gwkokab_scripts.flowMC_info
===========================

.. py:module:: gwkokab_scripts.flowMC_info


Attributes
----------

.. autoapisummary::

   gwkokab_scripts.flowMC_info.console


Functions
---------

.. autoapisummary::

   gwkokab_scripts.flowMC_info.display_config_table
   gwkokab_scripts.flowMC_info.display_diagnostics_panel
   gwkokab_scripts.flowMC_info.display_loop_table
   gwkokab_scripts.flowMC_info.display_suggestions
   gwkokab_scripts.flowMC_info.generate_heuristics
   gwkokab_scripts.flowMC_info.infer_n_dims
   gwkokab_scripts.flowMC_info.load_config
   gwkokab_scripts.flowMC_info.main
   gwkokab_scripts.flowMC_info.plot_history
   gwkokab_scripts.flowMC_info.print_header
   gwkokab_scripts.flowMC_info.print_section


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

.. py:function:: display_config_table(data: List[tuple])

.. py:function:: display_diagnostics_panel(info_dict: Dict[str, str], memory_notes: List[str])

   Display the detailed sample breakdown and memory notes in a clean panel.


.. py:function:: display_loop_table(rows: List[tuple], max_loops: int)

.. py:function:: display_suggestions(current: dict, suggested: dict)

.. py:function:: generate_heuristics(n_dims: int, n_chains: int, kept_total_per_loop: int) -> Dict[str, Any]

   Generate heuristic suggestions for FlowMC parameters.


.. py:function:: infer_n_dims(bundle_config: dict, cli_n_dims: Optional[int]) -> int

   Infer n_dims from CLI argument or config (mass_matrix length).


.. py:function:: load_config(path: str) -> Dict[str, Any]

   Load and validate the JSON configuration.


.. py:function:: main()

.. py:function:: plot_history(loops: List[int], candidates: List[int], n_train: List[int], save_path: str)

   Generate a matplotlib plot of the training history.


.. py:function:: print_header(title: str)

.. py:function:: print_section(title: str)

.. py:data:: console