def make_plots(prefix,
im,
tr=None,
plots=['data', 'model', 'chi'],
mags=['i'],
radecroi_in=None):
import pylab as plt
import tractor
if radecroi_in:
global radecroi
radecroi = radecroi_in
if tr is None:
srcs = get_cfht_catalog(mags=mags)
tr = tractor.Tractor(tractor.Images(im), srcs)
mod = tr.getModelImage(im)
ima = dict(interpolation='nearest',
origin='lower',
vmin=im.zr[0],
vmax=im.zr[1])
imchi = dict(interpolation='nearest', origin='lower', vmin=-5, vmax=+5)
if hasattr(im, 'extent'):
ima.update(extent=im.extent)
imchi.update(extent=im.extent)
fignum = 1
if 'data' in plots:
plt.figure(fignum)
fignum += 1
plt.clf()
plt.imshow(im.getImage(), **ima)
plt.gray()
plt.title(im.name + ': data')
plt.savefig(prefix + 'data.png')
if 'model' in plots:
plt.figure(fignum)
fignum += 1
plt.clf()
plt.imshow(mod, **ima)
plt.gray()
plt.title(im.name + ': model')
plt.savefig(prefix + 'mod.png')
if 'chi' in plots:
plt.figure(fignum)
fignum += 1
plt.clf()
plt.imshow((mod - im.getImage()) * im.getInvError(), **imchi)
plt.gray()
plt.title(im.name + ': chi')
plt.savefig(prefix + 'chi.png')
def fit_general_gaussian(self,
img,
sig1,
xi,
yi,
fluxi,
psf_r=15,
ps=None):
import tractor
H, W = img.shape
ix = int(np.round(xi))
iy = int(np.round(yi))
xlo = max(0, ix - psf_r)
xhi = min(W, ix + psf_r + 1)
ylo = max(0, iy - psf_r)
yhi = min(H, iy + psf_r + 1)
xx, yy = np.meshgrid(np.arange(xlo, xhi), np.arange(ylo, yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
keep = (r2 < psf_r**2)
pix = img[ylo:yhi, xlo:xhi].copy()
ie = np.zeros_like(pix)
ie[keep] = 1. / sig1
psf = tractor.NCircularGaussianPSF([4.], [1.])
tim = tractor.Image(data=pix, inverr=ie, psf=psf)
src = tractor.PointSource(tractor.PixPos(xi - xlo, yi - ylo),
tractor.Flux(fluxi))
tr = tractor.Tractor([tim], [src])
src.pos.addGaussianPrior('x', 0., 1.)
doplot = (ps is not None)
if doplot:
mod0 = tr.getModelImage(0)
tim.freezeAllBut('psf')
psf.freezeAllBut('sigmas')
# print('Optimizing params:')
# tr.printThawedParams()
#print('Parameter step sizes:', tr.getStepSizes())
optargs = dict(priors=False, shared_params=False)
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
if dlnp == 0:
break
# Now fit only the PSF size
tr.freezeParam('catalog')
# print('Optimizing params:')
# tr.printThawedParams()
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
if dlnp == 0:
break
# fwhms.append(psf.sigmas[0] * 2.35 * self.pixscale)
if doplot:
mod1 = tr.getModelImage(0)
chi1 = tr.getChiImage(0)
# Now switch to a non-isotropic PSF
s = psf.sigmas[0]
#print('Isotropic fit sigma', s)
s = np.clip(s, 1., 5.)
tim.psf = tractor.GaussianMixturePSF(1., 0., 0., s**2, s**2, 0.)
#print('Optimizing params:')
#tr.printThawedParams()
try:
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
#print('PSF:', tim.psf)
if dlnp == 0:
break
except:
import traceback
print('Error during fitting PSF in a focus frame; not to worry')
traceback.print_exc()
print(
'(The above was just an error during fitting one star in a focus frame; not to worry.)'
)
# Don't need to re-fit source params because PSF ampl and mean
# can fit for flux and position.
if doplot:
mod2 = tr.getModelImage(0)
chi2 = tr.getChiImage(0)
kwa = dict(vmin=-3 * sig1, vmax=50 * sig1, cmap='gray')
plt.clf()
plt.subplot(2, 3, 1)
plt.title('Image')
dimshow(pix, ticks=False, **kwa)
plt.subplot(2, 3, 2)
plt.title('Initial model')
dimshow(mod0, ticks=False, **kwa)
plt.subplot(2, 3, 3)
plt.title('Isotropic model')
dimshow(mod1, ticks=False, **kwa)
plt.subplot(2, 3, 4)
plt.title('Final model')
dimshow(mod2, ticks=False, **kwa)
plt.subplot(2, 3, 5)
plt.title('Isotropic chi')
dimshow(chi1, vmin=-10, vmax=10, ticks=False)
plt.subplot(2, 3, 6)
plt.title('Final chi')
dimshow(chi2, vmin=-10, vmax=10, ticks=False)
plt.suptitle('PSF fit')
ps.savefig()
return tim.psf.getParams()[-3:]
def main():
# In LSST meas-deblend (on lsst6):
# python examples/suprimePlot.py --data ~dstn/lsst/ACT-data -v 126969 -c 5 --data-range -100 300 --roi 0 500 0 500 --psf psf.fits --image img.fits --sources srcs.fits
from optparse import OptionParser
import sys
parser = OptionParser(usage=('%prog <img> <psf> <srcs>'))
parser.add_option('-v',
'--verbose',
dest='verbose',
action='count',
default=0,
help='Make more verbose')
opt, args = parser.parse_args()
if len(args) != 3:
parser.print_help()
sys.exit(-1)
if opt.verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
imgfn, psffn, srcfn = args
pimg = pyfits.open(imgfn)
if len(pimg) != 4:
print('Image must have 3 extensions')
sys.exit(-1)
img = pimg[1].data
mask = pimg[2].data
maskhdr = pimg[2].header
var = pimg[3].data
del pimg
print('var', var.shape)
#print var
print('mask', mask.shape)
#print mask
print('img', img.shape)
#print img
mask = mask.astype(np.int16)
for bit in range(16):
on = ((mask & (1 << bit)) != 0)
print('Bit', bit, 'has', np.sum(on), 'pixels set')
'''
MP_BAD = 0
Bit 0 has 2500 pixels set
MP_SAT = 1
Bit 1 has 5771 pixels set
MP_INTRP= 2
Bit 2 has 11269 pixels set
MP_CR = 3
Bit 3 has 136 pixels set
MP_EDGE = 4
Bit 4 has 11856 pixels set
HIERARCH MP_DETECTED = 5
Bit 5 has 37032 pixels set
'''
print('Mask header:', maskhdr)
maskplanes = {}
print('Mask planes:')
for card in maskhdr.ascardlist():
if not card.key.startswith('MP_'):
continue
print(card.value, card.key)
maskplanes[card.key[3:]] = card.value
print('Variance range:', var.min(), var.max())
print('Image median:', np.median(img.ravel()))
invvar = 1. / var
invvar[var == 0] = 0.
invvar[var < 0] = 0.
sig = np.sqrt(np.median(var))
H, W = img.shape
for k, v in maskplanes.items():
plt.clf()
I = ((mask & (1 << v)) != 0)
rgb = np.zeros((H, W, 3))
clipimg = np.clip((img - (-3. * sig)) / (13. * sig), 0, 1)
cimg = clipimg.copy()
cimg[I] = 1
rgb[:, :, 0] = cimg
cimg = clipimg.copy()
cimg[I] = 0
rgb[:, :, 1] = cimg
rgb[:, :, 2] = cimg
plt.imshow(rgb, interpolation='nearest', origin='lower')
plt.title(k)
plt.savefig('mask-%s.png' % k.lower())
badmask = sum([(1 << maskplanes[k])
for k in ['BAD', 'SAT', 'INTRP', 'CR']])
# HACK -- left EDGE sucks
badmask += (1 << maskplanes['EDGE'])
#badmask = (1 << 0) | (1 << 1) | (1 << 2) | (1 << 3)
#badmask |= (1 << 4)
print('Masking out: 0x%x' % badmask)
invvar[(mask & badmask) != 0] = 0.
assert (all(np.isfinite(img.ravel())))
assert (all(np.isfinite(invvar.ravel())))
psf = pyfits.open(psffn)[0].data
print('psf', psf.shape)
psf /= psf.sum()
from tractor.emfit import em_fit_2d
from tractor.fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
S = psf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = psf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
print('Multi-Gaussian PSF fit...')
xm, ym = -(S / 2), -(S / 2)
em_fit_2d(II, xm, ym, w, mu, sig)
print('w,mu,sig', w, mu, sig)
mypsf = tractor.GaussianMixturePSF(w, mu, sig)
P = mypsf.getPointSourcePatch(S / 2, S / 2)
mn, mx = psf.min(), psf.max()
ima = dict(interpolation='nearest', origin='lower', vmin=mn, vmax=mx)
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(psf, **ima)
plt.subplot(1, 2, 2)
pimg = np.zeros_like(psf)
P.addTo(pimg)
plt.imshow(pimg, **ima)
plt.savefig('psf.png')
sig = np.sqrt(np.median(var))
plt.clf()
plt.hist(img.ravel(), 100, range=(-3. * sig, 3. * sig))
plt.savefig('imghist.png')
srcs = fits_table(srcfn)
print('Initial:', len(srcs), 'sources')
# Trim sources with x=0 or y=0
srcs = srcs[(srcs.x != 0) * (srcs.y != 0)]
print('Trim on x,y:', len(srcs), 'sources left')
# Zero out nans & infs
for c in ['theta', 'a', 'b']:
I = np.logical_not(np.isfinite(srcs.get(c)))
srcs.get(c)[I] = 0.
