""" Simulation controlling mechanisms. """ import json import os import tempfile import threading import traceback import tinytroupe import tinytroupe.utils as utils import uuid import logging logger = logging.getLogger("tinytroupe") # to protect from race conditions when running in parallel concurrent_execution_lock = threading.Lock() class Simulation: STATUS_STOPPED = "stopped" STATUS_STARTED = "started" def __init__(self, id="default", cached_trace:list=None): self.id = id self.agents = [] self.name_to_agent = {} # {agent_name: agent, ...} self.environments = [] self.factories = [] # e.g., TinyPersonFactory instances self.name_to_factory = {} # {factory_name: factory, ...} self.name_to_environment = {} # {environment_name: environment, ...} self.status = Simulation.STATUS_STOPPED self.cache_path = f"./tinytroupe-{id}.cache.json" # default cache path # should we always automatically checkpoint at the every transaction? self.auto_checkpoint = False # whether there are changes not yet saved to the cache file self.has_unsaved_cache_changes = False # whether the agent is under a transaction or not, used for managing # simulation caching later self._under_transaction = {None: False} # whether the agent is under a parallel transactions segment or not, used for managing # simulation caching later self._under_parallel_transactions = False # Cache chain mechanism. # # stores a list of simulation states. # Each state is a tuple (prev_node_hash, event_hash, event_output, state), where prev_node_hash is a hash of the previous node in this chain, # if any, event_hash is a hash of the event that triggered the transition to this state, if any, event_output is the output of the event, # if any, and state is the actual complete state that resulted. if cached_trace is None: self.cached_trace = [] else: self.cached_trace = cached_trace self.cache_misses = 0 self.cache_hits = 0 # Execution chain mechanism. # # The actual, current, execution trace. Each state is a tuple (prev_node_hash, event_hash, state), where prev_node_hash is a hash # of the previous node in this chain, if any, event_hash is a hash of the event that triggered the transition to this state, if any, # event_output is the output of the event, if any, and state is the actual complete state that resulted. self.execution_trace = [] def begin(self, cache_path:str=None, auto_checkpoint:bool=False): """ Marks the start of the simulation being controlled. Args: cache_path (str): The path to the cache file. If not specified, defaults to the default cache path defined in the class. auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False. """ logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.") # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory from tinytroupe.factory.tiny_person_factory import TinyPersonFactory if self.status == Simulation.STATUS_STOPPED: self.status = Simulation.STATUS_STARTED else: raise ValueError("Simulation is already started.") if cache_path is not None: self.cache_path = cache_path # should we automatically checkpoint? self.auto_checkpoint = auto_checkpoint # clear the agents, environments and other simulated entities, we'll track them from now on TinyPerson.clear_agents() TinyWorld.clear_environments() TinyFactory.clear_factories() TinyPersonFactory.clear_factories() # All automated fresh ids will start from 0 again for this simulation utils.reset_fresh_id() # load the cache file, if any if self.cache_path is not None: self._load_cache_file(self.cache_path) def end(self): """ Marks the end of the simulation being controlled. """ logger.debug("Ending simulation.") if self.status == Simulation.STATUS_STARTED: self.status = Simulation.STATUS_STOPPED self.checkpoint() else: raise ValueError("Simulation is already stopped.") def checkpoint(self): """ Saves current simulation trace to a file. """ logger.debug("Checkpointing simulation state...") # save the cache file if self.has_unsaved_cache_changes: self._save_cache_file(self.cache_path) else: logger.debug("No unsaved cache changes to save to file.") def add_agent(self, agent): """ Adds an agent to the simulation. """ if agent.name in self.name_to_agent: raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.") agent.simulation_id = self.id self.agents.append(agent) self.name_to_agent[agent.name] = agent def add_environment(self, environment): """ Adds an environment to the simulation. """ if environment.name in self.name_to_environment: raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") environment.simulation_id = self.id self.environments.append(environment) self.name_to_environment[environment.name] = environment def add_factory(self, factory): """ Adds a factory to the simulation. """ if factory.name in self.name_to_factory: raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") factory.simulation_id = self.id self.factories.append(factory) self.name_to_factory[factory.name] = factory ################################################################################################### # Cache and execution chain mechanisms ################################################################################################### def _execution_trace_position(self) -> int: """ Returns the current position in the execution trace, or -1 if the execution trace is empty. """ return len(self.execution_trace) - 1 def _function_call_hash(self, function_name, *args, **kwargs) -> int: """ Computes the hash of the given function call. """ # if functions are passed as arguments to the function, there's the problem that their # string representation always changes due to memory position (e.g., ). # so we need to remove the changing suffix in those cases, while preserving the function name if it exists. # positional arguments # covnerts to a list of string representations first args_str = list(map(str, args)) for i, arg in enumerate(args): if callable(arg): args_str[i] = arg.