lenstronomy.Sampling.Samplers package¶
Submodules¶
lenstronomy.Sampling.Samplers.base_nested_sampler module¶
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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
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log_likelihood
(*args, **kwargs)[source]¶ compute the log-likelihood given list of parameters
Parameters: x – parameter values Returns: log-likelihood (from the likelihood module)
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lenstronomy.Sampling.Samplers.dynesty_sampler module¶
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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={})[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/
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log_likelihood
(x)[source]¶ compute the log-likelihood given list of parameters
Parameters: x – parameter values Returns: log-likelihood (from the likelihood module)
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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
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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
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lenstronomy.Sampling.Samplers.multinest_sampler module¶
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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
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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)
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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
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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
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lenstronomy.Sampling.Samplers.polychord_sampler module¶
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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={}, 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
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log_likelihood
(args)[source]¶ compute the log-likelihood given list of parameters
Parameters: args – parameter values Returns: log-likelihood (from the likelihood module)
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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
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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
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