def makeTractor(bands, ra, dec, S, RCFS):
TI = []
for i, (run, camcol, field) in enumerate(RCFS):
for bandname in bands:
im, inf = st.get_tractor_image(run,
camcol,
field,
bandname,
useMags=True,
roiradecsize=(ra, dec, S))
#im.dxdy = (fxc - xc, fyc - yc)
TI.append((im, inf))
if i == 0 and bandname == 'r':
wcs = im.getWcs()
# im,info = TI[bands.index('r')]
#wcs = im.getWcs()
# this is a shifted WCS; S,S is the center.
# rd = wcs.pixelToPosition(None, (S,S))
# ra,dec = rd.ra,rd.dec
# print 'RA,Dec', ra,dec
cd = wcs.cdAtPixel(xc, yc)
pixscale = np.sqrt(np.abs(np.linalg.det(cd)))
print('pixscale', pixscale * 3600.)
extent = pixscale * S
#for timg,info in TI:
# print timg.hashkey()
sources = st.get_tractor_sources(run, camcol, field, roi=roi, bands=bands)
print('Sources:')
for s in sources:
print(s)
print()
print('Tractor images:', TI)
cftimg, cfsky, cfstd = get_cfht_img(ra, dec, extent)
# Create fake tractor.Image
# psf = NCircularGaussianPSF([0.1], [1.])
# sky = timg.sky
# print 'SDSS Sky:', timg.sky
# photocal = timg.photocal
# fakescale = 3
# #wcs = ScaledWCS(fakescale, timg.wcs)
# #(h,w) = timg.data.shape
# #fakedata = np.zeros((h*fakescale, w*fakescale))
# # 0.066
# # make-wcs.py -r 333.55503 -d 0.36438 -s 0.02 -W 1200 -H 1200 fake-wcs.fits
# # # flip parity
# # modhead fake-wcs.fits CD1_1 0
# # modhead fake-wcs.fits CD1_2 1.666666667e-5
# # modhead fake-wcs.fits CD2_1 1.666666667e-5
# # modhead fake-wcs.fits CD2_2 0
# wcs = FitsWcs(Tan('fake-wcs.fits', 0))
# #h,w = 600,600
# h,w = 1200,1200
# fakescale = 0.396 * w / (0.02 * 3600.)
# fakedata = np.zeros((h, w))
# # print '0,0', wcs.pixelToPosition(None, (0,0))
# # print 'W,0', wcs.pixelToPosition(None, (w,0))
# # print '0,H', wcs.pixelToPosition(None, (0,h))
# # print 'W,H', wcs.pixelToPosition(None, (w,h))
# fakewcs = wcs
# fakeimg = Image(data=fakedata, invvar=fakedata, psf=psf,
# wcs=wcs, sky=sky, photocal=photocal, name='Fake')
# del psf
# del wcs
# del photocal
tims = [cftimg] + [timg for timg, tinf in TI]
# for tim in tims:
# try:
# print 'Test pickling im', tim.name
# pickle_to_file(tim, 'test.pickle')
# except:
# print 'failed:'
# traceback.print_exc()
# pass
# for src in sources:
# try:
# print 'Test pickling source', src
# pickle_to_file(src, 'test.pickle')
# except:
# print 'failed:'
# traceback.print_exc()
# pass
# sys.exit(0)
tractor = Tractor(tims)
tractor.addSources(sources)
skyvals = ([(cfstd, cfsky)] + [(info['skysig'], info['sky'])
for timg, info in TI])
return tractor, skyvals
roi = [1850, 1950, 620, 720]
x0, y0 = roi[0], roi[2]
