def __init__(
self,
config: Config,
real_job: typing.Callable[..., typing.Any],
) -> None:
self.config = config
local_read_pipe, local_write_pipe = Pipe(duplex=False)
process_read_pipe, process_write_pipe = Pipe(duplex=False)
self.local_read_pipe = local_read_pipe # type: Connection
self.local_write_pipe = local_write_pipe # type: Connection
self.process_read_pipe = process_read_pipe # type: Connection
self.process_write_pipe = process_write_pipe # type: Connection
self.real_job = real_job
self.process = Process(
target=self.work,
args=(
self.local_write_pipe,
self.process_read_pipe,
),
kwargs={'seed': random.random()},
name='Worker',
)
self.db = None # TODO delete
self.process.start()
def __init__(self,
cluster_cls: Type[Cluster],
cluster_kwargs: Optional[dict] = None):
self.cluster_cls = cluster_cls
self.cluster_kwargs = cluster_kwargs or {}
# must initialize the pipes before calling `run()`
self.__cmd_pipe = Pipe()
self.__result_pipe = Pipe()
super().__init__()
def load(self):
control_receiver, control_sender = Pipe(duplex=False)
events_receiver, events_sender = Pipe(duplex=False)
self.add_control_connection(control_receiver, "Manager")
self.add_events_connection(events_sender, "Manager")
# Start the process, this will call _run()
self.process.start()
return control_sender, events_receiver
def test_cython_wrapper(self):
serials = bladerf.get_device_list()
print("Connected serials", serials)
bladerf.open()
bladerf.set_tx(True)
bladerf.set_channel(0)
print("set gain", bladerf.set_gain(20))
print("set gain", bladerf.set_gain(21))
bladerf.set_tx(False)
bladerf.set_channel(1)
print("Sample Rate", bladerf.get_sample_rate())
print("Set sample rate to 2e6", bladerf.set_sample_rate(int(2e6)))
print("sample rate", bladerf.get_sample_rate())
print("Set sample rate to 40e6", bladerf.set_sample_rate(int(40e6)))
print("sample rate", bladerf.get_sample_rate())
print("Set sample rate to 200e6", bladerf.set_sample_rate(int(200e6)))
print("sample rate", bladerf.get_sample_rate())
bladerf.set_tx(True)
bladerf.set_channel(1)
print("Bandwidth", bladerf.get_bandwidth())
print("Set Bandwidth to 2e6", bladerf.set_bandwidth(int(2e6)))
print("Bandwidth", bladerf.get_bandwidth())
bladerf.set_tx(False)
bladerf.set_channel(0)
print("Frequency", bladerf.get_center_freq())
print("Set Frequency to 433.92e6",
bladerf.set_center_freq(int(433.92e6)))
print("Frequency", bladerf.get_center_freq())
bladerf.prepare_sync()
parent_conn, child_conn = Pipe()
for i in range(3):
bladerf.receive_sync(child_conn, 4096)
data = parent_conn.recv_bytes()
print(data)
bladerf.close()
bladerf.open()
bladerf.set_tx(True)
bladerf.set_channel(0)
bladerf.prepare_sync()
for i in range(10):
print("Send", bladerf.send_sync(np.fromstring(data,
dtype=np.int16)))
bladerf.close()
def create_worker(self):
a, b = Pipe()
with a:
cmd = 'from {0} import run_main, {1}; run_main({2!r}, {1})'.format(
self.__class__.__module__, 'worker_main', a.fileno())
p = start_worker(cmd, (a.fileno(), ))
sys.stdout.flush()
p.stdin.close()
w = Worker(p, b, self.events, self.name)
if self.common_data != pickle_dumps(None):
w.set_common_data(self.common_data)
return w
def get_state_value(conn: Connection = None, key=None, prgh_queue=None):
h, c = Pipe(duplex=True)
state_message = ModuleMessage("PRGH", "get_state_data", (key, c))
if conn is not None:
conn.send(state_message)
elif prgh_queue is not None:
prgh_queue.put(state_message)
else:
raise Exception("get_state_value() was called without a pipe or queue")
msg = h.recv()
h.close()
return msg
def test_c_wrapper(self):
def pycallback(data):
arr = np.asarray(data)
#result = np.empty(len(arr) // 2, dtype=np.complex64)
#result.real = (arr[::2] + 0.5) / 32767.5
#result.imag = (arr[1::2] + 0.5) / 32767.5
print(sdrplay.get_api_version())
print(sdrplay.get_devices())
print(sdrplay.set_device_index(0))
parent_conn, child_conn = Pipe()
p = Process(target=recv, args=(parent_conn, ))
p.daemon = True
p.start()
null_ptr = ctypes.POINTER(ctypes.c_voidp)()
print("Init stream",
sdrplay.init_stream(50, 2e6, 433.92e6, 2e6, 500, child_conn))
time.sleep(2)
print("settings sample rate")
print("Set sample rate", sdrplay.set_sample_rate(2e6))
time.sleep(1)
p.terminate()
p.join()
def __enter__(self):
