imchi = dict(interpolation='nearest',
origin='lower',
vmin=-5,
vmax=5)
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
F.flux = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
F.flux_ivar = R.IV.astype(np.float32)
F.fracflux = R.fitstats.profracflux.astype(np.float32)
F.rchi2 = R.fitstats.prochi2.astype(np.float32)
program_name = sys.argv[0]
version_hdr = get_version_header(program_name, decals.decals_dir)
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn)
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
fname = os.path.join(d2, d1, fname)
print('Trimmed filename to', fname)
#version_hdr.add_record(dict(name='CPFILE', value=im.imgfn, comment='DECam comm.pipeline file'))
version_hdr.add_record(
dict(name='CPFILE', value=fname, comment='DECam comm.pipeline file'))
version_hdr.add_record(
dict(name='CPHDU', value=im.hdu, comment='DECam comm.pipeline ext'))
version_hdr.add_record(
dict(name='CAMERA', value='DECam', comment='Dark Energy Camera'))
version_hdr.add_record(
dict(name='EXPNUM', value=im.expnum, comment='DECam exposure num'))
version_hdr.add_record(
dict(name='CCDNAME', value=im.ccdname, comment='DECam CCD name'))
version_hdr.add_record(
dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(
dict(name='EXPOSURE',
value='decam-%s-%s' % (im.expnum, im.ccdname),
comment='Name of this image'))
keys = [
'TELESCOP', 'OBSERVAT', 'OBS-LAT', 'OBS-LONG', 'OBS-ELEV', 'INSTRUME'
]
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
hdr = fitsio.FITSHDR()
units = {
'mjd': 'sec',
'exptime': 'sec',
'flux': 'nanomaggy',
'flux_ivar': '1/nanomaggy^2'
}
columns = F.get_columns()
for i, col in enumerate(columns):
if col in units:
hdr.add_record(dict(name='TUNIT%i' % (i + 1), value=units[col]))
outdir = os.path.dirname(opt.outfn)
if len(outdir):
trymakedirs(outdir)
fitsio.write(opt.outfn, None, header=version_hdr, clobber=True)
F.writeto(opt.outfn, header=hdr, append=True)
print('Wrote', opt.outfn)
print('Finished forced phot:', Time() - t0)
return 0
photocal=LinearPhotoCal(1., 'w1'))
tim2 = Image(data=w2, inverr=ie2,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w2'))
# ExpGalaxy at pixel (423.64, 444.09) with Fluxes: w1=9.06254e+08, w2=1.02335e+09 and Galaxy Shape: re=400.31, ab=0.38, phi=89.8
cx,cy = 423.64, 444.09
flux1 = 9.06e8
flux2 = 1.02e9
#shape = GalaxyShape(400.31, 0.38, 89.8)
shape = EllipseESoft.fromRAbPhi(400.31, 0.38, 89.8)
gal = ExpGalaxy(PixPos(cx, cy), Fluxes(w1=flux1, w2=flux2), shape)
tractor = Tractor([tim1, tim2],[gal])
tractor.freezeParam('images')
# Create emcee sampler
nw = 30
ndim = len(tractor.getParams())
print('N dim:', ndim)
sampler = emcee.EnsembleSampler(nw, ndim, tractor)
p0 = tractor.getParams()
#std = tractor.getStepSizes()
std = np.array([1.0, 1.0, 1e7, 1e7, 0.01, 0.01, 0.01])
pp = emcee.utils.sample_ball(p0, std, size=nw)
print('Fitting params: (%i):' % len(p0))
GalaxyShape(5., 0.5, 45.))
gal2 = DevGalaxy(RaDecPos(ra + 10 * pscale, dec), brightness(50.),
GalaxyShape(5., 0.25, 135.))
gal3 = FixedCompositeGalaxy(
RaDecPos(ra, dec + 10 * pscale),
brightness(50.),
0.6, # fraction of light in deV component
GalaxyShape(5., 0.6, 30.),
GalaxyShape(5., 0.3, 45.))
cat = Catalog(ptsrc, gal1, gal2, gal3)
# Create Tractor object
tr = Tractor([tim], cat)
# Evaluate likelihood
lnp = tr.getLogProb()
print('Logprob:', lnp)
for nm, val in zip(tr.getParamNames(), tr.getParams()):
print(' ', nm, val)
# Or, without creating a Tractor object:
model = np.zeros_like(data)
sky.addTo(model)
for src in cat:
patch = src.getModelPatch(tim)
if patch is None:
ps.savefig()
subtim = tim.subimage(x0, x1, y0, y1)
#mn,mx = np.percentile(subtim.getImage().ravel(), [25, 98])
# plt.clf()
# plt.imshow(subtim.getImage(), interpolation='nearest', origin='lower',
# cmap='gray', vmin=mn, vmax=mx)
# ps.savefig()
cat.cut((cat.xx >= x0) * (cat.xx <= x1) * (cat.yy >= y0) * (cat.yy <= y1))
print('Cut to', len(cat), 'sources in subimage')
srcs = gim2d_catalog(cat, band)
tractor = Tractor([subtim], srcs)
if False:
plt.clf()
plt.imshow(img, **ima)
ax = plt.axis()
I = np.flatnonzero(cat.type == 1)
plt.plot(cat.xx[I], cat.yy[I], 'r.')
I = np.flatnonzero(cat.type == 2)
plt.plot(cat.xx[I], cat.yy[I], 'b.')
I = np.flatnonzero(cat.type == 3)
plt.plot(cat.xx[I], cat.yy[I], 'm.')
plt.axis(ax)
ps.savefig()
plt.clf()
psf=psf,
wcs = ConstantFitsWcs(wcs),
photocal = LinearPhotoCal(1., band=band))
for i,sn in enumerate(sn_vals):
for j,re in enumerate(re_vals):
## HACK -- this is the flux required for a PSF to be
## detected at target S/N... adjust for galaxy?
flux = sn * detsig1
# Create round EXP galaxy
#PixPos(sz/2, sz/2),
true_src = ExpGalaxy(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}),
EllipseE(re, 0., 0.))
tr = Tractor([tim], [true_src])
tr.freezeParams('images')
true_mod = tr.getModelImage(0)
ima = dict(interpolation='nearest', origin='lower',
vmin=-2.*sig1, vmax=5.*sig1, cmap='hot')
this_dchisqs = []
flux_sns = []
for k in range(Nper):
noise = np.random.normal(scale=sig1, size=true_mod.shape)
tim.data = true_mod + noise
if k == 0 and False:
def main():
'''
This function generates the plots in the paper.
Some files and directories are assumed to exist in the current directory:
* WISE atlas tiles, from http://unwise.me/data/allsky-atlas.fits
* unwise-neo1-coadds, from http://unwise.me/data/neo1/
* unwise-neo1-coadds-half, unwise-neo1-coadds-quarter: directories
'''
# First, create the WCS into which we want to render
# degrees width to render in galactic coords
# |l| < 60
# |b| < 30
width = 120
# ~2 arcmin per pixel
W = int(width * 60.) / 2
H = W/2
zoom = 360. / width
wcs = anwcs_create_hammer_aitoff(0., 0., zoom, W, H, 0)
# Select WISE tiles that overlap. This atlas table is available
# from http://unwise.me/data/allsky-atlas.fits
# Select WISE tiles that overlap.
T = fits_table('allsky-atlas.fits')
print(len(T), 'tiles total')
T.ll,T.bb = radectolb(T.ra, T.dec)
I = np.flatnonzero(np.logical_or(T.ll < width+1,
T.ll > (360-width-1)) *
(T.bb > -width/2-1) * (T.bb < width/2+1))
T.cut(I)
print(len(I), 'tiles in L,B range')
# Create a coadd for each WISE band
lbpat = 'unwise-neo1-w%i-lb.fits'
imgs = []
for band in [1,2]:
outfn = lbpat % (band)
if os.path.exists(outfn):
print('Exists:', outfn)
img = fitsio.read(outfn)
imgs.append(img)
continue
coimg = np.zeros((H,W), np.float32)
conimg = np.zeros((H,W), np.float32)
for i,brick in enumerate(T.coadd_id):
# We downsample by 2, twice, just to make repeat runs a
# little faster.
# unWISE
fn = os.path.join('unwise-neo1-coadds', brick[:3], brick,
'unwise-%s-w%i-img-u.fits' % (brick, band))
qfn = os.path.join('unwise-neo1-coadds-quarter',
'unwise-%s-w%i.fits' % (brick, band))
hfn = os.path.join('unwise-neo1-coadds-half',
'unwise-%s-w%i.fits' % (brick, band))
if not os.path.exists(qfn):
if not os.path.exists(hfn):
print('Reading', fn)
halfsize(fn, hfn)
halfsize(hfn, qfn)
fn = qfn
print('Reading', fn)
img = fitsio.read(fn)
bwcs = Tan(fn, 0)
bh,bw = img.shape
# Coadd each unWISE pixel into the nearest target pixel.
xx,yy = np.meshgrid(np.arange(bw), np.arange(bh))
rr,dd = bwcs.pixelxy2radec(xx, yy)
ll,bb = radectolb(rr.ravel(), dd.ravel())
ll = ll.reshape(rr.shape)
bb = bb.reshape(rr.shape)
ok,ox,oy = wcs.radec2pixelxy(ll, bb)
ox = np.round(ox - 1).astype(int)
oy = np.round(oy - 1).astype(int)
K = (ox >= 0) * (ox < W) * (oy >= 0) * (oy < H) * ok
#print('ok:', np.unique(ok), 'x', ox.min(), ox.max(), 'y', oy.min(), oy.max())
assert(np.all(np.isfinite(img)))
if np.sum(K) == 0:
# no overlap
print('No overlap')
continue
np.add.at( coimg, (oy[K], ox[K]), img[K])
np.add.at(conimg, (oy[K], ox[K]), 1)
img = coimg / np.maximum(conimg, 1)
# Hack -- write and then read FITS WCS header.
fn = 'wiselb.wcs'
wcs.writeto(fn)
hdr = fitsio.read_header(fn)
hdr['CTYPE1'] = 'GLON-AIT'
hdr['CTYPE2'] = 'GLAT-AIT'
fitsio.write(outfn, img, header=hdr, clobber=True)
fitsio.write(outfn.replace('.fits', '-n.fits'), conimg,
header=hdr, clobber=True)
imgs.append(img)
w1,w2 = imgs
# Get/confirm L,B bounds...
H,W = w1.shape
print('Image size', W, 'x', H)
ok,l1,b1 = wcs.pixelxy2radec(1, (H+1)/2.)
ok,l2,b2 = wcs.pixelxy2radec(W, (H+1)/2.)
ok,l3,b3 = wcs.pixelxy2radec((W+1)/2., 1)
ok,l4,b4 = wcs.pixelxy2radec((W+1)/2., H)
print('L,B', (l1,b1), (l2,b2), (l3,b3), (l4,b4))
llo,lhi = l2,l1+360
blo,bhi = b3,b4
# Set plot sizes
plt.figure(1, figsize=(10,5))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
plt.figure(2, figsize=(5,5))
plt.subplots_adjust(left=0.11, right=0.96, bottom=0.1, top=0.95)
suffix = '.pdf'
rgb = wise_rgb(w1, w2)
xlo,ylo = 0,0
plt.figure(1)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo, ylo, lticks=[60,30,0,330,300], bticks=[-30,-15,0,15,30])
plt.savefig('xbulge-00' + suffix)
# Compute the median of each row as a crude way of suppressing the
# Galactic plane
medy1 = np.median(w1, axis=1)
medy2 = np.median(w2, axis=1)
rgb = wise_rgb(w1 - medy1[:,np.newaxis],
w2 - medy2[:,np.newaxis])
# Zoom in a bit for Galactic plane subtracted version
lhi,llo,blo,bhi = 40, 320, -20, 20
okxy = np.array([wcs.radec2pixelxy(l,b) for l,b in [
(llo, blo), (llo, bhi), (lhi, blo), (lhi, bhi)]])
xlo = int(np.floor(min(okxy[:,-2])))
xhi = int(np.ceil (max(okxy[:,-2])))
ylo = int(np.floor(min(okxy[:,-1])))
yhi = int(np.ceil (max(okxy[:,-1])))
plt.clf()
plt.imshow(rgb[ylo:yhi, xlo:xhi, :],origin='lower', interpolation='nearest')
#lbticks(wcs, xlo, ylo, lticks=[40,20,0,340,320], bticks=[-20,-10,0,10,20])
lbticks(wcs, xlo, ylo, lticks=[30,15,0,345,330], bticks=[-20,-10,0,10,20])
plt.savefig('xbulge-01' + suffix)
# Zoom in on the core
lhi,llo,blo,bhi = 15, 345, -15, 15
ok,x1,y1 = wcs.radec2pixelxy(llo, blo)
ok,x2,y2 = wcs.radec2pixelxy(llo, bhi)
ok,x3,y3 = wcs.radec2pixelxy(lhi, blo)
ok,x4,y4 = wcs.radec2pixelxy(lhi, bhi)
xlo = int(np.floor(min(x1,x2,x3,x4)))
xhi = int(np.ceil (max(x1,x2,x3,x4)))
ylo = int(np.floor(min(y1,y2,y3,y4)))
yhi = int(np.ceil (max(y1,y2,y3,y4)))
print('xlo,ylo', xlo, ylo)
w1 = w1[ylo:yhi, xlo:xhi]
w2 = w2[ylo:yhi, xlo:xhi]
plt.figure(2)
# Apply color cut
w1mag = -2.5*(np.log10(w1) - 9.)
w2mag = -2.5*(np.log10(w2) - 9.)