# rd1 is near the position of the SDSS-catalog object
# rd2 is four pixels above.
rd1 = RaDecPos(225.67954, 11.265948)
rd2 = RaDecPos(225.67991, 11.265852)
lvl = logging.INFO
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
bandname = band
sdssprefix = '%06i-%s%i-%04i' % (run, bandname, camcol, field)
timg, info = st.get_tractor_image(run, camcol, field, bandname, roi=roi)
sources = st.get_tractor_sources(run, camcol, field, bandname, roi=roi)
wcs = timg.getWcs()
for source in sources:
x, y = wcs.positionToPixel(source, source.getPosition())
print(' (%.2f, %.2f):' % (x + x0, y + y0), source)
tractor = st.SDSSTractor([timg])
tractor.addSources(sources)
lnlorig = tractor.getLogLikelihood()
zr = np.array([-5., +20.]) * info['skysig']
save(sdssprefix + '-orig', tractor, zr)
# find closest source
mini = -1
def main():
from optparse import OptionParser
parser = OptionParser(usage=('%prog'))
parser.add_option('--steps', dest='steps', type=int, help='Number of steps',
default=100)
parser.add_option('--step0', dest='step0', type=int, help='Starting step',
default=0)
parser.add_option('--nomodplot', dest='modplot', action='store_false', default=True)
opt,args = parser.parse_args()
lvl = logging.INFO
#lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
np.seterr(all='warn')
np.seterr(divide='raise')
bands = ['r']
#RCFS = [(2728, 4, 236),]
#ra,dec = (333.5596, 0.3671)
#S = 40
RCFS = [(756, 3, 243),]
ra,dec = (152.208922638573, -0.202430118237826)
S = 18
TI = []
for i,(run,camcol,field) in enumerate(RCFS):
for bandname in bands:
im,inf = st.get_tractor_image(run, camcol, field, bandname,
useMags=True,
roiradecsize=(ra,dec,S))
#im.dxdy = (fxc - xc, fyc - yc)
TI.append((im,inf))
tims = [im for im,inf in TI]
skyvals = [(info['skysig'], info['sky']) for timg,info in TI]
zrs = [np.array([-1.,+6.]) * std + sky for std,sky in skyvals]
# Grab sources from the LAST RCF
roi = inf['roi']
sources = st.get_tractor_sources(run, camcol, field, roi=roi,
bands=bands)
# DevGalaxy
s0 = sources[0]
s0.re = 0.01
s0.brightness.r = 22.
tractor = Tractor(tims)
tractor.addSources(sources)
lnp0 = tractor.getLogProb()
print 'Catalog:'
print tractor.catalog
print tractor.catalog.getParams()
nthreads = 16
p0 = np.array(tractor.catalog.getParams())
ndim = len(p0)
nw = max(50, 2*ndim)
print 'ndim', ndim
print 'nw', nw
sampler = emcee.EnsembleSampler(nw, ndim, tractor,
threads=nthreads)
im0 = tractor.getImages()[0]
steps = np.hstack([src.getStepSizes(im0) for src in tractor.catalog])
#print 'steps', steps
print 'p0', p0
pp0 = np.vstack([p0 + 1e-2 * steps * np.random.normal(size=len(steps))
for i in range(nw)])
plt.figure(1, figsize=(6,6))
#plt.figure(2, figsize=(18,18))
plt.figure(2, figsize=(12,12))
plt.figure(3, figsize=(4,4))
plt.figure(1)
plt.clf()
plotpos0 = [0.01, 0.01, 0.98, 0.94]
alllnp = []
allp = []
lnp = None #lnp0
pp = pp0
rstate = None
for step in range(opt.step0, opt.steps):
pfn = 'trample-%03i.pickle' % step
if os.path.exists(pfn):
print 'Unpickling', pfn
allp,alllnp = unpickle_from_file(pfn)
lnp = alllnp[-1]
pp = allp[-1]
else:
print 'Taking step', step
print 'pp shape', pp.shape
pp,lnp,rstate = sampler.run_mcmc(pp, 1, lnprob0=lnp, rstate0=rstate)
print 'lnprobs:', lnp
for p,x in zip(lnp,pp):
tractor.setAllSourceParams(x)
print 'lnp', p
print tractor.catalog
#print 'pp shape', pp.shape
# nw,ndim
alllnp.append(lnp.copy())
allp.append(pp.copy())
pickle_to_file((allp, alllnp), pfn)
plt.clf()
plt.plot(alllnp, 'k', alpha=0.5)
