def make_sampler(self):
ens_samp=EnsembleSampler(self.nwalkers, len(list(self.model.parameters)), self.model.lnposterior, threads=self.threads, args=[self.data])
if self.seed is not None:
seed_state=np.random.mtrand.RandomState(self.seed).get_state()
ens_samp.random_state=seed_state
return ens_samp
def sample_emcee(model,
data,
nwalkers,
nsamples,
walker_initial_pos,
threads='auto',
cleanup_threads=True,
seed=None):
sampler = EnsembleSampler(nwalkers,
len(list(model.parameters)),
model.lnposterior,
threads=autothreads(threads),
args=[data])
if seed is not None:
np.random.seed(seed)
seed_state = np.random.mtrand.RandomState(seed).get_state()
sampler.random_state = seed_state
sampler.run_mcmc(walker_initial_pos, nsamples)
if sampler.pool is not None and cleanup_threads:
sampler.pool.terminate()
sampler.pool.join()
return sampler
def sample_emcee(model, data, nwalkers, nsamples, walker_initial_pos,
threads='auto', cleanup_threads=True, seed=None):
sampler = EnsembleSampler(nwalkers, len(list(model.parameters)),
model.lnposterior,
threads=autothreads(threads), args=[data])
if seed is not None:
np.random.seed(seed)
seed_state = np.random.mtrand.RandomState(seed).get_state()
sampler.random_state=seed_state
sampler.run_mcmc(walker_initial_pos, nsamples)
if sampler.pool is not None and cleanup_threads:
sampler.pool.terminate()
sampler.pool.join()
return sampler