cc = w1mag - w2mag
goodcolor = np.isfinite(cc)
mlo,mhi = np.percentile(cc[goodcolor], [5,95])
print('W1 - W2 color masks:', mlo,mhi)
mask = goodcolor * (cc > mlo) * (cc < mhi)
plt.clf()
rgb = wise_rgb(w1, w2)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data')
plt.savefig('xbulge-fit-data' + suffix)
plt.clf()
rgb = wise_rgb(w1 * mask, w2 * mask)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data (masked)')
plt.savefig('xbulge-fit-masked' + suffix)
ie = mask.astype(np.float32)
from tractor import (Image, NCircularGaussianPSF, LinearPhotoCal, Tractor,
PixPos, Fluxes)
from tractor.galaxy import ExpGalaxy, GalaxyShape
# Create Tractor images
tim1 = Image(data=w1 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w1'))
tim2 = Image(data=w2 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w2'))
H,W = w1.shape
gal = ExpGalaxy(PixPos(W/2, H/2), Fluxes(w1=w1.sum(), w2=w2.sum()),
GalaxyShape(200, 0.4, 90.))
tractor = Tractor([tim1, tim2],[gal])
# fitsio.write('data-w1.fits', w1 * mask, clobber=True)
# fitsio.write('data-w2.fits', w2 * mask, clobber=True)
# fitsio.write('mask.fits', mask.astype(np.uint8), clobber=True)
# Optimize galaxy model
tractor.freezeParam('images')
for step in range(50):
dlnp,x,alpha = tractor.optimize()
print('dlnp', dlnp)
print('x', x)
print('alpha', alpha)
print('Galaxy', gal)
if dlnp == 0:
break
# Get galaxy model images, compute residuals
mod1 = tractor.getModelImage(0)
resid1 = w1 - mod1
mod2 = tractor.getModelImage(1)
resid2 = w2 - mod2
rgb = wise_rgb(mod1, mod2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Model')
plt.savefig('xbulge-fit-model' + suffix)
rgb = resid_rgb(resid1, resid2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals')
plt.savefig('xbulge-fit-resid' + suffix)
rgb = resid_rgb(resid1*mask, resid2*mask)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (masked)')
plt.savefig('xbulge-fit-residmasked' + suffix)
# fitsio.write('resid1.fits', resid1, clobber=True)
# fitsio.write('resid2.fits', resid2, clobber=True)
# Compute median-smoothed residuals
fr1 = np.zeros_like(resid1)
fr2 = np.zeros_like(resid2)
median_smooth(resid1, np.logical_not(mask), 25, fr1)
median_smooth(resid2, np.logical_not(mask), 25, fr2)
rgb = resid_rgb(fr1, fr2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (smoothed)')
plt.savefig('xbulge-fit-smooth2' + suffix)
ps.savefig()
subtim = tim.subimage(x0, x1, y0, y1)
#mn,mx = np.percentile(subtim.getImage().ravel(), [25, 98])
# plt.clf()
# plt.imshow(subtim.getImage(), interpolation='nearest', origin='lower',
# cmap='gray', vmin=mn, vmax=mx)
# ps.savefig()
cat.cut((cat.xx >= x0) * (cat.xx <= x1) * (cat.yy >= y0) * (cat.yy <= y1))
print('Cut to', len(cat), 'sources in subimage')
srcs = gim2d_catalog(cat, band)
tractor = Tractor([subtim], srcs)
if False:
plt.clf()
plt.imshow(img, **ima)
ax = plt.axis()
I = np.flatnonzero(cat.type == 1)
plt.plot(cat.xx[I], cat.yy[I], 'r.')
I = np.flatnonzero(cat.type == 2)
plt.plot(cat.xx[I], cat.yy[I], 'b.')
I = np.flatnonzero(cat.type == 3)
plt.plot(cat.xx[I], cat.yy[I], 'm.')
plt.axis(ax)
ps.savefig()
plt.clf()
def main():
fn = '/global/cscratch1/sd/dstn/c4d_190730_024955_ori/c4d_190730_024955_ori.52.fits'
survey_dir = '/global/cscratch1/sd/dstn/subtractor-survey-dir'
imagedir = os.path.join(survey_dir, 'images')
trymakedirs(imagedir)
calibdir = os.path.join(survey_dir, 'calib')
psfexdir = os.path.join(calibdir, 'decam', 'psfex-merged')
trymakedirs(psfexdir)
skydir = os.path.join(calibdir, 'decam', 'splinesky-merged')
trymakedirs(skydir)
basename = os.path.basename(fn)
basename = basename.replace('.fits', '')
# Output filenames for legacyzpts calibration/zeropoint files
f, photfn = tempfile.mkstemp()
os.close(f)
surveyfn = os.path.join(survey_dir, 'survey-ccds-%s.fits.gz' % basename)
annfn = os.path.join(survey_dir, 'annotated-%s.fits' % basename)
mp = multiproc()
survey = LegacySurveyData(survey_dir)
# Use the subclass above to handle DECam images!
survey.image_typemap.update(decam=GoldsteinDecamImage)
# Grab the exposure number and CCD name
hdr = fitsio.read_header(fn)
expnum = hdr['EXPNUM']
ccdname = hdr['EXTNAME'].strip()
print('Exposure', expnum, 'CCD', ccdname)
import logging
lvl = logging.INFO
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# tractor logging is *soooo* chatty
logging.getLogger('tractor.engine').setLevel(lvl + 10)
if not os.path.exists(surveyfn):
# Run calibrations and zeropoints
runit(fn,
photfn,
surveyfn,
annfn,
mp,
survey=survey,
camera='decam',
debug=False,
choose_ccd=ccdname,
splinesky=True,
calibdir=calibdir,
measureclass=GoldsteinDecamMeasurer)
# Find catalog sources touching this CCD
ccds = survey.find_ccds(expnum=expnum, ccdname=ccdname)
assert (len(ccds) == 1)
ccd = ccds[0]
print('Got CCD', ccd)
# Create Tractor image
im = survey.get_image_object(ccd)
print('Got image:', im)
# Look at this sub-image, or the whole chip?
#zoomslice=None
zoomslice = (slice(0, 1000), slice(0, 1000))
tim = im.get_tractor_image(slc=zoomslice,
pixPsf=True,
splinesky=True,
hybridPsf=True,
normalizePsf=True,
old_calibs_ok=True)
print('Got tim:', tim)
# Read catalog files touching this CCD
catsurvey = LegacySurveyData(
'/global/project/projectdirs/cosmo/work/legacysurvey/dr8/south')
T = get_catalog_in_wcs(tim.subwcs, catsurvey)
print('Got', len(T), 'DR8 catalog sources within CCD')
# Gaia stars: move RA,Dec to the epoch of this image.
I = np.flatnonzero(T.ref_epoch > 0)
if len(I):
from legacypipe.survey import radec_at_mjd
print('Moving', len(I), 'Gaia stars to MJD', tim.time.toMjd())
ra, dec = radec_at_mjd(T.ra[I], T.dec[I], T.ref_epoch[I].astype(float),
T.pmra[I], T.pmdec[I], T.parallax[I],
tim.time.toMjd())
T.ra[I] = ra
T.dec[I] = dec
# Create Tractor Source objects from the catalog
cat = read_fits_catalog(T, bands=tim.band)
print('Created', len(cat), 'source objects')
# Render model image!
tr = Tractor([tim], cat)
mod = tr.getModelImage(0)
# plots
ima = dict(interpolation='nearest',
origin='lower',
vmin=-2 * tim.sig1,
vmax=10 * tim.sig1,
cmap='gray')
plt.clf()
plt.imshow(tim.getImage(), **ima)
plt.title('Image')
plt.savefig('img.jpg')
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model')
plt.savefig('mod.jpg')
plt.clf()
plt.imshow(tim.getImage() - mod, **ima)
plt.title('Residual')
plt.savefig('res.jpg')
def main():
# Where are the data?
datadir = os.path.join(os.path.dirname(__file__), 'data-decam')
name = 'decam-520206-S16'
imagefn = os.path.join(datadir, '%s-image-sub.fits' % name)
invvarfn = os.path.join(datadir, '%s-invvar-sub.fits' % name)
psfexfn = os.path.join(datadir, '%s-psfex.fits' % name)
catfn = os.path.join(datadir, 'tractor-1816p325-sub.fits')
# Read the image and inverse-variance maps.
image = fitsio.read(imagefn)
invvar = fitsio.read(invvarfn)
# The DECam inverse-variance maps are unfortunately corrupted
# by fpack, causing zeros to become negative. Fix those.
invvar[invvar < np.median(invvar) * 0.1] = 0.
H, W = image.shape
print('Subimage size:', image.shape)
# For the PSF model, we need to know what subimage region this is:
subimage_offset = (35, 1465)
# We also need the calibrated zeropoint.
zeropoint = 24.7787
# What filter was this image taken in? (z)
prim_header = fitsio.read_header(imagefn)
band = prim_header['FILTER'].strip()[0]
print('Band:', band)
# These DECam images were calibrated so that the zeropoints need
# an exposure-time factor, so add that in.
exptime = prim_header['EXPTIME']
zeropoint += 2.5 * np.log10(exptime)
# Read the PsfEx model file
psf = PixelizedPsfEx(psfexfn)
# Instantiate a constant pixelized PSF at the image center
# (of the subimage)
x0, y0 = subimage_offset
psf = psf.constantPsfAt(x0 + W / 2., y0 + H / 2.)
# Load the WCS model from the header
# We convert from the RA---TPV type to RA---SIP
header = fitsio.read_header(imagefn, ext=1)
wcsobj = wcs_pv2sip_hdr(header, stepsize=10)
# We'll just use a rough sky estimate...
skyval = np.median(image)
# Create the Tractor Image (tim).
tim = Image(data=image,
invvar=invvar,
psf=psf,
wcs=ConstantFitsWcs(wcsobj),
sky=ConstantSky(skyval),
photocal=LinearPhotoCal(
NanoMaggies.zeropointToScale(zeropoint), band=band))
# Read the official DECaLS DR3 catalog -- it has only two sources in this subimage.
catalog = fits_table(catfn)
print('Read', len(catalog), 'sources')
print('Source types:', catalog.type)
# Create Tractor sources corresponding to these two catalog
# entries.
# In DECaLS, the "SIMP" type is a round Exponential galaxy with a
# fixed 0.45" radius, but we'll treat it as a general Exp galaxy.
sources = []
for c in catalog:
# Create a "position" object given the catalog RA,Dec
position = RaDecPos(c.ra, c.dec)
# Create a "brightness" object; in the catalog, the fluxes are
# stored in a [ugrizY] array, so pull out the right index
band_index = 'ugrizY'.index(band)
flux = c.decam_flux[band_index]
brightness = NanoMaggies(**{band: flux})
# Depending on the source classification in the catalog, pull
# out different fields for the galaxy shape, and for the
# galaxy type. The DECaLS catalogs, conveniently, store
# galaxy shapes as (radius, e1, e2) ellipses.
if c.type.strip() == 'DEV':
shape = EllipseE(c.shapedev_r, c.shapedev_e1, c.shapedev_e2)
galclass = DevGalaxy
elif c.type.strip() == 'SIMP':
shape = EllipseE(c.shapeexp_r, c.shapeexp_e1, c.shapeexp_e2)
galclass = ExpGalaxy
else:
assert (False)
# Create the tractor galaxy object
source = galclass(position, brightness, shape)
print('Created', source)
sources.append(source)
# Create the Tractor object -- a list of tractor Images and a list of tractor sources.
tractor = Tractor([tim], sources)
# Render the initial model image.
print('Getting initial model...')
mod = tractor.getModelImage(0)
make_plot(tim, mod, 'Initial Scene', 'mod0.png')
# Instantiate a new source at the location of the unmodelled peak.
print('Adding new source...')
# Find the peak very naively...
ipeak = np.argmax((image - mod) * tim.inverr)
iy, ix = np.unravel_index(ipeak, tim.shape)
print('Residual peak at', ix, iy)
# Compute the RA,Dec location of the peak...
radec = tim.getWcs().pixelToPosition(ix, iy)
print('RA,Dec', radec)
# Try modelling it as a point source.
# We'll initialize the brightness arbitrarily to 1 nanomaggy (= mag 22.5)
brightness = NanoMaggies(**{band: 1.})
source = PointSource(radec, brightness)
# Add it to the catalog!
tractor.catalog.append(source)
# Render the new model image with this source added.
mod = tractor.getModelImage(0)
make_plot(tim, mod, 'New Source (Before Fit)', 'mod1.png')
print('Fitting new source...')
# Now we're going to fit for the properties of the new source we
# added.
# We don't want to fit for any of the image calibration properties:
tractor.freezeParam('images')
# And we don't (yet) want to fit the existing sources. The new
# source is index number 2, so freeze everything else in the catalog.
tractor.catalog.freezeAllBut(2)
print('Fitting parameters:')
tractor.printThawedParams()
# Do the actual optimization:
tractor.optimize_loop()
mod = tractor.getModelImage(0)
make_plot(tim, mod, 'New Source Fit', 'mod2.png')
print('Fitting sources simultaneously...')