# Set sources with flux=NaN to something more sensible...
I = np.logical_not(np.isfinite(srcs.flux))
srcs.flux[I] = 1.
# Sort sources by flux.
srcs = srcs[np.argsort(-srcs.flux)]
# Trim sources that are way outside the image.
margin = 8. * np.maximum(srcs.a, srcs.b)
H, W = img.shape
srcs = srcs[(srcs.x > -margin) * (srcs.y > -margin) *
(srcs.x < (W + margin) * (srcs.y < (H + margin)))]
print('Trim out-of-bounds:', len(srcs), 'sources left')
wcs = tractor.FitsWcs(Sip(imgfn, 1))
#wcs = tractor.NullWCS()
timg = tractor.Image(data=img,
invvar=invvar,
psf=mypsf,
wcs=wcs,
sky=tractor.ConstantSky(0.),
photocal=tractor.NullPhotoCal(),
name='image')
inverr = timg.getInvError()
assert (all(np.isfinite(inverr.ravel())))
tsrcs = []
for s in srcs:
#pos = tractor.PixPos(s.x, s.y)
pos = tractor.RaDecPos(s.ra, s.dec)
if s.a > 0 and s.b > 0:
eflux = tractor.Flux(s.flux / 2.)
dflux = tractor.Flux(s.flux / 2.)
re, ab, phi = s.a, s.b / s.a, 90. - s.theta
eshape = gal.GalaxyShape(re, ab, phi)
dshape = gal.GalaxyShape(re, ab, phi)
print('Fluxes', eflux, dflux)
tsrc = gal.CompositeGalaxy(pos, eflux, eshape, dflux, dshape)
else:
flux = tractor.Flux(s.flux)
print('Flux', flux)
tsrc = tractor.PointSource(pos, flux)
tsrcs.append(tsrc)
chug = tractor.Tractor([timg])
for src in tsrcs:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
continue
chug.addSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
ima = dict(interpolation='nearest',
origin='lower',
vmin=-3. * sig,
vmax=10. * sig)
chia = dict(interpolation='nearest', origin='lower', vmin=-5., vmax=5.)
plt.clf()
plt.imshow(img, **ima)
plt.colorbar()
plt.savefig('img.png')
plt.clf()
plt.imshow(invvar, interpolation='nearest', origin='lower')
plt.colorbar()
plt.savefig('invvar.png')
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-0.png')
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-0.png')
for step in range(5):
cat = chug.getCatalog()
for src in cat:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
chug.removeSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
#cat = chug.getCatalog()
#for i,src in enumerate([]):
#for i,src in enumerate(chug.getCatalog()):
#for i in range(len(cat)):
i = 0
while i < len(cat):
src = cat[i]
#print 'Step', i
#for j,s in enumerate(cat):
# x,y = timg.getWcs().positionToPixel(s, s.getPosition())
# print ' ',
# if j == i:
# print '*',
# print '(%6.1f, %6.1f)'%(x,y), s
print('Optimizing source', i, 'of', len(cat))
x, y = timg.getWcs().positionToPixel(src.getPosition(), src)
print('(%6.1f, %6.1f)' % (x, y), src)
# pre = src.getModelPatch(timg)
s1 = str(src)
print('src1 ', s1)
dlnp1, X, a = chug.optimizeCatalogFluxes(srcs=[src])
s2 = str(src)
dlnp2, X, a = chug.optimizeCatalogAtFixedComplexityStep(srcs=[src],
sky=False)
s3 = str(src)
#post = src.getModelPatch(timg)
print('src1 ', s1)
print('src2 ', s2)
print('src3 ', s3)
print('dlnp', dlnp1, dlnp2)
if chug.getModelPatch(timg, src) is None:
print('After optimizing, no overlap!')
print('Removing source', src)
chug.removeSource(src)
i -= 1
i += 1
# plt.clf()
# plt.subplot(2,2,1)
# img = timg.getImage()
# (x0,x1,y0,y1) = pre.getExtent()
# plt.imshow(img, **ima)
# ax = plt.axis()
# plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'k-', lw=2)
# plt.axis(ax)
# plt.subplot(2,2,3)
# plt.imshow(pre.getImage(), **ima)
# plt.subplot(2,2,4)
# plt.imshow(post.getImage(), **ima)
# plt.savefig('prepost-s%i-s%03i.png' % (step, i))
#
# mod = chug.getModelImages()[0]
# plt.clf()
# plt.imshow(mod, **ima)
# plt.colorbar()
# plt.savefig('mod-s%i-s%03i.png' % (step, i))
# chi = chug.getChiImage(0)
# plt.clf()
# plt.imshow(chi, **chia)
# plt.colorbar()
# plt.savefig('chi-s%i-s%03i.png' % (step, i))
#dlnp,x,a = chug.optimizeCatalogFluxes()
#print 'fluxes: dlnp', dlnp
#dlnp,x,a = chug.optimizeCatalogAtFixedComplexityStep()
#print 'opt: dlnp', dlnp
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-%i.png' % (step + 1))
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-%i.png' % (step + 1))
return
for step in range(5):
chug.optimizeCatalogFluxes()
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-s%i.png' % step)
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-s%i.png' % step)
def main():
"""
NAME
LensTractor.py
PURPOSE
Run the Tractor on a deck of single object cutout images.
Read in an image and its weight map, guess the PSF, put an object at the
centre image of the field and then optimize the catalog and PSF.
COMMENTS
FLAGS
-h --help Print this message
-v --verbose Verbose operation
-s --sample Sample the posterior PDF instead of optimizing
-x --no-plots Do not plot progress
-l --lens Only fit lens model
-n --nebula Only fit nebula model
INPUTS
*.fits Deck of postcard images
OPTIONAL INPUTS
OUTPUTS
stdout Useful information
*.png Plots in png format
To be implemented:
lenstractor_progress.log Logged output
lenstractor_results.txt Model comparison results
lenstractor_lens.cat Lens model parameters, including lightcurves
lenstractor_nebula.cat Nebula model parameters, including lightcurves
EXAMPLES
python LensTractor.py -x examples/H1413+117_10x10arcsec_55*fits > examples/H1413+117_10x10arcsec_lenstractor.log
DEPENDENCIES
* The Tractor astrometry.net/svn/trunk/projects/tractor
* emcee github.com/danfm/emcee
* astrometry.net astrometry.net/svn/trunk/util
BUGS
- PSFs not being optimized correctly - permafrost?
HISTORY
2012-07-06 First predicted Lens images Marshall/Hogg (Oxford/NYU)
"""
# --------------------------------------------------------------------
from optparse import OptionParser
import sys
# Set available options:
parser = OptionParser(usage=('%prog *.fits'))
# Verbosity:
parser.add_option('-v',
'--verbose',
dest='verbose',
action='count',
default=False,
help='Make more verbose')
vb = True # for usual outputs.
# Sampling:
parser.add_option('-s',
'--sample',
dest='MCMC',
action='count',
default=False,
help='Sample posterior PDF')
# Plotting:
parser.add_option('-x',
'--no-plots',
dest='noplots',
action='count',
default=False,
help='Skip plotting')
# Lens model only:
parser.add_option('-l',
'--lens',
dest='lens',
action='count',
default=False,
help='Fit lens model')
# Nebula model only:
parser.add_option('-n',
'--nebula',
dest='nebula',
action='count',
default=False,
help='Fit nebula model')
# Read in options and arguments - note only sci and wht images are supplied:
opt, args = parser.parse_args()
if len(args) < 2:
parser.print_help()
sys.exit(-1)
# The rest of the command line is assumed to be a list of files:
inputfiles = args
# Workflow:
if opt.lens:
models = ['lens']
elif opt.nebula:
models = ['nebula']
else:
models = ['nebula', 'lens']
BIC = dict(zip(models, np.zeros(len(models))))
# NB. default operation is to fit both and compare.
# Do nebula first: PSF and sky then roll over into lens.
if vb:
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
print " LensTractor "
print " Fitting", models, " models to a deck of FITS postcards"
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# -------------------------------------------------------------------------
# Organise the deck of inputfiles into scifiles and varfiles:
scifiles, varfiles = lenstractor.Riffle(inputfiles, vb=vb)
# Read into Tractor Image objects, and see what filters we have:
images, total_mags, bands = lenstractor.Deal(scifiles,
varfiles,
SURVEY='PS1',
vb=vb)