plt.axhline(lnp0, color='r', lw=2, alpha=0.5)
mx = np.max([p.max() for p in alllnp])
plt.ylim(mx-300, mx+10)
plt.xlim(0, len(alllnp)-1)
plt.savefig('lnp.png')
for i in range(len(tractor.getImages())):
zr = zrs[i]
ima = dict(interpolation='nearest', origin='lower',
vmin=zr[0], vmax=zr[1], cmap='gray')
imchi = dict(interpolation='nearest', origin='lower',
vmin=-5., vmax=+5., cmap='gray')
if step == 0:
tim = tractor.getImage(i)
data = tim.getImage()
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(data, **ima)
plt.title('Data %s' % tim.name)
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('data%02i.png' % i)
modsum = None
#KK =
for k in xrange(nw):
tractor.setAllSourceParams(pp[k,:])
mod = tractor.getModelImage(i)
if k == 0:
modsum = mod
else:
modsum += mod
if step%50 == 0 and opt.modplot:
print 'mod,chi'
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(mod, **ima)
plt.title('Model')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('mod%02i-%02i-%03i.png' % (i,step,k))
chi = tractor.getChiImage(i)
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(chi, **imchi)
plt.title('Chi')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('chi%02i-%02i-%03i.png' % (i,step,k))
if step % 10 == 0:
print 'modbest, chibest'
ibest = np.argmax(lnp)
print 'ibest', ibest
tractor.setAllSourceParams(pp[ibest,:])
mod = tractor.getModelImage(i)
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(mod, **ima)
plt.title('Best Model')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('modbest%02i-%02i.png' % (i,step))
chi = tractor.getChiImage(i)
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(chi, **imchi)
plt.title('Best Chi')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('chibest%02i-%02i.png' % (i,step))
if step % 1 == 0:
print 'modsum'
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(modsum/float(nw), **ima)
plt.title('Model')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('modsum%02i-%02i.png' % (i,step))
if step % 1 == 0 and False:
# Plot models, exaggerating the variance
# pp shape is (nw,ndim)
print 'exag'
p0 = pp.mean(axis=0)
print 'p0 shape', p0
for i in range(nw):
delta = pp[i,:] - p0
exagg = 10.
pnew = pp[i,:] + (exagg-1.)*delta
tractor.setAllSourceParams(pnew)
modex = tractor.getModelImage(0)
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(modex, **ima)
plt.title('Model (exaggerated x %g)' % exagg)
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('modex%02i-%02i.png' % (i,step))
if step % 50 == 0:
pnames = tractor.catalog[0].getParamNames()
print 'Params', pnames
NP = len(pnames)
# select objid, ra,raerr,dec,decerr,devmag_r,devmagerr_r,devrad_r,devraderr_r,devphi_r,devab_r,devaberr_r from mydb.mytable_2 where run=756 and camcol=3 and field=243
#objid,ra,raerr,dec,decerr,devmag_r,devmagerr_r,devrad_r,devraderr_r,devphi_r,devab_r,devaberr_r
#1237648721218568377,152.208922638573,0.00391166133082899,-0.202430118237826,0.00423603626875907,17.59624,0.005895343,1.081388,0.0371073,26.40831,0.435521,0.01508615
# DR8
sdssvals = [ 152.208922638573,-0.202430118237826,17.59624,1.081388,0.435521, 26.40831 * -1. ]
sdsserrs = [ 0.00391166133082899, 0.00423603626875907,0.005895343,0.0371073, 0.01508615, 0. ]
# DR7; phi*-1; raErr, decErr are in arcsec, and raErr is err in ra*cos(dec)
sdssvals7 = [ 152.20891415,-0.20242878,17.60266,1.044212,0.4157184,26.3763*-1.,]
sdsserrs7 = [ 0.0443575305964374 / 3600., 0.041734802988402 / 3600.,0.006056787,0.03344231,0.0143005, 0.]
