def get_cfht_catalog(mags=['i'], maglim=27., returnTable=False):
from astrometry.util.pyfits_utils import fits_table
from tractor import Mags, RaDecPos, PointSource, Images, Catalog
from tractor.galaxy import DevGalaxy, ExpGalaxy, CompositeGalaxy, GalaxyShape
T = fits_table(
'/project/projectdirs/bigboss/data/cs82/W4p1m1_i.V2.7A.swarp.cut.deVexp.fit',
hdunum=2)
print 'Read', len(T), 'sources'
# Cut to ROI
(ra0, ra1, dec0, dec1) = radecroi
T.ra = T.alpha_j2000
T.dec = T.delta_j2000
T = T[(T.ra > ra0) * (T.ra < ra1) * (T.dec > dec0) * (T.dec < dec1)]
print 'Cut to', len(T), 'objects in ROI.'
srcs = Catalog()
keepi = []
for i, t in enumerate(T):
if t.chi2_psf < t.chi2_model and t.mag_psf <= maglim:
#print 'PSF'
themag = t.mag_psf
m = Mags(order=mags, **dict([(k, themag) for k in mags]))
srcs.append(PointSource(RaDecPos(t.ra, t.dec), m))
keepi.append(i)
continue
if t.mag_disk > maglim and t.mag_spheroid > maglim:
#print 'Faint'
continue
keepi.append(i)
themag = t.mag_spheroid
m_exp = Mags(order=mags, **dict([(k, themag) for k in mags]))
themag = t.mag_disk
m_dev = Mags(order=mags, **dict([(k, themag) for k in mags]))
# SPHEROID_REFF [for Sersic index n= 1] = 1.68 * DISK_SCALE
shape_dev = GalaxyShape(t.disk_scale_world * 1.68 * 3600.,
t.disk_aspect_world, t.disk_theta_world + 90.)
shape_exp = GalaxyShape(t.spheroid_reff_world * 3600.,
t.spheroid_aspect_world,
t.spheroid_theta_world + 90.)
pos = RaDecPos(t.alphamodel_j2000, t.deltamodel_j2000)
if t.mag_disk > maglim and t.mag_spheroid <= maglim:
#print 'Exp'
srcs.append(ExpGalaxy(pos, m_exp, shape_exp))
continue
if t.mag_disk <= maglim and t.mag_spheroid > maglim:
#print 'deV'
srcs.append(DevGalaxy(pos, m_dev, shape_dev))
continue
# exp + deV
srcs.append(CompositeGalaxy(pos, m_exp, shape_exp, m_dev, shape_dev))
if returnTable:
import numpy as np
T.cut(np.array(keepi))
return srcs, T
return srcs
def getModelPatch(self, img): # We start by rendering the visible lens galaxy. patch = self.light.getModelPatch(img) # We will use the lens model to predict the quasar's image positions. positions,mags = self.mass.getLensedImages(self.light.position, self.quasar) # 'positions' should be a list of RaDecPos objects # 'mags' should be a list of Mags objects for pos,mag,fudge in zip(positions, mags, self.magfudge): # For each image of the quasar, we will create a PointSource ps = PointSource(pos, mag + fudge) # ... and add it to the patch. patch += ps.getModelPatch(img) return patch
def getModelPatch(self, img):
# We start by rendering the visible lens galaxy.
patch = self.light.getModelPatch(img)
# We will use the lens model to predict the quasar's image positions.
positions, mags = self.mass.getLensedImages(self.light.position,
self.quasar)
# 'positions' should be a list of RaDecPos objects
# 'mags' should be a list of Mags objects
for pos, mag, fudge in zip(positions, mags, self.magfudge):
# For each image of the quasar, we will create a PointSource
ps = PointSource(pos, mag + fudge)
# ... and add it to the patch.
patch += ps.getModelPatch(img)
return patch
#x0,y0 = s.ix - margin, s.iy - margin
x0,y0 = s.x0, s.y0
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)
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():
# 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 main():
import optparse
from astrometry.util.plotutils import PlotSequence
from astrometry.util.util import Tan
parser = optparse.OptionParser(usage='%prog [options] incat.fits out.fits')
parser.add_option('-r', '--ralo', dest='ralo', type=float,
help='Minimum RA')
parser.add_option('-R', '--rahi', dest='rahi', type=float,
help='Maximum RA')
parser.add_option('-d', '--declo', dest='declo', type=float,
help='Minimum Dec')
parser.add_option('-D', '--dechi', dest='dechi', type=float,
help='Maximum Dec')
parser.add_option('-b', '--band', dest='bands', action='append', type=int,
default=[], help='WISE band to photometer (default: 1,2)')
parser.add_option('-u', '--unwise', dest='unwise_dir',
default='unwise-coadds',
help='Directory containing unWISE coadds')
parser.add_option('--no-ceres', dest='ceres', action='store_false',
default=True,
help='Use scipy lsqr rather than Ceres Solver?')