# -------------------------------------------------------------------------
# Generic items needed to initialize the Tractor's catalog.
# Get rough idea of object position from wcs of first image- works
# well if all images are the same size and well registered!
wcs = images[0].wcs
NX, NY = np.shape(images[0].data)
if vb: print "Generic initial position ", NX, NY, "(pixels)"
# m0 = 15.0
# magnitudes = m0*np.ones(len(bandnames))
# MAGIC initial magnitude. This should be estimated from
# the total flux in each (background-subtracted) image...
bandnames = np.unique(bands)
magnitudes = np.zeros(len(bandnames))
for i, bandname in enumerate(bandnames):
index = np.where(bands == bandname)
magnitudes[i] = np.median(total_mags[index])
if vb: print "Generic initial SED ", dict(zip(bandnames, magnitudes))
# -------------------------------------------------------------------------
# Loop over models:
for model in models:
if vb:
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
print "Initializing model: " + model
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if model == 'nebula':
# srcs = [InitializeNebula(wcs,bandnames)]
# Nebula - a flexible galaxy plus four point sources,
# 5 sources in total. Start them off with equal magnitudes:
x, y = 0.5 * NX, 0.5 * NY
fudge = 2
equalmagnitudes = magnitudes + 2.5 * np.log10(5 * fudge)
mags = tractor.Mags(order=bandnames,
**dict(zip(bandnames, equalmagnitudes)))
# Exponential galaxy...
galpos = wcs.pixelToPosition(x, y)
mg = tractor.Mags(order=bandnames,
**dict(zip(bandnames, equalmagnitudes)))
re = 0.5 # arcsec
q = 1.0 # axis ratio
theta = 0.0 # degrees
galshape = tractor.sdss_galaxy.GalaxyShape(re, q, theta)
nebulousgalaxy = tractor.sdss_galaxy.ExpGalaxy(
galpos, mags.copy(), galshape)
if vb: print nebulousgalaxy
# ...plus 4 independent point sources, arranged in a small cross:
e = 3.0 # pixels
srcs = [
nebulousgalaxy,
tractor.PointSource(wcs.pixelToPosition(x + e, y),
mags.copy()),
tractor.PointSource(wcs.pixelToPosition(x - e, y),
mags.copy()),
tractor.PointSource(wcs.pixelToPosition(x, y + e),
mags.copy()),
tractor.PointSource(wcs.pixelToPosition(x, y - e), mags.copy())
]
# Old setup - just 4 point sources:
# srcs = [tractor.PointSource(wcs.pixelToPosition(x+e,y),mags),
# tractor.PointSource(wcs.pixelToPosition(x-e,y),mags),
# tractor.PointSource(wcs.pixelToPosition(x,y+e),mags),
# tractor.PointSource(wcs.pixelToPosition(x,y-e),mags)]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
elif model == 'lens':
# srcs = [InitializeLens(wcs,bandnames)]
# Source to be lensed:
xs, ys = 0.5 * NX, 0.5 * NY
unlensedmagnitudes = magnitudes + 2.5 * np.log10(40.0)
ms = tractor.Mags(order=bandnames,
**dict(zip(bandnames, unlensedmagnitudes)))
if vb: print ms
sourcepos = wcs.pixelToPosition(xs, ys)
if vb: print sourcepos
pointsource = tractor.PointSource(sourcepos, ms)
if vb: print pointsource
# Lens mass:
thetaE = lenstractor.EinsteinRadius(0.75) # arcsec
if vb: print thetaE
gamma = 0.2 # to make quad
phi = 0.0 # deg
xshear = lenstractor.ExternalShear(gamma, phi)
if vb: print xshear
# Lens light:
x, y = 0.5 * NX, 0.5 * NY
lenspos = wcs.pixelToPosition(x, y)
if vb: print lenspos
halfmagnitudes = magnitudes + 2.5 * np.log10(2.0)
md = tractor.Mags(order=bandnames,
**dict(zip(bandnames, halfmagnitudes)))
if vb: print md
re = 1.0 # arcsec
q = 1.0 # axis ratio
theta = 0.0 # degrees
galshape = tractor.sdss_galaxy.GalaxyShape(re, q, theta)
if vb: print galshape
lensgalaxy = lenstractor.LensGalaxy(lenspos, md, galshape, thetaE,
xshear)
if vb: print lensgalaxy
srcs = [lenstractor.PointSourceLens(lensgalaxy, pointsource)]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if vb:
print "Model =", srcs
print " "
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Set up logging to the terminal by The Tractor:
if opt.verbose:
lvl = logging.DEBUG
else:
lvl = logging.INFO
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Start a tractor, and let it make a catalog one src at a time.
# Pass in a copy of the image list, so that PSF etc are
# initialised correctly for each model.
chug = tractor.Tractor(list(images))
for src in srcs:
chug.addSource(src)
# Plot initial state:
lenstractor.Plot_state(chug, model + '_progress_initial')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if not opt.MCMC:
# Optimize the model parameters:
if model == 'nebula':
Nrounds = 2
Nsteps_optimizing_catalog = 20
Nsteps_optimizing_PSFs = 10
elif model == 'lens':
Nrounds = 3
Nsteps_optimizing_catalog = 7
Nsteps_optimizing_PSFs = 3
if vb:
print "Optimizing model:"
print " - no. of iterations per round to be spent on catalog: ", Nsteps_optimizing_catalog
print " - no. of iterations per round to be spent on PSFs: ", Nsteps_optimizing_PSFs
print " - no. of rounds: ", Nrounds
k = 0
for round in range(Nrounds):
print "Fitting " + model + ": seconds out, round", round
# Freeze the PSF, sky and photocal, leaving the sources:
chug.thawParam('catalog')
for image in chug.getImages():
image.thawParams('sky')
image.freezeParams('photocal', 'wcs', 'psf')
print "Fitting " + model + ": Catalog parameters to be optimized are:", chug.getParamNames(
)
print "Fitting " + model + ": Initial values are:", chug.getParams(
)
print "Fitting " + model + ": Step sizes:", chug.getStepSizes()
# Optimize sources with initial PSF:
for i in range(Nsteps_optimizing_catalog):
dlnp, X, a = chug.optimize()
if not opt.noplots:
lenstractor.Plot_state(
chug, model +
'_progress_optimizing_step-%02d_catalog' % k)
print "Fitting " + model + ": at step", k, "parameter values are:", chug.getParams(
)
k += 1
# Freeze the sources and thaw the psfs:
chug.freezeParam('catalog')
for image in chug.getImages():
image.thawParams('psf')
image.freezeParams('photocal', 'wcs', 'sky')
print "Fitting PSF: After thawing, zeroth PSF = ", chug.getImage(
0).psf
print "Fitting PSF: PSF parameters to be optimized are:", chug.getParamNames(
)
print "Fitting PSF: Initial values are:", chug.getParams()
print "Fitting PSF: Step sizes:", chug.getStepSizes()
# Optimize everything that is not frozen:
for i in range(Nsteps_optimizing_PSFs):
dlnp, X, a = chug.optimize()
if not opt.noplots:
lenstractor.Plot_state(
chug, model +
'_progress_optimizing_step-%02d_catalog' % k)
print "Fitting PSF: at step", k, "parameter values are:", chug.getParams(
)
k += 1
print "Fitting PSF: After optimizing, zeroth PSF = ", chug.getImage(
0).psf
# BUG: PSF not being optimized correctly - missing derivatives?
lenstractor.Plot_state(chug,
model + '_progress_optimizing_zcomplete')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
elif opt.MCMC:
# MCMC sample the model parameters.
if vb:
print "Sampling model parameters with emcee:"
# Freeze the sky and photocal, leaving the PSFs and sources:
for image in chug.getImages():
image.freezeParams('photocal', 'wcs')
# Get the thawed parameters:
p0 = np.array(chug.getParams())
print 'Tractor parameters:'
for i, parname in enumerate(chug.getParamNames()):
print ' ', parname, '=', p0[i]
ndim = len(p0)
print 'Number of parameter space dimensions: ', ndim
# Make an emcee sampler that uses our tractor to compute its logprob:
nw = 8 * ndim
sampler = emcee.EnsembleSampler(nw,
ndim,
chug,
live_dangerously=True)
# Start the walkers off near the initialisation point -
# We need it to be ~1 pixel in position, and not too much
# flux restrction...
if model == 'lens':
# The following gets us 0.2" in dec:
psteps = np.zeros_like(p0) + 0.00004
# This could be optimized, to allow more initial freedom in eg flux.
elif model == 'nebula':
# Good first guess should be some fraction of the optimization step sizes:
psteps = 0.2 * np.array(chug.getStepSizes())
print "Initial size (in each dimension) of sample ball = ", psteps
pp = emcee.EnsembleSampler.sampleBall(p0, psteps, nw)
rstate = None
lnp = None
# Take a few steps - memory leaks fast! (~10Mb per sec)
for step in range(1, 4):
print 'Run MCMC step set:', step
t0 = tractor.Time()
pp, lnp, rstate = sampler.run_mcmc(pp,
5,
lnprob0=lnp,
rstate0=rstate)
print 'Mean acceptance fraction after', sampler.iterations, 'iterations =', np.mean(
sampler.acceptance_fraction)
t_mcmc = (tractor.Time() - t0)
print 'Runtime:', t_mcmc
# Find the current posterior means:
pbar = np.mean(pp, axis=0)
print "Mean parameters: ", pbar, np.mean(lnp)
# Find the current best sample:
maxlnp = np.max(lnp)
best = np.where(lnp == maxlnp)
pbest = np.ravel(pp[best, :])
print "Best parameters: ", pbest, maxlnp
if not opt.noplots:
chug.setParams(pbest)
lenstractor.Plot_state(
chug, model + '_progress_sampling_step-%02d' % step)
print 'Best lnprob:', np.max(lnp)
# print 'dlnprobs:', ', '.join(['%.1f' % d for d in lnp - np.max(lnp)])
# print 'MCMC took', t_mcmc, 'sec'
# Take the last best sample and call it a result:
chug.setParams(pbest)
lenstractor.Plot_state(chug,
model + '_progress_sampling_zcomplete')
if vb:
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Collect statistics about this model's fit:
chisq = -2.0 * chug.getLogLikelihood()
chug.thawParam('catalog')
for image in chug.getImages():
image.thawParams('sky', 'psf')
image.freezeParams('photocal', 'wcs')
K = len(chug.getParams())
N = chug.getNdata()
BIC[model] = chisq + K * np.log(1.0 * N)
print "Fitting " + model + ": chisq, K, N, BIC =", chisq, K, N, BIC[
model]
# -------------------------------------------------------------------------
if len(models) == 2:
# Compare models and report:
print "BIC = ", BIC
print "Fitting result: Bayes factor in favour of nebula is exp[", -0.5 * (
BIC['nebula'] - BIC['lens']), "]"
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# -------------------------------------------------------------------------
print "Tractor stopping."
