dddm package
Subpackages
- dddm.detectors package
- Submodules
- dddm.detectors.examples module
ArgonSimple
GermaniumSimple
GermaniumSimple.background_function()
GermaniumSimple.cut_efficiency
GermaniumSimple.detection_efficiency
GermaniumSimple.detector_name
GermaniumSimple.energy_threshold_kev
GermaniumSimple.exposure_tonne_year
GermaniumSimple.interaction_type
GermaniumSimple.location
GermaniumSimple.resolution()
GermaniumSimple.target_material
XenonSimple
- dddm.detectors.experiment module
Experiment
Experiment.background_function()
Experiment.config
Experiment.cut_efficiency
Experiment.detection_efficiency
Experiment.detector_hash
Experiment.detector_name
Experiment.e_max_kev
Experiment.e_min_kev
Experiment.effective_exposure
Experiment.energy_threshold_kev
Experiment.exposure_tonne_year
Experiment.interaction_type
Experiment.location
Experiment.n_energy_bins
Experiment.resolution()
Experiment.target_material
- dddm.detectors.super_cdms module
SuperCdmsHvGeMigdal
SuperCdmsHvGeMigdal.background_function()
SuperCdmsHvGeMigdal.cut_efficiency
SuperCdmsHvGeMigdal.detection_efficiency
SuperCdmsHvGeMigdal.detector_name
SuperCdmsHvGeMigdal.energy_threshold_kev
SuperCdmsHvGeMigdal.exposure_tonne_year
SuperCdmsHvGeMigdal.interaction_type
SuperCdmsHvGeMigdal.resolution()
SuperCdmsHvGeMigdal.target_material
SuperCdmsHvGeNr
SuperCdmsHvGeNr.background_function()
SuperCdmsHvGeNr.cut_efficiency
SuperCdmsHvGeNr.detection_efficiency
SuperCdmsHvGeNr.detector_name
SuperCdmsHvGeNr.energy_threshold_kev
SuperCdmsHvGeNr.exposure_tonne_year
SuperCdmsHvGeNr.interaction_type
SuperCdmsHvGeNr.resolution()
SuperCdmsHvGeNr.target_material
SuperCdmsHvSiMigdal
SuperCdmsHvSiMigdal.background_function()
SuperCdmsHvSiMigdal.cut_efficiency
SuperCdmsHvSiMigdal.detection_efficiency
SuperCdmsHvSiMigdal.detector_name
SuperCdmsHvSiMigdal.energy_threshold_kev
SuperCdmsHvSiMigdal.exposure_tonne_year
SuperCdmsHvSiMigdal.interaction_type
SuperCdmsHvSiMigdal.resolution()
SuperCdmsHvSiMigdal.target_material
SuperCdmsHvSiNr
SuperCdmsHvSiNr.background_function()
SuperCdmsHvSiNr.cut_efficiency
SuperCdmsHvSiNr.detection_efficiency
SuperCdmsHvSiNr.detector_name
SuperCdmsHvSiNr.energy_threshold_kev
SuperCdmsHvSiNr.exposure_tonne_year
SuperCdmsHvSiNr.interaction_type
SuperCdmsHvSiNr.resolution()
SuperCdmsHvSiNr.target_material
SuperCdmsIzipGeMigdal
SuperCdmsIzipGeMigdal.background_function()
SuperCdmsIzipGeMigdal.cut_efficiency
SuperCdmsIzipGeMigdal.detection_efficiency
SuperCdmsIzipGeMigdal.detector_name
SuperCdmsIzipGeMigdal.energy_threshold_kev
SuperCdmsIzipGeMigdal.exposure_tonne_year
SuperCdmsIzipGeMigdal.interaction_type
SuperCdmsIzipGeMigdal.resolution()
SuperCdmsIzipGeMigdal.target_material
SuperCdmsIzipGeNr
SuperCdmsIzipGeNr.background_function()
SuperCdmsIzipGeNr.cut_efficiency
SuperCdmsIzipGeNr.detection_efficiency
SuperCdmsIzipGeNr.detector_name
SuperCdmsIzipGeNr.energy_threshold_kev
SuperCdmsIzipGeNr.exposure_tonne_year
SuperCdmsIzipGeNr.interaction_type
SuperCdmsIzipGeNr.resolution()
SuperCdmsIzipGeNr.target_material
SuperCdmsIzipSiMigdal
SuperCdmsIzipSiMigdal.background_function()
SuperCdmsIzipSiMigdal.cut_efficiency
SuperCdmsIzipSiMigdal.detection_efficiency
SuperCdmsIzipSiMigdal.detector_name
SuperCdmsIzipSiMigdal.energy_threshold_kev
SuperCdmsIzipSiMigdal.exposure_tonne_year
SuperCdmsIzipSiMigdal.interaction_type
SuperCdmsIzipSiMigdal.resolution()
SuperCdmsIzipSiMigdal.target_material
SuperCdmsIzipSiNr
SuperCdmsIzipSiNr.background_function()
SuperCdmsIzipSiNr.cut_efficiency
SuperCdmsIzipSiNr.detection_efficiency
SuperCdmsIzipSiNr.detector_name
SuperCdmsIzipSiNr.energy_threshold_kev
SuperCdmsIzipSiNr.exposure_tonne_year
SuperCdmsIzipSiNr.interaction_type
SuperCdmsIzipSiNr.resolution()
SuperCdmsIzipSiNr.target_material
- dddm.detectors.xenon_nt module
- Module contents
- dddm.plotting package
- Submodules
