BioSANS2020.propagation.deterministic.euler_mod¶

Functions¶

BioSANS2020.propagation.deterministic.euler_mod.euler_model(sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, d_time, del_coef, molar=False)¶

This function returns the ODE model as a list of evaluated deri- vative of each components at a particular intance given by the state of the inputs.

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 (numpy.ndarray): stoichiometric matrix. For example

stch_var = np.array([

[ -1, 0 ] # species A [ 1, -1 ] # species B [ 0, 1 ] # species C

#1st rxn 2nd rxn

])

d_time ([type]): [description] del_coef (float, optional): factor for modifying time steps used

in the integration/propagation of ODE. Defaults to 10.

molarTrue if concentration or amount is in molar otherwise it

is False. Defaults to False. If molar is True, macroscopic propensity is used. If it is False, microscopic propensity is used.

Returns:

list: 2 element list i.e. [value of dx/dt, d_time] where x are

the components

BioSANS2020.propagation.deterministic.euler_mod.euler_int(t_var, sp_comp, ks_dict, sconc, r_dict, p_dict, stch_var, del_coef=10, lna_solve=False, items=None, implicit=False, molar=False, rfile='')¶

This function performs tau-adpative euler integration. The tau is adjusted in such a way that limit the change of the fastest reaction to del_coef amounts.

Args:

t_var (list): list of time points in the simulation 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

}

sconc (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

}

stoch_var (numpy.ndarray): stoichiometric matrix. For example

stoch_var = np.array([

[ -1, 0 ] # species A [ 1, -1 ] # species B [ 0, 1 ] # species C

#1st rxn 2nd rxn

])

del_coef (float, optional): factor for modifying time steps used

in the integration/propagation of ODE. Defaults to 10.

lna_solve (bool, optional): if True, proceed to linar noise

approximation calculations. Defaults to False.

items (list, optional): 3 item list of [canvas, scroll_x,

scroll_y], Defaults to None.

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.

molar (bool, optional): True if concentration or amount is in

molar and you want to use macroscopic equations otherwise it should be False and using microscopic equations. Defaults to False.

rfile (str): file name of BioSANS topology file.

Returns:

tuple: (time, trajectory)

BioSANS2020.propagation.deterministic.euler_mod.euler2_model(sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, molar=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

}

stoch_var (numpy.ndarray): stoichiometric matrix. For example

stoch_var = np.array([

[ -1, 0 ] # species A [ 1, -1 ] # species B [ 0, 1 ] # species C

#1st rxn 2nd rxn

])

molar (bool, optional): True if concentration or amount is in

molar and you want to use macroscopic equations otherwise it should be False and using microscopic equations. Defaults to False.

Returns:

np.ndarray: derivative of components with respect to time at a particular time or instant based on the current state of the inputs.

BioSANS2020.propagation.deterministic.euler_mod.euler_wer_est(h_var, sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, molar)¶

This function serves to do some part of the task in euler_help and euler2_int.

BioSANS2020.propagation.deterministic.euler_mod.euler_help(htry, eps, yscal, sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, molar)¶

This function serves to do some part of the task in euler2_int.

BioSANS2020.propagation.deterministic.euler_mod.euler2_int(t_var, sp_comp, ks_dict, conc, r_dict, p_dict, stch_var, yscal=10, lna_solve=False, items=None, implicit=False, molar=False, rfile='')¶

This function performs tau-adpative euler integration. The tau is adjusted to limit the error in each integration step as compared to what a second order runge-kutta would have predicted.

Args:

t_var (list): list of time points in the simulation 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

}

sconc (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

}

stoch_var (numpy.ndarray): stoichiometric matrix. For example

stoch_var = np.array([

[ -1, 0 ] # species A [ 1, -1 ] # species B [ 0, 1 ] # species C

#1st rxn 2nd rxn

])

yscal (float, optional): factor for modifying time steps used

in the integration/propagation of ODE. Defaults to 10.

lna_solve (bool, optional): if True, proceed to linar noise

approximation calculations. Defaults to False.

items (list, optional): 3 item list of [canvas, scroll_x,

scroll_y], Defaults to None.

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.

molar (bool, optional): True if concentration or amount is in

molar and you want to use macroscopic equations otherwise it should be False and using microscopic equations. Defaults to False.

rfile (str): file name of BioSANS topology file.

Returns:

tuple: (time, trajectory)