bilby_pipe.data_analysis

Script to perform data analysis

Classes

DataAnalysisInput

Handles user-input for the data analysis script

Functions

`sighandler`(signum, frame)
`create_analysis_parser`()	Data analysis parser creation
`main`()	Data analysis main logic
`reweight`()

Module Contents

bilby_pipe.data_analysis.sighandler(signum, frame)[source]

class bilby_pipe.data_analysis.DataAnalysisInput(args, unknown_args, test=False)[source]

Bases: bilby_pipe.input.Input

Handles user-input for the data analysis script

Parameters:

parser: BilbyArgParser, optional: The parser containing the command line / ini file inputs
args_list: list, optional: A list of the arguments to parse. Defaults to sys.argv[1:]

meta_data[source]

result = None[source]

ini[source]

scheduler[source]

periodic_restart_time[source]

request_cpus[source]

run_local[source]

outdir[source]: The path to the directory where output will be stored

label[source]

result_format[source]

data_dump_file[source]

detectors[source]: A list of the detectors to include, e.g., [‘H1’, ‘L1’]

sampler[source]

sampler_kwargs[source]

property sampling_seed[source]

sampling_frequency[source]

minimum_frequency[source]

The minimum frequency

If a per-detector dictionary is given, this will return the minimum frequency value. To access the dictionary, see self.minimum_frequency_dict

maximum_frequency[source]

The maximum frequency

If a per-detector dictionary is given, this will return the maximum frequency value. To access the dictionary, see self.maximum_frequency_dict

reference_frequency[source]

waveform_generator_class[source]

waveform_approximant[source]

catch_waveform_errors[source]

pn_spin_order[source]

pn_tidal_order[source]

pn_phase_order[source]

pn_amplitude_order[source]

mode_array[source]

waveform_arguments_dict[source]

numerical_relativity_file[source]

frequency_domain_source_model[source]: String of which frequency domain source model to use

conversion_function[source]

generation_function[source]

likelihood_type[source]

reference_frame[source]

time_reference[source]

extra_likelihood_kwargs[source]

enforce_signal_duration[source]

roq_folder[source]

roq_scale_factor[source]

calibration_model[source]

spline_calibration_envelope_dict[source]

spline_calibration_amplitude_uncertainty_dict[source]

spline_calibration_phase_uncertainty_dict[source]

spline_calibration_nodes[source]

calibration_prior_boundary[source]

distance_marginalization[source]

distance_marginalization_lookup_table = None[source]

phase_marginalization[source]

time_marginalization[source]

jitter_time[source]

calibration_marginalization[source]

calibration_lookup_table[source]

number_of_response_curves[source]

property interferometers[source]

static print_detector_information(interferometers)[source]

property data_dump[source]

_load_data_dump()[source]

property result_class[source]: The bilby result class to store results in

property result_directory[source]: The path to the directory where result output will be stored

get_likelihood_and_priors()[source]

Read in the likelihood and prior from the data dump

This reads in the data dump values and reconstructs the likelihood and priors. Note, care must be taken to use the “search_priors” which differ from the true prior when using marginalization

Returns:

likelihood, priors: The bilby likelihood and priors

run_sampler()[source]

get_likelihood_and_priors_for_reweighting()[source]

reweight_result()[source]

bilby_pipe.data_analysis.create_analysis_parser()[source]: Data analysis parser creation

bilby_pipe.data_analysis.main()[source]: Data analysis main logic

bilby_pipe.data_analysis.reweight()[source]