def logP(value=0,p=pars):
x0 = pars[0].value
y0 = pars[1].value
sig = pars[2].value
psfObj = SBObjects.Gauss('psf',{'x':0,'y':0,'sigma':sig,'q':1,'pa':0,'amp':1})
psf = psfObj.pixeval(xp,yp)
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
g1a,g2a,s1a,s2a,l1a,sha = g1.copy(),g2.copy(),s1.copy(),s2.copy(),l1.copy(),sh.copy()
g1a['x'],g2a['x'] = g1a['x']+x0,g2a['x']+x0
g1a['y'],g2a['y'] = g1a['y']+y0,g2a['y']+y0
s1a['x'] = g1a['x'] + 5*(s1a['x']-(g1a['x']-x0))
s2a['x'] = g1a['x'] + 5*(s2a['x']-(g1a['x']-x0))
s1a['y'] = g1a['y'] + 5*(s1a['y']-(g1a['y']-y0))
s2a['y'] = g1a['y'] + 5*(s2a['y']-(g1a['y']-y0))
l1a['x'] = g1a['x'] + 5*(l1a['x']-(g1a['x']-x0))
l1a['y'] = g1a['y'] + 5*(l1a['y']-(g1a['y']-y0))
s1a['re'],s2a['re'],g1a['re'],g2a['re'],l1a['b'],sha['b'] = s1a['re']*5,s2a['re']*5,g1a['re']*5,g2a['re']*5,l1a['b']*5,sha['b']*5
srcs = []
gals = []
lenses = []
srcs.append(SBModels.Sersic('Source 1',s1a))
srcs.append(SBModels.Sersic('Source 2',s2a))
gals.append(SBModels.Sersic('Galaxy 1',g1a))
gals.append(SBModels.Sersic('Galaxy 2',g2a))
lenses.append(MassModels.PowerLaw('Lens 1',l1a))
sha['x'] = lenses[0].pars['x']
sha['y'] = lenses[0].pars['y']
lenses.append(MassModels.ExtShear('shear',sha))
return lensModel.lensFit(None,image,sigma,gals,lenses,srcs,xc+x0,yc+y0,1,
verbose=False,psf=psf,csub=1)
def logP(value=0,p=pars):
x0 = pars[0].value
y0 = pars[1].value
sig1 = pars[2].value.item()
#q1 = pars[3].value.item()
#pa1 = pars[4].value.item()
amp1 = pars[3].value.item()
sig2 = pars[4].value.item()
#q2 = pars[7].value.item()
#pa2 = pars[8].value.item()
amp2 = pars[5].value.item()
sig3 = pars[6].value.item()
#q3 = pars[11].value.item()
#pa3 = pars[12].value.item()
amp3 = pars[7].value.item()
tamp = amp1+amp2+amp3
q1,q2,q3 = 1,1,1
pa1,pa2,pa3 = 0,0,0
amp1,amp2,amp3 = amp1/tamp, amp2/tamp, amp3/tamp
psfObj1 = SBObjects.Gauss('psf 1',{'x':0,'y':0,'sigma':sig1,'q':q1,'pa':pa1,'amp':10})
psfObj2 = SBObjects.Gauss('psf 2',{'x':0,'y':0,'sigma':sig2,'q':q2,'pa':pa2,'amp':10})
psfObj3 = SBObjects.Gauss('psf 3',{'x':0,'y':0,'sigma':sig3,'q':q3,'pa':pa3,'amp':10})
psf1 = psfObj1.pixeval(xp,yp) * amp1 / (np.pi*2.*sig1**2.)
psf2 = psfObj2.pixeval(xp,yp) * amp2 / (np.pi*2.*sig2**2.)
psf3 = psfObj3.pixeval(xp,yp) * amp3 / (np.pi*2.*sig3**2.)
psf = psf1 + psf2 + psf3
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)
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)
def logP(value=0.,p=pars):
lp = 0.
for i in range(len(imgs)):
if i==0:
x0 = 0.
y0 = 0.
