None:\\n try:\\n while not self._client.closed:\\n msg = await self._client.receive()\\n\\n if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSED, WSMsgType.CLOSING):\\n break\\n\\n if msg.type == WSMsgType.ERROR:\\n raise ConnectionFailed()\\n\\n if msg.type != WSMsgType.TEXT:\\n raise InvalidMessage(f\\\"Received non-Text message: {msg.type}\\\")\\n\\n try:\\n data = msg.json(loads=ujson.loads)\\n except ValueError as err:\\n raise InvalidMessage(\\\"Received invalid JSON.\\\") from err\\n\\n if LOGGER.isEnabledFor(logging.DEBUG):\\n LOGGER.debug(\\\"Received message:\\\\n%s\\\\n\\\", pprint.pformat(msg))\\n\\n self._handle_incoming_message(data)\\n\\n finally:\\n # TODO: handle reconnect!\\n LOGGER.debug(\\\"Listen completed. Cleaning up\\\")\\n\\n for future in self._result_futures.values():\\n future.cancel()\\n\\n if not self._client.closed:\\n await self._client.close()\\n\\n if self._shutdown_complete_event:\\n self._shutdown_complete_event.set()\\n else:\\n LOGGER.debug(\\\"Connection lost, will reconnect in 10 seconds...\\\")\\n self._loop.create_task(self._auto_reconnect())\",\n \"def receive(self):\\n while True:\\n if self.pending_request:\\n request = self.unpack(self.pending_request)\\n self.pending_request = None\\n else: \\n request = self.unpack(self.mh.receive_message())\\n if request:\\n yield request\\n else: break\",\n \"def process_received_message(self, message):\\n self.log.debug('Received \\\"%s\\\"', message)\\n self.receive_queue.put(message)\",\n \"def receive(self, msg):\\n pass\",\n \"def _recv(self) -> None:\\n if not self.connected or now() < self.next_poll:\\n return\\n self.next_poll += self.poll_interval\\n data = []\\n while True:\\n try:\\n data.append(self.endpoint.recv(BUFFSIZE))\\n except BlockingIOError:\\n break\\n if data:\\n stream = io.BytesIO(b\\\"\\\".join(data))\\n while True:\\n try:\\n info = pickle.load(stream)\\n msg = Message(*info)\\n self.inq.append(msg)\\n except EOFError:\\n break\",\n \"def run(self):\\n\\n self.make_connection()\\n self.channel()\\n self.declare_queue()\\n self.publish_message()\\n self.close_connection()\",\n \"def handle_read(self):\\n packet = self.recv(8192)\\n if packet == \\\"\\\":\\n #print \\\"[WARNING] Socket closed by remote host %s:%s\\\" % (\\n # self.address,self.port)\\n self.close()\\n return\\n packet_list = messages.separate_messages(packet)\\n #received_types = \\\" + \\\".join(\\n # messages.get_message_type(messages.parse(packet))\\n # for packet in packet_list)\\n #print \\\"From %s:%s received: \\\" % (self.address, self.port), received_types\\n # Process a single message at a time\\n for packet in packet_list:\\n message = messages.parse(packet)\\n if messages.get_message_type(message) == \\\"OFPT_ECHO_REQUEST\\\":\\n self.buffer.append(messages.of_echo_reply)\\n else:\\n self.handle_message(message)\",\n \"def process_frontend_msg(self):\\n logging.debug('Received message on the frontend socket')\\n\\n _id = self.frontend_socket.recv()\\n _empty = self.frontend_socket.recv()\\n msg = self.frontend_socket.recv_json()\\n\\n logging.debug('ID: %s', _id)\\n logging.debug('Message: %s', msg)\\n\\n if not isinstance(msg, dict):\\n self.frontend_socket.send(_id, zmq.SNDMORE)\\n self.frontend_socket.send(\\\"\\\", zmq.SNDMORE)\\n self.frontend_socket.send_json({ 'success': -1, 'msg': 'Request message should be in JSON format' })\\n return\\n\\n logging.debug('Generating client id for result collecting')\\n\\n # Generate a service request id for our client and ask them to\\n # subscribe to the result publisher endpoint in order to receive\\n # their results\\n req_id = uuid.uuid4().get_hex()\\n self.frontend_socket.send(_id, zmq.SNDMORE)\\n self.frontend_socket.send(\\\"\\\", zmq.SNDMORE)\\n self.frontend_socket.send_json({'uuid': req_id, 'port': self.result_pub_port})\\n \\n logging.debug('Client service request id is: %s', req_id)\\n \\n # The message we send to the backend also contains the client\\n # service request id as well. This is done so later when we receive\\n # the results in the sink we can route the results to the clients properly\\n msg['uuid'] = req_id\\n\\n logging.debug('Sending message to backend for processing')\\n \\n self.backend_socket.send_unicode(msg['topic'], zmq.SNDMORE)\\n self.backend_socket.send_json(msg)\",\n \"def process_task(self, body, message):\\n logger.info(\\\"Listener: New message received in MQ\\\")\\n try:\\n json_payload = json.loads(body)\\n except ValueError:\\n logger.error(\\\"Listener: Invalid JSON data received: ignoring the message\\\\n{body}\\\")\\n return\\n try:\\n message.ack()\\n except ConnectionResetError:\\n logger.error(\\\"Listener: ConnectionResetError: message may have not been ack...\\\")\\n message.properties['id'] = json_payload[0]['id']\\n logger.debug(\\\"Listener: Processing request message in a new thread...\\\")\\n try:\\n if self.thread_support:\\n thread = getattr(\\n self.sub_worker_mod,\\n self.sub_worker.split(\\\".\\\")[-1])(\\n message_worker = self,\\n data = json_payload,\\n message = message\\n )\\n thread.start() # threading usage\\n else:\\n pass # not yet implemented\\n except Exception as exc:\\n logger.error('Listener: Task raised exception: %r', exc)\",\n \"def _r_on_incoming_message(self, string, protocol):\\n #print(\\\"Incoming: %s\\\" % string)\\n d = threads.deferToThread(parse_message_string, string)\\n d.addCallback(self._r_handle_message_contents, protocol)\",\n \"def handle_messages(self):\\n\\n #Get the time at which the code started running\\n current_time = datetime.datetime.now()\\n\\n #get all messages between now and the time where a message was last received\\n messages = self.client.messages.list(\\n date_sent_before = datetime.datetime.now()+ datetime.timedelta(hours = TIMEDIFFERENCE),\\n date_sent_after = self.last_message_timing + datetime.timedelta(hours = TIMEDIFFERENCE)\\n )\\n\\n #Iterate through all the new messages\\n for record in messages:\\n #If it is not from the Twilio Client\\n if record.from_ != 'whatsapp:+14155238886':\\n #Then update the timing of the last message to the current time\\n self.last_message_timing = current_time\\n #If the message sent is the '?' that seeks to get the number\\n #of people in the queue\\n if record.body == '?':\\n #Get the data about people from firebase\\n people_data = self.firebase.get_data('people_count')\\n #Get the number of people queueing\\n no_of_people = people_data['people_count']\\n #Create a message from the API to tell the person\\n #asking the number of people in the queue\\n message = self.client.messages.create(\\n body='The number of the people in the queue is {}'.format(no_of_people),\\n from_='whatsapp:{sender_number}'.format(**self.config),\\n to=record.from_\\n )\",\n \"def consumeMsg():\\n\\tosuser = 'osdev'\\n\\tospass = 'osdev'\\n\\toshost = '10.32.29.94'\\n\\tosport = '5672'\\n\\tosvhost = '/openstack'\\n\\tneutronExchange = Exchange('quantum', type='topic', durable=False)\\n\\tinfoQueue = Queue('exthook', exchange=neutronExchange , durable=False,\\n\\t\\t\\trouting_key='notifications.info')\\n\\twith Connection(\\\"\\\".join(['amqp://', osuser, ':', ospass, '@', \\n\\t\\toshost, ':',osport, '/', osvhost])) as conn:\\n\\t\\twith conn.Consumer(infoQueue, callbacks=[msgParse]):\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\ttry: \\n\\t\\t\\t\\t\\tconn.drain_events()\\n\\t\\t\\t\\texcept Exception, e:\\n\\t\\t\\t\\t\\tlogging.exception('Draining events from AMQP stop')\\n\\t\\t\\t\\t\\tbreak\",\n \"def messageHandler(self):\\n\\n while len(self.ReceiveMessageBuffer) > 0: # if message handler is called all received messages will be processed\\n #print 'entered message handler of ID {0}'.format(self.CommID)\\n msg = self.ReceiveMessageBuffer.popleft()\\n self.MsgReceiveCount += 1\\n self.MsgReceiveCount_interval += 1\\n type = msg.getType()\\n # for communication test:\\n if type == 0: #System message\\n print 'ID {0} has received msg {1} from ID {2}'.format(self.CommID, msg.getData(), msg.getIDSender())\\n # send reply\\n data = msg.getData()\\n if data == 'ping':\\n retval = self.sendMessage(msg.getIDSender(), 0, 'pong')\\n return retval\\n elif data == 'pong':\\n retval = self.sendMessage(msg.getIDSender(), 0, 'ping')\\n return retval\\n # elif data[0] == 'system':\\n # if(data[1] == 'startRONOPT'):\\n # #save fluctuation curve of cluster\\n # self.EFluctuationCurve = data[4]\\n # #begin with local optimization (data[2] = fromTime, data[3]=toTime)\\n # self.stateRONOPT = 0\\n # for n in range(len(self.Neighbors)):\\n # self.NeighborMessageRec[n] = 0\\n # self.RemainderOfNeighborsOpt(data[2],data[3],1)\\n #########################################################################################################\\n\\n elif type == 20: # pseudo tree generation message\\n ret = self.messageHandler_PseudoTree(msg)\\n if ret == -1:\\n break\\n\\n elif type == 40: # load propagation message\\n self.messageHandler_LoadProp(msg)\\n\\n elif type == 70:\\n self.messageHandler_RemainderMulticast(msg) #remainder multicast optimization\\n\\n return 0\",\n \"def start(self):\\n while True:\\n ident = self.reply_socket.recv()\\n assert self.reply_socket.rcvmore(), \\\"Missing message part.\\\"\\n msg = self.reply_socket.recv_json()\\n omsg = Message(msg)\\n print>>sys.__stdout__\\n print>>sys.__stdout__, omsg\\n handler = self.handlers.get(omsg.msg_type, None)\\n if handler is None:\\n print >> sys.__stderr__, \\\"UNKNOWN MESSAGE TYPE:\\\", omsg\\n else:\\n handler(ident, omsg)\",\n \"def queue_processor(self):\\n\\n while self.state != consts.SMPP_CLIENT_STATE_CLOSED:\\n try:\\n p = self.queue.get(timeout=1)\\n self._request_handler(p)\\n self.queue.task_done()\\n except Empty:\\n pass\",\n \"def handle_message_queue(self):\\n # Add some dummy messages to the queue\\n message = ['dummy1', 'dummy2', 'dummy3']\\n for message in messages:\\n self.queue.put(message)\\n\\n time.sleep(1)\\n # Send kill event\\n self.dut.kill.set()\\n\\n time.sleep(1)\\n\\n assertTrue(self.dut.queue_empty.is_set())\\n # _process_message_queue is done\\n\\n # Check that dummy messages are in server socket queue\\n for message in messages:\\n size, received = self.get_message_from_queue()\\n\\n self.assertIsNotNone(received)\\n if received is None:\\n return\\n\\n self.assertIsNotNone(size)\\n if size is None:\\n return\\n\\n fail_size = 'Message length {} Received length {}'.format(len(message),\\n size)\\n self.assertEqual(size, len(message), msg=fail_size)\\n\\n fail_contents = 'Message: ({}) Received: ({})'.format(message, received)\\n self.assertEqual(message, received, msg=fail_contents)\",\n \"def run(self):\\n self.logger.info(\\\"Starting messenger.\\\")\\n self.recv()\",\n \"def process(self, msg):\\n print \\\"HANDLER: received a msg: %s\\\" % msg\",\n \"def process_sink_msg(self):\\n logging.debug('Received message on the sink socket')\\n \\n msg = self.sink_socket.recv_json()\\n \\n logging.debug('Message: %s', msg)\\n\\n # Publish the results to the clients using the\\n # request id of the service request as the topic\\n self.result_pub_socket.send_unicode(msg['uuid'], zmq.SNDMORE)\\n self.result_pub_socket.send_json(msg)\",\n \"def _process_messages(self, room, new_messages):\\n\\t\\tfor message in new_messages:\\n\\t\\t\\tself._log.info(\\\"handling message {}\\\".format(message[\\\"id\\\"]))\\n\\n\\t\\t\\tfor reactive in self._reactives:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\treactive(room, message, self, self._hipchat)\\n\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\tself._log.error(\\\"reactive {!r} errored while handling message\\\".format(reactive), exc_info=True)\",\n \"def on_message(self, data):\\n req = json.loads(data)\\n self.serve(req)\",\n \"def on_message(self, data):\\n req = json.loads(data)\\n self.serve(req)\",\n \"def run(self):\\n while True:\\n msg = self.recv()\",\n \"def _hear_message_from_server(self):\\n while self.is_alive:\\n data = self._socket.recv(1024)\\n content = loads(data)\\n self._current_list = content\\n print(\\\"Servidor: {}\\\".format(content))\",\n \"def receive_message(self, message):\\r\\n return\",\n \"def _dispatch(self, msg):\\n self.debug(\\\"Dispatching message CMD %r %s\\\", msg.cmd, msg)\\n if msg.seqno in self.listeners:\\n # self.debug(\\\"Dispatching sequence number %d\\\", msg.seqno)\\n sem = self.listeners[msg.seqno]\\n if isinstance(sem, asyncio.Semaphore):\\n self.listeners[msg.seqno] = msg\\n sem.release()\\n else:\\n self.debug(\\\"Got additional message without request - skipping: %s\\\", sem)\\n elif msg.cmd == HEART_BEAT:\\n self.debug(\\\"Got heartbeat response\\\")\\n if self.HEARTBEAT_SEQNO in self.listeners:\\n sem = self.listeners[self.HEARTBEAT_SEQNO]\\n self.listeners[self.HEARTBEAT_SEQNO] = msg\\n sem.release()\\n elif msg.cmd == UPDATEDPS:\\n self.debug(\\\"Got normal updatedps response\\\")\\n if self.RESET_SEQNO in self.listeners:\\n sem = self.listeners[self.RESET_SEQNO]\\n self.listeners[self.RESET_SEQNO] = msg\\n sem.release()\\n elif msg.cmd == SESS_KEY_NEG_RESP:\\n self.debug(\\\"Got key negotiation response\\\")\\n if self.SESS_KEY_SEQNO in self.listeners:\\n sem = self.listeners[self.SESS_KEY_SEQNO]\\n self.listeners[self.SESS_KEY_SEQNO] = msg\\n sem.release()\\n elif msg.cmd == STATUS:\\n if self.RESET_SEQNO in self.listeners:\\n self.debug(\\\"Got reset status update\\\")\\n sem = self.listeners[self.RESET_SEQNO]\\n self.listeners[self.RESET_SEQNO] = msg\\n sem.release()\\n else:\\n self.debug(\\\"Got status update\\\")\\n self.listener(msg)\\n else:\\n if msg.cmd == CONTROL_NEW:\\n self.debug(\\\"Got ACK message for command %d: will ignore it\\\", msg.cmd)\\n else:\\n self.debug(\\n \\\"Got message type %d for unknown listener %d: %s\\\",\\n msg.cmd,\\n msg.seqno,\\n msg,\\n )\",\n \"def test_process_message_queue(self):\\n t = threading.Thread(target=self.handle_message_queue)\\n t.start()\\n\\n self.dut._process_message_queue()\\n\\n t.join()\",\n \"def received_message(self, m):\\n self.receiver.handle_message(m)\",\n \"def _r_on_incoming_message(self, string, protocol):\\n #print(\\\"Incoming: %s\\\" % string)\\n d = threads.deferToThread(self._parse_message, string, protocol)\\n d.addCallback(self._r_process_message, protocol)\\n d.addCallbacks(callback=self._r_send_result, errback=self._r_send_error, callbackArgs=(protocol,), errbackArgs=(protocol,))\",\n \"async def _order_book_snapshot_router(self):\\n while True:\\n try:\\n ob_message: OrderBookMessage = await self._order_book_snapshot_stream.get()\\n trading_pair: str = ob_message.trading_pair\\n if trading_pair not in self._tracking_message_queues:\\n continue\\n message_queue: asyncio.Queue = self._tracking_message_queues[trading_pair]\\n await message_queue.put(ob_message)\\n except asyncio.CancelledError:\\n raise\\n except Exception:\\n self.logger().error(\\\"Unknown error. Retrying after 5 seconds.\\\", exc_info=True)\\n await asyncio.sleep(5.0)\",\n \"def on_messages(self, priority=-1):\\n\\n def decorator(coro):\\n router = ListRouter(priority=priority)\\n\\n @functools.wraps(coro)\\n async def _wrapper(update, context):\\n if not isinstance(update, Message):\\n return SKIPPED\\n return await coro(update, context)\\n\\n router.add_handler(_wrapper)\\n\\n self._routers.append(router)\\n\\n return coro\\n\\n return decorator\",\n \"def sendall(self, message):\\n reg = re.compile(':::(\\\\d+):::')\\n match = reg.match(message)\\n self.test.assertIsNotNone(match)\\n offset = len(match.group(0))\\n\\n try:\\n size = int(match.group(1))\\n except ValueError:\\n size = None\\n\\n message = message[offset:]\\n message = pickle.loads(message)\\n self.queue.put((size, message))\",\n \"def handleMessage(msg):\",\n \"def run(self):\\n\\n try:\\n if not self._connected:\\n self.connect()\\n\\n while self._connected:\\n msg = self._recvmsg()\\n self.handle(msg)\\n finally:\\n if self._connected:\\n self.disconnect()\",\n \"def handle_read(self):\\n while True:\\n try:\\n content = self.recv(1024)\\n if content:\\n self.rbuf.write(content.decode('utf-8'))\\n if len(content) < 1024:\\n break\\n except Exception as e:\\n print(e)\\n self.handle_rpc()\",\n \"def _process_incoming_queue_messages(self):\\n while self._queue.qsize():\\n msg = self._queue.get()\\n if msg == MAP_UPDATE:\\n self._clear_measurement_progress_label()\\n self._presenter.update_map(self.chosen_value.get())\",\n \"def _dispatch_to_client_request(self):\\n # Listen for client connection\\n self._to_client_request.listen()\\n\\n while not self._exit_request:\\n readable, _, _ = select([self._to_client_request], [], [self._to_client_request], 0.1)\\n if readable:\\n client_conn, client_addr = readable[0].accept()\\n client_conn.setblocking(False)\\n self._to_client_connections.append(client_conn)\\n print(\\\"Sending replies to [\\\" + client_addr[0] + \\\", \\\" + str(client_addr[1]) + ']')\",\n \"def processEvent(self):\\n # Note: break out of event dispatch loop when closedown event is received\\n # and closing flag is set. This is to prevent DoS attack by faked closedown\\n # event type, and to ensure that prior events received are all processed.\\n delay_on_error_min = 0.125 # Back off retry interval on error..\\n delay_on_error_max = 20.0 # ..\\n delay_on_error = delay_on_error_min # ..\\n while True:\\n if delay_on_error < delay_on_error_max:\\n delay_on_error *= 2\\n try:\\n # PLEASE NOTE: In the event that the HTTPC is run as duplex, not simplex\\n # then the post methods will be delayed if nothing is sent down to the client\\n # from the server. This timeout is controlled by QUEUE_WAIT_TIMEOUT in EventRouterHTTPS.py\\n if self._simplex == True:\\n self._queueEvent.wait()\\n self._queueEvent.clear()\\n \\n if not self._queue.empty():\\n Trace(\\\"%s queue.get ...\\\"%(self.getUri()), \\\"EventLib.EventRelayHTTPC\\\")\\n ###msgbody = self._queue.get()\\n ###Trace(\\\"%s get msgbody: %s\\\"%(self.getUri(),msgbody), \\\"EventLib.EventRelayHTTPC\\\")\\n ###self._event.set()\\n msgbody = self.getQueuedItem()\\n [typ,env] = msgbody\\n if typ == \\\"closedown\\\":\\n if self._closing: break\\n else:\\n # process request as an HTTP POST request\\n data = makeEnvelopeData(env)\\n headers = { \\\"Content-type\\\": \\\"text/plain\\\",\\n \\\"Accept\\\": \\\"text/plain\\\",\\n \\\"Content-length\\\": str(len(data)) }\\n self._httpcon.request(\\\"POST\\\", \\\"/request_path_ignored\\\", data, headers)\\n response = self._httpcon.getresponse()\\n delay_on_error = delay_on_error_min\\n elif self._simplex == False:\\n # Nothing in queue:\\n # issue a GET for incoming events\\n _log.info(\\\"%s HTTP get ...\\\"%(self.getUri()))\\n headers = { \\\"Accept\\\": \\\"text/plain\\\" }\\n self._httpcon.request(\\\"GET\\\", \\\"/request_path_ignored\\\", None, headers)\\n response = self._httpcon.getresponse()\\n if response.status == 200:\\n delay_on_error = delay_on_error_min\\n msgbody = response.read()\\n Trace(\\\"%s get msgbody: %s\\\"%(self.getUri(),msgbody), \\\"EventLib.EventRelayHTTPC\\\")\\n # Parse message and act accordingly\\n msgdata = parseMessageData(msgbody)\\n Trace(\\\"%s get msgdata: %s\\\"%(self.getUri(),str(msgdata)), \\\"EventLib.EventRelayHTTPC\\\")\\n if msgdata == None:\\n #TODO: Log \\\"Request body malformed\\\"\\n pass\\n elif msgdata[0] == \\\"forward\\\":\\n # msgdata = [\\\"forward\\\", [['R1', 'R2', 'R3'], 'ev:typ', 'ev:src', 'payload']]\\n event = makeEvent(evtype=msgdata[1][1],source=msgdata[1][2],payload=msgdata[1][3])\\n env = constructEnvelope(msgdata[1][0], event)\\n self.forward(event, env)\\n elif msgdata[0] == \\\"idle\\\":\\n # Idle response gives client a chance to send if anything is queued\\n pass\\n else:\\n #TODO: handle closedown message?\\n Warn( \\\"%s Request body unrecognized option: %s\\\"%(self.getUri(),msgdata[0]), \\\"EventRelayHTTPC\\\")\\n pass\\n elif response.status == 503:\\n Trace( \\\"%s processEvent error response: %u, %s\\\"%(self.getUri(),response.status,response.reason), \\\"EventLib.EventRelayHTTPC\\\")\\n # Remote end closed down\\n break\\n else:\\n # TODO: (log error response)\\n Warn( \\\"%s processEvent error response: %u, %s\\\"%(self.getUri(),response.status,response.reason), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n \\n except httplib.BadStatusLine, e:\\n # This can happen at closedown\\n Info( \\\"%s processEvent bad response: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except httplib.CannotSendRequest, e:\\n # This can happen at closedown\\n Info( \\\"%s Cannot send request: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except httplib.ResponseNotReady, e:\\n # This can happen at startup and sometimes other times:\\n # maybe multiple requests on a single HTTP connection object?\\n Info( \\\"%s Response not ready: (%s)\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n except socket.error, e:\\n Warn( \\\"%s Socket error: %s\\\"%(self.getUri(), str(e)), \\\"EventLib.EventRelayHTTPC\\\")\\n time.sleep(delay_on_error)\\n return\",\n \"def activate(self):\\n self.socket.listen(self.request_queue_size)\",\n \"def receive_messages(self):\\n messages = self.incoming_messages\\n self.incoming_messages = []\\n return messages\",\n \"async def _listen(self,sub_params): \\n async with websockets.connect(self.url) as websocket:\\n await websocket.send(json.dumps(sub_params))\\n # self.keepalive.start()\\n start_time = time.time()\\n while not self.shutdown_event.is_set():\\n try:\\n now = time.time()\\n if((now - start_time) > 0.5):\\n self.calculate_order_depth()\\n start_time = now \\n data = await websocket.recv()\\n msg = json.loads(data)\\n except ValueError as e:\\n self.on_error(e)\\n except Exception as e:\\n self.on_error(e)\\n else:\\n self.on_message(msg)\",\n \"def handle_messages(data_socket):\\r\\n size = 1\\r\\n message_number = 1\\r\\n while size != 0:\\r\\n lines = read_message(data_socket)\\r\\n size = len(lines)\\r\\n if size != 0:\\r\\n file_name = str(message_number) + \\\".txt\\\"\\r\\n message_number += 1\\r\\n with open(file_name, \\\"wb\\\") as file:\\r\\n write_lines(lines, file)\\r\\n data_socket.sendall(b'A')\\r\\n data_socket.sendall(b'Q')\",\n \"async def _process_queue(self, callback, socket_info,\\n has_heartbeat_seq=True):\\n pending_callback = False\\n while True:\\n unparsed_message = await socket_info.queue.get()\\n #log.debug(\\\"Received: \\\" + unparsed_message)\\n response = json.loads(unparsed_message)\\n # Sometimes the response is a list sometimes not. Convert to list.\\n message_list = response if type(response) == list else [response]\\n if not message_list:\\n log.warning(\\\"Received empty message from Gemini. This isn't a \\\"\\n \\\"type of response documented in their API docs.\\\")\\n continue\\n if message_list[0]['type'] == 'heartbeat':\\n if has_heartbeat_seq:\\n self._process_heartbeat(message_list[0], socket_info)\\n self._check_sequence(message_list[0], socket_info)\\n continue\\n # A non heartbeat message.\\n for message in message_list:\\n self._check_sequence(message, socket_info)\\n state_update = callback(message)\\n if state_update:\\n pending_callback = True\\n if not socket_info.queue.empty():\\n continue\\n if pending_callback and self.is_setup():\\n self.exchange_state.update_publisher.notify()\\n pending_callback = False\",\n \"def receive(self):\\n pass\",\n \"def subscribe2API():\\n\\tconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\\n\\tchannel = connection.channel()\\n\\n\\tchannel.queue_declare(queue='ToAPIgatewayQueue')\\n\\n\\tdef callback(ch, method, properties, body):\\n\\t\\tif(body != ''):\\t\\n\\t\\t\\tconnection.close()\\n \\t\\tpublish2apiHandler(body)\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\t\\n\\tchannel.basic_consume(callback, queue='ToAPIgatewayQueue', no_ack=True)\\n\\n\\tprint(' [*] Waiting for messages. To exit press CTRL+C')\\n\\tchannel.start_consuming()\\n\\t\\n\\treturn\",\n \"def msgsToSock(self, recv_queue):\\n\\t\\twhile True:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tmsg = recv_queue.get(True, 1)\\n\\t\\t\\t\\tif self.debug:\\n\\t\\t\\t\\t print(\\\"Message processed in the OS:\\\")\\n\\t\\t\\t\\t print(\\\" Dest IP: {}\\\".format(msg.getHeader(0)))\\n\\t\\t\\t\\t print(\\\" Src IP: {}\\\".format(msg.getHeader(1)))\\n\\t\\t\\t\\t print(\\\" Dest Port: {}\\\".format(msg.getPayload().getHeader(0)))\\n\\t\\t\\t\\t print(\\\" Src Port: {}\\\".format(msg.getPayload().getHeader(1)))\\n\\t\\t\\t\\t print(\\\" Message: {}\\\".format(msg.getPayload().getPayload()))\\n\\t\\t\\t\\t print(\\\" \\\")\\n\\t\\t\\t\\tif isinstance(msg, ms.IPLayer):\\n\\t\\t\\t\\t\\tdest_port = ord(msg.getPayload().getHeader(0))\\n\\t\\t\\t\\t\\tif dest_port in self.sock_dict:\\n\\t\\t\\t\\t\\t\\tself.sock_dict[dest_port].put(msg)\\n\\n\\t\\t\\texcept q.Empty:\\n\\t\\t\\t\\tcontinue\",\n \"def __receive_messages(self) -> [str]:\\n while True:\\n try:\\n data = self.__socket.recv(4096)\\n if data:\\n msgs = self.__json_serializer.bytes_to_jsons(data)\\n if RemotePlayerProxy.DEBUG:\\n for msg in msgs:\\n print(f'[RPP] [RECV] <- [{self.name}]: {msg}')\\n return msgs\\n except Exception as e:\\n if RemotePlayerProxy.DEBUG:\\n print(f'Lost client {self.name} because: ', e)\\n return []\",\n \"def handle(self):\\n req_lines = self._read_lines()\\n if not req_lines:\\n self.cleanup()\\n for req in req_lines:\\n log.debug('%s => %s', self.client, req)\\n req = req.split()\\n cmd = req.pop(0)\\n try:\\n self.get_command(cmd)(req)\\n result = [OK]\\n except Exception as error:\\n result = [ERROR, error.message]\\n self.send_line(' '.join(result))\\n self.flush()\",\n \"def serve_requests(self):\\n while True:\\n self.server_socket.listen(self.request_queue_size)\\n client_connection, client_address = self.server_socket.accept()\\n self.request_handler(client_connection)\",\n \"def received_message(self, textMessage):\\n\\n debug(\\\"Recieved: %s\\\" % str(textMessage))\\n try:\\n data = json.loads(str(textMessage))\\n except Exception:\\n error(\\\"Decoding json %s \\\\n%s\\\" % (str(textMessage),\\n traceback.format_exc()))\\n return\\n\\n if 'target' not in data:\\n error(\\\"No target in message %s \\\" % str(textMessage))\\n return;\\n\\n if 'message' not in data:\\n data['message'] = ''\\n\\n target = data['target']\\n message = data['message']\\n\\n if target == SERVER_KEY:\\n debug(\\\"%s - %s - %s\\\" % ('registering', str(id(self)), message))\\n\\n if message in WebSocketRouter.nodemap:\\n error(\\\"Attempt to register existing name %s\\\" % message)\\n return;\\n\\n WebSocketRouter.nodemap[message] = self\\n\\n # send register message to all users\\n registerData = {'target':'nodemanager', 'message': message }\\n registerJSON = json.dumps(registerData)\\n for ws in WebSocketRouter.usermap:\\n if type(ws) is WebSocketRouter:\\n debug(\\\"Found one\\\")\\n ws.send(registerJSON)\\n\\n elif target in WebSocketRouter.nodemap:\\n debug(\\\"Sending to node id:%s\\\" % \\\\\\n str(id(WebSocketRouter.nodemap[target])))\\n data['target'] = self.getSender()\\n debug(str(WebSocketRouter.nodemap[target]))\\n try:\\n WebSocketRouter.nodemap[target].send(json.dumps(data))\\n except Exception, e:\\n error(\\\" Function: %s\\\\nSender: %s\\\\nMessage: %s\\\\nError: %s\\\" % (\\n target, self.getSender(), message,\\n traceback.format_exc()))\\n return\\n\\n debug(\\\"Sent\\\")\\n\\n elif target in WebSocketRouter.usermap:\\n debug(\\\"Sending to user\\\")\\n data['target'] = self.getSender()\\n WebSocketRouter.usermap[target].send(json.dumps(data))\\n\\n elif target in WebSocketRouter.funcmap:\\n self.getSender() # places user in usermap if not already\\n try:\\n result = WebSocketRouter.funcmap[target](self, message)\\n debug('performed func with result %s' % str(result))\\n if result is not None:\\n JSON = json.dumps({'target':target, 'message': result})\\n self.send(JSON)\\n\\n except Exception:\\n error(\\\" Function: %s\\\\nSender: %s\\\\nMessage: %s\\\\nError: %s\\\" % (\\n target, self.getSender(), message,\\n traceback.format_exc()))\\n return\\n else:\\n error(\\\"No Target: %s message: %s sender: %s\\\" % (target, message,\\n self.getSender()))\\n return\",\n \"def handle_request(self):\\n try:\\n data = self.sock.recv(1024)\\n except socket.error as e: # ...,e:\\n if e == 10040:\\n print('Message too long, ignoring.')\\n return\\n raise\\n self.append_to_seq(parse_packet(data))\",\n \"def send_messages(self):\\n if self.messages:\\n messages, self.messages = self.messages, []\\n self.mpub(\\\"events.