BioSANS2020.propagation.law_of_localization¶

Functions¶

BioSANS2020.propagation.law_of_localization.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

BioSANS2020.propagation.law_of_localization.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

BioSANS2020.propagation.law_of_localization.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

BioSANS2020.propagation.law_of_localization.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]

BioSANS2020.propagation.law_of_localization.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