def _fit_simple_emcee_guess(self, mb_obs_list, model, epars):
"""
Fit one of the "simple" models, e.g. exp or dev
use flat g prior
"""
from ngmix.fitting import MCMCSimple
# note flat on g!
prior=self['model_pars'][model]['gflat_prior']
guess=self.guesser(n=epars['nwalkers'], prior=prior)
self._print_pars(guess.mean(axis=0), front=" emcee guess:")
fitter=MCMCSimple(mb_obs_list,
model,
nu=self['nu'],
margsky=self['margsky'],
prior=prior,
nwalkers=epars['nwalkers'],
mca_a=epars['a'],
random_state=self.random_state)
pos=fitter.run_mcmc(guess,epars['burnin'])
pos=fitter.run_mcmc(pos,epars['nstep'])
return fitter
def _fit_simple_mcmc(self, obs, model, guesser):
"""
Fit the simple model
"""
from ngmix.fitting import MCMCSimple
# note flat on g!
prior = self['search_prior']
epars = self['emcee_pars']
guess = guesser(n=epars['nwalkers'], prior=prior)
fitter = MCMCSimple(obs,
model,
prior=prior,
use_logpars=self['use_logpars'],
nwalkers=epars['nwalkers'],
mca_a=epars['a'])
try:
pos = fitter.run_mcmc(guess, epars['burnin'])
pos = fitter.run_mcmc(pos, epars['nstep'], thin=epars['thin'])
except ValueError as err:
print("problem with emcee: %s" % str(err))
return None
if self['g_prior_during']:
weights = None
else:
g_prior = self['prior'].g_prior
trials = fitter.get_trials()
weights = g_prior.get_prob_array2d(trials[:, 2], trials[:, 3])
fitter.calc_result(weights=weights)
self.weights = weights
return fitter
def _fit_simple_mcmc(self, obs, model, guesser):
"""
Fit the simple model
"""
from ngmix.fitting import MCMCSimple
# note flat on g!
prior=self['search_prior']
epars=self['emcee_pars']
guess=guesser(n=epars['nwalkers'], prior=prior)
fitter=MCMCSimple(obs,
model,
prior=prior,
use_logpars=self['use_logpars'],
nwalkers=epars['nwalkers'],
mca_a=epars['a'])
try:
pos=fitter.run_mcmc(guess,epars['burnin'])
pos=fitter.run_mcmc(pos,epars['nstep'],thin=epars['thin'])
except ValueError as err:
print("problem with emcee: %s" % str(err))
return None
if self['g_prior_during']:
weights=None
else:
g_prior = self['prior'].g_prior
trials = fitter.get_trials()
weights = g_prior.get_prob_array2d(trials[:,2], trials[:,3])
fitter.calc_result(weights=weights)
self.weights=weights
return fitter
