automata4cps.cps.CPSComponent

class automata4cps.cps.CPSComponent(id, initial_q=(), initial_xt: list = (), initial_xk: list = (), dt=-1.0, p=None, blocked_states=None, cont_state_names=None, discr_state_names=None, discr_output_names=None, cont_output_names=None, use_observed_timings=False)

General hybrid system class based on scipy and simpy. Based on

__init__(id, initial_q=(), initial_xt: list = (), initial_xk: list = (), dt=-1.0, p=None, blocked_states=None, cont_state_names=None, discr_state_names=None, discr_output_names=None, cont_output_names=None, use_observed_timings=False)

Methods

__init__(id[, initial_q, initial_xt, ...])
apply_event(e)
context(q, past_t, past_p, t)
discrete_event_dynamics(previous_q, e, xt, xk, p)	The function updates system state given the event e.
finish(t, **kwargs)
finish_condition()
get_execution_data()
get_sim_state()
input(q, clock)
invariants(q, clock, xc, xd, y)
load_context(context)	Loads artifacts from the specified PythonModelContext that can be used by predict() when evaluating inputs.
on_entry(q, context)
output(q, e, clock, xt, xk, u, p)
predict(context, model_input)	Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
predict_stream(context, model_input[, params])	Evaluates a pyfunc-compatible input and produces an iterator of output.
react(q, e, clock, xt, xk, u, p)	You can set self._d and self.x, but you must not
read_event(t, e[, clear_p, keep_p])
set_sim_state(q, e, p, y, block_event)
start(t[, q])
time_continuous_dynamics(t, xt, xk, q, u, p, y)
time_discrete_dynamics(t, xt, xk, q, u, p, y)
timed_transition(q, xc, xd, y[, ...])
update_input(u)
wait(q)

Attributes

overall_system