# Now let's unfreeze all the sources and fit them simultaneously.
tractor.catalog.thawAllParams()
tractor.printThawedParams()
tractor.optimize_loop()
mod = tractor.getModelImage(0)
make_plot(tim, mod, 'Simultaneous Fit', 'mod3.png')
def scan_dchisq(seeing, target_dchisq, ps, e1=0.):
pixscale = 0.262
psfsigma = seeing / pixscale / 2.35
print('PSF sigma:', psfsigma, 'pixels')
psf = GaussianMixturePSF(1., 0., 0., psfsigma**2, psfsigma**2, 0.)
sig1 = 0.01
psfnorm = 1./(2. * np.sqrt(np.pi) * psfsigma)
detsig1 = sig1 / psfnorm
sz = 50
cd = pixscale / 3600.
wcs = Tan(0., 0., float(sz/2), float(sz/2), -cd, 0., 0., cd,
float(sz), float(sz))
band = 'r'
tim = Image(data=np.zeros((sz,sz)), inverr=np.ones((sz,sz)) / sig1,
psf=psf,
wcs = ConstantFitsWcs(wcs),
photocal = LinearPhotoCal(1., band=band))
re_vals = np.logspace(-1., 0., 50)
all_runs = []
mods = []
for i,re in enumerate(re_vals):
true_src = ExpGalaxy(RaDecPos(0., 0.),
NanoMaggies(**{band: 1.}),
EllipseE(re, e1, 0.))
print('True source:', true_src)
tr = Tractor([tim], [true_src])
tr.freezeParams('images')
true_mod = tr.getModelImage(0)
dchisq_none = np.sum((true_mod * tim.inverr)**2)
scale = np.sqrt(target_dchisq / dchisq_none)
true_src.brightness.setParams([scale])
true_mod = tr.getModelImage(0)
dchisq_none = np.sum((true_mod * tim.inverr)**2)
mods.append(true_mod)
tim.data = true_mod
exp_src = true_src.copy()
psf_src = PointSource(true_src.pos.copy(), true_src.brightness.copy())
simp_src = SimpleGalaxy(true_src.pos.copy(), true_src.brightness.copy())
dchisqs = []
#for src in [psf_src, simp_src, exp_src]:
for src in [psf_src, simp_src]:
src.freezeParam('pos')
#print('Fitting source:', src)
#src.printThawedParams()
tr.catalog[0] = src
tr.optimize_loop()
#print('Fitted:', src)
mod = tr.getModelImage(0)
dchisqs.append(dchisq_none - np.sum(((true_mod - mod) * tim.inverr)**2))
#print('dchisq:', dchisqs[-1])
dchisqs.append(dchisq_none)
all_runs.append([re,] + dchisqs)
all_runs = np.array(all_runs)
re = all_runs[:,0]
dchi_psf = all_runs[:,1]
dchi_simp = all_runs[:,2]
dchi_exp = all_runs[:,3]
dchi_ps = np.maximum(dchi_psf, dchi_simp)
dchi_cut1 = dchi_ps + 3+9
dchi_cut2 = dchi_ps + dchi_psf * 0.02
dchi_cut3 = dchi_ps + dchi_psf * 0.008
plt.clf()
plt.plot(re, dchi_psf, 'k-', label='PSF')
plt.plot(re, dchi_simp, 'b-', label='SIMP')
plt.plot(re, dchi_exp, 'r-', label='EXP')
plt.plot(re, dchi_cut2, 'm--', alpha=0.5, lw=2, label='Cut: 2%')
plt.plot(re, dchi_cut3, 'm:', alpha=0.5, lw=2, label='Cut: 0.08%')
plt.plot(re, dchi_cut1, 'm-', alpha=0.5, lw=2, label='Cut: 12')
plt.xlabel('True r_e (arcsec)')
plt.ylabel('dchisq')
#plt.legend(loc='lower left')
plt.legend(loc='upper right')
tt = 'Seeing = %g arcsec, S/N ~ %i' % (seeing, int(np.round(np.sqrt(target_dchisq))))
if e1 != 0.:
tt += ', Ellipticity %g' % e1
plt.title(tt)
plt.ylim(0.90 * target_dchisq, 1.05 * target_dchisq)
# aspect = 1.2
# ax = plt.axis()
# dre = (ax[1]-ax[0]) / 20 / aspect
# dchi = (ax[3]-ax[2]) / 20
# I = np.linspace(0, len(re_vals)-1, 8).astype(int)
# for mod,re in [(mods[i], re_vals[i]) for i in I]:
# print('extent:', [re-dre, re+dre, ax[2], ax[2]+dchi])
# plt.imshow(mod, interpolation='nearest', origin='lower', aspect='auto',
# extent=[re-dre, re+dre, ax[2], ax[2]+dchi], cmap='gray')
# plt.axis(ax)
ps.savefig()
def main():
# I read a DESI DR8 target catalog, cut to ELGs, then took a narrow
# r-mag slice around the peak of that distribution;
# desi/target/catalogs/dr8/0.31.1/targets/main/resolve/targets-dr8-hp-1,5,11,50,55,60,83,84,86,89,91,98,119,155,158,174,186,187,190.fits')
# Then took the median g and z mags
# And opened the coadd invvar mags for a random brick in that footprint
# (0701p000) to find the median per-pixel invvars.
r = 23.0
g = 23.4
z = 22.2
# Selecting EXPs, the peak of the shapeexp_r was ~ 0.75".
r_e = 0.75
# Image properties:
giv = 77000.
riv = 27000.
ziv = 8000.
# PSF sizes were roughly equal, 1.16, 1.17, 1.19"
# -> sigma = 1.9 DECam pixels
psf_sigma = 1.9
H, W = 1000, 1000
seed = 42
np.random.seed(seed)
ra, dec = 70., 1.
brick = BrickDuck(ra, dec, 'custom-0700p010')
wcs = wcs_for_brick(brick, W=W, H=H)
bands = 'grz'
tims = []
for band, iv in zip(bands, [giv, riv, ziv]):
img = np.zeros((H, W), np.float32)
tiv = np.zeros_like(img) + iv
s = psf_sigma**2
psf = GaussianMixturePSF(1., 0., 0., s, s, 0.)
twcs = ConstantFitsWcs(wcs)
sky = ConstantSky(0.)
photocal = LinearPhotoCal(1., band=band)
tai = TAITime(None, mjd=55700.)
tim = tractor.Image(data=img,
invvar=tiv,
psf=psf,
wcs=twcs,
sky=sky,
photocal=photocal,
name='fake %s' % band,
time=tai)
tim.skyver = ('1', '1')
tim.psfver = ('1', '1')
tim.plver = '1'
tim.x0 = tim.y0 = 0
tim.subwcs = wcs
tim.psfnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
tim.galnorm = tim.psfnorm
tim.propid = '2020A-000'
tim.band = band
tim.dq = None
tim.sig1 = 1. / np.sqrt(iv)
tim.psf_sigma = psf_sigma
tim.primhdr = fitsio.FITSHDR()
tims.append(tim)
# Simulated catalog
gflux = NanoMaggies.magToNanomaggies(g)
rflux = NanoMaggies.magToNanomaggies(r)
zflux = NanoMaggies.magToNanomaggies(z)
box = 50
CX, CY = np.meshgrid(np.arange(box // 2, W, box),
np.arange(box // 2, H, box))
ny, nx = CX.shape
BA, PHI = np.meshgrid(np.linspace(0.1, 1.0, nx), np.linspace(0., 180., ny))
cat = []
for cx, cy, ba, phi in zip(CX.ravel(), CY.ravel(), BA.ravel(),
PHI.ravel()):
#print('x,y %.1f,%.1f, ba %.2f, phi %.2f' % (cx, cy, ba, phi))
r, d = wcs.pixelxy2radec(cx + 1, cy + 1)
src = ExpGalaxy(RaDecPos(r, d),
NanoMaggies(order=bands, g=gflux, r=rflux, z=zflux),
EllipseE.fromRAbPhi(r_e, ba, phi))
cat.append(src)
from legacypipe.catalog import prepare_fits_catalog
TT = fits_table()
TT.bx = CX.ravel()
TT.by = CY.ravel()
TT.ba = BA.ravel()
TT.phi = PHI.ravel()
tcat = tractor.Catalog(*cat)
T2 = prepare_fits_catalog(tcat, None, TT, bands, save_invvars=False)
T2.writeto('sim-cat.fits')
tr = Tractor(tims, cat)
for band, tim in zip(bands, tims):
mod = tr.getModelImage(tim)
mod += np.random.standard_normal(
size=tim.shape) * 1. / tim.getInvError()
fitsio.write('sim-%s.fits' % band, mod, clobber=True)
tim.data = mod
ccds = fits_table()
ccds.filter = np.array([f for f in bands])
ccds.ccd_cuts = np.zeros(len(ccds), np.int16)
ccds.imgfn = np.array([tim.name for tim in tims])
ccds.propid = np.array(['2020A-000'] * len(ccds))
ccds.fwhm = np.zeros(len(ccds), np.float32) + psf_sigma * 2.35
ccds.mjd_obs = np.zeros(len(ccds))
ccds.camera = np.array(['fake'] * len(ccds))
survey = FakeLegacySurvey(ccds, tims)
import logging
verbose = False
if verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# tractor logging is *soooo* chatty
logging.getLogger('tractor.engine').setLevel(lvl + 10)
run_brick(None,
survey,
radec=(ra, dec),
width=W,
height=H,
do_calibs=False,
gaia_stars=False,
large_galaxies=False,
tycho_stars=False,
forceall=True,
outliers=False) #, stages=['image_coadds'])
def unwise_forcedphot(cat, tiles, bands=[1, 2, 3, 4], roiradecbox=None,
unwise_dir='.',
use_ceres=True, ceres_block=8,
save_fits=False, ps=None,
psf_broadening=None):
'''
Given a list of tractor sources *cat*
and a list of unWISE tiles *tiles* (a fits_table with RA,Dec,coadd_id)
runs forced photometry, returning a FITS table the same length as *cat*.
'''
# Severely limit sizes of models
for src in cat:
if isinstance(src, PointSource):
src.fixedRadius = 10
else:
src.halfsize = 10
wantims = ((ps is not None) or save_fits)
wanyband = 'w'
fskeys = ['prochi2', 'pronpix', 'profracflux', 'proflux', 'npix',
'pronexp']
Nsrcs = len(cat)
phot = fits_table()
phot.tile = np.array([' '] * Nsrcs)
ra = np.array([src.getPosition().ra for src in cat])
dec = np.array([src.getPosition().dec for src in cat])
for band in bands:
print('Photometering WISE band', band)
wband = 'w%i' % band
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
tiledists = np.empty(Nsrcs)
tiledists[:] = 1e100
flux_invvars = np.zeros(Nsrcs, np.float32)
fitstats = dict([(k, np.zeros(Nsrcs, np.float32)) for k in fskeys])
nexp = np.zeros(Nsrcs, np.int16)
mjd = np.zeros(Nsrcs, np.float64)
for tile in tiles:
print('Reading tile', tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir, tile.coadd_id, band,
bandname=wanyband, roiradecbox=roiradecbox)
if tim is None:
print('Actually, no overlap with tile', tile.coadd_id)
continue
if psf_broadening is not None:
# psf_broadening is a factor by which the PSF FWHMs
# should be scaled; the PSF is a little wider
# post-reactivation.
psf = tim.getPsf()
from tractor import GaussianMixturePSF
if isinstance(psf, GaussianMixturePSF):
#
print('Broadening PSF: from', psf)
p0 = psf.getParams()
#print('Params:', p0)
pnames = psf.getParamNames()
#print('Param names:', pnames)
p1 = [p * psf_broadening**2 if 'var' in name else p
for (p, name) in zip(p0, pnames)]
#print('Broadened:', p1)
psf.setParams(p1)
print('Broadened PSF:', psf)
else:
print(
'WARNING: cannot apply psf_broadening to WISE PSF of type', type(psf))
print('Read image with shape', tim.shape)
# Select sources in play.
wcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wcs.radec2pixelxy(ra, dec)
x = (x - 1.).astype(np.float32)
y = (y - 1.).astype(np.float32)
margin = 10.
I = np.flatnonzero((x >= -margin) * (x < W + margin) *
(y >= -margin) * (y < H + margin))
print(len(I), 'within the image + margin')
inbox = ((x[I] >= -0.5) * (x[I] < (W - 0.5)) *
(y[I] >= -0.5) * (y[I] < (H - 0.5)))
print(sum(inbox), 'strictly within the image')
# Compute L_inf distance to (full) tile center.
tilewcs = unwise_tile_wcs(tile.ra, tile.dec)
cx, cy = tilewcs.crpix
ok, tx, ty = tilewcs.radec2pixelxy(ra[I], dec[I])
td = np.maximum(np.abs(tx - cx), np.abs(ty - cy))
closest = (td < tiledists[I])
tiledists[I[closest]] = td[closest]
keep = inbox * closest
# Source indices (in the full "cat") to keep (the fit values for)
srci = I[keep]
if not len(srci):
print('No sources to be kept; skipping.')
continue
phot.tile[srci] = tile.coadd_id
nexp[srci] = tim.nuims[np.clip(np.round(y[srci]).astype(int), 0, H - 1),
np.clip(np.round(x[srci]).astype(int), 0, W - 1)]
# Source indices in the margins
margi = I[np.logical_not(keep)]
# sources in the box -- at the start of the subcat list.
subcat = [cat[i] for i in srci]
# include *copies* of sources in the margins
# (that way we automatically don't save the results)
subcat.extend([cat[i].copy() for i in margi])
assert(len(subcat) == len(I))
# FIXME -- set source radii, ...?
minsb = 0.
fitsky = False
# Look in image and set radius based on peak height??
tractor = Tractor([tim], subcat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
tractor.optimizer = CeresOptimizer(BW=ceres_block,
BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(wanyband)
kwa = dict(fitstat_extras=[('pronexp', [tim.nims])])
t0 = Time()
R = tractor.optimize_forced_photometry(
minsb=minsb, mindlnp=1., sky=fitsky, fitstats=True,
variance=True, shared_params=False,
wantims=wantims, **kwa)
print('unWISE forced photometry took', Time() - t0)
if use_ceres:
term = R.ceres_status['termination']
print('Ceres termination status:', term)
# Running out of memory can cause failure to converge
# and term status = 2.
# Fail completely in this case.
if term != 0:
raise RuntimeError(
'Ceres terminated with status %i' % term)
if wantims:
ims0 = R.ims0
ims1 = R.ims1
IV, fs = R.IV, R.fitstats
if save_fits:
import fitsio
(dat, mod, ie, chi, roi) = ims1[0]
wcshdr = fitsio.FITSHDR()
tim.wcs.wcs.add_to_header(wcshdr)
tag = 'fit-%s-w%i' % (tile.coadd_id, band)
fitsio.write('%s-data.fits' %
tag, dat, clobber=True, header=wcshdr)
fitsio.write('%s-mod.fits' % tag, mod,
clobber=True, header=wcshdr)
fitsio.write('%s-chi.fits' % tag, chi,
clobber=True, header=wcshdr)
if ps:
tag = '%s W%i' % (tile.coadd_id, band)
(dat, mod, ie, chi, roi) = ims1[0]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
cmap='gray', vmin=-5, vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
# Save results for this tile.
# the "keep" sources are at the beginning of the "subcat" list
flux_invvars[srci] = IV[:len(srci)].astype(np.float32)
if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
mjd[srci] = (tim.mjdmin + tim.mjdmax) / 2.
if fs is None:
continue
for k in fskeys:
x = getattr(fs, k)
# fitstats are returned only for un-frozen sources
fitstats[k][srci] = np.array(x).astype(np.float32)[:len(srci)]
# Note, this is *outside* the loop over tiles.
# The fluxes are saved in the source objects, and will be set based on
# the 'tiledists' logic above.
nm = np.array([src.getBrightness().getBand(wanyband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their default
# (1-sigma or whatever) initial fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
phot.set(wband + '_nanomaggies', nm.astype(np.float32))
phot.set(wband + '_nanomaggies_ivar', nm_ivar)
dnm = np.zeros(len(nm_ivar), np.float32)
okiv = (nm_ivar > 0)
dnm[okiv] = (1. / np.sqrt(nm_ivar[okiv])).astype(np.float32)
okflux = (nm > 0)
mag = np.zeros(len(nm), np.float32)
mag[okflux] = (NanoMaggies.nanomaggiesToMag(nm[okflux])
).astype(np.float32)
dmag = np.zeros(len(nm), np.float32)
ok = (okiv * okflux)
dmag[ok] = (np.abs((-2.5 / np.log(10.)) * dnm[ok] / nm[ok])
).astype(np.float32)
mag[np.logical_not(okflux)] = np.nan
dmag[np.logical_not(ok)] = np.nan
phot.set(wband + '_mag', mag)
phot.set(wband + '_mag_err', dmag)
for k in fskeys:
phot.set(wband + '_' + k, fitstats[k])
phot.set(wband + '_nexp', nexp)
if not np.all(mjd == 0):
phot.set(wband + '_mjd', mjd)
return phot
def unwise_forcedphot(cat,
tiles,
band=1,
roiradecbox=None,
use_ceres=True,
ceres_block=8,
save_fits=False,
get_models=False,
ps=None,
psf_broadening=None,
pixelized_psf=False,
get_masks=None,
move_crpix=False,
modelsky_dir=None,
tag=None):
'''
Given a list of tractor sources *cat*
and a list of unWISE tiles *tiles* (a fits_table with RA,Dec,coadd_id)
runs forced photometry, returning a FITS table the same length as *cat*.
