def __init__(self, run, **keys):
"""
use config files
can over-ride any values with extra= for quick
tests.
"""
super(BAFitSim,self).__init__(run)
self.update(keys)
cw=self.simc.get('cen_width',None)
if cw is not None:
cen_dist=self.simc['cen_dist']
if cen_dist.lower()=="normal":
center_dist=gmix_image.priors.CenPrior([0.0]*2,[cw]*2)
else:
raise ValueError("implement non-normal cen dist")
self.center_dist=center_dist
else:
self.center_dist=None
if 'verbose' not in self:
self['verbose'] = False
self.gprior = GPriorBA(self.simc['g_width'])
class BAFitSim(shapesim.BaseSim):
def __init__(self, run, **keys):
"""
use config files
can over-ride any values with extra= for quick
tests.
"""
super(BAFitSim,self).__init__(run)
self.update(keys)
cw=self.simc.get('cen_width',None)
if cw is not None:
cen_dist=self.simc['cen_dist']
if cen_dist.lower()=="normal":
center_dist=gmix_image.priors.CenPrior([0.0]*2,[cw]*2)
else:
raise ValueError("implement non-normal cen dist")
self.center_dist=center_dist
else:
self.center_dist=None
if 'verbose' not in self:
self['verbose'] = False
self.gprior = GPriorBA(self.simc['g_width'])
def _set_s2n_info(self, is2n):
self._s2n_psf = self['s2n_psf']
self._s2n = shapesim.get_s2n(self, is2n)
self._s2n_method = self['s2n_method']
self._s2ncalc_fluxfrac =self['s2ncalc_fluxfrac']
def _get_npars(self):
fitmodels=self.get_fitmodels()
if 'coellip' in fitmodels[0]:
ngauss=self.get_coellip_ngauss(fitmodels[0])
npars=2*ngauss+4
elif 'bd' in fitmodels[0]:
npars=8
else:
npars=6
return npars
def process_trial_by_s2n(self, iT, is2n, isplit,
dowrite=False,
dolog=False):
t0=time.time()
nellip=self.get_nellip(is2n)
self._set_s2n_info(is2n)
gvals = self.get_gvals(nellip)
npars=self._get_npars()
out = zeros(nellip*2, dtype=self.out_dtype(npars))
i=0
for ipair,g in enumerate(gvals):
Tobj=self.shapesim._get_Tobj(iT)
counts=self.shapesim._get_counts()
ellip=lensing.util.g2e(g)
if self['verbose'] or ( ( (ipair+1) % 10) == 0 or ipair== 0):
stderr.write(" %s/%s pairs done g: %g\n" % ((ipair+1),nellip,g))
center_offset=None
while True:
theta1 = random.random()*360.0
theta2 = theta1 + 90.0
if self.center_dist is not None:
center_offset=self.center_dist.sample()
ci1=self._get_one_trial(Tobj, counts, ellip, theta1, center_offset=center_offset)
ci2=self._get_one_trial(Tobj, counts, ellip, theta2, center_offset=center_offset)
try:
res1,res2=self._process_pair(ci1,ci2)
break
except TryAgainError:
pass
self._copy_to_output(out, i, ci1, res1)
i += 1
self._copy_to_output(out, i, ci2, res2)
i += 1
tm=time.time()-t0
print 'total time:',tm
print 'time per:',0.5*tm/nellip
if dowrite:
shapesim.write_output(self['run'], iT, is2n, out, itrial=isplit,
fs=self.fs)
return out
def _get_one_trial(self, Tobj, counts, ellip, theta, center_offset=None):
ci_nonoise = self.shapesim.get_trial(Tobj, ellip, theta, counts=counts,
center_offset=center_offset)
if self['retrim']:
if 'retrim_fluxfrac' not in self:
raise ValueError("you must set fluxfrac for a retrim")
retrim_fluxfrac = self['retrim_fluxfrac']
if self['verbose']:
print >>stderr,"trimming:",retrim_fluxfrac,"before:",ci_nonoise.image.shape,
ci_nonoise= fimage.convolved.TrimmedConvolvedImage(ci_nonoise,
fluxfrac=retrim_fluxfrac)
if self['verbose']:
print >>stderr,"after:",ci_nonoise.image.shape
