diff options
Diffstat (limited to 'lib/automata.py')
| -rwxr-xr-x | lib/automata.py | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/lib/automata.py b/lib/automata.py index 3913670..fbfaf36 100755 --- a/lib/automata.py +++ b/lib/automata.py @@ -249,7 +249,7 @@ class PTA: parameters -- names of PTA parameters initial_param_values -- initial value for each parameter """ - self.states = dict([[state_name, State(state_name)] for state_name in state_names]) + 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() @@ -258,7 +258,7 @@ class PTA: self.transitions = [] if not 'UNINITIALIZED' in state_names: - self.states['UNINITIALIZED'] = State('UNINITIALIZED') + self.state['UNINITIALIZED'] = State('UNINITIALIZED') @classmethod def from_json(cls, json_input: dict): @@ -272,7 +272,7 @@ class PTA: if key in json_input: kwargs[key] = json_input[key] pta = cls(**kwargs) - for name, state in json_input['states'].items(): + 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']: @@ -308,7 +308,7 @@ class PTA: ret = { 'parameters' : self.parameters, 'initial_param_values' : self.initial_param_values, - 'states' : dict([[state.name, state.to_json()] for state in self.states.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 @@ -322,7 +322,7 @@ class PTA: 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) + self.state[state_name] = State(state_name, **kwargs) def add_transition(self, orig_state: str, dest_state: str, function_name: str, **kwargs): """ @@ -334,8 +334,8 @@ class PTA: function_name -- function name kwargs -- see Transition() documentation """ - orig_state = self.states[orig_state] - dest_state = self.states[dest_state] + 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) @@ -352,12 +352,12 @@ class PTA: depth -- search depth orig_state -- initial state for depth-first search """ - return self.states[orig_state].dfs(depth, **kwargs) + 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.states[orig_state] + 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] @@ -383,7 +383,7 @@ class PTA: return total_energy, total_duration, state, param_dict def update(self, static_model, param_model): - for state in self.states.values(): + for state in self.state.values(): if state.name != 'UNINITIALIZED': state.power = static_model(state.name, 'power') if param_model(state.name, 'power'): |