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J1605_keck_inf_v4_moffat.py
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J1605_keck_inf_v4_moffat.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,vmax=8,cmap='afmhot',aspect='auto') #,vmin=vmin,vmax=vmax)
pl.colorbar()
#pl.title('data')
pl.subplot(222)
pl.imshow(im,origin='lower',interpolation='nearest',extent=ext,vmin=0,vmax=8,cmap='afmhot',aspect='auto') #,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,cmap='afmhot',aspect='auto')
pl.colorbar()
#pl.title('data-model')
pl.subplot(224)
pl.imshow((image-im)/sigma,origin='lower',interpolation='nearest',extent=ext,vmin=-5,vmax=5,cmap='afmhot',aspect='auto')
#pl.title('signal-to-noise residuals')
pl.colorbar()
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()
#sigma = np.ones(sigma.shape)*sigma[0,0]
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((170,170))-85
OVRS = 1
guiFile = '/data/ljo31/Lens/J1605/terminal_iterated_4'
G,L,S,offsets,_ = numpy.load(guiFile)
pars = []
cov = []
srcs = []
for name in 'Source 1', 'Source 2':
s = S[name]
p = {}
if name == 'Source 1':
for key in 'x','y','q','pa','re','n':
if s[key]['type']=='constant':
p[key] = s[key]['value']
else:
lo,hi,val = s[key]['lower'],s[key]['upper'],s[key]['value']
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
p[key] = pars[-1]
cov.append(s[key]['sdev'])
elif name == 'Source 2':
for key in 'q','pa','re','n':
if s[key]['type']=='constant':
p[key] = s[key]['value']
else:
lo,hi,val = s[key]['lower'],s[key]['upper'],s[key]['value']
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
p[key] = pars[-1]
cov.append(s[key]['sdev'])
for key in 'x','y':
p[key] = srcs[0].pars[key]
srcs.append(SBModels.Sersic(name,p))
gals = []
for name in 'Galaxy 1', 'Galaxy 2':
s = G[name]
p = {}
if name == 'Galaxy 1':
for key in 'x','y','q','pa','re','n':
if s[key]['type']=='constant':
p[key] = s[key]['value']
else:
lo,hi,val = s[key]['lower'],s[key]['upper'],s[key]['value']
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
p[key] = pars[-1]
cov.append(s[key]['sdev'])
elif name == 'Galaxy 2':
for key in 'q','pa','re','n':
if s[key]['type']=='constant':
p[key] = s[key]['value']
else:
lo,hi,val = s[key]['lower'],s[key]['upper'],s[key]['value']
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
p[key] = pars[-1]
cov.append(s[key]['sdev'])
for key in 'x','y':
p[key] = gals[0].pars[key]
gals.append(SBModels.Sersic(name,p))
lenses = []
for name in L.keys():
s = L[name]
p = {}
for key in 'x','y','q','pa','b','eta':
if s[key]['type']=='constant':
p[key] = s[key]['value']
else:
lo,hi,val = s[key]['lower'],s[key]['upper'],s[key]['value']
if key == 'pa':
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
else:
pars.append(pymc.Uniform('%s %s'%(name,key),lo,hi,value=val))
p[key] = pars[-1]
cov.append(s[key]['sdev'])
lenses.append(MassModels.PowerLaw(name,p))
p = {}
p['x'] = lenses[0].pars['x']
p['y'] = lenses[0].pars['y']
pars.append(pymc.Uniform('extShear',-0.3,0.3,value=0))
cov.append(0.01)
p['b'] = pars[-1]
pars.append(pymc.Uniform('extShear PA',-180.,180.,value=0.))
cov.append(5.)
p['pa'] = pars[-1]
lenses.append(MassModels.ExtShear('shear',p))
pars = []
cov = []
## try some way of fitting!
#pars = [xoffset,yoffset,sig]
pars.append(pymc.Uniform('xoffset',9,13,value=10.5))
pars.append(pymc.Uniform('yoffset',10,14,value=12))
cov += [5,5] # think about this!
