def simulation(self, sweep_dict: Dict[str, List[Any]],
states_list: List[Dict[str, Any]],
configs: List[Tuple[List[Callable], List[Callable]]],
env_processes: Dict[str, Callable], time_seq: range,
runs: int) -> List[List[Dict[str, Any]]]:
def execute_run(sweep_dict, states_list, configs, env_processes,
time_seq, run) -> List[Dict[str, Any]]:
run += 1
def generate_init_sys_metrics(genesis_states_list):
for d in genesis_states_list:
d['run'], d['substep'], d['timestep'] = run, 0, 0
yield d
states_list_copy: List[Dict[str, Any]] = list(
generate_init_sys_metrics(deepcopy(states_list)))
first_timestep_per_run: List[Dict[str, Any]] = self.run_pipeline(
sweep_dict, states_list_copy, configs, env_processes, time_seq,
run)
del states_list_copy
return first_timestep_per_run
tp = TPool(runs)
pipe_run: List[List[Dict[str, Any]]] = flatten(
tp.map(
lambda run: execute_run(sweep_dict, states_list, configs,
env_processes, time_seq, run),
list(range(runs))))
tp.clear()
return pipe_run
if __name__ == '__main__':
from pathos.pools import ThreadPool as TPool
tpool = TPool()
print("Evaluate 10 items on 1 thread")
tpool.nthreads = 1
res3 = tpool.map(host, range(10))
print(tpool)
print('\n'.join(res3))
print('')
print("Evaluate 10 items on 2 threads")
tpool.nthreads = 2
res5 = tpool.map(host, range(10))
print(tpool)
print('\n'.join(res5))
print('')
print("Evaluate 10 items on ? threads")
tpool.nthreads = None
res9 = tpool.map(host, range(10))
print(tpool)
print('\n'.join(res9))
print('')
tpool.clear()
# end of file