ci = NoisyConvolvedImage(ci_nonoise, self._s2n, self._s2n_psf,
s2n_method=self._s2n_method,
fluxfrac=self._s2ncalc_fluxfrac)
return ci
def _process_pair(self, ci1, ci2):
reslist=[]
for ci in [ci1,ci2]:
res=self._run_models(ci)
if res['flags'] != 0:
raise TryAgainError("failed")
reslist.append(res)
return reslist
def _run_models(self, ci):
# fit models, keep the one that most looks like random error
probrand=-9999e9
fitmodels=self.get_fitmodels()
for fitmodel in fitmodels:
self._run_fitter(ci, fitmodel)
res0 = self.fitter.get_result()
fit_prob=res0.get('fit_prob',-9999)
if len(fitmodels) > 1:
print ' model:',fitmodel,'probrand:',fit_prob
if fit_prob > probrand:
res=res0
probrand=fit_prob
if len(fitmodels) > 1:
print ' best model:',res['model']
return res
def _get_cen_prior(self, ci):
cen_width = self.simc.get('cen_width',0.1)
cen_dist = self.simc['cen_dist']
if cen_dist.lower()=="normal":
cen_prior=CenPrior(ci['cen'], [cen_width]*2)
else:
raise ValueError("implement non-normal cen dist")
return cen_prior
def _get_counts_prior(self, ci):
counts_prior=None
counts_dist = self.simc.get('counts_dist',None)
if counts_dist is not None:
counts_width_frac=self.simc['counts_width_frac']
counts_mean=self.shapesim._counts_mean
counts_width = counts_mean*counts_width_frac
counts_prior = eu.random.get_dist(counts_dist,
[counts_mean,
counts_width])
return counts_prior
def _get_T_prior(self, ci):
T_prior=None
T_dist = self.simc.get('T_dist',None)
if T_dist is not None:
T_width_frac=self.simc['T_width_frac']
T_mean = ci['Ttrue']
T_width = T_mean*T_width_frac
T_prior = eu.random.get_dist(T_dist, [T_mean, T_width])
return T_prior
def _run_fitter(self, ci, fitmodel):
from gmix_image.gmix_em import GMixEMBoot
psf_ivar=1./ci['skysig_psf']**2
gmpsf=GMixEMBoot(ci.psf, self['ngauss_psf'], ci['cen_psf'],
ivar=psf_ivar,
maxiter=self['em_maxiter'],
tol=self['em_tol'])
psf_gmix=gmpsf.get_gmix()
Tguess = ci['Ttrue']*(1. + 0.1*srandu())
ivar=1./ci['skysig']**2
cen_prior=self._get_cen_prior(ci)
counts_prior=self._get_counts_prior(ci)
T_prior=self._get_T_prior(ci)
if 'coellip' in fitmodel:
ngauss=self.get_coellip_ngauss(fitmodel)
self.fitter=MixMCCoellip(ci.image, ivar,
psf_gmix, self.gprior, ngauss,
cen=ci['cen'],
do_pqr=True,
nwalkers=self['nwalkers'],
nstep=self['nstep'],
burnin=self['burnin'],
mca_a=self['mca_a'],
iter=self.get('iter',False),
draw_gprior=self['draw_gprior'])
elif 'bd' in fitmodel:
raise ValueError("fix bd")
self.fitter=MixMCBD(ci.image, ivar,
psf_gmix, self.gprior,
cen=ci['cen'],
do_pqr=True,
nwalkers=self['nwalkers'],
nstep=self['nstep'],
burnin=self['burnin'],
mca_a=self['mca_a'],
iter=self.get('iter',False),
draw_gprior=self['draw_gprior'])
else:
sampler_type=self.get('sampler','mcmc')
T_guess=ci['Ttrue']*(1.+0.3*srandu())
counts_guess=ci['counts_true']*(1.0 + 0.3*srandu())
cen_guess=ci['cen']
if sampler_type=='mcmc':
keys={}
keys.update(self)
keys['do_pqr']=True
keys['make_plots']=self.get('make_plots',False)
keys['cen_prior']=cen_prior
keys['T_prior']=T_prior
keys['counts_prior']=counts_prior
keys['when_prior']=self['when_prior']
keys['nwalkers']=self['nwalkers']
keys['nstep']=self['nstep']
keys['burnin']=self['burnin']
keys['mca_a']=self['mca_a']
keys['iter']=self.get('iter',False),
keys['draw_gprior']=self['draw_gprior']
self.fitter=MixMCSimple(ci.image,