return
def run(self, ps=None, focus=False, momentsize=5, n_fwhm=100):
import pylab as plt
from astrometry.util.plotutils import dimshow, plothist
from legacyanalysis.ps1cat import ps1cat
import photutils
import tractor
fn = self.fn
ext = self.ext
pixsc = self.pixscale
F = fitsio.FITS(fn)
primhdr = F[0].read_header()
self.primhdr = primhdr
img, hdr = self.read_raw(F, ext)
self.hdr = hdr
# pre sky-sub
mn, mx = np.percentile(img.ravel(), [25, 98])
self.imgkwa = dict(vmin=mn, vmax=mx, cmap='gray')
if self.debug and ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Raw image')
ps.savefig()
M = 200
plt.clf()
plt.subplot(2, 2, 1)
dimshow(img[-M:, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 2)
dimshow(img[-M:, -M:], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 3)
dimshow(img[:M, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 4)
dimshow(img[:M, -M:], ticks=False, **self.imgkwa)
plt.suptitle('Raw image corners')
ps.savefig()
img, trim_x0, trim_y0 = self.trim_edges(img)
fullH, fullW = img.shape
if self.debug and ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Trimmed image')
ps.savefig()
M = 200
plt.clf()
plt.subplot(2, 2, 1)
dimshow(img[-M:, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 2)
dimshow(img[-M:, -M:], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 3)
dimshow(img[:M, :M], ticks=False, **self.imgkwa)
plt.subplot(2, 2, 4)
dimshow(img[:M, -M:], ticks=False, **self.imgkwa)
plt.suptitle('Trimmed corners')
ps.savefig()
band = self.get_band(primhdr)
exptime = primhdr['EXPTIME']
airmass = primhdr['AIRMASS']
print('Band', band, 'Exptime', exptime, 'Airmass', airmass)
zp0 = self.nom.zeropoint(band, ext=self.ext)
sky0 = self.nom.sky(band)
kx = self.nom.fiducial_exptime(band).k_co
# Find the sky value and noise level
sky, sig1 = self.get_sky_and_sigma(img)
sky1 = np.median(sky)
skybr = -2.5 * np.log10(sky1 / pixsc / pixsc / exptime) + zp0
print('Sky brightness: %8.3f mag/arcsec^2' % skybr)
print('Fiducial: %8.3f mag/arcsec^2' % sky0)
img -= sky
self.remove_sky_gradients(img)
# Post sky-sub
mn, mx = np.percentile(img.ravel(), [25, 98])
self.imgkwa = dict(vmin=mn, vmax=mx, cmap='gray')
if ps is not None:
plt.clf()
dimshow(img, **self.imgkwa)
plt.title('Sky-sub image: %s-%s' % (os.path.basename(fn).replace(
'.fits', '').replace('.fz', ''), ext))
plt.colorbar()
ps.savefig()
# Read WCS header and compute boresight
wcs = self.get_wcs(hdr)
ra_ccd, dec_ccd = wcs.pixelxy2radec((fullW + 1) / 2., (fullH + 1) / 2.)
# Detect stars
psfsig = self.nominal_fwhm / 2.35
detsn = self.detection_map(img, sig1, psfsig, ps)
slices = self.detect_sources(detsn, self.det_thresh, ps)
print(len(slices), 'sources detected')
if len(slices) < 20:
slices = self.detect_sources(detsn, 10., ps)
print(len(slices), 'sources detected')
ndetected = len(slices)
camera = primhdr.get('INSTRUME', '').strip().lower()
# -> "decam" / "mosaic3"
meas = dict(band=band,
airmass=airmass,
skybright=skybr,
pixscale=pixsc,
primhdr=primhdr,
hdr=hdr,
wcs=wcs,
ra_ccd=ra_ccd,
dec_ccd=dec_ccd,
extension=ext,
camera=camera,
ndetected=ndetected)
if ndetected == 0:
print('NO SOURCES DETECTED')
return meas
xx, yy = [], []
fx, fy = [], []
mx2, my2, mxy = [], [], []
wmx2, wmy2, wmxy = [], [], []
# "Peak" region to centroid
P = momentsize
H, W = img.shape
for i, slc in enumerate(slices):
y0 = slc[0].start
x0 = slc[1].start
subimg = detsn[slc]
imax = np.argmax(subimg)
y, x = np.unravel_index(imax, subimg.shape)
if (x0 + x) < P or (x0 + x) > W - 1 - P or (y0 + y) < P or (
y0 + y) > H - 1 - P:
#print('Skipping edge peak', x0+x, y0+y)
continue
xx.append(x0 + x)
yy.append(y0 + y)
pkarea = detsn[y0 + y - P:y0 + y + P + 1,
x0 + x - P:x0 + x + P + 1]
from scipy.ndimage.measurements import center_of_mass
cy, cx = center_of_mass(pkarea)
#print('Center of mass', cx,cy)
fx.append(x0 + x - P + cx)
fy.append(y0 + y - P + cy)
#print('x,y', x0+x, y0+y, 'vs centroid', x0+x-P+cx, y0+y-P+cy)
### HACK -- measure source ellipticity
# go back to the image (not detection map)
#subimg = img[slc]
subimg = img[y0 + y - P:y0 + y + P + 1,
x0 + x - P:x0 + x + P + 1].copy()
subimg /= subimg.sum()
ph, pw = subimg.shape
px, py = np.meshgrid(np.arange(pw), np.arange(ph))
mx2.append(np.sum(subimg * (px - cx)**2))
my2.append(np.sum(subimg * (py - cy)**2))
mxy.append(np.sum(subimg * (px - cx) * (py - cy)))
# Gaussian windowed version
s = 1.
wimg = subimg * np.exp(-0.5 * ((px - cx)**2 + (py - cy)**2) / s**2)
wimg /= np.sum(wimg)
wmx2.append(np.sum(wimg * (px - cx)**2))
wmy2.append(np.sum(wimg * (py - cy)**2))
wmxy.append(np.sum(wimg * (px - cx) * (py - cy)))
mx2 = np.array(mx2)
my2 = np.array(my2)
mxy = np.array(mxy)
wmx2 = np.array(wmx2)
wmy2 = np.array(wmy2)
wmxy = np.array(wmxy)
# semi-major/minor axes and position angle
theta = np.rad2deg(np.arctan2(2 * mxy, mx2 - my2) / 2.)
theta = np.abs(theta) * np.sign(mxy)
s = np.sqrt(((mx2 - my2) / 2.)**2 + mxy**2)
a = np.sqrt((mx2 + my2) / 2. + s)
b = np.sqrt((mx2 + my2) / 2. - s)
ell = 1. - b / a
wtheta = np.rad2deg(np.arctan2(2 * wmxy, wmx2 - wmy2) / 2.)
wtheta = np.abs(wtheta) * np.sign(wmxy)
ws = np.sqrt(((wmx2 - wmy2) / 2.)**2 + wmxy**2)
wa = np.sqrt((wmx2 + wmy2) / 2. + ws)
wb = np.sqrt((wmx2 + wmy2) / 2. - ws)
well = 1. - wb / wa
fx = np.array(fx)
fy = np.array(fy)
xx = np.array(xx)
yy = np.array(yy)
if ps is not None:
plt.clf()
dimshow(detsn, vmin=-3, vmax=50, cmap='gray')
ax = plt.axis()
plt.plot(fx, fy, 'go', mec='g', mfc='none', ms=10)
plt.colorbar()
plt.title('Detected sources')
plt.axis(ax)
ps.savefig()
