def test_fit_1gauss_noisy(ellip=0.2, s2n=10000):
import images
import fimage
numpy.random.seed(35)
sigma = 1.4
T=2*sigma**2
nsig=5
dim = int(nsig*T)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1.)/2.
theta=23.7*numpy.pi/180.
#eta=-0.7
#ellip=(1+tanh(eta))/2
eta = ellip2eta(ellip+1.e-8)
print >>stderr,'ellip:',ellip
pars=array([cen[0],cen[1],eta,theta,1.,T])
print >>stderr,'pars'
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype='eta')
nsub=1
im0=gmix2image_em(gmix,dims,nsub=nsub)
im,skysig = fimage.add_noise(im0, s2n,check=True)
images.multiview(im,title='nsub: %d' % nsub)
p0=pars.copy()
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # eta
p0[3] += 0.5*(randu()-0.5) # theta radians
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0, ptype='eta',verbose=True)
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
print_pars(gf.perr, front='perr: ')
images.imprint(gf.pcov)
def test_fit_1gauss_galsim(ellip=0.2, s2n=10000):
import images
import galsim
import admom
numpy.random.seed(35)
#sigma = 1.4
sigma = 1
T=2*sigma**2
e = 0.2
theta=23.7
e1,e2 = etheta2e1e2(e,theta)
fimage_cov = ellip2mom(T, e=e, theta=theta)
print 'e: ',e
print 'e1:',e1
print 'e2:',e2
pixel_scale = 1.
nsig=5
dim = int(nsig*T)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2
pix = galsim.Pixel(xw=pixel_scale, yw=pixel_scale)
gobj = galsim.Gaussian(sigma=sigma)
gobj.applyDistortion(galsim.Ellipse(e1=e1,e2=e2))
gcobj = galsim.Convolve([gobj,pix])
im0 = galsim.ImageD(int(dims[1]),int(dims[0]))
gcobj.draw(image=im0, dx=pixel_scale)
images.multiview(im0.array)
galsim_nsub=16
ares = admom.admom(im0.array,cen[0],cen[1],guess=T/2,nsub=galsim_nsub)
print 'galsim sigma:',sqrt( (ares['Irr']+ares['Icc'])/2 )
print 'galsim admom e1:',ares['e1']
print 'galsim admom e2:',ares['e2']
print 'galsim center:',ares['row'],ares['col']
fnsub=16
fim0 = model_image('gauss',dims,cen,fimage_cov,nsub=fnsub)
fares = admom.admom(fim0,cen[0],cen[1],guess=T/2,nsub=fnsub)
print 'fimage sigma:',sqrt( (fares['Irr']+fares['Icc'])/2 )
print 'fimage admom e1:',fares['e1']
print 'fimage admom e2:',fares['e2']
print 'fimage center:',fares['row'],fares['col']
return
theta=23.7*numpy.pi/180.
print >>stderr,'ellip:',ellip
pars=array([cen[0],cen[1],eta,theta,1.,T])
print >>stderr,'pars'
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype='eta')
nsub=1
im0=gmix2image_em(gmix,dims,nsub=nsub)
im,skysig = fimage.add_noise(im0, s2n,check=True)
images.multiview(im,title='nsub: %d' % nsub)
p0=pars.copy()
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # eta
p0[3] += 0.5*(randu()-0.5) # theta radians
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0, ptype='eta',verbose=True)
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
print_pars(gf.perr, front='perr: ')
images.imprint(gf.pcov)
def test_fit_1gauss_e1e2(ellip=0.2,
seed=35,
s2n=-9999,
dopsf=False):
import images
from fimage import etheta2e1e2, add_noise, ellip2mom
ptype='e1e2'
numpy.random.seed(seed)
if dopsf:
print >>stderr,"DOING PSF"
Tpsf = 2.0
epsf = 0.2
theta_psf = 80.0
cov_psf = ellip2mom(Tpsf, e=epsf, theta=theta_psf)
psf=[{'p':1.0,
'irr':cov_psf[0],
'irc':cov_psf[1],
'icc':cov_psf[2]}]
else:
psf=None
theta=23.7
e1,e2 = etheta2e1e2(ellip, theta)
sigma = 1.4
T=2*sigma**2
nsig=5
dim = int(nsig*T)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1.)/2.
pars=array([cen[0],cen[1],e1,e2,1.,T])
print >>stderr,'pars'
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype=ptype)
nsub=1
im0=gmix2image_em(gmix,dims,nsub=nsub,psf=psf)
if s2n > 0:
im,skysig = add_noise(im0, s2n,check=True)
else:
im=im0
#images.multiview(im,title='nsub: %d' % nsub)
p0=pars.copy()
"""
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # e1
p0[3] += 0.2*(randu()-0.5) # e2
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
"""
'''
p0[0] += 0.2*(randu()-0.5) # cen0
p0[1] += 0.2*(randu()-0.5) # cen1
p0[2] += 0.1*(randu()-0.5) # e1
p0[3] += 0.1*(randu()-0.5) # e2
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
'''
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0,
psf=psf,
ptype=ptype,
verbose=True)
print >>stderr,'numiter:',gf.numiter
print_pars(gf.popt,front='popt: ')
print_pars(gf.perr,front='perr: ')
def test_fit_dev_e1e2(ngauss=4, s2n=1.e5):
import biggles
import admom
import fimage
numpy.random.seed(35)
ptype='e1e2'
e = 0.2
theta = 23.7
e1,e2 = etheta2e1e2(e,theta)
print >>stderr,"e:",e,"e1:",e1,"e2:",e2
nsig=15
#nsig=7
npars=2*ngauss+4
nsigma_vals=20
f='test-opt-dev-bysigma'
f += '-s2n%d' % s2n
f += '.rec'
pngf=f.replace('.rec','.png')
if not os.path.exists(f):
data=numpy.zeros(nsigma_vals,dtype=[('sigma','f8'),('pars','f8',npars)])
#sigvals = numpy.linspace(1.5,5.0,nsigma_vals)
sigvals = numpy.linspace(3.,5.0,nsigma_vals)
#sigvals=array([3.0])
for isigma,sigma in enumerate(sigvals):
print '-'*70
print 'sigma:',sigma
T = 2*sigma**2
cov = ellip2mom(T, e=e, theta=theta)
dim = int(2*nsig*sigma)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2.
