def GetFits(self):
bands = dict([('J0837','F606W'),('J0901','F606W'),('J0913','F555W'),('J1125','F606W'),('J1144','F606W'),('J1218','F606W'),('J1248','F555W'),('J1323','F555W'),('J1347','F606W'),('J1446','F606W'),('J1605','F555W'),('J1606','F606W'),('J1619','F606W'),('J2228','F606W')])
# not working yet - this will need us to have all the images somewhere, and all the xc,yc offsets!
# get nnls values! Ideally, these should be already saved
print "why didn't you save these before?!?"
yc,xc = iT.coords(self.img1.shape)
OVRS=1
yo,xo=iT.overSample(self.img1.shape,OVRS)
colours = [bands[self.name], 'F814W']
models = []
fits = []
for i in range(len(self.imgs)):
if i == 0:
dx,dy = 0,0
else:
dx = self.Ddic['xoffset']
dy = self.Ddic['yoffset']
xp,yp = xc+dx+self.Dx,yc+dy+self.Dx
xop,yop = xo+dy+self.Dy,yo+dy+self.Dy
image = self.imgs[i]
sigma = self.sigs[i]
psf = self.PSFs[i]
imin,sigin,xin,yin = image.flatten(), sigma.flatten(),xp.flatten(),yp.flatten()
n = 0
model = np.empty(((len(self.gals) + len(self.srcs)+1),imin.size))
for gal in self.gals:
gal.setPars()
tmp = xc*0.
tmp = gal.pixeval(xp,yp,1./OVRS,csub=11) # evaulate on the oversampled grid. OVRS = number of new pixels per old pixel.
tmp = iT.resamp(tmp,OVRS,True) # convert it back to original size
tmp = convolve.convolve(tmp,psf,False)[0]
model[n] = tmp.ravel()
n +=1
for lens in self.lenses:
lens.setPars()
x0,y0 = pylens.lens_images(self.lenses,self.srcs,[xp,yp],1./OVRS,getPix=True)
for src in self.srcs:
src.setPars()
tmp = xc*0.
tmp = src.pixeval(x0,y0,1./OVRS,csub=11)
tmp = iT.resamp(tmp,OVRS,True)
tmp = convolve.convolve(tmp,psf,False)[0]
model[n] = tmp.ravel()
n +=1
model[n] = np.ones(model[n].shape)
n +=1
rhs = (imin/sigin) # data
op = (model/sigin).T # model matrix
fit, chi = optimize.nnls(op,rhs)
components = (model.T*fit).T.reshape((n,image.shape[0],image.shape[1]))
model = components.sum(0)
models.append(model)
#SotPleparately(image,model,sigma,colours[i])
#NotPlicely(image,model,sigma)
comps = False
if comps == True:
CotSomponents(components,colours[i])
fits.append(fit)
self.fits = fits
def addImg(self,name,img,sig,psf=None,scale=1.):
self.imgs.append(img)
self.sigs.append(sig)
if psf is not None:
PSF = psf/psf.sum()
self.psfImgs.append(PSF)
PSF = convolve.convolve(img,PSF)[1]
self.psfs.append(PSF)
else:
self.psfs.append(None)
self.psfImgs.append(None)
yc,xc = iT.overSample(img.shape,1.)
self.xc.append(xc*scale)
self.yc.append(yc*scale)
if len(self.imgs)>1:
self.offsets.append(pymc.Uniform('xoff_%s'%name,-5.*scale,5.*scale,value=0.))
self.offsets.append(pymc.Uniform('yoff_%s'%name,-5.*scale,5.*scale,value=0.))
