gwkokab.analysis.n_pls_m_gs.analytical
======================================

.. py:module:: gwkokab.analysis.n_pls_m_gs.analytical


Classes
-------

.. autoapisummary::

   gwkokab.analysis.n_pls_m_gs.analytical.NPowerlawMGaussianAnalyticalAnalysis
   gwkokab.analysis.n_pls_m_gs.analytical.NPowerlawMGaussianFAnalyticalAnalysis
   gwkokab.analysis.n_pls_m_gs.analytical.NPowerlawMGaussianNAnalyticalAnalysis


Functions
---------

.. autoapisummary::

   gwkokab.analysis.n_pls_m_gs.analytical.main


Module Contents
---------------

.. py:class:: NPowerlawMGaussianAnalyticalAnalysis(N_pl: int, N_g: 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_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, use_cos_iota: bool, use_phi_12: bool, use_polarization_angle: bool, use_right_ascension: bool, use_sin_declination: bool, use_detection_time: bool, use_phi_1: bool, use_phi_2: bool, use_phi_orb: bool, use_mean_anomaly: 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: :py:obj:`gwkokab.analysis.n_pls_m_gs.common.NPowerlawMGaussianCore`, :py:obj:`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.

   :param likelihood_fn: A function that takes the model parameters and returns a function that computes the log-likelihood.
   :type likelihood_fn: Callable[..., Callable[..., Array]]
   :param model: model to be used in the AnalyticalBase class. It can be a Distribution or a callable
                 that returns a Distribution.
   :type model: Union[Distribution, Callable[..., Distribution]]
   :param data_loader: data loader for the analytical PE data.
   :type data_loader: AnalyticalPELoader
   :param seed: seed for the random number generator.
   :type seed: int
   :param prior_filename: path to the JSON file containing the prior distributions.
   :type prior_filename: str
   :param poisson_mean_filename: path to the JSON file containing the Poisson mean configuration.
   :type poisson_mean_filename: str
   :param flowMC_settings_filename: path to the JSON file containing the flowMC settings.
   :type flowMC_settings_filename: str
   :param debug_nans: 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
   :type debug_nans: bool, optional
   :param profile_memory: If True, enables memory profiling, by default False
   :type profile_memory: bool, optional
   :param check_leaks: 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
   :type check_leaks: bool, optional
   :param analysis_name: Name of the analysis, by default ""
   :type analysis_name: str, optional


.. py:class:: NPowerlawMGaussianFAnalyticalAnalysis(N_pl: int, N_g: 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_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, use_cos_iota: bool, use_phi_12: bool, use_polarization_angle: bool, use_right_ascension: bool, use_sin_declination: bool, use_detection_time: bool, use_phi_1: bool, use_phi_2: bool, use_phi_orb: bool, use_mean_anomaly: 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: :py:obj:`NPowerlawMGaussianAnalyticalAnalysis`, :py:obj:`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.

   :param likelihood_fn: A function that takes the model parameters and returns a function that computes the log-likelihood.
   :type likelihood_fn: Callable[..., Callable[..., Array]]
   :param model: model to be used in the AnalyticalBase class. It can be a Distribution or a callable
                 that returns a Distribution.
   :type model: Union[Distribution, Callable[..., Distribution]]
   :param data_loader: data loader for the analytical PE data.
   :type data_loader: AnalyticalPELoader
   :param seed: seed for the random number generator.
   :type seed: int
   :param prior_filename: path to the JSON file containing the prior distributions.
   :type prior_filename: str
   :param poisson_mean_filename: path to the JSON file containing the Poisson mean configuration.
   :type poisson_mean_filename: str
   :param flowMC_settings_filename: path to the JSON file containing the flowMC settings.
   :type flowMC_settings_filename: str
   :param debug_nans: 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
   :type debug_nans: bool, optional
   :param profile_memory: If True, enables memory profiling, by default False
   :type profile_memory: bool, optional
   :param check_leaks: 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
   :type check_leaks: bool, optional
   :param analysis_name: Name of the analysis, by default ""
   :type analysis_name: str, optional


.. py:class:: NPowerlawMGaussianNAnalyticalAnalysis(N_pl: int, N_g: 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_powerlaw_redshift: bool, use_madau_dickinson_redshift: bool, use_cos_iota: bool, use_phi_12: bool, use_polarization_angle: bool, use_right_ascension: bool, use_sin_declination: bool, use_detection_time: bool, use_phi_1: bool, use_phi_2: bool, use_phi_orb: bool, use_mean_anomaly: 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: :py:obj:`NPowerlawMGaussianAnalyticalAnalysis`, :py:obj:`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.

   :param likelihood_fn: A function that takes the model parameters and returns a function that computes the log-likelihood.
   :type likelihood_fn: Callable[..., Callable[..., Array]]
   :param model: model to be used in the AnalyticalBase class. It can be a Distribution or a callable
                 that returns a Distribution.
   :type model: Union[Distribution, Callable[..., Distribution]]
   :param data_loader: data loader for the analytical PE data.
   :type data_loader: AnalyticalPELoader
   :param seed: seed for the random number generator.
   :type seed: int
   :param prior_filename: path to the JSON file containing the prior distributions.
   :type prior_filename: str
   :param poisson_mean_filename: path to the JSON file containing the Poisson mean configuration.
   :type poisson_mean_filename: str
   :param flowMC_settings_filename: path to the JSON file containing the flowMC settings.
   :type flowMC_settings_filename: str
   :param debug_nans: 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
   :type debug_nans: bool, optional
   :param profile_memory: If True, enables memory profiling, by default False
   :type profile_memory: bool, optional
   :param check_leaks: 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
   :type check_leaks: bool, optional
   :param analysis_name: Name of the analysis, by default ""
   :type analysis_name: str, optional


.. py:function:: main() -> None