im0 = model_image('dev',dims,cen,cov,nsub=16)
im,skysig = fimage.add_noise(im0, s2n, check=True)
dim = int(2*nsig*sigma)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2.
ares = admom.admom(im0,cen[0],cen[1],guess=T/2,nsub=16)
Tadmom=ares['Irr'] + ares['Icc']
if ngauss == 4:
Tmax = Tadmom*100
# 0.02620127 0.09348825 0.23987656 0.63958437
p0 = array([cen[0],
cen[1],
e1,
e2,
0.026,
0.093,
0.24,
0.64,
Tmax,
Tmax*0.18,
Tmax*0.04,
Tmax*0.0027])
else:
p0 = array([cen[0],
cen[1],
e1,
e2,
1./ngauss,
1./ngauss,
1./ngauss,
Tadmom*4.9,
Tadmom*0.82,
Tadmom*0.18])
print_pars(p0, front='guess: ')
verbose=False
gf=gmix_fit.GMixFitCoellip(im, p0,
ptype=ptype,
verbose=verbose)
chi2per = gf.get_chi2per(gf.popt,skysig)
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
if gf.perr is not None:
print_pars(gf.perr, front='perr: ')
print 'chi2/deg:',chi2per
if gf.flags != 0:
gmix_image.printflags('fit',gf.flags)
raise RuntimeError("failed")
data['sigma'][isigma] = sigma
data['pars'][isigma,:] = gf.popt
tvals = gf.popt[4+ngauss:]
tmax=tvals.max()
print 't ratios:',tvals/tmax
print 'p values:',gf.popt[4:4+ngauss]
print 'T vals/Tadmom:',tvals/Tadmom
# plot the last one
gmix = gmix_fit.pars2gmix_coellip_pick(gf.popt,ptype=ptype)
model = gmix2image_em(gmix,im.shape)
images.compare_images(im,model)
else:
data=eu.io.read(f)
biggles.configure('fontsize_min', 1.0)
biggles.configure('linewidth',1.0) # frame only
nrows=3
ncols=4
tab=biggles.Table(nrows,ncols)
for par in xrange(npars):
plt=biggles.FramedPlot()
plt.add(biggles.Curve(data['sigma'],data['pars'][:,par]))
plt.add(biggles.Points(data['sigma'],data['pars'][:,par],
type='filled circle'))
plt.xlabel = r'$\sigma$'
plt.ylabel = 'p%d' % par
tab[par//ncols,par%ncols] = plt
tab.show()
tab.write_img(1024,1024,pngf)
def test_fit_2gauss_e1e2(ellip=0.2,
seed=35,
s2n=-9999,
dopsf=False):
import images
from fimage import etheta2e1e2, add_noise, ellip2mom
numpy.random.seed(seed)
ptype='e1e2'
nsub=1
if dopsf:
print >>stderr,"DOING PSF"
Tpsf = 2.0
epsf = 0.2
#epsf = 0.2
theta_psf = 80.0
cov_psf = ellip2mom(Tpsf, e=epsf, theta=theta_psf)
psf=[{'p':1.0,
'irr':cov_psf[0],
'irc':cov_psf[1],
'icc':cov_psf[2]}]
else:
psf=None
theta=23.7
e1,e2 = etheta2e1e2(ellip, theta)
T1=3.0
T2=6.0
nsig=5
dim = int(nsig*T2)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2.
p1=0.4
p2=0.6
pars=array([cen[0],cen[1],e1,e2,p1,p2,T1,T2])
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype=ptype)
im0=gmix2image_em(gmix,dims,psf=psf,nsub=nsub)
if s2n > 0:
im,skysig = add_noise(im0, s2n,check=True)
else:
im=im0
p0=pars.copy()
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # e1
p0[3] += 0.2*(randu()-0.5) # e2
p0[4] += 0.2*(randu()-0.5) # p1
p0[5] += 0.2*(randu()-0.5) # p2
p0[6] += 1*(randu()-0.5) # p2
p0[7] += 1*(randu()-0.5) # p2
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0,
ptype=ptype,
psf=psf,
verbose=True)
print >>stderr,'numiter:',gf.numiter
print_pars(gf.popt,front='popt: ')
if gf.perr is not None:
print_pars(gf.perr,front='perr: ')
return pars, gf.popt, gf.perr, gf.pcov