def lensModel(inpars,
image,
sig,
gals,
lenses,
sources,
xc,
yc,
OVRS=1,
csub=11,
psf=None,
noResid=False,
verbose=False):
import pylens, numpy
import indexTricks as iT
model = xc * 0.
for gal in gals:
gal.setPars(inpars)
model += gal.pixeval(xc, yc, 1. / OVRS, csub=csub)
for src in sources:
src.setPars(inpars)
for lens in lenses:
lens.setPars(inpars)
model = model + pylens.lens_images(lenses, sources, [xc, yc], 1. / OVRS)
if numpy.isnan(model.sum()):
if verbose == True:
print 'nan model'
return -1e300
if OVRS > 1:
model = iT.resamp(model, OVRS, True)
if psf is not None:
from imageSim import convolve
global psfFFT
if psfFFT is None:
psf /= psf.sum()
model, psfFFT = convolve.convolve(model, psf)
else:
model, psfFFT = convolve.convolve(model, psfFFT, False)
if noResid is True:
return model
resid = ((model - image) / sig).ravel()
if verbose == True:
print "%f %5.2f %d %dx%d" % (
(resid**2).sum(), (resid**2).sum() / resid.size, resid.size,
image.shape[1], image.shape[0])
return -0.5 * (resid**2).sum()
def lensModel(inpars,image,sig,gals,lenses,sources,xc,yc,OVRS=1,csub=11,psf=None,noResid=False,verbose=False):
import pylens,numpy
import indexTricks as iT
model = xc*0.
for gal in gals:
gal.setPars(inpars)
model += gal.pixeval(xc,yc,1./OVRS,csub=csub)
for src in sources:
src.setPars(inpars)
for lens in lenses:
lens.setPars(inpars)
model = model + pylens.lens_images(lenses,sources,[xc,yc],1./OVRS)
if numpy.isnan(model.sum()):
if verbose==True:
print 'nan model'
return -1e300
if OVRS>1:
model = iT.resamp(model,OVRS,True)
if psf is not None:
from imageSim import convolve
global psfFFT
if psfFFT is None:
psf /= psf.sum()
model,psfFFT = convolve.convolve(model,psf)
else:
model,psfFFT = convolve.convolve(model,psfFFT,False)
if noResid is True:
return model
resid = ((model-image)/sig).ravel()
if verbose==True:
print "%f %5.2f %d %dx%d"%((resid**2).sum(),(resid**2).sum()/resid.size,resid.size,image.shape[1],image.shape[0])
return -0.5*(resid**2).sum()
def lensFit(inpars,image,sig,gals,lenses,sources,xc,yc,OVRS=1,csub=5,psf=None,mask=None,noResid=False,verbose=False,getModel=False,showAmps=False,allowNeg=False):
import pylens,numpy
import indexTricks as iT
from imageSim import convolve
from scipy import optimize
if noResid==True or getModel==True:
mask = None
if mask is None:
xin = xc.copy()
yin = yc.copy()
imin = image.flatten()
sigin = sig.flatten()
else:
xin = xc[mask].copy()
yin = yc[mask].copy()
imin = image[mask].flatten()
sigin = sig[mask].flatten()
n = 0
model = numpy.empty((len(gals)+len(sources),imin.size))
for gal in gals:
gal.setPars()
gal.amp = 1
if mask is None:
tmp = gal.pixeval(xin,yin,1./OVRS,csub=csub)
else:
tmp = xc*0.
tmp[mask] = gal.pixeval(xin,yin,1./OVRS,csub=csub)
if numpy.isnan(tmp).any():
if verbose==True:
print 'nan model'
return -1e300
# if psf is not None:
# tmp = convolve.convolve(tmp,psfFFT,False)[0]
if OVRS>1:
tmp = iT.resamp(tmp,OVRS,True)
if psf is not None and gal.convolve is not None:
tmp = convolve.convolve(tmp,psf,False)[0]
if mask is None:
model[n] = tmp.ravel()
else:
model[n] = tmp[mask].ravel()
n += 1
for lens in lenses:
lens.setPars()
x0,y0 = pylens.lens_images(lenses,sources,[xin,yin],1./OVRS,getPix=True)
for src in sources:
src.setPars()
src.amp = 1
if mask is None:
tmp = src.pixeval(x0,y0,1./OVRS,csub=csub)
else:
tmp = xc*0.
