def test_fit_dev_eta_bysigma():
"""
Round object as a function of sigma
"""
import admom
import biggles
from fimage import model_image, ellip2mom
numpy.random.seed(35)
ngauss=4
nsig=15
npars=2*ngauss+4
nsigma_vals=20
e=0.3
eta = ellip2eta(e)
theta=23.7
print >>stderr,'ellip:',e
print >>stderr,'eta:',eta
print >>stderr,'theta:',theta*pi/180.
f='test-opt-dev-bysigma.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([7.0])
for isigma,sigma in enumerate(sigvals):
print '-'*70
print 'sigma:',sigma
T = 2*sigma**2
dim = int(2*nsig*sigma)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2.
cov=ellip2mom(T, e=e, theta=theta)
im = model_image('dev',dims,cen,cov,nsub=16)
#images.multiview(im)
ares = admom.admom(im,cen[0],cen[1],guess=T/2,nsub=16)
if ares['whyflag'] != 0:
raise ValueError("admom failed")
Tadmom = ares['Irr']+ares['Icc']
print >>stderr,'admom sigma:',sqrt(Tadmom/2)
print >>stderr,'admom T:',Tadmom
print >>stderr,'admom e:',sqrt(ares['e1']**2 + ares['e2']**2)
print >>stderr,'T input:',T
#Tuse = Tadmom
Tuse = T
p0 = array([cen[0],
cen[1],
eta,
theta*pi/180.,
0.22,
0.35,
0.25,
0.15,
Tuse*0.15,
Tuse*0.5,
Tuse*2.0,
Tuse*5.0])
#0.18450384 2.09205287 10.31125635 67.13233512
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0, ptype='eta',verbose=True)
flags = gf.flags
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
#for i in xrange(len(gf.popt)):
# print '%.6g' % gf.popt[i]
if gf.flags != 0:
raise RuntimeError("failed")
print >>stderr,'T relative to T uw:',gf.popt[4+ngauss:]/T
print >>stderr,'T relative to T admom:',gf.popt[4+ngauss:]/Tadmom
data['sigma'][isigma] = sigma
data['pars'][isigma,:] = gf.popt
# plot the last one
gmix = gmix_fit.pars2gmix_coellip_eta(gf.popt)
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_exp_eta():
import biggles
from fimage import model_image
numpy.random.seed(35)
nsig=7
ngauss=3
npars=2*ngauss+4
nsigma=20
data=numpy.zeros(nsigma,dtype=[('sigma','f8'),('pars','f8',npars)])
sigvals = numpy.linspace(1.5,5.0,nsigma)
for isigma,sigma in enumerate(sigvals):
print '-'*70
T = 2*sigma**2
e = 0.3
eta = ellip2eta(e)
#theta = randu()*360.*pi/180.
theta = 23.7*pi/180.
e1 = e*cos(2*theta)
e2 = e*sin(2*theta)
Irc = e2*T/2.
Icc = (1+e1)*T/2.
Irr = (1-e1)*T/2.
sigma = sqrt( (Irr+Icc)/2. )
dim = int(2*nsig*sigma)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2.
cov=[Irr,Irc,Icc]
im = model_image('exp',dims,cen,cov,nsub=16)
ngauss=3
p0 = [cen[0],# + 0.1*(randu()-0.5),
cen[1],# + 0.1*(randu()-0.5),
eta,# + 0.2*(randu()-0.5),
theta,# + 10.*pi/180.*(randu()-0.5),
0.2,
0.5,
0.3,
T,
0.05*T,
3.8*T]
gf=gmix_fit.GMixFitCoellip(im, p0,
ptype='eta',
verbose=True)
if gf.flags != 0:
raise RuntimeError("failed")
print 'numiter:',gf.numiter
if gf.flags != 0:
stop
#pcov = gf.pcov
#err = sqrt(diag(pcov))
for i in xrange(len(gf.popt)):
#print '%.6g %.6g' % (gf.popt[i],err[i])
print '%.6g %.6g' % (gf.popt[i],gf.perr[i])
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]
# plot the last one
gmix = gmix_fit.pars2gmix_coellip_eta(gf.popt)
model = gmix2image_em(gmix,im.shape)
plt=images.compare_images(im,model)
epsfile='test-opt-exp.eps'
print >>stderr,'epsfile of image compare:',epsfile
plt.write_eps(epsfile)
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.xlabel = r'$\sigma$'
plt.ylabel = 'p%d' % par
tab[par//ncols,par%ncols] = plt
tab.show()