parent_pipe, child_pipe = Pipe()
childpid = os.fork()
if childpid != 0:
self.childpid = childpid
self._parent_setup()
self.__pipe = parent_pipe
frame = self.__get_context_frame()
self.__inject_trace_func(frame, self.__exit_context)
return self
else:
frame = self.__get_context_frame()
self.__child_orig_locals = dict(frame.f_locals)
self.__pipe = child_pipe
try:
self._child_setup()
# pylint: disable=W0703
# need to catch all exceptions here since we are passing them to
# the parent process
except Exception as exc:
exc.__traceback_list__ = traceback.format_exc()
self._child_exit(exc)
return self
def get_config_value(conn: Connection = None, key=None, prgh_queue=None):
h, c = Pipe(duplex=True)
cfg_msg = cfg_transaction(key, is_set_var=False, reply=c)
config_message = ModuleMessage("PRGH", CFGT.CFG_GET, cfg_msg)
if conn is not None:
conn.send(config_message)
elif prgh_queue is not None:
prgh_queue.put(config_message)
else:
raise Exception(
"get_config_value() was called without a pipe or queue")
msg = h.recv()
h.close()
return msg
def __init__(self):
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._poll = self._reader.poll
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
def __init__(self):
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._poll = self._reader.poll
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
self._make_methods()
def Pipe(duplex=True):
'''
Returns two connection object connected by a pipe
'''
if sys.version_info[0] == 3:
from multiprocessing.connection import Pipe
else:
from billiard._connection import Pipe
return Pipe(duplex)
def test_cython_wrapper(self):
print(usrp.find_devices(""))
usrp.set_tx(False)
return_code = usrp.open("addr=192.168.10.2")
print("open", return_code)
usrp.setup_stream()
print("Made rx_streame handler")
print(usrp.get_device_representation())
print("Set sample rate", usrp.set_sample_rate(2e6))
print("Set freq", usrp.set_center_freq(433.92e6))
print("Set bandwidth", usrp.set_bandwidth(1e6))
print("Set gain", usrp.set_rf_gain(0.5))
buffer = bytearray()
num_samples = 32768 // 2
usrp.start_stream(num_samples)
parent_conn, child_conn = Pipe()
for i in range(500):
usrp.recv_stream(child_conn, num_samples)
received_bytes = parent_conn.recv_bytes()
#print(received_bytes)
print(i)
buffer.extend(received_bytes)
#print(USRP.unpack_complex(received_bytes, len(received_bytes) // 8))
f = open("/tmp/test.complex", "wb")
f.write(buffer)
f.close()
usrp.destroy_stream()
print("Freed rx streamer handler")
return_code = usrp.close()
print("close", return_code)
def on_input(self, func):
*_, last = self.all_steps # Get the last step
receiver, sender = Pipe(duplex=False)
for plugin in last[1]:
connection = ("Manager", sender)
control_manager = self.control_manager(plugin)
control_manager.send("output")
control_manager.send(connection)
self.receiver = receiver
self.on_input_func = func
def __init__(self, maxsize=0):
if maxsize <= 0:
maxsize = _multiprocessing.SemLock.SEM_VALUE_MAX
self._maxsize = maxsize
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._opid = os.getpid()
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
self._sem = BoundedSemaphore(maxsize)
# For use by concurrent.futures
self._ignore_epipe = False
self._after_fork()
if sys.platform != 'win32':
register_after_fork(self, Queue._after_fork)
def __init__(self, source):
self.source = source
self.tmp_dir = PropertyManager.getSharedInstance(
)["temporary_directory"]
(self.wavefilename, self.wavefile) = self.getWaveFile()
self.switchingLock = threading.Lock()
self.timer = None
(self.outputReader, self.outputWriter) = Pipe()
self.doRun = True
super().__init__()
def __enter__(self):
parent_pipe, child_pipe = Pipe()
if pid := os.fork():
self.childpid = pid
self._parent_setup()
self.__pipe = parent_pipe
frame = self.__get_context_frame()
self.__inject_trace_func(frame, self.__exit_context)
return self
def __init__(self, dsp, source, profile: AudioChopperProfile):
self.dsp = dsp
self.source = source
self.profile = profile
self.tmp_dir = Config.get()["temporary_directory"]
self.wavefile = None
self.wavefilename = None
self.switchingLock = threading.Lock()
self.timer = None
(self.outputReader, self.outputWriter) = Pipe()
