gwkokab.analysis.ecc_matters.analytical
=======================================

.. py:module:: gwkokab.analysis.ecc_matters.analytical


Classes
-------

.. autoapisummary::

   gwkokab.analysis.ecc_matters.analytical.EccentricityMattersFAnalyticalAnalysis
   gwkokab.analysis.ecc_matters.analytical.EccentricityMattersNAnalyticalAnalysis


Functions
---------

.. autoapisummary::

   gwkokab.analysis.ecc_matters.analytical.main


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

.. py:class:: EccentricityMattersFAnalyticalAnalysis(likelihood_fn: collections.abc.Callable[Ellipsis, collections.abc.Callable], model: Union[numpyro.distributions.Distribution, collections.abc.Callable[Ellipsis, numpyro.distributions.Distribution]], 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, analysis_name: str = '')

   Bases: :py:obj:`gwkokab.analysis.ecc_matters.common.EccentricityMattersCore`, :py:obj:`gwkokab.analysis.core.analytical_base.AnalyticalBase`, :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:: EccentricityMattersNAnalyticalAnalysis(likelihood_fn: collections.abc.Callable[Ellipsis, collections.abc.Callable], model: Union[numpyro.distributions.Distribution, collections.abc.Callable[Ellipsis, numpyro.distributions.Distribution]], 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, analysis_name: str = '')

   Bases: :py:obj:`gwkokab.analysis.ecc_matters.common.EccentricityMattersCore`, :py:obj:`gwkokab.analysis.core.analytical_base.AnalyticalBase`, :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