ivar,
psf_gmix,
self.gprior,
T_guess,
counts_guess,
cen_guess,
fitmodel,
**keys)
elif sampler_type=='cmcmc':
config={}
config.update(self)
config['model'] = fitmodel
config['cen1_mean']=ci['cen'][0]
config['cen1_width']=self.simc['cen_width']
config['cen2_mean']=ci['cen'][1]
config['cen2_width']=self.simc['cen_width']
config['g_width']=self.simc['g_width']
config['T_mean']=T_prior.get_mean()
config['T_width']=T_prior.get_sigma()
config['counts_mean']=counts_prior.get_mean()
config['counts_width']=counts_prior.get_sigma()
guess=self._get_cmcmc_simple_guess(ci,config)
self.fitter=gmix_image.gmix_mcmc.MixMCC(ci.image,
ivar,
psf_gmix,
guess,
config,
gprior=self.gprior)
elif sampler=='isample':
keys['nsample']=self['nsample']
g1_guess,g2_guess=self.gprior.sample2d(1)
guess=numpy.zeros(6)
guess[0:2] = cen_guess
guess[2] = g1_guess[0]
guess[3] = g2_guess[0]
guess[4] = T_guess
guess[5] = counts_guess
#prior_samples=self._presample_prior_simple(cen_prior,
# self.gprior,
# T_prior,
# counts_prior)
prior_samples=self._presample_gprior()
self.fitter=gmix_image.gmix_isamp.GMixIsampSimple(ci.image,
ivar,
psf_gmix,
cen_prior,
self.gprior,
T_prior,
counts_prior,
prior_samples,
guess,
fitmodel,
**keys)
def _get_cmcmc_simple_guess(self, ci, config):
nwalkers=config['nwalkers']
guess=numpy.zeros( (nwalkers, 6) )
# cen uniform within 0.1 pixels of truth
guess[:,0] = ci['cen'][0] + 0.1*srandu(nwalkers)
guess[:,1] = ci['cen'][0] + 0.1*srandu(nwalkers)
g_draw=config['g_draw']
if g_draw=="prior":
g1rand,g2rand=self.gprior.sample2d(nwalkers)
guess[:,2]=g1rand
guess[:,3]=g2rand
elif g_draw=="truth":
sh=lensing.Shear(e1=ci['e1true'],e2=ci['e2true'])
g1=sh.g1
g2=sh.g2
g1rand=numpy.zeros( nwalkers )
g2rand=numpy.zeros( nwalkers )
nleft=nwalkers
ngood=0
while nleft > 0:
g1rand_t = g1 + 0.01*srandu(nleft)
g2rand_t = g2 + 0.01*srandu(nleft)
g2tot=g1rand_t**2 + g2rand_t**2
w,=numpy.where(g2tot < 0.999)
if w.size > 0:
g1rand[ngood:ngood+w.size] = g1rand_t[w]
g2rand[ngood:ngood+w.size] = g2rand_t[w]
ngood += w.size
nleft -= w.size
guess[:,2]=g1rand
guess[:,3]=g2rand
elif g_draw=="maxlike":
raise ValueError("implement getting maxlike as guess")
guess[:,4] = ci['Ttrue']*(1.+0.1*srandu(nwalkers))
guess[:,5] = ci['counts_true']*(1.0 + 0.1*srandu(nwalkers))
return guess
def _presample_gprior(self):
if not hasattr(self,'_gprior_samples'):
npre=self.get('n_pre_sample',10000)
print >>stderr,' pre-sampling gprior:',npre
g1,g2= self.gprior.sample2d(npre)
self._gprior_samples= {'g1':g1, 'g2':g2}
return self._gprior_samples
def _presample_prior_simple(self,cen_prior,g_prior,T_prior,counts_prior):
from gmix_image import priors
if not hasattr(self,'_prior_samples'):
npre=self.get('n_pre_sample',100000)
print >>stderr,'pre-sampling the prior',npre
npars=6
nwalkers=20
# per walker, 2000
burnin=100
start=numpy.zeros( (nwalkers,npars) )
start[:,0:2] = cen_prior.sample(nwalkers)
start[:,2] = 0.1*srandu(nwalkers)
start[:,3] = 0.1*srandu(nwalkers)
start[:,4] = T_prior.sample(nwalkers)
start[:,5] = counts_prior.sample(nwalkers)
comb=priors.CombinedPriorSimple(cen_prior,
g_prior,
T_prior,
counts_prior)
sampler = comb.sample(start,
npre,
burnin=burnin,
nwalkers=nwalkers,
get_sampler=True)
prand = sampler.flatchain
lnp = sampler.lnprobability
lnp = lnp.reshape(lnp.shape[0]*lnp.shape[1])