def test_terminate_method_with_Pipe(self, num_threads=50):
readable, writable = Pipe(duplex=False)
threads = self._start_threads(readable.recv_bytes, num_threads)
writable.send_bytes(b"spam")
time.sleep(self.WAITSEC)
self.assertNumAliveThreads(num_threads - 1, threads,
"No thread has ended, %r" % threads)
self._terminate_still_alive(threads)
for t in threads:
self.assertRaises(OSError, t.terminate)
self.assertNumAliveThreads(
0, threads, "Some threads are still alive, %r" % threads)
for i in range(num_threads):
data = ("%d" % i).encode("ascii")
writable.send_bytes(data)
self.assertEqual(data, readable.recv_bytes(),
"Someone seems to intercept pipe, %r" % threads)
def __init__(self, active_dsp, mode_str: str):
self.read_fn = None
self.doRun = True
self.dsp = active_dsp
self.writers = []
mode = Modes.findByModulation(mode_str)
if mode is None or not isinstance(mode, AudioChopperMode):
raise ValueError(
"Mode {} is not an audio chopper mode".format(mode_str))
self.profile_source = mode.get_profile_source()
(self.outputReader, self.outputWriter) = Pipe()
super().__init__()
def _connect(self, node: Node) -> bool:
if isinstance(node, Analyzer):
from_analyzer, node.output = Pipe(duplex=False)
self._from_analyzers.append(from_analyzer)
node.input, to_analyzer = Pipe(duplex=False)
self._to_analyzers.append(to_analyzer)
self._analyzer_counter[to_analyzer] = 0
elif isinstance(node, Fetcher):
# Fetcher does not need to listen from Router
self._from_fetcher, node.output = Pipe(duplex=False)
elif isinstance(node, Broker):
self._from_broker, node.output = Pipe(duplex=False)
node.input, self._to_broker = Pipe(duplex=False)
elif isinstance(node, Ledger):
node.input, self._to_ledger = Pipe(duplex=False)
elif isinstance(node, Router):
pass
else:
raise TypeError(type(node))
logger.debug(f'Connected to {node.name}')
return True
def connect(self, plugin1, plugin2, extra_label=''):
""" create plugin1 output to plugin2 input """
receiver, sender = Pipe(duplex=False)
connection = (plugin2.instance_id, sender)
control_manager = self.control_manager(plugin1)
control_manager.send("output " + extra_label)
control_manager.send(connection)
connection = (plugin1.instance_id, receiver)
control_manager = self.control_manager(plugin2)
control_manager.send("input")
control_manager.send(connection)
def mult_fast(A, B):
thread_cnt = 4
n = len(A) // thread_cnt
threads = []
cs = []
for i in range(thread_cnt):
p_conn, c_conn = Pipe()
# th = MyThread(A[n*i:n*(i+1)], B)
th = Process(target=mult_s, args=(A[n * i:n * (i + 1)], B, c_conn))
cs.append(p_conn)
threads.append(th)
th.start()
res = []
# for t in threads:
# t.join()
# res += t.res
for (t, c) in zip(threads, cs):
res += c.recv()
return res
def __init__(self, maxsize=0):
if maxsize <= 0:
maxsize = _multiprocessing.SemLock.SEM_VALUE_MAX
self._maxsize = maxsize
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._opid = os.getpid()
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
self._sem = BoundedSemaphore(maxsize)
# For use by concurrent.futures
self._ignore_epipe = False
self._after_fork()
if sys.platform != 'win32':
register_after_fork(self, Queue._after_fork)
def create_local_worker(self, wclass, *args, **kwargs):
ours, theirs = Pipe()
self.pipes.append(ours)
spawnf = multiprocessing.Process
if 'spawnf' in kwargs:
spawnf = kwargs['spawnf']
del kwargs['spawnf']
proc = spawnf(target=create_worker,
args=(
theirs,
wclass,
*args,
),
kwargs=kwargs)
self.local_processes.append(proc)
proc.start()
self.on_new_worker(len(self.pipes) - 1)
def _process_execute(child_pipe: Pipe,
*args,
network_type: NetworkTypes = None,
**kwargs):
last_update = time.time()
AGENT = network_factory(network_type=network_type)
AGENT.load()
while True:
if not child_pipe.poll():
time.sleep(0.01)
continue
request = child_pipe.recv()
if isinstance(request, PredictionRequest):
if np.random.rand(
) <= AGENT._session_epsilon or request.no_random:
result = AGENT.predict_random(request.data)
else:
result = AGENT.predict(request.data)
child_pipe.send(PredictionResult(result=result))
elif isinstance(request, EpisodeResult):
for item in request.game_data:
AGENT.memory.append(item)
if len(AGENT.memory
) > AGENT.config.observe_frames_before_learning:
AGENT.fit_batch()