__name__ # keyword arguments # converts to a list of string representations first kwargs_str = {k: str(v) for k, v in kwargs.items()} for k, v in kwargs.items(): if callable(v): kwargs_str[k] = v.__name__ # then, convert to a single string, to obtain a unique hash event = str((function_name, args_str, kwargs_str)) # TODO actually compute a short hash of the event string, e.g., using SHA256 ? # event_hash = utils.custom_hash(event) return event def _skip_execution_with_cache(self): """ Skips the current execution, assuming there's a cached state at the same position. """ assert len(self.cached_trace) > self._execution_trace_position() + 1, "There's no cached state at the current execution position." self.execution_trace.append(self.cached_trace[self._execution_trace_position() + 1]) def _is_transaction_event_cached(self, event_hash, parallel=False) -> bool: """ Checks whether the given event hash matches the corresponding cached one, if any. If there's no corresponding cached state, returns True. """ if not parallel: # there's cache that could be used if len(self.cached_trace) > self._execution_trace_position() + 1: if self._execution_trace_position() >= -1: # here's a graphical depiction of the logic: # # Cache: c0:(c_prev_node_hash_0, c_event_hash_0, _, c_state_0) ------------------> c1:(c_prev_node_hash_1, c_event_hash_1, _, c_state_1) -> ... # Execution: e0:(e_prev_node_hash_0, e_event_hash_0, _, e_state_0) --> e1:(e_prev_node_hash_1, , , ) # position = 0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Must satisfy: # - event_hash == c_event_hash_1 # - hash(e0) == c_prev_node_hash_1 try: event_hash_match = event_hash == self.cached_trace[self._execution_trace_position() + 1][1] except Exception as e: logger.error(f"Error while checking event hash match: {e}") event_hash_match = False prev_node_match = True # TODO implement real check return event_hash_match and prev_node_match else: raise ValueError("Execution trace position is invalid, must be >= -1, but is ", self._execution_trace_position()) else: # no cache to use return False else: # parallel if len(self.cached_trace) >= self._execution_trace_position(): if self._execution_trace_position() >= 0: # parallel stores ignore order, so we need to check instead whether the event hash is a key in the parallel store, # regardless of the order of the events generated the data therein. if isinstance(self.cached_trace[self._execution_trace_position()], dict): event_hash_match = event_hash in self.cached_trace[self._execution_trace_position()].keys() else: event_hash_match = False prev_node_match = True # TODO implement real check return event_hash_match and prev_node_match else: raise ValueError("Execution trace position is invalid, must be >= 0, but is ", self._execution_trace_position()) def _get_cached_parallel_value(self, event_hash, key): parallel_store = self.cached_trace[self._execution_trace_position()] value = parallel_store[event_hash][key] return value def _drop_cached_trace_suffix(self): """ Drops the cached trace suffix starting at the current execution trace position. This effectively refreshes the cache to the current execution state and starts building a new cache from there. """ self.cached_trace = self.cached_trace[:self._execution_trace_position()+1] def _add_to_execution_trace(self, state: dict, event_hash: int, event_output, parallel=False): """ Adds a state to the execution_trace list and computes the appropriate hash. The computed hash is compared to the hash of the cached trace at the same position, and if they don't match, the execution is aborted. Similarly, the event_hash is compared to the hash of the event in the cached trace at the same position, and if they don't match, the execution is aborted. """ # Compute the hash of the previous execution pair, if any previous_hash = None if not parallel: # Create a tuple of (hash, state) and append it to the execution_trace list self.execution_trace.append((previous_hash, event_hash, event_output, state)) else: with concurrent_execution_lock: # state is not stored in parallel segments, only outputs self.execution_trace[-1][event_hash] = {"prev_node_hash": previous_hash, "encoded_output": event_output} def _add_to_cache_trace(self, state: dict, event_hash: int, event_output, parallel=False): """ Adds a state to the cached_trace list and computes the appropriate hash. """ # Compute the hash of the previous cached pair, if any previous_hash = None if self.cached_trace: previous_hash = utils.custom_hash(self.cached_trace[-1]) if not parallel: # Create a tuple of (hash, state) and append it to the cached_trace list self.cached_trace.append((previous_hash, event_hash, event_output, state)) else: with concurrent_execution_lock: # state is not stored in parallel segments, only outputs self.cached_trace[-1][event_hash] = {"prev_node_hash": previous_hash, "encoded_output": event_output} self.has_unsaved_cache_changes = True def _load_cache_file(self, cache_path:str): """ Loads the cache file from the given path. """ try: self.cached_trace = json.load(open(cache_path, "r", encoding="utf-8", errors="replace")) except FileNotFoundError: logger.info(f"Cache file not found on path: {cache_path}.") self.cached_trace = [] def _save_cache_file(self, cache_path:str): """ Saves the cache file to the given path. Always overwrites. """ logger.debug(f"Now saving cache file to {cache_path}.") try: # Create a temporary file with tempfile.NamedTemporaryFile('w', delete=False) as temp: json.dump(self.cached_trace, temp, indent=4) # Replace the original file with the temporary file os.replace(temp.name, cache_path) except Exception as e: traceback_string = ''.join(traceback.format_tb(e.