self.cov += [0.1,0.1]
def trimandmaskPSFMatrix(PSF, imshape, mask, size=None):
import indexTricks as iT
if size != None:
prad = size + 0.0001
pc = (PSF.shape[0] - 1) / 2
PSF = PSF[pc - prad:pc + prad, pc - prad:pc + prad]
pc = (PSF.shape[0] - 1) / 2
l, m = iT.overSample(PSF.shape, 1)
l -= pc
m -= pc
l = l.flatten()
m = m.flatten()
f = ((l**2 + m**2) < prad**2)
f = f.reshape(PSF.shape)
pmask = numpy.zeros(PSF.shape)
pmask[f] = 1
pmask = pmask.reshape(PSF.shape)
PSF[pmask != 1] = 0
psf = PSF / PSF.sum()
psf /= psf.sum()
Pfull = getPSFMatrix(psf, imshape)
Pmasked = maskPSFMatrix(Pfull, mask)
return Pmasked, Pfull
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 = py.open('/data/ljo31/Lens/J1125/Kp_J1125_nirc2_n.fits')[0].data.copy()[650:905,640:915]
sigma = np.ones(image.shape)
result = np.load('/data/ljo31/Lens/LensModels/J1125_212_nonconcentric')
lp= result[0]
a2=0
a1,a3 = numpy.unravel_index(lp[:,0].argmax(),lp[:,0].shape)
trace = result[1]
dic = result[2]
OVRS = 1
yc,xc = iT.overSample(image.shape,OVRS)
yo,xo = iT.overSample(image.shape,1)
xc,xo,yc,yo = xc+80,xo+80,yc+90,yo+90
mask = np.zeros(image.shape)
tck = RectBivariateSpline(yo[:,0],xo[0],mask)
mask2 = tck.ev(xc,yc)
mask2[mask2<0.5] = 0
mask2[mask2>0.5] = 1
mask2 = mask2==0
mask = mask==0
pars = []
cov = []
### four PSF components
pars.append(pymc.Uniform('xoffset',-40.,40.,value=5))
pars.append(pymc.Uniform('yoffset',-40.,40.,value=5))
guiFile = '/data/ljo31/Lens/J1605/BM2'
guiFile = '/data/ljo31/Lens/J1605/terminal_bestfit_iterated'
#guiFile = '/data/ljo31/Lens/J1605/terminal_bestfit_iterated_2'
#guiFile = '/data/ljo31/Lens/J1605/terminal_bestfit_iterated_3'
guiFile = '/data/ljo31/Lens/J1605/terminal_iterated_4'
#guiFile = '/data/ljo31/Lens/J1605/SingleSource'
print 'schon aus Terminal'
imgs = [img1,img2]
sigs = [sig1,sig2]
psfs = [psf1,psf2]
PSFs = []
yc,xc = iT.overSample(img1.shape,1.)
yc,xc = yc-15.,xc-15.
for i in range(len(imgs)):
psf = psfs[i]
image = imgs[i]
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
PSFs.append(psf)
OVRS = 1
G,L,S,offsets,_ = numpy.load(guiFile)
pars = []
cov = []
image = py.open('/data/ljo31/Lens/J1144/J1144_Kp_narrow_cutout.fits')[0].data.copy()
sigma = np.ones(image.shape)
guiFile = '/data/ljo31/Lens/J1144/FINAL_1src_30'
G,L,S,offsets,shear = numpy.load(guiFile)
print guiFile
result = np.load('/data/ljo31/Lens/J1144/KeckPSF_7')
lp= result[0]
a2=0
a1,a3 = numpy.unravel_index(lp[:,0].argmax(),lp[:,0].shape)
trace = result[1]
dic = result[2]
OVRS = 1
PVRS=2
yc,xc = iT.overSample(image.shape,OVRS)
yo,xo = iT.overSample(image.shape,1)
xc,xo,yc,yo=xc*0.2,xo*0.2,yc*0.2,yo*0.2
xc,xo = xc+12 , xo+12
yc,yo = yc+20 , yo+20
mask = np.zeros(image.shape)
tck = RectBivariateSpline(yo[:,0],xo[0],mask)
mask2 = tck.ev(xc,yc)
mask2[mask2<0.5] = 0
mask2[mask2>0.5] = 1
mask2 = mask2==0
mask = mask==0
xpsf,ypsf = iT.coords((340,340))-170
xpsf,ypsf = iT.overSample((340,340),PVRS) # oversampling
xpsf,ypsf=xpsf-170.,ypsf-170.