# logger.debug("[KerasProcess] Fit Batch Complete", runtime=time.time()-start_time, batch_size=batch_size)
# Let the runner know that we're done and ready for another task.
child_pipe.send(True)
elif request is None:
AGENT.save()
# Shutdown request
if request is None:
return
if (time.time() - last_update) / 60 > 5:
# Only print updates and save every 5 minutes
last_update = time.time()
logger.debug("KERAS PROCESS UPDATE",
epsilon=AGENT._session_epsilon,
memory_len=len(AGENT.memory))
# logger.debug("Stats", loss=np.mean(AGENT.loss_history), acc=np.mean(AGENT.acc_history))
AGENT.save()
def __init__(
self,
name: str,
target: Callable,
context: SpawnContext,
log_queue: Queue,
signal_handler: Callable[[int, FrameType], None],
event_callback: Callable[[Event], None],
):
self._name = name
self._target = target
self._context = context
self._log_queue = log_queue
self._signal_handler = signal_handler
self._process = None
self._ep_conn, self._mp_conn = Pipe()
self._event_listener = PipeListener(
conn=self._mp_conn,
action=event_callback,
name=f"{name} listener",
)
def __enter__(self):
parent_pipe, child_pipe = Pipe()
childpid = os.fork()
if childpid != 0:
self.parent_setup()
self.childpid = childpid
self.__pipe = parent_pipe
frame = self.__get_context_frame()
self.__inject_trace_func(frame, self.__exit_context)
return self
else:
self.__pipe = child_pipe
try:
self.child_setup()
# pylint: disable=W0703
# need to catch all exceptions here since we are passing them to
# the parent process
except Exception as e:
e.__traceback_list__ = traceback.format_exc()
self.__pipe.send(e)
try:
self.__pipe.send(SystemExit())
except (
BrokenPipeError if sys.hexversion >= 0x03030000 # pylint: disable=E0602
else OSError,
IOError) as e:
if e.errno in (errno.EPIPE, errno.ESHUTDOWN):
pass
else:
raise
os._exit(0) # pylint: disable=W0212
# we don't want SystemExit being caught here
return self
def launch_worker_process(self):
from calibre.utils.ipc.pool import start_worker
control_a, control_b = Pipe()
data_a, data_b = Pipe()
with control_a, data_a:
pass_fds = control_a.fileno(), data_a.fileno()
self.worker_process = p = start_worker(
'from {0} import run_main, {1}; run_main({2}, {3}, {1})'.