%s\\\" % config.pool, messages)\",\n \"def __listener__(self):\\n frame_interval = 0.1\\n str_list = []\\n c = ''\\n while True:\\n with Timeout(frame_interval, False):\\n while True:\\n try:\\n c = self.ser.read()\\n except:\\n self.ser.close()\\n self.make_connection.go()\\n self.connection_made.wait()\\n str_list.append(c)\\n if c == \\\"\\\\n\\\" or c == '':\\n break\\n received = ''.join(str_list)\\n str_list = []\\n if received:\\n for i in self.read_handlers:\\n gevent.spawn(i, received)\\n sleep(0.001)\",\n \"async def receiver(self):\\n socket_input = await self.websocket.recv()\\n logger.debug(\\\"<<< Received:\\\\n{}\\\".format(socket_input))\\n\\n # Showdown sends this response on initial connection\\n if socket_input == \\\"o\\\":\\n logger.info(\\\"Connected on {}\\\".format(self.websocket_url))\\n self.connected = True\\n self.add_task(self.on_connect())\\n return\\n\\n inputs = utils.parse_socket_input(socket_input)\\n for room_id, inp in inputs:\\n room_id = room_id or \\\"lobby\\\"\\n logger.debug(\\\"||| Parsing:\\\\n{}\\\".format(inp))\\n inp_type, params = utils.parse_text_input(inp)\\n\\n # Set challstr attributes and autologin\\n if inp_type == \\\"challstr\\\":\\n self.challengekeyid, self.challstr = params\\n if self.name and self.password and self.autologin:\\n await self.login()\\n elif self.autologin:\\n msg = (\\n \\\"Cannot login without username and password. If \\\"\\n \\\"you don't want your client to be logged in, \\\"\\n \\\"you can use Client.start(autologin=False).\\\"\\n )\\n raise Exception(msg)\\n\\n # Process query response\\n elif inp_type == \\\"queryresponse\\\":\\n response_type, data = params[0], \\\"|\\\".join(params[1:])\\n data = json.loads(data)\\n self.add_task(\\n self.on_query_response(response_type, data), transient=True\\n )\\n if response_type == \\\"savereplay\\\":\\n self.add_task(\\n self.server.save_replay_async(data), transient=True\\n )\\n\\n # Challenge updates\\n elif inp_type == \\\"updatechallenges\\\":\\n self.challenges = json.loads(params[0])\\n self.add_task(\\n self.on_challenge_update(self.challenges), transient=True\\n )\\n\\n # Messages\\n elif inp_type == \\\"c:\\\" or inp_type == \\\"c\\\":\\n timestamp = None\\n if inp_type == \\\"c:\\\":\\n timestamp, params = int(params[0]), params[1:]\\n author_str, *content = params\\n content = \\\"|\\\".join(content)\\n chat_message = message.ChatMessage(\\n room_id, timestamp, author_str, content, client=self\\n )\\n self.add_task(\\n self.on_chat_message(chat_message), transient=True\\n )\\n elif inp_type == \\\"pm\\\":\\n author_str, recipient_str, *content = params\\n content = \\\"|\\\".join(content)\\n private_message = message.PrivateMessage(\\n author_str, recipient_str, content, client=self\\n )\\n self.add_task(\\n self.on_private_message(private_message), transient=True\\n )\\n\\n # Rooms\\n elif inp_type == \\\"init\\\":\\n room_type = params[0]\\n room_obj = room.class_map.get(room_type, room.Room)(\\n room_id, client=self, max_logs=self.max_room_logs\\n )\\n self.rooms[room_id] = room_obj\\n self.add_task(self.on_room_init(room_obj), transient=True)\\n elif inp_type == \\\"deinit\\\":\\n if room_id in self.rooms:\\n self.add_task(\\n self.on_room_deinit(self.rooms.pop(room_id)),\\n transient=True,\\n )\\n\\n # add content to proper room\\n if isinstance(self.rooms.get(room_id, None), room.Room):\\n self.rooms[room_id].add_content(inp)\\n\\n self.add_task(\\n self.on_receive(room_id, inp_type, params), transient=True\\n )\",\n \"def handle(self, message):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69763803","0.63376343","0.6313037","0.62614304","0.62203604","0.6201189","0.6163866","0.61292046","0.6127975","0.60782707","0.60757583","0.6066614","0.6057568","0.6052637","0.6049549","0.6036926","0.60339874","0.60330033","0.60038686","0.5997388","0.59721","0.59465736","0.59287274","0.5921095","0.5920825","0.5917532","0.59102213","0.5902322","0.5898464","0.58868957","0.5881477","0.58695185","0.58664876","0.5865328","0.5863873","0.58636934","0.5810151","0.58059686","0.5803103","0.5803016","0.578942","0.5785212","0.5773676","0.5772197","0.57693064","0.57676524","0.57674664","0.57625574","0.57578695","0.5755704","0.5751001","0.5750513","0.5743363","0.5735322","0.5728757","0.5719356","0.5714945","0.5713018","0.57096016","0.5706943","0.5706703","0.56946075","0.56882215","0.56814164","0.56766367","0.5674658","0.5674658","0.56602204","0.56593335","0.56584984","0.5652429","0.5635758","0.5626339","0.562183","0.5618266","0.56176525","0.5615941","0.561519","0.56149155","0.5609464","0.5607916","0.56072503","0.5607099","0.5602759","0.55932707","0.5591552","0.55913556","0.55833113","0.5571275","0.5564289","0.55625695","0.5558606","0.5551219","0.55505383","0.55450326","0.5540545","0.553077","0.5518565","0.55167323","0.5511307"],"string":"[\n \"0.69763803\",\n \"0.63376343\",\n \"0.6313037\",\n \"0.62614304\",\n \"0.62203604\",\n \"0.6201189\",\n \"0.6163866\",\n \"0.61292046\",\n \"0.6127975\",\n \"0.60782707\",\n \"0.60757583\",\n \"0.6066614\",\n \"0.6057568\",\n \"0.6052637\",\n \"0.6049549\",\n \"0.6036926\",\n \"0.60339874\",\n \"0.60330033\",\n \"0.60038686\",\n \"0.5997388\",\n \"0.59721\",\n \"0.59465736\",\n \"0.59287274\",\n \"0.5921095\",\n \"0.5920825\",\n \"0.5917532\",\n \"0.59102213\",\n \"0.5902322\",\n \"0.5898464\",\n \"0.58868957\",\n \"0.5881477\",\n \"0.58695185\",\n \"0.58664876\",\n \"0.5865328\",\n \"0.5863873\",\n \"0.58636934\",\n \"0.5810151\",\n \"0.58059686\",\n \"0.5803103\",\n \"0.5803016\",\n \"0.578942\",\n \"0.5785212\",\n \"0.5773676\",\n \"0.5772197\",\n \"0.57693064\",\n \"0.57676524\",\n \"0.57674664\",\n \"0.57625574\",\n \"0.57578695\",\n \"0.5755704\",\n \"0.5751001\",\n \"0.5750513\",\n \"0.5743363\",\n \"0.5735322\",\n \"0.5728757\",\n \"0.5719356\",\n \"0.5714945\",\n \"0.5713018\",\n \"0.57096016\",\n \"0.5706943\",\n \"0.5706703\",\n \"0.56946075\",\n \"0.56882215\",\n \"0.56814164\",\n \"0.56766367\",\n \"0.5674658\",\n \"0.5674658\",\n \"0.56602204\",\n \"0.56593335\",\n \"0.56584984\",\n \"0.5652429\",\n \"0.5635758\",\n \"0.5626339\",\n \"0.562183\",\n \"0.5618266\",\n \"0.56176525\",\n \"0.5615941\",\n \"0.561519\",\n \"0.56149155\",\n \"0.5609464\",\n \"0.5607916\",\n \"0.56072503\",\n \"0.5607099\",\n \"0.5602759\",\n \"0.55932707\",\n \"0.5591552\",\n \"0.55913556\",\n \"0.55833113\",\n \"0.5571275\",\n \"0.5564289\",\n \"0.55625695\",\n \"0.5558606\",\n \"0.5551219\",\n \"0.55505383\",\n \"0.55450326\",\n \"0.5540545\",\n \"0.553077\",\n \"0.5518565\",\n \"0.55167323\",\n \"0.5511307\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55585676"},"document_rank":{"kind":"string","value":"92"}}},{"rowIdx":7170,"cells":{"query":{"kind":"string","value":"Shut down the event router thread"},"document":{"kind":"string","value":"def close(self):\n Trace(\"%s close\"%(self.getUri()), \"EventLib.EventRelayHTTPC\")\n self._httpcon.close()\n self._closing = True\n self._event.set()\n self._queueEvent.set()\n self._queue.put([\"closedown\",[]])\n self._thread.join()\n Trace(\"%s closed\"%(self.getUri()), \"EventLib.EventRelayHTTPC\")\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def shutdown(self):\n self.socket_thread.stop()","def shutdown(self):\n if not self.stop_event.is_set():\n self.stop_event.set()\n\n if self.pusher_thread:\n self.pusher_thread.join()","def __del__(self):\n AppHelper.stopEventLoop()","def __del__(self):\n AppHelper.stopEventLoop()","def shutDown(self):\n self.host = None\n self.port = None\n if(self.loop is not None):\n test = asyncio.run_coroutine_threadsafe(self.stopLoop(), self.loop)\n self.thread.join()\n if(self.loop.is_running()):\n self.loop.stop()\n else:\n self.loop.close()\n self.pool.shutDown()\n self.pool = None\n self.loop = None\n self.thread = None","def shutdown(self):\n\t\tself._log.info('shutting down DHT')\n\t\tself._threads.shutdown() # Trigger shutdown of maintainance threads\n\t\tself._krpc.shutdown() # Stop listening for incoming connections\n\t\tself._nodes.shutdown()\n\t\tself._threads.join() # Trigger shutdown of maintainance threads","def __exit__(self, exc_type, exc_val, exc_tb):\n if self.event_loop:\n self.event_loop.stop()","def Quit(self):\n t = threading.Thread(target=self.server.shutdown)\n t.start()","def shutdown(self):\n self.exit_event.set()","def shutdown(self):\n ...","def shutdown(self):\n self.thread.server.shutdown()\n self.thread.join()","def shutdown(self):","def shutdown(self):\r\n self.socket.close()\r\n # self.socket_video.close()\r\n self.socket_state.close()","def stop(self):\n logging.info(\"Shutting down thread...\")\n self.disconnect()\n self.running = False","def shutdown(self):\n self.log.info(\"Purging event queue...\")\n self.kill_all()\n self.log.info(\"done\")\n self.log.info(\"Exiting...\")\n sys.exit()","def shutdown():\n global handler, transport, protocol\n if handler is not None:\n handler.close()\n transport.close()\n handler = None\n transport = None\n protocol = None","def __shutdown(self):\n\n self._rpyReader.shutdownEvent.set()\n self._gpsReader.shutdownEvent.set()\n self._tcpSender.shutdownEvent.set()\n\n self._rpyReader.join()\n self._gpsReader.join()\n self._tcpSender.join()\n\n self._serverSocket.close()","def stop(self):\n if self.socket is not None:\n self.socket.close()\n self.__stopevent.set()\n del self._cb_handle","def shutdown(self) -> None:","def shutdown(self) -> None:","def _shutdown(self):\n self.control_socket.send(zmqmessage.IPC_END)\n self.end_threads = True\n self.timeout = 1","def shutdown(self):\n self.req_shutdown = True","def stop(self):\n self._transport = None\n self._cleanup()\n self._disconnected_callback = None","def shutdown(self):\n pass","def shutdown(self):\n pass","def shutdown(self):\n pass","async def shutdown(self):","def tearDownZServerThread(self):\n self.zserver.close()\n self.zthread.stop()","def teardownClass(cls):\n shutil.rmtree(cls._tmp_dir)\n cls.shutdown_event.set()\n cls.server_proc.join()","def shutdown(self):\n\n pass","def shutdown(self):\n\n reactor.callLater(0, reactor.stop)","def stop(self):\n\n self.__stop_threads = True\n self.__new_bus_Socket.close()\n self.__bus_stations_Socket.close()\n print(f\"stopped {self}\")","def shutdown() -> None: # TODO Better place for this code\n # TODO Safe landing\n pass","def clean_up(self):\n self.stop_full()\n self.ctrl_sock.close()\n self.context.term()","def shutdown(self):\n self.exit_app()","def Release(self):\n print(\"Release : stopping event loop\", self.loop)\n self.loop.quit()\n pass","def shutdown(self):\n self.sock.close()","def tearDown(self):\n self.server.stop()\n self._server_thread.join()","def shutdown(self):\n\n self.active = False\n\n try:\n self.listen_socket.shutdown(socket.SHUT_RDWR)\n except:\n self.logger.info(\"Ignoring listen soc shutdown error\")\n self.listen_socket = None\n\n with self.connect_cv:\n self.connect_cv.notifyAll()\n\n self.wakeup()\n self.dbg_state = \"down\"","def quit(self):\n self.stop()\n self.handler = None","def _shutdown(self):","def __del__(self):\n self.shutdown()","def main_thread_exit(self):\n ...","def shutdown(self) -> None:\n pass","def terminate(self):\n print('Terminating Revshell thread.')\n self.server.close()","def shutdown(self):\n try:\n if self.working and self.exiting.acquire():\n self.bot('shutting down...')\n self.working = False\n self._handleEvent(self.getEvent('EVT_STOP'))\n if self._cron:\n self._cron.stop()\n self.bot('shutting down database connections...')\n self.storage.shutdown()\n except Exception, e:\n self.error(e)","def __exit__(self, exc_type, exc_val, exc_tb):\n self.server.stop()\n self.driver.quit()","def shutup(self):\n logging.debug(\"shutup...\")\n self._eng.stop()","def teardown(self):\n if self.ae:\n self.ae.shutdown()","def shutdown(self):\n debug_print('Stopping JSONRPCTCPServer thread...')\n self.__rpc_server.shutdown()","def shutdown(self):\n self._shutdown(None, None)\n self._running = False","def shutdown(self):\n self._msg_disp.abort()\n self._conn_mgr.shutdown_connections()","def shutdown(self):\n self._ifccountslock.acquire()\n self._ifccounts.clear()\n self._ifccountslock.release()\n self._objslock.acquire()\n if len(self._objs) == 0:\n self._objslock.release()\n return\n logger.info(\"Stopping EMANE daemons.\")\n self.deinstallnetifs()\n self.stopdaemons()\n self.stopeventmonitor()\n self._objslock.release()","def teardown(self):\n\n\t\tself.shutdown = True\n\t\tself.terminate_process()","def __exit__(self, exc_type, exc_value, traceback): \n self.shutdown()","def shutdown(self):\n ts.client.transport.close()","def stop(self):\n self._running = False\n self._logger.info(\"Locator finished main loop\")","async def cleanup(self) -> None:\n if self.args.sync:\n self.stop_events_sync()\n self._processing.join()\n else:\n await self.stop_events_async()\n await self._processing\n try:\n raise self._error\n except TypeError:\n pass","def __exit__(self):\n self._stop_all()","def takedown(self):\n GPIO.cleanup()\n\n self.running = False\n self.data_handler.send_all_to_backlog()\n\n \n del(self)","def stop(self):\n self.stopped = True\n self.broadcast('host down')","def shutdown(self):\n if self.alive:\n libplasma.disconnect(self.conn)\n self.alive = False","def cleanupAtExit():\n \n global client\n \n client.stop()","def stop(self):\n self.bus.log('Stopping down OLA Plugin.')\n self.bus.unsubscribe(\n self.channel_names['channel_request'],\n self.handle_channel\n )\n self.bus.unsubscribe(\n self.channel_names['channel_set'],\n self.handle_channel_set\n )\n self.ola_connection.disconnect()\n # wait for thread to finish.\n self.ola_connection.join()","def stop(self):\n\n self.stop_thread = True","async def _internalStop(self):\n self.wserver.close()\n await self.wserver.wait_closed()\n self.server.stop()\n while (self.server.is_running()):\n time.sleep(0.5)\n self.server.close()\n self.server = None","def shutdown(self):\n self.broadcast(self.server_socket, '[server shutdown]', 'server')\n self.selector.unregister(self.server_socket)\n self.server_socket.close()","def shutdown(self):\n self._shutdown_requested_event.set()\n SimpleJSONRPCServer.SimpleJSONRPCServer.shutdown(self)\n logging.info('Server shutdown complete')","def shutdown(self):\n self.shutdown_requested = True","def __shutdown(self):\n\n pass","def shutdown(self):\n for param in [self._mic_name_param, self._lm_param, self._dic_param,\n self._audio_topic_param, self._hmm_param, self._kws_param]:\n if rospy.has_param(param):\n rospy.delete_param(param)\n\n # Stop & disconnect pipeline\n self.pipeline.remove(self.asr)\n self.bus.disconnect(self.bus_id)\n\n self.pipeline.set_state(gst.State.NULL)\n self.pipeline.get_state(gst.CLOCK_TIME_NONE)\n \"\"\" Shutdown the GTK thread. \"\"\"\n gtk.main_quit()","def teardown_test(self):\n self.log.info('Tearing down the test case')\n self.iperf_server.stop()\n self.access_point.bridge.teardown(self.brconfigs)\n self.access_point.close()\n wputils.reset_host_interface(self.pkt_sender.interface)\n self.mon.usb('on')","def stop() -> None:\n global _server\n if _server:\n try:\n _server.shutdown()\n except Exception:\n pass","def shutdown(self):\n #=======================================================================\n #\n # TODO: Place any cleanup code here.\n #\n #=======================================================================\n return","def atexit(self):\n self.stop_listen()\n for driver in self.drivers.values():\n driver.stop()\n if hasattr(driver, \"atexit\"):\n driver.atexit()\n try:\n self.processor_thread.join()\n except AttributeError:\n pass","def shutdown(self):\n self.running = False\n ev3.Leds.all_off()\n self.left_motor.stop()\n self.right_motor.stop()","def stopThreads():\n global listen, root\n\n listen = False\n root.destroy()","def __exit__(self, *args):\n self.stop()","async def __aexit__(self, *args) -> None:\n self.stop()\n await self.join()","def shutdown(self):\n print(\"shutting down resource manager\")","async def callback_homeassistant_stop(self, event: \"Event\") -> NoReturn:\n _LOGGER.debug(\"Hekr system is shutting down\")\n for device_id, device in self.devices.items():\n connector = device.connector\n listener = connector.listener\n if listener is not None and listener.is_running:\n _LOGGER.debug('Shutting down listener for device ID \"%s\"' % device_id)\n listener.stop()\n\n if connector.is_connected:\n _LOGGER.debug('Shutting down connector for device ID \"%s\"' % device_id)\n await connector.close_connection()","def socket_thread_stopped(self):\n self.done = True","def stop(self):\n self._Thread__stop()","def shutdown(self):\n self.action('shutdown')","async def shutdown(self) -> int:","def stop():\n current_event_loop().stop()","def quit_application(self, event):\n self.Close()\n server.closeSocket()","def stop(self):\n if self.started:\n try:\n self.server.shutdown()\n self.server.server_close()\n self.server_thread.join()\n self.server_thread = None\n except AttributeError:\n pass\n self.started = False\n self.server = None","def slot_stop(self):\n\n self.thread.working = False","def REBshutdown(self):\n pass","def shutdown():\n\n logger.debug(\"GiantbombHandler shutdown\")","def OnClose(self, event):\r\n if self.worker: #stop main GPIB thread\r\n self.worker.abort()\r\n time.sleep(0.3)\r\n self.Destroy()","async def shutdown(self):\n if self._unsub_stop:\n self._unsub_stop()\n self._unsub_stop = None\n await self.device.shutdown()","def Shutdown(self):\n self.conn.send(False)\n self.process.join()","def shutdown(self):\n\n if self.isBound() and not self.connectionCorrupted:\n self.log.warning(\"Shutdown requested...unbinding\")\n self.unbind().addBoth(lambda result: self.disconnect())\n elif self.sessionState not in (SMPPSessionStates.UNBIND_RECEIVED, SMPPSessionStates.UNBIND_PENDING):\n self.log.warning(\"Shutdown requested...disconnecting\")\n self.disconnect()\n else:\n self.log.debug(\"Shutdown already in progress\")","def stop(self):\n # print \"process shutdown complete\"","def shutdown(self, signum, frame):\n self.log('WARNING', -1, 'Shutting down normally ...')\n main_thread = threading.current_thread()\n\n for t in threading.enumerate():\n if t is main_thread:\n continue\n t.join()\n self.server_socket.close()\n sys.exit(0)","def stop_connection(self):\n self.libEDK.EE_EngineDisconnect()\n self.libEDK.EE_EmoStateFree(self.e_state)\n self.libEDK.EE_EmoEngineEventFree(self.e_event)","def stop(self):\n with self.stop_event_lock:\n self.stop_event.set()\n self.clean_up()","def shutdown(self):\n TCPServer.shutdown(self)\n self.server_close()\n self.ae._servers.remove(self)","def _shutdown(self, *args):\n self.server.shutdown()"],"string":"[\n \"def shutdown(self):\\n self.socket_thread.stop()\",\n \"def shutdown(self):\\n if not self.stop_event.is_set():\\n self.stop_event.set()\\n\\n if self.pusher_thread:\\n self.pusher_thread.join()\",\n \"def __del__(self):\\n AppHelper.stopEventLoop()\",\n \"def __del__(self):\\n AppHelper.stopEventLoop()\",\n \"def shutDown(self):\\n self.host = None\\n self.port = None\\n if(self.loop is not None):\\n test = asyncio.run_coroutine_threadsafe(self.stopLoop(), self.loop)\\n self.thread.join()\\n if(self.loop.is_running()):\\n self.loop.stop()\\n else:\\n self.loop.close()\\n self.pool.shutDown()\\n self.pool = None\\n self.loop = None\\n self.thread = None\",\n \"def shutdown(self):\\n\\t\\tself._log.info('shutting down DHT')\\n\\t\\tself._threads.shutdown() # Trigger shutdown of maintainance threads\\n\\t\\tself._krpc.shutdown() # Stop listening for incoming connections\\n\\t\\tself._nodes.shutdown()\\n\\t\\tself._threads.join() # Trigger shutdown of maintainance threads\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n if self.event_loop:\\n self.event_loop.stop()\",\n \"def Quit(self):\\n t = threading.Thread(target=self.server.shutdown)\\n t.start()\",\n \"def shutdown(self):\\n self.exit_event.set()\",\n \"def shutdown(self):\\n ...\",\n \"def shutdown(self):\\n self.thread.server.shutdown()\\n self.thread.join()\",\n \"def shutdown(self):\",\n \"def shutdown(self):\\r\\n self.socket.close()\\r\\n # self.socket_video.close()\\r\\n self.socket_state.close()\",\n \"def stop(self):\\n logging.info(\\\"Shutting down thread...\\\")\\n self.disconnect()\\n self.running = False\",\n \"def shutdown(self):\\n self.log.info(\\\"Purging event queue...\\\")\\n self.kill_all()\\n self.log.info(\\\"done\\\")\\n self.log.info(\\\"Exiting...\\\")\\n sys.exit()\",\n \"def shutdown():\\n global handler, transport, protocol\\n if handler is not None:\\n handler.close()\\n transport.close()\\n handler = None\\n transport = None\\n protocol = None\",\n \"def __shutdown(self):\\n\\n self._rpyReader.shutdownEvent.set()\\n self._gpsReader.shutdownEvent.set()\\n self._tcpSender.shutdownEvent.set()\\n\\n self._rpyReader.join()\\n self._gpsReader.join()\\n self._tcpSender.join()\\n\\n self._serverSocket.close()\",\n \"def stop(self):\\n if self.socket is not None:\\n self.socket.close()\\n self.__stopevent.set()\\n del self._cb_handle\",\n \"def shutdown(self) -> None:\",\n \"def shutdown(self) -> None:\",\n \"def _shutdown(self):\\n self.control_socket.send(zmqmessage.IPC_END)\\n self.end_threads = True\\n self.timeout = 1\",\n \"def shutdown(self):\\n self.req_shutdown = True\",\n \"def stop(self):\\n self._transport = None\\n self._cleanup()\\n self._disconnected_callback = None\",\n \"def shutdown(self):\\n pass\",\n \"def shutdown(self):\\n pass\",\n \"def shutdown(self):\\n pass\",\n \"async def shutdown(self):\",\n \"def tearDownZServerThread(self):\\n self.zserver.close()\\n self.zthread.stop()\",\n \"def teardownClass(cls):\\n shutil.rmtree(cls._tmp_dir)\\n cls.shutdown_event.set()\\n cls.server_proc.join()\",\n \"def shutdown(self):\\n\\n pass\",\n \"def shutdown(self):\\n\\n reactor.callLater(0, reactor.stop)\",\n \"def stop(self):\\n\\n self.__stop_threads = True\\n self.__new_bus_Socket.close()\\n self.__bus_stations_Socket.close()\\n print(f\\\"stopped {self}\\\")\",\n \"def shutdown() -> None: # TODO Better place for this code\\n # TODO Safe landing\\n pass\",\n \"def clean_up(self):\\n self.stop_full()\\n self.ctrl_sock.close()\\n self.context.term()\",\n \"def shutdown(self):\\n self.exit_app()\",\n \"def Release(self):\\n print(\\\"Release : stopping event loop\\\", self.loop)\\n self.loop.quit()\\n pass\",\n \"def shutdown(self):\\n self.sock.close()\",\n \"def tearDown(self):\\n self.server.stop()\\n self._server_thread.join()\",\n \"def shutdown(self):\\n\\n self.active = False\\n\\n try:\\n self.listen_socket.shutdown(socket.SHUT_RDWR)\\n except:\\n self.logger.info(\\\"Ignoring listen soc shutdown error\\\")\\n self.listen_socket = None\\n\\n with self.connect_cv:\\n self.connect_cv.notifyAll()\\n\\n self.wakeup()\\n self.dbg_state = \\\"down\\\"\",\n \"def quit(self):\\n self.stop()\\n self.handler = None\",\n \"def _shutdown(self):\",\n \"def __del__(self):\\n self.shutdown()\",\n \"def main_thread_exit(self):\\n ...\",\n \"def shutdown(self) -> None:\\n pass\",\n \"def terminate(self):\\n print('Terminating Revshell thread.')\\n self.server.close()\",\n \"def shutdown(self):\\n try:\\n if self.working and self.exiting.acquire():\\n self.bot('shutting down...')\\n self.working = False\\n self._handleEvent(self.getEvent('EVT_STOP'))\\n if self._cron:\\n self._cron.stop()\\n self.bot('shutting down database connections...')\\n self.storage.shutdown()\\n except Exception, e:\\n self.error(e)\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.server.stop()\\n self.driver.quit()\",\n \"def shutup(self):\\n logging.debug(\\\"shutup...\\\")\\n self._eng.stop()\",\n \"def teardown(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def shutdown(self):\\n debug_print('Stopping JSONRPCTCPServer thread...')\\n self.__rpc_server.shutdown()\",\n \"def shutdown(self):\\n self._shutdown(None, None)\\n self._running = False\",\n \"def shutdown(self):\\n self._msg_disp.abort()\\n self._conn_mgr.shutdown_connections()\",\n \"def shutdown(self):\\n self._ifccountslock.acquire()\\n self._ifccounts.clear()\\n self._ifccountslock.release()\\n self._objslock.acquire()\\n if len(self._objs) == 0:\\n self._objslock.release()\\n return\\n logger.info(\\\"Stopping EMANE daemons.\\\")\\n self.deinstallnetifs()\\n self.stopdaemons()\\n self.stopeventmonitor()\\n self._objslock.release()\",\n \"def teardown(self):\\n\\n\\t\\tself.shutdown = True\\n\\t\\tself.terminate_process()\",\n \"def __exit__(self, exc_type, exc_value, traceback): \\n self.shutdown()\",\n \"def shutdown(self):\\n ts.client.transport.close()\",\n \"def stop(self):\\n self._running = False\\n self._logger.info(\\\"Locator finished main loop\\\")\",\n \"async def cleanup(self) -> None:\\n if self.args.sync:\\n self.stop_events_sync()\\n self._processing.join()\\n else:\\n await self.stop_events_async()\\n await self._processing\\n try:\\n raise self._error\\n except TypeError:\\n pass\",\n \"def __exit__(self):\\n self._stop_all()\",\n \"def takedown(self):\\n GPIO.cleanup()\\n\\n self.running = False\\n self.data_handler.send_all_to_backlog()\\n\\n \\n del(self)\",\n \"def stop(self):\\n self.stopped = True\\n self.broadcast('host down')\",\n \"def shutdown(self):\\n if self.alive:\\n libplasma.disconnect(self.conn)\\n self.alive = False\",\n \"def cleanupAtExit():\\n \\n global client\\n \\n client.stop()\",\n \"def stop(self):\\n self.bus.log('Stopping down OLA Plugin.')\\n self.bus.unsubscribe(\\n self.channel_names['channel_request'],\\n self.handle_channel\\n )\\n self.bus.unsubscribe(\\n self.channel_names['channel_set'],\\n self.handle_channel_set\\n )\\n self.ola_connection.disconnect()\\n # wait for thread to finish.\\n self.ola_connection.join()\",\n \"def stop(self):\\n\\n self.stop_thread = True\",\n \"async def _internalStop(self):\\n self.wserver.close()\\n await self.wserver.wait_closed()\\n self.server.stop()\\n while (self.server.is_running()):\\n time.sleep(0.5)\\n self.server.close()\\n self.server = None\",\n \"def shutdown(self):\\n self.broadcast(self.server_socket, '[server shutdown]', 'server')\\n self.selector.unregister(self.server_socket)\\n self.server_socket.close()\",\n \"def shutdown(self):\\n self._shutdown_requested_event.set()\\n SimpleJSONRPCServer.SimpleJSONRPCServer.shutdown(self)\\n logging.info('Server shutdown complete')\",\n \"def shutdown(self):\\n self.shutdown_requested = True\",\n \"def __shutdown(self):\\n\\n pass\",\n \"def shutdown(self):\\n for param in [self._mic_name_param, self._lm_param, self._dic_param,\\n self._audio_topic_param, self._hmm_param, self._kws_param]:\\n if rospy.has_param(param):\\n rospy.delete_param(param)\\n\\n # Stop & disconnect pipeline\\n self.pipeline.remove(self.asr)\\n self.bus.disconnect(self.bus_id)\\n\\n self.pipeline.set_state(gst.State.NULL)\\n self.pipeline.get_state(gst.CLOCK_TIME_NONE)\\n \\\"\\\"\\\" Shutdown the GTK thread. \\\"\\\"\\\"\\n gtk.main_quit()\",\n \"def teardown_test(self):\\n self.log.info('Tearing down the test case')\\n self.iperf_server.stop()\\n self.access_point.bridge.teardown(self.brconfigs)\\n self.access_point.close()\\n wputils.reset_host_interface(self.pkt_sender.interface)\\n self.mon.usb('on')\",\n \"def stop() -> None:\\n global _server\\n if _server:\\n try:\\n _server.shutdown()\\n except Exception:\\n pass\",\n \"def shutdown(self):\\n #=======================================================================\\n #\\n # TODO: Place any cleanup code here.\\n #\\n #=======================================================================\\n return\",\n \"def atexit(self):\\n self.stop_listen()\\n for driver in self.drivers.values():\\n driver.stop()\\n if hasattr(driver, \\\"atexit\\\"):\\n driver.atexit()\\n try:\\n self.processor_thread.join()\\n except AttributeError:\\n pass\",\n \"def shutdown(self):\\n self.running = False\\n ev3.Leds.all_off()\\n self.left_motor.stop()\\n self.right_motor.stop()\",\n \"def stopThreads():\\n global listen, root\\n\\n listen = False\\n root.destroy()\",\n \"def __exit__(self, *args):\\n self.stop()\",\n \"async def __aexit__(self, *args) -> None:\\n self.stop()\\n await self.join()\",\n \"def shutdown(self):\\n print(\\\"shutting down resource manager\\\")\",\n \"async def callback_homeassistant_stop(self, event: \\\"Event\\\") -> NoReturn:\\n _LOGGER.debug(\\\"Hekr system is shutting down\\\")\\n for device_id, device in self.devices.items():\\n connector = device.connector\\n listener = connector.listener\\n if listener is not None and listener.is_running:\\n _LOGGER.debug('Shutting down listener for device ID \\\"%s\\\"' % device_id)\\n listener.stop()\\n\\n if connector.is_connected:\\n _LOGGER.debug('Shutting down connector for device ID \\\"%s\\\"' % device_id)\\n await connector.close_connection()\",\n \"def socket_thread_stopped(self):\\n self.done = True\",\n \"def stop(self):\\n self._Thread__stop()\",\n \"def shutdown(self):\\n self.action('shutdown')\",\n \"async def shutdown(self) -> int:\",\n \"def stop():\\n current_event_loop().stop()\",\n \"def quit_application(self, event):\\n self.Close()\\n server.closeSocket()\",\n \"def stop(self):\\n if self.started:\\n try:\\n self.server.shutdown()\\n self.server.server_close()\\n self.server_thread.join()\\n self.server_thread = None\\n except AttributeError:\\n pass\\n self.started = False\\n self.server = None\",\n \"def slot_stop(self):\\n\\n self.thread.working = False\",\n \"def REBshutdown(self):\\n pass\",\n \"def shutdown():\\n\\n logger.debug(\\\"GiantbombHandler shutdown\\\")\",\n \"def OnClose(self, event):\\r\\n if self.worker: #stop main GPIB thread\\r\\n self.worker.abort()\\r\\n time.sleep(0.3)\\r\\n self.Destroy()\",\n \"async def shutdown(self):\\n if self._unsub_stop:\\n self._unsub_stop()\\n self._unsub_stop = None\\n await self.device.shutdown()\",\n \"def Shutdown(self):\\n self.conn.send(False)\\n self.process.join()\",\n \"def shutdown(self):\\n\\n if self.isBound() and not self.connectionCorrupted:\\n self.log.warning(\\\"Shutdown requested...unbinding\\\")\\n self.unbind().addBoth(lambda result: self.disconnect())\\n elif self.sessionState not in (SMPPSessionStates.UNBIND_RECEIVED, SMPPSessionStates.UNBIND_PENDING):\\n self.log.warning(\\\"Shutdown requested...disconnecting\\\")\\n self.disconnect()\\n else:\\n self.log.debug(\\\"Shutdown already in progress\\\")\",\n \"def stop(self):\\n # print \\\"process shutdown complete\\\"\",\n \"def shutdown(self, signum, frame):\\n self.log('WARNING', -1, 'Shutting down normally ...')