dx,dy,sig1,q1,pa1,amp1,sig2,q2,pa2 = trace[a1,a2,a3]
def optimize(data, niter, oname=None, first=True):
import pymc, pyfits, numpy
import indexTricks as iT
priors = data['PRIORS']
models = data['MODELS']
pars = data['PARAMS']
image = {}
for key in data['IMG'].keys():
image[key] = data['IMG'][key].copy()
ZP = data['ZP']
filters = [filt for filt in data['FILTERS']]
sigmas = data['SIGMA']
if 'GAIN' in data.keys():
gain = data['GAIN']
doSigma = True
else:
doSigma = False
if 'OVRS' in data.keys():
OVRS = data['OVRS']
else:
OVRS = 1
MASK = data['MASK'].copy()
mask = MASK == 0
mask_r = mask.ravel()
key2index = {}
i = 0
for key in filters:
key2index[key] = i
i += 1
model2index = {}
i = 0
for key in filters:
for model in models[key]:
model2index[model.name] = i
i += 1
imshape = MASK.shape
yc, xc = iT.overSample(imshape, OVRS)
if doSigma == True:
nu = {}
eta = {}
background = {}
counts = {}
sigmask = {}
for key in filters:
nu[key] = pymc.Uniform('nu_%s' % key,
-6,
6,
value=log10(gain[key]))
eta[key] = pymc.Uniform('eta_%s' % key, -4, 5, value=1.)
background[key] = sigmas[key]
sigmask[key] = image[key] > 1.5 * sigmas[key]**0.5
counts[key] = image[key][sigmask[key]].copy()
pars.append(nu[key])
pars.append(eta[key])
def getSigma(n=nu, e=eta, b=background, c=counts, m=mask):
sigma = b.copy()
sigma[m] += ((10**n) * c)**e
return numpy.sqrt(sigma).ravel()
sigmas = []
for key in filters:
parents = {
'n': nu[key],
'e': eta[key],
'b': background[key],
'c': counts[key],
'm': sigmask[key]
}
sigmas.append(
pymc.Deterministic(eval=getSigma,
name='sigma_%s' % key,
parents=parents,
doc='',
trace=False,
verbose=False))
else:
for key in filters:
sigmas[key] = sigmas[key].ravel()
for key in filters:
image[key] = image[key].ravel()
@pymc.deterministic(trace=False)
def logpAndMags(p=pars):
lp = 0.
mags = []
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
lp += linearmodelSB(p,
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
OVRS=OVRS)
mags += [model.Mag(ZP[key]) for model in models[key]]
return lp, mags
@pymc.deterministic
def lp(lpAM=logpAndMags):
return lpAM[0]
@pymc.deterministic
def Mags(lpAM=logpAndMags):
return lpAM[1]
@pymc.observed
def logpCost(value=0., logP=lp):
return logP
costs = [logpCost]
if priors is not None:
@pymc.observed
def colorPrior(value=0., M=Mags):
lp = 0.
for p in priors:
color = M[model2index[p[0]]] - M[model2index[p[1]]]
lp += p[2](color)
return lp
costs.append(colorPrior)
def resid(p):
model = numpy.empty(0)
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
model = numpy.append(
model,
linearmodelSB(p,
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
levMar=True,
OVRS=OVRS))
return model
print "Optimizing", niter
from SampleOpt import AMAOpt as Opt, levMar as levMar
default = numpy.empty(0)
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
default = numpy.append(default, simage)
# levMar(pars,resid,default)
cov = None
if 'COV' in data.keys():
cov = data['COV']
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.sample(niter / 10)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 4.
O.sample(niter / 4)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 10.
O.sample(niter / 4)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 10.