format(self.__class__.__module__, self.worker_entry_point,
*pass_fds), pass_fds)
p.stdin.close()
self.control_conn = control_b
self.data_conn = data_b
self.data_thread = t = Thread(name='CWData',
target=self.handle_data_requests)
t.daemon = True
t.start()
self.connected.set()
class SimpleQueue(object):
def __init__(self):
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._poll = self._reader.poll
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
def empty(self):
return not self._poll()
def __getstate__(self):
assert_spawning(self)
return (self._reader, self._writer, self._rlock, self._wlock)
def __setstate__(self, state):
(self._reader, self._writer, self._rlock, self._wlock) = state
def get(self):
with self._rlock:
res = self._reader.recv_bytes()
# unserialize the data after having released the lock
return ForkingPickler.loads(res)
def put(self, obj):
# serialize the data before acquiring the lock
obj = ForkingPickler.dumps(obj)
if self._wlock is None:
# writes to a message oriented win32 pipe are atomic
self._writer.send_bytes(obj)
else:
with self._wlock:
self._writer.send_bytes(obj)
def runBaseline(config, data_dictionary, train_test_folds):
program_start_time = time()
# assign all program arguments to local variables
Runs = config['runs']
LeadTimes = config['lead_times']
SliceLength = config['slice_size']
TestFraction = config['test_fraction']
BatchSize = config['batch_size']
stations = config['stations']
n_stations = len(stations)
n_loaders = config['n_loaders']
n_workers = config['n_workers']
# update general static model information
experiment_info = config
experiment_info['experiment'] = 'bias_corrected_baseline'
experiment_info['train_test_distribution'] = []
# output path for experiment information
setting_string = 'bias_corrected_baseline_tf_%s_sl_%s' % (TestFraction, SliceLength)
output_path = '%s/%s' % (config['experiment_path'], setting_string)
# time for the set up until first run
experiment_info['set_up_time'] = time() - program_start_time
print('[Time]: Set-up %s' % strftime("%H:%M:%S", gmtime(experiment_info['set_up_time'])))
sys.stdout.flush()
# initialize runtime statistic
run_times = None
# cross validation
for run in range(Runs):
# time stamp at start of run
run_start_time = time()
print('[Run %s] Cross-validation test fold %s' % (str(run + 1), str(run + 1)))
# take the right preprocessed train/test data set for the current run
train_fold, test_fold = train_test_folds[run]
n_train, n_test = len(train_fold), len(test_fold)
# initialize train and test dataloaders
trainset = DataLoaders.BiasCorrectionCosmoData(
config=config,
files=train_fold,
station_data_dict=data_dictionary)
trainloader = DataLoader(trainset, batch_size=BatchSize, shuffle=False,
num_workers=n_loaders, collate_fn=DataLoaders.collate_fn)
testset = DataLoaders.BiasCorrectionCosmoData(
config=config,
files=test_fold,
station_data_dict=data_dictionary)
testloader = DataLoader(testset, batch_size=BatchSize, shuffle=False,
num_workers=n_loaders, collate_fn=DataLoaders.collate_fn)
# -----------------------------------#
# ----------- TRAINING ----------- #
# ----------------------------------#
# initialize variables for epoch statistics
train_start_time = time()
run_output_path = output_path + '/run_%s' % run
# load existing bias data
if (os.path.exists(run_output_path + '/station_bias.pkl') and
os.path.exists(run_output_path + '/station_hour_bias.pkl') and
os.path.exists(run_output_path + '/station_hour_month_bias.pkl')):
print('Load existing bias data and skip training...')
with open(run_output_path + '/station_bias.pkl', 'rb') as handle:
station_bias = pkl.load(handle)
with open(run_output_path + '/station_hour_bias.pkl', 'rb') as handle:
station_hour_bias = pkl.load(handle)
with open(run_output_path + '/station_hour_month_bias.pkl', 'rb') as handle:
station_hour_month_bias = pkl.load(handle)
print('Existing bias data loaded.')
# calculate bias on training data and store it
else:
# set up multiprocessing workers per hour in [0, 23]
pipes = []
processes = []
station_mapping = {}
for p in range(n_workers):
parent, child = Pipe()
pipes += [parent]
process_stations = [stations[s] for s in range(p, n_stations, n_workers)]
for ps in process_stations:
station_mapping[ps] = p
processes += [TrainWorker(child, process_stations, n_train // n_stations, n_test // n_stations, run)]
for p in processes:
p.start()
# loop over complete train set and sample all data points the same way as with the NN models
for i, data in enumerate(trainloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and external features
Label, Prediction, Station, Month, Hour = tuple(map(lambda x: x.numpy().squeeze(), data[:5]))
except ValueError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Skipped ValueError in train.')
continue
for batch_sample in range(Label.shape[0]):
station = Station[batch_sample]
process_station = station_mapping[station]
pipe = pipes[process_station]
pipe.send((LeadTimes, Prediction[batch_sample], Label[batch_sample], station,
Hour[batch_sample], Month[batch_sample]))
print("All training samples have been queued. Waiting on results...")