else:
x0 = pars[i*2-2].value
y0 = pars[i*2-1].value
img = imgs[i]
sig = sigs[i]
psf = psfs[i]
lp += lensModel.lensFit(None,img,sig,gals,lenses,srcs,xc[i]+x0,
yc[i]+y0,1,verbose=False,psf=psf,
mask=mask,csub=1)
return lp
def logP(value=0,p=pars):
x0 = pars[0].value
y0 = pars[1].value
sig1 = pars[2].value.item()
q1 = pars[3].value.item()
pa1 = pars[4].value.item()
amp1 = pars[5].value.item()
sig2 = pars[6].value.item()
q2 = pars[7].value.item()
pa2 = pars[8].value.item()
amp2 = 1.-amp1
psfObj1 = SBObjects.Gauss('psf 1',{'x':0,'y':0,'sigma':sig1,'q':q1,'pa':pa1,'amp':10})
psfObj2 = SBObjects.Gauss('psf 2',{'x':0,'y':0,'sigma':sig2,'q':q2,'pa':pa2,'amp':10})
psf1 = psfObj1.pixeval(xp,yp) * amp1 / (np.pi*2.*sig1**2.)
psf2 = psfObj2.pixeval(xp,yp) * amp2 / (np.pi*2.*sig2**2.)
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)
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)
def logP(value=0, p=pars):
x0 = pars[0].value
y0 = pars[1].value
sig1 = pars[2].value.item()
q1 = pars[3].value.item()
pa1 = pars[4].value.item()
amp1 = pars[5].value.item()
sig2 = pars[6].value.item()
q2 = pars[7].value.item()
pa2 = pars[8].value.item()
# q2,pa2 = 1,0
amp2 = 1.0 - amp1
psfObj1 = SBObjects.Gauss("psf 1", {"x": 0, "y": 0, "sigma": sig1, "q": q1, "pa": pa1, "amp": 10})
psfObj2 = SBObjects.Gauss("psf 2", {"x": 0, "y": 0, "sigma": sig2, "q": q2, "pa": pa2, "amp": 10})
psf1 = psfObj1.pixeval(xp, yp) * amp1 / (np.pi * 2.0 * sig1 ** 2.0)
psf2 = psfObj2.pixeval(xp, yp) * amp2 / (np.pi * 2.0 * sig2 ** 2.0)
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
)
def logP(value=0,p=pars):
lp = 0.
for i in range(3):
#print i
image = imgs[i]
sigma = sigs[i]
if i == 0:
psf = PSFs[i]
x0,y0 = 0,0
xm,ym = xc,yc
#print i, x0,y0
if i == 1:
psf = PSFs[i]
x0,y0 = pars[9].value,pars[10].value
#print i, x0,y0
xm,ym = xc,yc
elif i ==2:
x0,y0 = pars[0].value,pars[1].value
#print i,x0,y0
sigma1 = pars[2].value.item()
q1 = pars[3].value.item()
pa1 = pars[4].value.item()
amp1 = pars[5].value.item()
sigma2 = pars[6].value.item()
q2 = pars[7].value.item()
pa2 = pars[8].value.item()
amp2 = 1.-amp1
psfObj1 = SBObjects.Gauss('psf 1',{'x':0,'y':0,'sigma':sigma1,'q':q1,'pa':pa1,'amp':10})
psfObj2 = SBObjects.Gauss('psf 2',{'x':0,'y':0,'sigma':sigma2,'q':q2,'pa':pa2,'amp':10})
psf1 = psfObj1.pixeval(xp,yp) * amp1 / (np.pi*2.*sigma1**2.)
psf2 = psfObj2.pixeval(xp,yp) * amp2 / (np.pi*2.*sigma2**2.)
psf = psf1 + psf2
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
xm,ym = xc3,yc3
lp += lensModel.lensFit(None,image,sigma,gals,lenses,srcs,xm+x0,ym+y0,1,
verbose=False,psf=psf,csub=1)
return lp
def runInference(self,optCov=None,getModel=False):
import numpy
from SampleOpt import AMAOpt,levMar
cov = [c for c in self.cov]
pars = [o for o in self.offsets]
gals = []
srcs = []
lenses = []
self.gals = self.parent.galaxyManager.objs
self.lenses = self.parent.lensManager.objs
self.srcs = self.parent.srcManager.objs
mask = self.parent.mask
if len(self.gals)+len(self.srcs)==0:
return None
# if len(self.gals)+len(self.srcs)+len(self.lenses)==0:
# return None
for g in self.gals.keys():
gal = self.gals[g]
gal.makeModel()
gal,gpars,gcov = gal.model,gal.modelPars,gal.cov
gals.append(gal)
pars += gpars
cov += gcov
for s in self.srcs.keys():
src = self.srcs[s]
src.makeModel()
src,spars,scov = src.model,src.modelPars,src.cov
srcs.append(src)
pars += spars
cov += scov
for l in self.lenses.keys():
lens = self.lenses[l]
lens.makeModel()
lens,lpars,lcov = lens.model,lens.modelPars,lens.cov
lenses.append(lens)
pars += lpars
cov += lcov
if self.parent.shearFlag==True:
shear = self.parent.shear
shear.makeModel()
lenses.append(shear.model)
pars += shear.modelPars
cov += shear.cov
if getModel==True or len(pars)==0:
if len(pars)==0:
self.outPars = []
models = []
for i in range(len(self.imgs)):
if i==0:
x0 = 0.
