:py:mod:`BioSANS2020.propagation.law_of_localization`
=====================================================

.. py:module:: BioSANS2020.propagation.law_of_localization

.. autoapi-nested-parse::

               This module is the law_of_localization module

   The sole purpose of this module is to implement the law of localization




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


Functions
~~~~~~~~~

.. autoapisummary::

   BioSANS2020.propagation.law_of_localization.subs2
   BioSANS2020.propagation.law_of_localization.prepare_ffrrint
   BioSANS2020.propagation.law_of_localization.prepare_dict_list1
   BioSANS2020.propagation.law_of_localization.prepare_list2
   BioSANS2020.propagation.law_of_localization.law_loc_symbolic



.. py:function:: subs2(zvar, cval)

   This function substitute actual values to sympy symbols.

   Args:
       zvar (sympy.core): sympy expression
       cval (disct): dictionary of Symbols : value

   Returns:
       (sympy.core): substituted expression


.. py:function:: prepare_ffrrint(items)

   This function instantiate how the output will be printed by crea-
   thing the function ffprint

   Args:
       text (Text): text area where the outputs are written
       items (tuple): 3 item list of (canvas, scroll_x, scroll_y)

   Returns:
       function: either print in console or in text area


.. py:function:: prepare_dict_list1(sp_comp, conc, ks_dict)

   This function prepares cs_var, cso_var, time

   Args:
       sp_comp (dict): dictionary of components position in reaction
       conc (dict) : dictionary of initail concentration

   Returns:
       (dict/list): cs_var, cso_var, equivals, equi_ks


.. py:function:: prepare_list2(stch_var, slabels)

   This function prepares two list from stoichiometric matrix

   Args:
       stch_var (np.ndarray): toichiometric matrix

   Returns:
       list: [non zero row values of  stch_var,
           nonzero rows index of stch_var]


.. py:function:: law_loc_symbolic(sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, items=None, molar=False, mode=None, numer=False)

   [summary]

   Args:
       sp_comp (dict): dictionary of appearance or position of species
           or component in the reaction tag of BioSANS topology file.

           For example;

               #REACTIONS
               A => B, -kf1    # negative means to be estimated
               B => C, kf2

           The value of sp_comp is

               sp_comp = {'A': {0}, 'B': {0, 1}, 'C': {1}}

               A appears in first reaction with index 0
               B appears in first and second reaction with index 0, 1
               C appears in second reaction with index 1
       ks_dict (dict): dictionary of rate constant that appear in each
           reactions.

           For example;

               #REACTIONS
               A => B , 0.3        # first reaction
               B <=> C, 0.1, 0.2   # second reaction

           The value of ks_dict is

               ks_dict = {
                   0 : [0.3],      # first reaction
                   1 : [0.1, 0.2]  # second reaction
               }
       conc (dict): dictionary of initial concentration.

           For example;

               {'A': 100.0, 'B': -1.0, 'C': 0.0}
               negative means unknown or for estimation
       r_dict (dict): dictionary of reactant stoichiometry. For example

           r_dict = {
               0: {'A': 1},  # first reaction, coefficient of A is 1
               1: {'B': 1}   # second reaction, coefficient of B is 1
           }
       p_dict (dict): dictionary of product stoichiometry. For example

           p_dict = {
               0: {'B': 1},  # first reaction, coefficient of B is 1
               1: {'C': 1}   # second reaction, coefficient of C is 1
           }
       stch_var (np.ndarray): stoichiometric matrix
       items (tuple): 3 item list of (canvas, scroll_x, scroll_y)
       molar (bool, optional): [description]. Defaults to False.
       mode (str, optional): either "Numeric", "fofks", "fofCo".
       numer (bool, optional): If True, numeric substitution will be
           done. Defaults to False.

   Returns:
       list: [0, 0] - useless return