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J1605_keck_inf_v2.py
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J1605_keck_inf_v2.py
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import cPickle,numpy,pyfits
import pymc
from pylens import *
from imageSim import SBModels,convolve,SBObjects
import indexTricks as iT
from SampleOpt import AMAOpt
import pylab as pl
import numpy as np
from pylens import lensModel
# 0.02 arcsec/pixel
# HST: 0.05 arcsec/pixel --> so need to scale and somehow recentre.
# p_k = p_h * 2.5
def MakeCuts():
image = pyfits.open('/data/mauger/EELs/SDSSJ1605+3811/J1605_Kp_narrow_med.fits')[0].data.copy()[550:750,600:850]
header = pyfits.open('/data/mauger/EELs/SDSSJ1605+3811/J1605_Kp_narrow_med.fits')[0].header
pyfits.writeto('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_cutout.fits',image,header,clobber=True)
def MakeMaps():
image = pyfits.open('/data/mauger/EELs/SDSSJ1605+3811/J1605_Kp_narrow_med.fits')[0].data.copy()
header = pyfits.open('/data/mauger/EELs/SDSSJ1605+3811/J1605_Kp_narrow_med.fits')[0].header
cut1 = image[680:725,525:575]
cut2 = image[700:750,850:925]
cut3 = image[525:575,865:950]
var1,var2,var3 = np.var(cut1),np.var(cut2),np.var(cut3)
poisson = np.mean((var1,var2,var3))
sigma = poisson**0.5
im = pyfits.open('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_cutout.fits')[0].data.copy()
smooth = ndimage.gaussian_filter(im,0.7)
noisemap = np.where((smooth>0.7*sigma)&(im>0),im/120.+poisson, poisson)**0.5 # for now - nut find out the actual exposure time from Matt...
pyfits.writeto('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_sigma.fits',noisemap,header,clobber=True)
pl.figure()
pl.imshow(noisemap)
pl.colorbar()
# plot things
def NotPlicely(image,im,sigma):
ext = [0,image.shape[0],0,image.shape[1]]
#vmin,vmax = numpy.amin(image), numpy.amax(image)
pl.figure()
pl.subplot(221)
pl.imshow(image,origin='lower',interpolation='nearest',extent=ext,vmin=0) #,vmin=vmin,vmax=vmax)
pl.colorbar()
pl.title('data')
pl.subplot(222)
pl.imshow(im,origin='lower',interpolation='nearest',extent=ext) #,vmin=vmin,vmax=vmax)
pl.colorbar()
pl.title('model')
pl.subplot(223)
pl.imshow(image-im,origin='lower',interpolation='nearest',extent=ext,vmin=-0.25,vmax=0.25)
pl.colorbar()
pl.title('data-model')
pl.subplot(224)
pl.imshow((image-im)/sigma,origin='lower',interpolation='nearest',extent=ext,vmin=-5,vmax=5)
pl.title('signal-to-noise residuals')
pl.colorbar()
#pl.suptitle(str(V))
#pl.savefig('/data/ljo31/Lens/TeXstuff/plotrun'+str(X)+'.png')
image = pyfits.open('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_cutout.fits')[0].data.copy()
sigma = pyfits.open('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_sigma.fits')[0].data.copy()
yc,xc = iT.coords(image.shape)*0.2
# Model the PSF as a Gaussian to start with. We'll do this over a grid of sigmas, and then maybe also ellitpicity and position anlge (will get kompliziert!!)
xp,yp = iT.coords((50,50))-25
OVRS = 1
# we should get these from the iterated terminal version.
det = np.load('/data/ljo31/Lens/J1605/det8.npy')[()]
g1,g2,l1,s1,s2,sh = {},{},{},{},{},{}
srcs = []
gals = []
lenses = []
coeff=[]
for name in det.keys():
s = det[name]
coeff.append(s[-1])
if name[:8] == 'Source 1':
s1[name[9:]] = s[-1]
elif name[:8] == 'Source 2':
s2[name[9:]] = s[-1]
elif name[:6] == 'Lens 1':
l1[name[7:]] = s[-1]
elif name[:8] == 'Galaxy 1':
g1[name[9:]] = s[-1]
elif name[:8] == 'Galaxy 2':
g2[name[9:]] = s[-1]
elif name[:8] == 'extShear':
if len(name)<9:
sh['b'] = s[-1]
elif name == 'extShear PA':
sh['pa'] = s[-1]
srcs.append(SBModels.Sersic('Source 1',s1))
srcs.append(SBModels.Sersic('Source 2',s2))
gals.append(SBModels.Sersic('Galaxy 1',g1))
gals.append(SBModels.Sersic('Galaxy 2',g2))
lenses.append(MassModels.PowerLaw('Lens 1',l1))
sh['x'] = lenses[0].pars['x']
sh['y'] = lenses[0].pars['y']
lenses.append(MassModels.ExtShear('shear',sh))
## try some way of fitting!
#pars = [xoffset,yoffset,sig]
pars = []
cov = []
pars.append(pymc.Uniform('xoffset',0,20,value=10))
pars.append(pymc.Uniform('yoffset',0,20,value=10))
cov += [20,20] # think about this!