tractor.setAllSourceParams(sdssvals7)
sdssmod = tractor.getModelImage(0)
sdsschi = tractor.getChiImage(0)
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(sdssmod, **ima)
plt.title('SDSS Model')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('sdssmod.png')
plt.clf()
plt.gca().set_position(plotpos0)
plt.imshow(sdsschi, **imchi)
plt.title('SDSS Model Chi')
plt.xticks([],[])
plt.yticks([],[])
plt.savefig('sdsschi.png')
ra0, dec0 = sdssvals7[:2]
#152.209, -0.202
pnames = [#'RA - %.3f (arcsec)' % np.abs(ra0),
#'Dec + %.3f (arcsec)' % np.abs(dec0),
'RA - SDSS (milli-arcsec)',
'Dec - SDSS (milli-arcsec)',
'r (mag)',
'effective radius (arcsec)', 'axis ratio',
'position angle (deg)']
nsteps = 20
ppp = allp[-nsteps:]
tmn = {}
tstd = {}
paris = {}
for i in range(NP):
pari = np.hstack([p[:,i] for p in ppp])
mn,std,c =(sdssvals7[i],sdsserrs7[i],'m')
# in [#(sdssvals[i],sdsserrs[i],'r'),
if i == 0:
pari -= ra0
mn -= ra0
pari *= 3600. * 1000.
mn *= 3600. * 1000.
std *= 3600. * 1000.
elif i == 1:
pari -= dec0
mn -= dec0
pari *= 3600. * 1000.
mn *= 3600. * 1000.
std *= 3600. * 1000.
paris[i] = pari
tmn[i] = np.mean(pari)
tstd[i] = np.std(pari)
plt.figure(3)
plt.clf()
xlo, xhi = min(pari.min(), mn-std), max(pari.max(), mn+std)
n,b,p1 = plt.hist(pari, 25, ec='none', alpha=0.5)
plt.hist(pari, 25, histtype='step', ec='b', lw=2)
xx = np.linspace(xlo, xhi, 100)
ax = plt.axis()
dx = b[1]-b[0]
#yy = np.exp(-0.5 * (xx - mn)**2 / std**2)
#yy /= sum(yy)
#yy *= sum(n)*dx / (xx[1]-xx[0])
#print 'yy', yy
#plt.plot(xx, yy, 'b-', lw=3, alpha=0.8)
print 'mn', mn, 'std', std
#plt.axvline(mn, color='b', lw=3, alpha=0.5)
#for mn,std,c in [#(sdssvals[i],sdsserrs[i],'r'),
# (sdssvals7[i],sdsserrs7[i],'m')]:
if std != 0:
yy = np.exp(-0.5 * (xx - mn)**2 / std**2)
yy /= sum(yy)
yy *= sum(n)*dx / (xx[1]-xx[0])
plt.plot(xx, yy, '-', color=c, lw=3, alpha=0.8)
print 'sdss', mn, std
p2 = plt.axvline(mn, color=c, lw=3, alpha=0.8)
#plt.axis(ax)
#plt.xlim(min(xx.min(), mn-std), max(xx.max(), mn+std))
plt.xlim(xlo,xhi)
plt.xlabel(pnames[i])
plt.legend((p1[0],p2), ('Tractor', 'SDSS DR7'))
fn = 'modparam%02i-%02i.png' % (step,i)
print 'save', fn
plt.savefig(fn)
plt.figure(2)
plt.clf()
plt.subplots_adjust(left=0.01, right=0.98, bottom=0.03, top=0.98,
wspace=0.1, hspace=0.1)
for i in range(NP):
pari = paris[i]
for j in range(i, NP):
parj = paris[j]
plt.subplot(NP, NP, (i*NP)+j+1)
mn,std = tmn[i],tstd[i]
irng = (mn-3*std, mn+3*std)
mn,std = tmn[j],tstd[j]
jrng = (mn-3*std, mn+3*std)
if i == j:
plt.hist(pari, 25, ec='none', alpha=0.5, range=irng)
plt.hist(pari, 25, histtype='step', ec='b', lw=1, range=irng)
plt.xlabel(pnames[i])
else:
plt.plot(pari, parj, 'b.', alpha=0.1)
plt.ylim(*jrng)
#plt.ylabel(pnames[j])
#plt.xlabel(pnames[i])
plt.xticks([])
plt.yticks([])
plt.xlim(*irng)
plt.savefig('modhist%02i.png' % step)
plt.savefig('modhist%02i.pdf' % step)
plt.figure(1)
