def dynesty(test_statistic, transform, n_dim, observed, n_live=100, **kwargs):
"""
Nested sampling with dynesty
"""
sampler = NestedSampler(test_statistic,
transform,
n_dim,
nlive=n_live,
**kwargs)
it = 0
for it, state in enumerate(sampler.sample(dlogz=0.)):
threshold = state[3]
calls = state[9]
if it % 100 == 0:
log_convergence(threshold, observed)
if threshold > observed:
logging.info("threshold reached observed - stopping")
break
calls = sampler.results["ncall"].sum()
n_iter = it + 1
return Result.from_ns(n_iter, n_live, calls)
),
**init_sampler_kwargs,
)
else:
sampler.prior_transform = prior_transform_function
sampler.loglikelihood = log_likelihood_function
sampler.evolve_point = sample_rwalk_parallel_with_act
#sampler.sampling = sample_rwalk_parallel_with_act
sampler.pool = pool
sampler.M = pool.map
logger.info(
f"Starting sampling for job {label}, with pool size={POOL_SIZE} "
f"and check_point_deltaT={check_point_deltaT}")
sampler_kwargs = dict(dlogz=dlogz)
for it, res in enumerate(sampler.sample(**sampler_kwargs)):
(
worst,
ustar,
vstar,
loglstar,
logvol,
logwt,
logz,
logzvar,
h,
nc,
worst_it,
boundidx,
bounditer,