O.sample(niter)
logp, trace, result = O.result()
mags = numpy.array(result['Mags'])
for key in model2index.keys():
result[key] = mags[:, model2index[key]].copy()
del result['Mags']
output = {}
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
m = linearmodelSB([p.value for p in pars],
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
noResid=True,
OVRS=OVRS)
output[key] = m
return output, (logp, trace, result)
sigName = '/data/ljo31/Lens/J1606/dump/SDSSJ1606+2235_F606W_noise_cutout_2.fits'
sigName = '/data/ljo31/Lens/J1606/SDSSJ1606+2235_F606W_noisemap.fits'
psfName = '/data/ljo31/Lens/J1606/SDSSJ1606+2235_F606W_psf.fits'
#img2Name = '/data/ljo31/Lens/SDSSJ1606+2235_F814W_sci_cutout.fits'
#sig2Name = '/data/ljo31/Lens/SDSSJ1606+2235_F814W_noise3_cutout.fits'
#psf2Name = '/data/ljo31/Lens/SDSSJ1606+2235_F814W_psf.fits'
guiFile = '/data/ljo31/Lens/J1606/fits/ModelFit20_uniform'
image = pyfits.open(imgName)[0].data.copy()
sigma = pyfits.open(sigName)[0].data.copy()
psf = pyfits.open(psfName)[0].data.copy()
yc,xc = iT.overSample(image.shape,1.)
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
OVRS = 1
G,L,S,_,_ = numpy.load(guiFile)
pars = []
srcs = []
cov = []
for name in S.keys():
s = S[name]
p = {}
for key in 'x','y','q','pa','re','n':
if s[key]['type']=='constant':
psf1 = psf1[15:-15,15:-15]
psf1 /= psf1.sum()
img2 = pyfits.open('/data/ljo31/Lens/J1347/SDSSJ1347-0101_F814W_sci_cutout.fits')[0].data.copy()
sig2 = pyfits.open('/data/ljo31/Lens/J1347/SDSSJ1347-0101_F814W_noisemap.fits')[0].data.copy()
psf2 = pyfits.open('/data/ljo31/Lens/J1347/SDSSJ1347-0101_F814W_psf_#2.fits')[0].data.copy()
psf2 = psf2[15:-15,15:-16]
psf2 /= psf2.sum()
imgs = [img1,img2]
sigs = [sig1,sig2]
psfs = [psf1,psf2]
PSFs = []
OVRS = 4
yc,xc = iT.overSample(img1.shape,OVRS)
yc,xc = yc,xc
for i in range(len(imgs)):
psf = psfs[i]
image = imgs[i]
psf /= psf.sum()
psf = convolve.convolve(image,psf)[1]
PSFs.append(psf)
lp = 0.
for i in range(len(imgs)):
if i == 0:
x0,y0 = 0,0
else:
x0 = dx
y0 = dy
guiFile = '/data/ljo31/Lens/J1347/1_src3'
'''
result = np.load('/data/ljo31/Lens/J1347/emcee_FINAL_uncertainties_TWO')
lp= result[0]
a1,a2 = numpy.unravel_index(lp.argmax(),lp.shape)
trace = result[1]
dx,dy,x1,y1,q1,pa1,re1,n1,q2,pa2,re2,n2,x3,y3,q3,pa3,re3,n3,x4,y4,q4,pa4,b,eta,shear,shearpa = trace[a1,a2,:]
'''
imgs = [img1,img2,img3]
sigs = [sig1,sig2,sig3]
psfs = [psf1,psf2,psf3]
PSFs = []
OVRS = 3
yc,xc = iT.overSample(img1.shape,OVRS)
yo,xo = iT.overSample(img1.shape,1)
yck,xck = iT.overSample(img3.shape,1)
yck,xck=yck*0.6,xck*0.6
xck,yck=xck,yck
mask=np.ones(img1.shape)
tck = RectBivariateSpline(yo[:,0],xo[0],mask)
mask2 = tck.ev(yc,xc)
mask2[mask2<0.5] = 0
mask2[mask2>0.5] = 1
print mask2.shape, mask.shape
mask2 = mask2==1
mask = mask==1
for i in range(len(imgs)):
def GetPDFs(self,kpc=False):
self.muPDF = []
self.murestPDF = []
self.RePDF = []
self.magrestPDF = []
self.LumPDF = []
self.magPDF = []
self.fitPDF = []
self.bbandPDF = []
OVRS=self.OVRS
yo,xo = iT.coords(self.img1.shape)
yc,xc=iT.overSample(self.img1.shape,OVRS)
colours = [bands[self.name], 'F814W']
for b1 in range(0,len(self.dictionaries),100):
srcs,gals,lenses = [],[],[]
for number in range(1,1+self.srcno):
name = 'Source '+str(number)
p = {}
for key in 'q','re','n','pa':
p[key] = self.dictionaries[b1][name+' '+key]
for key in 'x','y': # subtract lens potition - to be added back on later in each likelihood iteration!
