Python queue.deque方法代碼示例

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def fill_queue(self):
        if self.results is None:
            self.results = queue.deque(maxlen=self.max_queue)
        if self.num_workers > 0:
            if self.pool is None:
                self.pool = Pool(processes=self.num_workers)

        while len(self.results) < self.max_queue:
            if self.distinct_levels is not None and self.idx >= self.distinct_levels:
                break
            elif not self.repeat_levels and self.idx >= len(self.file_data):
                break
            else:
                data = self.get_next_parameters()
                if data is None:
                    break
            self.idx += 1
            kwargs = {'seed': self._seed.spawn(1)[0]}
            if self.num_workers > 0:
                result = self.pool.apply_async(_game_from_data, data, kwargs)
            else:
                result = _game_from_data(*data, **kwargs)
            self.results.append((data, result)) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def __init__(self, name, topic: (str, list) = None, *args, **kwargs):
        self.name = name
        self.topic = set(topic if isinstance(topic, list) else
                         [topic]) if topic is not None else set()
        self.ctx = zmq_ctx
        self.sub_socket = self.ctx.socket(zmq.SUB)
        self.sub_socket.setsockopt(zmq.RCVTIMEO, 3000)
        self._thread_pool = ThreadPoolExecutor(max_workers=1)
        self.inproc = set()

        if self.topic:  # 如果topic默認為None，則對所有的topic做處理
            for t in self.topic:
                self.sub_socket.setsockopt(zmq.SUBSCRIBE, pickle.dumps(t))
        else:
            self.sub_socket.subscribe('')

        if kwargs.get('latest', False):  # 可以通過latest(bool)來訂閱最新的數據
            self.data_queue = deque(maxlen=1)
            self.latest = True
        else:
            self.data_queue = deque()
            self.latest = False
        self.__active = False 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def reset(self):
        obs = self.env.reset()
        self._prior_positions = queue.deque(
            [self.game.agent_loc], self.movement_bonus_period)
        return obs 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def __getstate__(self):
        state = self.__dict__.copy()
        if self.num_workers > 0:
            # Don't pickle the multiprocessing pool, and wait on all queued results.
            state['pool'] = None
            state['results'] = queue.deque([
                r.get() if isinstance(r, ApplyResult) else r
                for r in self.results
            ], maxlen=self.max_queue)

        return state 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def profile(func):
    name = func.__name__
    samples = queue.deque(maxlen=5)

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        if not self.debug_enabled:
            return func(self, *args, **kwargs)
        start = clock()
        ret = func(self, *args, **kwargs)
        n = tfloat(clock() - start)

        if len(samples) < 2:
            m = 0
            d = 0
            n.color = 36
        else:
            m = mean(samples)
            if stdev:
                d = tfloat(stdev(samples))
            else:
                d = 0

            if n <= m + d:
                n.color = 32
            elif n > m + d * 2:
                n.color = 31
            else:
                n.color = 33
        samples.append(n)
        self.info("\x1b[34m%s\x1b[m t = %s, \u00b5 = %s, \u03c3 = %s)", name, n, m, d)
        return ret

    return wrapper 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def __init__(self, app):
        """
        :type app: feeluown.app.App
        """
        self._app = app

        # store songs that are going to be added to playlist
        self._queue = deque()
        self._activated = False
        self._is_fetching_songs = False
        self._fetch_songs_task_name = 'fm-fetch-songs'
        self._fetch_songs_func = None
        self._minimum_per_fetch = 3

        self._app.playlist.mode_changed.connect(self._on_playlist_mode_changed) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def profile(func):
    name = func.__name__
    samples = queue.deque(maxlen=5)

    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        if not self.is_debug_enabled:
            return func(self, *args, **kwargs)
        start = clock()
        ret = func(self, *args, **kwargs)
        n = tfloat(clock() - start)

        if len(samples) < 2:
            m = 0
            d = 0
            n.color = 36
        else:
            m = mean(samples)
            if stdev:
                d = tfloat(stdev(samples))
            else:
                d = 0

            if n <= m + d:
                n.color = 32
            elif n > m + d * 2:
                n.color = 31
            else:
                n.color = 33
        samples.append(n)
        self.info('\x1b[34m%s\x1b[m t = %s, \u00b5 = %s, \u03c3 = %s)',
                  name, n, m, d)
        return ret
    return wrapper 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def __init__(self):
        self.values = {}
        self.lock = threading.Lock()
        self.queue = queue.deque()
        self.index = 0 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def min_subnet_depth(topology):
    """Find the minumum depth of each subnet in the network graph in terms of steps
    from an exposed subnet to each subnet