probs = numpy.exp(lnp)
self._prior_samples={'samples':prand,
'prob':probs}
return self._prior_samples
def get_coellip_ngauss(self, model):
if model=='coellip1':
return 1
elif model=='coellip2':
return 2
elif model=='coellip3':
return 3
else:
raise ValueError("implement '%s'" % model)
def get_fitmodels(self):
fitmodels=self['fitmodel']
if not isinstance(fitmodels,list):
fitmodels=[fitmodels]
return fitmodels
def _copy_to_output(self, out, i, ci, res):
sampler_type=self.get('sampler','mcmc')
out['flags'][i] = res['flags']
if res['flags'] != 0:
return
out['s2'][i] = self.simc['Tpsf']/ci['Ttrue']
out['sratio'][i] = sqrt(1./out['s2'][i])
e1true=ci['e1true']
e2true=ci['e2true']
g1true,g2true=lensing.util.e1e2_to_g1g2(e1true,e2true)
out['gtrue'][i,0] = g1true
out['gtrue'][i,1] = g2true
out['shear_true'][i,0] = ci['shear1']
out['shear_true'][i,1] = ci['shear2']
out['Ttrue'][i] = ci['Ttrue']
out['s2n_admom'][i] = ci['s2n_admom']
out['s2n_matched'][i] = ci['s2n_matched']
out['s2n_uw'][i] = ci['s2n_uw']
out['s2n_admom_psf'][i] = ci['s2n_admom_psf']
out['s2n_matched_psf'][i] = ci['s2n_matched_psf']
out['s2n_uw_psf'][i] = ci['s2n_uw_psf']
out['model'][i] = res['model']
out['pars'][i,:] = res['pars']
out['pcov'][i,:,:] = res['pcov']
if 'gcov' in res:
out['g'][i,:] = res['g']
out['gsens'][i,:] = res['gsens']
out['gcov'][i,:,:] = res['gcov']
else:
out['e'][i,:] = res['e']
out['ecov'][i,:,:] = res['ecov']
out['emed'][i,:] = res['emed']
if 'Ts2n' in res:
for tn in ['Tmean','Terr','Ts2n']:
out[tn][i] = res[tn]
for tn in ['flux','flux_err','flux_s2n']:
if tn in res:
out[tn][i] = res[tn]
out['s2n_meas_w'][i] = res['s2n_w']
out['loglike'][i] = res['loglike']
out['chi2per'][i] = res['chi2per']
out['dof'][i] = res['dof']
out['fit_prob'][i] = res['fit_prob']
if 'arate' in res:
out['arate'][i] = res['arate']
if 'P' in res:
out['P'][i] = res['P']
out['Q'][i] = res['Q']
out['R'][i] = res['R']
if sampler_type=='isample':
out['lm_flux'][i] = res['lm_result']['pars'][5]
out['lm_flux_err'][i] = res['lm_result']['perr'][5]
def get_nellip(self, is2n):
s2n = shapesim.get_s2n(self, is2n)
s2n_fac = self['s2n_fac']
nellip = shapesim.get_s2n_nrepeat(s2n, fac=s2n_fac)
if nellip < self['min_gcount']:
nellip=self['min_gcount']
return nellip
def get_gvals(self, nellip):
numpy.random.seed(None)
gvals = self.gprior.sample1d(nellip)
return gvals
def out_dtype(self, npars):
dt=[('model','S20'),
('s2n_admom','f8'),
('s2n_matched','f8'),
('s2n_uw','f8'),
('s2n_admom_psf','f8'),
('s2n_matched_psf','f8'),
('s2n_uw_psf','f8'),
('sratio','f8'),
('s2','f8'),
('shear_true','f8',2),
('gtrue','f8',2),
('Ttrue','f8'),
('g','f8',2),
('gsens','f8',2),
('gcov','f8',(2,2)),
('pars','f8',npars),
('pcov','f8',(npars,npars)),
('Tmean','f8'),
('Terr','f8'),
('Ts2n','f8'),
('flux','f8'),
('flux_err','f8'),
('flux_s2n','f8'),
('s2n_meas_w','f8'), # weighted s/n based on most likely point
('loglike','f8'), # loglike of fit
('chi2per','f8'), # chi^2/degree of freedom
('dof','i4'), # degrees of freedom
('fit_prob','f8'), # probability of the fit happening randomly
('arate','f8'),
('P','f8'), # parameters from BA13
('Q','f8',2),
('R','f8',(2,2)),
('flags','i4')
]
sampler_type=self.get('sampler','mcmc')
if sampler_type=='isample':
dt += [('lm_flux','f8'),
('lm_flux_err','f8')]
return dt