*get_masks*: the WCS to resample mask bits into.
'''
from tractor import PointSource, Tractor, ExpGalaxy, DevGalaxy
from tractor.sersic import SersicGalaxy
if tag is None:
tag = ''
else:
tag = tag + ': '
if not pixelized_psf and psf_broadening is None:
# PSF broadening in post-reactivation data, by band.
# Newer version from Aaron's email to decam-chatter, 2018-06-14.
broadening = {1: 1.0405, 2: 1.0346, 3: None, 4: None}
psf_broadening = broadening[band]
if False:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence('wise-forced-w%i' % band)
plots = (ps is not None)
if plots:
import pylab as plt
wantims = (plots or save_fits or get_models)
wanyband = 'w'
if get_models:
models = []
wband = 'w%i' % band
Nsrcs = len(cat)
phot = fits_table()
# Filled in based on unique tile overlap
phot.wise_coadd_id = np.array([' '] * Nsrcs, dtype='U8')
phot.wise_x = np.zeros(Nsrcs, np.float32)
phot.wise_y = np.zeros(Nsrcs, np.float32)
phot.set('psfdepth_%s' % wband, np.zeros(Nsrcs, np.float32))
nexp = np.zeros(Nsrcs, np.int16)
mjd = np.zeros(Nsrcs, np.float64)
central_flux = np.zeros(Nsrcs, np.float32)
ra = np.array([src.getPosition().ra for src in cat])
dec = np.array([src.getPosition().dec for src in cat])
fskeys = ['prochi2', 'profracflux']
fitstats = {}
if get_masks:
mh, mw = get_masks.shape
maskmap = np.zeros((mh, mw), np.uint32)
tims = []
for tile in tiles:
info(tag + 'Reading WISE tile', tile.coadd_id, 'band', band)
tim = get_unwise_tractor_image(tile.unwise_dir,
tile.coadd_id,
band,
bandname=wanyband,
roiradecbox=roiradecbox)
if tim is None:
debug('Actually, no overlap with WISE coadd tile', tile.coadd_id)
continue
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage(),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data' % tag)
ps.savefig()
plt.clf()
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
range=(-5, 10),
bins=100)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.title(tag)
ps.savefig()
if move_crpix and band in [1, 2]:
realwcs = tim.wcs.wcs
x, y = realwcs.crpix
tile_crpix = tile.get('crpix_w%i' % band)
dx = tile_crpix[0] - 1024.5
dy = tile_crpix[1] - 1024.5
realwcs.set_crpix(x + dx, y + dy)
debug('unWISE', tile.coadd_id, 'band', band, 'CRPIX', x, y,
'shift by', dx, dy, 'to', realwcs.crpix)
if modelsky_dir and band in [1, 2]:
fn = os.path.join(modelsky_dir,
'%s.%i.mod.fits' % (tile.coadd_id, band))
if not os.path.exists(fn):
raise RuntimeError('WARNING: does not exist:', fn)
x0, x1, y0, y1 = tim.roi
bg = fitsio.FITS(fn)[2][y0:y1, x0:x1]
assert (bg.shape == tim.shape)
if plots:
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(tim.getImage(),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=5 * sig1)
plt.subplot(1, 2, 2)
plt.imshow(bg,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=5 * sig1)
tag = '%s W%i' % (tile.coadd_id, band)
plt.suptitle(tag)
ps.savefig()
plt.clf()
ha = dict(range=(-5, 10), bins=100, histtype='step')
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
color='b',
label='Original',
**ha)
plt.hist(((tim.getImage() - bg) *
tim.inverr)[tim.inverr > 0].ravel(),
color='g',
label='Minus Background',
**ha)
plt.axvline(0, color='k', alpha=0.5)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.legend()
plt.title(tag + ': background')
ps.savefig()
# Actually subtract the background!
tim.data -= bg
# Floor the per-pixel variances,
# and add Poisson contribution from sources
if band in [1, 2]:
# in Vega nanomaggies per pixel
floor_sigma = {1: 0.5, 2: 2.0}
poissons = {1: 0.15, 2: 0.3}
with np.errstate(divide='ignore'):
new_ie = 1. / np.sqrt(
(1. / tim.inverr)**2 + floor_sigma[band] +
poissons[band]**2 * np.maximum(0., tim.data))
new_ie[tim.inverr == 0] = 0.
if plots:
plt.clf()
plt.plot((1. / tim.inverr[tim.inverr > 0]).ravel(),
(1. / new_ie[tim.inverr > 0]).ravel(), 'b.')
plt.title('unWISE per-pixel error: %s band %i' %
(tile.coadd_id, band))
plt.xlabel('original')
plt.ylabel('floored')
ps.savefig()
assert (np.all(np.isfinite(new_ie)))
assert (np.all(new_ie >= 0.))
tim.inverr = new_ie
# Expand a 3-pixel radius around weight=0 (saturated) pixels
# from Eddie via crowdsource
# https://github.com/schlafly/crowdsource/blob/7069da3e7d9d3124be1cbbe1d21ffeb63fc36dcc/python/wise_proc.py#L74
## FIXME -- W3/W4 ??
satlimit = 85000
msat = ((tim.data > satlimit) | ((tim.nims == 0) &
(tim.nuims > 1)))
from scipy.ndimage.morphology import binary_dilation
xx, yy = np.mgrid[-3:3 + 1, -3:3 + 1]
dilate = xx**2 + yy**2 <= 3**2
msat = binary_dilation(msat, dilate)
nbefore = np.sum(tim.inverr == 0)
tim.inverr[msat] = 0
nafter = np.sum(tim.inverr == 0)
debug('Masking an additional', (nafter - nbefore),
'near-saturated pixels in unWISE', tile.coadd_id, 'band',
band)
# Read mask file?
if get_masks:
from astrometry.util.resample import resample_with_wcs, OverlapError
# unwise_dir can be a colon-separated list of paths
tilemask = None
for d in tile.unwise_dir.split(':'):
fn = os.path.join(d, tile.coadd_id[:3], tile.coadd_id,
'unwise-%s-msk.fits.gz' % tile.coadd_id)
if os.path.exists(fn):
debug('Reading unWISE mask file', fn)
x0, x1, y0, y1 = tim.roi
tilemask = fitsio.FITS(fn)[0][y0:y1, x0:x1]
break
if tilemask is None:
info('unWISE mask file for tile', tile.coadd_id,
'does not exist')
else:
try:
tanwcs = tim.wcs.wcs
assert (tanwcs.shape == tilemask.shape)
Yo, Xo, Yi, Xi, _ = resample_with_wcs(get_masks,
tanwcs,
intType=np.int16)
# Only deal with mask pixels that are set.
I, = np.nonzero(tilemask[Yi, Xi] > 0)
# Trim to unique area for this tile
rr, dd = get_masks.pixelxy2radec(Xo[I] + 1, Yo[I] + 1)
good = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2,
tile.dec1, tile.dec2)
I = I[good]
maskmap[Yo[I], Xo[I]] = tilemask[Yi[I], Xi[I]]
except OverlapError:
# Shouldn't happen by this point
print('Warning: no overlap between WISE tile',
tile.coadd_id, 'and brick')
if plots:
plt.clf()
plt.imshow(tilemask, interpolation='nearest', origin='lower')
plt.title('Tile %s: mask' % tile.coadd_id)
ps.savefig()
plt.clf()
plt.imshow(maskmap, interpolation='nearest', origin='lower')
plt.title('Tile %s: accumulated maskmap' % tile.coadd_id)
ps.savefig()
# The tiles have some overlap, so zero out pixels outside the
# tile's unique area.
th, tw = tim.shape
xx, yy = np.meshgrid(np.arange(tw), np.arange(th))
rr, dd = tim.wcs.wcs.pixelxy2radec(xx + 1, yy + 1)
unique = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1,
tile.dec2)
debug('Tile', tile.coadd_id, '- total of', np.sum(unique),
'unique pixels out of', len(unique.flat), 'total pixels')
if get_models:
# Save the inverr before blanking out non-unique pixels, for making coadds with no gaps!
# (actually, slightly more subtly, expand unique area by 1 pixel)
from scipy.ndimage.morphology import binary_dilation
du = binary_dilation(unique)
tim.coadd_inverr = tim.inverr * du
tim.inverr[unique == False] = 0.
del xx, yy, rr, dd, unique
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage() * (tim.inverr > 0),
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data (unique)' % tag)
ps.savefig()
if pixelized_psf:
from unwise_psf import unwise_psf
if (band == 1) or (band == 2):
# we only have updated PSFs for W1 and W2
psfimg = unwise_psf.get_unwise_psf(band,
tile.coadd_id,
modelname='neo6_unwisecat')
else:
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id)
if band == 4:
# oversample (the unwise_psf models are at native W4 5.5"/pix,
# while the unWISE coadds are made at 2.75"/pix.
ph, pw = psfimg.shape
subpsf = np.zeros((ph * 2 - 1, pw * 2 - 1), np.float32)
from astrometry.util.util import lanczos3_interpolate
xx, yy = np.meshgrid(
np.arange(0., pw - 0.51, 0.5, dtype=np.float32),
np.arange(0., ph - 0.51, 0.5, dtype=np.float32))
xx = xx.ravel()
yy = yy.ravel()
ix = xx.astype(np.int32)
iy = yy.astype(np.int32)
dx = (xx - ix).astype(np.float32)
dy = (yy - iy).astype(np.float32)
psfimg = psfimg.astype(np.float32)
rtn = lanczos3_interpolate(ix, iy, dx, dy, [subpsf.flat],
[psfimg])
if plots:
plt.clf()
plt.imshow(psfimg, interpolation='nearest', origin='lower')
plt.title('Original PSF model')
ps.savefig()
plt.clf()
plt.imshow(subpsf, interpolation='nearest', origin='lower')
plt.title('Subsampled PSF model')
ps.savefig()
psfimg = subpsf
del xx, yy, ix, iy, dx, dy
from tractor.psf import PixelizedPSF
psfimg /= psfimg.sum()
fluxrescales = {1: 1.04, 2: 1.005, 3: 1.0, 4: 1.0}
psfimg *= fluxrescales[band]
tim.psf = PixelizedPSF(psfimg)
if psf_broadening is not None and not pixelized_psf:
# psf_broadening is a factor by which the PSF FWHMs
# should be scaled; the PSF is a little wider
# post-reactivation.
psf = tim.getPsf()
from tractor import GaussianMixturePSF
if isinstance(psf, GaussianMixturePSF):
debug('Broadening PSF: from', psf)
p0 = psf.getParams()
pnames = psf.getParamNames()
p1 = [
p * psf_broadening**2 if 'var' in name else p
for (p, name) in zip(p0, pnames)
]
psf.setParams(p1)
debug('Broadened PSF:', psf)
else:
print(
'WARNING: cannot apply psf_broadening to WISE PSF of type',
type(psf))
wcs = tim.wcs.wcs
_, fx, fy = wcs.radec2pixelxy(ra, dec)
x = np.round(fx - 1.).astype(int)
y = np.round(fy - 1.).astype(int)
good = (x >= 0) * (x < tw) * (y >= 0) * (y < th)
# Which sources are in this brick's unique area?
usrc = radec_in_unique_area(ra, dec, tile.ra1, tile.ra2, tile.dec1,
tile.dec2)
I, = np.nonzero(good * usrc)
nexp[I] = tim.nuims[y[I], x[I]]
if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
mjd[I] = (tim.mjdmin + tim.mjdmax) / 2.
phot.wise_coadd_id[I] = tile.coadd_id
phot.wise_x[I] = fx[I] - 1.
phot.wise_y[I] = fy[I] - 1.
