gwkokab.analysis.subpopulation.analytical

Classes

SubPopulationModelAnalyticalAnalysis	AnalysisBase is a class which contains all the common functionality among the
SubPopulationModelFAnalyticalAnalysis	AnalysisBase is a class which contains all the common functionality among the
SubPopulationModelNAnalyticalAnalysis	AnalysisBase is a class which contains all the common functionality among the

Functions

main(→ None)

Module Contents

class gwkokab.analysis.subpopulation.analytical.SubPopulationModelAnalyticalAnalysis(N_spl: int, N_bpl: int, N_gpl: int, use_beta_spin_magnitude: bool, use_spin_magnitude_mixture: bool, use_truncated_normal_spin_x: bool, use_truncated_normal_spin_y: bool, use_truncated_normal_spin_z: bool, use_chi_eff_mixture: bool, use_skew_normal_chi_eff: bool, use_truncated_normal_chi_p: bool, use_tilt: bool, use_eccentricity_mixture: bool, use_eccentricity_powerlaw: bool, use_mean_anomaly: bool, use_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, likelihood_fn: collections.abc.Callable[Ellipsis, collections.abc.Callable], data_loader: gwkokab.analysis.core.inference_io.AnalyticalPELoader, prior_filename: str, poisson_mean_filename: str, sampler_cfg, variance_cut_threshold: float | None, n_samples: int, debug_nans: bool = False, profile_memory: bool = False, check_leaks: bool = False)

Bases: gwkokab.analysis.subpopulation.common.SubPopulationModelCore, gwkokab.analysis.core.analytical_base.AnalyticalBase

AnalysisBase is a class which contains all the common functionality among the different analyses.

It is not meant to be used directly, but rather to be subclassed by the specific analyses.

Parameters:

• likelihood_fn (Callable[..., Callable[..., Array]]) – A function that takes the model parameters and returns a function that computes the log-likelihood.

• model (Union[Distribution, Callable[..., Distribution]]) – model to be used in the AnalyticalBase class. It can be a Distribution or a callable that returns a Distribution.

• data_loader (AnalyticalPELoader) – data loader for the analytical PE data.

• seed (int) – seed for the random number generator.

• prior_filename (str) – path to the JSON file containing the prior distributions.

• poisson_mean_filename (str) – path to the JSON file containing the Poisson mean configuration.

• flowMC_settings_filename (str) – path to the JSON file containing the flowMC settings.

• debug_nans (bool, optional) – If True, checks for NaNs in each computation. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug_nans.html#jax.debug_nans), by default False

• profile_memory (bool, optional) – If True, enables memory profiling, by default False

• check_leaks (bool, optional) – If True, checks for JAX Tracer leaks. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.checking_leaks.html#jax.checking_leaks), by default False

• analysis_name (str, optional) – Name of the analysis, by default “”

class gwkokab.analysis.subpopulation.analytical.SubPopulationModelFAnalyticalAnalysis(N_spl: int, N_bpl: int, N_gpl: int, use_beta_spin_magnitude: bool, use_spin_magnitude_mixture: bool, use_truncated_normal_spin_x: bool, use_truncated_normal_spin_y: bool, use_truncated_normal_spin_z: bool, use_chi_eff_mixture: bool, use_skew_normal_chi_eff: bool, use_truncated_normal_chi_p: bool, use_tilt: bool, use_eccentricity_mixture: bool, use_eccentricity_powerlaw: bool, use_mean_anomaly: bool, use_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, likelihood_fn: collections.abc.Callable[Ellipsis, collections.abc.Callable], data_loader: gwkokab.analysis.core.inference_io.AnalyticalPELoader, prior_filename: str, poisson_mean_filename: str, sampler_cfg, variance_cut_threshold: float | None, n_samples: int, debug_nans: bool = False, profile_memory: bool = False, check_leaks: bool = False)

Bases: SubPopulationModelAnalyticalAnalysis, gwkokab.analysis.core.flowMC_base.FlowMCBase

AnalysisBase is a class which contains all the common functionality among the different analyses.

It is not meant to be used directly, but rather to be subclassed by the specific analyses.

Parameters:

• likelihood_fn (Callable[..., Callable[..., Array]]) – A function that takes the model parameters and returns a function that computes the log-likelihood.

• model (Union[Distribution, Callable[..., Distribution]]) – model to be used in the AnalyticalBase class. It can be a Distribution or a callable that returns a Distribution.

• data_loader (AnalyticalPELoader) – data loader for the analytical PE data.

• seed (int) – seed for the random number generator.

• prior_filename (str) – path to the JSON file containing the prior distributions.

• poisson_mean_filename (str) – path to the JSON file containing the Poisson mean configuration.

• flowMC_settings_filename (str) – path to the JSON file containing the flowMC settings.

• debug_nans (bool, optional) – If True, checks for NaNs in each computation. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug_nans.html#jax.debug_nans), by default False

• profile_memory (bool, optional) – If True, enables memory profiling, by default False

• check_leaks (bool, optional) – If True, checks for JAX Tracer leaks. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.checking_leaks.html#jax.checking_leaks), by default False

• analysis_name (str, optional) – Name of the analysis, by default “”

class gwkokab.analysis.subpopulation.analytical.SubPopulationModelNAnalyticalAnalysis(N_spl: int, N_bpl: int, N_gpl: int, use_beta_spin_magnitude: bool, use_spin_magnitude_mixture: bool, use_truncated_normal_spin_x: bool, use_truncated_normal_spin_y: bool, use_truncated_normal_spin_z: bool, use_chi_eff_mixture: bool, use_skew_normal_chi_eff: bool, use_truncated_normal_chi_p: bool, use_tilt: bool, use_eccentricity_mixture: bool, use_eccentricity_powerlaw: bool, use_mean_anomaly: bool, use_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, likelihood_fn: collections.abc.Callable[Ellipsis, collections.abc.Callable], data_loader: gwkokab.analysis.core.inference_io.AnalyticalPELoader, prior_filename: str, poisson_mean_filename: str, sampler_cfg, variance_cut_threshold: float | None, n_samples: int, debug_nans: bool = False, profile_memory: bool = False, check_leaks: bool = False)

Bases: SubPopulationModelAnalyticalAnalysis, gwkokab.analysis.core.numpyro_base.NumpyroBase

AnalysisBase is a class which contains all the common functionality among the different analyses.

It is not meant to be used directly, but rather to be subclassed by the specific analyses.

Parameters:

• likelihood_fn (Callable[..., Callable[..., Array]]) – A function that takes the model parameters and returns a function that computes the log-likelihood.

• model (Union[Distribution, Callable[..., Distribution]]) – model to be used in the AnalyticalBase class. It can be a Distribution or a callable that returns a Distribution.

• data_loader (AnalyticalPELoader) – data loader for the analytical PE data.

• seed (int) – seed for the random number generator.

• prior_filename (str) – path to the JSON file containing the prior distributions.

• poisson_mean_filename (str) – path to the JSON file containing the Poisson mean configuration.

• flowMC_settings_filename (str) – path to the JSON file containing the flowMC settings.

• debug_nans (bool, optional) – If True, checks for NaNs in each computation. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug_nans.html#jax.debug_nans), by default False

• profile_memory (bool, optional) – If True, enables memory profiling, by default False

• check_leaks (bool, optional) – If True, checks for JAX Tracer leaks. See details in the [documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.checking_leaks.html#jax.checking_leaks), by default False

• analysis_name (str, optional) – Name of the analysis, by default “”

gwkokab.analysis.subpopulation.analytical.main() → None