# img.shape = data.shape
# del img.data
# del img.invvar
# R = super(Tractor2,self).getModelPatchNoCache(img, src)
# img.data, img.invvar = data,invvar
#run,camcol,field = 7164,4,273
#band='g'
run,camcol,field = 2662, 4, 111
band='i'
roi=[0,300,0,300]
im,info = st.get_tractor_image(run, camcol, field, band,
useMags=True, roi=roi)
sources = st.get_tractor_sources(run, camcol, field, band, roi=roi)
tractor = Tractor2([im], sources)
print tractor
print tractor.getLogProb()
tractor.freezeParam('images')
p0 = tractor.getParams()
tractor.setParams(p0)
print
print 'With Debug:'
tractor.setParams(p0)
tractor.mp = dmup
t0 = Time()
tractor.opt2()
print 'With Debug:', Time()-t0
import numpy as np from tractor import * from tractor import sdss as st from tractor.cache import * from astrometry.util.ttime import * import gc #gc.set_debug(gc.DEBUG_LEAK) #gc.enable() run,camcol,field = 3384, 4, 198 bandname = 'r' timg,info = st.get_tractor_image(run, camcol, field, bandname, useMags=True) sources = st.get_tractor_sources(run, camcol, field,bandname, bands=[bandname]) # mags = [] # for i,src in enumerate(sources): # m = getattr(src.getBrightness(), bandname) # mags.append(m) # J = np.argsort(mags) #sources = [sources[J[i]] for i in range(10)] sources = sources[:20] tractor = Tractor([timg], sources) tractor.cache = Cache(maxsize=100) for step in range(10):
def main():
run = 2863
field = 180
camcol = 4
x0 = 200 #Field of view for the image
x1 = 900
y0 = 400
y1 = 850
starx = 200. #Star location
stary = 300.
starr = 50. #Star radius
roi = [x0,x1,y0,y1]
bands=['r','g','i','u','z']
bandname = 'r'
flipBands = ['r']
rerun = 0
TI = []
TI.extend([st.get_tractor_image(run, camcol, field, bandname,roi=roi,useMags=True) for bandname in bands])
sources = st.get_tractor_sources(run, camcol, field,bandname, bands=bands,roi=roi)
timg,info = TI[0]
photocal = timg.getPhotoCal()
wcs = timg.getWcs()
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
tims = [timg for timg,tinf in TI]
tractor = st.SDSSTractor(tims)
tractor.addSources(sources)
zr = np.array([-5.,+5.]) * info['skysig']
print bands
ra,dec = 152.041958,53.083472
r = 200
itune = 10
ntune = 5
prefix = 'ugc5459'
saveBands('initial-'+prefix, tractor,zr,flipBands,debug=True)
xtr,ytr = wcs.positionToPixel(RaDecPos(ra,dec))
print xtr,ytr
bright = None
lowbright = 1000
xt = 250. #Moving to the left for better results
yt = 210.
for src in sources:
xs,ys = wcs.positionToPixel(src.getPosition(), src)
if (xs-xt)**2+(ys-yt)**2 <= r**2:
if isinstance(src,st.CompositeGalaxy):
brightE = src.brightnessExp
brightD = src.brightnessDev
sumbright = sum([brightE.getMag(bandname)+brightD.getMag(bandname) for bandname in bands])
if sumbright < lowbright:
print("GREATER")
lowBrightE = brightE
lowBrightD = brightD
lowShapeE = src.shapeExp
lowShapeD = src.shapeDev
print "Removed:", src
print xs,ys
tractor.removeSource(src)
saveBands('removed-'+prefix, tractor,zr,flipBands,debug=True)