parser.add_option('--ceres-block', '-B', dest='ceresblock', type=int,
default=8,
help='Ceres image block size (default: %default)')
parser.add_option('--plots', dest='plots',
default=False, action='store_true')
parser.add_option('--save-fits', dest='save_fits',
default=False, action='store_true')
# parser.add_option('--ellipses', action='store_true',
# help='Assume catalog shapes are ellipse descriptions (not r,ab,phi)')
# parser.add_option('--ra', help='Center RA')
# parser.add_option('--dec', help='Center Dec')
# parser.add_option('--width', help='Degrees width (in RA*cos(Dec))')
# parser.add_option('--height', help='Degrees height (Dec)')
opt, args = parser.parse_args()
if len(args) != 2:
parser.print_help()
sys.exit(-1)
if len(opt.bands) == 0:
opt.bands = [1, 2]
# Allow specifying bands like "123"
bb = []
for band in opt.bands:
for s in str(band):
bb.append(int(s))
opt.bands = bb
print('Bands', opt.bands)
ps = None
if opt.plots:
ps = PlotSequence('unwise')
infn, outfn = args
T = fits_table(infn)
print('Read', len(T), 'sources from', infn)
if opt.declo is not None:
T.cut(T.dec >= opt.declo)
if opt.dechi is not None:
T.cut(T.dec <= opt.dechi)
# Let's be a bit smart about RA wrap-around. Compute the 'center'
# of the RA points, use the cross product against that to define
# inequality (clockwise-of).
r = np.deg2rad(T.ra)
x = np.mean(np.cos(r))
y = np.mean(np.sin(r))
rr = np.hypot(x, y)
x /= rr
y /= rr
midra = np.rad2deg(np.arctan2(y, x))
midra += 360. * (midra < 0)
xx = np.cos(r)
yy = np.sin(r)
T.cross = x * yy - y * xx
minra = T.ra[np.argmin(T.cross)]
maxra = T.ra[np.argmax(T.cross)]
if opt.ralo is not None:
r = np.deg2rad(opt.ralo)
xx = np.cos(r)
yy = np.sin(r)
crosscut = x * yy - y * xx
T.cut(T.cross >= crosscut)
print('Cut to', len(T), 'with RA >', opt.ralo)
if opt.rahi is not None:
r = np.deg2rad(opt.rahi)
xx = np.cos(r)
yy = np.sin(r)
crosscut = x * yy - y * xx
T.cut(T.cross <= crosscut)
print('Cut to', len(T), 'with RA <', opt.rahi)
if opt.declo is None:
opt.declo = T.dec.min()
if opt.dechi is None:
opt.dechi = T.dec.max()
if opt.ralo is None:
opt.ralo = T.ra[np.argmin(T.cross)]
if opt.rahi is None:
opt.rahi = T.ra[np.argmax(T.cross)]
T.delete_column('cross')
print('RA range:', opt.ralo, opt.rahi)
print('Dec range:', opt.declo, opt.dechi)
x = np.mean([np.cos(np.deg2rad(r)) for r in (opt.ralo, opt.rahi)])
y = np.mean([np.sin(np.deg2rad(r)) for r in (opt.ralo, opt.rahi)])
midra = np.rad2deg(np.arctan2(y, x))
midra += 360. * (midra < 0)
middec = (opt.declo + opt.dechi) / 2.
print('RA,Dec center:', midra, middec)
pixscale = 2.75 / 3600.