pars.append(pymc.Uniform('fwhm 1',0,8,value=4))
cov += [5]
pars.append(pymc.Uniform('q 1',0,1,value=0.7))
cov += [1]
pars.append(pymc.Uniform('pa 1',-180,180,value= 90 )) #-30 )) #-30.0))
cov += [50]
pars.append(pymc.Uniform('amp 1',0,1,value=0.7))
cov += [4]
pars.append(pymc.Uniform('fwhm 2',0,150,value= 15 )) #30eb )) #15))
cov += [40]
pars.append(pymc.Uniform('q 2',0,1,value=0.9))
cov += [1]
pars.append(pymc.Uniform('pa 2',-180,180,value= 90 )) #-100 )) ##-100))
cov += [100]
pars.append(pymc.Uniform('index 2',0,10,value= 4.5 )) #30eb )) #15))
cov += [40]
pars.append(pymc.Uniform('index 1',0,10,value= 4.5 )) #30eb )) #15))
cov += [40]
@pymc.deterministic
def logP(value=0,p=pars):
x0 = pars[0].value
y0 = pars[1].value
fwhm1 = pars[2].value.item()
q1 = pars[3].value.item()
pa1 = pars[4].value.item()
amp1 = pars[5].value.item()
fwhm2 = pars[6].value.item()
q2 = pars[7].value.item()
pa2 = pars[8].value.item()
index2 = pars[9].value.item()
index1 = pars[10].value.item()
amp2 = 1.-amp1
psfObj1 = SBObjects.Moffat('psf 1',{'x':0,'y':0,'fwhm':fwhm1,'q':q1,'pa':pa1,'amp':10,'index':index1})
psfObj2 = SBObjects.Moffat('psf 2',{'x':0,'y':0,'fwhm':fwhm2,'q':q2,'pa':pa2,'amp':10,'index':index2})
psf1 = psfObj1.pixeval(xp,yp) * amp1
psf2 = psfObj2.pixeval(xp,yp) * amp2
psf = psf1 + psf2
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(250*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!
sigma = pyfits.open('/data/ljo31/Lens/J1605/J1605_Kp_narrow_med_sigma.fits')[0].data.copy()
x0 = det['xoffset'][-1]
y0 = det['yoffset'][-1]
fwhm1 = det['fwhm 1'][-1]
q1 = det['q 1'][-1]
pa1 = det['pa 1'][-1]
amp1 = det['amp 1'][-1]
fwhm2 = det['fwhm 2'][-1]
q2 = det['q 2'][-1]
pa2 = det['pa 2'][-1]
index2 = det['index 2'][-1]
index1 = det['index 1'][-1]
amp2 = 1.-amp1
psfObj1 = SBObjects.Moffat('psf 1',{'x':0,'y':0,'fwhm':fwhm1,'q':q1,'pa':pa1,'amp':10,'index':index1})
psfObj2 = SBObjects.Moffat('psf 2',{'x':0,'y':0,'fwhm':fwhm2,'q':q2,'pa':pa2,'amp':10,'index':index2})
psf1 = psfObj1.pixeval(xp,yp) * amp1
psf2 = psfObj2.pixeval(xp,yp) * amp2
psf = psf1 + psf2
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.savefig('/data/ljo31/Lens/J1605/Keckpsf.eps')
for key in det.keys():
print key, det[key][-1]
print 'KECK - x & y & sigma 1 & sigma 2 & pa 1 & pa 2 & q 1 & q 2 & amp 1 & index 2 \\'
print '%.1f'%det['xoffset'][-1], '&', '%.1f'%det['yoffset'][-1], '&', '%.1f'%det['fwhm 1'][-1], '&', '%.1f'%det['fwhm 2'][-1], '&', '%.1f'%det['pa 1'][-1], '&', '%.1f'%det['pa 2'][-1], '&', '%.1f'%det['q 1'][-1], '&', '%.1f'%det['q 2'][-1], '&', '%.1f'%det['amp 1'][-1], '&', '%.1f'%det['index 2'][-1], '&', '%.1f'%det['index 1'][-1], '\\'