# bright = Mags(r=15.,u=15.,g=15.,z=15.,i=15.,order=['r','u','g','z','i'])
# shape = st.GalaxyShape(re,ab,phi)
# shape2 = st.GalaxyShape(30.,0.3,45.)
# print bright
# print shape
# print shape2
CG = st.CompositeGalaxy(RaDecPos(ra,dec),lowBrightE,lowShapeE,lowBrightD,lowShapeD)
print CG
tractor.addSource(CG)
saveBands('added-'+prefix,tractor,zr,flipBands,debug=True)
for img in tractor.getImages():
star = [(x,y) for x in range(img.getWidth()) for y in range(img.getHeight()) if (x-starx)**2+(y-stary)**2 <= starr**2]
for (x,y) in star:
img.getInvError()[y][x] = 0
saveBands('nostar-'+prefix,tractor,zr,flipBands,debug=True)
for i in range(itune):
tractor.optimizeCatalogLoop(nsteps=1,srcs=[CG],sky=False)
tractor.clearCache()
saveBands('itune-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
for i in range(ntune):
tractor.optimizeCatalogLoop(nsteps=1,sky=True)
saveBands('ntune-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
tractor.clearCache()
makeflipbook(prefix,flipBands,itune,ntune)
def main():
run = 2863
field = 180
camcol = 4
x0 = 200 #Field of view for the image
x1 = 900
y0 = 400
y1 = 850
starx = 200. #Star location
stary = 300.
starr = 50. #Star radius
roi = [x0,x1,y0,y1]
bands=['r','g','i','u','z']
bandname = 'r'
flipBands = ['r']
rerun = 0
TI = []
TI.extend([st.get_tractor_image(run, camcol, field, bandname,roi=roi,useMags=True) for bandname in bands])
sources = st.get_tractor_sources(run, camcol, field,bandname, bands=bands,roi=roi)
timg,info = TI[0]
photocal = timg.getPhotoCal()
wcs = timg.getWcs()
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
tims = [timg for timg,tinf in TI]
tractor = st.SDSSTractor(tims)
tractor.addSources(sources)
zr = np.array([-5.,+5.]) * info['skysig']
print(bands)
ra,dec = 152.041958,53.083472
r = 200
itune = 10
ntune = 5
prefix = 'ugc5459'
saveBands('initial-'+prefix, tractor,zr,flipBands,debug=True)
xtr,ytr = wcs.positionToPixel(RaDecPos(ra,dec))
print(xtr,ytr)
bright = None
lowbright = 1000
xt = 250. #Moving to the left for better results
yt = 210.
for src in sources:
xs,ys = wcs.positionToPixel(src.getPosition(), src)
if (xs-xt)**2+(ys-yt)**2 <= r**2:
if isinstance(src,st.CompositeGalaxy):
brightE = src.brightnessExp
brightD = src.brightnessDev
sumbright = sum([brightE.getMag(bandname)+brightD.getMag(bandname) for bandname in bands])
if sumbright < lowbright:
print("GREATER")
lowBrightE = brightE
lowBrightD = brightD
lowShapeE = src.shapeExp
lowShapeD = src.shapeDev
print("Removed:", src)
print(xs,ys)
tractor.removeSource(src)
saveBands('removed-'+prefix, tractor,zr,flipBands,debug=True)
# bright = Mags(r=15.,u=15.,g=15.,z=15.,i=15.,order=['r','u','g','z','i'])
# shape = st.GalaxyShape(re,ab,phi)
# shape2 = st.GalaxyShape(30.,0.3,45.)
# print bright
# print shape
# print shape2
CG = st.CompositeGalaxy(RaDecPos(ra,dec),lowBrightE,lowShapeE,lowBrightD,lowShapeD)
print(CG)
tractor.addSource(CG)
saveBands('added-'+prefix,tractor,zr,flipBands,debug=True)
for img in tractor.getImages():
star = [(x,y) for x in range(img.getWidth()) for y in range(img.getHeight()) if (x-starx)**2+(y-stary)**2 <= starr**2]
for (x,y) in star:
img.getInvError()[y][x] = 0
saveBands('nostar-'+prefix,tractor,zr,flipBands,debug=True)
for i in range(itune):
tractor.optimizeCatalogLoop(nsteps=1,srcs=[CG],sky=False)
tractor.clearCache()
saveBands('itune-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
for i in range(ntune):
tractor.optimizeCatalogLoop(nsteps=1,sky=True)
saveBands('ntune-%d-' % (i+1)+prefix,tractor,zr,flipBands,debug=True)
tractor.clearCache()
makeflipbook(prefix,flipBands,itune,ntune)
def makeTractor(bands, ra, dec, S, RCFS):
TI = []
for i,(run,camcol,field) in enumerate(RCFS):
for bandname in bands:
im,inf = st.get_tractor_image(run, camcol, field, bandname,
useMags=True,
roiradecsize=(ra,dec,S))
#im.dxdy = (fxc - xc, fyc - yc)
TI.append((im,inf))
if i == 0 and bandname == 'r':
wcs = im.getWcs()
# im,info = TI[bands.index('r')]
#wcs = im.getWcs()