\\n main_thread = threading.current_thread()\\n\\n for t in threading.enumerate():\\n if t is main_thread:\\n continue\\n t.join()\\n self.server_socket.close()\\n sys.exit(0)\",\n \"def stop_connection(self):\\n self.libEDK.EE_EngineDisconnect()\\n self.libEDK.EE_EmoStateFree(self.e_state)\\n self.libEDK.EE_EmoEngineEventFree(self.e_event)\",\n \"def stop(self):\\n with self.stop_event_lock:\\n self.stop_event.set()\\n self.clean_up()\",\n \"def shutdown(self):\\n TCPServer.shutdown(self)\\n self.server_close()\\n self.ae._servers.remove(self)\",\n \"def _shutdown(self, *args):\\n self.server.shutdown()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.73733056\",\n \"0.7205434\",\n \"0.7066241\",\n \"0.7066241\",\n \"0.68949795\",\n \"0.6849437\",\n \"0.6786754\",\n \"0.6766629\",\n \"0.67512125\",\n \"0.6745822\",\n \"0.6738774\",\n \"0.67175186\",\n \"0.6715772\",\n \"0.6702667\",\n \"0.669924\",\n \"0.66910434\",\n \"0.6680487\",\n \"0.66720706\",\n \"0.66561806\",\n \"0.66561806\",\n \"0.6640596\",\n \"0.6633338\",\n \"0.6625795\",\n \"0.6624714\",\n \"0.6624714\",\n \"0.6624714\",\n \"0.66127837\",\n \"0.65947753\",\n \"0.6586154\",\n \"0.6579285\",\n \"0.65784246\",\n \"0.65445685\",\n \"0.65369576\",\n \"0.6499835\",\n \"0.64899814\",\n \"0.64832884\",\n \"0.64760804\",\n \"0.6473791\",\n \"0.64658004\",\n \"0.64625186\",\n \"0.64503926\",\n \"0.6444075\",\n \"0.6439922\",\n \"0.6439393\",\n \"0.642913\",\n \"0.6415193\",\n \"0.6402132\",\n \"0.63923174\",\n \"0.63755137\",\n \"0.63626736\",\n \"0.6360209\",\n \"0.6347473\",\n \"0.63458365\",\n \"0.634125\",\n \"0.63295794\",\n \"0.6316964\",\n \"0.6309852\",\n \"0.63009876\",\n \"0.6297995\",\n \"0.6295834\",\n \"0.6287845\",\n \"0.627978\",\n \"0.62795264\",\n \"0.6275598\",\n \"0.6273465\",\n \"0.6267022\",\n \"0.6258988\",\n \"0.62557817\",\n \"0.6253495\",\n \"0.62532836\",\n \"0.62448627\",\n \"0.62421167\",\n \"0.6220931\",\n \"0.6216546\",\n \"0.6215381\",\n \"0.62049055\",\n \"0.6204861\",\n \"0.6203862\",\n \"0.6203142\",\n \"0.6195443\",\n \"0.6188418\",\n \"0.6183701\",\n \"0.6175124\",\n \"0.61748403\",\n \"0.6174007\",\n \"0.617141\",\n \"0.61682653\",\n \"0.61624485\",\n \"0.6158489\",\n \"0.6158123\",\n \"0.61576205\",\n \"0.615355\",\n \"0.61520743\",\n \"0.61518145\",\n \"0.6151423\",\n \"0.61479354\",\n \"0.61379737\",\n \"0.6132271\",\n \"0.6127136\",\n \"0.612246\",\n \"0.6117748\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7171,"cells":{"query":{"kind":"string","value":"Add item to the queue, and return a deferred object that fires when an item is removed (or the queue is empty)."},"document":{"kind":"string","value":"def queueItem(self, item): \n Trace(\"%s queueItem (%s)\"%(self.getUri(),item), \"EventLib.EventRelayHTTPC\")\n if not self._closing:\n self._queue.put(item)\n self._queueEvent.set()\n return makeQueueDeferred(StatusVal.OK, self._queue, self._event)\n return makeDeferred(StatusVal.OK)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add(self, item):\n completeDeferred = defer.Deferred()\n self.queue.append((item, completeDeferred))","def add(self, item: T) -> None:\n self._queue.append(item)\n if not self.is_empty():\n self._queue.sort(reverse=True)","def enqueue(self, item):\n self.queue.append(item)","def enqueue(self, item):\n self.queue.append(item)","def enqueue(self, item):\n self._queue.append(item)","def put(self, item: Any):\n has_item = True\n with self._lock:\n if item not in self._items:\n self._items.add(item)\n has_item = False\n if not has_item:\n self._queue.put(item)","def enqueue(self, item):\n self.__queue.insert(0, item)","def put_nowait(self, item: _T) -> None:\n self._consume_expired()\n if self._getters:\n assert self.empty(), \"queue non-empty, why are getters waiting?\"\n getter = self._getters.popleft()\n self.__put_internal(item)\n future_set_result_unless_cancelled(getter, self._get())\n elif self.full():\n raise QueueFull\n else:\n self.__put_internal(item)","def getQueuedItem(self):\n Trace(\"%s getQueuedItem ...\"%(self.getUri()), context=\"EventLib.EventRelayHTTPC\")\n item = self._queue.get()\n Trace(\"%s getQueuedItem (%s)\"%(self.getUri(),item), context=\"EventLib.EventRelayHTTPC\")\n self._event.set()\n return item","def _put(self, item, queue):","def enqueue(self, item):\n self.list.append(item)","def put_nowait(self, item):\r\n if self.full():\r\n raise QueueFull\r\n self._put(item)\r\n self._unfinished_tasks += 1\r\n self._finished.clear()\r\n self._wakeup_next(self._getters)","def put(\n self, item: _T, timeout: Optional[Union[float, datetime.timedelta]] = None\n ) -> \"Future[None]\":\n future = Future() # type: Future[None]\n try:\n self.put_nowait(item)\n except QueueFull:\n self._putters.append((item, future))\n _set_timeout(future, timeout)\n else:\n future.set_result(None)\n return future","def deque_non_blocking_put(self, item):\n try:\n self.q.put(item, block=False)\n return True\n except queue.Full:\n try:\n _ = self.q.get(block=False)\n dropped = False\n except queue.Empty:\n dropped = True\n # TODO - could crash due to a race condition, could be solved with a lock\n self.q.put(item, block=False)\n return dropped","def add_to_queue(self, msg):\n if not self.queue.full():\n self.queue.put(msg)","def enqueue(self, item):\n\n self.__items__.append(item)","def enqueue(Q, x):\n # Q.append(x)\n Q.put_nowait(x)\n if debug: \n print(\"enqueue\", x, \":\", end=\" \")\n show_queue(Q)\n return Q","def enqueue(self, item):\n\t\tself.items.insert(0, item)","def enqueue(self, item):\n self.items.insert(0, item)","def enqueue(self, item):\n self.items.insert(0, item)","def append ( self , item ) :\n self.cond.acquire()\n try:\n if self.closed :\n raise Exception( \"Trying to append to a closed queue\" )\n else :\n self.weight += int( item['size'] )\n self.push( item )\n self.cond.notify()\n finally:\n self.cond.release()","def push(self, value):\n if self.please_stop and not self.allow_add_after_close:\n Log.error(\"Do not push to closed queue\")\n\n with self.lock:\n self._wait_for_queue_space()\n if not self.please_stop:\n self.queue.appendleft(value)\n return self","def add(self, item):\n self.sleeping.reveille() # wake items whose sleep timer has expired\n self.stack.push(item)","def enqueue(self, x):\r\n self.queue.append(x)\r\n return self.queue","def put(self, item):\r\n while self.full():\r\n putter = self._loop.create_future()\r\n self._putters.append(putter)\r\n try:\r\n yield from putter\r\n except:\r\n putter.cancel() # Just in case putter is not done yet.\r\n if not self.full() and not putter.cancelled():\r\n # We were woken up by get_nowait(), but can't take\r\n # the call. Wake up the next in line.\r\n self._wakeup_next(self._putters)\r\n raise\r\n return self.put_nowait(item)","def __add__(self, value):\n self.queue.append(value)","def non_blocking_put(self, item):\n try:\n self.q.put(item, block=False)\n return True\n except queue.Full:\n return False","def add(self, item):\n if self.has_item(item):\n return\n\n self.cache.append(item)\n\n if self.size() > self.max_size:\n self.cache.popleft()","def add_message_to_queue(self, message):\n\t\t\t\tself.message_queue.append(message)\n\t\t\t\treturn self.message_queue","def maybe_enqueue(self):\n if len(self._vals) > 0:\n self.enqueued = True\n return self._queue.enqueue(self._vals)\n else:\n return None","def append(self, item):\n\t\theapq.heappush(self.heap, (self.f(item), item))","def put(self,item):\n\t\ttry:\n\t\t\tself.logger.debug('Im trying to put new item to queue %s'%(item))\n\t\t\tself.queue.put(item)\n\t\t\tself.logger.debug('Successfull put new item to queue')\n\t\t\treturn True\n\t\texcept Exception, e:\n\t\t\tself.logger.error('Error method put, item: %s, error: %s'%(item,e),exc_info=True)\n\t\t\treturn False","def enqueue(self, item):\n\n self._data.append(item)","def enqueue_rear(self, item):\n self._items.append(item)","def enqueue(self, item):\r\n raise QueueException(\"Unimplemented Abstract Queue Function\")","def add(self, item):\r\n if len(self.buff)==self.size: self.buff.pop(0)\r\n self.buff.append(item)","def enqueue(self, val):\r\n self.queue.append(val)","def deque_timeout_put(self, item, timeout):\n try:\n self.q.put(item, timeout=timeout)\n return True\n except queue.Full:\n try:\n _ = self.q.get(block=False)\n dropped = False\n except queue.Empty:\n dropped = True\n # TODO - could crash due to a race condition, could be solved with a lock\n self.q.put(item, block=False)\n return dropped","async def added(self) -> Tuple[bool, List[Any]]:\n more = True\n # Make sure waiting on event_added is done by one coroutine at a time.\n # Multiple might be waiting and if there is only one event in the queue\n # they would all otherwise be triggered\n async with self.parent.event_added_lock:\n await self.parent.event_added.wait()\n async with self.parent.lock:\n notification_item = self.parent.notification_items.pop(0)\n # If there are still more items that the added event hasn't\n # processed then make sure we will return immediately if called\n # again\n if not self.parent.notification_items:\n more = False\n self.parent.event_added.clear()\n return more, notification_item","def queue_append(self, obj, value):\n self.queue.append((obj, value))\n if len(self.queue) > self.queue_size:\n self.dump_queue()","def add_queue(self, queue):\n with self.mutex:\n self.queues.append(queue)","def enqueue(self, element):\n self.the_queue.append(element)","def add(self, item):\n if not (item in self.set):\n self.set[item] = True\n heapq.heappush(self.heap, item)","def push(self, item):\n if item not in self._items:\n self._items.append(item)","def addEvent(self, IEvent):\n self.queue.put(IEvent)","def enqueue(self, value: object) -> None:\n enqueue = self.da.append(value)\n return enqueue","def push(self, val):\r\n return self.deque.append(val)","def push(self, x):\r\n self.queue.append(x)","def test_the_queue_enqueue(the_queue):\n the_queue.enqueue(2)\n assert the_queue._new_dll.head.data == the_queue._new_dll.tail.data == 2","def put(self, item, block=True, timeout=None):\n self.q.put(item, block, timeout)","def insert_and_check(self, item) -> bool:\n with Monitor.acquire(self):\n if item in self:\n return False\n self.add(item)\n return True","def enqueue(self, item=None):\n if item:\n self._queue_items.insert(0, item)\n else:\n raise QueueException('queue element cannot be an empty string')","def add_to_queue(self, removed):\n if self.exploration_strategy == 'uniform':\n self._add_to_queue_uniform(removed)\n elif self.exploration_strategy == 'uniform-exhaustive':\n self._add_to_queue_uniform_exhaustive(removed)\n elif self.exploration_strategy == 'valence-weighted':\n self._add_to_queue_valence_weighted(removed)\n elif self.exploration_strategy == 'count-weighted':\n self._add_to_queue_count_weighted(removed)\n else:\n raise ValueError('Did not recognise exploration strategy: {}'.format(\n self.exploration_strategy))","def worker_take_item(self):\r\n worker = self.get_waiting_worker()\r\n if worker:\r\n worker.take_item(self.item)\r\n self.item = Item.E\r\n return worker","def enqueue(queue, item):\n new_node = Node(item, None)\n if empty_queue(queue):\n queue.front = new_node\n queue.back = new_node\n else:\n queue.back.next = new_node\n queue.back = new_node\n queue.size = queue.size + 1","def _dequeue(self) -> Optional[torch.cuda.Event]:\n if self._queue:\n event = self._queue.popleft()\n return event\n return None","def enqueueItem(item):\n if item.type not in AgentInventory.__idQueue__:\n AgentInventory.__idQueue__[item.type] = []\n\n # If item id is already in queue, move it to front. Otherwise, just prepend it\n if item.id in AgentInventory.__idQueue__[item.type]:\n idx = AgentInventory.__idQueue__[item.type].index(item.id)\n del AgentInventory.__idQueue__[item.type][idx]\n AgentInventory.__idQueue__[item.type].insert(0, item.id)\n else:\n AgentInventory.__idQueue__[item.type].insert(0, item.id)","def enqueue(self, item):\n # double size of array if necessary and recopy to front of array\n if self._N == len(self._q):\n self._resize(2*len(self._q)) # double size of array if necessary\n self._q[self._last] = item # add item\n self._last += 1\n if self._last == len(self._q):\n self._last = 0 # wrap-around\n self._N += 1","def push(self, transition):\n # if we reached the capacity, delete oldest item\n if (self.size == self.capacity):\n del self.queue[0]\n self.queue.append(transition)","def active_item(self, remove=True):\n self.sleeping.reveille() # wake items whose sleep timer has expired\n if not self.stack.empty():\n pass\n elif not self.backlog.empty():\n # feed the stack the top priority item from the queue\n self.stack.push(self.backlog.get())\n else: # both the stack & queue are empty\n raise queue.Empty\n\n assert not self.stack.empty(), \"BUG: empty stack\"\n\n if remove:\n return self.stack.pop()\n\n return self.stack.peek()","def remove(self) -> T:\n if not self.is_empty():\n return self._queue.pop()","def get_nowait(self):\r\n if self.empty():\r\n raise QueueEmpty\r\n item = self._get()\r\n self._wakeup_next(self._putters)\r\n return item","def maybe_dequeue(self):\n if self._queue.enqueued:\n return self._queue.dequeue()\n else:\n return None","def get_nowait(self) -> _T:\n self._consume_expired()\n if self._putters:\n assert self.full(), \"queue not full, why are putters waiting?\"\n item, putter = self._putters.popleft()\n self.__put_internal(item)\n future_set_result_unless_cancelled(putter, None)\n return self._get()\n elif self.qsize():\n return self._get()\n else:\n raise QueueEmpty","def add_and_process(self, item, followers, users):\n mod = self.modder\n mod.set(item.seq)\n current = self.q[mod.val]\n if current:\n # cannot wait any longer\n current.dispatch(followers, users)\n\n self.q[mod.val] = item\n\n # try to dispatch all events that are in order\n mod.set(self.last_valid + 1)\n while self.q[mod.val]:\n ev = self.q[mod.val]\n ev.dispatch(followers, users)\n\n # clear place in queue\n self.q[mod.val] = None\n\n self.last_valid = ev.seq\n mod.inc()","def on_item(self, ch, method, header, body):\n try:\n # Get the item from the playlist store\n item = self.playlist_store.find_one({'_id': ObjectId(body)})\n print \" [x] Received %r\" % (item['track']['track']['name'],)\n \n except:\n print \" [x] Not found: %r\" % (body,)\n \n else:\n # Add item to our list\n self.items.append(item)\n \n # Mark item as 'queued'\n item['status'] = 'queued'\n self.playlist_store.update({'_id': item['_id']}, item)\n \n # If no items 'sent' or 'playing', broadcast next item in queue\n self.send()\n \n # Acknowledge\n ch.basic_ack(delivery_tag=method.delivery_tag)","def test_the_queue_dequeue(the_queue):\n the_queue.enqueue(2)\n assert the_queue.dequeue() == 2","def dequeue(self):\r\n return self.queue.pop(0)","def dequeue_message(self) -> MessageQueueItem:\n return heapq.heappop(self._message_queue)","def add_to_queue(self, word):\n self.q.put(word)\n print(\"word \\'{}\\' added in clients queue\".format(word))","def enqueue_message(self, item: MessageQueueItem):\n heapq.heappush(self._message_queue, item)","def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except queue.Empty:\n return None\n return item","def enQueue(self, value):\n if not self.isFull():\n self.queue.append(value)\n return True\n else:\n return False","def enQueue(self, value):\n if not self.isFull():\n self.queue.append(value)\n self.rear += 1\n return True\n else:\n return False","def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except Queue.Empty:\n return None\n return item","def request_already_queued(self, request: str):\n try:\n self.create_request_queue_if_not_exists()\n queue = []\n db = self.get_db_safely()\n cursor = db.cursor()\n cursor.execute(\n \"\"\"SELECT rowid FROM queue WHERE request = ?\"\"\",\n (request,))\n for row in cursor:\n queue.append(row)\n if len(queue) == 0:\n return False\n else:\n return True\n except sqlite3.Error:\n # This is a lie, but we don't want to try and enqueue something if we got an error here.\n return True","def pop(self, timeout=None):\n item = super(ExclusiveQueue, self).pop(timeout)\n try:\n self.remove(item)\n except ValueError:\n pass\n return item","def _add_to_chat_queue(self, message):\n self.chat_message_queue.appendleft(message)","def put_req(self, item):\n self.req_q.put(item)","def add_request_to_queue(self,request):\n self.queue.append(request)","def add(self, data):\n wasquiet = True if (self.tail == self.curr) else False\n\n # Assert the queue is clean\n qtail = self.base + \".\" + str(self.tail)\n print \"creating %s\" % qtail\n assert not os.path.exists(qtail)\n qt = open(qtail, \"w\")\n qt.write(data)\n qt.close()\n\n # Where does the next item go\n self.tail += 1\n self._settail(self.tail)\n\n return wasquiet","def push(self, item) -> None:\n self.items.append(item)","def enqueue(self, record):\r\n self.queue.put_nowait(record)","def _dequeue(self):\n return self._queue.popleft()","def push(self, item) -> None:\n self._items.append(item)","def add(self, item):\n self._set(item, None)","def remove(self):\n return self.queue.popleft()","def push(self, item):\n heapq.heappush(self.heap, item)","def process_queue(self, queue):\n\n while queue:\n deferred, data = queue.popleft()\n deferred.callback(data)","def timeout_put(self, item, timeout):\n try:\n self.q.put(item, timeout=timeout)\n return True\n except queue.Full:\n return False","def put(self, item):\n self.url_queue.put(item)","def dequeue(self):\n return self.__queue.pop()","def test_appended(self):\n genFn = Mock(return_value=None)\n expected = 123\n \n wrapper = KaoGenerator(genFn)\n wrapper.queue(expected)\n actual = wrapper._queue.pop()\n self.assertEqual(expected, actual)","def evict_or_add (self, item):","def register_queue(self, queue) -> None:\r\n if queue is None:\r\n raise ValueError('queue is None')\r\n if not hasattr(queue, 'empty'):\r\n raise ValueError(f'queue {queue} is missing empty member')\r\n if not hasattr(queue, 'get_nowait'):\r\n raise ValueError(f'queue {queue} is missing get_nowait member')\r\n self.receive_queues.append(queue)","def dequeue(self):\n return self.the_queue.pop(0)","def dequeue(self):\n return self.queue.pop(0)","def send(self, item):\n self.input_queue.put(item)","def push(self, item):\n self.items.append(item)","def push(self, item):\n self.items.append(item)"],"string":"[\n \"def add(self, item):\\n completeDeferred = defer.Deferred()\\n self.queue.append((item, completeDeferred))\",\n \"def add(self, item: T) -> None:\\n self._queue.append(item)\\n if not self.is_empty():\\n self._queue.sort(reverse=True)\",\n \"def enqueue(self, item):\\n self.queue.append(item)\",\n \"def enqueue(self, item):\\n self.queue.append(item)\",\n \"def enqueue(self, item):\\n self._queue.append(item)\",\n \"def put(self, item: Any):\\n has_item = True\\n with self._lock:\\n if item not in self._items:\\n self._items.add(item)\\n has_item = False\\n if not has_item:\\n self._queue.put(item)\",\n \"def enqueue(self, item):\\n self.__queue.insert(0, item)\",\n \"def put_nowait(self, item: _T) -> None:\\n self._consume_expired()\\n if self._getters:\\n assert self.empty(), \\\"queue non-empty, why are getters waiting?\\\"\\n getter = self._getters.popleft()\\n self.__put_internal(item)\\n future_set_result_unless_cancelled(getter, self._get())\\n elif self.full():\\n raise QueueFull\\n else:\\n self.__put_internal(item)\",\n \"def getQueuedItem(self):\\n Trace(\\\"%s getQueuedItem ...\\\"%(self.getUri()), context=\\\"EventLib.EventRelayHTTPC\\\")\\n item = self._queue.get()\\n Trace(\\\"%s getQueuedItem (%s)\\\"%(self.getUri(),item), context=\\\"EventLib.EventRelayHTTPC\\\")\\n self._event.set()\\n return item\",\n \"def _put(self, item, queue):\",\n \"def enqueue(self, item):\\n self.list.append(item)\",\n \"def put_nowait(self, item):\\r\\n if self.full():\\r\\n raise QueueFull\\r\\n self._put(item)\\r\\n self._unfinished_tasks += 1\\r\\n self._finished.clear()\\r\\n self._wakeup_next(self._getters)\",\n \"def put(\\n self, item: _T, timeout: Optional[Union[float, datetime.timedelta]] = None\\n ) -> \\\"Future[None]\\\":\\n future = Future() # type: Future[None]\\n try:\\n self.put_nowait(item)\\n except QueueFull:\\n self._putters.append((item, future))\\n _set_timeout(future, timeout)\\n else:\\n future.set_result(None)\\n return future\",\n \"def deque_non_blocking_put(self, item):\\n try:\\n self.q.put(item, block=False)\\n return True\\n except queue.Full:\\n try:\\n _ = self.q.get(block=False)\\n dropped = False\\n except queue.Empty:\\n dropped = True\\n # TODO - could crash due to a race condition, could be solved with a lock\\n self.q.put(item, block=False)\\n return dropped\",\n \"def add_to_queue(self, msg):\\n if not self.queue.full():\\n self.queue.put(msg)\",\n \"def enqueue(self, item):\\n\\n self.__items__.append(item)\",\n \"def enqueue(Q, x):\\n # Q.append(x)\\n Q.put_nowait(x)\\n if debug: \\n print(\\\"enqueue\\\", x, \\\":\\\", end=\\\" \\\")\\n show_queue(Q)\\n return Q\",\n \"def enqueue(self, item):\\n\\t\\tself.items.insert(0, item)\",\n \"def enqueue(self, item):\\n self.items.insert(0, item)\",\n \"def enqueue(self, item):\\n self.items.insert(0, item)\",\n \"def append ( self , item ) :\\n self.cond.acquire()\\n try:\\n if self.closed :\\n raise Exception( \\\"Trying to append to a closed queue\\\" )\\n else :\\n self.weight += int( item['size'] )\\n self.push( item )\\n self.cond.notify()\\n finally:\\n self.cond.release()\",\n \"def push(self, value):\\n if self.please_stop and not self.allow_add_after_close:\\n Log.error(\\\"Do not push to closed queue\\\")\\n\\n with self.lock:\\n self._wait_for_queue_space()\\n if not self.please_stop:\\n self.queue.appendleft(value)\\n return self\",\n \"def add(self, item):\\n self.sleeping.reveille() # wake items whose sleep timer has expired\\n self.stack.push(item)\",\n \"def enqueue(self, x):\\r\\n self.queue.append(x)\\r\\n return self.queue\",\n \"def put(self, item):\\r\\n while self.full():\\r\\n putter = self._loop.create_future()\\r\\n self._putters.append(putter)\\r\\n try:\\r\\n yield from putter\\r\\n except:\\r\\n putter.cancel() # Just in case putter is not done yet.\\r\\n if not self.full() and not putter.cancelled():\\r\\n # We were woken up by get_nowait(), but can't take\\r\\n # the call. Wake up the next in line.\\r\\n self._wakeup_next(self._putters)\\r\\n raise\\r\\n return self.put_nowait(item)\",\n \"def __add__(self, value):\\n self.queue.append(value)\",\n \"def non_blocking_put(self, item):\\n try:\\n self.q.put(item, block=False)\\n return True\\n except queue.Full:\\n return False\",\n \"def add(self, item):\\n if self.has_item(item):\\n return\\n\\n self.cache.append(item)\\n\\n if self.size() > self.max_size:\\n self.cache.popleft()\",\n \"def add_message_to_queue(self, message):\\n\\t\\t\\t\\tself.message_queue.append(message)\\n\\t\\t\\t\\treturn self.message_queue\",\n \"def maybe_enqueue(self):\\n if len(self._vals) > 0:\\n self.enqueued = True\\n return self._queue.enqueue(self._vals)\\n else:\\n return None\",\n \"def append(self, item):\\n\\t\\theapq.heappush(self.heap, (self.f(item), item))\",\n \"def put(self,item):\\n\\t\\ttry:\\n\\t\\t\\tself.logger.debug('Im trying to put new item to queue %s'%(item))\\n\\t\\t\\tself.queue.put(item)\\n\\t\\t\\tself.logger.debug('Successfull put new item to queue')\\n\\t\\t\\treturn True\\n\\t\\texcept Exception, e:\\n\\t\\t\\tself.logger.error('Error method put, item: %s, error: %s'%(item,e),exc_info=True)\\n\\t\\t\\treturn False\",\n \"def enqueue(self, item):\\n\\n self._data.append(item)\",\n \"def enqueue_rear(self, item):\\n self._items.append(item)\",\n \"def enqueue(self, item):\\r\\n raise QueueException(\\\"Unimplemented Abstract Queue Function\\\")\",\n \"def add(self, item):\\r\\n if len(self.buff)==self.size: self.buff.pop(0)\\r\\n self.buff.append(item)\",\n \"def enqueue(self, val):\\r\\n self.queue.append(val)\",\n \"def deque_timeout_put(self, item, timeout):\\n try:\\n self.q.put(item, timeout=timeout)\\n return True\\n except queue.Full:\\n try:\\n _ = self.q.get(block=False)\\n dropped = False\\n except queue.Empty:\\n dropped = True\\n # TODO - could crash due to a race condition, could be solved with a lock\\n self.q.put(item, block=False)\\n return dropped\",\n \"async def added(self) -> Tuple[bool, List[Any]]:\\n more = True\\n # Make sure waiting on event_added is done by one coroutine at a time.\\n # Multiple might be waiting and if there is only one event in the queue\\n # they would all otherwise be triggered\\n async with self.parent.event_added_lock:\\n await self.parent.event_added.wait()\\n async with self.parent.lock:\\n notification_item = self.parent.notification_items.pop(0)\\n # If there are still more items that the added event hasn't\\n # processed then make sure we will return immediately if called\\n # again\\n if not self.parent.notification_items:\\n more = False\\n self.parent.event_added.clear()\\n return more, notification_item\",\n \"def queue_append(self, obj, value):\\n self.queue.append((obj, value))\\n if len(self.queue) > self.queue_size:\\n self.dump_queue()\",\n \"def add_queue(self, queue):\\n with self.mutex:\\n self.queues.append(queue)\",\n \"def enqueue(self, element):\\n self.the_queue.append(element)\",\n \"def add(self, item):\\n if not (item in self.set):\\n self.set[item] = True\\n heapq.heappush(self.heap, item)\",\n \"def push(self, item):\\n if item not in self._items:\\n self._items.append(item)\",\n \"def addEvent(self, IEvent):\\n self.queue.put(IEvent)\",\n \"def enqueue(self, value: object) -> None:\\n enqueue = self.da.append(value)\\n return enqueue\",\n \"def push(self, val):\\r\\n return self.deque.append(val)\",\n \"def push(self, x):\\r\\n self.queue.append(x)\",\n \"def test_the_queue_enqueue(the_queue):\\n the_queue.enqueue(2)\\n assert the_queue._new_dll.head.data == the_queue._new_dll.tail.data == 2\",\n \"def put(self, item, block=True, timeout=None):\\n self.q.put(item, block, timeout)\",\n \"def insert_and_check(self, item) -> bool:\\n with Monitor.acquire(self):\\n if item in self:\\n return False\\n self.add(item)\\n return True\",\n \"def enqueue(self, item=None):\\n if item:\\n self._queue_items.insert(0, item)\\n else:\\n raise QueueException('queue element cannot be an empty string')\",\n \"def add_to_queue(self, removed):\\n if self.exploration_strategy == 'uniform':\\n self._add_to_queue_uniform(removed)\\n elif self.exploration_strategy == 'uniform-exhaustive':\\n self._add_to_queue_uniform_exhaustive(removed)\\n elif self.exploration_strategy == 'valence-weighted':\\n self._add_to_queue_valence_weighted(removed)\\n elif self.exploration_strategy == 'count-weighted':\\n self._add_to_queue_count_weighted(removed)\\n else:\\n raise ValueError('Did not recognise exploration strategy: {}'.format(\\n self.exploration_strategy))\",\n \"def worker_take_item(self):\\r\\n worker = self.get_waiting_worker()\\r\\n if worker:\\r\\n worker.take_item(self.item)\\r\\n self.item = Item.E\\r\\n return worker\",\n \"def enqueue(queue, item):\\n new_node = Node(item, None)\\n if empty_queue(queue):\\n queue.front = new_node\\n queue.back = new_node\\n else:\\n queue.back.next = new_node\\n queue.back = new_node\\n queue.size = queue.size + 1\",\n \"def _dequeue(self) -> Optional[torch.cuda.Event]:\\n if self._queue:\\n event = self._queue.popleft()\\n return event\\n return None\",\n \"def enqueueItem(item):\\n if item.type not in AgentInventory.__idQueue__:\\n AgentInventory.__idQueue__[item.type] = []\\n\\n # If item id is already in queue, move it to front. Otherwise, just prepend it\\n if item.id in AgentInventory.__idQueue__[item.type]:\\n idx = AgentInventory.__idQueue__[item.type].index(item.id)\\n del AgentInventory.__idQueue__[item.type][idx]\\n AgentInventory.__idQueue__[item.type].insert(0, item.id)\\n else:\\n AgentInventory.