# show centroids too
# plt.plot(xx, yy, 'go', mec='g', mfc='none', ms=8)
# plt.axis(ax)
# ps.savefig()
# if ps is not None:
# plt.clf()
# plt.subplot(2,1,1)
# mx = np.percentile(np.append(mx2,my2), 99)
# ha = dict(histtype='step', range=(0,mx), bins=50)
# plt.hist(mx2, color='b', label='mx2', **ha)
# plt.hist(my2, color='r', label='my2', **ha)
# plt.hist(mxy, color='g', label='mxy', **ha)
# plt.legend()
# plt.xlim(0,mx)
# plt.subplot(2,1,2)
# mx = np.percentile(np.append(wmx2,wmy2), 99)
# ha = dict(histtype='step', range=(0,mx), bins=50, lw=3, alpha=0.3)
# plt.hist(wmx2, color='b', label='wx2', **ha)
# plt.hist(wmy2, color='r', label='wy2', **ha)
# plt.hist(wmxy, color='g', label='wxy', **ha)
# plt.legend()
# plt.xlim(0,mx)
# plt.suptitle('Source moments')
# ps.savefig()
#
# #mx = np.percentile(np.abs(np.append(mxy,wmxy)), 99)
# plt.clf()
# plt.subplot(2,1,1)
# ha = dict(histtype='step', range=(0,1), bins=50)
# plt.hist(ell, color='g', label='ell', **ha)
# plt.hist(well, color='g', lw=3, alpha=0.3, label='windowed ell', **ha)
# plt.legend()
# plt.subplot(2,1,2)
# ha = dict(histtype='step', range=(-90,90), bins=50)
# plt.hist(theta, color='g', label='theta', **ha)
# plt.hist(wtheta, color='g', lw=3, alpha=0.3,
# label='windowed theta', **ha)
# plt.xlim(-90,90)
# plt.legend()
# plt.suptitle('Source ellipticities & angles')
# ps.savefig()
# Cut down to stars whose centroids are within 1 pixel of their peaks...
#keep = (np.hypot(fx - xx, fy - yy) < 2)
#print(sum(keep), 'of', len(keep), 'stars have centroids within 2 of peaks')
#print('mean dx', np.mean(fx-xx), 'dy', np.mean(fy-yy), 'pixels')
#assert(float(sum(keep)) / len(keep) > 0.9)
#fx = fx[keep]
#fy = fy[keep]
apxy = np.vstack((fx, fy)).T
ap = []
aprad_pix = self.aprad / pixsc
aper = photutils.CircularAperture(apxy, aprad_pix)
p = photutils.aperture_photometry(img, aper)
apflux = p.field('aperture_sum')
# Manual aperture photometry to get clipped means in sky annulus
sky_inner_r, sky_outer_r = [r / pixsc for r in self.skyrad]
sky = []
for xi, yi in zip(fx, fy):
ix = int(np.round(xi))
iy = int(np.round(yi))
skyR = int(np.ceil(sky_outer_r))
xlo = max(0, ix - skyR)
xhi = min(W, ix + skyR + 1)
ylo = max(0, iy - skyR)
yhi = min(H, iy + skyR + 1)
xx, yy = np.meshgrid(np.arange(xlo, xhi), np.arange(ylo, yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
inannulus = ((r2 >= sky_inner_r**2) * (r2 < sky_outer_r**2))
skypix = img[ylo:yhi, xlo:xhi][inannulus]
#print('ylo,yhi, xlo,xhi', ylo,yhi, xlo,xhi, 'img subshape', img[ylo:yhi, xlo:xhi].shape, 'inann shape', inannulus.shape)
s, nil = sensible_sigmaclip(skypix)
sky.append(s)
sky = np.array(sky)
apflux2 = apflux - sky * (np.pi * aprad_pix**2)
good = (apflux2 > 0) * (apflux > 0)
apflux = apflux[good]
apflux2 = apflux2[good]
fx = fx[good]
fy = fy[good]
# Read in the PS1 catalog, and keep those within 0.25 deg of CCD center
# and those with main sequence colors
pscat = ps1cat(ccdwcs=wcs)
stars = pscat.get_stars()
#print('Got PS1 stars:', len(stars))
# we add the color term later
ps1band = ps1cat.ps1band[band]
stars.mag = stars.median[:, ps1band]
ok, px, py = wcs.radec2pixelxy(stars.ra, stars.dec)
px -= 1
py -= 1
if ps is not None:
#kwa = dict(vmin=-3*sig1, vmax=50*sig1, cmap='gray')
# Add to the 'detected sources' plot
# mn,mx = np.percentile(img.ravel(), [50,99])
# kwa = dict(vmin=mn, vmax=mx, cmap='gray')
# plt.clf()
# dimshow(img, **kwa)
ax = plt.axis()
#plt.plot(fx, fy, 'go', mec='g', mfc='none', ms=10)
K = np.argsort(stars.mag)
plt.plot(px[K[:10]] - trim_x0,
py[K[:10]] - trim_y0,
'o',
mec='m',
mfc='none',
ms=12,
mew=2)
plt.plot(px[K[10:]] - trim_x0,
py[K[10:]] - trim_y0,
'o',
mec='m',
mfc='none',
ms=8)
plt.axis(ax)
plt.title('PS1 stars')
#plt.colorbar()
ps.savefig()
# we trimmed the image before running detection; re-add that margin
fullx = fx + trim_x0
fully = fy + trim_y0
# Match PS1 to our detections, find offset
radius = self.maxshift / pixsc
I, J, dx, dy = self.match_ps1_stars(px, py, fullx, fully, radius,
stars)
print(len(I), 'spatial matches with large radius', self.maxshift,
'arcsec,', radius, 'pix')
bins = 2 * int(np.ceil(radius))
#print('Histogramming with', bins, 'bins')
histo, xe, ye = np.histogram2d(dx,
dy,
bins=bins,
range=((-radius, radius), (-radius,
radius)))
# smooth histogram before finding peak -- fuzzy matching
from scipy.ndimage.filters import gaussian_filter
histo = gaussian_filter(histo, 1.)
histo = histo.T
mx = np.argmax(histo)
my, mx = np.unravel_index(mx, histo.shape)
shiftx = (xe[mx] + xe[mx + 1]) / 2.
shifty = (ye[my] + ye[my + 1]) / 2.
if ps is not None:
plt.clf()
plothist(dx, dy, range=((-radius, radius), (-radius, radius)))
plt.xlabel('dx (pixels)')
plt.ylabel('dy (pixels)')
plt.title('Offsets to PS1 stars')
ax = plt.axis()
plt.axhline(0, color='b')
plt.axvline(0, color='b')
plt.plot(shiftx, shifty, 'o', mec='m', mfc='none', ms=15, mew=3)
plt.axis(ax)
ps.savefig()
# Refine with smaller search radius
radius2 = 3. / pixsc
I, J, dx, dy = self.match_ps1_stars(px, py, fullx + shiftx,
fully + shifty, radius2, stars)
print(len(J), 'matches to PS1 with small radius', 3, 'arcsec')
shiftx2 = np.median(dx)
shifty2 = np.median(dy)
#print('Stage-1 shift', shiftx, shifty)
#print('Stage-2 shift', shiftx2, shifty2)
sx = shiftx + shiftx2
sy = shifty + shifty2
print('Astrometric shift (%.0f, %.0f) pixels' % (sx, sy))
if self.debug and ps is not None:
plt.clf()
plothist(dx, dy, range=((-radius2, radius2), (-radius2, radius2)))
plt.xlabel('dx (pixels)')
plt.ylabel('dy (pixels)')
plt.title('Offsets to PS1 stars')
ax = plt.axis()
plt.axhline(0, color='b')
plt.axvline(0, color='b')
plt.plot(shiftx2, shifty2, 'o', mec='m', mfc='none', ms=15, mew=3)
plt.axis(ax)
ps.savefig()
if ps is not None:
mn, mx = np.percentile(img.ravel(), [50, 99])
kwa2 = dict(vmin=mn, vmax=mx, cmap='gray')
plt.clf()
dimshow(img, **kwa2)
ax = plt.axis()
plt.plot(fx[J], fy[J], 'go', mec='g', mfc='none', ms=10, mew=2)
plt.plot(px[I] - sx - trim_x0,
py[I] - sy - trim_y0,
'm+',
ms=10,
mew=2)
plt.axis(ax)
plt.title('Matched PS1 stars')
plt.colorbar()
ps.savefig()
plt.clf()
dimshow(img, **kwa2)
ax = plt.axis()
plt.plot(fx[J], fy[J], 'go', mec='g', mfc='none', ms=10, mew=2)
K = np.argsort(stars.mag)
plt.plot(px[K[:10]] - sx - trim_x0,
py[K[:10]] - sy - trim_y0,
'o',
mec='m',
mfc='none',
ms=12,
mew=2)
plt.plot(px[K[10:]] - sx - trim_x0,
py[K[10:]] - sy - trim_y0,
'o',
mec='m',
mfc='none',
ms=8,
mew=2)
plt.axis(ax)
plt.title('All PS1 stars')
plt.colorbar()
ps.savefig()
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
if len(stars) == 0:
print('No overlap or too few stars in PS1')
return None
px = px[keep]
py = py[keep]
# Re-match
I, J, dx, dy = self.match_ps1_stars(px, py, fullx + sx, fully + sy,
radius2, stars)
print('Cut to', len(stars), 'PS1 stars with good colors; matched',
len(I))
nmatched = len(I)
meas.update(dx=sx, dy=sy, nmatched=nmatched)
if focus:
meas.update(img=img,
hdr=hdr,
primhdr=primhdr,
fx=fx,
fy=fy,
px=px - trim_x0 - sx,
py=py - trim_y0 - sy,
sig1=sig1,
stars=stars,
moments=(mx2, my2, mxy, theta, a, b, ell),
wmoments=(wmx2, wmy2, wmxy, wtheta, wa, wb, well),
apflux=apflux,
apflux2=apflux2)
return meas
#print('Mean astrometric shift (arcsec): delta-ra=', -np.mean(dy)*0.263, 'delta-dec=', np.mean(dx)*0.263)
# Compute photometric offset compared to PS1
# as the PS1 minus observed mags
colorterm = self.colorterm_ps1_to_observed(stars.median, band)
stars.mag += colorterm
ps1mag = stars.mag[I]
if False and ps is not None:
plt.clf()
plt.semilogy(ps1mag, apflux2[J], 'b.')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap flux (with sky sub)')
ps.savefig()
plt.clf()
plt.semilogy(ps1mag, apflux[J], 'b.')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap flux (no sky sub)')
ps.savefig()
apmag2 = -2.5 * np.log10(apflux2) + zp0 + 2.5 * np.log10(exptime)
apmag = -2.5 * np.log10(apflux) + zp0 + 2.5 * np.log10(exptime)
if ps is not None:
plt.clf()
plt.plot(ps1mag, apmag[J], 'b.', label='No sky sub')
plt.plot(ps1mag, apmag2[J], 'r.', label='Sky sub')
# ax = plt.axis()
# mn = min(ax[0], ax[2])
# mx = max(ax[1], ax[3])
# plt.plot([mn,mx], [mn,mx], 'k-', alpha=0.1)
# plt.axis(ax)
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap mag')
plt.legend(loc='upper left')
plt.title('Zeropoint')
ps.savefig()
dm = ps1mag - apmag[J]
dmag, dsig = sensible_sigmaclip(dm, nsigma=2.5)
print('Mag offset: %8.3f' % dmag)
print('Scatter: %8.3f' % dsig)
if not np.isfinite(dmag) or not np.isfinite(dsig):
print('FAILED TO GET ZEROPOINT!')
