"""Classes and helper functions for PTA and other automata.""" from functions import AnalyticFunction import itertools def _dict_to_list(input_dict: dict) -> list: return [input_dict[x] for x in sorted(input_dict.keys())] def _attribute_to_json(static_value: float, param_function: AnalyticFunction) -> dict: ret = { 'static' : static_value } if param_function: ret['function'] = { 'raw' : param_function._model_str, 'regression_args' : list(param_function._regression_args) } return ret class State: """A single PTA state.""" def __init__(self, name: str, power: float = 0, power_function: AnalyticFunction = None): u""" Create a new PTA state. arguments: name -- state name power -- static state power in µW power_function -- parameterized state power in µW """ self.name = name self.power = power self.power_function = power_function self.outgoing_transitions = {} def add_outgoing_transition(self, new_transition: object): """Add a new outgoing transition.""" self.outgoing_transitions[new_transition.name] = new_transition def get_energy(self, duration: float, param_dict: dict = {}) -> float: u""" Return energy spent in state in pJ. arguments: duration -- duration in µs param_dict -- current parameters """ if self.power_function: return self.power_function.eval(_dict_to_list(param_dict)) * duration return self.power * duration def get_transition(self, transition_name: str) -> object: """Return Transition object for outgoing transtion transition_name.""" return self.outgoing_transitions[transition_name] def has_interrupt_transitions(self) -> bool: """Check whether this state has any outgoing interrupt transitions.""" for trans in self.outgoing_transitions.values(): if trans.is_interrupt: return True return False def get_next_interrupt(self, parameters: dict) -> object: """ Return the outgoing interrupt transition with the lowet timeout. Must only be called if has_interrupt_transitions returned true. arguments: parameters -- current parameter values """ interrupts = filter(lambda x: x.is_interrupt, self.outgoing_transitions.values()) interrupts = sorted(interrupts, key = lambda x: x.get_timeout(parameters)) return interrupts[0] def dfs(self, depth: int, with_arguments: bool = False): """ Return a generator object for depth-first search over all outgoing transitions. arguments: depth -- search depth with_arguments -- perform dfs with function+argument transitions instead of just function transitions. """ if depth == 0: for trans in self.outgoing_transitions.values(): if with_arguments: for args in itertools.product(*trans.argument_values): yield [[trans.name, args]] else: yield [trans.name] else: for trans in self.outgoing_transitions.values(): for suffix in trans.destination.dfs(depth - 1, with_arguments = with_arguments): if with_arguments: for args in itertools.product(*trans.argument_values): new_suffix = [[trans.name, args]] new_suffix.extend(suffix) yield new_suffix else: new_suffix = [trans.name] new_suffix.extend(suffix) yield new_suffix def to_json(self) -> dict: """Return JSON encoding of this state object.""" ret = { 'name' : self.name, 'power' : _attribute_to_json(self.power, self.power_function) } return ret class Transition: """A single PTA transition with one origin and one destination state.""" def __init__(self, orig_state: State, dest_state: State, name: str, energy: float = 0, energy_function: AnalyticFunction = None, duration: float = 0, duration_function: AnalyticFunction = None, timeout: float = 0, timeout_function: AnalyticFunction = None, is_interrupt: bool = False, arguments: list = [], argument_values: list = [], param_update_function = None, arg_to_param_map: dict = None, set_param = None): """ Create a new transition between two PTA states. arguments: orig_state -- origin state dest_state -- destination state name -- transition name, typically the same as a driver/library function name """ self.name = name self.origin = orig_state self.destination = dest_state self.energy = energy self.energy_function = energy_function self.duration = duration self.duration_function = duration_function self.timeout = timeout self.timeout_function = timeout_function self.is_interrupt = is_interrupt self.arguments = arguments.copy() self.argument_values = argument_values.copy() self.param_update_function = param_update_function self.arg_to_param_map = arg_to_param_map self.set_param = set_param def get_duration(self, param_dict: dict = {}, args: list = []) -> float: u""" Return transition duration in µs. arguments: param_dict -- current parameter values args -- function arguments """ if self.duration_function: return self.duration_function.eval(_dict_to_list(param_dict), args) return self.duration def get_energy(self, param_dict: dict = {}, args: list = []) -> float: u""" Return transition energy cost in pJ. arguments: param_dict -- current parameter values args -- function arguments """ if self.energy_function: return self.energy_function.eval(_dict_to_list(param_dict), args) return self.energy def get_timeout(self, param_dict: dict = {}) -> float: u""" Return transition timeout in µs. Returns 0 if the transition does not have a timeout. arguments: param_dict -- current parameter values args -- function arguments """ if self.timeout_function: return self.timeout_function.eval(_dict_to_list(param_dict)) return self.timeout def get_params_after_transition(self, param_dict: dict, args: list = []) -> dict: """ Return the new parameter dict after taking this transition. parameter values may be affected by this transition's update function, it's argument-to-param map, and its set_param settings. """ if self.param_update_function: return self.param_update_function(param_dict, args) ret = param_dict.copy() if self.arg_to_param_map: for k, v in self.arg_to_param_map.items(): ret[k] = args[v] if self.set_param: for k, v in self.set_param.items(): ret[k] = v return ret def to_json(self) -> dict: """Return JSON encoding of this transition object.""" ret = { 'name' : self.name, 'origin' : self.origin.name, 'destination' : self.destination.name, 'is_interrupt' : self.is_interrupt, 'arguments' : self.arguments, 'arg_to_param_map' : self.arg_to_param_map, 'set_param' : self.set_param, 