tmp[mask] = src.pixeval(x0,y0,1./OVRS,csub=csub)
if numpy.isnan(tmp).any():
if verbose==True:
print 'nan model'
return -1e300
# if psf is not None:
# tmp = convolve.convolve(tmp,psfFFT,False)[0]
if OVRS>1:
tmp = iT.resamp(tmp,OVRS,True)
if psf is not None:
tmp = convolve.convolve(tmp,psf,False)[0]
if mask is None:
model[n] = tmp.ravel()
else:
model[n] = tmp[mask].ravel()
n += 1
rhs = (imin/sigin)
op = (model/sigin).T
fit,chi = optimize.nnls(op,rhs)
if getModel is True:
j = 0
for m in gals+sources:
m.amp = fit[j]
j += 1
return (model.T*fit).T.reshape((n,image.shape[0],image.shape[1]))
elif noResid is True:
model = (model.T*fit).sum(1).reshape(image.shape)
j = 0
for m in gals+sources:
m.amp = fit[j]
j += 1
return model
model = (model.T*fit).sum(1)
if mask is None:
model = model.reshape(image.shape)
resid = ((model-image)/sig).ravel()
else:
resid = (model-imin)/sigin
if verbose==True:
print "%f %5.2f %d %dx%d"%((resid**2).sum(),(resid**2).sum()/resid.size,resid.size,image.shape[1],image.shape[0])
return -0.5*(resid**2).sum()
def lensFit(inpars,
image,
sig,
gals,
lenses,
sources,
xc,
yc,
OVRS=1,
csub=5,
psf=None,
mask=None,
noResid=False,
verbose=False,
getModel=False,
showAmps=False,
allowNeg=False):
import pylens, numpy
import indexTricks as iT
from imageSim import convolve
from scipy import optimize
"""
if psf is not None:
from imageSim import convolve
global psfFFT
if psfFFT is None:
psf /= psf.sum()
#if OVRS>1:
# from scipy import ndimage
# psf = ndimage.zoom(psf,OVRS)
tmp,psfFFT = convolve.convolve(image,psf)
"""
if noResid == True or getModel == True:
mask = None
if mask is None:
xin = xc.copy()
yin = yc.copy()
imin = image.flatten()
sigin = sig.flatten()
else:
xin = xc[mask].copy()
yin = yc[mask].copy()
imin = image[mask].flatten()
sigin = sig[mask].flatten()
n = 0
model = numpy.empty((len(gals) + len(sources), imin.size))
for gal in gals:
gal.setPars()
gal.amp = 1
if mask is None:
tmp = gal.pixeval(xin, yin, 1. / OVRS, csub=csub)
else:
tmp = xc * 0.
tmp[mask] = gal.pixeval(xin, yin, 1. / OVRS, csub=csub)
if numpy.isnan(tmp).any():
if verbose == True:
print 'nan model'
return -1e300
# if psf is not None:
# tmp = convolve.convolve(tmp,psfFFT,False)[0]
if OVRS > 1:
tmp = iT.resamp(tmp, OVRS, True)
if psf is not None and gal.convolve is not None:
tmp = convolve.convolve(tmp, psf, False)[0]
if mask is None:
model[n] = tmp.ravel()
else:
model[n] = tmp[mask].ravel()
n += 1
for lens in lenses:
lens.setPars()
x0, y0 = pylens.lens_images(lenses,
sources, [xin, yin],
1. / OVRS,
getPix=True)
for src in sources:
src.setPars()
src.amp = 1
if mask is None:
tmp = src.pixeval(x0, y0, 1. / OVRS, csub=csub)
else:
tmp = xc * 0.
tmp[mask] = src.pixeval(x0, y0, 1. / OVRS, csub=csub)
if numpy.isnan(tmp).any():
if verbose == True:
print 'nan model'
return -1e300
# if psf is not None:
# tmp = convolve.convolve(tmp,psfFFT,False)[0]
if OVRS > 1:
tmp = iT.resamp(tmp, OVRS, True)
if psf is not None:
tmp = convolve.convolve(tmp, psf, False)[0]
if mask is None:
model[n] = tmp.ravel()
else:
model[n] = tmp[mask].ravel()
n += 1
rhs = (imin / sigin)
op = (model / sigin).T
fit, chi = optimize.nnls(op, rhs)
#print fit
#exit()
if getModel is True:
j = 0
for m in gals + sources:
m.amp = fit[j]
j += 1
return (model.T * fit).T.reshape((n, image.shape[0], image.shape[1]))
elif noResid is True:
model = (model.T * fit).sum(1).reshape(image.shape)
j = 0
for m in gals + sources:
m.amp = fit[j]
j += 1
return model
model = (model.T * fit).sum(1)
if mask is None:
model = model.reshape(image.shape)
resid = ((model - image) / sig).ravel()
else:
resid = (model - imin) / sigin
if verbose == True:
print "%f %5.2f %d %dx%d" % (
(resid**2).sum(), (resid**2).sum() / resid.size, resid.size,
image.shape[1], image.shape[0])
return -0.5 * (resid**2).sum()