sys.stdout.flush()
# send poison pill to worker, to signalize end of training phase
for pipe in pipes:
pipe.send(None)
station_hour_month_bias_data_set = None
for pipe_idx, pipe in enumerate(pipes):
while True:
result = pipe.recv()
# check if it is the last result sent by worker
if result is None:
print("All results of process %s have been consumed." % pipe_idx)
break
try:
station_hour_month_bias_data_set = station_hour_month_bias_data_set.combine_first(result)
except AttributeError:
station_hour_month_bias_data_set = result
for pipe in pipes:
pipe.close()
sys.stdout.flush()
station_labels = station_hour_month_bias_data_set.coords['station'].data
hour_labels = station_hour_month_bias_data_set.coords['hour'].data
month_labels = station_hour_month_bias_data_set.coords['month'].data
# calculate the average error made for a specific station
station_bias = xr.DataArray(np.nanmean(station_hour_month_bias_data_set.data, axis=(2, 3, 4))[..., None],
dims=('lead', 'station', 'bias'),
coords=(LeadTimes, station_labels, ['bias']))
# calculate the average error made for specific station at a specific hour
station_hour_bias = xr.DataArray(np.nanmean(station_hour_month_bias_data_set.data, axis=(3, 4))[..., None],
dims=('lead', 'station', 'hour', 'bias'),
coords=(LeadTimes, station_labels, hour_labels, ['bias']))
station_hour_month_bias = xr.DataArray(np.nanmean(station_hour_month_bias_data_set.data, axis=(4))[..., None],
dims=('lead', 'station', 'hour', 'month', 'bias'),
coords=(LeadTimes, station_labels, hour_labels, month_labels, ['bias']))
# fill missing values (nan) with 0
station_hour_month_bias = station_hour_month_bias.fillna(0)
station_bias = station_bias.fillna(0)
station_hour_bias = station_hour_bias.fillna(0)
# store time to process complete train set
train_time = time() - train_start_time
experiment_info['run_time_train'] = train_time
# dump experiment statistic
print('Store bias data after training...')
run_output_path = output_path + '/run_%s' % run
if not os.path.exists(run_output_path):
os.makedirs(run_output_path)
with open(run_output_path + '/station_bias.pkl', 'wb') as handle:
pkl.dump(station_bias, handle, protocol=pkl.HIGHEST_PROTOCOL)
with open(run_output_path + '/station_hour_bias.pkl', 'wb') as handle:
pkl.dump(station_hour_bias, handle, protocol=pkl.HIGHEST_PROTOCOL)
with open(run_output_path + '/station_hour_month_bias.pkl', 'wb') as handle:
pkl.dump(station_hour_month_bias, handle, protocol=pkl.HIGHEST_PROTOCOL)
print('Stored bias data after training.')
# -----------------------------------#
# ----------- TESTING ----------- #
# ----------------------------------#
test_start_time = time()
# set up multiprocessing workers per hour in [0, 23]
pipes = []
processes = []
station_mapping = {}
for p in range(n_workers):
parent, child = Pipe()
pipes += [parent]
process_stations = [stations[s] for s in range(p, n_stations, n_workers)]
for ps in process_stations:
station_mapping[ps] = p
processes += [TestWorker(child, process_stations, n_test // n_stations, n_test // n_stations, run)]
for p in processes:
p.start()
trained_stations = set()
# loop over complete test set and sample all data points the same way as with the NN models
for i, data in enumerate(testloader, 0):
try:
# get training batch, e.g. label, cosmo-1 output and external features
Label, Prediction, Station, Month, Hour = tuple(map(lambda x: x.numpy().squeeze(), data[:5]))
except ValueError:
# when the batch size is small, it could happen, that all labels have been corrupted and therefore
# collate_fn would return an empty list
print('Skipped ValueError in test.')
continue
for batch_sample in range(Label.shape[0]):
station = Station[batch_sample]
trained_stations.add(station)
prediction = Prediction[batch_sample]
hour = Hour[batch_sample]
month = Month[batch_sample]
label = Label[batch_sample]
# calculate the prediction with subtracting the learned mean error of the station over all hours
try:
bias_corrected_output = prediction - station_bias.sel(station=station).data.squeeze()
except KeyError:
# if we have no data for a station and hour, we do not correct the data
print('Station bias not corrected, because no bias for station %s available.' % station)
bias_corrected_output = prediction
# calculate the prediction with subtracting the learned mean error of the station at a specific hour
time_bias_corrected_output = []
time_month_bias_corrected_output = []
for lead_idx, lead in enumerate(LeadTimes):
lead_hour = hour[lead_idx]
lead_month = month[lead_idx]
try:
time_bias_corrected_output += [
prediction[lead_idx] - station_hour_bias.sel(lead=lead, station=station,
hour=lead_hour).data.squeeze()]
except KeyError:
# if we have no data for a station and hour, we do not correct the data
print('Station time-bias not corrected, because no bias for station %s and time %s available.'
