autoreduce.reductions.timescale.Reduce

class Reduce(x, f, params=None, C=None, g=None, h=None, u=None, params_values=None, x_init=None, input_values=None, timepoints_ode=None, timepoints_ssm=None, error_tol=None, nstates_tol=None, nstates_tol_min=None, **kwargs)

The class can be used to compute the various possible reduced models for the System object and then find out the best reduced model choice using doi : https://doi.org/10.1101/640276

__init__(x, f, params=None, C=None, g=None, h=None, u=None, params_values=None, x_init=None, input_values=None, timepoints_ode=None, timepoints_ssm=None, error_tol=None, nstates_tol=None, nstates_tol_min=None, **kwargs)

The general system dynamics : x_dot = f(x, P) + g(x, P)u, y = h(x,P) Use the utility function ode_to_sympy to write these.

x : (Symbolic) state variable vector

fThe system model dynamics.

Written symbolically with symbols x = [x_0, x_1, …] for states and P = [P_0, P_1, …] for parameters.

params(Symbolic) parameters used to

define f, g, h. None if no symbolic parameters.

gThe actuator / input dynamics.

None by default if the system is autonomous.

CThe output matrix for y = Cx,

size of C must be #outputs times #states. If None, the argument h is expected. Cannot set C and h both.

hThe output description y = h(x, P)

where x are states and P are parameters.

params_values : Values for model parameters

u : List of inputs

x_init : Model initial conditions

Methods

__init__(x, f[, params, C, g, h, u, ...])	The general system dynamics : x_dot = f(x, P) + g(x, P)u, y = h(x,P) Use the utility function ode_to_sympy to write these.
compute_reduced_model()	Dispatch to the reduction workflow matching the system structure.
create_C_hat(x_hat)	Returns C_hat matrix for the reduced system given the x_hat (reduced system state vector) and using the C matrix for the full system (if any)
evaluate(f, x, P[, u])	Evaluate the given symbolic function (f) that is part of the System at the values given by x for self.x and P for self.params
generate_sbml_model([show_warnings])	Creates an new SBML model and populates with the species and their ODE in the System object :param show_warnings: bool, to display warnings :param kwargs: extra keywords pass onto create_sbml_model() :return: tuple: (document,model) SBML objects
get_T(attempt)	Construct transformation matrices for retained and collapsed states.
get_all_combinations()	Combinatorially create sets of all states that can be reduced in self.all_reductions.
get_conservation_laws([...])	Find conserved species sets using the conservation module.
get_error_metric(reduced_sys)	Returns the error defined as the 2-norm of y - y_hat.
get_error_metric_with_input(reduced_sys)	Returns the error defined as the 2-norm of y - y_hat.
get_invariant_manifold(reduced_sys)	Evaluate collapsed-state expressions along reduced trajectories.
get_output_states()	Return state symbols that appear in the system output.
get_robustness_metric(reduced_sys, **kwargs)	Compute robustness metrics comparing full and reduced systems.
get_robustness_metric_with_input(reduced_sys)	Return the robustness metric for systems with inputs.
get_solutions()	Return cached full-model ODE, SSM-time ODE, and SSM objects.
get_system()	Return the current reduction object as a base System.
pretty_print()	Print a concise model summary with LaTeX system equations.
reduce_general()	Return the placeholder result set for the general reduction path.
reduce_simple(**kwargs)	Compute candidate reductions for autonomous systems.
reduce_with_input()	Compute candidate reductions for systems with explicit inputs.
set_conservation_laws(conservation_laws, ...)	Apply conservation laws using the conservation module.
set_dynamics([f, g, h, C, u, params])	Set either f, g, h, or C to the System object or parameter values using P.
set_ic_from_params(x_init, ic_param, ic_index)	Set System initial conditions using parameter values
set_parameters([params_values, x_init])	Set model parameters and initial conditions
setup_conservation_laws(total_quantities, ...)	Create conservation-law expressions from conserved species sets.
solve_approximations()	Run abundance-based approximations from the abundance module.
solve_conservation_laws([conservation_laws, ...])	Apply conservation laws using the conservation module.
solve_timescale_separation(slow_states[, ...])	This function solves the time-scale separation problem for the System object passed through.
solve_timescale_separation_with_input(...)	Solve time-scale separation for systems with explicit inputs.
write_sbml(filename, **kwargs)	Writes an SBML file for AutoReduce System model.