H = (opt.dechi - opt.declo) / pixscale
dra = 2. * min(np.abs(midra - opt.ralo), np.abs(midra - opt.rahi))
W = dra * np.cos(np.deg2rad(middec)) / pixscale
margin = 5
W = int(W) + margin * 2
H = int(H) + margin * 2
print('W,H', W, H)
targetwcs = Tan(midra, middec, (W + 1) / 2., (H + 1) / 2.,
-pixscale, 0., 0., pixscale, float(W), float(H))
#print('Target WCS:', targetwcs)
ra0, dec0 = targetwcs.pixelxy2radec(0.5, 0.5)
ra1, dec1 = targetwcs.pixelxy2radec(W + 0.5, H + 0.5)
roiradecbox = [ra0, ra1, dec0, dec1]
#print('ROI RA,Dec box', roiradecbox)
tiles = unwise_tiles_touching_wcs(targetwcs)
print('Cut to', len(tiles), 'unWISE tiles')
disable_galaxy_cache()
cols = T.get_columns()
all_ptsrcs = not('type' in cols)
if not all_ptsrcs:
assert('shapeexp' in cols)
assert('shapedev' in cols)
assert('fracdev' in cols)
wanyband = 'w'
print('Creating Tractor catalog...')
cat = []
for i, t in enumerate(T):
pos = RaDecPos(t.ra, t.dec)
flux = NanoMaggies(**{wanyband: 1.})
if all_ptsrcs:
cat.append(PointSource(pos, flux))
continue
tt = t.type.strip()
if tt in ['PTSRC', 'STAR', 'S']:
cat.append(PointSource(pos, flux))
elif tt in ['EXP', 'E']:
shape = EllipseE(*t.shapeexp)
cat.append(ExpGalaxy(pos, flux, shape))
elif tt in ['DEV', 'D']:
shape = EllipseE(*t.shapedev)
cat.append(DevGalaxy(pos, flux, shape))
elif tt in ['COMP', 'C']:
eshape = EllipseE(*t.shapeexp)
dshape = EllipseE(*t.shapedev)
cat.append(FixedCompositeGalaxy(pos, flux, t.fracdev,
eshape, dshape))
else:
print('Did not understand row', i, 'of input catalog:')
t.about()
assert(False)
W = unwise_forcedphot(cat, tiles, roiradecbox=roiradecbox,
bands=opt.bands, unwise_dir=opt.unwise_dir,
use_ceres=opt.ceres, ceres_block=opt.ceresblock,
save_fits=opt.save_fits, ps=ps)
W.writeto(outfn)
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()
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:
plt.clf()
plt.subplot(1,2,1)
plt.imshow(true_mod, **ima)
plt.subplot(1,2,2)
plt.imshow(tim.data, **ima)
plt.title('S/N %f, r_e %f' % (sn, re))
ps.savefig()
## run one_blob code? Or shortcut?
src = PointSource(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}))
nblob,iblob,Isrcs = 0, 1, np.array([0])
brickwcs = wcs
bx0, by0, blobw, blobh = 0, 0, sz, sz
blobmask = np.ones((sz,sz), bool)
timargs = [(tim.data, tim.getInvError(), tim.wcs, tim.wcs.wcs,
tim.getPhotoCal(), tim.getSky(), tim.psf,
'tim', 0, sz, 0, sz, band, sig1,
tim.modelMinval, None)]
srcs = [src]
bands = band
plots,psx = False, None
simul_opt, use_ceres, hastycho = False, False, False
X = (nblob, iblob, Isrcs, brickwcs, bx0, by0, blobw, blobh,
def run_sed_matched_filters(SEDs,
bands,
detmaps,
detivs,
omit_xy,
targetwcs,
nsigma=5,
saddle_fraction=0.1,
saddle_min=2.,
saturated_pix=None,
exclusion_radius=4.,
veto_map=None,
mp=None,
plots=False,
ps=None,
rgbimg=None):
'''
Runs a given set of SED-matched filters.
Parameters
----------
SEDs : list of (name, sed) tuples
The SEDs to run. The `sed` values are lists the same length
as `bands`.
bands : list of string
The band names of `detmaps` and `detivs`.
detmaps : numpy array, float
Detection maps for each of the listed `bands`.
detivs : numpy array, float
Inverse-variances of the `detmaps`.
omit_xy : None, or (xx,yy,rr) tuple
Existing sources to avoid: x, y, radius.
targetwcs : WCS object
WCS object to use to convert pixel values into RA,Decs for the
returned Tractor PointSource objects.
nsigma : float, optional
Detection threshold
saturated_pix : None or list of numpy arrays, booleans
Passed through to sed_matched_detection.
