:py:mod:`BioSANS2020.propagation.stochastic.mytauleap`
======================================================

.. py:module:: BioSANS2020.propagation.stochastic.mytauleap

.. autoapi-nested-parse::

                     This module is the mytauleap module

   The purpose of this module is to propagate stochastic trajectories using
   the tau-leaping algorithm.

   The functions in this module are the following;

   1. sim_tauLeap
   2.




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


Functions
~~~~~~~~~

.. autoapisummary::

   BioSANS2020.propagation.stochastic.mytauleap.sim_tauLeap



.. py:function:: sim_tauLeap(tvar, sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, rand_seed, del_coef, implicit=False, rfile='')

   This functions performs the tau-leaping integration

   Args:
       tvar ([type]): [description]
       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 (numpy.ndarray): stoichiometric matrix. For example

           v_stoich = np.array([
               [   -1,           0   ]            # species A
               [    1,          -1   ]            # species B
               [    0,           1   ]            # species C
                 #1st rxn    2nd rxn
           ])
       rand_seed ([type]): [description]
       rand_seed (float): random  seed value picked  at random for each
           trajectory. They have been sampled from the calling program.
       implicit (bool, optional): True means report in time intervals
           similar to the input time intervals even if actual step is
           more or less. Defaults to False.
       rfile (string, optional): name of topology file where some
           parameters or components are negative indicating  they  have
           to be estimated. Defaults to None.

   Returns:
       tuple : (time, trajectories)