gwkokab.analysis.core.analysis_base
===================================

.. py:module:: gwkokab.analysis.core.analysis_base


Classes
-------

.. autoapisummary::

   gwkokab.analysis.core.analysis_base.AnalysisBase


Functions
---------

.. autoapisummary::

   gwkokab.analysis.core.analysis_base.analysis_base_arg_parser


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

.. py:class:: AnalysisBase(*, analysis_name: str, check_leaks: bool, debug_nans: bool, model: Union[numpyro.distributions.distribution.Distribution, collections.abc.Callable[Ellipsis, numpyro.distributions.distribution.Distribution]], poisson_mean_filename: str, prior_filename: str, profile_memory: bool, sampler_cfg, variance_cut_threshold: float | None)

   Bases: :py:obj:`gwkokab.analysis.core.utils.PRNGKeyMixin`


   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.


   .. py:method:: classify_model_parameters() -> Tuple[Dict[str, Union[int, float, bool, None]], gwkokab.models.utils.JointDistribution, Dict[str, int], Dict[str, int]]

      Classify model parameters into constants, priors, and variables index.

      :returns: A tuple containing the constants, the distribution function, the prior
                distribution, and the variables index.
      :rtype: Tuple[ Dict[str, Union[int, float, bool, None]], Callable[..., Distribution], JointDistribution, Dict[str, int], ]



   .. py:method:: driver(*, logpdf: collections.abc.Callable[[jaxtyping.Array, Dict[str, Any]], jaxtyping.Array], priors: gwkokab.models.utils.JointDistribution, data: Any, labels: List[str]) -> None
      :abstractmethod:



   .. py:method:: modify_model_params(params: dict) -> dict

      Hook for subclasses to modify parameters before model instantiation.



   .. py:attribute:: analysis_name


   .. py:attribute:: check_leaks


   .. py:attribute:: debug_nans


   .. py:attribute:: model


   .. py:property:: model_parameters
      :type: List[str]

      :abstractmethod:


      Returns the model parameters.

      :returns: List of model parameters.
      :rtype: List[str]

      :raises NotImplementedError: If the AnalysisBase class is used directly, this method raises a NotImplementedError.
          It is expected that subclasses of AnalysisBase will implement this method.


   .. py:property:: parameters
      :type: Union[List[str], Tuple[str, Ellipsis]]

      :abstractmethod:


      Returns the parameters (intrinsic + extrinsic).

      :returns: List or tuple of parameter names.
      :rtype: Union[List[str], Tuple[str, ...]]

      :raises NotImplementedError: If the AnalysisBase class is used directly, this method raises a NotImplementedError.
          It is expected that subclasses of AnalysisBase will implement this method.


   .. py:attribute:: poisson_mean_filename


   .. py:attribute:: prior_filename


   .. py:attribute:: profile_memory


   .. py:attribute:: sampler_cfg


   .. py:attribute:: variance_cut_threshold


.. py:function:: analysis_base_arg_parser(parser: argparse.ArgumentParser) -> argparse.ArgumentParser

   Populate the command line argument parser with the arguments for the AnalysisBase
   script.

   :param parser: Parser to add the arguments to
   :type parser: ArgumentParser

   :returns: the command line argument parser
   :rtype: ArgumentParser