central_flux[I] = tim.getImage()[y[I], x[I]]
del x, y, good, usrc
# PSF norm for depth
psf = tim.getPsf()
h, w = tim.shape
patch = psf.getPointSourcePatch(h // 2, w // 2).patch
psfnorm = np.sqrt(np.sum(patch**2))
# To handle zero-depth, we return 1/nanomaggies^2 units rather than mags.
# In the small empty patches of the sky (eg W4 in 0922p702), we get sig1 = NaN
if np.isfinite(tim.sig1):
phot.get('psfdepth_%s' % wband)[I] = 1. / (tim.sig1 / psfnorm)**2
tim.tile = tile
tims.append(tim)
if plots:
plt.clf()
mn, mx = 0.1, 20000
plt.hist(np.log10(np.clip(central_flux, mn, mx)),
bins=100,
range=(np.log10(mn), np.log10(mx)))
logt = np.arange(0, 5)
plt.xticks(logt, ['%i' % i for i in 10.**logt])
plt.title('Central fluxes (W%i)' % band)
plt.axvline(np.log10(20000), color='k')
plt.axvline(np.log10(1000), color='k')
ps.savefig()
# Eddie's non-secret recipe:
#- central pixel <= 1000: 19x19 pix box size
#- central pixel in 1000 - 20000: 59x59 box size
#- central pixel > 20000 or saturated: 149x149 box size
#- object near "bright star": 299x299 box size
nbig = nmedium = nsmall = 0
for src, cflux in zip(cat, central_flux):
if cflux > 20000:
R = 100
nbig += 1
elif cflux > 1000:
R = 30
nmedium += 1
else:
R = 15
nsmall += 1
if isinstance(src, PointSource):
src.fixedRadius = R
else:
### FIXME -- sizes for galaxies..... can we set PSF size separately?
galrad = 0
# RexGalaxy is a subclass of ExpGalaxy
if isinstance(src, (ExpGalaxy, DevGalaxy, SersicGalaxy)):
galrad = src.shape.re
pixscale = 2.75
src.halfsize = int(np.hypot(R, galrad * 5 / pixscale))
debug('Set WISE source sizes:', nbig, 'big', nmedium, 'medium', nsmall,
'small')
tractor = Tractor(tims, cat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
tractor.optimizer = CeresOptimizer(BW=ceres_block, BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(wanyband)
t0 = Time()
R = tractor.optimize_forced_photometry(fitstats=True,
variance=True,
shared_params=False,
wantims=wantims)
info(tag + 'unWISE forced photometry took', Time() - t0)
if use_ceres:
term = R.ceres_status['termination']
# Running out of memory can cause failure to converge and term
# status = 2. Fail completely in this case.
if term != 0:
info(tag + 'Ceres termination status:', term)
raise RuntimeError('Ceres terminated with status %i' % term)
if wantims:
ims1 = R.ims1
# can happen if empty source list (we still want to generate coadds)
if ims1 is None:
ims1 = R.ims0
flux_invvars = R.IV
if R.fitstats is not None:
for k in fskeys:
x = getattr(R.fitstats, k)
fitstats[k] = np.array(x).astype(np.float32)
if save_fits:
for i, tim in enumerate(tims):
tile = tim.tile
(dat, mod, _, chi, _) = ims1[i]
wcshdr = fitsio.FITSHDR()
tim.wcs.wcs.add_to_header(wcshdr)
tag = 'fit-%s-w%i' % (tile.coadd_id, band)
fitsio.write('%s-data.fits' % tag,
dat,
clobber=True,
header=wcshdr)
fitsio.write('%s-mod.fits' % tag, mod, clobber=True, header=wcshdr)
fitsio.write('%s-chi.fits' % tag, chi, clobber=True, header=wcshdr)
if plots:
# Create models for just the brightest sources
bright_cat = [
src for src in cat if src.getBrightness().getBand(wanyband) > 1000
]
debug('Bright soures:', len(bright_cat))
btr = Tractor(tims, bright_cat)
for tim in tims:
mod = btr.getModelImage(tim)
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
sig1 = tim.sig1
plt.clf()
plt.imshow(mod,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: bright-star models' % tag)
ps.savefig()
if get_models:
for i, tim in enumerate(tims):
tile = tim.tile
(dat, mod, _, _, _) = ims1[i]
models.append(
(tile.coadd_id, band, tim.wcs.wcs, dat, mod, tim.coadd_inverr))
if plots:
for i, tim in enumerate(tims):
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
(dat, mod, _, chi, _) = ims1[i]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-3 * sig1,
vmax=25 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi,
interpolation='nearest',
origin='lower',
cmap='gray',
vmin=-5,
vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
nm = np.array([src.getBrightness().getBand(wanyband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their initial
# fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
phot.set('flux_%s' % wband, nm.astype(np.float32))
phot.set('flux_ivar_%s' % wband, nm_ivar.astype(np.float32))
for k in fskeys:
phot.set(k + '_' + wband,
fitstats.get(k, np.zeros(len(phot), np.float32)))
phot.set('nobs_%s' % wband, nexp)
phot.set('mjd_%s' % wband, mjd)
rtn = wphotduck()
rtn.phot = phot
rtn.models = None
rtn.maskmap = None
if get_models:
rtn.models = models
if get_masks:
rtn.maskmap = maskmap
return rtn
def run_one_ccd(survey, catsurvey_north, catsurvey_south, resolve_dec, ccd,
opt, zoomslice, ps):
from functools import reduce
from legacypipe.bits import DQ_BITS
tlast = Time()
#print('Opt:', opt)
im = survey.get_image_object(ccd)
print('Run_one_ccd: checking cache', survey.cache_dir)
if survey.cache_dir is not None:
im.check_for_cached_files(survey)
if opt.do_calib:
im.run_calibs(splinesky=True)
tim = im.get_tractor_image(slc=zoomslice,
pixPsf=True,
constant_invvar=opt.constant_invvar,
hybridPsf=opt.hybrid_psf,
normalizePsf=opt.normalize_psf,
old_calibs_ok=True,
trim_edges=False)
print('Got tim:', tim) #, 'x0,y0', tim.x0, tim.y0)
chipwcs = tim.subwcs
H, W = tim.shape
tnow = Time()
print('Read image:', tnow - tlast)
tlast = tnow
if ccd.camera == 'decam':
# Halo subtraction
from legacypipe.halos import subtract_one
from legacypipe.reference import mask_radius_for_mag, read_gaia
ref_margin = mask_radius_for_mag(0.)
mpix = int(np.ceil(ref_margin * 3600. / chipwcs.pixel_scale()))
marginwcs = chipwcs.get_subimage(-mpix, -mpix, W + 2 * mpix,
H + 2 * mpix)
gaia = read_gaia(marginwcs, None)
keeprad = np.ceil(gaia.keep_radius * 3600. /
chipwcs.pixel_scale()).astype(int)
_, xx, yy = chipwcs.radec2pixelxy(gaia.ra, gaia.dec)
# cut to those touching the chip
gaia.cut((xx > -keeprad) * (xx < W + keeprad) * (yy > -keeprad) *
(yy < H + keeprad))
Igaia, = np.nonzero(gaia.isgaia * gaia.pointsource)
halostars = gaia[Igaia]
print('Got', len(gaia), 'Gaia stars,', len(halostars),
'for halo subtraction')
moffat = True
halos = subtract_one((tim, halostars, moffat))
tim.data -= halos
# The "north" and "south" directories often don't have
# 'survey-bricks" files of their own -- use the 'survey' one
# instead.
if catsurvey_south is not None:
try:
catsurvey_south.get_bricks_readonly()
except:
catsurvey_south.bricks = survey.get_bricks_readonly()
if catsurvey_north is not None:
try:
catsurvey_north.get_bricks_readonly()
except:
catsurvey_north.bricks = survey.get_bricks_readonly()
# Apply outlier masks
outlier_header = None
outlier_mask = None
posneg_mask = None
if opt.outlier_mask is not None:
posneg_mask = np.zeros(tim.shape, np.uint8)
# Outliers masks are computed within a survey (north/south for dr9), and are stored
# in a brick-oriented way, in the results directories.
north_ccd = (ccd.camera.strip() != 'decam')
catsurvey = catsurvey_north
if not north_ccd and catsurvey_south is not None:
catsurvey = catsurvey_south
bricks = bricks_touching_wcs(chipwcs, survey=catsurvey)
for b in bricks:
print(
'Reading outlier mask for brick', b.brickname, ':',
catsurvey.find_file('outliers_mask',
brick=b.brickname,
output=False))
ok = read_outlier_mask_file(catsurvey, [tim],
b.brickname,
pos_neg_mask=posneg_mask,
subimage=False,
output=False,
ps=ps)
if not ok:
print('WARNING: failed to read outliers mask file for brick',
b.brickname)
if opt.outlier_mask is not None:
outlier_mask = np.zeros((ccd.height, ccd.width), np.uint8)
outlier_mask[tim.y0:tim.y0 + H, tim.x0:tim.x0 + W] = posneg_mask
del posneg_mask
# Grab original image headers (including WCS)
im = survey.get_image_object(ccd)
imhdr = im.read_image_header()
imhdr['CAMERA'] = ccd.camera
imhdr['EXPNUM'] = ccd.expnum
imhdr['CCDNAME'] = ccd.ccdname
imhdr['IMGFILE'] = ccd.image_filename.strip()
outlier_header = imhdr
if opt.catalog:
T = fits_table(opt.catalog)
else:
chipwcs = tim.subwcs
T = get_catalog_in_wcs(chipwcs,
survey,
catsurvey_north,
catsurvey_south=catsurvey_south,
resolve_dec=resolve_dec)
if T is None:
print('No sources to photometer.')
return None
if opt.write_cat:
T.writeto(opt.write_cat)
print('Wrote catalog to', opt.write_cat)
surveydir = survey.get_survey_dir()
del survey
if opt.move_gaia:
# Gaia stars: move RA,Dec to the epoch of this image.
I = np.flatnonzero(T.ref_epoch > 0)
if len(I):
print('Moving', len(I), 'Gaia stars to MJD', tim.time.toMjd())
ra, dec = radec_at_mjd(T.ra[I], T.dec[I],
T.ref_epoch[I].astype(float), T.pmra[I],
T.pmdec[I], T.parallax[I], tim.time.toMjd())
T.ra[I] = ra
T.dec[I] = dec
tnow = Time()
print('Read catalog:', tnow - tlast)
tlast = tnow
# Find SGA galaxies outside this chip and subtract them before we begin.
chipwcs = tim.subwcs
_, xx, yy = chipwcs.radec2pixelxy(T.ra, T.dec)
W, H = chipwcs.get_width(), chipwcs.get_height()
sga_out = (T.ref_cat == 'L3') * np.logical_not(
(xx >= 1) * (xx <= W) * (yy >= 1) * (yy <= H))
I = np.flatnonzero(sga_out)
if len(I):
print(len(I), 'SGA galaxies are outside the image. Subtracting...')
cat = read_fits_catalog(T[I], bands=[tim.band])
tr = Tractor([tim], cat)
mod = tr.getModelImage(0)
tim.data -= mod
I = np.flatnonzero(np.logical_not(sga_out))
T.cut(I)
cat = read_fits_catalog(T, bands='r')
# Replace the brightness (which will be a NanoMaggies with g,r,z)
# with a NanoMaggies with this image's band only.
for src in cat:
src.brightness = NanoMaggies(**{tim.band: 1.})
tnow = Time()
print('Parse catalog:', tnow - tlast)
tlast = tnow
print('Forced photom...')
F = run_forced_phot(cat,
tim,
ceres=opt.ceres,
derivs=opt.derivs,
fixed_also=True,
agn=opt.agn,
do_forced=opt.forced,
do_apphot=opt.apphot,
get_model=opt.save_model,
ps=ps,
timing=True,
ceres_threads=opt.ceres_threads)
if opt.save_model:
# unpack results
F, model_img = F
F.release = T.release
F.brickid = T.brickid
F.brickname = T.brickname
F.objid = T.objid
F.camera = np.array([ccd.camera] * len(F))
F.expnum = np.array([im.expnum] * len(F), dtype=np.int64)
F.ccdname = np.array([im.ccdname] * len(F))
# "Denormalizing"
F.filter = np.array([tim.band] * len(F))
F.mjd = np.array([tim.primhdr['MJD-OBS']] * len(F))
F.exptime = np.array([tim.primhdr['EXPTIME']] * len(F), dtype=np.float32)
F.psfsize = np.array([tim.psf_fwhm * tim.imobj.pixscale] * len(F),
dtype=np.float32)
F.ccd_cuts = np.array([ccd.ccd_cuts] * len(F))
F.airmass = np.array([ccd.airmass] * len(F), dtype=np.float32)
### --> also add units to the dict below so the FITS headers have units
F.sky = np.array([tim.midsky / tim.zpscale / tim.imobj.pixscale**2] *
len(F),
dtype=np.float32)
# in the same units as the depth maps -- flux inverse-variance.
F.psfdepth = np.array([(1. / (tim.sig1 / tim.psfnorm)**2)] * len(F),
dtype=np.float32)
F.galdepth = np.array([(1. / (tim.sig1 / tim.galnorm)**2)] * len(F),
dtype=np.float32)
F.fwhm = np.array([tim.psf_fwhm] * len(F), dtype=np.float32)
F.skyrms = np.array([ccd.skyrms] * len(F), dtype=np.float32)
F.ccdzpt = np.array([ccd.ccdzpt] * len(F), dtype=np.float32)
F.ccdrarms = np.array([ccd.ccdrarms] * len(F), dtype=np.float32)
F.ccddecrms = np.array([ccd.ccddecrms] * len(F), dtype=np.float32)
F.ccdphrms = np.array([ccd.ccdphrms] * len(F), dtype=np.float32)
if opt.derivs:
cosdec = np.cos(np.deg2rad(T.dec))
with np.errstate(divide='ignore', invalid='ignore'):
F.dra = (F.flux_dra / F.flux) * 3600. / cosdec
F.ddec = (F.flux_ddec / F.flux) * 3600.
F.dra[F.flux == 0] = 0.
F.ddec[F.flux == 0] = 0.