__idQueue__[item.type].insert(0, item.id)\",\n \"def enqueue(self, item):\\n # double size of array if necessary and recopy to front of array\\n if self._N == len(self._q):\\n self._resize(2*len(self._q)) # double size of array if necessary\\n self._q[self._last] = item # add item\\n self._last += 1\\n if self._last == len(self._q):\\n self._last = 0 # wrap-around\\n self._N += 1\",\n \"def push(self, transition):\\n # if we reached the capacity, delete oldest item\\n if (self.size == self.capacity):\\n del self.queue[0]\\n self.queue.append(transition)\",\n \"def active_item(self, remove=True):\\n self.sleeping.reveille() # wake items whose sleep timer has expired\\n if not self.stack.empty():\\n pass\\n elif not self.backlog.empty():\\n # feed the stack the top priority item from the queue\\n self.stack.push(self.backlog.get())\\n else: # both the stack & queue are empty\\n raise queue.Empty\\n\\n assert not self.stack.empty(), \\\"BUG: empty stack\\\"\\n\\n if remove:\\n return self.stack.pop()\\n\\n return self.stack.peek()\",\n \"def remove(self) -> T:\\n if not self.is_empty():\\n return self._queue.pop()\",\n \"def get_nowait(self):\\r\\n if self.empty():\\r\\n raise QueueEmpty\\r\\n item = self._get()\\r\\n self._wakeup_next(self._putters)\\r\\n return item\",\n \"def maybe_dequeue(self):\\n if self._queue.enqueued:\\n return self._queue.dequeue()\\n else:\\n return None\",\n \"def get_nowait(self) -> _T:\\n self._consume_expired()\\n if self._putters:\\n assert self.full(), \\\"queue not full, why are putters waiting?\\\"\\n item, putter = self._putters.popleft()\\n self.__put_internal(item)\\n future_set_result_unless_cancelled(putter, None)\\n return self._get()\\n elif self.qsize():\\n return self._get()\\n else:\\n raise QueueEmpty\",\n \"def add_and_process(self, item, followers, users):\\n mod = self.modder\\n mod.set(item.seq)\\n current = self.q[mod.val]\\n if current:\\n # cannot wait any longer\\n current.dispatch(followers, users)\\n\\n self.q[mod.val] = item\\n\\n # try to dispatch all events that are in order\\n mod.set(self.last_valid + 1)\\n while self.q[mod.val]:\\n ev = self.q[mod.val]\\n ev.dispatch(followers, users)\\n\\n # clear place in queue\\n self.q[mod.val] = None\\n\\n self.last_valid = ev.seq\\n mod.inc()\",\n \"def on_item(self, ch, method, header, body):\\n try:\\n # Get the item from the playlist store\\n item = self.playlist_store.find_one({'_id': ObjectId(body)})\\n print \\\" [x] Received %r\\\" % (item['track']['track']['name'],)\\n \\n except:\\n print \\\" [x] Not found: %r\\\" % (body,)\\n \\n else:\\n # Add item to our list\\n self.items.append(item)\\n \\n # Mark item as 'queued'\\n item['status'] = 'queued'\\n self.playlist_store.update({'_id': item['_id']}, item)\\n \\n # If no items 'sent' or 'playing', broadcast next item in queue\\n self.send()\\n \\n # Acknowledge\\n ch.basic_ack(delivery_tag=method.delivery_tag)\",\n \"def test_the_queue_dequeue(the_queue):\\n the_queue.enqueue(2)\\n assert the_queue.dequeue() == 2\",\n \"def dequeue(self):\\r\\n return self.queue.pop(0)\",\n \"def dequeue_message(self) -> MessageQueueItem:\\n return heapq.heappop(self._message_queue)\",\n \"def add_to_queue(self, word):\\n self.q.put(word)\\n print(\\\"word \\\\'{}\\\\' added in clients queue\\\".format(word))\",\n \"def enqueue_message(self, item: MessageQueueItem):\\n heapq.heappush(self._message_queue, item)\",\n \"def get_item_from_queue(Q, timeout=0.01):\\n try:\\n item = Q.get(True, 0.01)\\n except queue.Empty:\\n return None\\n return item\",\n \"def enQueue(self, value):\\n if not self.isFull():\\n self.queue.append(value)\\n return True\\n else:\\n return False\",\n \"def enQueue(self, value):\\n if not self.isFull():\\n self.queue.append(value)\\n self.rear += 1\\n return True\\n else:\\n return False\",\n \"def get_item_from_queue(Q, timeout=0.01):\\n try:\\n item = Q.get(True, 0.01)\\n except Queue.Empty:\\n return None\\n return item\",\n \"def request_already_queued(self, request: str):\\n try:\\n self.create_request_queue_if_not_exists()\\n queue = []\\n db = self.get_db_safely()\\n cursor = db.cursor()\\n cursor.execute(\\n \\\"\\\"\\\"SELECT rowid FROM queue WHERE request = ?\\\"\\\"\\\",\\n (request,))\\n for row in cursor:\\n queue.append(row)\\n if len(queue) == 0:\\n return False\\n else:\\n return True\\n except sqlite3.Error:\\n # This is a lie, but we don't want to try and enqueue something if we got an error here.\\n return True\",\n \"def pop(self, timeout=None):\\n item = super(ExclusiveQueue, self).pop(timeout)\\n try:\\n self.remove(item)\\n except ValueError:\\n pass\\n return item\",\n \"def _add_to_chat_queue(self, message):\\n self.chat_message_queue.appendleft(message)\",\n \"def put_req(self, item):\\n self.req_q.put(item)\",\n \"def add_request_to_queue(self,request):\\n self.queue.append(request)\",\n \"def add(self, data):\\n wasquiet = True if (self.tail == self.curr) else False\\n\\n # Assert the queue is clean\\n qtail = self.base + \\\".\\\" + str(self.tail)\\n print \\\"creating %s\\\" % qtail\\n assert not os.path.exists(qtail)\\n qt = open(qtail, \\\"w\\\")\\n qt.write(data)\\n qt.close()\\n\\n # Where does the next item go\\n self.tail += 1\\n self._settail(self.tail)\\n\\n return wasquiet\",\n \"def push(self, item) -> None:\\n self.items.append(item)\",\n \"def enqueue(self, record):\\r\\n self.queue.put_nowait(record)\",\n \"def _dequeue(self):\\n return self._queue.popleft()\",\n \"def push(self, item) -> None:\\n self._items.append(item)\",\n \"def add(self, item):\\n self._set(item, None)\",\n \"def remove(self):\\n return self.queue.popleft()\",\n \"def push(self, item):\\n heapq.heappush(self.heap, item)\",\n \"def process_queue(self, queue):\\n\\n while queue:\\n deferred, data = queue.popleft()\\n deferred.callback(data)\",\n \"def timeout_put(self, item, timeout):\\n try:\\n self.q.put(item, timeout=timeout)\\n return True\\n except queue.Full:\\n return False\",\n \"def put(self, item):\\n self.url_queue.put(item)\",\n \"def dequeue(self):\\n return self.__queue.pop()\",\n \"def test_appended(self):\\n genFn = Mock(return_value=None)\\n expected = 123\\n \\n wrapper = KaoGenerator(genFn)\\n wrapper.queue(expected)\\n actual = wrapper._queue.pop()\\n self.assertEqual(expected, actual)\",\n \"def evict_or_add (self, item):\",\n \"def register_queue(self, queue) -> None:\\r\\n if queue is None:\\r\\n raise ValueError('queue is None')\\r\\n if not hasattr(queue, 'empty'):\\r\\n raise ValueError(f'queue {queue} is missing empty member')\\r\\n if not hasattr(queue, 'get_nowait'):\\r\\n raise ValueError(f'queue {queue} is missing get_nowait member')\\r\\n self.receive_queues.append(queue)\",\n \"def dequeue(self):\\n return self.the_queue.pop(0)\",\n \"def dequeue(self):\\n return self.queue.pop(0)\",\n \"def send(self, item):\\n self.input_queue.put(item)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def push(self, item):\\n self.items.append(item)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.809805","0.66659725","0.6658853","0.6658853","0.649351","0.6432891","0.6412939","0.6350026","0.6251586","0.623737","0.6233821","0.6162664","0.6149751","0.6137725","0.6091136","0.60882497","0.60764444","0.6054781","0.5985582","0.5985582","0.598518","0.5972382","0.5959985","0.594677","0.5944127","0.59432197","0.5928539","0.5911735","0.5863586","0.58527267","0.58521014","0.5792889","0.57886845","0.57781714","0.57729894","0.57705414","0.5765411","0.57586163","0.5730444","0.57259667","0.57148486","0.5711285","0.5698054","0.5694351","0.56763774","0.56485","0.564033","0.56346565","0.56319374","0.56220984","0.561701","0.5609047","0.56063515","0.56063116","0.5604719","0.5604132","0.5597111","0.55937856","0.5590126","0.55730575","0.55616546","0.5548362","0.5545486","0.5542501","0.5536824","0.5525351","0.5510965","0.5493038","0.5478092","0.54703623","0.5465571","0.54600996","0.5457797","0.54523635","0.5452208","0.54438645","0.5441057","0.5440856","0.5435963","0.5433496","0.54205495","0.5403471","0.5402086","0.5398602","0.5388546","0.53846675","0.5379767","0.53776264","0.5376609","0.5375024","0.53739196","0.53698903","0.5364151","0.53617215","0.535743","0.53409535","0.53388923","0.5337402","0.53285813","0.53285813"],"string":"[\n \"0.809805\",\n \"0.66659725\",\n \"0.6658853\",\n \"0.6658853\",\n \"0.649351\",\n \"0.6432891\",\n \"0.6412939\",\n \"0.6350026\",\n \"0.6251586\",\n \"0.623737\",\n \"0.6233821\",\n \"0.6162664\",\n \"0.6149751\",\n \"0.6137725\",\n \"0.6091136\",\n \"0.60882497\",\n \"0.60764444\",\n \"0.6054781\",\n \"0.5985582\",\n \"0.5985582\",\n \"0.598518\",\n \"0.5972382\",\n \"0.5959985\",\n \"0.594677\",\n \"0.5944127\",\n \"0.59432197\",\n \"0.5928539\",\n \"0.5911735\",\n \"0.5863586\",\n \"0.58527267\",\n \"0.58521014\",\n \"0.5792889\",\n \"0.57886845\",\n \"0.57781714\",\n \"0.57729894\",\n \"0.57705414\",\n \"0.5765411\",\n \"0.57586163\",\n \"0.5730444\",\n \"0.57259667\",\n \"0.57148486\",\n \"0.5711285\",\n \"0.5698054\",\n \"0.5694351\",\n \"0.56763774\",\n \"0.56485\",\n \"0.564033\",\n \"0.56346565\",\n \"0.56319374\",\n \"0.56220984\",\n \"0.561701\",\n \"0.5609047\",\n \"0.56063515\",\n \"0.56063116\",\n \"0.5604719\",\n \"0.5604132\",\n \"0.5597111\",\n \"0.55937856\",\n \"0.5590126\",\n \"0.55730575\",\n \"0.55616546\",\n \"0.5548362\",\n \"0.5545486\",\n \"0.5542501\",\n \"0.5536824\",\n \"0.5525351\",\n \"0.5510965\",\n \"0.5493038\",\n \"0.5478092\",\n \"0.54703623\",\n \"0.5465571\",\n \"0.54600996\",\n \"0.5457797\",\n \"0.54523635\",\n \"0.5452208\",\n \"0.54438645\",\n \"0.5441057\",\n \"0.5440856\",\n \"0.5435963\",\n \"0.5433496\",\n \"0.54205495\",\n \"0.5403471\",\n \"0.5402086\",\n \"0.5398602\",\n \"0.5388546\",\n \"0.53846675\",\n \"0.5379767\",\n \"0.53776264\",\n \"0.5376609\",\n \"0.5375024\",\n \"0.53739196\",\n \"0.53698903\",\n \"0.5364151\",\n \"0.53617215\",\n \"0.535743\",\n \"0.53409535\",\n \"0.53388923\",\n \"0.5337402\",\n \"0.53285813\",\n \"0.53285813\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6929844"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":7172,"cells":{"query":{"kind":"string","value":"Wait for an item to be queued, then return it."},"document":{"kind":"string","value":"def getQueuedItem(self):\n Trace(\"%s getQueuedItem ...\"%(self.getUri()), context=\"EventLib.EventRelayHTTPC\")\n item = self._queue.get()\n Trace(\"%s getQueuedItem (%s)\"%(self.getUri(),item), context=\"EventLib.EventRelayHTTPC\")\n self._event.set()\n return item"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except queue.Empty:\n return None\n return item","def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except Queue.Empty:\n return None\n return item","def worker_take_item(self):\r\n worker = self.get_waiting_worker()\r\n if worker:\r\n worker.take_item(self.item)\r\n self.item = Item.E\r\n return worker","def get_nowait(self):\r\n if self.empty():\r\n raise QueueEmpty\r\n item = self._get()\r\n self._wakeup_next(self._putters)\r\n return item","def get(self):\n with self.__lock:\n while True:\n try:\n job = self.__queue.get(False)\n self.__lock.notify_all()\n return job\n except Queue.Empty:\n self.__lock.wait()","def get_nowait(self) -> _T:\n self._consume_expired()\n if self._putters:\n assert self.full(), \"queue not full, why are putters waiting?\"\n item, putter = self._putters.popleft()\n self.__put_internal(item)\n future_set_result_unless_cancelled(putter, None)\n return self._get()\n elif self.qsize():\n return self._get()\n else:\n raise QueueEmpty","def get_next_item(self, timeout=None):\n if self.current_item is not None:\n raise error_classes.UVMSequenceError(\"You must call item_done() before calling get_next_item again\")\n self.current_item = self.req_q.get(timeout=timeout)\n return self.current_item","def _wait_for_event_in_queue(self):\n try:\n event = self._queue.get(timeout=SendTelemetryEventsHandler._MAX_TIMEOUT)\n self._queue.task_done()\n except Empty:\n # No elements in Queue, return None\n event = None\n\n return event","def get(self):\n\t\ttry:\n\t\t\tself.logger.debug('Im trying to get item from queue')\n\t\t\titem = self.queue.get()\n\t\t\tself.logger.debug('Recevie item from queue %s'%(item))\n\t\t\treturn True, item\n\t\texcept Exception, e:\n\t\t\tself.logger.error('Error method get, error: %s'%(e),exc_info=True)\n\t\t\treturn False, None","def non_blocking_get(self):\n try:\n return self.q.get(block=False)\n except queue.Empty:\n time.sleep(0)\n return None","def _wait_queue(self):\n while True:\n time.sleep(0.1)\n if self.queue.unfinished_tasks == 0 or self.stop_signal.is_set():\n return","def wait(self, timeout=None):\r\n with self._lock:\r\n if self._finished:\r\n raise RuntimeError(\"wait() does not support re-entry!\")\r\n if not self._started:\r\n for thread in self._wait_events:\r\n thread.timeout = timeout\r\n thread.start()\r\n self._started = True\r\n try:\r\n if self._queue.get(timeout=timeout):\r\n return True\r\n return False\r\n except Empty:\r\n return False\r\n finally:\r\n with self._lock:\r\n self._finished = True","def dequeue(self, timeout=0):\n result = self.connection.dequeue_any([self], timeout)\n if result:\n job, queue = result\n return job\n else:\n return None","def queueItem(self, item): \n Trace(\"%s queueItem (%s)\"%(self.getUri(),item), \"EventLib.EventRelayHTTPC\")\n if not self._closing:\n self._queue.put(item)\n self._queueEvent.set()\n return makeQueueDeferred(StatusVal.OK, self._queue, self._event)\n return makeDeferred(StatusVal.OK)","def wait_for_queue(self, q, stop=True):\n return _loader.wait_for_queue(q, stop=stop)","def get(self):\r\n try:\r\n # get with block=False returns an item if one\r\n # is immediately available, else raises the Empty exception\r\n return self._queue.get(block=False)\r\n except queue.Empty:\r\n return self._create_connection()","def get(self, block=True, timeout=None):\n return self.queue.get(block, timeout)","def wait(self):\n self.queue.join()","def pollTillAvailable(self):\n item = self.getItem()\n while item is None:\n item = self.getItem()\n\n return item","def wait_for_message(self, tag, timeout=None):\n def done_check():\n if self._message_queue.setdefault(tag,[]):\n value=heapq.heappop(self._message_queue[tag])[-1]\n return True,value\n return False,None\n return self._wait_in_process_loop(done_check,timeout=timeout)","def q_get(self):\n while not self.stopped():\n try:\n return self.in_q.get(timeout=self.heart_beat)\n except queue.Empty:\n pass","def _get_nowait(self):\n # Fulfills a waiting producer, returning its value, or raising Empty if\n # no fulfillable producers are waiting.\n def fulfill_waiting_producer():\n while True:\n if self._waiting_producers:\n produce_wish = self._waiting_producers.pop(0)\n with produce_wish.group.lock:\n if not produce_wish.group.fulfilled:\n return produce_wish.fulfill()\n else:\n raise Empty()\n\n if self._buf is not None and not self._buf.empty:\n value = self._buf.pop()\n try:\n # Cycles a producer's value onto the buffer\n produced = fulfill_waiting_producer()\n self._buf.push(produced)\n except Empty:\n pass\n return value\n else:\n return fulfill_waiting_producer()","def put(self, item):\r\n while self.full():\r\n putter = self._loop.create_future()\r\n self._putters.append(putter)\r\n try:\r\n yield from putter\r\n except:\r\n putter.cancel() # Just in case putter is not done yet.\r\n if not self.full() and not putter.cancelled():\r\n # We were woken up by get_nowait(), but can't take\r\n # the call. Wake up the next in line.\r\n self._wakeup_next(self._putters)\r\n raise\r\n return self.put_nowait(item)","def _wait_empty(self):\n while True:\n if self.queue.empty():\n # We still have to wait for the last queue item being processed\n # (queue.empty() returns True before queue.task_done() is\n # called).\n self.queue.join()\n return\n time.sleep(1)","def put(\n self, item: _T, timeout: Optional[Union[float, datetime.timedelta]] = None\n ) -> \"Future[None]\":\n future = Future() # type: Future[None]\n try:\n self.put_nowait(item)\n except QueueFull:\n self._putters.append((item, future))\n _set_timeout(future, timeout)\n else:\n future.set_result(None)\n return future","def worker_process(self, item):\n g_sleep()\n return item","def put_nowait(self, item: _T) -> None:\n self._consume_expired()\n if self._getters:\n assert self.empty(), \"queue non-empty, why are getters waiting?\"\n getter = self._getters.popleft()\n self.__put_internal(item)\n future_set_result_unless_cancelled(getter, self._get())\n elif self.full():\n raise QueueFull\n else:\n self.__put_internal(item)","def ztest_get_item(self):\n \n queue = NMSQueue()\n \n result_set = queue.get_items_with_priority(1,1,0,1)\n \n for item in result_set:\n print(\"\\nItem = %s\\n\" % (item) )\n newitem = queue.get_item(item.uuid)\n print(\"\\nRetrieve the same from queue Item = %s\\n\" % (newitem) )","def timeout_get(self, timeout):\n try:\n return self.q.get(timeout=timeout)\n except queue.Empty:\n time.sleep(0)\n return None","def get(self) -> Any:\n return self._queue.get()","def active_item(self, remove=True):\n self.sleeping.reveille() # wake items whose sleep timer has expired\n if not self.stack.empty():\n pass\n elif not self.backlog.empty():\n # feed the stack the top priority item from the queue\n self.stack.push(self.backlog.get())\n else: # both the stack & queue are empty\n raise queue.Empty\n\n assert not self.stack.empty(), \"BUG: empty stack\"\n\n if remove:\n return self.stack.pop()\n\n return self.stack.peek()","def non_blocking_put(self, item):\n try:\n self.q.put(item, block=False)\n return True\n except queue.Full:\n return False","def __await__(self):\n return self.waiter.__await__()","def __await__(self):\n return self.waiter.__await__()","def wait(self):\n while self._worker is None:\n # wait() before self._run()\n time.sleep(0.1)\n self._worker.join()\n return self.poll()","def get_waiting_worker(self):\r\n if self.item in (Item.A, Item.B):\r\n for worker in self.workers:\r\n if not worker.working and self.item not in worker.items:\r\n return worker","def get(self, q_idx, data_id):\n\n while True:\n # Make sure no other get threads are pulling from the queue\n self.nsyncs[q_idx].done_lock.acquire()\n\n # Check every item currently in the queue\n done_queue = self.nsyncs[q_idx].done_queue\n size = done_queue.qsize() if self.os_supported else 1 # Workaround for non-UNIX systems\n for i in range(size):\n tup = done_queue.get()\n\n # Check if item is our item (ie. if the ids match)\n if tup[0] == data_id:\n self.nsyncs[q_idx].done_lock.release()\n return tup[1]\n\n # Nope, not our item, put it back on the done_queue \n # for someone else to grab\n done_queue.put(tup)\n\n # Didn't find our item, try again..\n self.nsyncs[q_idx].done_lock.release()","def maybe_dequeue(self):\n if self._queue.enqueued:\n return self._queue.dequeue()\n else:\n return None","def next_ele(self):\n\t\ttry:\n\t\t\tret = self._queue.get(block = True, timeout=0.5)\n\t\t\tself._queue.task_done()\n\t\t\treturn ret\n\t\texcept queue.Empty:\n\t\t\tif not self.is_running():\n\t\t\t\traise\n\t\t\telse:\n\t\t\t\treturn None","def next(self):\n try:\n return self.queue.get()\n except Empty:\n raise StopIteration","def pop(self, timeout=None):\n item = super(ExclusiveQueue, self).pop(timeout)\n try:\n self.remove(item)\n except ValueError:\n pass\n return item","def wait(timeout=None, flush=True):\n if timeout is not None:\n timeout = timeout + _time.clock() # timeout at this time\n while True:\n if _eventQueue:\n return _eventQueue.pop(0)\n if flush:\n # a full 'round' of events need to be processed before flushing\n _tdl.flush()\n if timeout and _time.clock() >= timeout:\n return None # return None on timeout\n _time.sleep(0.001) # sleep 1ms\n _processEvents()","def await(self):\n self.simulation_queue.join()\n return self.result_queue, self.fail_queue","def next_ele(self):\n\t\ttry:\n\t\t\tret = self._queue.get(block=True, timeout=0.5)\n\t\t\tself._queue.task_done()\n\t\t\treturn ret\n\t\texcept queue.Empty:\n\t\t\tif not self.isAlive():\n\t\t\t\traise\n\t\t\telse:\n\t\t\t\treturn None","def deque_non_blocking_put(self, item):\n try:\n self.q.put(item, block=False)\n return True\n except queue.Full:\n try:\n _ = self.q.get(block=False)\n dropped = False\n except queue.Empty:\n dropped = True\n # TODO - could crash due to a race condition, could be solved with a lock\n self.q.put(item, block=False)\n return dropped","def wait(self):\n self.drain_call_queue()","def put_nowait(self, item):\r\n if self.full():\r\n raise QueueFull\r\n self._put(item)\r\n self._unfinished_tasks += 1\r\n self._finished.clear()\r\n self._wakeup_next(self._getters)","def wait(self, timeout=None):\n self.ev_done.wait(timeout=timeout)\n\n if not self.ev_done.is_set():\n raise TaskTimeout(\"Task %s timed out.\" % self)\n\n # --> self.result is set\n # If it is an exception, then raise it in this waiter\n if isinstance(self.result, Exception):\n raise self.result\n\n # Release waiters and perform callbacks\n # done() has already been called, because of self.ev_done check\n # \"asynchronous\" tasks should could call done() here\n #self.done(self.result)\n\n return self.result","def wait(self, **kwargs):\n return self.client.api.wait(self.id, **kwargs)","def get_message_from_queue(self):\n message = None, None\n\n try:\n message = self.queue.get(block=True, timeout=3)\n except Empty:\n self.fail(msg='Queue get() failed empty')\n\n return message","def wait(self, timeout=None):\n self._do_work_thread.join(timeout)\n return self.finished()","def wait(self, timeout=600):\n s = datetime.datetime.now()\n status = json.loads(self.get())\n while status['status'] != 'COMPLETE':\n status = self.get()\n e = datetime.datetime.now()\n if (e - s).seconds > timeout:\n raise RuntimeError('timeout')\n return status","def _getqueue(self):\n go = self.tickqueue.get()\n for index in range(len(self.outqueues)):\n if not self.outqueues[index].empty(): return self.outqueues[index]","def Wait(self, request_id, timeout):\n end_time = time.time() + timeout\n while not self._IsComplete(request_id):\n if end_time < time.time():\n self.AbortRequest(request_id)\n raise WorkQueueTimeout(request_id, timeout)\n time.sleep(self._WAIT_POLL_INTERVAL)\n completion_file = self._GetRequestPathname(request_id, self._COMPLETE)\n with open(completion_file, 'r') as f:\n result = pickle.load(f)\n os.remove(completion_file)\n if isinstance(result, Exception):\n raise result\n assert not isinstance(result, BaseException)\n return result","def pop(self):\n record = self.db.crawl_queue.find_and_modify(\n query={'status': self.WAITING},\n update={'$set': {'status': self.PROCESSING, 'timestamp': datetime.now()}}\n )\n if record:\n return record\n else:\n self.repair()\n raise KeyError()","def get_task(self):\n return self.queue.get()","def _getqueue(self):\n\n go = self.tickqueue.get()\n for index in range(len(self.outqueues)):\n if not self.outqueues[index].empty():\n return self.outqueues[index]","def wait(self):\n self.drain_call_queue()\n DaskWrapper.wait(self._data)","def get_msg(self, block=True, timeout=None):\n return self.in_queue.get(block, timeout)","def get_msg(self, block=True, timeout=None):\n return self.in_queue.get(block, timeout)","def get_result(self, wait=-1):\n\n if not self.is_done():\n\n if wait >= 0:\n self.thread.join(wait)\n\n else:\n raise Asynchronous.NotYetDoneException(\n 'the call has not yet completed its task'\n )\n\n if self.result is None:\n self.result = self.queue.get()\n\n return self.result","def drainQueue(q):\n buf = []\n while True:\n # Get as much as possible without blocking\n try:\n while True:\n item = q.get_nowait()\n if item is None:\n return buf\n else:\n buf.append(item)\n except Queue.Empty:\n pass\n\n if buf:\n return buf\n\n # Nothing in the queue. Block for\n # one item, then go back and get any\n # that we can without blocking.\n item = q.get()\n if item is None:\n return buf\n else:\n buf.append(item)","def wait(self, timeout):\n if not hasattr(self, '_value'):\n try:\n value = self.broker.pop_result(self, timeout=timeout)\n except KeyError:\n return False\n except TaskExpired as err:\n value = err\n self._value = value\n return hasattr(self, '_value')","def get(self, block=True, timeout=None):\n if not self.connected:\n raise QueueNotConnectedError(\"Queue is not Connected\")\n\n if block:\n payload = self.__db.brpop(self._key, timeout=timeout)\n else:\n payload = self.__db.rpop(self._key)\n\n if not payload:\n return None\n\n task = self.task_class(payload[1])\n\n # if task was marked as unique then\n # remove the unique_hash from lock table\n if task.unique:\n self.__db.srem(self._lock_key, task.unique_hash())\n\n return task","def wait_for_messages(self):\n msg = self.inbox.get()\n return msg","def pop(self):\n if not self.empty():\n return self.queue.pop()\n return None","def _wait_for_queue_space(self, timeout=DEFAULT_WAIT_TIME):\n wait_time = 5\n\n (DEBUG and len(self.queue) > 1 * 1000 * 1000) and Log.warning(\"Queue {{name}} has over a million items\")\n\n now = time()\n if timeout != None:\n time_to_stop_waiting = now + timeout\n else:\n time_to_stop_waiting = now + DEFAULT_WAIT_TIME\n\n if self.next_warning < now:\n self.next_warning = now + wait_time\n\n while not self.please_stop and len(self.queue) >= self.max:\n if now > time_to_stop_waiting:\n Log.error(THREAD_TIMEOUT)\n\n if self.silent:\n self.lock.wait(Till(till=time_to_stop_waiting))\n else:\n self.lock.wait(Till(seconds=wait_time))\n if len(self.queue) >= self.max:\n now = time()\n if self.next_warning < now:\n self.next_warning = now + wait_time\n Log.alert(\n \"Queue by name of {{name|quote}} is full with ({{num}} items), thread(s) have been waiting {{wait_time}} sec\",\n name=self.name,\n num=len(self.queue),\n wait_time=wait_time\n )","def wait(self):\n with self.__lock:\n while not self.__complete:\n self.__lock.wait()","def dequeue(self):\n return self.the_queue.pop(0)","def _queue_thread(self):\n while self.running:\n try:\n msg = self.q.get(True, max(self.blocktime / 1000, 1))\n self.busy = True\n self.send(msg)\n self.update()\n except Empty:\n self.busy = False\n pass\n\n # Prune the events list of dead events\n self.events_lock.acquire()\n self.events = filter(lambda t: t.is_alive(), self.events)\n self.events_lock.release()","def getitem(self, index):\n #FIXME: A better way to get item without removing it.\n priority,size,trace=self.queues[index].get()\n self.enqueue(index,trace,priority)\n return trace","def wait(self, msecs = sys.maxint):\n\t\tcall_sdk_function('PrlJob_Wait', self.handle, msecs)\n\t\terr_obj = None\n\t\ttry:\n\t\t\terr_obj = self.get_error()\n\t\texcept:\n\t\t\tpass\n\t\tsdk_check_result(self.get_ret_code(), err_obj)\n\t\treturn self.get_result()","def dequeue(self):\r\n return self.queue.pop(0)","def get(self, block=True, timeout=None):\n return self.q.get(block, timeout)","def get_data(self):\n try:\n data = self._queue.get(block=False)\n except Empty:\n data = None\n return data","def waitForItem(self, itemName, timeout, index=1,containerObject=None, relatedAreaEnd=None):\r\n t = time.time()\r\n\r\n while t+timeout/1000.0>time.time():\r\n #self.getCurrentState(refresh=True)\r\n isScrollable = self.isItemScrollable(itemName,containerObject=containerObject, relatedAreaEnd=relatedAreaEnd)\r\n itemVisibility = self.isItemVisible(itemName, index=index, containerObject=containerObject, relatedAreaEnd=relatedAreaEnd)[0]\r\n if (isScrollable and itemVisibility != self.NOT_FOUND) or (not isScrollable and itemVisibility == self.VISIBLE):\r\n return True\r\n self.phone.delay(1000, False)\r\n\r\n return False","def get(self):\n if not self.finished():\n self.wait()\n return self._result","def wait_request(self, req_id):\n self.infer_queue[req_id].wait()\n return self.outputs.pop(req_id, None)","def __next__(self):\n waitable = queue.Queue(maxsize=1)\n getter = functools.partial(waitable.get, timeout=self._timeout)\n self._latest_item = self._async_wrapper_class(\n func=getter,\n concurrency_provider=self._provider\n )\n with self._lock:\n try:\n # get latest notification\n data = self._notifications.get_nowait()\n except queue.Empty:\n # store the consumer\n self._waitables.put(waitable)\n LOG.debug('no data for new consumer')\n else:\n # if we have a notification, pass it to the consumer immediately\n waitable.put_nowait(data)\n LOG.debug('new consumer taking next data immediately')\n return self._latest_item","def wait_message(self):\n if self._state != states['open']:\n return False\n if len(self._read_queue) > 0:\n return True\n\n assert self._read_waiter is None or self._read_waiter.cancelled(), \\\n \"You may only use one wait_message() per connection.