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)

prior(*args, **kwargs)[source]

compute the mapping between the unit cube and parameter cube

Returns

hypercube in parameter space

run(kwargs_run)[source]

run the nested sampling algorithm

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

Module contents