'duration' : _attribute_to_json(self.duration, self.duration_function), 'energy' : _attribute_to_json(self.energy, self.energy_function), 'timeout' : _attribute_to_json(self.timeout, self.timeout_function), } return ret def _json_function_to_analytic_function(base, attribute: str, parameters: list): if attribute in base and 'function' in base[attribute]: base = base[attribute]['function'] return AnalyticFunction(base['raw'], parameters, 0, regression_args = base['regression_args']) return None def _json_get_static(base, attribute: str): if attribute in base: return base[attribute]['static'] return 0 class PTA: """ A parameterized priced timed automaton. All states are accepting. Suitable for simulation, model storage, and (soon) benchmark generation. """ def __init__(self, state_names: list = [], parameters: list = [], initial_param_values: list = None): """ Return a new PTA object. arguments: state_names -- names of PTA states. Note that the PTA always contains an initial UNINITIALIZED state, regardless of the content of state_names. parameters -- names of PTA parameters initial_param_values -- initial value for each parameter """ self.state = dict([[state_name, State(state_name)] for state_name in state_names]) self.parameters = parameters.copy() if initial_param_values: self.initial_param_values = initial_param_values.copy() else: self.initial_param_values = [None for x in self.parameters] self.transitions = [] if not 'UNINITIALIZED' in state_names: self.state['UNINITIALIZED'] = State('UNINITIALIZED') @classmethod def from_json(cls, json_input: dict): """ Return a PTA created from the provided JSON data. Compatible with the to_json method. """ kwargs = {} for key in ('state_names', 'parameters', 'initial_param_values'): if key in json_input: kwargs[key] = json_input[key] pta = cls(**kwargs) for name, state in json_input['state'].items(): power_function = _json_function_to_analytic_function(state, 'power', pta.parameters) pta.add_state(name, power = _json_get_static(state, 'power'), power_function = power_function) for transition in json_input['transitions']: duration_function = _json_function_to_analytic_function(transition, 'duration', pta.parameters) energy_function = _json_function_to_analytic_function(transition, 'energy', pta.parameters) timeout_function = _json_function_to_analytic_function(transition, 'timeout', pta.parameters) arg_to_param_map = None if 'arg_to_param_map' in transition: arg_to_param_map = transition['arg_to_param_map'] origins = transition['origin'] if type(origins) != list: origins = [origins] for origin in origins: pta.add_transition(origin, transition['destination'], transition['name'], duration = _json_get_static(transition, 'duration'), duration_function = duration_function, energy = _json_get_static(transition, 'energy'), energy_function = energy_function, timeout = _json_get_static(transition, 'timeout'), timeout_function = timeout_function, arg_to_param_map = arg_to_param_map ) return pta def to_json(self) -> dict: """ Return JSON encoding of this PTA. Compatible with the from_json method. """ ret = { 'parameters' : self.parameters, 'initial_param_values' : self.initial_param_values, 'state' : dict([[state.name, state.to_json()] for state in self.state.values()]), 'transitions' : [trans.to_json() for trans in self.transitions] } return ret def add_state(self, state_name: str, **kwargs): """ Add a new state. See the State() documentation for acceptable arguments. """ if 'power_function' in kwargs and type(kwargs['power_function']) != AnalyticFunction: kwargs['power_function'] = AnalyticFunction(kwargs['power_function'], self.parameters, 0) self.state[state_name] = State(state_name, **kwargs) def add_transition(self, orig_state: str, dest_state: str, function_name: str, **kwargs): """ Add function_name as new transition from orig_state to dest_state. arguments: orig_state -- origin state name. Must be known to PTA dest_state -- destination state name. Must be known to PTA. function_name -- function name kwargs -- see Transition() documentation """ orig_state = self.state[orig_state] dest_state = self.state[dest_state] for key in ('duration_function', 'energy_function', 'timeout_function'): if key in kwargs and type(kwargs[key]) != AnalyticFunction: kwargs[key] = AnalyticFunction(kwargs[key], self.parameters, 0) new_transition = Transition(orig_state, dest_state, function_name, **kwargs) self.transitions.append(new_transition) orig_state.add_outgoing_transition(new_transition) def dfs(self, depth: int = 10, orig_state: str = 'UNINITIALIZED', **kwargs): """ Return a generator object for depth-first search starting at orig_state. arguments: depth -- search depth orig_state -- initial state for depth-first search """ return self.state[orig_state].dfs(depth, **kwargs) def simulate(self, trace: list, orig_state: str = 'UNINITIALIZED'): total_duration = 0. total_energy = 0. state = self.state[orig_state] param_dict = dict([[self.parameters[i], self.initial_param_values[i]] for i in range(len(self.parameters))]) for function in trace: function_name = function[0] function_args = function[1 : ] if function_name == 'sleep': duration = function_args[0] total_energy += state.get_energy(duration, param_dict) total_duration += duration else: transition = state.get_transition(function_name) total_duration += transition.get_duration(param_dict, function_args) total_energy += transition.get_energy(param_dict, function_args) param_dict = transition.get_params_after_transition(param_dict, function_args) state = transition.destination while (state.has_interrupt_transitions()): transition = state.get_next_interrupt(param_dict) duration = transition.get_timeout(param_dict) total_duration += duration total_energy += state.get_energy(duration, param_dict) param_dict = transition.get_params_after_transition(param_dict) state = transition.destination return total_energy, total_duration, state, param_dict def update(self, static_model, param_model): for state in self.state.values(): if state.name != 'UNINITIALIZED': state.power = static_model(state.name, 'power') if param_model(state.name, 'power'): state.power_function = param_model(state.name, 'power')['function'] print(state.name, state.power, state.power_function.__dict__)