lenstronomy.Sampling.Samplers package¶
Submodules¶
lenstronomy.Sampling.Samplers.base_nested_sampler module¶
- class NestedSampler(likelihood_module, prior_type, prior_means, prior_sigmas, width_scale, sigma_scale)[source]¶
Bases:
object
Base class for nested samplers.
- __init__(likelihood_module, prior_type, prior_means, prior_sigmas, width_scale, sigma_scale)[source]¶
- Parameters:
likelihood_module – likelihood_module like in likelihood.py (should be callable)
prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube
prior_means – if prior_type is ‘gaussian’, mean for each param
prior_sigmas – if prior_type is ‘gaussian’, std dev for each param
width_scale – scale the widths of the parameters space by this factor
sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor
- prior(u, *args)[source]¶
Compute the mapping between the unit cube and parameter cube.
- Parameters:
u – unit hypercube, sampled by the algorithm
- Returns:
hypercube in parameter space
lenstronomy.Sampling.Samplers.dynesty_sampler module¶
- class 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:
NestedSampler
Wrapper for dynamical nested sampling algorithm Dynesty by J. Speagle.
paper : https://arxiv.org/abs/1904.02180 doc : https://dynesty.readthedocs.io/
- __init__(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]¶
- Parameters:
likelihood_module – likelihood_module like in likelihood.py (should be callable)
prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube
prior_means – if prior_type is ‘gaussian’, mean for each param
prior_sigmas – if prior_type is ‘gaussian’, std dev for each param
width_scale – scale the widths of the parameters space by this factor
sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor
bound – specific to Dynesty, see https://dynesty.readthedocs.io
sample – specific to Dynesty, see https://dynesty.readthedocs.io
use_mpi – Use MPI computing if True
use_pool – specific to Dynesty, see https://dynesty.readthedocs.io
- 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 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:
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
- __init__(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]¶
- Parameters:
likelihood_module – likelihood_module like in likelihood.py (should be callable)
prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube
prior_means – if prior_type is ‘gaussian’, mean for each param
prior_sigmas – if prior_type is ‘gaussian’, std dev for each param
width_scale – scale the widths of the parameters space by this factor
sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor
output_dir – name of the folder that will contain output files
output_basename – prefix for output files
remove_output_dir – remove the output_dir folder after completion
use_mpi – flag directly passed to MultInest sampler (NOT TESTED)
- 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 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:
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
- __init__(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]¶
- Parameters:
likelihood_module – likelihood_module like in likelihood.py (should be callable)
prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube
prior_means – if prior_type is ‘gaussian’, mean for each param
prior_sigmas – if prior_type is ‘gaussian’, std dev for each param
width_scale – scale the widths of the parameters space by this factor
sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor
output_dir – name of the folder that will contain output files
output_basename – prefix for output files
resume_dyn_run – if True, previous resume files will not be deleted so that previous run can be resumed
polychord_settings – settings dictionary to send to pypolychord. Check dypolychord documentation for details.
remove_output_dir – remove the output_dir folder after completion
use_mpi – Use MPI computing if True
- 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