def _fit_simple_max(self, obs, model, guesser):
from ngmix.fitting import MaxSimple
max_pars = self['max_pars']
if max_pars['method'] == 'lm':
return self._fit_simple_lm(obs, model, guesser)
prior = self['search_prior']
#print("search prior:",prior)
fitter = MaxSimple(obs,
model,
prior=prior,
use_logpars=self['use_logpars'],
**max_pars)
for i in xrange(max_pars['ntry']):
guess = guesser(prior=prior)
print_pars(guess, front=' max_guess: ')
fitter.run_max(guess, **max_pars)
res = fitter.get_result()
if res['flags'] == 0:
break
return fitter
def _fit_simple_max(self, obs, model, guesser):
from ngmix.fitting import MaxSimple
max_pars=self['max_pars']
if max_pars['method']=='lm':
return self._fit_simple_lm(obs, model, guesser)
prior=self['search_prior']
#print("search prior:",prior)
fitter=MaxSimple(obs,
model,
prior=prior,
use_logpars=self['use_logpars'],
**max_pars)
for i in xrange(max_pars['ntry']):
guess=guesser(prior=prior)
print_pars(guess,front=' max_guess: ')
fitter.run_max(guess, **max_pars)
res=fitter.get_result()
if res['flags']==0:
break
return fitter
def _go_nm(self, ntry=1):
from ngmix.fitting import MaxSimple
for i in xrange(ntry):
guess=self.guesser()
fitter=MaxSimple(self.obs, self.model,
method='Nelder-Mead',
use_logpars=self.use_logpars,
prior=self.prior)
fitter.run_max(guess, **self.send_pars)
res=fitter.get_result()
if res['flags']==0:
break
self.fitter=fitter
def _fit_psf_max(self, obs):
from ngmix.fitting import MaxSimple,LMSimple
assert self['psf_model'] in ["turb","gauss"],"gauss,turb only for now"
max_pars=self['psf_max_pars']
if self['psf_method']=='Nelder-Mead':
fitter=MaxSimple(obs, self['psf_model'], method=self['psf_method'])
else:
fitter=LMSimple(obs, self['psf_model'], lm_pars=max_pars)
Tguess=2*self['psf_sigma_guess']**2
Fguess=obs.image.sum()
guess0=array([0.0, 0.0, 0.0, 0.0, Tguess, Fguess])
for i in xrange(self['psf_ntry']):
guess=guess0.copy()
guess[0:0+2] += 0.01*srandu(2)
guess[2:2+2] += 0.1*srandu(2)
guess[4] *= (1.0 + 0.1*srandu())
guess[5] *= (1.0 + 0.1*srandu())
print_pars(guess,front=' guess: ')
if self['psf_method']=='lm':
fitter.run_max(guess)
else:
fitter.run_max(guess, **max_pars)
res=fitter.get_result()
if res['flags']==0:
print(" max_nfev:",res['nfev'])
break
return fitter
def _go_nm(self, ntry=1):
from ngmix.fitting import MaxSimple
for i in xrange(ntry):
guess = self.guesser()
fitter = MaxSimple(self.obs,
self.model,
method='Nelder-Mead',
use_logpars=self.use_logpars,
prior=self.prior)
fitter.run_max(guess, **self.send_pars)
res = fitter.get_result()
if res['flags'] == 0:
break
self.fitter = fitter
def _fit_simple_max(self, mb_obs_list, model, nm_pars, ntry=1):
"""
parameters
----------
mb_obs_list: MultiBandObsList
The observations to fit
model: string
model to fit
params: dict
from the config file 'me_iter' or 'coadd_iter'
"""
prior=self['model_pars'][model]['gflat_prior']
method = 'Nelder-Mead'
fitter=MaxSimple(mb_obs_list,
model,
prior=prior,
method=method)
guess=self.guesser(prior=prior)
for i in xrange(ntry):
fitter.run_max(guess, **nm_pars)
res=fitter.get_result()
errbad=False
if 'pars_err' in res:
errmin=res['pars_err'][2:2+3].min()
errbad = (errmin < 1.0e-8)
if (ntry > 1) and ( (res['flags'] & EIG_NOTFINITE) != 0 or errbad ):
# bad covariance matrix, need to get a new guess
self._print_pars(res['pars'],front=' bad cov at pars')
if 'pars_err' in res:
self._print_pars(res['pars_err'],front=' perr')
self.guesser=FromParsGuesser(guess, guess*0.1)
guess=self.guesser(prior=prior)
self._print_pars(guess,front=' new guess')
else:
break
return fitter
def _fit_psf_max(self, obs):
from ngmix.fitting import MaxSimple, LMSimple
assert self['psf_model'] in ["turb",
"gauss"], "gauss,turb only for now"
max_pars = self['psf_max_pars']
if self['psf_method'] == 'Nelder-Mead':
fitter = MaxSimple(obs,
self['psf_model'],
method=self['psf_method'])
else:
fitter = LMSimple(obs, self['psf_model'], lm_pars=max_pars)
Tguess = 2 * self['psf_sigma_guess']**2
Fguess = obs.image.sum()
guess0 = array([0.0, 0.0, 0.0, 0.0, Tguess, Fguess])
for i in xrange(self['psf_ntry']):
guess = guess0.copy()
guess[0:0 + 2] += 0.01 * srandu(2)
guess[2:2 + 2] += 0.1 * srandu(2)
guess[4] *= (1.0 + 0.1 * srandu())
guess[5] *= (1.0 + 0.1 * srandu())
print_pars(guess, front=' guess: ')
if self['psf_method'] == 'lm':
fitter.run_max(guess)
else:
fitter.run_max(guess, **max_pars)
res = fitter.get_result()
if res['flags'] == 0:
print(" max_nfev:", res['nfev'])
break
return fitter