p[key] = self.dictionaries[b1][name+' '+key]+self.dictionaries[b1]['Lens 1 '+key]
srcs.append(SBBModels.Sersic(name,p))
for number in range(1,1+self.galno):
name = 'Galaxy '+str(number)
p = {}
for key in 'x','y','q','re','n','pa':
p[key] = self.dictionaries[b1][name+' '+key]
gals.append(SBBModels.Sersic(name,p))
p = {}
for key in 'x','y','q','pa','b','eta':
p[key] = self.dictionaries[b1]['Lens 1 '+key]
lenses.append(MassModels.PowerLaw('Lens 1',p))
p = {}
p['x'] = lenses[0].pars['x']
p['y'] = lenses[0].pars['y']
p['b'] = self.dictionaries[b1]['extShear']
p['pa'] = self.dictionaries[b1]['extShear PA']
lenses.append(MassModels.ExtShear('shear',p))
# fits
fits = []
for i in range(len(self.imgs)):
if i == 0:
dx,dy = 0,0
else:
dx = self.dictionaries[b1]['xoffset']
dy = self.dictionaries[b1]['yoffset']
xp,yp = xc+dx+self.Dx,yc+dy+self.Dy
xop,yop = xo+dy+self.Dx,yo+dy+self.Dy
image = self.imgs[i]
sigma = self.sigs[i]
psf = self.PSFs[i]
imin,sigin,xin,yin = image.flatten(), sigma.flatten(),xp.flatten(),yp.flatten()
n = 0
model = np.empty(((len(gals) + len(srcs)+1),imin.size))
for gal in gals:
gal.setPars()
tmp = xc*0.
tmp = gal.pixeval(xp,yp,1./OVRS,csub=11) # evaulate on the oversampled grid. OVRS = number of new pixels per old pixel.
tmp = iT.resamp(tmp,OVRS,True) # convert it back to original size
tmp = convolve.convolve(tmp,psf,False)[0]
model[n] = tmp.ravel()
n +=1
for lens in lenses:
lens.setPars()
x0,y0 = pylens.lens_images(lenses,srcs,[xp,yp],1./OVRS,getPix=True)
for src in srcs:
src.setPars()
tmp = xc*0.
if 'boxiness' in self.dic.keys():
if src.name == 'Source 2':
#print 'this source has a boxy/disky component with c = ', '%.2f'%self.Ddic['boxiness'], '. I hope this is J1606!'
tmp = src.boxypixeval(x0,y0,1./OVRS,csub=11,c=self.Ddic['boxiness'])
else:
tmp = src.pixeval(x0,y0,1./OVRS,csub=11)
else:
tmp = src.pixeval(x0,y0,1./OVRS,csub=11)
tmp = iT.resamp(tmp,OVRS,True)
tmp = convolve.convolve(tmp,psf,False)[0]
model[n] = tmp.ravel()
if self.name == 'J0837':
#print "making J0837's dust lane..."