\"\n\n self._read_waiter = asyncio.Future(loop=self._loop)\n yield from self._read_waiter\n return self.wait_message()","def get(self):\n if self.result_data.get(self.get_idx + 1) is not None:\n self.get_idx += 1\n res = self.result_data[self.get_idx]\n del self.result_data[self.get_idx]\n return res\n while True:\n res = self.result_queue.get(block=False)\n idx = res.id\n if idx == self.get_idx + 1:\n self.get_idx += 1\n return res\n self.result_data[idx] = res","def queue_peek(queue_instance, timeout=60):\r\n while True:\r\n try:\r\n yield queue_instance.get(timeout=timeout)\r\n except Empty:\r\n break","def pop(self, till=None):\n if till is not None and not isinstance(till, Signal):\n Log.error(\"expecting a signal\")\n\n with self.lock:\n while True:\n if self.queue:\n value = self.queue.popleft()\n return value\n if self.please_stop:\n break\n if not self.lock.wait(till=till | self.please_stop):\n if self.please_stop:\n break\n return None\n (DEBUG or not self.silent) and Log.note(self.name + \" queue stopped\")\n return THREAD_STOP","def dequeue(self):\n if not self.is_empty():\n return self._queue_items.pop()\n else:\n raise QueueException('dequeue operation not supported on an empty queue')","def wait(self):\n ident = get_ident()\n if ident not in self.events:\n # this is a new client\n # add an entry for it in the self.events dict\n # each entry has two elements, a threading.Event() and a timestamp\n self.events[ident] = [threading.Event(), time.time()]\n return self.events[ident][0].wait()","def dequeue(self):\n return self.queue.pop(0)","def popleft(self, timeout=None):\n item = super(ExclusiveQueue, self).popleft(timeout)\n try:\n self.remove(item)\n except ValueError:\n pass\n return item","def deque_timeout_put(self, item, timeout):\n try:\n self.q.put(item, timeout=timeout)\n return True\n except queue.Full:\n try:\n _ = self.q.get(block=False)\n dropped = False\n except queue.Empty:\n dropped = True\n # TODO - could crash due to a race condition, could be solved with a lock\n self.q.put(item, block=False)\n return dropped","def wait(self, timeout=None):\n with self.condition:\n if not self.ready:\n self.condition.wait(timeout)","def dequeue(self):\n return self.__queue.pop()","def get_queue_data():\n global grove_data\n try:\n grove_data = grove_queue.get_nowait()\n except Empty:\n # Just use old loopstate if queue is empty\n pass\n return grove_data","def first(self, timeout=None):\n while True:\n with self._jobfinished:\n if self._results or not self._jobs.unfinished_tasks:\n break\n self._jobfinished.wait(timeout)\n return self._results[0] if self._results else None","def dequeue(self):\n return self._queue.dequeue()","async def wait_for(self, seqno, cmd, timeout=5):\n if seqno in self.listeners:\n raise Exception(f\"listener exists for {seqno}\")\n\n self.debug(\"Command %d waiting for seq. number %d\", cmd, seqno)\n self.listeners[seqno] = asyncio.Semaphore(0)\n try:\n await asyncio.wait_for(self.listeners[seqno].acquire(), timeout=timeout)\n except asyncio.TimeoutError:\n self.warning(\n \"Command %d timed out waiting for sequence number %d\", cmd, seqno\n )\n del self.listeners[seqno]\n raise\n\n return self.listeners.pop(seqno)","def pop(self):\r\n return self.queue.pop(0)","def get(self, timeout=None):\n if timeout is not None:\n timeout_deadline = time.time() + timeout\n\n with self._lock:\n try:\n return self._get_nowait()\n except Empty:\n pass\n\n if self._closed:\n raise ChanClosed(which=self)\n\n # Shortcut for if the operation shouldn't block.\n if timeout is not None and timeout <= 0:\n raise Timeout()\n\n group = WishGroup()\n wish = Wish(group, WISH_CONSUME, self)\n self._waiting_consumers.append(wish)\n\n with group.lock:\n while not group.fulfilled:\n if timeout is None:\n group.cond.wait()\n else:\n group.cond.wait(timeout_deadline - time.time())\n\n if time.time() >= timeout_deadline:\n # Only time out if the wish wasn't fulfilled\n if not group.fulfilled:\n self._waiting_consumers.remove(wish)\n raise Timeout()\n\n if wish.closed:\n raise ChanClosed(which=self)\n return wish.value","def dequeue(self):\n return self.queue.popleft()","def get_next_event(self, timeout=None):\n ret = self.inq.Wait(timeout)\n return ret","def wait_send(self, timeout = None):\n\t\tself._send_queue_cleared.clear()\n\t\tself._send_queue_cleared.wait(timeout = timeout)","def request(self, *args, **kwargs):\n self.work_request_queue.put((args, kwargs))\n return self.result_queue.get()"],"string":"[\n \"def get_item_from_queue(Q, timeout=0.01):\\n try:\\n item = Q.get(True, 0.01)\\n except queue.Empty:\\n return None\\n return item\",\n \"def get_item_from_queue(Q, timeout=0.01):\\n try:\\n item = Q.get(True, 0.01)\\n except Queue.Empty:\\n return None\\n return item\",\n \"def worker_take_item(self):\\r\\n worker = self.get_waiting_worker()\\r\\n if worker:\\r\\n worker.take_item(self.item)\\r\\n self.item = Item.E\\r\\n return worker\",\n \"def get_nowait(self):\\r\\n if self.empty():\\r\\n raise QueueEmpty\\r\\n item = self._get()\\r\\n self._wakeup_next(self._putters)\\r\\n return item\",\n \"def get(self):\\n with self.__lock:\\n while True:\\n try:\\n job = self.__queue.get(False)\\n self.__lock.notify_all()\\n return job\\n except Queue.Empty:\\n self.__lock.wait()\",\n \"def get_nowait(self) -> _T:\\n self._consume_expired()\\n if self._putters:\\n assert self.full(), \\\"queue not full, why are putters waiting?\\\"\\n item, putter = self._putters.popleft()\\n self.__put_internal(item)\\n future_set_result_unless_cancelled(putter, None)\\n return self._get()\\n elif self.qsize():\\n return self._get()\\n else:\\n raise QueueEmpty\",\n \"def get_next_item(self, timeout=None):\\n if self.current_item is not None:\\n raise error_classes.UVMSequenceError(\\\"You must call item_done() before calling get_next_item again\\\")\\n self.current_item = self.req_q.get(timeout=timeout)\\n return self.current_item\",\n \"def _wait_for_event_in_queue(self):\\n try:\\n event = self._queue.get(timeout=SendTelemetryEventsHandler._MAX_TIMEOUT)\\n self._queue.task_done()\\n except Empty:\\n # No elements in Queue, return None\\n event = None\\n\\n return event\",\n \"def get(self):\\n\\t\\ttry:\\n\\t\\t\\tself.logger.debug('Im trying to get item from queue')\\n\\t\\t\\titem = self.queue.get()\\n\\t\\t\\tself.logger.debug('Recevie item from queue %s'%(item))\\n\\t\\t\\treturn True, item\\n\\t\\texcept Exception, e:\\n\\t\\t\\tself.logger.error('Error method get, error: %s'%(e),exc_info=True)\\n\\t\\t\\treturn False, None\",\n \"def non_blocking_get(self):\\n try:\\n return self.q.get(block=False)\\n except queue.Empty:\\n time.sleep(0)\\n return None\",\n \"def _wait_queue(self):\\n while True:\\n time.sleep(0.1)\\n if self.queue.unfinished_tasks == 0 or self.stop_signal.is_set():\\n return\",\n \"def wait(self, timeout=None):\\r\\n with self._lock:\\r\\n if self._finished:\\r\\n raise RuntimeError(\\\"wait() does not support re-entry!\\\")\\r\\n if not self._started:\\r\\n for thread in self._wait_events:\\r\\n thread.timeout = timeout\\r\\n thread.start()\\r\\n self._started = True\\r\\n try:\\r\\n if self._queue.get(timeout=timeout):\\r\\n return True\\r\\n return False\\r\\n except Empty:\\r\\n return False\\r\\n finally:\\r\\n with self._lock:\\r\\n self._finished = True\",\n \"def dequeue(self, timeout=0):\\n result = self.connection.dequeue_any([self], timeout)\\n if result:\\n job, queue = result\\n return job\\n else:\\n return None\",\n \"def queueItem(self, item): \\n Trace(\\\"%s queueItem (%s)\\\"%(self.getUri(),item), \\\"EventLib.EventRelayHTTPC\\\")\\n if not self._closing:\\n self._queue.put(item)\\n self._queueEvent.set()\\n return makeQueueDeferred(StatusVal.OK, self._queue, self._event)\\n return makeDeferred(StatusVal.OK)\",\n \"def wait_for_queue(self, q, stop=True):\\n return _loader.wait_for_queue(q, stop=stop)\",\n \"def get(self):\\r\\n try:\\r\\n # get with block=False returns an item if one\\r\\n # is immediately available, else raises the Empty exception\\r\\n return self._queue.get(block=False)\\r\\n except queue.Empty:\\r\\n return self._create_connection()\",\n \"def get(self, block=True, timeout=None):\\n return self.queue.get(block, timeout)\",\n \"def wait(self):\\n self.queue.join()\",\n \"def pollTillAvailable(self):\\n item = self.getItem()\\n while item is None:\\n item = self.getItem()\\n\\n return item\",\n \"def wait_for_message(self, tag, timeout=None):\\n def done_check():\\n if self._message_queue.setdefault(tag,[]):\\n value=heapq.heappop(self._message_queue[tag])[-1]\\n return True,value\\n return False,None\\n return self._wait_in_process_loop(done_check,timeout=timeout)\",\n \"def q_get(self):\\n while not self.stopped():\\n try:\\n return self.in_q.get(timeout=self.heart_beat)\\n except queue.Empty:\\n pass\",\n \"def _get_nowait(self):\\n # Fulfills a waiting producer, returning its value, or raising Empty if\\n # no fulfillable producers are waiting.\\n def fulfill_waiting_producer():\\n while True:\\n if self._waiting_producers:\\n produce_wish = self._waiting_producers.pop(0)\\n with produce_wish.group.lock:\\n if not produce_wish.group.fulfilled:\\n return produce_wish.fulfill()\\n else:\\n raise Empty()\\n\\n if self._buf is not None and not self._buf.empty:\\n value = self._buf.pop()\\n try:\\n # Cycles a producer's value onto the buffer\\n produced = fulfill_waiting_producer()\\n self._buf.push(produced)\\n except Empty:\\n pass\\n return value\\n else:\\n return fulfill_waiting_producer()\",\n \"def put(self, item):\\r\\n while self.full():\\r\\n putter = self._loop.create_future()\\r\\n self._putters.append(putter)\\r\\n try:\\r\\n yield from putter\\r\\n except:\\r\\n putter.cancel() # Just in case putter is not done yet.\\r\\n if not self.full() and not putter.cancelled():\\r\\n # We were woken up by get_nowait(), but can't take\\r\\n # the call. Wake up the next in line.\\r\\n self._wakeup_next(self._putters)\\r\\n raise\\r\\n return self.put_nowait(item)\",\n \"def _wait_empty(self):\\n while True:\\n if self.queue.empty():\\n # We still have to wait for the last queue item being processed\\n # (queue.empty() returns True before queue.task_done() is\\n # called).\\n self.queue.join()\\n return\\n time.sleep(1)\",\n \"def put(\\n self, item: _T, timeout: Optional[Union[float, datetime.timedelta]] = None\\n ) -> \\\"Future[None]\\\":\\n future = Future() # type: Future[None]\\n try:\\n self.put_nowait(item)\\n except QueueFull:\\n self._putters.append((item, future))\\n _set_timeout(future, timeout)\\n else:\\n future.set_result(None)\\n return future\",\n \"def worker_process(self, item):\\n g_sleep()\\n return item\",\n \"def put_nowait(self, item: _T) -> None:\\n self._consume_expired()\\n if self._getters:\\n assert self.empty(), \\\"queue non-empty, why are getters waiting?\\\"\\n getter = self._getters.popleft()\\n self.__put_internal(item)\\n future_set_result_unless_cancelled(getter, self._get())\\n elif self.full():\\n raise QueueFull\\n else:\\n self.__put_internal(item)\",\n \"def ztest_get_item(self):\\n \\n queue = NMSQueue()\\n \\n result_set = queue.get_items_with_priority(1,1,0,1)\\n \\n for item in result_set:\\n print(\\\"\\\\nItem = %s\\\\n\\\" % (item) )\\n newitem = queue.get_item(item.uuid)\\n print(\\\"\\\\nRetrieve the same from queue Item = %s\\\\n\\\" % (newitem) )\",\n \"def timeout_get(self, timeout):\\n try:\\n return self.q.get(timeout=timeout)\\n except queue.Empty:\\n time.sleep(0)\\n return None\",\n \"def get(self) -> Any:\\n return self._queue.get()\",\n \"def active_item(self, remove=True):\\n self.sleeping.reveille() # wake items whose sleep timer has expired\\n if not self.stack.empty():\\n pass\\n elif not self.backlog.empty():\\n # feed the stack the top priority item from the queue\\n self.stack.push(self.backlog.get())\\n else: # both the stack & queue are empty\\n raise queue.Empty\\n\\n assert not self.stack.empty(), \\\"BUG: empty stack\\\"\\n\\n if remove:\\n return self.stack.pop()\\n\\n return self.stack.peek()\",\n \"def non_blocking_put(self, item):\\n try:\\n self.q.put(item, block=False)\\n return True\\n except queue.Full:\\n return False\",\n \"def __await__(self):\\n return self.waiter.__await__()\",\n \"def __await__(self):\\n return self.waiter.__await__()\",\n \"def wait(self):\\n while self._worker is None:\\n # wait() before self._run()\\n time.sleep(0.1)\\n self._worker.join()\\n return self.poll()\",\n \"def get_waiting_worker(self):\\r\\n if self.item in (Item.A, Item.B):\\r\\n for worker in self.workers:\\r\\n if not worker.working and self.item not in worker.items:\\r\\n return worker\",\n \"def get(self, q_idx, data_id):\\n\\n while True:\\n # Make sure no other get threads are pulling from the queue\\n self.nsyncs[q_idx].done_lock.acquire()\\n\\n # Check every item currently in the queue\\n done_queue = self.nsyncs[q_idx].done_queue\\n size = done_queue.qsize() if self.os_supported else 1 # Workaround for non-UNIX systems\\n for i in range(size):\\n tup = done_queue.get()\\n\\n # Check if item is our item (ie. if the ids match)\\n if tup[0] == data_id:\\n self.nsyncs[q_idx].done_lock.release()\\n return tup[1]\\n\\n # Nope, not our item, put it back on the done_queue \\n # for someone else to grab\\n done_queue.put(tup)\\n\\n # Didn't find our item, try again..\\n self.nsyncs[q_idx].done_lock.release()\",\n \"def maybe_dequeue(self):\\n if self._queue.enqueued:\\n return self._queue.dequeue()\\n else:\\n return None\",\n \"def next_ele(self):\\n\\t\\ttry:\\n\\t\\t\\tret = self._queue.get(block = True, timeout=0.5)\\n\\t\\t\\tself._queue.task_done()\\n\\t\\t\\treturn ret\\n\\t\\texcept queue.Empty:\\n\\t\\t\\tif not self.is_running():\\n\\t\\t\\t\\traise\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn None\",\n \"def next(self):\\n try:\\n return self.queue.get()\\n except Empty:\\n raise StopIteration\",\n \"def pop(self, timeout=None):\\n item = super(ExclusiveQueue, self).pop(timeout)\\n try:\\n self.remove(item)\\n except ValueError:\\n pass\\n return item\",\n \"def wait(timeout=None, flush=True):\\n if timeout is not None:\\n timeout = timeout + _time.clock() # timeout at this time\\n while True:\\n if _eventQueue:\\n return _eventQueue.pop(0)\\n if flush:\\n # a full 'round' of events need to be processed before flushing\\n _tdl.flush()\\n if timeout and _time.clock() >= timeout:\\n return None # return None on timeout\\n _time.sleep(0.001) # sleep 1ms\\n _processEvents()\",\n \"def await(self):\\n self.simulation_queue.join()\\n return self.result_queue, self.fail_queue\",\n \"def next_ele(self):\\n\\t\\ttry:\\n\\t\\t\\tret = self._queue.get(block=True, timeout=0.5)\\n\\t\\t\\tself._queue.task_done()\\n\\t\\t\\treturn ret\\n\\t\\texcept queue.Empty:\\n\\t\\t\\tif not self.isAlive():\\n\\t\\t\\t\\traise\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn None\",\n \"def deque_non_blocking_put(self, item):\\n try:\\n self.q.put(item, block=False)\\n return True\\n except queue.Full:\\n try:\\n _ = self.q.get(block=False)\\n dropped = False\\n except queue.Empty:\\n dropped = True\\n # TODO - could crash due to a race condition, could be solved with a lock\\n self.q.put(item, block=False)\\n return dropped\",\n \"def wait(self):\\n self.drain_call_queue()\",\n \"def put_nowait(self, item):\\r\\n if self.full():\\r\\n raise QueueFull\\r\\n self._put(item)\\r\\n self._unfinished_tasks += 1\\r\\n self._finished.clear()\\r\\n self._wakeup_next(self._getters)\",\n \"def wait(self, timeout=None):\\n self.ev_done.wait(timeout=timeout)\\n\\n if not self.ev_done.is_set():\\n raise TaskTimeout(\\\"Task %s timed out.\\\" % self)\\n\\n # --> self.result is set\\n # If it is an exception, then raise it in this waiter\\n if isinstance(self.result, Exception):\\n raise self.result\\n\\n # Release waiters and perform callbacks\\n # done() has already been called, because of self.ev_done check\\n # \\\"asynchronous\\\" tasks should could call done() here\\n #self.done(self.result)\\n\\n return self.result\",\n \"def wait(self, **kwargs):\\n return self.client.api.wait(self.id, **kwargs)\",\n \"def get_message_from_queue(self):\\n message = None, None\\n\\n try:\\n message = self.queue.get(block=True, timeout=3)\\n except Empty:\\n self.fail(msg='Queue get() failed empty')\\n\\n return message\",\n \"def wait(self, timeout=None):\\n self._do_work_thread.join(timeout)\\n return self.finished()\",\n \"def wait(self, timeout=600):\\n s = datetime.datetime.now()\\n status = json.loads(self.get())\\n while status['status'] != 'COMPLETE':\\n status = self.get()\\n e = datetime.datetime.now()\\n if (e - s).seconds > timeout:\\n raise RuntimeError('timeout')\\n return status\",\n \"def _getqueue(self):\\n go = self.tickqueue.get()\\n for index in range(len(self.outqueues)):\\n if not self.outqueues[index].empty(): return self.outqueues[index]\",\n \"def Wait(self, request_id, timeout):\\n end_time = time.time() + timeout\\n while not self._IsComplete(request_id):\\n if end_time < time.time():\\n self.AbortRequest(request_id)\\n raise WorkQueueTimeout(request_id, timeout)\\n time.sleep(self._WAIT_POLL_INTERVAL)\\n completion_file = self._GetRequestPathname(request_id, self._COMPLETE)\\n with open(completion_file, 'r') as f:\\n result = pickle.load(f)\\n os.remove(completion_file)\\n if isinstance(result, Exception):\\n raise result\\n assert not isinstance(result, BaseException)\\n return result\",\n \"def pop(self):\\n record = self.db.crawl_queue.find_and_modify(\\n query={'status': self.WAITING},\\n update={'$set': {'status': self.PROCESSING, 'timestamp': datetime.now()}}\\n )\\n if record:\\n return record\\n else:\\n self.repair()\\n raise KeyError()\",\n \"def get_task(self):\\n return self.queue.get()\",\n \"def _getqueue(self):\\n\\n go = self.tickqueue.get()\\n for index in range(len(self.outqueues)):\\n if not self.outqueues[index].empty():\\n return self.outqueues[index]\",\n \"def wait(self):\\n self.drain_call_queue()\\n DaskWrapper.wait(self._data)\",\n \"def get_msg(self, block=True, timeout=None):\\n return self.in_queue.get(block, timeout)\",\n \"def get_msg(self, block=True, timeout=None):\\n return self.in_queue.get(block, timeout)\",\n \"def get_result(self, wait=-1):\\n\\n if not self.is_done():\\n\\n if wait >= 0:\\n self.thread.join(wait)\\n\\n else:\\n raise Asynchronous.NotYetDoneException(\\n 'the call has not yet completed its task'\\n )\\n\\n if self.result is None:\\n self.result = self.queue.get()\\n\\n return self.result\",\n \"def drainQueue(q):\\n buf = []\\n while True:\\n # Get as much as possible without blocking\\n try:\\n while True:\\n item = q.get_nowait()\\n if item is None:\\n return buf\\n else:\\n buf.append(item)\\n except Queue.Empty:\\n pass\\n\\n if buf:\\n return buf\\n\\n # Nothing in the queue. Block for\\n # one item, then go back and get any\\n # that we can without blocking.\\n item = q.get()\\n if item is None:\\n return buf\\n else:\\n buf.append(item)\",\n \"def wait(self, timeout):\\n if not hasattr(self, '_value'):\\n try:\\n value = self.broker.pop_result(self, timeout=timeout)\\n except KeyError:\\n return False\\n except TaskExpired as err:\\n value = err\\n self._value = value\\n return hasattr(self, '_value')\",\n \"def get(self, block=True, timeout=None):\\n if not self.connected:\\n raise QueueNotConnectedError(\\\"Queue is not Connected\\\")\\n\\n if block:\\n payload = self.__db.brpop(self._key, timeout=timeout)\\n else:\\n payload = self.__db.rpop(self._key)\\n\\n if not payload:\\n return None\\n\\n task = self.task_class(payload[1])\\n\\n # if task was marked as unique then\\n # remove the unique_hash from lock table\\n if task.unique:\\n self.__db.srem(self._lock_key, task.unique_hash())\\n\\n return task\",\n \"def wait_for_messages(self):\\n msg = self.inbox.get()\\n return msg\",\n \"def pop(self):\\n if not self.empty():\\n return self.queue.pop()\\n return None\",\n \"def _wait_for_queue_space(self, timeout=DEFAULT_WAIT_TIME):\\n wait_time = 5\\n\\n (DEBUG and len(self.queue) > 1 * 1000 * 1000) and Log.warning(\\\"Queue {{name}} has over a million items\\\")\\n\\n now = time()\\n if timeout != None:\\n time_to_stop_waiting = now + timeout\\n else:\\n time_to_stop_waiting = now + DEFAULT_WAIT_TIME\\n\\n if self.next_warning < now:\\n self.next_warning = now + wait_time\\n\\n while not self.please_stop and len(self.queue) >= self.max:\\n if now > time_to_stop_waiting:\\n Log.error(THREAD_TIMEOUT)\\n\\n if self.silent:\\n self.lock.wait(Till(till=time_to_stop_waiting))\\n else:\\n self.lock.wait(Till(seconds=wait_time))\\n if len(self.queue) >= self.max:\\n now = time()\\n if self.next_warning < now:\\n self.next_warning = now + wait_time\\n Log.alert(\\n \\\"Queue by name of {{name|quote}} is full with ({{num}} items), thread(s) have been waiting {{wait_time}} sec\\\",\\n name=self.name,\\n num=len(self.queue),\\n wait_time=wait_time\\n )\",\n \"def wait(self):\\n with self.__lock:\\n while not self.__complete:\\n self.__lock.wait()\",\n \"def dequeue(self):\\n return self.the_queue.pop(0)\",\n \"def _queue_thread(self):\\n while self.running:\\n try:\\n msg = self.q.get(True, max(self.blocktime / 1000, 1))\\n self.busy = True\\n self.send(msg)\\n self.update()\\n except Empty:\\n self.busy = False\\n pass\\n\\n # Prune the events list of dead events\\n self.events_lock.acquire()\\n self.events = filter(lambda t: t.is_alive(), self.events)\\n self.events_lock.release()\",\n \"def getitem(self, index):\\n #FIXME: A better way to get item without removing it.\\n priority,size,trace=self.queues[index].get()\\n self.enqueue(index,trace,priority)\\n return trace\",\n \"def wait(self, msecs = sys.maxint):\\n\\t\\tcall_sdk_function('PrlJob_Wait', self.handle, msecs)\\n\\t\\terr_obj = None\\n\\t\\ttry:\\n\\t\\t\\terr_obj = self.get_error()\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\t\\tsdk_check_result(self.get_ret_code(), err_obj)\\n\\t\\treturn self.get_result()\",\n \"def dequeue(self):\\r\\n return self.queue.pop(0)\",\n \"def get(self, block=True, timeout=None):\\n return self.q.get(block, timeout)\",\n \"def get_data(self):\\n try:\\n data = self._queue.get(block=False)\\n except Empty:\\n data = None\\n return data\",\n \"def waitForItem(self, itemName, timeout, index=1,containerObject=None, relatedAreaEnd=None):\\r\\n t = time.time()\\r\\n\\r\\n while t+timeout/1000.0>time.time():\\r\\n #self.getCurrentState(refresh=True)\\r\\n isScrollable = self.isItemScrollable(itemName,containerObject=containerObject, relatedAreaEnd=relatedAreaEnd)\\r\\n itemVisibility = self.isItemVisible(itemName, index=index, containerObject=containerObject, relatedAreaEnd=relatedAreaEnd)[0]\\r\\n if (isScrollable and itemVisibility != self.NOT_FOUND) or (not isScrollable and itemVisibility == self.VISIBLE):\\r\\n return True\\r\\n self.phone.delay(1000, False)\\r\\n\\r\\n return False\",\n \"def get(self):\\n if not self.finished():\\n self.wait()\\n return self._result\",\n \"def wait_request(self, req_id):\\n self.infer_queue[req_id].wait()\\n return self.outputs.pop(req_id, None)\",\n \"def __next__(self):\\n waitable = queue.Queue(maxsize=1)\\n getter = functools.partial(waitable.get, timeout=self._timeout)\\n self._latest_item = self._async_wrapper_class(\\n func=getter,\\n concurrency_provider=self._provider\\n )\\n with self._lock:\\n try:\\n # get latest notification\\n data = self._notifications.get_nowait()\\n except queue.Empty:\\n # store the consumer\\n self._waitables.put(waitable)\\n LOG.debug('no data for new consumer')\\n else:\\n # if we have a notification, pass it to the consumer immediately\\n waitable.put_nowait(data)\\n LOG.debug('new consumer taking next data immediately')\\n return self._latest_item\",\n \"def wait_message(self):\\n if self._state != states['open']:\\n return False\\n if len(self._read_queue) > 0:\\n return True\\n\\n assert self._read_waiter is None or self._read_waiter.cancelled(), \\\\\\n \\\"You may only use one wait_message() per connection.\\\"\\n\\n self._read_waiter = asyncio.Future(loop=self._loop)\\n yield from self._read_waiter\\n return self.wait_message()\",\n \"def get(self):\\n if self.result_data.get(self.get_idx + 1) is not None:\\n self.get_idx += 1\\n res = self.result_data[self.get_idx]\\n del self.result_data[self.get_idx]\\n return res\\n while True:\\n res = self.result_queue.get(block=False)\\n idx = res.id\\n if idx == self.get_idx + 1:\\n self.get_idx += 1\\n return res\\n self.result_data[idx] = res\",\n \"def queue_peek(queue_instance, timeout=60):\\r\\n while True:\\r\\n try:\\r\\n yield queue_instance.get(timeout=timeout)\\r\\n except Empty:\\r\\n break\",\n \"def pop(self, till=None):\\n if till is not None and not isinstance(till, Signal):\\n Log.error(\\\"expecting a signal\\\")\\n\\n with self.lock:\\n while True:\\n if self.queue:\\n value = self.queue.popleft()\\n return value\\n if self.please_stop:\\n break\\n if not self.lock.wait(till=till | self.please_stop):\\n if self.please_stop:\\n break\\n return None\\n (DEBUG or not self.silent) and Log.note(self.name + \\\" queue stopped\\\")\\n return THREAD_STOP\",\n \"def dequeue(self):\\n if not self.is_empty():\\n return self._queue_items.pop()\\n else:\\n raise QueueException('dequeue operation not supported on an empty queue')\",\n \"def wait(self):\\n ident = get_ident()\\n if ident not in self.events:\\n # this is a new client\\n # add an entry for it in the self.events dict\\n # each entry has two elements, a threading.Event() and a timestamp\\n self.events[ident] = [threading.Event(), time.time()]\\n return self.events[ident][0].wait()\",\n \"def dequeue(self):\\n return self.queue.pop(0)\",\n \"def popleft(self, timeout=None):\\n item = super(ExclusiveQueue, self).popleft(timeout)\\n try:\\n self.remove(item)\\n except ValueError:\\n pass\\n return item\",\n \"def deque_timeout_put(self, item, timeout):\\n try:\\n self.q.put(item, timeout=timeout)\\n return True\\n except queue.Full:\\n try:\\n _ = self.q.get(block=False)\\n dropped = False\\n except queue.Empty:\\n dropped = True\\n # TODO - could crash due to a race condition, could be solved with a lock\\n self.q.put(item, block=False)\\n return dropped\",\n \"def wait(self, timeout=None):\\n with self.condition:\\n if not self.ready:\\n self.condition.wait(timeout)\",\n \"def dequeue(self):\\n return self.__queue.pop()\",\n \"def get_queue_data():\\n global grove_data\\n try:\\n grove_data = grove_queue.get_nowait()\\n except Empty:\\n # Just use old loopstate if queue is empty\\n pass\\n return grove_data\",\n \"def first(self, timeout=None):\\n while True:\\n with self._jobfinished:\\n if self._results or not self._jobs.unfinished_tasks:\\n break\\n self._jobfinished.wait(timeout)\\n return self._results[0] if self._results else None\",\n \"def dequeue(self):\\n return self._queue.dequeue()\",\n \"async def wait_for(self, seqno, cmd, timeout=5):\\n if seqno in self.listeners:\\n raise Exception(f\\\"listener exists for {seqno}\\\")\\n\\n self.debug(\\\"Command %d waiting for seq. number %d\\\", cmd, seqno)\\n self.listeners[seqno] = asyncio.Semaphore(0)\\n try:\\n await asyncio.wait_for(self.listeners[seqno].acquire(), timeout=timeout)\\n except asyncio.TimeoutError:\\n self.warning(\\n \\\"Command %d timed out waiting for sequence number %d\\\", cmd, seqno\\n )\\n del self.listeners[seqno]\\n raise\\n\\n return self.listeners.pop(seqno)\",\n \"def pop(self):\\r\\n return self.queue.pop(0)\",\n \"def get(self, timeout=None):\\n if timeout is not None:\\n timeout_deadline = time.time() + timeout\\n\\n with self._lock:\\n try:\\n return self._get_nowait()\\n except Empty:\\n pass\\n\\n if self._closed:\\n raise ChanClosed(which=self)\\n\\n # Shortcut for if the operation shouldn't block.