y0 = 0.
else:
x0 = pars[i*2-2].value
y0 = pars[i*2-1].value
print 'x0,y0',x0,y0
img = self.imgs[i]
sig = self.sigs[i]
psf = self.psfs[i]
xc = self.xc[i]
yc = self.yc[i]
model = lensModel.lensFit(None,img,sig,gals,lenses,srcs,xc+x0,
yc+y0,1,verbose=False,psf=psf,
noResid=True,csub=1)
models.append(model)
return models
# Trim images for faster convolution if masking
xc = []
yc = []
imgs = []
sigs = []
psfs = []
if mask is not None:
Y,X = numpy.where(mask)
ylo,yhi,xlo,xhi = Y.min(),Y.max()+1,X.min(),X.max()+1
mask = mask[ylo:yhi,xlo:xhi]
for i in range(len(self.imgs)):
xc.append(self.xc[i][ylo:yhi,xlo:xhi].copy())
yc.append(self.yc[i][ylo:yhi,xlo:xhi].copy())
imgs.append(self.imgs[i][ylo:yhi,xlo:xhi].copy())
sigs.append(self.sigs[i][ylo:yhi,xlo:xhi].copy())
if self.psfs[i] is not None:
PSF = self.psfImgs[i]
psfs.append(convolve.convolve(imgs[-1],PSF)[1])
else:
xc = [i for i in self.xc]
yc = [i for i in self.yc]
imgs = [i for i in self.imgs]
sigs = [i for i in self.sigs]
psfs = [i for i in self.psfs]
@pymc.deterministic
def logP(value=0.,p=pars):
lp = 0.
for i in range(len(imgs)):
if i==0:
x0 = 0.
y0 = 0.
else:
x0 = pars[i*2-2].value
y0 = pars[i*2-1].value
img = imgs[i]
sig = sigs[i]
psf = psfs[i]
lp += lensModel.lensFit(None,img,sig,gals,lenses,srcs,xc[i]+x0,
yc[i]+y0,1,verbose=False,psf=psf,
mask=mask,csub=1)
return lp
@pymc.observed
def likelihood(value=0.,lp=logP):
return lp
def resid(p):
lp = -2*logP.value
return self.imgs[0].ravel()*0 + lp
if optCov is None:
optCov = numpy.array(cov)
#S = levMar(pars,resid)
#self.outPars = pars
#return
niter = 2*len(pars)**2
if niter<20:
niter = 20
S = AMAOpt(pars,[likelihood],[logP],cov=optCov)
S.set_minprop(10*len(pars))
S.sample(niter)
self.Sampler = S
self.outPars = pars
return self.getModel()
pylab.subplot(222)
pylab.imshow(oimg,origin='lower',interpolation='nearest',extent=ext)
pylab.colorbar()
pylab.subplot(223)
pylab.imshow((img-oimg)/sig,origin='lower',interpolation='nearest',extent=ext)
pylab.colorbar()
pylab.subplot(224)
pylab.imshow(osrc,origin='lower',interpolation='nearest',extent=ext2)
pylab.colorbar()
return osrc
# Setup data for adaptive source modelling
cpsf = convolve.convolve(img,psf)[1]
y,x = iT.coords(img.shape)-15
model = lensModel.lensFit(None,img,sig,gals,lenses,srcs,x,y,1,noResid=True,psf=cpsf,verbose=True,getModel=True,showAmps=True)
galaxy = model[0] + model[1]
img = img - galaxy
pl.figure()
pl.imshow(img,origin='lower',interpolation='nearest')
#pyfits.writeto('/data/ljo31/Lens/J1605/F555W_galsub.fits',img)
ifltm = img[mask]
sfltm = sig[mask]
vfltm = sfltm**2