% (station, lead_hour))
time_bias_corrected_output += [prediction[lead_idx]]
# calculate the prediction with subtracting the learned mean b
# of the station at a specific hour for specific month
try:
time_month_bias_corrected_output += [
prediction[lead_idx] - station_hour_month_bias.sel(lead=lead, station=station,
hour=lead_hour,
month=lead_month).data.squeeze()]
except KeyError:
# if we have no data for a station and hour, we do not correct the data
print(
'Station time-month-bias not corrected, because no bias for station %s, time %s and month % s available.'
% (station, lead_hour, lead_month))
time_month_bias_corrected_output += [prediction[lead_idx]]
process_station = station_mapping[station]
pipe = pipes[process_station]
pipe.send((LeadTimes, label, prediction, station, hour, month, bias_corrected_output,
time_bias_corrected_output, time_month_bias_corrected_output))
# send poison pill to worker, to signalize end of training phase
for pipe in pipes:
pipe.send(None)
# reset bias to not memorize test data from previous run
error_statistics = None
for pipe_idx, pipe in enumerate(pipes):
while True:
result = pipe.recv()
# check if it is the last result sent by worker
if result is None:
print("All results of process %s have been consumed." % pipe_idx)
break
try:
error_statistics = error_statistics.combine_first(result)
except AttributeError:
error_statistics = result
for pipe in pipes:
pipe.close()
# flush output to see progress
sys.stdout.flush()
# time for test
test_time = time() - test_start_time
run_time = time() - run_start_time
# flush output to see progress
sys.stdout.flush()
experiment_info['run_time_test'] = test_time
experiment_info['run_time'] = run_time
# generate data set of all experiment statistics and additional information
experiment_statistic = xr.Dataset({
'error_statistic': error_statistics,
'run_time_statistic': run_times,
})
experiment_statistic.attrs['experiment_info'] = experiment_info
with open(run_output_path + '/experiment_statistic.pkl', 'wb') as handle:
pkl.dump(experiment_statistic, handle, protocol=pkl.HIGHEST_PROTOCOL)
# print program execution time
m, s = divmod(time() - program_start_time, 60)
h, m = divmod(m, 60)
print('Program has successfully finished in %dh %02dmin %02ds' % (h, m, s))
def Pipe(duplex=True):
"""
Returns two connection object connected by a pipe
"""
from multiprocessing.connection import Pipe
return Pipe(duplex)
from multiprocessing.connection import Client, Listener, wait, Pipe
from multiprocessing import Queue, Process, Pool, Process, Lock, Value, Array, Manager
import socket
import time
#=====================================pip and queue===============================================================
switch_to_queue = False
parent_conn, child_conn = Pipe()
queue = Queue()
address = ('localhost', 6000)
family = 'AF_UNIX'
def server_start(child_conn=None, queue=None):
def task(conn):
print('server side start')
# if switch_to_queue and queue:
# for i in range(0,10):
# queue.put('[queue] coming from server side')
# time.sleep(1)
# elif conn:
# for i in range(0,10):
# conn.send('[pip] coming from server side...{}'.format(i))
# time.sleep(0.3)
# conn.send('exit')
with Client(address) as client:
for i in range(0, 10):
client.send('push {} msg to client '.format(i))
p = Process(target=task, args=(child_conn,))
p.start()
# p.join()
class Queue(object):
def __init__(self, maxsize=0):
if maxsize <= 0:
maxsize = _multiprocessing.SemLock.SEM_VALUE_MAX
self._maxsize = maxsize
self._reader, self._writer = Pipe(duplex=False)
self._rlock = Lock()
self._opid = os.getpid()
if sys.platform == 'win32':
self._wlock = None
else:
self._wlock = Lock()
self._sem = BoundedSemaphore(maxsize)
# For use by concurrent.futures
self._ignore_epipe = False
self._after_fork()
if sys.platform != 'win32':
register_after_fork(self, Queue._after_fork)
def __getstate__(self):
assert_spawning(self)
return (self._ignore_epipe, self._maxsize, self._reader, self._writer,
self._rlock, self._wlock, self._sem, self._opid)
def __setstate__(self, state):
(self._ignore_epipe, self._maxsize, self._reader, self._writer,
self._rlock, self._wlock, self._sem, self._opid) = state
self._after_fork()
def _after_fork(self):
debug('Queue._after_fork()')
self._notempty = threading.Condition(threading.Lock())
self._buffer = collections.deque()
