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from dfatool import AnalyticFunction
def _parse_function(input_function):
if type('input_function') == 'str':
raise NotImplemented
if type('input_function') == 'function':
return 'raise ValueError', input_function
raise ValueError('Function description must be provided as string or function')
def _dict_to_list(input_dict):
return [input_dict[x] for x in sorted(input_dict.keys())]
class Transition:
def __init__(self, orig_state, dest_state, name,
energy = 0, energy_function = None,
duration = 0, duration_function = None,
timeout = 0, timeout_function = None,
is_interrupt = False,
arguments = [], param_update_function = None,
arg_to_param_map = None, set_param = None):
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.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 = {}, args = []):
if self.duration_function:
return self.duration_function.eval(_dict_to_list(param_dict), args)
return self.duration
def get_energy(self, param_dict = {}, args = []):
if self.energy_function:
return self.energy_function.eval(_dict_to_list(param_dict), args)
return self.energy
def get_timeout(self, param_dict = {}):
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, args = []):
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
class State:
def __init__(self, name, power = 0, power_function = None):
self.name = name
self.power = power
self.power_function = power_function
self.outgoing_transitions = {}
"""@classmethod
def from_json(cls, serialized_state):
if 'power' in serialized_state:
cls.power = serialized_state['power']['static']
if 'function' in serialized_state:
cls.power_function = """
def add_outgoing_transition(self, new_transition):
self.outgoing_transitions[new_transition.name] = new_transition
def get_energy(self, duration, param_dict = {}):
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):
return self.outgoing_transitions[transition_name]
def has_interrupt_transitions(self):
for trans in self.outgoing_transitions.values():
if trans.is_interrupt:
return True
return False
def get_next_interrupt(self, parameters):
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):
if depth == 0:
for trans in self.outgoing_transitions.values():
yield [trans.name]
else:
for trans in self.outgoing_transitions.values():
for suffix in trans.destination.dfs(depth - 1):
new_suffix = [trans.name]
new_suffix.extend(suffix)
yield new_suffix
def _json_function_to_analytic_function(base, attribute, parameters):
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):
if attribute in base:
return base[attribute]['static']
return 0
class PTA:
def __init__(self, state_names = [], parameters = [], initial_param_values = None):
self.states = 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.states['UNINITIALIZED'] = State('UNINITIALIZED')
@classmethod
def from_json(cls, json_input):
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['states'].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']
pta.add_transition(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 add_state(self, state_name, **kwargs):
if 'power_function' in kwargs and type(kwargs['power_function']) != AnalyticFunction:
kwargs['power_function'] = AnalyticFunction(kwargs['power_function'],
self.parameters, 0)
self.states[state_name] = State(state_name, **kwargs)
def add_transition(self, orig_state, dest_state, function_name, **kwargs):
orig_state = self.states[orig_state]
dest_state = self.states[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 = 10, orig_state = 'UNINITIALIZED'):
return self.states[orig_state].dfs(depth)
def simulate(self, trace, orig_state = 'UNINITIALIZED'):
total_duration = 0.
total_energy = 0.
state = self.states[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
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