__traceback__)) logger.error(f"An error occurred while saving the cache file: {e}\nTraceback:\n{traceback_string}") self.has_unsaved_cache_changes = False ################################################################################################### # Transactional control ################################################################################################### # # Regular sequential transactions # def begin_transaction(self, id=None): """ Starts a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = True self._clear_communications_buffers() # TODO <---------------------------------------------------------------- def end_transaction(self, id=None): """ Ends a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = False def is_under_transaction(self, id=None): """ Checks if the agent is under a transaction. """ with concurrent_execution_lock: return self._under_transaction.get(id, False) def _clear_communications_buffers(self): """ Cleans the communications buffers of all agents and environments. """ for agent in self.agents: agent.clear_communications_buffer() for environment in self.environments: environment.clear_communications_buffer() # # Parallel transactions # def begin_parallel_transactions(self): """ Starts parallel transactions. """ with concurrent_execution_lock: self._under_parallel_transactions = True # add a new parallel segment to the execution and cache traces self.execution_trace.append({}) self.cached_trace.append({}) def end_parallel_transactions(self): """ Ends parallel transactions. """ self._under_parallel_transactions = False def is_under_parallel_transactions(self): """ Checks if the agent is under parallel transactions. """ return self._under_parallel_transactions ################################################################################################### # Simulation state handling ################################################################################################### def _encode_simulation_state(self) -> dict: """ Encodes the current simulation state, including agents, environments, and other relevant information. """ state = {} # Encode agents state["agents"] = [] for agent in self.agents: state["agents"].append(agent.encode_complete_state()) # Encode environments state["environments"] = [] for environment in self.environments: state["environments"].append(environment.encode_complete_state()) # Encode factories state["factories"] = [] for factory in self.factories: state["factories"].append(factory.encode_complete_state()) return state def _decode_simulation_state(self, state: dict): """ Decodes the given simulation state, including agents, environments, and other relevant information. Args: state (dict): The state to decode. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld logger.debug(f"Decoding simulation state: {state['factories']}") logger.debug(f"Registered factories: {self.name_to_factory}") logger.debug(f"Registered agents: {self.name_to_agent}") logger.debug(f"Registered environments: {self.name_to_environment}") # Decode factories for factory_state in state["factories"]: factory = self.name_to_factory[factory_state["name"]] factory.decode_complete_state(factory_state) # Decode environments ###self.environments = [] for environment_state in state["environments"]: try: environment = self.name_to_environment[environment_state["name"]] environment.decode_complete_state(environment_state) if TinyWorld.communication_display: environment.pop_and_display_latest_communications() except Exception as e: raise ValueError(f"Environment {environment_state['name']} is not in the simulation, thus cannot be decoded there.") from e # Decode agents (if they were not already decoded by the environment) ####self.agents = [] for agent_state in state["agents"]: try: agent = self.name_to_agent[agent_state["name"]] agent.decode_complete_state(agent_state) # The agent has not yet been decoded because it is not in any environment. So, decode it. if agent.environment is None: if TinyPerson.communication_display: agent.pop_and_display_latest_communications() except Exception as e: raise ValueError(f"Agent {agent_state['name']} is not in the simulation, thus cannot be decoded there.") from e class Transaction: def __init__(self, obj_under_transaction, simulation, function, *args, **kwargs): # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory self.obj_under_transaction = obj_under_transaction self.simulation = simulation self.function_name = function.