cmatm = diags(1./sfltm,0)
xm = x[mask]
ym = y[mask]
coords = [xm,ym]
PSF = pT.getPSFMatrix(psf,img.shape)
PSFm = pT.maskPSFMatrix(PSF,mask)
amp3 = det['amp 3'][-1]
tamp = amp1+amp2+amp3
amp1,amp2,amp3 = amp1/tamp,amp2/tamp,amp3/tamp
q1,q2,q3 = 1,1,1
pa1,pa2,pa3 = 0,0,0
psfObj1 = SBObjects.Gauss('psf 1',{'x':0,'y':0,'sigma':sig1,'q':q1,'pa':pa1,'amp':10})
psfObj2 = SBObjects.Gauss('psf 2',{'x':0,'y':0,'sigma':sig2,'q':q2,'pa':pa2,'amp':10})
psfObj3 = SBObjects.Gauss('psf 3',{'x':0,'y':0,'sigma':sig3,'q':q3,'pa':pa3,'amp':10})
psf1 = psfObj1.pixeval(xp,yp) * amp1 / (2.*np.pi*sig1**2)
psf2 = psfObj2.pixeval(xp,yp) * amp2 / (2.*np.pi*sig2**2)
psf3 = psfObj3.pixeval(xp,yp) * amp3 / (2.*np.pi*sig3**2)
psf = psf1 + psf2 + psf3
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 & sigma 3 & pa 1 & pa 2 & pa 3 & q 1 & q 2 & q 3 & amp 1 & amp2 & amp3 \\'
#print '%.1f'%det['xoffset'][-1], '&', '%.1f'%det['yoffset'][-1], '&', '%.1f'%det['sigma 1'][-1], '&', '%.1f'%det['sigma 2'][-1], '&', '%.1f'%det['sigma 3'][-1], '&','%.1f'%det['pa 1'][-1], '&', '%.1f'%det['pa 2'][-1], '&','%.1f'%det['pa 3'][-1], '&', '%.1f'%det['q 1'][-1], '&', '%.1f'%det['q 2'][-1], '&', '%.1f'%det['q 3'][-1], '&','%.1f'%det['amp 1'][-1], '&', '%.1f'%det['amp 2'][-1], '&', '%.1f'%det['amp 3'][-1],'\\'
print 'KECK - x & y & sigma 1 & sigma 2 & sigma 3 & pa 1 & pa 2 & pa 3 & q 1 & q 2 & q 3 & amp 1 & amp2 & amp3 \\'
print '%.1f'%det['xoffset'][-1], '&', '%.1f'%det['yoffset'][-1], '&', '%.1f'%det['sigma 1'][-1], '&', '%.1f'%det['sigma 2'][-1], '&', '%.1f'%det['sigma 3'][-1], '&','%.1f'%det['amp 1'][-1], '&', '%.1f'%det['amp 2'][-1], '&', '%.1f'%det['amp 3'][-1],'\\'
sig1 = det['sigma 1'][-1]
q1 = det['q 1'][-1]
pa1 = det['pa 1'][-1]
amp1 = det['amp 1'][-1]
sig2 = det['sigma 2'][-1]
q2 = det['q 2'][-1]
pa2 = det['pa 2'][-1]
amp2 = 1.-amp1
psfObj1 = SBObjects.Gauss('psf 1',{'x':0,'y':0,'sigma':sig1,'q':q1,'pa':pa1,'amp':10})
psfObj2 = SBObjects.Gauss('psf 2',{'x':0,'y':0,'sigma':sig2,'q':q2,'pa':pa2,'amp':10})
psf1 = psfObj1.pixeval(xp,yp) * amp1 / (2.*np.pi*sig1**2)
psf2 = psfObj2.pixeval(xp,yp) * amp2 / (2.*np.pi*sig2**2)
psf = psf1 + psf2
psf /= psf.sum()
psf = convolve.convolve(img3,psf)[1]
im = lensModel.lensFit(None,img3,sigma,gals,lenses,srcs,xc3+x0,yc3+y0,OVRS,noResid=True,psf=psf,verbose=True) # return model
model = lensModel.lensFit(None,img3,sigma,gals,lenses,srcs,xc3+x0,yc3+y0,OVRS,noResid=True,psf=psf,verbose=True,getModel=True,showAmps=True)
NotPlicely(img3,im,sigma,8)
ims = []
models = []
for i in range(len(PSFs)):
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