meas.update(zp=None)
return meas
from scipy.stats import sigmaclip
goodpix, lo, hi = sigmaclip(dm, low=3, high=3)
dmagmed = np.median(goodpix)
print(len(goodpix), 'stars used for zeropoint median')
print('Using median zeropoint:')
zp_med = zp0 + dmagmed
trans_med = 10.**(-0.4 * (zp0 - zp_med - kx * (airmass - 1.)))
print('Zeropoint %6.3f' % zp_med)
print('Transparency: %.3f' % trans_med)
dm = ps1mag - apmag2[J]
dmag2, dsig2 = sensible_sigmaclip(dm, nsigma=2.5)
#print('Sky-sub mag offset', dmag2)
#print('Scatter', dsig2)
if ps is not None:
plt.clf()
plt.plot(ps1mag,
apmag[J] + dmag - ps1mag,
'b.',
label='No sky sub')
plt.plot(ps1mag, apmag2[J] + dmag2 - ps1mag, 'r.', label='Sky sub')
plt.xlabel('PS1 mag')
plt.ylabel('DECam ap mag - PS1 mag')
plt.legend(loc='upper left')
plt.ylim(-0.25, 0.25)
plt.axhline(0, color='k', alpha=0.25)
plt.title('Zeropoint')
ps.savefig()
zp_obs = zp0 + dmag
transparency = 10.**(-0.4 * (zp0 - zp_obs - kx * (airmass - 1.)))
meas.update(zp=zp_obs, transparency=transparency)
print('Zeropoint %6.3f' % zp_obs)
print('Fiducial %6.3f' % zp0)
print('Transparency: %.3f' % transparency)
# print('Using sky-subtracted values:')
# zp_sky = zp0 + dmag2
# trans_sky = 10.**(-0.4 * (zp0 - zp_sky - kx * (airmass - 1.)))
# print('Zeropoint %6.3f' % zp_sky)
# print('Transparency: %.3f' % trans_sky)
fwhms = []
psf_r = 15
if n_fwhm not in [0, None]:
Jf = J[:n_fwhm]
for i, (xi, yi, fluxi) in enumerate(zip(fx[Jf], fy[Jf], apflux[Jf])):
#print('Fitting source', i, 'of', len(Jf))
ix = int(np.round(xi))
iy = int(np.round(yi))
xlo = max(0, ix - psf_r)
xhi = min(W, ix + psf_r + 1)
ylo = max(0, iy - psf_r)
yhi = min(H, iy + psf_r + 1)
xx, yy = np.meshgrid(np.arange(xlo, xhi), np.arange(ylo, yhi))
r2 = (xx - xi)**2 + (yy - yi)**2
keep = (r2 < psf_r**2)
pix = img[ylo:yhi, xlo:xhi].copy()
ie = np.zeros_like(pix)
ie[keep] = 1. / sig1
#print('fitting source at', ix,iy)
#print('number of active pixels:', np.sum(ie > 0), 'shape', ie.shape)
psf = tractor.NCircularGaussianPSF([4.], [1.])
tim = tractor.Image(data=pix, inverr=ie, psf=psf)
src = tractor.PointSource(tractor.PixPos(xi - xlo, yi - ylo),
tractor.Flux(fluxi))
tr = tractor.Tractor([tim], [src])
#print('Posterior before prior:', tr.getLogProb())
src.pos.addGaussianPrior('x', 0., 1.)
#print('Posterior after prior:', tr.getLogProb())
doplot = (i < 5) * (ps is not None)
if doplot:
mod0 = tr.getModelImage(0)
tim.freezeAllBut('psf')
psf.freezeAllBut('sigmas')
# print('Optimizing params:')
# tr.printThawedParams()
#print('Parameter step sizes:', tr.getStepSizes())
optargs = dict(priors=False, shared_params=False)
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
#print('dlnp', dlnp)
#print('src', src)
#print('psf', psf)
if dlnp == 0:
break
# Now fit only the PSF size
tr.freezeParam('catalog')
# print('Optimizing params:')
# tr.printThawedParams()
for step in range(50):
dlnp, x, alpha = tr.optimize(**optargs)
#print('dlnp', dlnp)
#print('src', src)
#print('psf', psf)
if dlnp == 0:
break
fwhms.append(psf.sigmas[0] * 2.35 * pixsc)
if doplot:
mod1 = tr.getModelImage(0)
chi1 = tr.getChiImage(0)
plt.clf()
plt.subplot(2, 2, 1)
plt.title('Image')
dimshow(pix, **self.imgkwa)
plt.subplot(2, 2, 2)
plt.title('Initial model')
dimshow(mod0, **self.imgkwa)
plt.subplot(2, 2, 3)
plt.title('Final model')
dimshow(mod1, **self.imgkwa)
plt.subplot(2, 2, 4)
plt.title('Final chi')
dimshow(chi1, vmin=-10, vmax=10)
plt.suptitle('PSF fit')
ps.savefig()
fwhms = np.array(fwhms)
fwhm = np.median(fwhms)
print('Median FWHM: %.3f' % np.median(fwhms))
meas.update(seeing=fwhm)
if False and ps is not None:
lo, hi = np.percentile(fwhms, [5, 95])
lo -= 0.1
hi += 0.1
plt.clf()
plt.hist(fwhms, 25, range=(lo, hi), histtype='step', color='b')
plt.xlabel('FWHM (arcsec)')
ps.savefig()
if ps is not None:
plt.clf()
for i, (xi, yi) in enumerate(zip(fx[J], fy[J])[:50]):
ix = int(np.round(xi))
iy = int(np.round(yi))
xlo = max(0, ix - psf_r)
xhi = min(W, ix + psf_r + 1)
ylo = max(0, iy - psf_r)
yhi = min(H, iy + psf_r + 1)
pix = img[ylo:yhi, xlo:xhi]
slc = pix[iy - ylo, :].copy()
slc /= np.sum(slc)
p1 = plt.plot(slc, 'b-', alpha=0.2)
slc = pix[:, ix - xlo].copy()
slc /= np.sum(slc)
p2 = plt.plot(slc, 'r-', alpha=0.2)
ph, pw = pix.shape
cx, cy = pw / 2, ph / 2
if i == 0:
xx = np.linspace(0, pw, 300)
dx = xx[1] - xx[0]
sig = fwhm / pixsc / 2.35
yy = np.exp(-0.5 * (xx - cx)**2 / sig**2) # * np.sum(pix)
yy /= (np.sum(yy) * dx)
p3 = plt.plot(xx, yy, 'k-', zorder=20)
#plt.ylim(-0.2, 1.0)
plt.legend([p1[0], p2[0], p3[0]],
['image slice (y)', 'image slice (x)', 'fit'])
plt.title('PSF fit')
ps.savefig()
return meas
ra, dec = 40., 10.
psf_sigma = 1.4 # pixels
v = psf_sigma**2
ps = pixscale / 3600.
wcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -ps, 0., 0., ps, float(W),
float(H))
tim = tractor.Image(data=np.zeros((H, W), np.float32),
inverr=np.ones((H, W), np.float32),
psf=tractor.GaussianMixturePSF(1., 0., 0., v, v, 0.),
wcs=tractor.ConstantFitsWcs(wcs))
src = RexGalaxy(tractor.RaDecPos(ra, dec), tractor.Flux(100.),
LogRadius(0.))
tr = tractor.Tractor([tim], [src])
mod = tr.getModelImage(0)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.savefig('rex.png')
# add noise with std 1.
noisy = mod + np.random.normal(size=mod.shape)
# make that the tim's data
tim.data = noisy
# reset the source params
src.brightness.setParams([1.])
tr.freezeParam('images')
def main():
"""
NAME
LensTractor.py
PURPOSE
Run the Tractor on a deck of single object cutout images.
Read in an image and its weight map, guess the PSF, put an object at the
centre image of the field and then optimize the catalog and PSF.
COMMENTS
The idea is to identify good lens candidates by principled model
selection: two well-defined models competing against each other, given
multi-epoch imaging data. The Nebula model (1 extended source, plus
N=1,2,3 or 4 point sources, with sub-models denoted by "NebulaN") is very
flexible, so should be better at fitting the data in general than the
Lens model (1 extended source, plus 1 background point source). However,
when the Lens provides a good fit, it does so at lower cost (fewer
parameters), so should win by Bayesian information criteria (we use BIC
as a cheap proxy for evidence ratio).
The default workflow is as follows:
Is the target a lens?
* Try Nebula1
* Try Nebula2
if Nebula1 beats Nebula2:
Return NO
else:
Nebula = Nebula2
* Try Nebula4
if Nebula4 beats Nebula2:
Nebula = Nebula4
* Try Lens (via Nebula)
if Lens beats Nebula:
Return YES
else:
Return NO
Initialisation of Lens via Nebula depends on the point source
multiplicity of the final Nebula model: what we do with three point
image positions will be different from what we do with 4 point image
positions, particularly with regard to the deflector centroid.
