lenstronomy.Sampling.Samplers package¶
Submodules¶
lenstronomy.Sampling.Samplers.base_nested_sampler module¶
- class lenstronomy.Sampling.Samplers.base_nested_sampler.NestedSampler(likelihood_module, prior_type, prior_means, prior_sigmas, width_scale, sigma_scale)[source]¶
Bases:
object
Base class for nested samplers
- log_likelihood(*args, **kwargs)[source]¶
compute the log-likelihood given list of parameters
- Returns
log-likelihood (from the likelihood module)
lenstronomy.Sampling.Samplers.dynesty_sampler module¶
- class lenstronomy.Sampling.Samplers.dynesty_sampler.DynestySampler(likelihood_module, prior_type='uniform', prior_means=None, prior_sigmas=None, width_scale=1, sigma_scale=1, bound='multi', sample='auto', use_mpi=False, use_pool=None)[source]¶
Bases:
lenstronomy.Sampling.Samplers.base_nested_sampler.NestedSampler
Wrapper for dynamical nested sampling algorithm Dynesty by J. Speagle
paper : https://arxiv.org/abs/1904.02180 doc : https://dynesty.readthedocs.io/
- log_likelihood(x)[source]¶
compute the log-likelihood given list of parameters
- Parameters
x – parameter values
- Returns
log-likelihood (from the likelihood module)
- prior(u)[source]¶
compute the mapping between the unit cube and parameter cube
- Parameters
u – unit hypercube, sampled by the algorithm
- Returns
hypercube in parameter space
- run(kwargs_run)[source]¶
run the Dynesty nested sampler
see https://dynesty.readthedocs.io for content of kwargs_run
- Parameters
kwargs_run – kwargs directly passed to DynamicNestedSampler.run_nested
- Returns
samples, means, logZ, logZ_err, logL, results
lenstronomy.Sampling.Samplers.multinest_sampler module¶
- class lenstronomy.Sampling.Samplers.multinest_sampler.MultiNestSampler(likelihood_module, prior_type='uniform', prior_means=None, prior_sigmas=None, width_scale=1, sigma_scale=1, output_dir=None, output_basename='-', remove_output_dir=False, use_mpi=False)[source]¶
Bases:
lenstronomy.Sampling.Samplers.base_nested_sampler.NestedSampler
Wrapper for nested sampling algorithm MultInest by F. Feroz & M. Hobson papers : arXiv:0704.3704, arXiv:0809.3437, arXiv:1306.2144 pymultinest doc : https://johannesbuchner.github.io/PyMultiNest/pymultinest.html
- log_likelihood(args, ndim, nparams)[source]¶
compute the log-likelihood given list of parameters
- Parameters
args – parameter values
ndim – number of sampled parameters
nparams – total number of parameters
- Returns
log-likelihood (from the likelihood module)
- prior(cube, ndim, nparams)[source]¶
compute the mapping between the unit cube and parameter cube (in-place)
- Parameters
cube – unit hypercube, sampled by the algorithm
ndim – number of sampled parameters
nparams – total number of parameters
- run(kwargs_run)[source]¶
run the MultiNest nested sampler
see https://johannesbuchner.github.io/PyMultiNest/pymultinest.html for content of kwargs_run
- Parameters
kwargs_run – kwargs directly passed to pymultinest.run
- Returns
samples, means, logZ, logZ_err, logL, stats
lenstronomy.Sampling.Samplers.polychord_sampler module¶
- class lenstronomy.Sampling.Samplers.polychord_sampler.DyPolyChordSampler(likelihood_module, prior_type='uniform', prior_means=None, prior_sigmas=None, width_scale=1, sigma_scale=1, output_dir=None, output_basename='-', resume_dyn_run=False, polychord_settings=None, remove_output_dir=False, use_mpi=False)[source]¶
Bases:
lenstronomy.Sampling.Samplers.base_nested_sampler.NestedSampler
Wrapper for dynamical nested sampling algorithm DyPolyChord by E. Higson, M. Hobson, W. Handley, A. Lasenby
papers : arXiv:1704.03459, arXiv:1804.06406 doc : https://dypolychord.readthedocs.io
- log_likelihood(args)[source]¶
compute the log-likelihood given list of parameters
- Parameters
args – parameter values
- Returns
log-likelihood (from the likelihood module)
- prior(cube)[source]¶
compute the mapping between the unit cube and parameter cube
‘copy=True’ below because cube can not be modified in-place (read-only)
- Parameters
cube – unit hypercube, sampled by the algorithm
- Returns
hypercube in parameter space
- run(dynamic_goal, kwargs_run)[source]¶
run the DyPolyChord dynamical nested sampler
see https://dypolychord.readthedocs.io for content of kwargs_run
- Parameters
dynamic_goal – 0 for evidence computation, 1 for posterior computation
kwargs_run – kwargs directly passed to dyPolyChord.run_dypolychord
- Returns
samples, means, logZ, logZ_err, logL, ns_run