self._thread = None
self._jointhread = None
self._joincancelled = False
self._closed = False
self._close = None
self._send = self._writer.send
self._recv = self._reader.recv
self._poll = self._reader.poll
def put(self, obj, block=True, timeout=None):
assert not self._closed
if not self._sem.acquire(block, timeout):
raise Full
self._notempty.acquire()
try:
if self._thread is None:
self._start_thread()
self._buffer.append(obj)
self._notempty.notify()
finally:
self._notempty.release()
def get(self, block=True, timeout=None):
if block and timeout is None:
self._rlock.acquire()
try:
res = self._recv()
self._sem.release()
return res
finally:
self._rlock.release()
else:
if block:
deadline = time.time() + timeout
if not self._rlock.acquire(block, timeout):
raise Empty
try:
if block:
timeout = deadline - time.time()
if timeout < 0 or not self._poll(timeout):
raise Empty
elif not self._poll():
raise Empty
res = self._recv()
self._sem.release()
return res
finally:
self._rlock.release()
def qsize(self):
# Raises NotImplementedError on Mac OSX because of broken sem_getvalue()
return self._maxsize - self._sem._semlock._get_value()
def empty(self):
return not self._poll()
def full(self):
return self._sem._semlock._is_zero()
def get_nowait(self):
return self.get(False)
def put_nowait(self, obj):
return self.put(obj, False)
def close(self):
self._closed = True
self._reader.close()
if self._close:
self._close()
def join_thread(self):
debug('Queue.join_thread()')
assert self._closed
if self._jointhread:
self._jointhread()
def cancel_join_thread(self):
debug('Queue.cancel_join_thread()')
self._joincancelled = True
try:
self._jointhread.cancel()
except AttributeError:
pass
def _start_thread(self):
debug('Queue._start_thread()')
# Start thread which transfers data from buffer to pipe
self._buffer.clear()
self._thread = threading.Thread(
target=Queue._feed,
args=(self._buffer, self._notempty, self._send,
self._wlock, self._writer.close, self._ignore_epipe),
name='QueueFeederThread'
)
self._thread.daemon = True
debug('doing self._thread.start()')
self._thread.start()
debug('... done self._thread.start()')
# On process exit we will wait for data to be flushed to pipe.
#
# However, if this process created the queue then all
# processes which use the queue will be descendants of this
# process. Therefore waiting for the queue to be flushed
# is pointless once all the child processes have been joined.
created_by_this_process = (self._opid == os.getpid())
if not self._joincancelled and not created_by_this_process:
self._jointhread = Finalize(
self._thread, Queue._finalize_join,
[weakref.ref(self._thread)],
exitpriority=-5
)
# Send sentinel to the thread queue object when garbage collected
self._close = Finalize(
self, Queue._finalize_close,
[self._buffer, self._notempty],
exitpriority=10
)
@staticmethod
def _finalize_join(twr):
debug('joining queue thread')
thread = twr()
if thread is not None:
thread.join()
debug('... queue thread joined')
else:
debug('... queue thread already dead')
@staticmethod
def _finalize_close(buffer, notempty):
debug('telling queue thread to quit')
notempty.acquire()
try:
buffer.append(_sentinel)
notempty.notify()
finally:
notempty.release()
@staticmethod
def _feed(buffer, notempty, send, writelock, close, ignore_epipe):
debug('starting thread to feed data to pipe')
from .util import is_exiting
nacquire = notempty.acquire
nrelease = notempty.release
nwait = notempty.wait
bpopleft = buffer.popleft
sentinel = _sentinel
if sys.platform != 'win32':
wacquire = writelock.acquire
wrelease = writelock.release
else:
wacquire = None
try:
while 1:
nacquire()
try:
if not buffer:
nwait()
finally:
nrelease()
try:
while 1:
obj = bpopleft()
if obj is sentinel:
debug('feeder thread got sentinel -- exiting')
close()
return
if wacquire is None:
send(obj)
else:
wacquire()
try:
send(obj)
finally:
wrelease()
except IndexError:
pass
except Exception as e:
if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE:
return
# Since this runs in a daemon thread the resources it uses
# may be become unusable while the process is cleaning up.
# We ignore errors which happen after the process has
# started to cleanup.
try:
if is_exiting():
info('error in queue thread: %s', e)
else:
import traceback
traceback.print_exc()
except Exception:
pass
