ml4cps.cps.base module

The module provides base classes to represent the dynamics of cyber-physical systems (CPS): - CPS, and, - CPSComponent.

Author: Nemanja Hranisavljevic, hranisan@hsu-hh.de

class ml4cps.cps.base.CPS(sys_id, components)

Bases: object

CPS class represents a cyber-physical system. It is used to model the hierarchy in the system as each CPS can contain a mixture of other CPS and CPS components. The leaves of the hierarchy are only CPS component objects which define their dynamics and communication with other components.

id

Identifier of the object.

Type:

str

com

Property. A collection of components.

Type:

OrderedDict

copy()
finish_condition()
get_all_components(exclude=None)

Returns a list of all child components (not only direct) except those with ids possibly provided in exclude.

Parameters:

exclude (iterable) – A list of component ids to exclude in the returned list.

get_component(name)
get_component_by_full_id(full_id)
get_components(exclude=None)

Returns a list of all direct child components except those with ids possibly provided in exclude.

Parameters:

exclude (iterable) – A list of component ids to exclude in the returned list (default is None).

get_execution_data(flat=False)
items()
property overall_system
property parent_system

Gets the parent system by accessing _parent_system private attribute.

Returns:

The parent system.

Return type:

(CPS)

reinitialize(t=0, state=None)

The function re-initializes the CPS components of this CPS with the given state values. :param t: Current time to set to the components. :param state: State of the CPS (it’s components) given as a dictionary of dictionaries … of tuples (according to the CPS hierarchy). The tuplus are created as values concatenated values of discrete-event state variables, time-continuous state variables and time-discrete state variables.

Returns:

set_child_component(id, com)

Set component with the id.

Parameters:

  • id (str) – ID of the component to add.

  • com ((CPS, CPSComponent)) – Component of subsystem to add.

simulate(finish_time, verbose=False, save_choices=False)

Simulates behaviour of the system until the finish_time is reached.

Parameters:

  • finish_time (Time when simulation finishes.)

  • verbose (Should the execution log be printed in detail (default is False).)

  • save_choices (Should the choices for each component be saved to json files after)

  • False). (the simulation is finished (default is)

property state
class ml4cps.cps.base.CPSComponent(id, t=0, initial_q=(), initial_xt: list = (), initial_xk: list = (), dt=-1.0, p=None, cont_state_names=None, discr_state_names=None, discr_output_names=None, cont_output_names=None, unknown_state='Unknown')

Bases: PythonModel, Simulator

General hybrid system class based on scipy and simpy.

apply_sim_event(e, env=None)

The event e is applied in this component’s simpy execution, this means that the process must wait for an event. :param e: Event to apply. :return:

context(q, past_t, past_p, t) → tuple
continuous_dynamics_process(env, max_time, verbose=False)
copy()
discrete_dynamics_process(env, max_time, verbose=False)
discrete_event_dynamics(q, xt, xk, p) → tuple
property discrete_state
discrete_time_dynamics_process(env, max_time, verbose=False)
finish(t, **kwargs)
finish_condition()
property full_id
get_alternatives(state, system_state)
get_decision_state(state, overall_state)
get_execution_data()
get_sim_state()
guards(q, x)
input(q, clock) → tuple
invariants(q, clock, xc, xd, y)
is_decision(state, overall_state)
on_entry(q, context)
output_d(q, xt, xk)
output_y(q, xt, xk)
property overall_system
property parent_system
reinitialize(t, state=None)
set_sim_state(q, e, p, y, block_event)
simulate(finish_time, verbose=False, save_choices=False)

Simulates behaviour of the system until the finish_time is reached.

Parameters:

  • finish_time (Time when simulation finishes.)

  • verbose (Should the execution log be printed in detail (default is False).)

  • save_choices (Should the choices for each component be saved to json files after)

  • False). (the simulation is finished (default is)

simulation_process_simpy(env, max_time, verbose=False)

The function simulates single concurrent thread of one component (CPSComponent). :param env: It is simpy environment used to synchronize multiple parallel threads during simulation. :type env: simpy.Environment :param max_time: It is the maximum time that the simulation will perform. :type max_time: float :param verbose: Should the function print execution logs (default is False). :type verbose: bool

property state

System state is a vector of all state variables. :return:

time_continuous_dynamics(t, xt, xk, q, u, p, y)
time_discrete_dynamics(q, p, x, u)
timed_event(q, xc, xd)

Calculates if and when the next time event will happen and the new state values. :param t: Current time. :param q: Discrete-event part of the state. :param xc: Time-continuous part of the state. :param xd: Time-discrete part of the state. :return: Time delay of the timed event, new state value.

update_input(u)
wait(q)