if src.name == 'Source 2':
model[n] *= -1
n +=1
model[n] = np.ones(model[n].shape)
n +=1
rhs = (imin/sigin) # data
op = (model/sigin).T # model matrix
fit, chi = optimize.nnls(op,rhs)
fits.append(fit)
# source plane (observed) magnitudes
if len(self.srcs)==1 or self.name == 'J0837':
mag_v = srcs[0].getMag(fits[0][-2],self.ZPs[0])
mag_i = srcs[0].getMag(fits[1][-2],self.ZPs[1])
elif len(self.srcs)==2 and self.name != 'J0837':
mv1,mv2 = srcs[0].getMag(fits[0][-3],self.ZPs[0]), srcs[1].getMag(fits[0][-2],self.ZPs[0])
mi1,mi2 = srcs[0].getMag(fits[1][-3],self.ZPs[1]),srcs[1].getMag(fits[1][-2],self.ZPs[1])
Fv = 10**(0.4*(self.ZPs[0]-mv1)) + 10**(0.4*(self.ZPs[0]-mv2))
Fi = 10**(0.4*(self.ZPs[0]-mi1)) + 10**(0.4*(self.ZPs[0]-mi2))
mag_v, mag_i = -2.5*np.log10(Fv) + self.ZPs[0], -2.5*np.log10(Fi) + self.ZPs[1]
else:
print 'how many sources do you want from me?'
# intrinsic magnitudes
if bands[self.name] == 'F555W':
vband = MassB2
bband = MassB2toB
elif bands[self.name] == 'F606W':
vband = MassB1
bband = MassB1toB
iband, kband = MassR, MassK
Lv,mag_v_rest = vband(mag_v, self.z)
Li,mag_i_rest = iband(mag_i, self.z)
Lb,mag_b_rest = bband(mag_v,self.z)
# sizes
if self.srcno == 1 or self.name == 'J0837':
Re_v, Re_i = srcs[0].pars['re']*0.05, srcs[0].pars['re']*0.05
elif self.srcno == 2 and self.name != 'J0837':
Xgrid = np.logspace(-4,5,1501)
Ygrid = np.logspace(-4,5,1501)
bandRes = []
for i in range(len(self.imgs)):
source = fits[i][-3]*srcs[0].eval(Xgrid) + fits[i][-2]*srcs[1].eval(Xgrid)
R = Xgrid.copy()
light = source*2.*np.pi*R
mod = splrep(R,light,t=np.logspace(-3.8,4.8,1301))
intlight = np.zeros(len(R))
for i in range(len(R)):
intlight[i] = splint(0,R[i],mod)
model = splrep(intlight[:-300],R[:-300])
if len(model[1][np.where(np.isnan(model[1])==True)]>0):
#print 'here'
model = splrep(intlight[:-600],R[:-600])
reff = splev(0.5*intlight[-1],model)
bandRes.append(reff*0.05)
Re_v,Re_i = bandRes
# surface brightnesses
mu_v_rest = mag_v_rest + 2.5*np.log10(2.*np.pi*Re_v**2.)
mu_i_rest = mag_i_rest + 2.5*np.log10(2.*np.pi*Re_i**2.)
mu_b_rest = mag_b_rest + 2.5*np.log10(2.*np.pi*Re_v**2.)
mu_v = mag_v + 2.5*np.log10(2.*np.pi*Re_v**2.)
mu_i = mag_i + 2.5*np.log10(2.*np.pi*Re_i**2.)
self.muPDF.append([mu_v,mu_i])
self.murestPDF.append([mu_v_rest,mu_i_rest,mu_b_rest])
if kpc:
self.RePDF.append([Re_v*self.scale,Re_i*self.scale])
else:
self.RePDF.append([Re_v,Re_i])
self.magrestPDF.append([mag_v_rest,mag_i_rest,mag_b_rest])
self.LumPDF.append([Lv,Li,Lb])
self.magPDF.append([mag_v,mag_i])
self.fitPDF.append(fits)
