bahamas.cccg
============

.. py:module:: bahamas.cccg

.. autoapi-nested-parse::

   Created on September 7, 2025
   @author: wangc, chene



Attributes
----------

.. autoapisummary::

   bahamas.cccg.logger


Classes
-------

.. autoapisummary::

   bahamas.cccg.CCCG


Functions
---------

.. autoapisummary::

   bahamas.cccg.check_valid
   bahamas.cccg.clean_string
   bahamas.cccg.merge_multi_lists
   bahamas.cccg.drop_dup
   bahamas.cccg.unique_cells
   bahamas.cccg.match_CCCG_on
   bahamas.cccg.match_CCCG_list_on
   bahamas.cccg.match_multi_on
   bahamas.cccg.match_multi_list_on


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

.. py:data:: logger

.. py:function:: check_valid(src_arr)

   Check if group is empty

   :param src_arr: data to check
   :type src_arr: list or pd.DataFrame

   :returns: True if the group is not empty, else False
   :rtype: bool


.. py:function:: clean_string(s)

   Remove "None" and "nan" from str that are separated by ";"

   :param s: string to clean
   :type s: str

   :returns: cleaned string
   :rtype: str


.. py:function:: merge_multi_lists(*arg)

   Merge multiple pd.DataFrame CCCG data and remove duplicates

   :param arg: list of pd.DataFrame CCCG data
   :type arg: list

   :returns: merged CCCG data without duplications
   :rtype: list


.. py:function:: drop_dup(list_pd)

   Drop duplicated CCCG data

   :param list_pd: list of pd.DataFrame (CCCG data)
   :type list_pd: list

   :returns: list of pd.DataFrame (CCCG data) without duplications
   :rtype: list


.. py:function:: unique_cells(src_arr)

   Identify the unique inputs/designs/functions for the given coupling factor (i.e., Input Function and Design)

   :param src_arr: expanded coupling factor data for single coupling factor
   :type src_arr: pd.DataFrame

   :returns: list of unique inputs/designs/functions among all components
   :rtype: list


.. py:function:: match_CCCG_on(src_arr, col_name)

   Get a list of CCCGs based on single coupling factor, meaning the CCCGs are grouped because
   they have at least one shared variable of the given coupling factor

   :param src_arr: Component table with expanded coupling factors
   :type src_arr: pd.DataFrame
   :param col_name: coupling factor, Function\_, Input\_ or Design\_
   :type col_name: str

   :returns: list of CCCGs based on single coupling factor, arranged in the order \*_1, \*_2, \*_3, etc. where \* represents the coupling factor
   :rtype: list


.. py:function:: match_CCCG_list_on(list_pd, col_name)

   Identify CCCGs that are also have the "col_name" coupling factor. (This function is used for two or more shared coupling factors.)

   :param list_pd: list of pd.DataFrame of CCCGs
   :type list_pd: list
   :param col_name: coupling factor, Function\_, Input\_ or Design\_
   :type col_name: str

   :returns: CCCGs with the additional coupling factor ("col_name")
   :rtype: list


.. py:function:: match_multi_on(src_arr, col_name)

   Extract a CCCG based on col_name

   :param src_arr: pd.DataFrame of CCCGs
   :type src_arr: pd.DataFrame
   :param col_name: coupling factor
   :type col_name: str

   :returns: extracted the CCCGs based on the given column name
   :rtype: list


.. py:function:: match_multi_list_on(list_pd, col_name)

   Extract the CCCGs from a list of pandas.DataFrame

   :param list_pd: list of pd.DataFrame
   :type list_pd: list
   :param col_name: coupling factor name
   :type col_name: str

   :returns: list of extracted CCCGs
   :rtype: list


.. py:class:: CCCG(file)

   Bases: :py:obj:`object`


   Compute CCCGs for the given list of components of diversity and redundancy system


   .. py:attribute:: _sys_diagram


   .. py:attribute:: _cccg_final
      :value: []



   .. py:attribute:: _cccg_function
      :value: []



   .. py:attribute:: _cccg_input
      :value: []



   .. py:attribute:: _cccg_design
      :value: []



   .. py:attribute:: _cccg_single
      :value: []



   .. py:attribute:: _cccg_function_input
      :value: []



   .. py:attribute:: _cccg_function_design
      :value: []



   .. py:attribute:: _cccg_input_function
      :value: []



   .. py:attribute:: _cccg_input_design
      :value: []



   .. py:attribute:: _cccg_design_input
      :value: []



   .. py:attribute:: _cccg_design_function
      :value: []



   .. py:attribute:: _cccg_double
      :value: []



   .. py:attribute:: _cccg_function_input_design
      :value: []



   .. py:attribute:: _cccg_function_design_input
      :value: []



   .. py:attribute:: _cccg_input_design_function
      :value: []



   .. py:attribute:: _cccg_input_function_design
      :value: []



   .. py:attribute:: _cccg_design_input_function
      :value: []



   .. py:attribute:: _cccg_design_function_input
      :value: []



   .. py:attribute:: _cccg_triple
      :value: []



   .. py:attribute:: _function_all
      :value: []



   .. py:attribute:: _input_all
      :value: []



   .. py:attribute:: _design_all
      :value: []



   .. py:method:: generate(config=None)

      Generate CCCGs based on three coupling factors (i.e., Function, Input, and Design)

      :param config: config file for output control. Defaults to None.
      :type config: dict, optional



   .. py:method:: get(name)

      Get CCCGs

      :param name: name for CCCGs group
      :type name: str

      :returns: list of CCCGs
      :rtype: list (list of Pandas.DataFrame)



   .. py:method:: write(data, fname, style='csv')

      Dump data

      :param data: output data to dump
      :type data: list of pandas.DataFrame
      :param fname: file name to save the data
      :type fname: str
      :param style: type of file. Defaults to "csv".
      :type style: str, optional



   .. py:method:: aggregate(cccgs)

      Aggregate expanded Function, Input, and Design into Function_Config, Input_Config, and Design_Config

      :param cccgs: list of pd.DataFrame (i.e., CCCGs)
      :type cccgs: list

      :returns: list of aggregated CCCGs
      :rtype: list