\\n if timeout is not None and timeout <= 0:\\n raise Timeout()\\n\\n group = WishGroup()\\n wish = Wish(group, WISH_CONSUME, self)\\n self._waiting_consumers.append(wish)\\n\\n with group.lock:\\n while not group.fulfilled:\\n if timeout is None:\\n group.cond.wait()\\n else:\\n group.cond.wait(timeout_deadline - time.time())\\n\\n if time.time() >= timeout_deadline:\\n # Only time out if the wish wasn't fulfilled\\n if not group.fulfilled:\\n self._waiting_consumers.remove(wish)\\n raise Timeout()\\n\\n if wish.closed:\\n raise ChanClosed(which=self)\\n return wish.value\",\n \"def dequeue(self):\\n return self.queue.popleft()\",\n \"def get_next_event(self, timeout=None):\\n ret = self.inq.Wait(timeout)\\n return ret\",\n \"def wait_send(self, timeout = None):\\n\\t\\tself._send_queue_cleared.clear()\\n\\t\\tself._send_queue_cleared.wait(timeout = timeout)\",\n \"def request(self, *args, **kwargs):\\n self.work_request_queue.put((args, kwargs))\\n return self.result_queue.get()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69198966","0.69057333","0.6884813","0.6864645","0.68376034","0.6821141","0.6660425","0.6638609","0.65500504","0.6460967","0.6446401","0.63761413","0.63736504","0.6362188","0.6324351","0.62841207","0.6243727","0.61811554","0.6120136","0.61119175","0.60853547","0.60850537","0.6048267","0.6044677","0.60400003","0.6034453","0.60216844","0.59891033","0.59822494","0.5976261","0.59724563","0.59672153","0.59362185","0.59362185","0.5926343","0.5922113","0.59214455","0.5906842","0.5900956","0.5889584","0.5883158","0.5877458","0.58427584","0.58404225","0.5831381","0.5812643","0.5800383","0.57982075","0.5797517","0.5797063","0.5790493","0.5774333","0.5764621","0.5764605","0.57583904","0.57541966","0.57530516","0.5747142","0.5742784","0.5742784","0.57374597","0.5729477","0.5719461","0.5715755","0.57067496","0.5698188","0.5697237","0.5677781","0.56709236","0.56686866","0.565926","0.5652527","0.56482303","0.5642524","0.56383854","0.5626286","0.5623751","0.56225824","0.56221914","0.56217504","0.56206065","0.5611751","0.5607913","0.5602662","0.55906636","0.55847967","0.5574999","0.55687726","0.5561468","0.5537652","0.5534225","0.5530735","0.55302745","0.5523353","0.55214787","0.5520924","0.5519951","0.55187124","0.5516488","0.5500914"],"string":"[\n \"0.69198966\",\n \"0.69057333\",\n \"0.6884813\",\n \"0.6864645\",\n \"0.68376034\",\n \"0.6821141\",\n \"0.6660425\",\n \"0.6638609\",\n \"0.65500504\",\n \"0.6460967\",\n \"0.6446401\",\n \"0.63761413\",\n \"0.63736504\",\n \"0.6362188\",\n \"0.6324351\",\n \"0.62841207\",\n \"0.6243727\",\n \"0.61811554\",\n \"0.6120136\",\n \"0.61119175\",\n \"0.60853547\",\n \"0.60850537\",\n \"0.6048267\",\n \"0.6044677\",\n \"0.60400003\",\n \"0.6034453\",\n \"0.60216844\",\n \"0.59891033\",\n \"0.59822494\",\n \"0.5976261\",\n \"0.59724563\",\n \"0.59672153\",\n \"0.59362185\",\n \"0.59362185\",\n \"0.5926343\",\n \"0.5922113\",\n \"0.59214455\",\n \"0.5906842\",\n \"0.5900956\",\n \"0.5889584\",\n \"0.5883158\",\n \"0.5877458\",\n \"0.58427584\",\n \"0.58404225\",\n \"0.5831381\",\n \"0.5812643\",\n \"0.5800383\",\n \"0.57982075\",\n \"0.5797517\",\n \"0.5797063\",\n \"0.5790493\",\n \"0.5774333\",\n \"0.5764621\",\n \"0.5764605\",\n \"0.57583904\",\n \"0.57541966\",\n \"0.57530516\",\n \"0.5747142\",\n \"0.5742784\",\n \"0.5742784\",\n \"0.57374597\",\n \"0.5729477\",\n \"0.5719461\",\n \"0.5715755\",\n \"0.57067496\",\n \"0.5698188\",\n \"0.5697237\",\n \"0.5677781\",\n \"0.56709236\",\n \"0.56686866\",\n \"0.565926\",\n \"0.5652527\",\n \"0.56482303\",\n \"0.5642524\",\n \"0.56383854\",\n \"0.5626286\",\n \"0.5623751\",\n \"0.56225824\",\n \"0.56221914\",\n \"0.56217504\",\n \"0.56206065\",\n \"0.5611751\",\n \"0.5607913\",\n \"0.5602662\",\n \"0.55906636\",\n \"0.55847967\",\n \"0.5574999\",\n \"0.55687726\",\n \"0.5561468\",\n \"0.5537652\",\n \"0.5534225\",\n \"0.5530735\",\n \"0.55302745\",\n \"0.5523353\",\n \"0.55214787\",\n \"0.5520924\",\n \"0.5519951\",\n \"0.55187124\",\n \"0.5516488\",\n \"0.5500914\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.734153"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7173,"cells":{"query":{"kind":"string","value":"This function is the HTTP client worker thread."},"document":{"kind":"string","value":"def processEvent(self):\n # Note: break out of event dispatch loop when closedown event is received\n # and closing flag is set. This is to prevent DoS attack by faked closedown\n # event type, and to ensure that prior events received are all processed.\n delay_on_error_min = 0.125 # Back off retry interval on error..\n delay_on_error_max = 20.0 # ..\n delay_on_error = delay_on_error_min # ..\n while True:\n if delay_on_error < delay_on_error_max:\n delay_on_error *= 2\n try:\n # PLEASE NOTE: In the event that the HTTPC is run as duplex, not simplex\n # then the post methods will be delayed if nothing is sent down to the client\n # from the server. This timeout is controlled by QUEUE_WAIT_TIMEOUT in EventRouterHTTPS.py\n if self._simplex == True:\n self._queueEvent.wait()\n self._queueEvent.clear()\n \n if not self._queue.empty():\n Trace(\"%s queue.get ...\"%(self.getUri()), \"EventLib.EventRelayHTTPC\")\n ###msgbody = self._queue.get()\n ###Trace(\"%s get msgbody: %s\"%(self.getUri(),msgbody), \"EventLib.EventRelayHTTPC\")\n ###self._event.set()\n msgbody = self.getQueuedItem()\n [typ,env] = msgbody\n if typ == \"closedown\":\n if self._closing: break\n else:\n # process request as an HTTP POST request\n data = makeEnvelopeData(env)\n headers = { \"Content-type\": \"text/plain\",\n \"Accept\": \"text/plain\",\n \"Content-length\": str(len(data)) }\n self._httpcon.request(\"POST\", \"/request_path_ignored\", data, headers)\n response = self._httpcon.getresponse()\n delay_on_error = delay_on_error_min\n elif self._simplex == False:\n # Nothing in queue:\n # issue a GET for incoming events\n _log.info(\"%s HTTP get ...\"%(self.getUri()))\n headers = { \"Accept\": \"text/plain\" }\n self._httpcon.request(\"GET\", \"/request_path_ignored\", None, headers)\n response = self._httpcon.getresponse()\n if response.status == 200:\n delay_on_error = delay_on_error_min\n msgbody = response.read()\n Trace(\"%s get msgbody: %s\"%(self.getUri(),msgbody), \"EventLib.EventRelayHTTPC\")\n # Parse message and act accordingly\n msgdata = parseMessageData(msgbody)\n Trace(\"%s get msgdata: %s\"%(self.getUri(),str(msgdata)), \"EventLib.EventRelayHTTPC\")\n if msgdata == None:\n #TODO: Log \"Request body malformed\"\n pass\n elif msgdata[0] == \"forward\":\n # msgdata = [\"forward\", [['R1', 'R2', 'R3'], 'ev:typ', 'ev:src', 'payload']]\n event = makeEvent(evtype=msgdata[1][1],source=msgdata[1][2],payload=msgdata[1][3])\n env = constructEnvelope(msgdata[1][0], event)\n self.forward(event, env)\n elif msgdata[0] == \"idle\":\n # Idle response gives client a chance to send if anything is queued\n pass\n else:\n #TODO: handle closedown message?\n Warn( \"%s Request body unrecognized option: %s\"%(self.getUri(),msgdata[0]), \"EventRelayHTTPC\")\n pass\n elif response.status == 503:\n Trace( \"%s processEvent error response: %u, %s\"%(self.getUri(),response.status,response.reason), \"EventLib.EventRelayHTTPC\")\n # Remote end closed down\n break\n else:\n # TODO: (log error response)\n Warn( \"%s processEvent error response: %u, %s\"%(self.getUri(),response.status,response.reason), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n \n except httplib.BadStatusLine, e:\n # This can happen at closedown\n Info( \"%s processEvent bad response: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except httplib.CannotSendRequest, e:\n # This can happen at closedown\n Info( \"%s Cannot send request: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except httplib.ResponseNotReady, e:\n # This can happen at startup and sometimes other times:\n # maybe multiple requests on a single HTTP connection object?\n Info( \"%s Response not ready: (%s)\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n except socket.error, e:\n Warn( \"%s Socket error: %s\"%(self.getUri(), str(e)), \"EventLib.EventRelayHTTPC\")\n time.sleep(delay_on_error)\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __manage_client_thread(self, conn_socket: socket.socket, address: tuple):\n print(\"[THREAD] new thread started for client\")\n connected = True\n\n while connected:\n request_header = HttpServer.get_request_header(conn_socket)\n split_request_header = request_header.split()\n file = split_request_header[1]\n print(\"[RECV] header received from IPv4 address\", address[0], \":\", request_header)\n\n if not HttpServer.is_valid_http_request(split_request_header):\n http_message = HttpServer.create_400_response()\n pass\n else:\n if file == \"/\":\n file = \"/index.html\"\n\n put_or_post = HttpServer.is_put_or_post(split_request_header)\n try:\n if put_or_post:\n request_body = HttpServer.get_request_body(conn_socket, request_header)\n status_code = HttpServer.get_status_code_for_put_or_post(request_header, file)\n\n if status_code == 204:\n if split_request_header[0] == \"PUT\":\n f = open(file[1:], \"w\")\n else:\n f = open(file[1:], \"a\")\n\n f.write(request_body)\n f.close()\n http_message = HttpServer.create_204_response()\n elif status_code == 501:\n http_message = HttpServer.create_501_response()\n else: # status code is 201\n f = open(file[1:], \"x\")\n f.write(request_body)\n f.close()\n http_message = self.create_201_response(file)\n\n else:\n if split_request_header[0] == \"GET\":\n # GET needs filename\n status_code = HttpServer.get_status_code_for_get(request_header, file)\n else:\n status_code = HttpServer.get_status_code_for_head(file)\n\n if status_code == 404:\n http_message = HttpServer.create_404_response()\n elif status_code == 304:\n http_message = HttpServer.create_304_response()\n else: # status code is 200\n http_message = HttpServer.create_200_response(file)\n except Exception:\n http_message = HttpServer.create_500_response()\n\n conn_socket.send(http_message)\n\n # determine if connection has to be closed\n conn_str = \"Connection:\"\n conn_begin_ind = request_header.find(conn_str)\n\n if conn_begin_ind != -1:\n conn_end_ind = request_header[conn_begin_ind:].find(\"\\r\\n\") + conn_begin_ind\n conn_status = request_header[conn_begin_ind+len(conn_str):conn_end_ind].strip()\n if conn_status == \"close\":\n conn_socket.close()\n connected = False\n print(\"[THREAD] client thread ended\")","def _client(self):\n while True:\n body = self.queue.get(True)\n print \"Sending %s bytes (%s/%s)\" % (len(body), self.queue.qsize(), self.queue.maxsize)\n\n try:\n req = urllib2.Request(self.endpoint, body)\n urllib2.urlopen(req).read()\n except:\n print \"Cannot send request. Retrying in 5 seconds\"\n print_exception(*sys.exc_info())\n print \"continuing...\"\n self.enqueue(body)\n sleep(5)","def serveThread(self):\r\n while True:\r\n try:\r\n client = self.clients.get()\r\n self.serveClient(client)\r\n except Exception, x:\r\n logging.exception(x)","def run(self):\n # sends download range from offset to offset + block_size - 1 (including) in the header\n headers = {'User-Agent': self.user_agent, 'Refferer': '{}://{}/'.format(self.url.protocol, self.url.host), \n 'Range': 'bytes={}-{}'.format(self.offset, self.offset + self.block_size - 1)}\n status = 0 # set status to 0 that means a connection error\n try:\n self.conn.request('GET', self.url.request, headers=headers)\n response = self.conn.getresponse()\n # the server does not support partial downloading - error\n if response.status != 206:\n status = response.status\n raise MirrorError\n part_size = int(response.getheader('Content-Length')) # actual count of bytes sent by the server\n data = b'' # data buffer\n # loop while all data will be received\n while part_size > len(data):\n if self.cancelled.is_set(): # if the thread has been cancelled\n # stop the thread, the TaskError would not be processed\n # because a loop in the main thread already broken\n raise Exception\n data_fragment = response.read(self.FRAGMENT_SIZE)\n data += data_fragment # add data to the buffer\n # put progress information into the queue\n info = TaskProgress(self.url.host, response.status, len(data))\n self.data_queue.put(info)\n # when the downloading loop finished, create TaskData object\n info = TaskData(self.url.host, response.status, self.offset, data)\n response.close()\n except:\n # if an error has occurred - create a TaskError object\n info = TaskError(self.url.host, status, self.offset)\n finally:\n self.data_queue.put(info) # put result TaskInfo object into the queue\n self.ready.set() # mark the thread as comleted","def _http_thread_func(self):\r\n while not self._terminating:\r\n # pop queued request from the queue and process it\r\n (api_endpoint, params, reqid) = self.http_requests.get(True)\r\n translated = None\r\n try:\r\n answer = self.http_signed_call(api_endpoint, params)\r\n if answer[\"result\"] == \"success\":\r\n # the following will reformat the answer in such a way\r\n # that we can pass it directly to signal_recv()\r\n # as if it had come directly from the websocket\r\n translated = {\r\n \"op\": \"result\",\r\n \"result\": answer[\"data\"],\r\n \"id\": reqid\r\n }\r\n else:\r\n if \"error\" in answer:\r\n if answer[\"token\"] == \"unknown_error\":\r\n # enqueue it again, it will eventually succeed.\r\n self.enqueue_http_request(api_endpoint, params, reqid)\r\n else:\r\n\r\n # these are errors like \"Order amount is too low\"\r\n # or \"Order not found\" and the like, we send them\r\n # to signal_recv() as if they had come from the\r\n # streaming API beause Gox() can handle these errors.\r\n translated = {\r\n \"op\": \"remark\",\r\n \"success\": False,\r\n \"message\": answer[\"error\"],\r\n \"token\": answer[\"token\"],\r\n \"id\": reqid\r\n }\r\n\r\n else:\r\n self.debug(\"### unexpected http result:\", answer, reqid)\r\n\r\n except Exception as exc:\r\n # should this ever happen? HTTP 5xx wont trigger this,\r\n # something else must have gone wrong, a totally malformed\r\n # reply or something else.\r\n #\r\n # After some time of testing during times of heavy\r\n # volatility it appears that this happens mostly when\r\n # there is heavy load on their servers. Resubmitting\r\n # the API call will then eventally succeed.\r\n self.debug(\"### exception in _http_thread_func:\",\r\n exc, api_endpoint, params, reqid)\r\n\r\n # enqueue it again, it will eventually succeed.\r\n self.enqueue_http_request(api_endpoint, params, reqid)\r\n\r\n if translated:\r\n self.signal_recv(self, (json.dumps(translated)))\r\n\r\n self.http_requests.task_done()","def serve(self):\n\t\timport thread\n\t\tthread.start_new_thread(self._server_thread, tuple())","def run(self):\n\n\t\t#Begin running the clientHandler\n\t\tself.running = True\n\t\tself.rxThread.start()\n\n\t\twhile self.running:\n\t\t\ttime.sleep(0.1)\n\t\n\t\t\t#Keep a count of the number of missing Hello requests, over 5 kill client\n\t\t\tif self.missingCount >= 5:\n\t\t\t\tself.running = False","def __thread(self):\n\n _LOGGER.debug(\"Webserver is starting.\")\n\n monitor = self\n\n # Embedding this because it's so trivial.\n class Handler(BaseHTTPServer.BaseHTTPRequestHandler):\n def do_GET(self):\n\n # We have the first line of the response with the authorization code\n # passed as a query argument.\n #\n # Example:\n #\n # GET /?code=4/clwm0rESq8sqeC-JxIcfiSdjh2593hLej9CZxAcbe1A HTTP/1.1\n #\n\n try:\n unicode\n except NameError:\n request = self.requestline + \"\\n\\n\"\n else:\n request = unicode(self.requestline + \"\\n\\n\")\n\n # Use Python to parse the request. We need to add one newline for the\n # line and another for a subsequent blank line to terminate the block\n # and conform with the RFC.\n hr = _HTTPRequest(request)\n u = urlparse.urlparse(hr.path)\n arguments = urlparse.parse_qs(u.query)\n\n # It's not an authorization response. Bail with the same error\n # the library would normally send for unhandled requests.\n if 'code' not in arguments:\n self.send_error(\n 501,\n \"Unsupported method ({}): {}\".format(\n self.command, hr.path))\n\n return\n\n token = arguments['code'][0]\n _LOGGER.debug(\"Received auth-code [{}]\".format(token))\n\n monitor._token = token\n\n monitor._request_state_e.set()\n\n self.send_response(200, message='OK')\n\n self.send_header(\"Content-type\", 'text/html')\n self.end_headers()\n\n self.wfile.write(\"\"\"\\\n<html>\n<head></head>\n<body>\nAuthorization recorded.\n</body>\n</html>\n\"\"\")\n\n def log_message(self, format, *args):\n pass\n\n\n class Server(SocketServer.TCPServer):\n def server_activate(self, *args, **kwargs):\n r = SocketServer.TCPServer.server_activate(self, *args, **kwargs)\n\n # Sniff the port, now that we're running.\n monitor._effective_port = self.server_address[1]\n\n return r\n\n # Our little webserver. (0) for the port will automatically assign it\n # to some unused port.\n binding = ('localhost', self._webserver_port)\n self.__s = Server(binding, Handler)\n\n _LOGGER.debug(\"Created server.\")\n\n # Signal the startup routine that we're starting.\n self.__server_state_e.set()\n\n _LOGGER.debug(\"Running server.\")\n self.__s.serve_forever()\n\n _LOGGER.debug(\"Webserver is stopping.\")\n\n # Signal the startup routine that we're stopping.\n self.__server_state_e.set()","def run(self):\n try:\n # Initialize signal handler to be able to have a graceful shutdown.\n ServiceShutdownHandling.initServiceShutdownHandling()\n\n httpd = None\n # The HTTP server thread - our HTTP interface\n if self._port != None:\n httpd = RaspendHTTPServerThread(self._shutdownFlag, self._dataLock, self._sharedDict, self._cmdMap, self._port)\n # Start our threads.\n httpd.start()\n\n for worker in self._workers:\n worker.start()\n\n # Keep primary thread or main loop alive.\n while True:\n time.sleep(0.5)\n\n except ServiceShutdownHandling.ServiceShutdownException:\n # Signal the shutdown flag, so the threads can quit their work.\n self._shutdownFlag.set()\n\n # Wait for all threads to end.\n for worker in self._workers:\n worker.join()\n\n if httpd:\n httpd.join()\n\n except Exception as e:\n print (\"An unexpected error occured. Error: {}\".format(e))\n\n finally:\n pass\n\n return","def run(self):\n server = TCPServer((self.host, self.port), TCPHandler)\n server.lymphocytes_getter = self.lymphocytes_getter\n\n #runs forever - so make this thread daemon\n server.serve_forever()","def run(self):\n while self.running:\n self.handle_request()","async def main(self, loop: asyncio.get_event_loop) -> None:\n queue = asyncio.Queue()\n\n for url in self.url_list:\n queue.put_nowait(url)\n\n async with aiohttp.ClientSession(loop=loop) as session:\n workers = [\n asyncio.create_task(self.worker(queue, session))\n for _ in range(self.max_treads)\n ]\n await queue.join()\n\n for worker in workers:\n worker.cancel()\n\n await asyncio.gather(*workers, return_exceptions=True)","def initThread(self):\n thread1 = threading.Thread(target=self.processData)\n self.info(\"starting cod7http thread\")\n thread1.start()\n self.httpthreadinst = thread1","def __init__(self):\n\n # Every WSGI application must have an application object - a callable\n # object that accepts two arguments. For that purpose, we're going to\n # use a function (note that you're not limited to a function, you can\n # use a class for example). The first argument passed to the function\n # is a dictionary containing CGI-style environment variables and the\n # second variable is the callable object (see PEP 333).\n def application(environ, start_response):\n \"\"\"\n WSGI application object. Returns request status.\n For specific endpoints (e.g. get_with_params), returns\n specific response bodies.\n \"\"\"\n\n response_text = 'Hello World!'\n endpoint = environ['PATH_INFO'][1:]\n\n if endpoint == 'get_with_params':\n #echo back uri parameters as dict...\n response_text = str(dict(urlparse.parse_qsl(environ['QUERY_STRING'])))\n\n #set status code for response based on request...\n requested_status = environ['PATH_INFO'][1:]\n\n status = self.codes.get(requested_status, '200 OK') # HTTP Status\n headers = [('Content-type', 'text/plain')] # HTTP Headers\n start_response(status, headers)\n #print(environ)\n #print('pathInfo: {0}'.format(environ.get('PATH_INFO')))\n #print('queryString: {0}'.format(environ.get('QUERY_STRING')))\n #print('requestMethod:{0}'.format(environ['REQUEST_METHOD']))\n # The returned object is going to be printed\n return response_text\n\n threading.Thread.__init__(self)\n self.httpd = make_server('', 0, application)\n self.address = self.httpd.server_address","def handler(self):\n msg = self.create_http_request()\n self.send(msg)\n\n if self.file_name == \"/\":\n self.file_name = \"/index.html\"\n else:\n self.file_name = HttpClient.create_file_location(self.file_name)\n\n if self.http_command == \"HEAD\":\n recv_raw, _ = self.recv_header()\n recv = recv_raw.decode(self.format_body)\n recv_with_updated_imgs = self.update_images(recv)\n self.write_to_html_file(recv_with_updated_imgs)\n elif self.http_command == \"PUT\":\n recv_raw = self.recv_all_data()\n if recv_raw != b'':\n recv = recv_raw.decode(self.format_body)\n recv_with_updated_imgs = self.update_images(recv)\n self.write_to_html_file(recv_with_updated_imgs)\n elif self.http_command == \"POST\":\n recv_raw = self.recv_all_data()\n if recv_raw != b'':\n recv = recv_raw.decode(self.format_body)\n recv_with_updated_imgs = self.update_images(recv)\n self.write_to_html_file(recv_with_updated_imgs)\n else: # http_command == \"GET\" or it is a bad request\n recv_raw = self.recv_all_data()\n recv = recv_raw.decode(self.format_body)\n recv_with_updated_imgs = self.update_images(recv)\n self.write_to_html_file(recv_with_updated_imgs)\n\n self.disconnect()\n print(\"[CONNECTION] Client terminated\")","def main():\n host = ''\n port = 8088\n HTTPServer((host, port), HandleRequests).serve_forever()","async def main():\n # Create the queue of work\n work_queue = asyncio.Queue()\n\n # Put some work in the queue\n for url in [\n \"http://google.com\",\n \"http://yahoo.com\",\n \"http://linkedin.com\",\n \"http://apple.com\",\n \"http://microsoft.com\",\n \"http://facebook.com\",\n \"http://twitter.com\",\n ]:\n await work_queue.put(url)\n\n browser = HTTPBrowser(2,\n work_queue.qsize(),\n LoadBalancing()\n )\n\n await browser.browser_session(work_queue)","def run(self):\n while True:\n req = self._requests.get()[1]\n req.start()\n logging.info('Running request %s', req)","def request_handler(self):\n\n size = 1024\n while True:\n # accept message from client\n clientSock, addr = self.socket.accept()\n self.printLine()\n print('connect to {}'.format(addr))\n\n # print client message content\n msg = clientSock.recv(size).decode('utf-8')\n self.printLine()\n print(\"sent message :\")\n print(msg)\n\n self.printLine()\n self._set_fileName(msg)\n\n # check for existance of file in the server (with name of file.txt)\n data, isFileExist = self._send_file_handler()\n\n self.printLine()\n print('data of file :')\n print(data)\n\n # create header for response message\n if isFileExist:\n header = self._generate_headers(200)\n else:\n header = self._generate_headers(404)\n\n response = header.encode() + data.encode()\n\n # send response in http protocol\n clientSock.send(response)\n\n # close the signal\n clientSock.close()","def server():","def server():","def image_server():\n yield from http_server_thread(ImageHandler)","def client():","def run(self):\n while self._num_workers > 0:\n self.server.handle_request()\n self._graph = None","def run(self):\n try:\n # connect method implementation should return a TaskInfo object\n info = self.connect()\n except:\n # if an error has occurred create a TaskHeadError object\n info = TaskHeadError(self.url.host, 0)\n finally:\n self.data_queue.put(info) # put the result in the queue\n self.ready.set() # and mark the thread as completed","def run(self):\n print('ClientThread[{}] is running!'.format(self.threadID))\n while True:\n request = self.receive()\n try:\n requestcode = request.split(',')[0]\n if requestcode == 'SYNCFROM':\n self.syncToClient()\n continue\n elif requestcode == 'SYNCTO':\n self.syncFromClient()\n continue\n elif requestcode == 'GETINDEX':\n self.sendIndex()\n continue\n elif requestcode == 'CLOSE':\n print('Connection to {}:{} closed'.format(self.ip,self.port))\n self.tcpsock.close()\n break\n elif not request:\n continue\n else:\n print(request, type(request))\n raise Exception('Unexpected bytes from client.')\n except KeyboardInterrupt:\n sys.exit()\n except Exception as err:\n traceback.print_exc()\n continue\n self.tcpsock.close()\n print('ClientThread[{}] exiting..'.format(self.threadID))","def handle(self):\n self.request.recv(1024)\n self.request.sendall(pickle.dumps(self.server.lymphocytes_getter()))","def process_thread(self):","async def handle_client(self, reader: StreamReader, writer: StreamWriter):\n peername = writer.transport.get_extra_info(\"peername\")\n log.info(\"handle_client : %s\", peername)\n\n try:\n\n remote_host, remote_port, req = await parse_http_request_header(\n reader, writer\n )\n\n remote_reader, remote_writer = await asyncio.open_connection(\n remote_host, remote_port\n )\n if req:\n log.info(\"req: %s\", req)\n remote_writer.write(req)\n\n asyncio.create_task(http_channel(remote_reader, writer))\n\n asyncio.create_task(http_channel(reader, remote_writer))\n\n except Exception as ex:\n log.exception(ex)","def make_request_thread(self, service, request):\n requestResponse = self.callbacks.makeHttpRequest(service, request)\n print self.helpers.analyzeRequest(requestResponse).getUrl().toString()","def threadget(self, url, *args):\n\n self.logger.debug(\"Starting a thread to simulate a GET request to %s\" % url)\n api_get = threading.Thread(target=self.get, args=(url, *args,))\n api_get.start()","def client_request(self, evt):\n threads.deferToThread(self.cli_db.accept, evt)","def run(self):\n to_client_request_thread = threading.Thread(target=self._dispatch_to_client_request, daemon=True)\n to_client_request_thread.start()\n\n from_client_request_thread = threading.Thread(target=self._dispatch_from_client_request, daemon=True)\n from_client_request_thread.start()\n\n from_client_commands_thread = threading.Thread(target=self._from_client_commands, daemon=True)\n from_client_commands_thread.start()\n\n to_client_update_state_thread = threading.Thread(target=self._to_client_update_state, daemon=True)\n to_client_update_state_thread.start()\n\n server_control_thread = threading.Thread(target=self._server_control, daemon=True)\n server_control_thread.start()\n\n # Wait for threads to finish\n to_client_request_thread.join()\n from_client_request_thread.join()\n from_client_commands_thread.join()\n to_client_update_state_thread.join()\n server_control_thread.join()\n \n # Close server connection\n self._to_client_request.close()\n self._from_client_request.close()","def run(self):\n self.initialize()\n\n # run the start callback\n tools.run_callback(\"start\", {'request': self._request})\n\n data = self._request.getData()\n pyhttp = self._request.getHttp()\n config = self._request.getConfiguration()\n\n # allow anyone else to handle the request at this point\n handled = tools.run_callback(\"handle\", \n {'request': self._request},\n mappingfunc=lambda x,y:x,\n donefunc=lambda x:x)\n\n if not handled == 1:\n blosxom_handler(self._request)\n\n # do end callback\n tools.run_callback(\"end\", {'request': self._request})","async def worker(self) -> None:\n session = aiohttp.ClientSession()\n while 1:\n try:\n data = await self.task_queue.get()\n await self.add_page_views(url_sub=data, session=session)\n except Exception as e:\n print(e)\n await asyncio.sleep(delay=self.wait_frequency)","def run(self):\n # bind to the port\n self.serversocket.bind(('0.0.0.0', PORT))\n print(\"Listening on: \" + Colors.BOLD + HOST + \":\" + str(PORT) + Colors.ENDC)\n print(\"... waiting for a connection\", file=sys.stderr)\n try:\n while True:\n # queue up to 5 requests\n self.serversocket.listen(5)\n clientsocket, addr = self.serversocket.accept()\n print(\"Got a connection from \" + Colors.WARNING + \"%s\" % str(addr) + Colors.ENDC)\n self.clientsocket.append(clientsocket)\n newthread = ClientThread(addr, self.clientsocket[-1])\n newthread.start()\n finally:\n for cskt in self.clientsocket:\n cskt.close()","def threading_handler(client_connection, client_address):\r\n\r\n while True:\r\n request_method, request_path, request_proto, request_data = parse_request(client_connection)\r\n\r\n if not request_data:\r\n break\r\n\r\n collect_request_data(request_path, client_address)\r\n write_data(access_entries)\r\n\r\n if request_method == 'GET':\r\n if request_path in allowed_path:\r\n requested_file = open(os.getcwd() + request_path, 'rb')\r\n data = requested_file.read(os.path.getsize(os.getcwd() + request_path))\r\n requested_file.close()\r\n request_handler(client_connection, request_path, request_proto, '200', 'OK', data, True)\r\n break\r\n elif '/www' == request_path or '/' == request_path:\r\n request_handler(client_connection, request_path, request_proto, '403', 'Forbidden', '403 Unauthorized',\r\n False)\r\n break\r\n elif '/www/access_entries.html' == request_path:\r\n final_tag = construct_access_entries_data()\r\n request_handler(client_connection, '/www/access_list.txt', request_proto, '200', 'OK', final_tag, True)\r\n break\r\n else:\r\n request_handler(client_connection, request_path, request_proto, '404', 'Not found', '404 Not Found',\r\n False)\r\n break\r\n else:\r\n request_handler(client_connection, request_path, request_proto, '405', 'Method Not Allowed',\r\n '405 Method Not Allowed', False)\r\n\r\n client_connection.close()\r\n print (\" --- Connection Closed --- \", client_address)","def handle(self):\n global log_th\n sent = 1\n msg_body = ''\n get_recv = True\n get_data = True\n empty_check = 0\n # Looping session requests\n while 1:\n try:\n # If enabled sleep feauture\n if self.sleep_between != 0:\n time.sleep(self.sleep_between)\n # If no answer feauture\n if self.no_answer != 0:\n time.sleep(1)\n continue\n # Changing receive size if receiving data part\n if sent == 3 or sent == 4:\n data = self.request.recv(self.data_recv_size)\n else:\n data = self.request.recv(self.std_recv_size)\n if sent != 5:\n self.command_w_th_inc.write_commands(\n data=bytes(data).decode().encode('ascii', 'ignore')\n .decode().rstrip(), qid=self.message_id)\n # To many empty line received, closed thread\n if self.func_empty_check(data):\n if empty_check >= 3:\n break\n else:\n empty_check += 1\n continue\n # Logging session requests if steps not equal to data section\n if sent != 5:\n log_th.log_info('{} - {} client executed : \"{}\"'.format(\n self.message_id, self.client_ip, bytes(data).decode().rstrip()))\n # Break the loop\n if self.func_quit(data):\n break\n except Exception as ae:\n log_th.log_warning('{} encounter an error from {} thread : {}'.format(\n self.client_ip, threading.current_thread().name, str(ae)))\n break\n else:\n try:\n # Checking the all steps\n if self.func_rset(data):\n sent = 2\n continue\n if self.func_auth(data):\n continue\n if self.func_auth_plain(data):\n continue\n if self.func_starttls(data):\n continue\n # Starting the sent steps\n # Ehlo/hello\n if sent == 1:\n if self.func_ehlo(data) or self.func_helo(data):\n sent += 1\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('command not found'))\n # Mail from, rcpt to, data\n elif sent == 2:\n if bytes(data).decode().encode('ascii', 'ignore').decode().rstrip().splitlines().