A map of pixels that are always considered "hot" when
determining whether a new source touches hot pixels of an
existing source.
exclusion_radius: int
How many pixels around an existing source to veto
plots : boolean, optional
Create plots?
ps : PlotSequence object
Create plots?
mp : multiproc object
Multiprocessing
Returns
-------
Tnew : fits_table
Table of new sources detected
newcat : list of PointSource objects
Newly detected objects, with positions and fluxes, as Tractor
PointSource objects.
hot : numpy array of bool
"Hot pixels" containing sources.
See also
--------
sed_matched_detection : run a single SED-matched filter.
'''
from astrometry.util.fits import fits_table
from tractor import PointSource, RaDecPos, NanoMaggies
if omit_xy is not None:
xx, yy, rr = omit_xy
n0 = len(xx)
else:
xx, yy, rr = [], [], []
n0 = 0
H, W = detmaps[0].shape
hot = np.zeros((H, W), bool)
peaksn = []
apsn = []
for sedname, sed in SEDs:
if plots:
pps = ps
else:
pps = None
sedhot, px, py, peakval, apval = sed_matched_detection(
sedname,
sed,
detmaps,
detivs,
bands,
xx,
yy,
rr,
nsigma=nsigma,
saddle_fraction=saddle_fraction,
saddle_min=saddle_min,
saturated_pix=saturated_pix,
veto_map=veto_map,
ps=pps,
rgbimg=rgbimg)
if sedhot is None:
continue
info('SED', sedname, ':', len(px), 'new peaks')
hot |= sedhot
# With an empty xx, np.append turns it into a double!
xx = np.append(xx, px).astype(int)
yy = np.append(yy, py).astype(int)
rr = np.append(rr, np.zeros_like(px) + exclusion_radius).astype(int)
peaksn.extend(peakval)
apsn.extend(apval)
# New peaks:
peakx = xx[n0:]
peaky = yy[n0:]
Tnew = None
newcat = []
if len(peakx):
# Add sources for the new peaks we found
pr, pd = targetwcs.pixelxy2radec(peakx + 1, peaky + 1)
info('Adding', len(pr), 'new sources')
# Also create FITS table for new sources
Tnew = fits_table()
Tnew.ra = pr
Tnew.dec = pd
Tnew.ibx = peakx
Tnew.iby = peaky
assert (len(peaksn) == len(Tnew))
assert (len(apsn) == len(Tnew))
Tnew.peaksn = np.array(peaksn)
Tnew.apsn = np.array(apsn)
for r, d, x, y in zip(pr, pd, peakx, peaky):
fluxes = dict([(band, detmap[y, x])
for band, detmap in zip(bands, detmaps)])
newcat.append(
PointSource(RaDecPos(r, d), NanoMaggies(order=bands,
**fluxes)))
return Tnew, newcat, hot
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:
plt.clf()
plt.subplot(1,2,1)
plt.imshow(true_mod, **ima)
plt.subplot(1,2,2)
plt.imshow(tim.data, **ima)
plt.title('S/N %f, r_e %f' % (sn, re))
ps.savefig()
## run one_blob code? Or shortcut?
src = PointSource(RaDecPos(0., 0.),
NanoMaggies(**{band: flux}))
nblob,iblob,Isrcs = 0, 1, np.array([0])
brickwcs = wcs
bx0, by0, blobw, blobh = 0, 0, sz, sz
blobmask = np.ones((sz,sz), bool)
timargs = [(tim.data, tim.getInvError(), tim.wcs, tim.wcs.wcs,
tim.getPhotoCal(), tim.getSky(), tim.psf,
'tim', 0, sz, 0, sz, band, sig1,
tim.modelMinval, None)]
srcs = [src]
bands = band
plots,psx = False, None
simul_opt, use_ceres, hastycho = False, False, False
X = (nblob, iblob, Isrcs, brickwcs, bx0, by0, blobw, blobh,
# (but in SDSS we'll be using multi-band NanoMaggies)
band = 'r'
# image sensitivity:
photocal = LinearPhotoCal(10., band=band)
# flat sky
sky = ConstantSky(0.)
# Create tractor.Image object
tim = Image(data, iv, psf=psf, wcs=wcs, sky=sky, photocal=photocal)
def brightness(x):
return NanoMaggies(**{band: x})
# Create some sources
ptsrc = PointSource(RaDecPos(ra, dec), brightness(10.))
gal1 = ExpGalaxy(RaDecPos(ra - 10 * pscale, dec), brightness(50.),
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)