Open questions:
Does it make sense to dogmatically associate the extended object with
the deflector?
YES: detection of a deflector galaxy will be needed for a convincing
candidate anyway.
NO: using the extended object to model a high S/N merging image
system should not be punished
How are we going to interpret the point image positions if we do not
have an estimated deflector position?
FLAGS
-h --help Print this message
-v --verbose Verbose operation
-s --sample Sample the posterior PDF instead of optimizing
-x --no-plots Do not plot progress
-l --lens Only fit lens model, initialized from scratch
-n --nebula Only fit nebula model, initialized from scratch
INPUTS
*.fits Deck of postcard images
OPTIONAL INPUTS
--optimization-rounds Nr Number of rounds of optimization [2]
--optimization-steps-catalog Nc Number of steps per round spent
optimizing source catalog [10]
--optimization-steps-psf Np Number of steps per round spent
optimizing source catalog [2]
OUTPUTS
stdout Useful information
*.png Plots in png format
To be implemented:
lenstractor_progress.log Logged output
lenstractor_results.txt Model comparison results
lenstractor_lens.cat Lens model parameters, including lightcurves
lenstractor_nebula.cat Nebula model parameters, including lightcurves
EXAMPLES
python LensTractor.py -x examples/H1413+117_10x10arcsec_55*fits > examples/H1413+117_10x10arcsec_lenstractor.log
DEPENDENCIES
* The Tractor astrometry.net/svn/trunk/projects/tractor
* emcee github.com/danfm/emcee
* astrometry.net astrometry.net/svn/trunk/util
BUGS
HISTORY
2012-07-06 First predicted Lens images Marshall/Hogg (Oxford/NYU)
2013-01-23 Sequential Nebula 1,2,4 scheme implemented
"""
# --------------------------------------------------------------------
from argparse import ArgumentParser
import sys
# Set available options:
parser = ArgumentParser()
# List of files:
parser.add_argument('inputfiles', metavar='N', nargs='+')
# Verbosity:
parser.add_argument('-v',
'--verbose',
dest='verbose',
action='store_true',
default=False,
help='Make more verbose')
# Sampling:
parser.add_argument('-s',
'--sample',
dest='MCMC',
action='store_true',
default=False,
help='Sample posterior PDF')
# Plotting:
parser.add_argument('-x',
'--no-plots',
dest='noplots',
action='store_true',
default=False,
help='Skip plotting')
# Lens model only:
parser.add_argument('-l',
'--lens',
dest='lens',
action='store_true',
default=False,
help='Fit lens model')
# Nebula model only:
parser.add_argument('-n',
'--nebula',
dest='nebula',
action='store_true',
default=False,
help='Fit nebula model')
# optimization workflow:
parser.add_argument('--optimization-rounds',
dest='Nr',
type=int,
default=3,
help='No. of optimization rounds')
parser.add_argument(
'--optimization-steps-catalog',
dest='Nc',
type=int,
default=10,
help='No. of optimization steps spent on source catalog')
parser.add_argument('--optimization-steps-psf',
dest='Np',
type=int,
default=2,
help='No. of optimization steps spent on PSFs')
parser.add_argument('--survey',
dest='survey',
type=str,
default="PS1",
help="Survey, either PS1 or KIDS")
# Read in options and arguments - note only sci and wht images are supplied:
args = parser.parse_args()
if len(args.inputfiles) < 2:
parser.print_help()
sys.exit(-1)
vb = args.verbose
# Workflow:
if args.lens:
models = ['lens']
elif args.nebula:
models = [
'nebula1',
'nebula2',
'nebula4',
]
else:
models = ['nebula1', 'nebula2', 'nebula4', 'lens']
BIC = dict(zip(models, np.zeros(len(models))))
# NB. default operation is to fit both and compare.
if vb:
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
print " LensTractor "
print " Fitting", models, " models to a deck of FITS postcards"
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# -------------------------------------------------------------------------
# Organise the deck of inputfiles into scifiles and varfiles:
scifiles, varfiles = lenstractor.Riffle(args.inputfiles, vb=vb)
# Read into Tractor Image objects, and see what filters we have:
images, total_mags, bands = lenstractor.Deal(scifiles,
varfiles,
SURVEY=args.survey,
vb=vb)
# -------------------------------------------------------------------------
# Generic items needed to initialize the Tractor's catalog.
# Get rough idea of object position from wcs of first image- works
# well if all images are the same size and well registered!
wcs = images[0].wcs
NX, NY = np.shape(images[0].data)
if vb: print "Generic initial position ", NX, NY, "(pixels)"
# m0 = 15.0
# magnitudes = m0*np.ones(len(bandnames))
# MAGIC initial magnitude. This should be estimated from
# the total flux in each (background-subtracted) image...
bandnames = np.unique(bands)
magnitudes = np.zeros(len(bandnames))
for i, bandname in enumerate(bandnames):
index = np.where(bands == bandname)
magnitudes[i] = np.median(total_mags[index])
if vb: print "Generic initial SED ", dict(zip(bandnames, magnitudes))
# -------------------------------------------------------------------------
# Loop over models:
for model in models:
if vb:
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
print "Initializing model: " + model
# Figure out what type of model this is:
modeltype = model[0:6]
# - - - - - - - - - - - - - - - - - -
if modeltype == 'nebula':
# Nebula - a flexible galaxy plus K point sources,
# How many point sources?
K = int(model[6:7])
# The first nebula model we run has 1 point source and one
# galaxy, initialised sensibly but randomly.
# All subsequent models just have extra random point sources.
# Start both sources off with equal magnitudes:
x, y = 0.5 * NX, 0.5 * NY
fudge = 2
equalmagnitudes = magnitudes + 2.5 * np.log10(5 * fudge)
mags = tractor.Mags(order=bandnames,
**dict(zip(bandnames, equalmagnitudes)))
# Add an exponential galaxy:
galpos = wcs.pixelToPosition(x, y)
mg = tractor.Mags(order=bandnames,
**dict(zip(bandnames, equalmagnitudes)))
re = 0.5 # arcsec
q = 1.0 # axis ratio
theta = 0.0 # degrees
galshape = tractor.sdss_galaxy.GalaxyShape(re, q, theta)
nebulousgalaxy = tractor.sdss_galaxy.ExpGalaxy(
galpos, mags.copy(), galshape)
if vb: print nebulousgalaxy
srcs = [nebulousgalaxy]
for i in range(K):
# Add a point source with random position near nebula centre:
e = 2.0 # pixels
dx, dy = e * np.random.randn(2)
star = tractor.PointSource(wcs.pixelToPosition(x + dx, y + dy),
mags.copy())
if vb: print star
srcs.append(star)
# - - - - - - - - - - - - - - - - - -
# Original Nebula4 initialisation went as follows:
# tractor.PointSource(wcs.pixelToPosition(x+e,y),mags.copy()),
# tractor.PointSource(wcs.pixelToPosition(x-e,y),mags.copy()),
# tractor.PointSource(wcs.pixelToPosition(x,y+e),mags.copy()),
# tractor.PointSource(wcs.pixelToPosition(x,y-e),mags.copy())]
# - - - - - - - - - - - - - - - - - -
elif modeltype == 'lens':
# If nebula has been run, use the best nebula model (by BIC)
# to initialise the lens model. If it hasn't, do something
# sensible.
# Source to be lensed:
xs, ys = 0.5 * NX, 0.5 * NY
# Tiny random offset (pixels):
e = 0.5
dx, dy = e * np.random.randn(2)
xs, ys = xs + dx, ys + dy
unlensedmagnitudes = magnitudes + 2.5 * np.log10(40.0)
ms = tractor.Mags(order=bandnames,
**dict(zip(bandnames, unlensedmagnitudes)))
if vb: print ms
sourcepos = wcs.pixelToPosition(xs, ys)
if vb: print sourcepos
pointsource = tractor.PointSource(sourcepos, ms)
if vb: print pointsource
# Lens mass:
thetaE = lenstractor.EinsteinRadius(0.75) # arcsec
if vb: print thetaE
gamma = 0.2 # to make quad
phi = 0.0 # deg
xshear = lenstractor.ExternalShear(gamma, phi)
if vb: print xshear
# Lens light:
x, y = 0.5 * NX, 0.5 * NY
lenspos = wcs.pixelToPosition(x, y)
if vb: print lenspos
lensmagnitudes = magnitudes + 2.5 * np.log10(10.0)
md = tractor.Mags(order=bandnames,
**dict(zip(bandnames, lensmagnitudes)))
if vb: print md
re = 0.5 # arcsec
q = 0.8 # axis ratio
theta = 90.0 # degrees
galshape = tractor.sdss_galaxy.GalaxyShape(re, q, theta)
if vb: print galshape
lensgalaxy = lenstractor.LensGalaxy(lenspos, md, galshape, thetaE,
xshear)
if vb: print lensgalaxy
srcs = [lenstractor.PointSourceLens(lensgalaxy, pointsource)]
# - - - - - - - - - - - - - - - - - -
if vb:
print "Initialization complete."
print "Model =", srcs
print " "
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Set up logging to the terminal by The Tractor:
if vb:
lvl = logging.DEBUG
else:
lvl = logging.INFO
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Start a tractor, and let it make a catalog one src at a time.
