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 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
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)
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()
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
color='r')
plt.axis(ax)
ps.savefig()
tractor.freezeParam('images')
for src in srcs:
src.freezeAllBut('brightness')
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tractor.optimizer = opti
print('Forced phot...')
kwa = {}
R = tractor.optimize_forced_photometry(shared_params=False, variance=True, **kwa)
print('R:', R, dir(R))
mod = tractor.getModelImage(0)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Forced photometry model')
ps.savefig()
plt.clf()
plt.imshow(subtim.getImage(), **ima)
plt.title('CFHT data')
ps.savefig()
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
plt.axis(ax)
ps.savefig()
tractor.freezeParam('images')
for src in srcs:
src.freezeAllBut('brightness')
from tractor.ceres_optimizer import CeresOptimizer
B = 8
opti = CeresOptimizer(BW=B, BH=B)
tractor.optimizer = opti
print('Forced phot...')
kwa = {}
R = tractor.optimize_forced_photometry(shared_params=False,
variance=True,
**kwa)
print('R:', R, dir(R))
mod = tractor.getModelImage(0)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Forced photometry model')
ps.savefig()
plt.clf()
plt.imshow(subtim.getImage(), **ima)
plt.title('CFHT data')
ps.savefig()
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:')
tractor.printThawedParams()
tractor.optimize_forced_photometry(priors=False,
shared_params=False)
mod = tractor.getModelImage(0)
print('Tractor model (forced phot)', tim.name)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
ps.savefig()
comod[Yo, Xo] += wt * mod[Yi - y0, Xi - x0]
coimg /= np.maximum(cowt, 1e-18)
comod /= np.maximum(cowt, 1e-18)
coimgs.append(coimg)
comods.append(comod)
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
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 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 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