F.dra_ivar = F.flux_dra_ivar * (F.flux / 3600. * cosdec)**2
F.ddec_ivar = F.flux_ddec_ivar * (F.flux / 3600.)**2
F.delete_column('flux_dra')
F.delete_column('flux_ddec')
F.delete_column('flux_dra_ivar')
F.delete_column('flux_ddec_ivar')
F.flux = F.flux_fixed
F.flux_ivar = F.flux_fixed_ivar
F.delete_column('flux_fixed')
F.delete_column('flux_fixed_ivar')
for c in ['dra', 'ddec', 'dra_ivar', 'ddec_ivar', 'flux', 'flux_ivar']:
F.set(c, F.get(c).astype(np.float32))
F.ra = T.ra
F.dec = T.dec
_, x, y = tim.sip_wcs.radec2pixelxy(T.ra, T.dec)
F.x = (x - 1).astype(np.float32)
F.y = (y - 1).astype(np.float32)
h, w = tim.shape
ix = np.round(F.x).astype(int)
iy = np.round(F.y).astype(int)
F.dqmask = tim.dq[np.clip(iy, 0, h - 1), np.clip(ix, 0, w - 1)]
# Set an OUT-OF-BOUNDS bit.
F.dqmask[reduce(np.logical_or,
[ix < 0, ix >= w, iy < 0, iy >= h])] |= DQ_BITS['edge2']
program_name = sys.argv[0]
## FIXME -- from catalog?
release = 9999
version_hdr = get_version_header(program_name, surveydir, release)
filename = getattr(ccd, 'image_filename')
if filename is None:
# HACK -- print only two directory names + filename of CPFILE.
fname = os.path.basename(im.imgfn.strip())
d = os.path.dirname(im.imgfn)
d1 = os.path.basename(d)
d = os.path.dirname(d)
d2 = os.path.basename(d)
filename = os.path.join(d2, d1, fname)
print('Trimmed filename to', filename)
version_hdr.add_record(
dict(name='CPFILE', value=filename, comment='CP file'))
version_hdr.add_record(dict(name='CPHDU', value=im.hdu, comment='CP ext'))
version_hdr.add_record(
dict(name='CAMERA', value=ccd.camera, comment='Camera'))
version_hdr.add_record(
dict(name='EXPNUM', value=im.expnum, comment='Exposure num'))
version_hdr.add_record(
dict(name='CCDNAME', value=im.ccdname, comment='CCD name'))
version_hdr.add_record(
dict(name='FILTER', value=tim.band, comment='Bandpass of this image'))
version_hdr.add_record(
dict(name='PLVER', value=ccd.plver, comment='CP pipeline version'))
version_hdr.add_record(
dict(name='PLPROCID', value=ccd.plprocid, comment='CP pipeline id'))
version_hdr.add_record(
dict(name='PROCDATE', value=ccd.procdate, comment='CP image DATE'))
keys = [
'TELESCOP', 'OBSERVAT', 'OBS-LAT', 'OBS-LONG', 'OBS-ELEV', 'INSTRUME'
]
for key in keys:
if key in tim.primhdr:
version_hdr.add_record(dict(name=key, value=tim.primhdr[key]))
if opt.save_model or opt.save_data:
hdr = fitsio.FITSHDR()
tim.getWcs().wcs.add_to_header(hdr)
if opt.save_model:
fitsio.write(opt.save_model, model_img, header=hdr, clobber=True)
print('Wrote', opt.save_model)
if opt.save_data:
fitsio.write(opt.save_data, tim.getImage(), header=hdr, clobber=True)
print('Wrote', opt.save_data)
tnow = Time()
print('Forced phot:', tnow - tlast)
return F, version_hdr, outlier_mask, outlier_header
c = False
distance = 19
Coordinate.default_order = 'yx' #LatLng
scene = canvas(width=1200, height=700, background=color.white)
old_bearing = False
bearings = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
mesh = []
left = curve(color=color.orange, radius=0.3, retain=500)
right = curve(color=color.orange, radius=0.3, retain=500)
ab = curve(color=color.black, radius=0.5)
limit_line = curve(
color=color.white,
radius=1,
)
rate(5)
tractor = Tractor()
tractor_3d = box(color=color.red, size=vec(1, 1, 1))
qtemp = box(color=color.green, size=vec(1, 1, 1))
qobj = []
desv = 0
def move(LatLon):
global old_pos, init, old_bearing, mesh, bearings, tractor
utm_pos = Coordinate(x=LatLon.x, y=LatLon.y)
if init is False:
tractor.m_p_s = 2
tractor.run()
init = True
old_pos = utm_pos
scene.camera.pos = vec(utm_pos.x, 4, utm_pos.y - 50)
srcs = get_tractor_sources_dr8(4381,2,114,'u', roi=[0.0,1746.0,0.0,712.0])
labels.append((len(mem), 'sources'))
mem.append(memuse())
track('sources')
#im,inf = get_tractor_image_dr8(3063,4,60,'g', roi=[100,300,100,300])
im,inf = get_tractor_image_dr8(4381,2,114,'u', roi=[0.0,1746.0,0.0,712.0])
labels.append((len(mem), 'image'))
mem.append(memuse())
track('image')
tractor = Tractor(Images(im), Catalog(*srcs))
#tractor.cache = NullCache()
#disable_galaxy_cache()
tractor.cache = Cache(100)
set_galaxy_cache_size(100)
labels.append((len(mem), 'tractor'))
mem.append(memuse())
mod = tractor.getModelImage(im)
labels.append((len(mem), 'model'))
mem.append(memuse())
def galex_coadds(onegal,
galaxy=None,
radius_mosaic=30,
radius_mask=None,
pixscale=1.5,
ref_pixscale=0.262,
output_dir=None,
galex_dir=None,
log=None,
centrals=True,
verbose=False):
'''Generate custom GALEX cutouts.
radius_mosaic and radius_mask in arcsec
pixscale: GALEX pixel scale in arcsec/pixel.
'''
import fitsio
import matplotlib.pyplot as plt
from astrometry.libkd.spherematch import match_radec
from astrometry.util.resample import resample_with_wcs, OverlapError
from tractor import (Tractor, NanoMaggies, Image, LinearPhotoCal,
NCircularGaussianPSF, ConstantFitsWcs, ConstantSky)
from legacypipe.survey import imsave_jpeg
from legacypipe.catalog import read_fits_catalog
if galaxy is None:
galaxy = 'galaxy'
if galex_dir is None:
galex_dir = os.environ.get('GALEX_DIR')
if output_dir is None:
output_dir = '.'
if radius_mask is None:
radius_mask = radius_mosaic
radius_search = 5.0 # [arcsec]
else:
radius_search = radius_mask
W = H = np.ceil(2 * radius_mosaic / pixscale).astype('int') # [pixels]
targetwcs = Tan(onegal['RA'], onegal['DEC'], (W + 1) / 2.0, (H + 1) / 2.0,
-pixscale / 3600.0, 0.0, 0.0, pixscale / 3600.0, float(W),
float(H))
# Read the custom Tractor catalog
tractorfile = os.path.join(output_dir, '{}-tractor.fits'.format(galaxy))
if not os.path.isfile(tractorfile):
print('Missing Tractor catalog {}'.format(tractorfile))
return 0
cat = fits_table(tractorfile)
print('Read {} sources from {}'.format(len(cat), tractorfile),
flush=True,
file=log)
keep = np.ones(len(cat)).astype(bool)
if centrals:
# Find the large central galaxy and mask out (ignore) all the models
# which are within its elliptical mask.
# This algorithm will have to change for mosaics not centered on large
# galaxies, e.g., in galaxy groups.
m1, m2, d12 = match_radec(cat.ra,
cat.dec,
onegal['RA'],
onegal['DEC'],
radius_search / 3600.0,
nearest=False)
if len(m1) == 0:
print('No central galaxies found at the central coordinates!',
flush=True,
file=log)
else:
pixfactor = ref_pixscale / pixscale # shift the optical Tractor positions
for mm in m1:
morphtype = cat.type[mm].strip()
if morphtype == 'EXP' or morphtype == 'COMP':
e1, e2, r50 = cat.shapeexp_e1[mm], cat.shapeexp_e2[
mm], cat.shapeexp_r[mm] # [arcsec]
elif morphtype == 'DEV' or morphtype == 'COMP':
e1, e2, r50 = cat.shapedev_e1[mm], cat.shapedev_e2[
mm], cat.shapedev_r[mm] # [arcsec]
else:
r50 = None
if r50:
majoraxis = r50 * 5 / pixscale # [pixels]
ba, phi = SGA.misc.convert_tractor_e1e2(e1, e2)
these = SGA.misc.ellipse_mask(W / 2, W / 2, majoraxis,
ba * majoraxis,
np.radians(phi),
cat.bx * pixfactor,
cat.by * pixfactor)
if np.sum(these) > 0:
#keep[these] = False
pass
print('Hack!')
keep[mm] = False
#srcs = read_fits_catalog(cat)
#_srcs = np.array(srcs)[~keep].tolist()
#mod = SGA.misc.srcs2image(_srcs, ConstantFitsWcs(targetwcs), psf_sigma=3.0)
#import matplotlib.pyplot as plt
##plt.imshow(mod, origin='lower') ; plt.savefig('junk.png')
#plt.imshow(np.log10(mod), origin='lower') ; plt.savefig('junk.png')
#pdb.set_trace()
srcs = read_fits_catalog(cat)
for src in srcs:
src.freezeAllBut('brightness')
#srcs_nocentral = np.array(srcs)[keep].tolist()
# Find all overlapping GALEX tiles and then read the tims.
galex_tiles = _read_galex_tiles(targetwcs,
galex_dir,
log=log,
verbose=verbose)
gbands = ['n', 'f']
nicegbands = ['NUV', 'FUV']
zps = dict(n=20.08, f=18.82)
coimgs, comods, coresids, coimgs_central, comods_nocentral = [], [], [], [], []
for niceband, band in zip(nicegbands, gbands):
J = np.flatnonzero(galex_tiles.get('has_' + band))
print(len(J), 'GALEX tiles have coverage in band', band)
coimg = np.zeros((H, W), np.float32)
comod = np.zeros((H, W), np.float32)
cowt = np.zeros((H, W), np.float32)
comod_nocentral = np.zeros((H, W), np.float32)
for src in srcs:
src.setBrightness(NanoMaggies(**{band: 1}))
for j in J:
brick = galex_tiles[j]
fn = os.path.join(
galex_dir, brick.tilename.strip(),
'%s-%sd-intbgsub.fits.gz' % (brick.brickname, band))
#print(fn)
gwcs = Tan(*[
float(f) for f in [
brick.crval1, brick.crval2, brick.crpix1, brick.crpix2,
brick.cdelt1, 0., 0., brick.cdelt2, 3840., 3840.
]
])
img = fitsio.read(fn)
#print('Read', img.shape)
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(targetwcs, gwcs, [], 3)
except OverlapError:
continue
K = np.flatnonzero(img[Yi, Xi] != 0.)
if len(K) == 0:
continue
Yo, Xo, Yi, Xi = Yo[K], Xo[K], Yi[K], Xi[K]
wt = brick.get(band + 'exptime')
coimg[Yo, Xo] += wt * img[Yi, Xi]
cowt[Yo, Xo] += wt
x0, x1, y0, y1 = min(Xi), max(Xi), min(Yi), max(Yi)
subwcs = gwcs.get_subimage(x0, y0, x1 - x0 + 1, y1 - y0 + 1)
twcs = ConstantFitsWcs(subwcs)
timg = img[y0:y1 + 1, x0:x1 + 1]
tie = np.ones_like(timg) ## HACK!
#hdr = fitsio.read_header(fn)
#zp = hdr['']
zp = zps[band]
photocal = LinearPhotoCal(NanoMaggies.zeropointToScale(zp),
band=band)
tsky = ConstantSky(0.0)
# HACK -- circular Gaussian PSF of fixed size...
# in arcsec
#fwhms = dict(NUV=6.0, FUV=6.0)
# -> sigma in pixels
#sig = fwhms[band] / 2.35 / twcs.pixel_scale()
sig = 6.0 / np.sqrt(8 * np.log(2)) / twcs.pixel_scale()
tpsf = NCircularGaussianPSF([sig], [1.])
tim = Image(data=timg,
inverr=tie,
psf=tpsf,
wcs=twcs,
sky=tsky,
photocal=photocal,
name='GALEX ' + band + brick.brickname)
## Build the model image with and without the central galaxy model.
tractor = Tractor([tim], srcs)
mod = tractor.getModelImage(0)
tractor.freezeParam('images')
tractor.optimize_forced_photometry(priors=False,
shared_params=False)
mod = tractor.getModelImage(0)
srcs_nocentral = np.array(srcs)[keep].tolist()
#srcs_nocentral = np.array(srcs)[nocentral].tolist()
tractor_nocentral = Tractor([tim], srcs_nocentral)
mod_nocentral = tractor_nocentral.getModelImage(0)
comod[Yo, Xo] += wt * mod[Yi - y0, Xi - x0]
comod_nocentral[Yo, Xo] += wt * mod_nocentral[Yi - y0, Xi - x0]
coimg /= np.maximum(cowt, 1e-18)
comod /= np.maximum(cowt, 1e-18)
comod_nocentral /= np.maximum(cowt, 1e-18)
coresid = coimg - comod
# Subtract the model image which excludes the central (comod_nocentral)
# from the data (coimg) to isolate the light of the central
# (coimg_central).
coimg_central = coimg - comod_nocentral
coimgs.append(coimg)
comods.append(comod)
coresids.append(coresid)
comods_nocentral.append(comod_nocentral)
coimgs_central.append(coimg_central)
# Write out the final images with and without the central, making sure
# to apply the zeropoint to go from counts/s to AB nanomaggies.
# https://asd.gsfc.nasa.gov/archive/galex/FAQ/counts_background.html
for thisimg, imtype in zip((coimg, comod, comod_nocentral),
('image', 'model', 'model-nocentral')):
fitsfile = os.path.join(
output_dir, '{}-{}-{}.fits'.format(galaxy, imtype, niceband))
if verbose:
print('Writing {}'.format(fitsfile))
fitsio.write(fitsfile,
thisimg * 10**(-0.4 * (zp - 22.5)),
clobber=True)
# Build a color mosaic (but note that the images here are in units of
# background-subtracted counts/s).
#_galex_rgb = _galex_rgb_moustakas
#_galex_rgb = _galex_rgb_dstn
_galex_rgb = _galex_rgb_official
for imgs, imtype in zip(
(coimgs, comods, coresids, comods_nocentral, coimgs_central),
('image', 'model', 'resid', 'model-nocentral', 'image-central')):
rgb = _galex_rgb(imgs)
jpgfile = os.path.join(output_dir,
'{}-{}-FUVNUV.jpg'.format(galaxy, imtype))
if verbose:
print('Writing {}'.format(jpgfile))
imsave_jpeg(jpgfile, rgb, origin='lower')
return 1
if len(I):
print 'Converting', len(I), 'bogus COMP galaxies to DEV'
for i in I:
T.type[i] = 'DEV'
else:
T = fits_table(catfn)
T.shapeexp = np.vstack((T.shapeexp_r, T.shapeexp_e1, T.shapeexp_e2)).T
T.shapedev = np.vstack((T.shapedev_r, T.shapedev_e1, T.shapedev_e2)).T
cat = read_fits_catalog(T, ellipseClass=tractor.ellipses.EllipseE)
#print 'Got cat:', cat
print 'Forced photom...'