# this is a shifted WCS; S,S is the center.
# rd = wcs.pixelToPosition(None, (S,S))
# ra,dec = rd.ra,rd.dec
# print 'RA,Dec', ra,dec
cd = wcs.cdAtPixel(xc,yc)
pixscale = np.sqrt(np.abs(np.linalg.det(cd)))
print 'pixscale', pixscale * 3600.
extent = pixscale * S
#for timg,info in TI:
# print timg.hashkey()
sources = st.get_tractor_sources(run, camcol, field, roi=roi,
bands=bands)
print 'Sources:'
for s in sources:
print s
print
print 'Tractor images:', TI
cftimg,cfsky,cfstd = get_cfht_img(ra,dec, extent)
# Create fake tractor.Image
# psf = NCircularGaussianPSF([0.1], [1.])
# sky = timg.sky
# print 'SDSS Sky:', timg.sky
# photocal = timg.photocal
# fakescale = 3
# #wcs = ScaledWCS(fakescale, timg.wcs)
# #(h,w) = timg.data.shape
# #fakedata = np.zeros((h*fakescale, w*fakescale))
# # 0.066
# # make-wcs.py -r 333.55503 -d 0.36438 -s 0.02 -W 1200 -H 1200 fake-wcs.fits
# # # flip parity
# # modhead fake-wcs.fits CD1_1 0
# # modhead fake-wcs.fits CD1_2 1.666666667e-5
# # modhead fake-wcs.fits CD2_1 1.666666667e-5
# # modhead fake-wcs.fits CD2_2 0
# wcs = FitsWcs(Tan('fake-wcs.fits', 0))
# #h,w = 600,600
# h,w = 1200,1200
# fakescale = 0.396 * w / (0.02 * 3600.)
# fakedata = np.zeros((h, w))
# # print '0,0', wcs.pixelToPosition(None, (0,0))
# # print 'W,0', wcs.pixelToPosition(None, (w,0))
# # print '0,H', wcs.pixelToPosition(None, (0,h))
# # print 'W,H', wcs.pixelToPosition(None, (w,h))
# fakewcs = wcs
# fakeimg = Image(data=fakedata, invvar=fakedata, psf=psf,
# wcs=wcs, sky=sky, photocal=photocal, name='Fake')
# del psf
# del wcs
# del photocal
tims = [cftimg] + [timg for timg,tinf in TI]
# for tim in tims:
# try:
# print 'Test pickling im', tim.name
# pickle_to_file(tim, 'test.pickle')
# except:
# print 'failed:'
# traceback.print_exc()
# pass
# for src in sources:
# try:
# print 'Test pickling source', src
# pickle_to_file(src, 'test.pickle')
# except:
# print 'failed:'
# traceback.print_exc()
# pass
# sys.exit(0)
tractor = Tractor(tims)
tractor.addSources(sources)
skyvals = ( [(cfstd, cfsky)] +
[(info['skysig'], info['sky'])
for timg,info in TI] )
return tractor,skyvals
import numpy as np from tractor import * from tractor import sdss as st from tractor.cache import * from astrometry.util.ttime import * import gc # gc.set_debug(gc.DEBUG_LEAK) # gc.enable() run, camcol, field = 3384, 4, 198 bandname = "r" timg, info = st.get_tractor_image(run, camcol, field, bandname, useMags=True) sources = st.get_tractor_sources(run, camcol, field, bandname, bands=[bandname]) # mags = [] # for i,src in enumerate(sources): # m = getattr(src.getBrightness(), bandname) # mags.append(m) # J = np.argsort(mags) # sources = [sources[J[i]] for i in range(10)] sources = sources[:20] tractor = Tractor([timg], sources) tractor.cache = Cache(maxsize=100) for step in range(10):