    Parameters
    ----------
    topology : 2D matrix
        An adjacency matrix representing the network, with first subnet
        representing the internet (i.e. exposed)

    Returns
    -------
    depths : list
        depth of each subnet ordered by subnet index in topology
    """
    num_subnets = len(topology)

    assert len(topology[0]) == num_subnets

    depths = []
    Q = deque()
    for subnet in range(num_subnets):
        if topology[subnet][INTERNET] == 1:
            depths.append(0)
            Q.appendleft(subnet)
        else:
            depths.append(float('inf'))

    while len(Q) > 0:
        parent = Q.pop()
        for child in range(num_subnets):
            if topology[parent][child] == 1:
                # child is connected to parent
                if depths[child] > depths[parent] + 1:
                    depths[child] = depths[parent] + 1
                    Q.appendleft(child)
    return depths 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def statistic(self):
        now = time.time()
        if now > self.last_statistic_time + 60:
            rtt = 0
            sent = 0
            received = 0
            for stat in self.second_stats:
                rtt = max(rtt, stat["rtt"])
                sent += stat["sent"]
                received += stat["received"]
            self.minute_stat = {
                "rtt": rtt,
                "sent": sent,
                "received": received
            }
            self.second_stats = queue.deque()
            self.last_statistic_time = now

        if len(self.rtts):
            rtt = max(self.rtts)
        else:
            rtt = 0

        self.second_stat = {
            "rtt": rtt,
            "sent": self.total_sent - self.last_sent,
            "received": self.total_received - self.last_received
        }
        self.rtts = []
        self.last_sent = self.total_sent
        self.last_received = self.total_received
        self.second_stats.append(self.second_stat) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def numpy_mode():
    with chainer.using_config('train', False):
        with chainer.no_backprop_mode():
            netA_path = input('enter netA path...')
            netB_path = input('enter netB path...')
            wave_path = input('enter wave path...')

            ds = dataset.WaveDataset(wave_path, -1, True)

            netA = Generator()
            netB = Generator()
            chainer.serializers.load_npz(netA_path, netA)
            chainer.serializers.load_npz(netB_path, netB)

            que_a = queue.deque()
            que_ab = queue.deque()
            que_aba = queue.deque()

            gla = GLA()

            print('converting...')
            for i in tqdm.tqdm(range(ds.max//dataset.dif)):
                x_a = ds.get_example(i)
                x_a = chainer.dataset.convert.concat_examples([x_a], -1)
                x_a = chainer.Variable(x_a)

                x_ab = netA(x_a)
                x_aba = netB(x_ab)

                que_a  .append(x_a  .data[0])
                que_ab .append(x_ab .data[0])
                que_aba.append(x_aba.data[0])
            print('done')

            print('phase estimating...')
            for i, que, name in zip(range(3), [que_a, que_ab, que_aba], ['a.wav', 'ab.wav', 'aba.wav']):
                print()
                print(i+1, '/ 3')
                wave   = np.concatenate([gla.inverse(c_f) for i_f in tqdm.tqdm(que) for c_f in dataset.reverse(i_f)])
                print('done...')
                dataset.save(wave_path + name, 16000, wave)
                print('wave-file saved at', wave_path + name)

            print('all done') 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def cupy_mode(gpu):
    from gla.gla_gpu import GLA_GPU
    cp = chainer.cuda.cupy

    with chainer.using_config('train', False):
        with chainer.no_backprop_mode():
            netA_path = input('enter netA path...')
            netB_path = input('enter netB path...')
            wave_path = input('enter wave path...')
            batchsize = int(input('enter batch size...'))
            chainer.cuda.get_device_from_id(gpu).use()

            ds = dataset.WaveDataset(wave_path, -1, True)

            netA = Generator()
            netB = Generator()
            chainer.serializers.load_npz(netA_path, netA)
            chainer.serializers.load_npz(netB_path, netB)
            netA.to_gpu()
            netB.to_gpu()

            que_a = queue.deque()
            que_ab = queue.deque()
            que_aba = queue.deque()

            gla = GLA_GPU(batchsize*4)

            print('converting...')
            l = ds.max//dataset.dif
            for i in tqdm.tqdm(range(0, l, batchsize)):
                x_a = [ds.get_example(_i) for _i in range(i, min([i+batchsize, l]))]
                x_a = chainer.dataset.convert.concat_examples(x_a, gpu)
                x_a = chainer.Variable(x_a)

