dddm.samplers package

Submodules

dddm.samplers.emcee module

Do a likelihood fit. The class MCMCStatModel is used for fitting applying the MCMC algorithm emcee.

MCMC is:: slower than the nestle package; and harder to use since one has to choose the ‘right’ initial parameters

Nevertheless, the walkers give great insight in how the likelihood-function is felt by the steps that the walkers make

class dddm.samplers.emcee.MCMCStatModel(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: Union[DetectorSpectrum, GenSpectrum], prior: dict, tmp_folder: str, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nwalkers=50, nsteps=100, remove_frac=0.2, emcee_thin=15)[source]

Bases: StatModel

run()[source]

save_results(save_to_dir='emcee', force_index=False)[source]

set_sampler(mult=True)[source]: init the MCMC sampler

show_corner()[source]

show_walkers()[source]

dddm.samplers.multi_detectors module

class dddm.samplers.multi_detectors.CombinedMultinest(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: List[Union[DetectorSpectrum, GenSpectrum]], prior: dict, tmp_folder: str, results_dir: Optional[str] = None, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nlive=1024, tol=0.1)[source]: Bases: _CombinedInference, MultiNestSampler

class dddm.samplers.multi_detectors.CombinedNestle(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: List[Union[DetectorSpectrum, GenSpectrum]], prior: dict, tmp_folder: str, results_dir: Optional[str] = None, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nlive=1024, tol=0.1)[source]: Bases: _CombinedInference, NestleSampler

class dddm.samplers.multi_detectors.CombinedUltraNest(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: List[Union[DetectorSpectrum, GenSpectrum]], prior: dict, tmp_folder: str, results_dir: Optional[str] = None, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nlive=1024, tol=0.1)[source]: Bases: _CombinedInference, UltraNestSampler

dddm.samplers.nestle module

class dddm.samplers.nestle.NestleSampler(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: Union[DetectorSpectrum, GenSpectrum], prior: dict, tmp_folder: str, results_dir: Optional[str] = None, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nlive=1024, tol=0.1)[source]

Bases: MultiNestSampler

get_summary()[source]

run()[source]

save_results(force_index=False)[source]

show_corner()[source]

dddm.samplers.pymultinest module

Do a likelihood fit. The class NestedSamplerStatModel is used for fitting applying the bayesian algorithm nestle/multinest

class dddm.samplers.pymultinest.MultiNestSampler(wimp_mass: Union[float, int], cross_section: Union[float, int], spectrum_class: Union[DetectorSpectrum, GenSpectrum], prior: dict, tmp_folder: str, results_dir: Optional[str] = None, fit_parameters=('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density', 'k'), detector_name=None, verbose=False, notes='default', nlive=1024, tol=0.1)[source]