- dddm.plotting.confidence_figures module
DDDMResult
DDDMResult.config_summary()
DDDMResult.detector
DDDMResult.get_from_config()
DDDMResult.get_samples()
DDDMResult.halo_model
DDDMResult.mass
DDDMResult.n_parameters
DDDMResult.nlive
DDDMResult.notes
DDDMResult.result
DDDMResult.result_summary()
DDDMResult.setup()
DDDMResult.sigma
DDDMResult.summary()
ResultsManager
SeabornPlot
- dddm.plotting.plot_basics module
- dddm.plotting.seaborn_utils module
- Module contents
- dddm.recoil_rates package
- dddm.samplers package
- Submodules
- dddm.samplers.emcee module
- dddm.samplers.multi_detectors module
- dddm.samplers.nestle module
- dddm.samplers.pymultinest module
MultiNestSampler
MultiNestSampler.check_did_run()
MultiNestSampler.check_did_save()
MultiNestSampler.get_save_dir()
MultiNestSampler.get_summary()
MultiNestSampler.log_prior_transform_nested()
MultiNestSampler.log_probability_nested()
MultiNestSampler.run()
MultiNestSampler.save_results()
MultiNestSampler.show_corner()
- Module contents
Submodules
dddm.context module
Setup the file structure for the software. Specifies several folders: software_dir: path of installation
dddm.priors module
dddm.statistics module
Statistical model giving likelihoods for detecting a spectrum given a benchmark to compare it with.
- class dddm.statistics.StatModel(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')[source]
Bases:
object
- allow_multiple_detectors = False
- property bench_is_set
- benchmark_values = None
- eval_spectrum(values: Union[list, tuple, ndarray], parameter_names: Union[List[str], Tuple[str]])[source]
For given values and parameter names, return the spectrum one would have with these parameters. The values and parameter names should be array like objects of the same length. Usually, one fits either two (‘log_mass’, ‘log_cross_section’) or five parameters (‘log_mass’, ‘log_cross_section’, ‘v_0’, ‘v_esc’, ‘density’). :param values: array like object of :param parameter_names: names of parameters :return: a spectrum as specified by the parameter_names
- known_parameters = ('log_mass', 'log_cross_section', 'v_0', 'v_esc', 'density')
- property log_cross_section
- property log_mass
- log_prior(value, variable_name)[source]
Compute the prior of variable_name for a given value :param value: value of variable name :param variable_name: name of the ‘value’. This name should be in the config of the class under the priors with a similar content as the priors as specified in the get_prior function. :return: prior of value
- log_probability(parameter_vals, parameter_names)[source]
- Parameters:
parameter_vals – the values of the model/benchmark considered as the truth
parameter_names – the names of the parameter_values
- Returns:
- set_benchmark()[source]
Set up the benchmark used in this statistical model. Likelihood of other models can be evaluated for this ‘truth’
dddm.test_utils module
dddm.utils module
Basic functions for saving et cetera
- dddm.utils.deterministic_hash(thing, length=10)[source]
Return a base32 lowercase string of length determined from hashing a container hierarchy
- dddm.utils.exporter(export_self=False)[source]
Export utility modified from https://stackoverflow.com/a/41895194 Returns export decorator, __all__ list stolen from https://github.com/AxFoundation/strax/blob/d3608efc77acd52e1d5a208c3092b6b45b27a6e2/strax/utils.py#46
- dddm.utils.print_versions(modules=('dddm', 'numpy', 'numba', 'wimprates'), print_output=True, include_python=True, return_string=False, include_git=True)[source]
Print versions of modules installed.
- Parameters:
modules – Modules to print, should be str, tuple or list. E.g. print_versions(modules=(‘numpy’, ‘dddm’,))
return_string – optional. Instead of printing the message, return a string
include_git – Include the current branch and latest commit hash
- Returns:
optional, the message that would have been printed