async def on_message(self, message): # Check for command match = shortcut_regex.match(message.content) if (not match): return logging.debug( "Recognized command from {0.author}: {0.content}".format(message)) # Parse arguments try: raw_args = parser.convert_arg_line_to_args(match.group("args")) args = parser.parse_args(raw_args) except ValueError as err: await message.channel.send(err) return # Perform action player = self.get_player(message.author) if (args.command is None): help_msg = "```\n{0}\n```".format(parser.format_help()) await message.channel.send(help_msg) else: output = args.action(player, args) if (output and output.private): await message.author.send(output.message) elif (output): await message.channel.send(output.message)
def main(): call_args = parser.parse_args() if call_args.experiment: import pickle from experiments import testar_parametros_paralelo_por_arquivo print(call_args.tsp_queue) r = testar_parametros_paralelo_por_arquivo( list(glean_tsp_files(call_args.tsp_queue))) binary_file = open(call_args.tsp_queue[0] + '/my_pickled_results.bin', mode='wb') pickle.dump(r, binary_file) binary_file.close() import os os.system( 'play --no-show-progress --null --channels 1 synth %s sine %f' % (0.1, 440)) elif call_args.results: from experiments import process_results, process_one_result import pickle # r = pickle.loads(open(call_args.tsp_queue[0] + '/my_pickled_results.bin', mode='rb').read()) for tsp_path in glean_tsp_files(call_args.tsp_queue): # print(tsp_path) r = pickle.loads(open(tsp_path, mode='rb').read()) process_one_result(r) else: for tsp_path in glean_tsp_files(call_args.tsp_queue): process_from_tsp_path(call_args, tsp_path)
def main(): cli_args = parser.parse_args(sys.argv[1:]) config_name = cli_args.input output_file = cli_args.output config = configparser.ConfigParser() config.read(config_name) args = config['MAIN'] """ article_text = args['article'] article_url = args['url'] article_title = args['title'] article_doi = args['doi'] """ print('Внимание! Проверьте правильность входных данных:') timer = threading.Event() timer.clear() timer.wait(3) for key, value in config['MAIN'].items(): print(fields_definitions[key]) timer.wait(0.2) print(value) if not accept_key(): print('Завершение работы.') return print('Запуск поискового бота') timer.wait(3) results = run(**args) print('Поиск завершен. Найдены следующие ссылки:') header = 'Название страницы, Оценка точности, URL-адрес \n' with open(output_file, 'w', encoding='utf-8') as f: f.write(header) print(header) for line in results: f.write(line) print(line) print('Все ссылки сохранены в файле {}'.format(output_file)) input('Поиск завершен. Нажмите любую клавишу для завершения')
def main(): args = parser.parse_args() if (args.enc): print("encrypt") encryptFile(args.filepath) elif (args.dec): print("decrypt") decryptFile(args.filepath) elif (args.sgn): print("sighn") addSignature(args.filepath) elif (args.chcksign): print("check sign") if checkSignature(args.filepath, args.sgnpath): print('Sign correct') else: print('Sign incorrect')
def main(): call_args = parser.parse_args() for tsp_path in glean_tsp_files(call_args.tsp_queue): print_results_from_tsp_path(call_args,tsp_path)
def main(): call_args = parser.parse_args() for tsp_path in glean_tsp_files(call_args.tsp_queue): print_results_from_tsp_path(call_args, tsp_path)
logger = logging.getLogger('mastermind') logger.propagate = False logger.setLevel(logging.INFO) # create console handler with a higher log level ch = logging.StreamHandler() # create formatter and add it to the handlers formatter = logging.Formatter('%(levelname)s - %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) args = parser.parse_args() game_solver = Mastermind(args.k, args.n, logger=logger, pretty_printer=pretty_name_8_colors) def init_worker(shared_counter): global counter counter = shared_counter # hack to overcome limitation of Pool.map which is unable to execute instance methods def f(x): res = game_solver.batch_solve(x) counter.value += 1 logger.info("{}/{} {}%".format(counter.value, len(game_solver.all_possible_combinations), 100.0 * counter.value / len(game_solver.all_possible_combinations))) return res
n = n - 1 return D(result) def get_ramanujan_term(k): pid = getpid() log('...({}) is calculating term for k={}'.format(pid, k)) k = D(k) num = multiply(factorial(4 * k), addition(D(1103), multiply(D(26390), D(k)))) den = multiply(power(factorial(k), D(4)), power(D(396), multiply(D(4), D(k)))) term = divide(num, den) log('...({}) is done for k={}'.format(pid, k)) return term if __name__ == '__main__': args = parser.parse_args() if args.precision is 1: print('default precision 0 will be used') # start worker processes ts = time() log('Started at {}'.format(datetime.now()), True) with Pool(processes=args.tasks) as pool: factor = divide(multiply(D(2), sqrt(D(2))), D(9801)) all_terms = pool.map(get_ramanujan_term, range(args.precision)) the_sum = reduce(lambda x, y: addition(x, y), all_terms) with open(args.output_file, 'w') as f: f.write('Pi({}) = {}'.format(args.precision, 1 / multiply(factor, the_sum)))
translations = re.findall( r""" # userContextPersonal.label = Pa ngat moni userContext (Personal|Work|Banking|Shopping|None) \. label \ = \ (.*?) \n """, browser_properties.read().decode("utf-8"), flags=re.X ) logging.info(f"Got translations for {lang_name}.") await res_q.put((lang_name, lang_code, translations)) file_q.task_done() if __name__ == "__main__": args_dict = vars(parser.parse_args()) logging.basicConfig(level=args_dict["log"],format="%(message)s") # asyncio.run results in RuntimeError: Event loop is closed loop = asyncio.get_event_loop() loop.set_debug(args_dict["debug"]) loop.run_until_complete(main(args_dict["workers"]))
from MarioGym import MarioGym, MAP_MULTIPLIER, MAP_WIDTH, MAP_HEIGHT import tensorflow as tf if "../" not in sys.path: sys.path.append("../") from lib import plotting from collections import deque, namedtuple import matplotlib.pyplot as plt import numpy as np from constants import * from argparser import parser from PER import SumTree, Memory arguments = parser.parse_args() env = MarioGym(HEADLESS, step_size=STEP_SIZE, level_name=LEVEL_NAME, partial_observation=PARTIAL_OBSERVATION, distance_reward=DISTANCE_REWARD, experiment=EXPERIMENT) class StateProcessor(): """ Processes a raw Atari images. Resizes it and converts it to grayscale. """ def __init__(self): # Build the Tensorflow graph