                x_ab = netA(x_a)
                x_aba = netB(x_ab)

                que_a  .extend([dataset.reverse(_x) for _x in cp.asnumpy(x_a  .data)])
                que_ab .extend([dataset.reverse(_x) for _x in cp.asnumpy(x_ab .data)])
                que_aba.extend([dataset.reverse(_x) for _x in cp.asnumpy(x_aba.data)])
            img_a   = np.concatenate(que_a,   axis=0)
            img_ab  = np.concatenate(que_ab,  axis=0)
            img_aba = np.concatenate(que_aba, axis=0)
            print('done')

            print('phase estimating...')
            for i, img, name in zip(range(3), [img_a, img_ab, img_aba], ['a.wav', 'ab.wav', 'aba.wav']):
                print()
                print(i+1, '/ 3')
                wave = gla.auto_inverse(img)
                print('done...')
                dataset.save(wave_path + name, 16000, wave)
                print('wave-file saved at', wave_path + name)

            print('all done') 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def _download_multithreaded(self, input_items, num_threads, trace_custom_fields={}, traces_copy_out=None):
        """
        Starts an appropriate number of threads to download items from the input list.
        (This function is meant to be used as class internal only)

        :param input_items: list containing the input items to download
        :param num_threads: suggestion of how many threads should be started
        :param trace_custom_fields: Custom key value pairs to send with the traces
        :param traces_copy_out: reference to an external list, where the traces should be uploaded

        :returns: list with output items as dictionaries
        """
        logger = self.logger

        num_files = len(input_items)
        nlimit = 5
        num_threads = max(1, num_threads)
        num_threads = min(num_files, num_threads, nlimit)

        input_queue = Queue()
        output_queue = Queue()
        input_queue.queue = deque(input_items)

        if num_threads < 2:
            logger.info('Using main thread to download %d file(s)' % num_files)
            self._download_worker(input_queue, output_queue, trace_custom_fields, traces_copy_out, '')
            return list(output_queue.queue)

        logger.info('Using %d threads to download %d files' % (num_threads, num_files))
        threads = []
        for thread_num in range(0, num_threads):
            log_prefix = 'Thread %s/%s: ' % (thread_num, num_threads)
            kwargs = {'input_queue': input_queue,
                      'output_queue': output_queue,
                      'trace_custom_fields': trace_custom_fields,
                      'traces_copy_out': traces_copy_out,
                      'log_prefix': log_prefix}
            try:
                thread = Thread(target=self._download_worker, kwargs=kwargs)
                thread.start()
                threads.append(thread)
            except Exception as error:
                logger.warning('Failed to start thread %d' % thread_num)
                logger.debug(error)

        try:
            logger.debug('Waiting for threads to finish')
            for thread in threads:
                thread.join()
        except KeyboardInterrupt:
            logger.warning('You pressed Ctrl+C! Exiting gracefully')
            for thread in threads:
                thread.kill_received = True
        return list(output_queue.queue) 

# 需要導入模塊: import queue [as 別名]
# 或者: from queue import deque [as 別名]
def __init__(self, logger, config, ip_manager, connection_manager,
                 http1worker=Http1Worker,
                 http2worker=Http2Worker):
        self.logger = logger
        self.config = config
        self.ip_manager = ip_manager
        self.connection_manager = connection_manager
        self.connection_manager.set_ssl_created_cb(self.on_ssl_created_cb)

        self.http1worker = http1worker
        self.http2worker = http2worker

        self.request_queue = queue.Queue()
        self.workers = []
        self.working_tasks = {}
        self.h1_num = 0
        self.h2_num = 0
        self.last_request_time = time.time()
        self.task_count_lock = threading.Lock()
        self.task_count = 0
        self.running = True

        # for statistic
        self.success_num = 0
        self.fail_num = 0
        self.continue_fail_num = 0
        self.last_fail_time = 0
        self.rtts = []
        self.last_sent = self.total_sent = 0
        self.last_received = self.total_received = 0
        self.second_stats = queue.deque()
        self.last_statistic_time = time.time()
        self.second_stat = {
            "rtt": 0,
            "sent": 0,
            "received": 0
        }
        self.minute_stat = {
            "rtt": 0,
            "sent": 0,
            "received": 0
        }

        self.trigger_create_worker_cv = SimpleCondition()
        self.wait_a_worker_cv = simple_queue.Queue()

        threading.Thread(target=self.dispatcher).start()
        threading.Thread(target=self.create_worker_thread).start()
        threading.Thread(target=self.connection_checker).start()