tr = Tractor([tim], cat)
tr.freezeParam('images')
for src in cat:
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
kwa = {}
if opt.ceres:
B = 8
kwa.update(use_ceres=True, BW=B, BH=B)
if opt.plots is None:
kwa.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True, fitstats=True,
shared_params=False, **kwa)
# HACK -- circular Gaussian PSF of fixed size...
# in arcsec
#fwhms = dict(NUV=6.0, FUV=6.0)
# -> sigma in pixels
#sig = fwhms[band] / 2.35 / twcs.pixel_scale()
sig = 6.0 / 2.35 / twcs.pixel_scale()
tpsf = NCircularGaussianPSF([sig], [1.])
tim = Image(data=timg,
inverr=tie,
psf=tpsf,
wcs=twcs,
sky=tsky,
photocal=photocal,
name='GALEX ' + band + brick.brickname)
tractor = Tractor([tim], srcs)
mod = tractor.getModelImage(0)
print('Tractor image', tim.name)
plt.clf()
plt.imshow(timg, interpolation='nearest', origin='lower')
ps.savefig()
print('Tractor model', tim.name)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
ps.savefig()
tractor.freezeParam('images')
print('Params:')
def wise_cutouts(ra,
dec,
radius,
ps,
pixscale=2.75,
survey_dir=None,
unwise_dir=None):
'''
radius in arcsec.
pixscale: WISE pixel scale in arcsec/pixel;
make this smaller than 2.75 to oversample.
'''
if unwise_dir is None:
unwise_dir = os.environ.get('UNWISE_COADDS_DIR')
npix = int(np.ceil(radius / pixscale))
print('Image size:', npix)
W = H = npix
pix = pixscale / 3600.
wcs = Tan(ra, dec, (W + 1) / 2., (H + 1) / 2., -pix, 0., 0., pix, float(W),
float(H))
# Find DECaLS bricks overlapping
survey = LegacySurveyData(survey_dir=survey_dir)
B = bricks_touching_wcs(wcs, survey=survey)
print('Found', len(B), 'bricks overlapping')
TT = []
for b in B.brickname:
fn = survey.find_file('tractor', brick=b)
T = fits_table(fn)
print('Read', len(T), 'from', b)
primhdr = fitsio.read_header(fn)
TT.append(T)
T = merge_tables(TT)
print('Total of', len(T), 'sources')
T.cut(T.brick_primary)
print(len(T), 'primary')
margin = 20
ok, xx, yy = wcs.radec2pixelxy(T.ra, T.dec)
I = np.flatnonzero((xx > -margin) * (yy > -margin) * (xx < W + margin) *
(yy < H + margin))
T.cut(I)
print(len(T), 'within ROI')
#return wcs,T
# Pull out DECaLS coadds (image, model, resid).
dwcs = wcs.scale(2. * pixscale / 0.262)
dh, dw = dwcs.shape
print('DECaLS resampled shape:', dh, dw)
tags = ['image', 'model', 'resid']
coimgs = [np.zeros((dh, dw, 3), np.uint8) for t in tags]
for b in B.brickname:
fn = survey.find_file('image', brick=b, band='r')
bwcs = Tan(fn, 1) # ext 1: .fz
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(dwcs, bwcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print('Resampling', len(Yo), 'pixels from', b)
xl, xh, yl, yh = Xi.min(), Xi.max(), Yi.min(), Yi.max()
#print('python legacypipe/runbrick.py -b %s --zoom %i %i %i %i --outdir cluster --pixpsf --splinesky --pipe --no-early-coadds' %
# (b, xl-5, xh+5, yl-5, yh+5) + ' -P \'pickles/cluster-%(brick)s-%%(stage)s.pickle\'')
for i, tag in enumerate(tags):
fn = survey.find_file(tag + '-jpeg', brick=b)
img = plt.imread(fn)
img = np.flipud(img)
coimgs[i][Yo, Xo, :] = img[Yi, Xi, :]
tt = dict(image='Image', model='Model', resid='Resid')
for img, tag in zip(coimgs, tags):
plt.clf()
dimshow(img, ticks=False)
plt.title('DECaLS grz %s' % tt[tag])
ps.savefig()
# Find unWISE tiles overlapping
tiles = unwise_tiles_touching_wcs(wcs)
print('Cut to', len(tiles), 'unWISE tiles')
# Here we assume the targetwcs is axis-aligned and that the
# edge midpoints yield the RA,Dec limits (true for TAN).
r, d = wcs.pixelxy2radec(np.array([1, W, W / 2, W / 2]),
np.array([H / 2, H / 2, 1, H]))
# the way the roiradec box is used, the min/max order doesn't matter
roiradec = [r[0], r[1], d[2], d[3]]
ra, dec = T.ra, T.dec
srcs = read_fits_catalog(T)
wbands = [1, 2, 3, 4]
wanyband = 'w'
for band in wbands:
f = T.get('flux_w%i' % band)
f *= 10.**(primhdr['WISEAB%i' % band] / 2.5)
coimgs = [np.zeros((H, W), np.float32) for b in wbands]
comods = [np.zeros((H, W), np.float32) for b in wbands]
con = [np.zeros((H, W), np.uint8) for b in wbands]
for iband, band in enumerate(wbands):
print('Photometering WISE band', band)
wband = 'w%i' % band
for i, src in enumerate(srcs):
#print('Source', src, 'brightness', src.getBrightness(), 'params', src.getBrightness().getParams())
#src.getBrightness().setParams([T.wise_flux[i, band-1]])
src.setBrightness(
NanoMaggies(**{wanyband: T.get('flux_w%i' % band)[i]}))
# print('Set source brightness:', src.getBrightness())
# The tiles have some overlap, so for each source, keep the
# fit in the tile whose center is closest to the source.
for tile in tiles:
print('Reading tile', tile.coadd_id)
tim = get_unwise_tractor_image(unwise_dir,
tile.coadd_id,
band,
bandname=wanyband,
roiradecbox=roiradec)
if tim is None:
print('Actually, no overlap with tile', tile.coadd_id)
continue
print('Read image with shape', tim.shape)
# Select sources in play.
wisewcs = tim.wcs.wcs
H, W = tim.shape
ok, x, y = wisewcs.radec2pixelxy(ra, dec)
x = (x - 1.).astype(np.float32)
y = (y - 1.).astype(np.float32)
margin = 10.
I = np.flatnonzero((x >= -margin) * (x < W + margin) *
(y >= -margin) * (y < H + margin))
print(len(I), 'within the image + margin')
subcat = [srcs[i] for i in I]
tractor = Tractor([tim], subcat)
mod = tractor.getModelImage(0)
# plt.clf()
# dimshow(tim.getImage(), ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
# plt.clf()
# dimshow(mod, ticks=False)
# plt.title('WISE %s %s' % (tile.coadd_id, wband))
# ps.savefig()
try:
Yo, Xo, Yi, Xi, nil = resample_with_wcs(wcs, tim.wcs.wcs)
except ResampleError:
continue
if len(Yo) == 0:
continue
print('Resampling', len(Yo), 'pixels from WISE', tile.coadd_id,
band)
coimgs[iband][Yo, Xo] += tim.getImage()[Yi, Xi]
comods[iband][Yo, Xo] += mod[Yi, Xi]
con[iband][Yo, Xo] += 1
for img, mod, n in zip(coimgs, comods, con):
img /= np.maximum(n, 1)
mod /= np.maximum(n, 1)
for band, img, mod in zip(wbands, coimgs, comods):
lo, hi = np.percentile(img, [25, 99])
plt.clf()
dimshow(img, vmin=lo, vmax=hi, ticks=False)
plt.title('WISE W%i Data' % band)
ps.savefig()
plt.clf()
dimshow(mod, vmin=lo, vmax=hi, ticks=False)
plt.title('WISE W%i Model' % band)
ps.savefig()
resid = img - mod
mx = np.abs(resid).max()
plt.clf()
dimshow(resid, vmin=-mx, vmax=mx, ticks=False)
plt.title('WISE W%i Resid' % band)
ps.savefig()
#kwa = dict(mn=-0.1, mx=2., arcsinh = 1.)
kwa = dict(mn=-0.1, mx=2., arcsinh=None)
rgb = _unwise_to_rgb(coimgs[:2], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Data')
ps.savefig()
rgb = _unwise_to_rgb(comods[:2], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Model')
ps.savefig()
kwa = dict(mn=-1, mx=1, arcsinh=None)
rgb = _unwise_to_rgb(
[img - mod for img, mod in list(zip(coimgs, comods))[:2]], **kwa)
plt.clf()
dimshow(rgb, ticks=False)
plt.title('WISE W1/W2 Resid')
ps.savefig()
return wcs, T
def unwise_forcedphot(cat, tiles, band=1, roiradecbox=None,
use_ceres=True, ceres_block=8,
save_fits=False, get_models=False, ps=None,
psf_broadening=None,
pixelized_psf=False,
get_masks=None,
move_crpix=False,
modelsky_dir=None):
'''
Given a list of tractor sources *cat*
and a list of unWISE tiles *tiles* (a fits_table with RA,Dec,coadd_id)
runs forced photometry, returning a FITS table the same length as *cat*.
*get_masks*: the WCS to resample mask bits into.
'''
from tractor import NanoMaggies, PointSource, Tractor, ExpGalaxy, DevGalaxy, FixedCompositeGalaxy
if not pixelized_psf and psf_broadening is None:
# PSF broadening in post-reactivation data, by band.
# Newer version from Aaron's email to decam-chatter, 2018-06-14.
broadening = { 1: 1.0405, 2: 1.0346, 3: None, 4: None }
psf_broadening = broadening[band]
if False:
from astrometry.util.plotutils import PlotSequence
ps = PlotSequence('wise-forced-w%i' % band)
plots = (ps is not None)
if plots:
import pylab as plt
wantims = (plots or save_fits or get_models)
wanyband = 'w'
if get_models:
models = {}
wband = 'w%i' % band
fskeys = ['prochi2', 'pronpix', 'profracflux', 'proflux', 'npix',
'pronexp']
Nsrcs = len(cat)
phot = fits_table()
# Filled in based on unique tile overlap
phot.wise_coadd_id = np.array([' '] * Nsrcs)
phot.set(wband + '_psfdepth', np.zeros(len(phot), np.float32))
ra = np.array([src.getPosition().ra for src in cat])
dec = np.array([src.getPosition().dec for src in cat])
nexp = np.zeros(Nsrcs, np.int16)
mjd = np.zeros(Nsrcs, np.float64)
central_flux = np.zeros(Nsrcs, np.float32)
fitstats = {}
tims = []
if get_masks:
mh,mw = get_masks.shape
maskmap = np.zeros((mh,mw), np.uint32)
for tile in tiles:
print('Reading WISE tile', tile.coadd_id, 'band', band)
tim = get_unwise_tractor_image(tile.unwise_dir, tile.coadd_id, band,
bandname=wanyband, roiradecbox=roiradecbox)
if tim is None:
print('Actually, no overlap with tile', tile.coadd_id)
continue
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage(), interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data' % tag)
ps.savefig()
plt.clf()
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
range=(-5,10), bins=100)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.title(tag)
ps.savefig()
if move_crpix and band in [1, 2]:
realwcs = tim.wcs.wcs
x,y = realwcs.crpix
tile_crpix = tile.get('crpix_w%i' % band)
dx = tile_crpix[0] - 1024.5
dy = tile_crpix[1] - 1024.5
realwcs.set_crpix(x+dx, y+dy)
#print('CRPIX', x,y, 'shift by', dx,dy, 'to', realwcs.crpix)
if modelsky_dir and band in [1, 2]:
fn = os.path.join(modelsky_dir, '%s.%i.mod.fits' % (tile.coadd_id, band))
if not os.path.exists(fn):
raise RuntimeError('WARNING: does not exist:', fn)
x0,x1,y0,y1 = tim.roi
bg = fitsio.FITS(fn)[2][y0:y1, x0:x1]
#print('Read background map:', bg.shape, bg.dtype, 'vs image', tim.shape)
if plots:
plt.clf()
plt.subplot(1,2,1)
plt.imshow(tim.getImage(), interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=5 * sig1)
plt.subplot(1,2,2)
plt.imshow(bg, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=5 * sig1)
tag = '%s W%i' % (tile.coadd_id, band)
plt.suptitle(tag)
ps.savefig()
plt.clf()
ha = dict(range=(-5,10), bins=100, histtype='step')
plt.hist((tim.getImage() * tim.inverr)[tim.inverr > 0].ravel(),
color='b', label='Original', **ha)
plt.hist(((tim.getImage()-bg) * tim.inverr)[tim.inverr > 0].ravel(),
color='g', label='Minus Background', **ha)
plt.axvline(0, color='k', alpha=0.5)
plt.xlabel('Per-pixel intensity (Sigma)')
plt.legend()
plt.title(tag + ': background')
ps.savefig()
# Actually subtract the background!
tim.data -= bg
# Floor the per-pixel variances
if band in [1,2]:
# in Vega nanomaggies per pixel
floor_sigma = {1: 0.5, 2: 2.0}
with np.errstate(divide='ignore'):
new_ie = 1. / np.hypot(1./tim.inverr, floor_sigma[band])
new_ie[tim.inverr == 0] = 0.