# Pass in a copy of the image list, so that PSF etc are
# initialised correctly for each model.
chug = tractor.Tractor(list(images))
for src in srcs:
chug.addSource(src)
# Plot initial state:
lenstractor.Plot_state(chug,
model + '_progress_initial',
SURVEY=args.survey)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Do the fit - either by maximizing the posterior PDF ("optimizing")
# or by exploring the posterior PDF ("MCMC").
if not args.MCMC:
# Pre-op! Cycle over N initialisations, trying 5 steps each. Then
# Optimize from the best of these.
# Optimize the model parameters:
if modeltype == 'nebula':
Nrounds = args.Nr
Nsteps_optimizing_catalog = args.Nc
Nsteps_optimizing_PSFs = args.Np
elif modeltype == 'lens':
Nrounds = args.Nr
Nsteps_optimizing_catalog = args.Nc
Nsteps_optimizing_PSFs = args.Np
if vb:
print "Optimizing model:"
print " - no. of iterations per round to be spent on catalog: ", Nsteps_optimizing_catalog
print " - no. of iterations per round to be spent on PSFs: ", Nsteps_optimizing_PSFs
print " - no. of rounds: ", Nrounds
k = 0
for round in range(Nrounds):
print "Fitting " + model + ": seconds out, round", round
# Freeze the PSF, sky and photocal, leaving the sources:
print "Thawing catalog..."
chug.thawParam('catalog')
for image in chug.getImages():
print "Thawing sky..."
image.thawParams('sky')
print "Freezing photocal, WCS, PSF..."
image.freezeParams('photocal', 'wcs', 'psf')
print "Fitting " + model + ": Catalog parameters to be optimized are:", chug.getParamNames(
)
print "Fitting " + model + ": Initial values are:", chug.getParams(
)
print "Fitting " + model + ": Step sizes:", chug.getStepSizes()
# Optimize sources with initial PSF:
for i in range(Nsteps_optimizing_catalog):
dlnp, X, a = chug.optimize(damp=3)
# print "Fitting "+model+": at step",k,"parameter values are:",chug.getParams()
print "Progress: k,dlnp = ", k, dlnp
print ""
print "Catalog:", chug.getParams()
if dlnp == 0:
print "Converged? Exiting..."
# Although this only leaves *this* loop...
break
k += 1
if not args.noplots:
lenstractor.Plot_state(
chug,
model + '_progress_optimizing_step-%02d_catalog' % k,
SURVEY=args.survey)
# Freeze the sources and sky and thaw the psfs:
print "Freezing catalog..."
chug.freezeParam('catalog')
for image in chug.getImages():
print "Thawing PSF..."
image.thawParams('psf')
print "Freezing photocal, WCS, sky..."
image.freezeParams('photocal', 'wcs', 'sky')
print "Fitting PSF: After thawing, zeroth PSF = ", chug.getImage(
0).psf
print "Fitting PSF: PSF parameters to be optimized are:", chug.getParamNames(
)
print "Fitting PSF: Initial values are:", chug.getParams()
print "Fitting PSF: Step sizes:", chug.getStepSizes()
# Optimize everything that is not frozen:
for i in range(Nsteps_optimizing_PSFs):
dlnp, X, a = chug.optimize()
print "Fitting PSF: at step", k, "parameter values are:", chug.getParams(
)
k += 1
print "Fitting PSF: After optimizing, zeroth PSF = ", chug.getImage(
0).psf
if not args.noplots:
lenstractor.Plot_state(
chug,
model + '_progress_optimizing_step-%02d_catalog' % k,
SURVEY=args.survey)
# BUG: PSF not being optimized correctly - missing derivatives?
lenstractor.Plot_state(chug,
model + '_progress_optimizing_zcomplete',
SURVEY=args.survey)
# - - - - - - - - - - - - - - - - - -
elif args.MCMC:
# MCMC sample the model parameters.
if vb:
print "Sampling model parameters with emcee:"
# Freeze the wcs and photocal, leaving the PSFs, sky and sources:
for image in chug.getImages():
image.freezeParams('photocal', 'wcs')
# Temporary expt:
image.freezeParams('psf')
# Get the thawed parameters:
p0 = np.array(chug.getParams())
print 'Tractor parameters:'
for i, parname in enumerate(chug.getParamNames()):
print ' ', parname, '=', p0[i]
ndim = len(p0)
print 'Number of parameter space dimensions: ', ndim
# Make an emcee sampler that uses our tractor to compute its logprob:
nw = 8 * ndim
sampler = emcee.EnsembleSampler(nw, ndim, chug, threads=4)
# Start the walkers off near the initialisation point -
# We need it to be ~1 pixel in position, and not too much
# flux restrction...
if model == 'lens':
# The following gets us 0.2" in dec:
psteps = np.zeros_like(p0) + 0.00004
# This could be optimized, to allow more initial freedom in eg flux.
elif model == 'nebula':
# Good first guess should be some fraction of the optimization step sizes:
psteps = 0.2 * np.array(chug.getStepSizes())
# BUG - nebula+lens workflow not yet enabled!
print "Initial size (in each dimension) of sample ball = ", psteps
pp = emcee.EnsembleSampler.sampleBall(p0, psteps, nw)
rstate = None
lnp = None
# Take a few steps - memory leaks fast! (~10Mb per sec)
for step in range(10):
print 'EMCEE: Run MCMC step set:', step
t0 = tractor.Time()
pp, lnp, rstate = sampler.run_mcmc(pp,
50,
lnprob0=lnp,
rstate0=rstate)
print 'EMCEE: Mean acceptance fraction after', sampler.iterations, 'iterations =', np.mean(
sampler.acceptance_fraction)
t_mcmc = (tractor.Time() - t0)
print 'EMCEE: Runtime:', t_mcmc
# Find the current posterior means:
# pbar = np.mean(pp,axis=0)
# print "Mean parameters: ",pbar,np.mean(lnp)
# Find the current best sample:
maxlnp = np.max(lnp)
best = np.where(lnp == maxlnp)
pbest = np.ravel(pp[best, :])
print "EMCEE: Best parameters: ", maxlnp, pbest
chug.setParams(pbest)
chisq = -2.0 * chug.getLogLikelihood()
print "EMCEE: chisq at Best pt: ", chisq
if not args.noplots:
chug.setParams(pbest)
lenstractor.Plot_state(
chug,
model + '_progress_sampling_step-%02d' % step,
SURVEY=args.survey)
# Take the last best sample and call it a result:
chug.setParams(pbest)
print 'EMCEE: Best lnprob, chisq:', maxlnp, chisq
# print 'dlnprobs:', ', '.join(['%.1f' % d for d in lnp - np.max(lnp)])
# print 'MCMC took', t_mcmc, 'sec'
# Make the final plot:
lenstractor.Plot_state(chug,
model + '_progress_sampling_zcomplete',
SURVEY=args.survey)
# - - - - - - - - - - - - - - - - - -
if vb:
print "Fit complete."
print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Collect statistics about this model's fit:
chisq = -2.0 * chug.getLogLikelihood()
chug.thawParam('catalog')
for image in chug.getImages():
image.thawParams('sky', 'psf')
image.freezeParams('photocal', 'wcs')
K = len(chug.getParams())
N = chug.getNdata()
BIC[model] = chisq + K * np.log(1.0 * N)
print model + " results: chisq, K, N, BIC =", chisq, K, N, BIC[model]
# -------------------------------------------------------------------------
# Make some decision about the nature of this system.
# if len(models) > 1:
# # Compare models and report:
# print "BIC = ",BIC
# print "Hypothesis test result: Bayes factor in favour of nebula is exp[",-0.5*(BIC['nebula'] - BIC['lens']),"]"
# print "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *"
# -------------------------------------------------------------------------
print "Tractor stopping."
return
print(name)
print()
# We're simulating a single isolated point source
src = tractor.PointSource(tractor.PixPos(W // 2, H // 2),
tractor.Flux(trueflux))
# Produce tractor image objects with noise model, PSF model, etc
tims = []
for noise, psf_size in imageset:
tim = tractor.Image(np.zeros((H, W), np.float32),
inverr=np.ones((H, W), np.float32) * (1. / noise),
psf=tractor.NCircularGaussianPSF([psf_size], [1.]),
photocal=tractor.LinearPhotoCal(1.))
# Create noiseless model image (simulated image)
tr = tractor.Tractor([tim], [src])
mod = tr.getModelImage(0)
tim.data = mod
tims.append(tim)
# First we'll run without any noise added to the images, to get estimates of ideal performance.
# Run the Simultaneous Fitting method
tr = tractor.Tractor(tims, [src])
src.brightness.setParams([0.])
# Freeze the source position -- only fit for flux
src.freezeParam('pos')
# Freeze the image calibration parameters (PSF model, sky background, photometric calibration, etc)
tr.freezeParam('images')
phot = tr.optimize_forced_photometry(variance=True)
simult_err = 1. / np.sqrt(phot.IV[0])
tim = tractor.Image(data=np.zeros((H, W), np.float32),
inverr=np.ones((H, W), np.float32),
psf=psf,
wcs=wcs,
photcal=photcal)
## _tr = tractor.Tractor([tim], [src])
## mod = _tr.getModelImage(0)
tim.data = tim.data + noise.data ## + mod.data
tims.append(tim)
cat = tractor.Catalog(src)
##
tr = tractor.Tractor(tims, cat)
# Evaluate likelihood.
lnp = tr.getLogProb()
print('Logprob:', lnp)
for nm, val in zip(tr.getParamNames(), tr.getParams()):
print(' ', nm, val)
exit(0)
mod = tr.getModelImage(0)
np.savetxt('output/rex_noiseless.txt', mod)
# Reset the source params.
