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