pars.append(pymc.Uniform('sigma',0,8,value=4))
cov += [5]
pars.append(pymc.Uniform('q',0,1,value=0.6))
cov += [1]
pars.append(pymc.Uniform('pa',-180,180,value=150.0))
cov += [50]
@pymc.deterministic
def logP(value=0,p=pars):
x0 = pars[0].value
y0 = pars[1].value
sig = pars[2].value.item()
q = pars[3].value.item()
pa = pars[4].value.item()
psfObj = SBObjects.Gauss('psf',{'x':0,'y':0,'sigma':sig,'q':q,'pa':pa,'amp':10})
psf = psfObj.pixeval(xp,yp)
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
return lensModel.lensFit(None,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,1,
verbose=False,psf=psf,csub=1)
@pymc.observed
def likelihood(value=0.,lp=logP):
return lp
def resid(p):
lp = -2*logP.value
return self.imgs[0].ravel()*0 + lp
optCov = None
if optCov is None:
optCov = numpy.array(cov)
# use lensFit to calculate the likelihood at each point in the chain
for i in range(1):
S = AMAOpt(pars,[likelihood],[logP],cov=optCov/4.)
S.set_minprop(len(pars)*2)
S.sample(80*len(pars)**2)
logp,trace,det = S.result() # log likelihoods; chain (steps * params); det['extShear PA'] = chain in this variable
coeff = []
for i in range(len(pars)):
coeff.append(trace[-1,i])
coeff = numpy.asarray(coeff)
pars = coeff
o = 'npars = ['
for i in range(pars.size):
o += '%f,'%(pars)[i]
o = o[:-1]+"]"
keylist = []
dkeylist = []
chainlist = []
for key in det.keys():
keylist.append(key)
dkeylist.append(det[key][-1])
chainlist.append(det[key])
plot = False
if plot:
for i in range(len(keylist)):
pl.figure()
pl.plot(chainlist[i])
pl.title(str(keylist[i]))
# compare best model with data!
x0 = det['xoffset'][-1]
y0 = det['yoffset'][-1]
sig = det['sigma'][-1]
q = det['q'][-1]
pa = det['pa'][-1]
print x0,y0,sig,q,pa
psfObj = SBObjects.Gauss('psf',{'x':0,'y':0,'sigma':sig,'q':q,'pa':pa,'amp':10})
psf = psfObj.pixeval(xp,yp)
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
im = lensModel.lensFit(coeff,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,OVRS,noResid=True,psf=psf,verbose=True) # return model
model = lensModel.lensFit(coeff,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,OVRS,noResid=True,psf=psf,verbose=True,getModel=True,showAmps=True)
NotPlicely(image,im,sigma)
pl.suptitle('pa = 150')
for key in det.keys():
print key, det[key][-1]
'''
ims = []
models = []
for i in range(len(imgs)):
image = imgs[i]
sigma = sigs[i]
psf = PSFs[i]
if i == 0:
x0,y0 = 0,0
else:
x0,y0 = det['xoffset'][-1], det['yoffset'][-1] # xoffset, yoffset #
print x0,y0
im = lensModel.lensFit(coeff,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,OVRS,psf=psf,verbose=True) # return loglikelihood
print im
im = lensModel.lensFit(coeff,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,OVRS,noResid=True,psf=psf,verbose=True) # return model
model = lensModel.lensFit(coeff,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,OVRS,noResid=True,psf=psf,verbose=True,getModel=True,showAmps=True) # return the model decomposed into the separate galaxy and source components
ims.append(im)
models.append(model)
colours = ['F555W', 'F814W']
for i in range(len(imgs)):
image = imgs[i]
im = ims[i]
model = models[i]
sigma = sigs[i]
pyfits.PrimaryHDU(model).writeto('/data/ljo31/Lens/J1605/components_uniform'+str(colours[i])+str(X)+'.fits',clobber=True)
pyfits.PrimaryHDU(im).writeto('/data/ljo31/Lens/J1605/model_uniform'+str(colours[i])+str(X)+'.fits',clobber=True)
pyfits.PrimaryHDU(image-im).writeto('/data/ljo31/Lens/J1605/resid_uniform'+str(colours[i])+str(X)+'.fits',clobber=True)
f = open('/data/ljo31/Lens/J1605/coeff'+str(X),'wb')
cPickle.dump(coeff,f,2)
f.close()
NotPlicely(image,im,sigma)
pl.suptitle(str(colours[i]))
### OUTPUT THE THINGS IN LATEX-FRIENDLY FORM!
#print '%.1f'%det['Source 1 x'][-1], '&', '%.1f'%det['Source 1 y'][-1], '&', '%.1f'%det['Source 1 n'][-1], '&', '%.1f'%det['Source 1 re'][-1], '&', '%.1f'%det['Source 1 q'][-1], '&', '%.1f'%det['Source 1 pa'][-1], '\\'
#print '%.1f'%det['Source 2 n'][-1], '&', '%.1f'%det['Source 2 re'][-1], '&', '%.1f'%det['Source 2 q'][-1], '&', '%.1f'%det['Source 2 pa'][-1], '\\'
#numpy.save('/data/ljo31/Lens/J1606/trace'+str(Y), trace)
#numpy.save('/data/ljo31/Lens/J1606/logP'+str(Y), logp)
for key in det.keys():
print key, '%.1f'%det[key][-1]
print 'max lnL is ', max(logp)
#pl.figure()
#pl.imshow((im-image)/sigma)
#pl.colorbar()
print det['xoffset'], det['yoffset']
#print xoffset, yoffset
'''