if plots:
plt.clf()
plt.plot((1. / tim.inverr[tim.inverr>0]).ravel(), (1./new_ie[tim.inverr>0]).ravel(), 'b.')
plt.title('unWISE per-pixel error: %s band %i' % (tile.coadd_id, band))
plt.xlabel('original')
plt.ylabel('floored')
ps.savefig()
tim.inverr = new_ie
# Read mask file?
if get_masks:
from astrometry.util.resample import resample_with_wcs, OverlapError
# unwise_dir can be a colon-separated list of paths
tilemask = None
for d in tile.unwise_dir.split(':'):
fn = os.path.join(d, tile.coadd_id[:3], tile.coadd_id,
'unwise-%s-msk.fits.gz' % tile.coadd_id)
if os.path.exists(fn):
print('Reading unWISE mask file', fn)
x0,x1,y0,y1 = tim.roi
tilemask = fitsio.FITS(fn)[0][y0:y1,x0:x1]
break
if tilemask is None:
print('unWISE mask file for tile', tile.coadd_id, 'does not exist')
else:
try:
tanwcs = tim.wcs.wcs
assert(tanwcs.shape == tilemask.shape)
Yo,Xo,Yi,Xi,_ = resample_with_wcs(get_masks, tanwcs, intType=np.int16)
# Only deal with mask pixels that are set.
I, = np.nonzero(tilemask[Yi,Xi] > 0)
# Trim to unique area for this tile
rr,dd = get_masks.pixelxy2radec(Yo[I]+1, Xo[I]+1)
good = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
I = I[good]
maskmap[Yo[I],Xo[I]] = tilemask[Yi[I], Xi[I]]
except OverlapError:
# Shouldn't happen by this point
print('No overlap between WISE tile', tile.coadd_id, 'and brick')
# The tiles have some overlap, so zero out pixels outside the
# tile's unique area.
th,tw = tim.shape
xx,yy = np.meshgrid(np.arange(tw), np.arange(th))
rr,dd = tim.wcs.wcs.pixelxy2radec(xx+1, yy+1)
unique = radec_in_unique_area(rr, dd, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
#print(np.sum(unique), 'of', (th*tw), 'pixels in this tile are unique')
tim.inverr[unique == False] = 0.
del xx,yy,rr,dd,unique
if plots:
sig1 = tim.sig1
plt.clf()
plt.imshow(tim.getImage() * (tim.inverr > 0),
interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=10 * sig1)
plt.colorbar()
tag = '%s W%i' % (tile.coadd_id, band)
plt.title('%s: tim data (unique)' % tag)
ps.savefig()
if pixelized_psf:
import unwise_psf
if (band == 1) or (band == 2):
# we only have updated PSFs for W1 and W2
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id,
modelname='neo4_unwisecat')
else:
psfimg = unwise_psf.get_unwise_psf(band, tile.coadd_id)
if band == 4:
# oversample (the unwise_psf models are at native W4 5.5"/pix,
# while the unWISE coadds are made at 2.75"/pix.
ph,pw = psfimg.shape
subpsf = np.zeros((ph*2-1, pw*2-1), np.float32)
from astrometry.util.util import lanczos3_interpolate
xx,yy = np.meshgrid(np.arange(0., pw-0.51, 0.5, dtype=np.float32),
np.arange(0., ph-0.51, 0.5, dtype=np.float32))
xx = xx.ravel()
yy = yy.ravel()
ix = xx.astype(np.int32)
iy = yy.astype(np.int32)
dx = (xx - ix).astype(np.float32)
dy = (yy - iy).astype(np.float32)
psfimg = psfimg.astype(np.float32)
rtn = lanczos3_interpolate(ix, iy, dx, dy, [subpsf.flat], [psfimg])
if plots:
plt.clf()
plt.imshow(psfimg, interpolation='nearest', origin='lower')
plt.title('Original PSF model')
ps.savefig()
plt.clf()
plt.imshow(subpsf, interpolation='nearest', origin='lower')
plt.title('Subsampled PSF model')
ps.savefig()
psfimg = subpsf
del xx, yy, ix, iy, dx, dy
from tractor.psf import PixelizedPSF
psfimg /= psfimg.sum()
fluxrescales = {1: 1.04, 2: 1.005, 3: 1.0, 4: 1.0}
psfimg *= fluxrescales[band]
tim.psf = PixelizedPSF(psfimg)
if psf_broadening is not None and not pixelized_psf:
# psf_broadening is a factor by which the PSF FWHMs
# should be scaled; the PSF is a little wider
# post-reactivation.
psf = tim.getPsf()
from tractor import GaussianMixturePSF
if isinstance(psf, GaussianMixturePSF):
#
print('Broadening PSF: from', psf)
p0 = psf.getParams()
pnames = psf.getParamNames()
p1 = [p * psf_broadening**2 if 'var' in name else p
for (p, name) in zip(p0, pnames)]
psf.setParams(p1)
print('Broadened PSF:', psf)
else:
print('WARNING: cannot apply psf_broadening to WISE PSF of type', type(psf))
wcs = tim.wcs.wcs
ok,x,y = wcs.radec2pixelxy(ra, dec)
x = np.round(x - 1.).astype(int)
y = np.round(y - 1.).astype(int)
good = (x >= 0) * (x < tw) * (y >= 0) * (y < th)
# Which sources are in this brick's unique area?
usrc = radec_in_unique_area(ra, dec, tile.ra1, tile.ra2, tile.dec1, tile.dec2)
I, = np.nonzero(good * usrc)
nexp[I] = tim.nuims[y[I], x[I]]
if hasattr(tim, 'mjdmin') and hasattr(tim, 'mjdmax'):
mjd[I] = (tim.mjdmin + tim.mjdmax) / 2.
phot.wise_coadd_id[I] = tile.coadd_id
central_flux[I] = tim.getImage()[y[I], x[I]]
del x,y,good,usrc
# PSF norm for depth
psf = tim.getPsf()
h,w = tim.shape
patch = psf.getPointSourcePatch(h//2, w//2).patch
psfnorm = np.sqrt(np.sum(patch**2))
# To handle zero-depth, we return 1/nanomaggies^2 units rather than mags.
psfdepth = 1. / (tim.sig1 / psfnorm)**2
phot.get(wband + '_psfdepth')[I] = psfdepth
tim.tile = tile
tims.append(tim)
if plots:
plt.clf()
mn,mx = 0.1, 20000
plt.hist(np.log10(np.clip(central_flux, mn, mx)), bins=100,
range=(np.log10(mn), np.log10(mx)))
logt = np.arange(0, 5)
plt.xticks(logt, ['%i' % i for i in 10.**logt])
plt.title('Central fluxes (W%i)' % band)
plt.axvline(np.log10(20000), color='k')
plt.axvline(np.log10(1000), color='k')
ps.savefig()
# Eddie's non-secret recipe:
#- central pixel <= 1000: 19x19 pix box size
#- central pixel in 1000 - 20000: 59x59 box size
#- central pixel > 20000 or saturated: 149x149 box size
#- object near "bright star": 299x299 box size
nbig = nmedium = nsmall = 0
for src,cflux in zip(cat, central_flux):
if cflux > 20000:
R = 100
nbig += 1
elif cflux > 1000:
R = 30
nmedium += 1
else:
R = 15
nsmall += 1
if isinstance(src, PointSource):
src.fixedRadius = R
else:
### FIXME -- sizes for galaxies..... can we set PSF size separately?
galrad = 0
# RexGalaxy is a subclass of ExpGalaxy
if isinstance(src, (ExpGalaxy, DevGalaxy)):
galrad = src.shape.re
elif isinstance(src, FixedCompositeGalaxy):
galrad = max(src.shapeExp.re, src.shapeDev.re)
pixscale = 2.75
src.halfsize = int(np.hypot(R, galrad * 5 / pixscale))
#print('Set WISE source sizes:', nbig, 'big', nmedium, 'medium', nsmall, 'small')
minsb = 0.
fitsky = False
tractor = Tractor(tims, cat)
if use_ceres:
from tractor.ceres_optimizer import CeresOptimizer
tractor.optimizer = CeresOptimizer(BW=ceres_block, BH=ceres_block)
tractor.freezeParamsRecursive('*')
tractor.thawPathsTo(wanyband)
kwa = dict(fitstat_extras=[('pronexp', [tim.nims for tim in tims])])
t0 = Time()
R = tractor.optimize_forced_photometry(
minsb=minsb, mindlnp=1., sky=fitsky, fitstats=True,
variance=True, shared_params=False,
wantims=wantims, **kwa)
print('unWISE forced photometry took', Time() - t0)
if use_ceres:
term = R.ceres_status['termination']
# Running out of memory can cause failure to converge
# and term status = 2.
# Fail completely in this case.
if term != 0:
print('Ceres termination status:', term)
raise RuntimeError(
'Ceres terminated with status %i' % term)
if wantims:
ims1 = R.ims1
flux_invvars = R.IV
if R.fitstats is not None:
for k in fskeys:
x = getattr(R.fitstats, k)
fitstats[k] = np.array(x).astype(np.float32)
if save_fits:
for i,tim in enumerate(tims):
tile = tim.tile
(dat, mod, ie, chi, roi) = ims1[i]
wcshdr = fitsio.FITSHDR()
tim.wcs.wcs.add_to_header(wcshdr)
tag = 'fit-%s-w%i' % (tile.coadd_id, band)
fitsio.write('%s-data.fits' %
tag, dat, clobber=True, header=wcshdr)
fitsio.write('%s-mod.fits' % tag, mod,
clobber=True, header=wcshdr)
fitsio.write('%s-chi.fits' % tag, chi,
clobber=True, header=wcshdr)
if plots:
# Create models for just the brightest sources
bright_cat = [src for src in cat
if src.getBrightness().getBand(wanyband) > 1000]
print('Bright soures:', len(bright_cat))
btr = Tractor(tims, bright_cat)
for tim in tims:
mod = btr.getModelImage(tim)
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
sig1 = tim.sig1
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: bright-star models' % tag)
ps.savefig()
if get_models:
for i,tim in enumerate(tims):
tile = tim.tile
(dat, mod, ie, chi, roi) = ims1[i]
models[(tile.coadd_id, band)] = (mod, dat, ie, tim.roi, tim.wcs.wcs)
if plots:
for i,tim in enumerate(tims):
tile = tim.tile
tag = '%s W%i' % (tile.coadd_id, band)
(dat, mod, ie, chi, roi) = ims1[i]
sig1 = tim.sig1
plt.clf()
plt.imshow(dat, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: data' % tag)
ps.savefig()
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
cmap='gray', vmin=-3 * sig1, vmax=25 * sig1)
plt.colorbar()
plt.title('%s: model' % tag)
ps.savefig()
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
cmap='gray', vmin=-5, vmax=+5)
plt.colorbar()
plt.title('%s: chi' % tag)
ps.savefig()
nm = np.array([src.getBrightness().getBand(wanyband) for src in cat])
nm_ivar = flux_invvars
# Sources out of bounds, eg, never change from their default
# (1-sigma or whatever) initial fluxes. Zero them out instead.
nm[nm_ivar == 0] = 0.
phot.set(wband + '_nanomaggies', nm.astype(np.float32))
phot.set(wband + '_nanomaggies_ivar', nm_ivar.astype(np.float32))
dnm = np.zeros(len(nm_ivar), np.float32)
okiv = (nm_ivar > 0)
dnm[okiv] = (1. / np.sqrt(nm_ivar[okiv])).astype(np.float32)
okflux = (nm > 0)
mag = np.zeros(len(nm), np.float32)
mag[okflux] = (NanoMaggies.nanomaggiesToMag(nm[okflux])
).astype(np.float32)
dmag = np.zeros(len(nm), np.float32)
ok = (okiv * okflux)
dmag[ok] = (np.abs((-2.5 / np.log(10.)) * dnm[ok] / nm[ok])
).astype(np.float32)
mag[np.logical_not(okflux)] = np.nan
dmag[np.logical_not(ok)] = np.nan
phot.set(wband + '_mag', mag)
phot.set(wband + '_mag_err', dmag)
for k in fskeys:
phot.set(wband + '_' + k, fitstats[k])
phot.set(wband + '_nexp', nexp)
if not np.all(mjd == 0):
phot.set(wband + '_mjd', mjd)
rtn = wphotduck()
rtn.phot = phot
rtn.models = None
rtn.maskmap = None
if get_models:
rtn.models = models
if get_masks:
rtn.maskmap = maskmap
return rtn
slc = (slice(y0, s.iy + margin+1),
slice(x0, s.ix + margin+1))
subpsf = tim.psf.constantPsfAt(s.ix, s.iy)
subtim = Image(data=tim.data[slc],
inverr=tim.inverr[slc],
psf=subpsf, photocal=tim.photocal,
name=tim.name + '/star %i' % i)
flux = NanoMaggies.magToNanomaggies(s.mag)
print 'Flux:', flux
flux = max(flux, 1.)
src = PointSource(PixPos(s.xx - x0, s.yy - y0),
NanoMaggies(**{tim.band: flux}))
tr = Tractor([subtim], [src])
mod = tr.getModelImage(0)
#mod = src.getModelImage(tsubtim)
tractors.append(tr)
resids.append((subtim.data - mod) * (subtim.inverr > 0))
chis.append((subtim.data - mod) * subtim.inverr)
plt.subplot(R, C, i+1)
dimshow(mod, **ima)
plt.suptitle('Models for Pan-STARRS stars: ' + tim.name)
ps.savefig()
resa = dict(vmin=-5.*tim.sig1, vmax=5.*tim.sig1, ticks=False)
chia = dict(vmin=-5., vmax=5., ticks=False)
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra,dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0,x1,y0,y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1-x0, y1-y0)
r0,r1,d0,d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [brick_catalog_for_radec_box(ra,rb, d0-margin,d1+margin,
decals, catpattern)
for (ra,rb) in [(0, r1+margin), (r0-margin, 360.)]]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0-margin,r1+margin,d0-margin,
d1+margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0,y1), slice(x0,x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok,x,y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat,T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