__len__() > 2:\n get_data = False\n get_recv = False\n elif bytes(data).decode().encode('ascii',\n 'ignore').decode().rstrip().splitlines().__len__() > 1:\n get_recv = False\n if self.func_from(data, get_recv):\n sent += 1\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('mail from'))\n if not get_recv:\n if self.func_to(data, get_recv, get_data):\n sent += 1\n get_recv = True\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))\n if not get_data:\n if self.func_data(data, get_recv, get_data):\n sent += 1\n get_data = True\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\n # rcpt to and data\n elif sent == 3:\n if bytes(data).decode().encode('ascii', 'ignore').decode().rstrip().splitlines().__len__() > 1:\n get_data = False\n if self.func_to(data, get_recv, get_data):\n sent += 1\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))\n if not get_data:\n if self.func_data(data, get_recv, get_data):\n sent += 1\n get_data = True\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\n # data\n elif sent == 4:\n if self.func_to(data, get_recv, get_data):\n continue\n if self.func_data(data, get_recv, get_data):\n sent += 1\n else:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\n # content writing to file (if enabled) and quit statement\n elif sent == 5:\n data_list = bytes(data).decode().split('\\r\\n')\n for line in data_list:\n if str(line) == '.':\n if self.mail_save_enable != 0:\n out_file = open(self.mail_save_path + '/'\n + self.message_id + '.eml', 'w')\n out_file.write(msg_body)\n out_file.close()\n self.func_data_ok()\n sent = 1\n break\n else:\n msg_body += str(line) + '\\r\\n'\n except IndexError:\n if sent == 2:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('mail from'))\n elif sent == 3:\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))","def thread(self, request, *args, **kwargs):\n thread = self.get_thread()\n protocol = request.is_secure() and 'wss://' or 'ws://'\n url = protocol + request.get_host() + settings.WEBSOCKET_URL + 'thread-%i' % thread.id\n return Response(url)","def run(self):\n httpd = HTTPServer((self.host, self.port), self._Handler)\n sa = httpd.socket.getsockname()\n serve_message = \"Serving HTTP on {host} port {port} (http://{host}:{port}/) ...\"\n print(serve_message.format(host=sa[0], port=sa[1]))\n try:\n httpd.serve_forever()\n except KeyboardInterrupt:\n print(\"\\nKeyboard interrupt received, exiting.\")\n httpd.shutdown()","def run(self):\n self.log_id = 0\n\n # all active websockets and their state\n self.websocks = {}\n\n # all active python interpreter sessions\n self.pysessions = {}\n\n if self.DISABLE_REQUESTS_DEBUG_LOGS:\n disable_requests_debug_logs()\n\n self.threadpool = ThreadPool(self.THREADPOOL_WORKERS)\n\n self.api = None\n\n # tornado app object\n base_handlers = self.prepare_base_handlers()\n handlers = self.prepare_handlers()\n self.template_loader = TemplateLoader([resolve_path(self.TEMPLATE_PATH)])\n _ = self.prepare_template_loader(self.template_loader)\n if _ is not None:\n self.template_loader = _\n\n shclass = CustomStaticFileHandler\n shclass.PATHS.append(resolve_path(self.STATIC_PATH))\n _ = self.prepare_static_paths(shclass.PATHS)\n if _ is not None:\n shclass.PATHS = _\n\n self.static_handler_class = shclass\n\n self.nav_tabs = [(\"Home\", \"/\")]\n if self.args.debug:\n self.nav_tabs += [(\"Console\", \"/console\"), (\"Logs\", \"/logs\")]\n self.nav_tabs = self.prepare_nav_tabs(self.nav_tabs)\n\n settings = {\n \"static_path\": \"<DUMMY-INEXISTENT-PATH>\",\n \"static_handler_class\": self.static_handler_class,\n \"template_loader\": self.template_loader,\n \"compress_response\": True,\n \"debug\": self.args.debug,\n }\n\n all_handlers = handlers + base_handlers\n self.app = self.APP_CLASS(**settings)\n self.app.add_handlers(self.VIRTUAL_HOST, all_handlers)\n\n sys.funcserver = self.app.funcserver = self\n\n self.api = self.prepare_api()\n if self.api is not None and not hasattr(self.api, \"log\"):\n self.api.log = self.log\n\n if self.args.port != 0:\n self.app.listen(self.args.port)\n\n tornado.ioloop.IOLoop.instance().start()","def start(self):\n threading.Thread(target=self.serve_forever).start()","def main():\n server = ThreadedServer(MasterControllerService, port=5000)\n server.start()","def getServerInThread(data, onlyOnce=False):\n\tclass Handler(BaseHTTPServer.BaseHTTPRequestHandler):\n\t\tdef do_GET(self):\n\t\t\tself.wfile.write(data)\n\t\tdo_POST = do_GET\n\t\n\tport = 34000\n\thttpd = BaseHTTPServer.HTTPServer(('', port), Handler)\n\n\tif onlyOnce:\n\t\tserve = httpd.handle_request\n\telse:\n\t\tserve = httpd.serve_forever\n\n\tt = threading.Thread(target=serve)\n\tt.setDaemon(True)\n\tt.start()\n\treturn httpd, t, \"http://localhost:%s\"%port","def run(self, host):\n\n # Trying to connect to given host.\n while self.csrftoken == None:\n try:\n self.client.get(self.url)\n self.csrftoken = self.client.cookies['csrftoken']\n self.cookies = dict(self.client.cookies)\n except:\n pass\n\n while True:\n if self.queue.empty():\n self.sending = False\n time.sleep(1)\n else:\n self.sending = True\n\n # sending operations when queue reached batching size\n # after about 5 seconds send anyway...\n qsize = self.queue.qsize()\n if qsize < BATCH_SIZE and qsize != self.old_qsize:\n time.sleep(1)\n self.old_qsize = qsize\n elif qsize < BATCH_SIZE and qsize == self.old_qsize and self.count < 5:\n time.sleep(1)\n self.count += 1\n else:\n data_list = []\n while not self.queue.empty():\n op = self.queue.get()\n data_list.append(op)\n # just for testing\n # shuffle(data_list)\n data_dict = dict(list=json.dumps(data_list)) \n\n while True:\n try:\n self.send_post(data_dict)\n break\n except requests.exceptions.RequestException:\n time.sleep(2)\n print \"[THREAD \" + str(host['id']) + \"] Can't reach host \" + str(host['port'])\n continue\n\n self.count = 0","def do_GET(self):\r\n self.logger.debug(\"SseHTTPRequestHandler(Thread-%s): do_GET, path=%s\",\r\n threading.current_thread().ident, self.path)\r\n threading.current_thread().name = (self.path + \"_thread_\" +\r\n str(threading.current_thread().ident))\r\n if self.path == SseHTTPRequestHandler.eventsource_path:\r\n self._start_event_stream()\r\n else:\r\n http.server.SimpleHTTPRequestHandler.do_GET(self)","def __image_request_handler(self):\n self.__logger.info(\"Image Request Handling Thread started\")\n ticker = Ticker(2)\n while self._running:\n timeout = ticker.end_tick(False)\n try:\n task = self.__image_queue.get(True, timeout)\n except Queue.Empty:\n ticker.start_tick()\n continue\n\n # There is a task to process\n ticker.start_tick()\n source, connection = task\n\n # Check if the connection has been closed. If it was,\n # do not bother processing the request.\n if not connection.connected():\n self.__logger.info(\"Skipping request for image of source %s\" \\\n \" because requesting client disconnected\" \\\n % source)\n self.__image_queue.task_done()\n continue \n\n # Obtain new image\n error = \"No image available\"\n image = None\n mtime = time.time()\n if source in self.__video_modules:\n try:\n mtime, image = self.__get_image(source)\n except Exception as err:\n error = \"Obtaining image failed: %s\" % repr(err)\n else:\n error = \"Video source %s has not been started\" % source\n\n if connection.connected():\n if image:\n # Valid image was obtained\n img_str = image.tostring()\n data = {'name': 'image',\n 'source': source,\n 'time': mtime,\n 'shape': (image.width, image.height),\n 'depth': image.depth,\n 'nChannels': image.nChannels}\n else:\n # An error occured, notify the vision module\n self.__logger.info(\"Failed to obtain image for source %s. \"\\\n \" Error message: %s\" % (source, error))\n img_str = \"\"\n data = {'name': 'image',\n 'source': source,\n 'time': mtime,\n 'error': error}\n # Send the data to the vision module.\n if not connection.sendall(data, img_str):\n self.__logger.warning(\"Failed to send data to client. \" \\\n \"Probably disconnected\")\n else:\n self.__logger.info(\"Image of source %s obtained but not \" \\\n \"sending because requesting client \" \\\n \"disconnected\" % source)\n self.__image_queue.task_done()\n self.__logger.info(\"Image Request Handling Thread ended\")","def _worker(self, args):\n pass","def run(self):\n self._connection = self.open_connection()\n self._connection.ioloop.start()","def handle_client(self):\n e = threading.Event()\n reg_t = threading.Thread(target=self.handle_reg_client, args=(e,))\n stream_t = threading.Thread(target=self.handle_stream_client,\n args=(e,))\n reg_t.start()\n stream_t.start()","def start(self):\n if self._params.arq:\n s = udpsocket.UDPSocket()\n else:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n try:\n s.bind(('', self._params.port))\n s.listen(5)\n self.check_and_print_debug_message(\n 'HTTP Server is running on port: ' + str(self._params.port))\n while True:\n c, addr = s.accept()\n Thread(\n target=self.process_http_request,\n args=(c, addr)\n ).start()\n except Exception as e:\n self.check_and_print_debug_message(str(e))\n s.close()\n finally:\n self.check_and_print_debug_message(\n \"HTTP Server connection is closed.\")\n s.close()","def serve_forever(self):\n\t\tself.serverActive = 1\n\t\twhile self.serverActive:\n\t\t\trfds = self.procHelper.ReadFDs\n\t\t\tsockFile = self.socket.fileno()\n\t\t\trfds.append(sockFile)\n\t\t\ttry_select = 1\n\t\t\t#print \"HTTP RFDS:\", rfds\n\t\t\tif len(rfds) == 1:\n\t\t\t\tprint \"RPC-HTTP: We are lost our parents or socket closed.. Exiting Normally !\"\n\t\t\t\tself.procHelper.exit()\n\t\t\t\tprint \"Error.. We can't exit :(\"\n\n\t\t\twhile try_select:\n\t\t\t\ttry:\n\t\t\t\t\trds = select.select(rfds, [], [], 3)\n\t\t\t\t\ttry_select = 0\n\t\t\t\t\t#print \"Connection from:\", rds, rfds\n\t\t\t\texcept:\n\t\t\t\t\tprint \"Select Exception. Can be a SIGCHLD ?\"\n\n\t\t\tif rds != None and len(rds) == 1:\n\t\t\t\tprint \"RPC-HTTP: We are lost our parents or socket closed.. Exiting Normally !\"\n\t\t\t\tself.procHelper.exit()\n\t\t\txsock = 0\n\t\t\tif rds:\n\t\t\t\tfor rfd in rds[0]:\n\t\t\t\t\tif rfd == sockFile:\n\t\t\t\t\t\txsock = rfd\n\t\t\t\t\t\t#print \"HTTP Main Loop: Socket I/O\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tio = self.procHelper.readFD2io(rfd)\n\t\t\t\t\t\tsrcpid = self.procHelper.rfd2PID(rfd)\n\t\t\t\t\t\tppid = self.procHelper.parentRFD()\n\t\t\t\t\t\tcmd = io.getCommand()\n\t\t\t\t\t\t#print self.procHelper.myPID, os.getpid(), \"HTTP Main Loop: Read a command:\", str(cmd)[:20], ppid, srcpid\n\t\t\t\t\t\tif io.cmdrpoll.poll(1)[0][1] & select.POLLHUP:\n\t\t\t\t\t\t\tself.procHelper.removeChild(srcpid)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tpkPid = int(cmd[0])\n\t\t\t\t\t\t\tpkTid = int(cmd[1])\n\t\t\t\t\t\t\tpkData = cmd[3]\n\t\t\t\t\t\t\tcommand = cmd[2]\n\t\t\t\t\t\t\t#print \"ACCEPT READ FROM PIPE:\",rfd, ppid, str(cmd)[:20]\n\t\t\t\t\t\t\tif rfd != ppid:\n\t\t\t\t\t\t\t\t#print \"Send To Parent:\", pkPid, command\n\t\t\t\t\t\t\t\tself.procHelper.checkUpCmd(dir=\"P\", srcpid=srcpid, cmd=command, pktpid=pkPid, tid=pkTid, data=pkData)\n\t\t\t\t\t\t\t\tif cmd:\n\t\t\t\t\t\t\t\t\tself.procHelper.sendParentCommand(command, pkPid, pkTid, pkData)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif cmd:\n\t\t\t\t\t\t\t\t\tself.procHelper.checkUpCmd(dir=\"C\", srcpid=srcpid, cmd=command, pktpid=pkPid, tid=pkTid, data=pkData)\n\t\t\t\t\t\t\t\t\t#print \"Send To Child:\", pkPid, command\n\t\t\t\t\t\t\t\t\tself.procHelper.sendCommand(pkPid, command, pkPid, pkTid, pkData)\n\t\t\t\tif xsock > 0:\n\t\t\t\t\tself.handle_request()\n\t\t\telse:\n\t\t\t\tfor i in l:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tx = os.waitpid(i, os.WNOHANG)\n\t\t\t\t\t\tprint \"Child info:\", i, x\n\t\t\t\t\t\tif os.WIFEXITED(x[1]):\n\t\t\t\t\t\t\tprint \"Child Deleted:\", i, x\n\t\t\t\t\t\t\tdel l[p]\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass","def process_request(self, request, client_address):\n\t\t#traceback.print_stack()\n\t\tself.collect_children()\n\t\tchldPID = self.procHelper.makeChild()\n\t\tprint \"HTTP New Connection: Go Fork for accept: \", self.procHelper.PID2io(chldPID), chldPID\n\t\tparentrpid = os.getpid()\n\t\tpid = os.fork()\n\t\tif pid:\n\t\t\t# Parent process\n\t\t\tprint \"HTTPD Conn Parent:\", self.procHelper.myPID ,chldPID\n\t\t\tself.procHelper.setIODebug(chldPID, 0, \"HTTP->Connection\")\n\t\t\tself.procHelper.initForParent(chldPID)\n\t\t\tself.procHelper.registerChild(chldPID, self.procHelper.myPID)\n\t\t\tsigset = self.procHelper\n\t\t\tself.procHelper.sendCommand(child = chldPID, command = \"INTU_MCL\", PID = chldPID, TID = 0, data = None)\n\t\t\ttry:\n\t\t\t\tselect.select([self.procHelper.PID2rfd(chldPID)], [],[], 0.1)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\ttp = self.procHelper.readConn(chldPID)\n\t\t\tpid = int(tp[3])\n\t\t\tl = sigset.getUserData()\n\t\t\tif l == None:\n\t\t\t\tl = []\n\t\t\tl.append(pid)\n\t\t\tsigset.setUserData(l[:])\n\t\t\tsigchldhandler(0, sigset)\n\t\t\tif self.active_children is None:\n\t\t\t\tself.active_children = []\n\t\t\tself.active_children.append(pid)\n\t\t\tself.close_request(request)\n\t\t\t#print \"PID\", chldPID, \"created ppid:\", pid, sigset.getUserData()\n\t\t\treturn\n\t\telse:\n\t\t\t# Child process.\n\t\t\t# This must never return, hence os._exit()!\n\t\t\tself.procHelper.setIODebug(chldPID, 0, \"Connection->HTTP\")\n\t\t\tgloPIO = self.procHelper.PID2io(chldPID)\n\t\t\tgloPPid = self.procHelper.myPID + 0\n\t\t\tnew_ph = CHLDHELPER.childHelper(gloPIO, gloPPid, chldPID)\n\t\t\tnew_ph.setIODebug(chldPID, 0, \"Connection->HTTP\")\n\t\t\tnew_ph.initForChild(gloPPid)\n\t\t\tself.procHelper = new_ph\n\t\t\tself.procHelper.parentppid = parentrpid\n\t\t\tprint \"HTTPD Conn Child:\", chldPID, os.getpid(), self.procHelper.parentppid, self.procHelper.myPID, self.procHelper.gloPPid\n\t\t\ttry:\n\t\t\t\t#print \"I am new child:\", os.getpid()\n\t\t\t\tself.procHelper.waitForParentCmd(timeout = 1)\n\t\t\t\tpcmd = self.procHelper.getParentCommand()\n\t\t\t\tself.procHelper.sendParentCommand(cmd = \"IRSU_PPID\", pid = chldPID, tid = 0, data=str(os.getpid()))\n\t\t\t\tself.finish_request(request, client_address)\n\t\t\t\tos._exit(0)\n\t\t\texcept:\n\t\t\t\ttry:\n\t\t\t\t\tself.handle_error(request, client_address)\n\t\t\t\tfinally:\n\t\t\t\t\tos._exit(1)","def __ServiceClient(self,Client):\n\t\twhile True:\n\t\t\tDataClient = Client.recv(1024)\n\t\t\tprint(DataClient)\n\t\t\t# your source code here\n\t\t\tmessage = DataClient\n\t\t\t# data to be sent to api\n\t\t\tdata = {'message': message}\n\t\t\t# sending post request and saving response as response object\n\t\t\tr = requests.post(url = self.API_ENDPOINT, data = data)\n\t\t\t# extracting response text\n\t\t\t#pastebin_url = r.text\n\t\t\t#print(\"The pastebin URL is:%s\"%pastebin_url)","def handle(self):\n data = self.request.recv(1024)\n self.request.send(data)","def run(self):\n print(\"Client: Started\", flush=True)\n ack = Packet()\n ack_data = b''\n\n request = \"download\"\n req_pkt = Packet(0, request)\n req_packed = req_pkt.pkt_pack()\n\n self.client_socket.sendto(req_packed, self.server_addr)\n\n ack_data = self.client_socket.recv(self.pkt_size)\n ack.pkt_unpack(ack_data)\n\n self.recv_img(self.img_save_to)\n\n ack = Packet()\n ack_data = b''\n request = \"upload\"\n req_pkt = Packet(0, request)\n req_packed = req_pkt.pkt_pack()\n\n self.client_socket.sendto(req_packed, self.server_addr)\n\n ack_data = self.client_socket.recv(self.pkt_size)\n ack.pkt_unpack(ack_data)\n\n self.send_img(self.img_to_send)\n\n sleep(5)\n\n ack = Packet()\n ack_data = b''\n request = \"exit\"\n req_pkt = Packet(0, request)\n req_packed = req_pkt.pkt_pack()\n\n self.client_socket.sendto(req_packed, self.server_addr)\n\n ack_data = self.client_socket.recv(self.pkt_size)\n ack.pkt_unpack(ack_data)\n\n print(\"Client: Exiting...\")\n # close socket when finished\n self.client_socket.close()","async def worker(\n self, queue: asyncio.Queue, session: aiohttp.ClientSession\n ) -> None:\n while True:\n url = await queue.get()\n await self.fetch(url, session)\n queue.task_done()","def serve(self):\n\t\tself.keep_running=1\n\t\tif self.debug:\n\t\t\tprint \"server started\"\n\t\ttry:\n\t\t\twhile self.keep_running:\n\t\t\t\tself.handle_request()\n\t\tfinally:\n\t\t\tif self.debug:\n\t\t\t\tprint \"server finished\"\n\t\t\tself.keep_running=0\n\t\t\tself.close()","def start(self):\n #Starting the thread able to handle the administrator request\n t2 = threading.Thread(target=self.handleServer)\n t2.start()\n self.ActiveThreads.append(t2)\n #Listen continously\n while True:\n self.server.listen(50)\n self.Log.log(\"Waiting for connections...\")\n #Obtaining the parameters like the socket and the address/port of the incoming connection\n (conn, (ip,port)) = self.server.accept()\n #Creating a new thread able to handle the new connection with the client\n newClient = ClientHandler(conn,ip,port,self.DB,self.Users,self.Log,self.XML);\n #Starting the new thread\n newClient.start()\n #Appending the thread to the list of active threads\n self.ActiveThreads.append(newClient)","def run(self):\n self.debug(__name__ + \".run(): self.threadName=\" + str(self.threadName) + \"\\n\")\n self.debug(__name__ + \".run(): self.statusFile=\" + str(self.statusFile) + \"\\n\")\n self.debug(__name__ + \".run(): self.recvData=\" + str(self.recvData) + \"\\n\")\n self.debug(__name__ + \".run(): self.socketConn=\" + str(self.socketConn) + \"\\n\")\n\n status = True\n data = self.getFileData()\n self.mySocketObj.serverSend(self.socketConn, data)\n if self.socketConn: self.socketConn.close()\n # self.updateCounts()\n self.status = status\n if status:\n self.appendMsg(__name__ + \".run(): Completed successfully for \" + str(self.threadName) + \"\\n\")\n else:\n self.appendMsg(__name__ + \".run(): Failed for \" + str(self.threadName) + \"\\n\")\n # Endif","def _make_thread(self):\r\n pass","def _process_request(self):\n if not self._requests:\n if self._stream:\n self._stream.close()\n self._stream = None\n if self._processing:\n self._processing = False\n Engine.instance().stop()\n return\n\n request = self._requests[0]\n\n request.append(\n Engine.instance().defer(request[5], self._request_timeout, request))\n\n port = request[2].port\n if not port:\n if request[2].scheme.lower() == 'https':\n port = 443\n else:\n port = 80\n\n host = \"%s:%d\" % (request[2].hostname, port)\n\n if self._stream:\n if not self._server == host.lower() or not \\\n self._is_secure == (request[2].scheme.lower() == 'https'):\n self._stream.end()\n return\n\n if not self._stream:\n # Store the current server.\n self._server = host.lower()\n\n # Create a Stream, hook into it, and connect.\n self._stream = Stream()\n\n self._stream.on_close = self._on_close\n self._stream.on_connect = self._on_connect\n\n self._is_secure = request[2].scheme.lower() == 'https'\n if self._is_secure:\n raise Exception(\"SSL has not yet been implemented in this version of Pants.\")\n self._stream.startTLS()\n\n self._stream.connect((request[2].hostname, port))\n return\n\n # If we got here, we're connected, and to the right server. Do stuff.\n self._stream.write('%s %s HTTP/1.1%s' % (request[0], request[8], CRLF))\n for k, v in request[3].iteritems():\n self._stream.write('%s: %s%s' % (k, v, CRLF))\n\n if request[4]:\n self._stream.write('%s%s' % (CRLF, request[4]))\n else:\n self._stream.write(CRLF)\n\n # Now, wait for a response.\n self._stream.on_read = self._read_headers\n self._stream.read_delimiter = DOUBLE_CRLF","def run(self):\n self.logger.debug(\"Resource manager main thread started\")\n\n while not self._stop_flag:\n try:\n self._handle_requests()\n self._accept_requests()\n\n except Exception as ex:\n self.logger.exception(\"Resource manager failed. \"\n \"Reason: %s\", ex)\n\n self.logger.debug(\"Resource manager thread is down\")","def done(self, *arg, **kw):\r\n\r\n # ----------------------------------------\r\n # persistent connection management\r\n # ----------------------------------------\r\n\r\n # --- BUCKLE UP! ----\r\n\r\n connection = http_server.get_header(http_server.CONNECTION,self.header)\r\n connection = connection.lower()\r\n\r\n close_it = 0\r\n wrap_in_chunking = 0\r\n globbing = 1\r\n\r\n if self.version == '1.0':\r\n if connection == 'keep-alive':\r\n if not 'Content-Length' in self:\r\n close_it = 1\r\n else:\r\n self['Connection'] = 'Keep-Alive'\r\n else:\r\n close_it = 1\r\n elif self.version == '1.1':\r\n if connection == 'close':\r\n close_it = 1\r\n elif not 'Content-Length' in self:\r\n if 'Transfer-Encoding' in self:\r\n if not self['Transfer-Encoding'] == 'chunked':\r\n close_it = 1\r\n elif self.use_chunked:\r\n self['Transfer-Encoding'] = 'chunked'\r\n wrap_in_chunking = 1\r\n # globbing slows down tail -f output, so only use it if\r\n # we're not in chunked mode\r\n globbing = 0\r\n else:\r\n close_it = 1\r\n elif self.version is None:\r\n # Although we don't *really* support http/0.9 (because\r\n # we'd have to use \\r\\n as a terminator, and it would just\r\n # yuck up a lot of stuff) it's very common for developers\r\n # to not want to type a version number when using telnet\r\n # to debug a server.\r\n close_it = 1\r\n\r\n outgoing_header = producers.simple_producer(self.build_reply_header())\r\n\r\n if close_it:\r\n self['Connection'] = 'close'\r\n\r\n if wrap_in_chunking:\r\n outgoing_producer = deferring_chunked_producer(\r\n deferring_composite_producer(self.outgoing)\r\n )\r\n # prepend the header\r\n outgoing_producer = deferring_composite_producer(\r\n [outgoing_header, outgoing_producer]\r\n )\r\n else:\r\n # prepend the header\r\n self.outgoing.insert(0, outgoing_header)\r\n outgoing_producer = deferring_composite_producer(self.outgoing)\r\n\r\n # hook logging into the output\r\n outgoing_producer = deferring_hooked_producer(outgoing_producer,\r\n self.log)\r\n\r\n if globbing:\r\n outgoing_producer = deferring_globbing_producer(outgoing_producer)\r\n\r\n self.channel.push_with_producer(outgoing_producer)\r\n\r\n self.channel.current_request = None\r\n\r\n if close_it:\r\n self.channel.close_when_done()","def run(self):\n ioloop.IOLoop.current().start()","def _run(self) -> None:\n\n log.debug(\"Volumio 2 Web Service client starting ...\")\n log.debug(f\"Connecting to Volumio 2 Web Service on {self._server}:{self._port}\")\n\n with SocketIO(self._server, self._port) as socketIO:\n log.debug(\"Connected to Volumio 2 Web Service\")\n socketIO.on(\"pushState\", self._on_state_response)\n socketIO.emit(\"GetState\", on_GetState_response)\n\n # Request initial values\n socketIO.emit(\"getState\", \"\")\n\n while not self._stop_event.is_set():\n # rely on Volumio to push states mostly, but request an update\n # at a low frequency to get some lacy update.\n socketIO.wait_for_callbacks(seconds=10)\n socketIO.emit(\"getState\", \"\")","def run(self):\n \n try:\n logging.info('Thread:{} starting'.format(self.thread_id))\n\n self.crawl_url()\n self.parse_html()\n except IOError as e:\n self.thread_post_processing()\n logging.error('CrawlUrlError url:{} msg:{}'.format(self.url, e))\n\n self.thread_post_processing()","def process_request_thread(self):\n while True:\n try:\n request, client_address = self._request_queue.get(\n timeout=self.timeout_on_get,\n )\n except Queue.Empty:\n # You wouldn't believe how much crap this can end up leaking,\n # so we clear the exception.\n sys.exc_clear()\n if self._shutdown_event.isSet():\n return\n continue\n try:\n self.finish_request(request, client_address)\n self.shutdown_request(request)\n except:\n self.handle_error(request, client_address)\n self.shutdown_request(request)\n self._request_queue.task_done()","def proxy_thread(self, conn, client_addr):\n req = conn.recv(self.config['MAX_REQUEST_LENGTH'])\n line1 = req.split(b'\\n')[0]\n x = line1.split(b' ')\n if len(x) > 1:\n url = x[1]\n else:\n return\n \n # Check if the file requested is css/js file\n if bool(self.regex.findall(url.decode())):\n # send cached version if present\n if self.cache.retrieve(url.decode()):\n time_elapsed = time()\n conn.send(self.cache.retrieve(url.decode()).data)\n conn.close()\n time_elapsed = time() - time_elapsed\n print('Retrieving from cache: ' + url.decode())\n print('Time taken: {}'.format(time_elapsed))\n else:\n self.log(\"INFO\", client_addr, \"Request: \" + str(line1))\n time_elapsed = time()\n http_pos = url.find(b'://')\n if http_pos == -1:\n temp = url\n else:\n temp = url[(http_pos + 3):]\n port_pos = temp.find(b':')\n webserver_pos = temp.find(b'/')\n if webserver_pos == -1:\n webserver_pos = len(temp)\n\n webserver = \"\"\n port = -1\n if port_pos == -1 or webserver_pos < port_pos:\n port = 80\n webserver = temp[:webserver_pos]\n else:\n port = int((temp[port_pos + 1:])[:webserver_pos-port_pos-1])\n webserver = temp[:port_pos]\n\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(self.config['CONNECTION_TIMEOUT'])\n s.connect((webserver, port))\n s.sendall(req)\n while True:\n data = s.recv(self.config['MAX_REQUEST_LENGTH'])\n if len(data) > 0:\n self.cache.add(url.decode(), data)\n conn.send(data)\n else:\n break\n s.close()\n conn.close()\n\n except socket.error as error_msg:\n self.log('ERROR', client_addr, error_msg)\n if s:\n s.close()\n if conn:\n conn.close()\n self.log(\"WARNING\", client_addr, \"Peer Reset \" + str(line1))\n print('Recieving from website: ' + url.decode())\n time_elapsed = time() - time_elapsed\n print('Time Elapsed: {}'.format(time_elapsed))\n else:\n http_pos = url.find(b'://')\n if http_pos == -1:\n temp = url\n else:\n temp = url[(http_pos + 3):]\n port_pos = temp.find(b':')\n webserver_pos = temp.find(b'/')\n if webserver_pos == -1:\n webserver_pos = len(temp)\n\n webserver = \"\"\n port = -1\n if port_pos == -1 or webserver_pos < port_pos:\n port = 80\n webserver = temp[:webserver_pos]\n else:\n port = int((temp[port_pos + 1:])[:webserver_pos - port_pos - 1])\n webserver = temp[:port_pos]\n\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(self.config['CONNECTION_TIMEOUT'])\n s.connect((webserver, port))\n s.sendall(req)\n while True:\n data = s.recv(self.config['MAX_REQUEST_LENGTH'])\n if len(data) > 0:\n conn.send(data)\n else:\n break\n s.close()\n conn.close()\n except socket.error:\n if s:\n s.close()\n if conn:\n conn.close()\n self.log(\"WARNING\", client_addr, \"Peer Reset \" + str(line1))","def __init__(self, client_ident):\n\t\tthreading.Thread.__init__(self, None)\n\t\tself.client_ident\t\t= client_ident\n\t\tself.start()","def handle(self):\n\n global batch_no\n\n if self.client_address[0] in workers: # Established worker\n # self.request is a TCP socket connected to the client\n print('Worker connected... handling')\n self.data = recv_all(self.request, bytes_expected)\n\n try:\n network_weight_updates = deque(struct.unpack('{}f'.format(num_weights), self.data))\n print('\\tReceived data unpacked')\n except:\n print('\\tError unpacking data')\n else:\n print('\\tTraining master network')\n nn.train_master(network_weight_updates)\n batch_no += 1\n print('\\tCompleted batch #{} of {}'.format(batch_no, total_batches))\n\n print('\\tGetting network weights')\n network_weights = nn.get_weights()\n print('\\tPacking response')\n response = struct.pack('{}f'.format(num_weights), *network_weights)\n print('\\tSending back weights')\n self.request.sendall(response)\n\n elif self.client_address[0] in validators: # Established validator\n print('Validator connected... handling')\n print('\\tGetting network weights')\n network_weights = nn.get_weights()\n print('\\tPacking response')\n response = struct.pack('{}f'.format(num_weights), *network_weights)\n print('\\tSending back weights')\n self.request.sendall(response)\n\n else: # New node connected\n # Initialize client into set of workers\n print('Contacted by new node...')\n self.data = self.request.recv(1024).strip()\n print('\\tReceived init string')\n\n # New nodes should send b'w' to become a worker, or b'v' to become a validator\n if self.data == b'w':\n print('\\tNew WORKER node... handling')\n workers.add(self.client_address[0]) # Add IP to list of workers\n elif self.data == b'v':\n print('\\tNew VALIDATOR node... handling')\n validators.add(self.client_address[0]) # Add IP to list of validators\n else:\n print('\\tUnknown node request! Moving on...')