__name__ self.function = function self.args = args self.kwargs = kwargs # # If we have an ongoing simulation, set the simulation id of the object under transaction if it is not already set. # if simulation is not None: if hasattr(obj_under_transaction, 'simulation_id') and obj_under_transaction.simulation_id is not None: if obj_under_transaction.simulation_id != simulation.id: raise ValueError(f"Object {obj_under_transaction} is already captured by a different simulation (id={obj_under_transaction.simulation_id}), \ and cannot be captured by simulation id={simulation.id}.") logger.debug(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Object {obj_under_transaction} is already captured by simulation {simulation.id}.") else: # if is a TinyPerson, add the agent to the simulation if isinstance(obj_under_transaction, TinyPerson): simulation.add_agent(obj_under_transaction) logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added agent {obj_under_transaction} to simulation {simulation.id}.") # if is a TinyWorld, add the environment to the simulation elif isinstance(obj_under_transaction, TinyWorld): simulation.add_environment(obj_under_transaction) # if is a TinyFactory, add the factory to the simulation elif isinstance(obj_under_transaction, TinyFactory): simulation.add_factory(obj_under_transaction) logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added factory {obj_under_transaction} to simulation {simulation.id}.") else: raise ValueError(f"Object {obj_under_transaction} (type = {type(obj_under_transaction)}) is not a TinyPerson or TinyWorld instance, and cannot be captured by the simulation.") def execute(self, begin_parallel=False, parallel_id=None): output = None # Transaction caching will only operate if there is a simulation and it is started if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED: # Compute the function and return it, no caching, since the simulation is not started output = self.function(*self.args, **self.kwargs) elif self.simulation.status == Simulation.STATUS_STARTED: # Compute the event hash event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs) # Sequential and parallel transactions are handled in different ways if begin_parallel: self.simulation.begin_parallel_transactions() # CACHED? Check if the event hash is in the cache if self.simulation._is_transaction_event_cached(event_hash, parallel=self.simulation.is_under_parallel_transactions()): self.simulation.cache_hits += 1 # Restore the full state and return the cached output logger.debug(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.") # SEQUENTIAL if not self.simulation.is_under_parallel_transactions(): self.simulation._skip_execution_with_cache() state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state self.simulation._decode_simulation_state(state) # Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances # mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are # encoded/decoded as is. encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output output = self._decode_function_output(encoded_output) # PARALLEL else: # is under parallel transactions # in parallel segments, state is not restored, only outputs encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output") output = self._decode_function_output(encoded_output) else: # not cached if not begin_parallel: # in case of beginning a parallel segment, we don't want to count it as a cache miss, # since the segment itself will not be cached, but rather the events within it. self.simulation.cache_misses += 1 if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel: # BEGIN SEQUENTIAL TRANSACTION ############################################################### # # if this is the beginning of a parallel segment, we don't need to begin a transaction, since # we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy). if not begin_parallel: self.simulation.begin_transaction(id=parallel_id) # Compute the function and encode the relevant output and simulation state output = self.function(*self.args, **self.kwargs) self._save_output_with_simulation_state(event_hash, output) # END TRANSACTION ################################################################# if not begin_parallel: self.simulation.end_transaction(id=parallel_id) else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment # NOTES: # # - Reentrant sequential transactions are not cached, since what matters is the final result of # the top-level transaction. # # - The event that starts the parallel transactions segment WILL NOT itself be cached, since # it is not part of the parallel segment, but rather the beginning of it. This event will be # reconstructed during runtime from the parallel events within the segment. output = self.function(*self.args, **self.kwargs) if begin_parallel: self.simulation.end_parallel_transactions() # execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done. Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id) else: raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}") # Checkpoint if needed logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.") if self.simulation is not None and self.simulation.auto_checkpoint: logger.debug("Auto-checkpointing simulation state after transaction execution.") self.simulation.checkpoint() # after all the transaction is done, return the output - the client will never know about all the complexity we've # gone through to get here. return output def _save_output_with_simulation_state(self, event_hash, output): encoded_output = self._encode_function_output(output) state = self.simulation._encode_simulation_state() # immediately drop the cached trace suffix, since we are starting a new execution from this point on. # in the case of parallel transactions, this will drop everything _after_ the current parallel segment # (which itself occupies one position only, with a dictionary of event hashes and their outputs). self.simulation._drop_cached_trace_suffix() # Cache the result and update the current execution trace. If this is a parallel transaction, the # cache and execution traces will be updated in a different way. self.simulation._add_to_cache_trace(state, event_hash, encoded_output, parallel=self.simulation.is_under_parallel_transactions()) self.simulation._add_to_execution_trace(state, event_hash, encoded_output, parallel=self.simulation.is_under_parallel_transactions()) def _encode_function_output(self, output) -> dict: """ Encodes the given function output. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory # if the output is a supported object, encode it if output is None: return None elif isinstance(output, TinyPerson): return {"type": "TinyPersonRef", "name": output.name} elif isinstance(output, TinyWorld): return {"type": "TinyWorldRef", "name": output.name} elif isinstance(output, TinyFactory): return {"type": "TinyFactoryRef", "name": output.name} elif isinstance(output, list): encoded_list = [] for item in output: if isinstance(item, TinyPerson): encoded_list.append({"type": "TinyPersonRef", "name": item.name}) elif isinstance(item, TinyWorld): encoded_list.append({"type": "TinyWorldRef", "name": item.name}) elif isinstance(item, TinyFactory): encoded_list.append({"type": "TinyFactoryRef", "name": item.name}) else: encoded_list.append({"type": "JSON", "value": item}) return {"type": "List", "value": encoded_list} elif isinstance(output, (int, float, str, bool, dict, tuple)): return {"type": "JSON", "value": output} else: raise ValueError(f"Unsupported output type: {type(output)}") def _decode_function_output(self, encoded_output: dict): """ Decodes the given encoded function output. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory if encoded_output is None: return None elif encoded_output["type"] == "TinyPersonRef": return TinyPerson.get_agent_by_name(encoded_output["name"]) elif encoded_output["type"] == "TinyWorldRef": return TinyWorld.get_environment_by_name(encoded_output["name"]) elif encoded_output["type"] == "TinyFactoryRef": return TinyFactory.get_factory_by_name(encoded_output["name"]) elif encoded_output["type"] == "List": decoded_list = [] for item in encoded_output["value"]: if item["type"] == "TinyPersonRef": decoded_list.append(TinyPerson.get_agent_by_name(item["name"])) elif item["type"] == "TinyWorldRef": decoded_list.append(TinyWorld.get_environment_by_name(item["name"])) elif item["type"] == "TinyFactoryRef": decoded_list.append(TinyFactory.get_factory_by_name(item["name"])) else: decoded_list.append(item["value"]) return decoded_list elif encoded_output["type"] == "JSON": return encoded_output["value"] else: raise ValueError(f"Unsupported output type: {encoded_output['type']}") def transactional(parallel=False): """ A helper decorator that makes a function simulation-transactional. """ def decorator(func): def wrapper(*args, **kwargs): obj_under_transaction = args[0] simulation = current_simulation() obj_sim_id = obj_under_transaction.simulation_id if hasattr(obj_under_transaction, 'simulation_id') else None logger.debug(f"-----------------------------------------> Transaction: {func.__name__} with args {args[1:]} and kwargs {kwargs} under simulation {obj_sim_id}, parallel={parallel}.") parallel_id = str(threading.current_thread()) transaction = Transaction(obj_under_transaction, simulation, func, *args, **kwargs) result = transaction.execute(begin_parallel=parallel, parallel_id=parallel_id) return result return wrapper return decorator class SkipTransaction(Exception): pass class CacheOutOfSync(Exception): """ Raised when a cached and the corresponding freshly executed elements are out of sync. """ pass class ExecutionCached(Exception): """ Raised when a proposed execution is already cached. """ pass ################################################################################################### # Convenience functions ################################################################################################### def reset(): """ Resets the entire simulation control state. """ global _current_simulations, _current_simulation_id _current_simulations = {"default": None} # TODO Currently, only one simulation can be started at a time. In future versions, this should be # changed to allow multiple simulations to be started at the same time, e.g., for fast # analyses through parallelization. _current_simulation_id = None def _simulation(id="default"): global _current_simulations if _current_simulations[id] is None: _current_simulations[id] = Simulation() return _current_simulations[id] def begin(cache_path=None, id="default", auto_checkpoint=False): """ Marks the start of the simulation being controlled. """ global _current_simulation_id if _current_simulation_id is None: _simulation(id).begin(cache_path, auto_checkpoint) _current_simulation_id = id else: raise ValueError(f"Simulation is already started under id {_current_simulation_id}. Currently only one simulation can be started at a time.") def end(id="default"): """ Marks the end of the simulation being controlled. """ global _current_simulation_id _simulation(id).end() _current_simulation_id = None def checkpoint(id="default"): """ Saves current simulation state. """ _simulation(id).checkpoint() def current_simulation(): """ Returns the current simulation. """ global _current_simulation_id if _current_simulation_id is not None: return _simulation(_current_simulation_id) else: return None def cache_hits(id="default"): """ Returns the number of cache hits. """ return _simulation(id).cache_hits def cache_misses(id="default"): """ Returns the number of cache misses. """ return _simulation(id).cache_misses reset() # initialize the control state