\n return\n\n # Gather current network weights, pack as bytes, and send back to client\n network_weights = nn.get_weights()\n response = struct.pack('{}f'.format(num_weights), *network_weights)\n print('\\tSending network weights')\n self.request.sendall(response)\n\n print('\\tDONE with node')\n\n if batch_no > total_batches:\n # Done with desired number of batches! Finalize network training\n save_pkl(nn, 'saved_nn/iris_nn_relu_16n8n_100e_16b_l2.pkl') # Save trained network\n\n print('Eval training...')\n eval_tr = nn.eval_classification(train)\n print('Eval validation...')\n eval_v = nn.eval_classification(validation)\n print('Eval testing...')\n eval_t = nn.eval_classification(test)\n\n print('Batches: {}\\tTrain: {:.3f}\\tValidation: {:.3f}\\tTest: {:.3f}'\n .format(batch_no, eval_tr, eval_v, eval_t))\n exit()","def run(self):\r\n self.rpc_server.serve_forever(0.5)","def doctest_BackgroundWorkerThread():","def workerProcess(self):\r\n\r\n if self.postForkCallback:\r\n self.postForkCallback()\r\n\r\n while self.isRunning.value == True:\r\n try:\r\n client = self.serverTransport.accept()\r\n self.serveClient(client)\r\n except (KeyboardInterrupt, SystemExit):\r\n return 0\r\n except Exception, x:\r\n logging.exception(x)","def main():\n\n httpd = BaseHTTPServer.HTTPServer(\n ('localhost', int(ADDON.getSetting(\"port\"))),\n StupidHTTPRequestHandler)\n httpd_thread = threading.Thread(target=httpd.serve_forever)\n httpd_thread.start()\n\n monitor = xbmc.Monitor()\n \n while not monitor.abortRequested():\n # Sleep/wait for abort for 10 seconds\n if monitor.waitForAbort(10):\n # Abort was requested while waiting. We should exit\n break\n\n httpd.shutdown()\n httpd.server_close()","def http_server():\n #Set up the server socket.\n server_socket = socket.socket(\n socket.AF_INET,\n socket.SOCK_STREAM,\n socket.IPPROTO_IP)\n\n try:\n #Connect the server socket.\n server_socket.bind(('127.0.0.1', 50000))\n server_socket.listen(1)\n\n #Loop indefinitely while waiting for connections.\n\n while True:\n try:\n conn, addr = server_socket.accept()\n msg = receive_message(conn)\n uri = parse_request(msg)\n resource, mimetype = map_uri(uri)\n\n except (Error404, ParseException) as err:\n response = build_response(err.message, 'text/plain', err.code)\n\n except:\n response = build_response(\"500 Internal Server Error\",\n 'text/plain', '500')\n\n else:\n response = build_response(resource, mimetype)\n\n finally:\n conn.sendall(response)\n # conn.shutdown(socket.SHUT_WR)\n conn.close()\n\n finally:\n #Make sure the socket is closed if we can't continue.\n server_socket.close()","def do_GET(self):\n server_ip = Setup.parse_options()['ip_address']\n uri = \"http://\" + server_ip + self.path\n response = urllib.urlopen(uri)\n self.copyfile(response, self.wfile)\n headers = self.generate_header_dic(self.headers.headers)\n ip_address = self.client_address[0] # get client iP address\n if Setup.system_status != 'green':\n self.process_request(ip_address, headers, self.path)\n self.process_response(ip_address, response.headers)","def main(self):\n while True:\n if not self.data_server_command.empty():\n command_data_server = self.data_server_command.get()\n if command_data_server[0] == 4:\n thread.start_new_thread(self.get_file, (command_data_server[1],))\n else:\n self.data_server_command_def[command_data_server[0]](command_data_server[1])","def do_work(self):","def server(conn, address):\n print(\"Client Connection Open\")\n while True:\n request = server_read(conn)\n if request:\n print(request)\n manage_client(request, conn)","def handle_one_request(self):\n \n try:\n \n self.raw_requestline = self.rfile.readline(65537)\n \n if len(self.raw_requestline) > 65536:\n \n self.requestline = ''\n \n self.request_version = ''\n \n self.command = ''\n \n self.send_error(414)\n \n return\n \n if not self.raw_requestline:\n \n self.close_connection = 1\n \n return\n \n if not self.parse_request():\n \n # An error code has been sent, just exit\n \n return\n \n mname = 'do_' + self.command\n \n if not hasattr(self, mname):\n \n self.send_error(501, \"Unsupported method (%r)\" % self.command)\n \n return\n \n method = getattr(self, mname)\n \n print \"before call do_Get\"\n \n method()\n \n #增加 debug info 及 wfile 判断是否已经 close\n \n print \"after call do_Get\"\n \n if not self.wfile.closed:\n self.wfile.flush() #actually send the response if not already done.\n \n print \"after wfile.flush()\"\n \n except socket.timeout, e:\n \n #a read or a write timed out. Discard this connection\n self.log_error(\"Request timed out: %r\", e)\n self.close_connection = 1\n return","async def _handler(self, reader, writer):\n gc.collect()\n\n try:\n req = request(reader)\n resp = response(writer)\n # Read HTTP Request with timeout\n await asyncio.wait_for(self._handle_request(req, resp),\n self.request_timeout)\n\n # OPTIONS method is handled automatically\n if req.method == b'OPTIONS':\n resp.add_access_control_headers()\n # Since we support only HTTP 1.0 - it is important\n # to tell browser that there is no payload expected\n # otherwise some webkit based browsers (Chrome)\n # treat this behavior as an error\n resp.add_header('Content-Length', '0')\n await resp._send_headers()\n return\n\n # Ensure that HTTP method is allowed for this path\n if req.method not in req.params['methods']:\n raise HTTPException(405)\n\n # Handle URL\n gc.collect()\n if hasattr(req, '_param'):\n await req.handler(req, resp, req._param)\n else:\n await req.handler(req, resp)\n # Done here\n except (asyncio.CancelledError, asyncio.TimeoutError):\n pass\n except OSError as e:\n # Do not send response for connection related errors - too late :)\n # P.S. code 32 - is possible BROKEN PIPE error (TODO: is it true?)\n if e.args[0] not in (errno.ECONNABORTED, errno.ECONNRESET, 32):\n try:\n await resp.error(500)\n except Exception as e:\n log.exc(e, \"\")\n except HTTPException as e:\n try:\n await resp.error(e.code)\n except Exception as e:\n log.exc(e)\n except Exception as e:\n # Unhandled expection in user's method\n log.error(req.path.decode())\n log.exc(e, \"\")\n try:\n await resp.error(500)\n # Send exception info if desired\n if self.debug:\n sys.print_exception(e, resp.writer.s)\n except Exception:\n pass\n finally:\n await writer.aclose()\n # Max concurrency support -\n # if queue is full schedule resume of TCP server task\n if len(self.conns) == self.max_concurrency:\n self.loop.create_task(self._server_coro)\n # Delete connection, using socket as a key\n del self.conns[id(writer.s)]","def main(self):\n addr = (self.uri, self.port)\n try:\n self.client.connect(addr)\n except socket.gaierror:\n print(\"[ERROR] not a valid URI. Try again please...\")\n else:\n print(\"[SETUP] client connected to IPv4 address\", self.uri, \"on port\", self.port)\n self.handler()","def run(self):\n self._server = self._get_server()\n self._server.serve_forever()","def run(self):\n self.submit()\n self.start()","async def run(self) -> None:","async def run(self) -> None:","def start(self):\n #url = '{}://{}:{}/'.format('http',\n # self.ip,\n # self.port)\n #self.service_info = ServiceInfo(\n # '_webthing._sub._http._tcp.local.',\n # '{}._http._tcp.local.'.format(self.name),\n # address=socket.inet_aton(self.ip),\n # port=self.port,\n # properties={\n # 'url': url,\n # },\n # server='{}.local.'.format(socket.gethostname()))\n #self.zeroconf = Zeroconf()\n #self.zeroconf.register_service(self.service_info)\n\n # If WebSocketS used and NOT running in thread, and WebServer IS\n # running in thread make shure WebServer has enough stack size to\n # handle also the WebSocket requests.\n log.info('Starting Web Server')\n self.server.Start(threaded=srv_run_in_thread, stackSize=8192)","def _bg_thread_main(self) -> None:\n while not self._done:\n self._run_server_cycle()","async def main():\n await serve_websocket(handle_server, SERVER, PORT, ssl_context=None)","def main_thread_http_entry(self, msg):\n # V/nsHttp HttpBaseChannel::Init [this=c30d000]\n if msg['message'].startswith('HttpBaseChannel::Init'):\n match = re.search(r'^HttpBaseChannel::Init \\[this=(?P<channel>[\\w\\d]+)]',\n msg['message'])\n if match:\n self.http['current_channel'] = match.groupdict().get('channel')\n # D/nsHttp nsHttpChannel::Init [this=c30d000]\n elif 'current_channel' in self.http and msg['message'].startswith('nsHttpChannel::Init'):\n del self.http['current_channel']\n # V/nsHttp uri=http://www.webpagetest.org/?bare=1\n elif 'current_channel' in self.http and msg['message'].startswith('uri='):\n match = re.search(r'^uri=(?P<url>[^ \\r\\n]+)', msg['message'])\n if match:\n self.http['channels'][self.http['current_channel']] = \\\n match.groupdict().get('url')\n # V/nsHttp Creating nsHttpTransaction @0x7f88bb130400\n elif msg['message'].startswith('Creating nsHttpTransaction '):\n match = re.search(r'^Creating nsHttpTransaction @(?P<id>[\\w\\d]+)', msg['message'])\n if match:\n self.http['creating_trans_id'] = match.groupdict().get('id')\n # D/nsHttp nsHttpChannel c30d000 created nsHttpTransaction c138c00\n elif msg['message'].startswith('nsHttpChannel') and \\\n msg['message'].find(' created nsHttpTransaction ') > -1:\n match = re.search(r'^nsHttpChannel (?P<channel>[\\w\\d]+) created '\\\n r'nsHttpTransaction (?P<id>[\\w\\d]+)', msg['message'])\n if match:\n channel = match.groupdict().get('channel')\n if channel in self.http['channels']:\n url = self.http['channels'][channel]\n del self.http['channels'][channel]\n if 'creating_trans_id' in self.http:\n trans_id = self.http['creating_trans_id']\n del self.http['creating_trans_id']\n else:\n trans_id = match.groupdict().get('id')\n # If there is already an existing transaction with the same ID,\n # move it to a unique ID.\n if trans_id in self.http['requests']:\n tmp_request = self.http['requests'][trans_id]\n del self.http['requests'][trans_id]\n self.unique_id += 1\n new_id = '{0}.{1:d}'.format(trans_id, self.unique_id)\n self.http['requests'][new_id] = tmp_request\n self.http['requests'][trans_id] = {'url': url,\n 'request_headers': [],\n 'response_headers': [],\n 'status': None,\n 'bytes_in': 0,\n 'chunks': []}\n # D/nsHttp nsHttpTransaction::Init [this=c138c00 caps=21]\n elif msg['message'].startswith('nsHttpTransaction::Init '):\n match = re.search(r'^nsHttpTransaction::Init \\[this=(?P<id>[\\w\\d]+)', msg['message'])\n if match:\n trans_id = match.groupdict().get('id')\n self.http['current_transaction'] = trans_id\n # D/nsHttp nsHttpTransaction c138c00 SetRequestContext c15ba00\n elif 'current_transaction' in self.http and \\\n msg['message'].startswith('nsHttpTransaction ') and \\\n msg['message'].find(' SetRequestContext ') > -1:\n del self.http['current_transaction']\n # I/nsHttp http request [\n elif 'current_transaction' in self.http and msg['message'] == 'http request [':\n self.http['request_headers'] = self.http['current_transaction']\n elif 'request_headers' in self.http and msg['message'] == ']':\n del self.http['request_headers']\n # Individual request headers\n elif 'request_headers' in self.http and msg['message'][0:2] == ' ':\n trans_id = self.http['request_headers']\n if trans_id in self.http['requests']:\n self.http['requests'][trans_id]['request_headers'].append(msg['message'][2:])","def handle(self):\n self.ip = self.client_address[0]\n self.port = self.client_address[1]\n self.connection = self.request\n #self.logged_in = False\n\n # Loop that listens for messages from the client\n while True:\n received_string = self.connection.recv(4096).strip()\n if received_string:\n jsonObject = json.loads(received_string)\n request = jsonObject.get('request')\n #print(received_string)\n #self.handle_data(received_string)\n if request == 'login':\n print 'logging in'\n self.login(jsonObject)\n elif request == 'logout':\n self.logout()\n elif request == 'msg':\n self.send(jsonObject)\n elif request == 'names':\n self.getNames()\n elif request == 'help':\n return \"geiegohruuhiegr\"\n else:\n return \"you suck\"\n\n else:\n print('The client is disconnected.')\n break \n # TODO: Add handling of received payload from client","async def _main(self):\n while True:\n time.sleep(1)","def serve_requests(self):\n while True:\n self.server_socket.listen(self.request_queue_size)\n client_connection, client_address = self.server_socket.accept()\n self.request_handler(client_connection)","def run(self):\n try:\n self.server = socket(AF_INET, SOCK_STREAM)\n self.server.bind(self.address)\n self.server.listen(5) # Allows up to 5 waiting clients\n\n while True:\n self.myView.updateStatus('Waiting for connection ...')\n client, address = self.server.accept()\n self.myView.updateStatus('... connected from ' + str(address))\n handler = ClientHandler(client, self.bank, self.myView)\n handler.start()\n\n except Exception as message:\n self.myView.updateStatus(message)\n self.server.close()\n self.myView.updateStatus(\"Server shutting down.\")","def background_stuff():\n print \"BACKGROUND\"\n\n\n\n # # global socketio\n\n # # print(wsClient.url, wsClient.products)\n # while (wsClient.MessageCount < 30):\n # print(\"\\nMessageCount =\", \"%i \\n\" % wsClient.MessageCount)\n # # time.sleep(1)\n # # socketio.emit('my response', {'data': [\"TEST\"]}, namespace=\"/test\", broadcast=True)\n # #\n # wsClient.close()\n #\n # while True:\n # time.sleep(1)\n # t = str(time.clock())\n # print t\n\n def minute_passed(oldepoch):\n return time.time() - oldepoch >= .1\n\n global wsClient\n\n # t = time.time()\n # for i in range(3000):\n # # while True:\n # # print time.clock(), t\n # # if time.clock() > ( t + .1 ):\n # # show = True #minute_passed(t)\n # # if show:\n # # print show, time.time(), t, time.time() - t\n # t = time.time()\n # cur_time = str(t)\n # socketio.emit('message', {'data': cur_time, \"msg\": wsClient.message['price'] }, namespace=\"/test\", broadcast=True)\n\n # global thread\n # thread = None","def handle(self):\n thread_name = get_threadname()\n\n style_args = {}\n style_args['label'] = thread_name\n\n axis_args = {}\n axis_args['name'] = 'default'\n axis_args['x_label'] = ''\n axis_args['y_label'] = ''\n\n # client_init blocks until client sends 'BEGIN'\n first_value = self._handle_client_init(style_args, axis_args)\n if type(first_value) != float:\n if first_value == \"RETURN\":\n # done with client\n return\n else:\n print \"Unknown status\"\n return\n\n self._handle_setup_axis(axis_args)\n\n print \"Assigning to axis:\", axis_args['name']\n axes = self.server.axes[axis_args['name']]\n line_name = self._handle_create_line(axes, style_args)\n self._handle_update_legend(axes)\n\n # NOTE: client_read_data will block until client disconnects.\n self._handle_client_read_data(first_value, line_name)\n print \"Exiting:\", thread_name","def run(self):\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect(self.address)\n\n #send dummy data\n sock.sendall(bytes(\"Give me\", \"utf-8\"))\n received = sock.recv(1024)\n while True:\n data = sock.recv(1024)\n if not data: break\n received += data\n lymphocytes = pickle.loads(received)\n self.lymphocytes_setter(lymphocytes)\n except ConnectionRefusedError:\n #Don't bother. May be it's better to add more logic to determine\n #permanent connection errors.\n pass\n finally:\n sock.close()","def main():\n try:\n # persistent('infected', os.path.abspath(__file__)) # DON'T REMOVE\n\n # rename me to client.py\n # os.rename(__file__, os.getcwd() + \"/client1.py\")\n # print os.path.abspath(__file__)\n global my_socket\n my_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n my_socket.connect((SERVER_ADDRESS, PORT))\n\n handle = Handle()\n handle.say_hello()\n print my_socket.getsockname()\n msg = ''\n while True:\n # ----------send data----------\n if msvcrt.kbhit():\n char = msvcrt.getch()\n if char == chr(MY_CHAR):\n break\n if msg == '':\n sys.stdout.write('[Me] ')\n msg += char\n sys.stdout.write(char)\n sys.stdout.flush()\n if char == chr(CHAR_ENTER):\n my_socket.send(msg)\n msg = ''\n sys.stdout.write('\\n')\n\n # ----------receive data-------------\n rlist, wlist, xlist = select.select([my_socket], [my_socket], [], 1)\n if my_socket in rlist:\n data = my_socket.recv(MAX_SIZE_RESPONSE)\n if not data or data == \"\":\n my_socket.close()\n print \"Connection with server closed.\"\n return\n handle.handle_request(data)\n if msg != '':\n sys.stdout.write('[Me] ' + msg)\n sys.stdout.flush()\n\n except Exception as e:\n print e.message\n my_socket.close()\n print 'connection closed'\n return\n\n my_socket.close()\n print 'connection closed'","def _worker(self):\n while True:\n request = self.queue.get()\n self.worker(request)\n self.queue.task_done()","def process_request(self, request, client_address):\r\n t = self.thread_class(target = self.process_request_thread,\r\n args = (request, client_address))\r\n if self.daemon_threads:\r\n t.setDaemon (1)\r\n t.start()"],"string":"[\n \"def __manage_client_thread(self, conn_socket: socket.socket, address: tuple):\\n print(\\\"[THREAD] new thread started for client\\\")\\n connected = True\\n\\n while connected:\\n request_header = HttpServer.get_request_header(conn_socket)\\n split_request_header = request_header.split()\\n file = split_request_header[1]\\n print(\\\"[RECV] header received from IPv4 address\\\", address[0], \\\":\\\", request_header)\\n\\n if not HttpServer.is_valid_http_request(split_request_header):\\n http_message = HttpServer.create_400_response()\\n pass\\n else:\\n if file == \\\"/\\\":\\n file = \\\"/index.html\\\"\\n\\n put_or_post = HttpServer.is_put_or_post(split_request_header)\\n try:\\n if put_or_post:\\n request_body = HttpServer.get_request_body(conn_socket, request_header)\\n status_code = HttpServer.get_status_code_for_put_or_post(request_header, file)\\n\\n if status_code == 204:\\n if split_request_header[0] == \\\"PUT\\\":\\n f = open(file[1:], \\\"w\\\")\\n else:\\n f = open(file[1:], \\\"a\\\")\\n\\n f.write(request_body)\\n f.close()\\n http_message = HttpServer.create_204_response()\\n elif status_code == 501:\\n http_message = HttpServer.create_501_response()\\n else: # status code is 201\\n f = open(file[1:], \\\"x\\\")\\n f.write(request_body)\\n f.close()\\n http_message = self.create_201_response(file)\\n\\n else:\\n if split_request_header[0] == \\\"GET\\\":\\n # GET needs filename\\n status_code = HttpServer.get_status_code_for_get(request_header, file)\\n else:\\n status_code = HttpServer.get_status_code_for_head(file)\\n\\n if status_code == 404:\\n http_message = HttpServer.create_404_response()\\n elif status_code == 304:\\n http_message = HttpServer.create_304_response()\\n else: # status code is 200\\n http_message = HttpServer.create_200_response(file)\\n except Exception:\\n http_message = HttpServer.create_500_response()\\n\\n conn_socket.send(http_message)\\n\\n # determine if connection has to be closed\\n conn_str = \\\"Connection:\\\"\\n conn_begin_ind = request_header.find(conn_str)\\n\\n if conn_begin_ind != -1:\\n conn_end_ind = request_header[conn_begin_ind:].find(\\\"\\\\r\\\\n\\\") + conn_begin_ind\\n conn_status = request_header[conn_begin_ind+len(conn_str):conn_end_ind].strip()\\n if conn_status == \\\"close\\\":\\n conn_socket.close()\\n connected = False\\n print(\\\"[THREAD] client thread ended\\\")\",\n \"def _client(self):\\n while True:\\n body = self.queue.get(True)\\n print \\\"Sending %s bytes (%s/%s)\\\" % (len(body), self.queue.qsize(), self.queue.maxsize)\\n\\n try:\\n req = urllib2.Request(self.endpoint, body)\\n urllib2.urlopen(req).read()\\n except:\\n print \\\"Cannot send request. Retrying in 5 seconds\\\"\\n print_exception(*sys.exc_info())\\n print \\\"continuing...\\\"\\n self.enqueue(body)\\n sleep(5)\",\n \"def serveThread(self):\\r\\n while True:\\r\\n try:\\r\\n client = self.clients.get()\\r\\n self.serveClient(client)\\r\\n except Exception, x:\\r\\n logging.exception(x)\",\n \"def run(self):\\n # sends download range from offset to offset + block_size - 1 (including) in the header\\n headers = {'User-Agent': self.user_agent, 'Refferer': '{}://{}/'.format(self.url.protocol, self.url.host), \\n 'Range': 'bytes={}-{}'.format(self.offset, self.offset + self.block_size - 1)}\\n status = 0 # set status to 0 that means a connection error\\n try:\\n self.conn.request('GET', self.url.request, headers=headers)\\n response = self.conn.getresponse()\\n # the server does not support partial downloading - error\\n if response.status != 206:\\n status = response.status\\n raise MirrorError\\n part_size = int(response.getheader('Content-Length')) # actual count of bytes sent by the server\\n data = b'' # data buffer\\n # loop while all data will be received\\n while part_size > len(data):\\n if self.cancelled.is_set(): # if the thread has been cancelled\\n # stop the thread, the TaskError would not be processed\\n # because a loop in the main thread already broken\\n raise Exception\\n data_fragment = response.read(self.FRAGMENT_SIZE)\\n data += data_fragment # add data to the buffer\\n # put progress information into the queue\\n info = TaskProgress(self.url.host, response.status, len(data))\\n self.data_queue.put(info)\\n # when the downloading loop finished, create TaskData object\\n info = TaskData(self.url.host, response.status, self.offset, data)\\n response.close()\\n except:\\n # if an error has occurred - create a TaskError object\\n info = TaskError(self.url.host, status, self.offset)\\n finally:\\n self.data_queue.put(info) # put result TaskInfo object into the queue\\n self.ready.set() # mark the thread as comleted\",\n \"def _http_thread_func(self):\\r\\n while not self._terminating:\\r\\n # pop queued request from the queue and process it\\r\\n (api_endpoint, params, reqid) = self.http_requests.get(True)\\r\\n translated = None\\r\\n try:\\r\\n answer = self.http_signed_call(api_endpoint, params)\\r\\n if answer[\\\"result\\\"] == \\\"success\\\":\\r\\n # the following will reformat the answer in such a way\\r\\n # that we can pass it directly to signal_recv()\\r\\n # as if it had come directly from the websocket\\r\\n translated = {\\r\\n \\\"op\\\": \\\"result\\\",\\r\\n \\\"result\\\": answer[\\\"data\\\"],\\r\\n \\\"id\\\": reqid\\r\\n }\\r\\n else:\\r\\n if \\\"error\\\" in answer:\\r\\n if answer[\\\"token\\\"] == \\\"unknown_error\\\":\\r\\n # enqueue it again, it will eventually succeed.\\r\\n self.enqueue_http_request(api_endpoint, params, reqid)\\r\\n else:\\r\\n\\r\\n # these are errors like \\\"Order amount is too low\\\"\\r\\n # or \\\"Order not found\\\" and the like, we send them\\r\\n # to signal_recv() as if they had come from the\\r\\n # streaming API beause Gox() can handle these errors.\\r\\n translated = {\\r\\n \\\"op\\\": \\\"remark\\\",\\r\\n \\\"success\\\": False,\\r\\n \\\"message\\\": answer[\\\"error\\\"],\\r\\n \\\"token\\\": answer[\\\"token\\\"],\\r\\n \\\"id\\\": reqid\\r\\n }\\r\\n\\r\\n else:\\r\\n self.debug(\\\"### unexpected http result:\\\", answer, reqid)\\r\\n\\r\\n except Exception as exc:\\r\\n # should this ever happen? HTTP 5xx wont trigger this,\\r\\n # something else must have gone wrong, a totally malformed\\r\\n # reply or something else.\\r\\n #\\r\\n # After some time of testing during times of heavy\\r\\n # volatility it appears that this happens mostly when\\r\\n # there is heavy load on their servers. Resubmitting\\r\\n # the API call will then eventally succeed.\\r\\n self.debug(\\\"### exception in _http_thread_func:\\\",\\r\\n exc, api_endpoint, params, reqid)\\r\\n\\r\\n # enqueue it again, it will eventually succeed.\\r\\n self.enqueue_http_request(api_endpoint, params, reqid)\\r\\n\\r\\n if translated:\\r\\n self.signal_recv(self, (json.dumps(translated)))\\r\\n\\r\\n self.http_requests.task_done()\",\n \"def serve(self):\\n\\t\\timport thread\\n\\t\\tthread.start_new_thread(self._server_thread, tuple())\",\n \"def run(self):\\n\\n\\t\\t#Begin running the clientHandler\\n\\t\\tself.running = True\\n\\t\\tself.rxThread.start()\\n\\n\\t\\twhile self.running:\\n\\t\\t\\ttime.sleep(0.1)\\n\\t\\n\\t\\t\\t#Keep a count of the number of missing Hello requests, over 5 kill client\\n\\t\\t\\tif self.missingCount >= 5:\\n\\t\\t\\t\\tself.running = False\",\n \"def __thread(self):\\n\\n _LOGGER.debug(\\\"Webserver is starting.\\\")\\n\\n monitor = self\\n\\n # Embedding this because it's so trivial.\\n class Handler(BaseHTTPServer.BaseHTTPRequestHandler):\\n def do_GET(self):\\n\\n # We have the first line of the response with the authorization code\\n # passed as a query argument.\\n #\\n # Example:\\n #\\n # GET /?code=4/clwm0rESq8sqeC-JxIcfiSdjh2593hLej9CZxAcbe1A HTTP/1.1\\n #\\n\\n try:\\n unicode\\n except NameError:\\n request = self.requestline + \\\"\\\\n\\\\n\\\"\\n else:\\n request = unicode(self.requestline + \\\"\\\\n\\\\n\\\")\\n\\n # Use Python to parse the request. We need to add one newline for the\\n # line and another for a subsequent blank line to terminate the block\\n # and conform with the RFC.\\n hr = _HTTPRequest(request)\\n u = urlparse.urlparse(hr.path)\\n arguments = urlparse.parse_qs(u.query)\\n\\n # It's not an authorization response. Bail with the same error\\n # the library would normally send for unhandled requests.\\n if 'code' not in arguments:\\n self.send_error(\\n 501,\\n \\\"Unsupported method ({}): {}\\\".format(\\n self.command, hr.path))\\n\\n return\\n\\n token = arguments['code'][0]\\n _LOGGER.debug(\\\"Received auth-code [{}]\\\".format(token))\\n\\n monitor._token = token\\n\\n monitor._request_state_e.set()\\n\\n self.send_response(200, message='OK')\\n\\n self.send_header(\\\"Content-type\\\", 'text/html')\\n self.end_headers()\\n\\n self.wfile.write(\\\"\\\"\\\"\\\\\\n<html>\\n<head></head>\\n<body>\\nAuthorization recorded.\\n</body>\\n</html>\\n\\\"\\\"\\\")\\n\\n def log_message(self, format, *args):\\n pass\\n\\n\\n class Server(SocketServer.TCPServer):\\n def server_activate(self, *args, **kwargs):\\n r = SocketServer.TCPServer.server_activate(self, *args, **kwargs)\\n\\n # Sniff the port, now that we're running.\\n monitor._effective_port = self.server_address[1]\\n\\n return r\\n\\n # Our little webserver. (0) for the port will automatically assign it\\n # to some unused port.\\n binding = ('localhost', self._webserver_port)\\n self.__s = Server(binding, Handler)\\n\\n _LOGGER.debug(\\\"Created server.\\\")\\n\\n # Signal the startup routine that we're starting.\\n self.__server_state_e.set()\\n\\n _LOGGER.debug(\\\"Running server.\\\")\\n self.__s.serve_forever()\\n\\n _LOGGER.debug(\\\"Webserver is stopping.\\\")\\n\\n # Signal the startup routine that we're stopping.\\n self.__server_state_e.set()\",\n \"def run(self):\\n try:\\n # Initialize signal handler to be able to have a graceful shutdown.\\n ServiceShutdownHandling.initServiceShutdownHandling()\\n\\n httpd = None\\n # The HTTP server thread - our HTTP interface\\n if self._port != None:\\n httpd = RaspendHTTPServerThread(self._shutdownFlag, self._dataLock, self._sharedDict, self._cmdMap, self._port)\\n # Start our threads.\\n httpd.start()\\n\\n for worker in self._workers:\\n worker.start()\\n\\n # Keep primary thread or main loop alive.\\n while True:\\n time.sleep(0.5)\\n\\n except ServiceShutdownHandling.ServiceShutdownException:\\n # Signal the shutdown flag, so the threads can quit their work.\\n self._shutdownFlag.set()\\n\\n # Wait for all threads to end.\\n for worker in self._workers:\\n worker.join()\\n\\n if httpd:\\n httpd.join()\\n\\n except Exception as e:\\n print (\\\"An unexpected error occured. Error: {}\\\".format(e))\\n\\n finally:\\n pass\\n\\n return\",\n \"def run(self):\\n server = TCPServer((self.host, self.port), TCPHandler)\\n server.lymphocytes_getter = self.lymphocytes_getter\\n\\n #runs forever - so make this thread daemon\\n server.serve_forever()\",\n \"def run(self):\\n while self.running:\\n self.handle_request()\",\n \"async def main(self, loop: asyncio.get_event_loop) -> None:\\n queue = asyncio.Queue()\\n\\n for url in self.url_list:\\n queue.put_nowait(url)\\n\\n async with aiohttp.ClientSession(loop=loop) as session:\\n workers = [\\n asyncio.create_task(self.worker(queue, session))\\n for _ in range(self.max_treads)\\n ]\\n await queue.join()\\n\\n for worker in workers:\\n worker.cancel()\\n\\n await asyncio.gather(*workers, return_exceptions=True)\",\n \"def initThread(self):\\n thread1 = threading.Thread(target=self.processData)\\n self.info(\\\"starting cod7http thread\\\")\\n thread1.start()\\n self.httpthreadinst = thread1\",\n \"def __init__(self):\\n\\n # Every WSGI application must have an application object - a callable\\n # object that accepts two arguments. For that purpose, we're going to\\n # use a function (note that you're not limited to a function, you can\\n # use a class for example). The first argument passed to the function\\n # is a dictionary containing CGI-style environment variables and the\\n # second variable is the callable object (see PEP 333).\\n def application(environ, start_response):\\n \\\"\\\"\\\"\\n WSGI application object. Returns request status.\\n For specific endpoints (e.g. get_with_params), returns\\n specific response bodies.\\n \\\"\\\"\\\"\\n\\n response_text = 'Hello World!'\\n endpoint = environ['PATH_INFO'][1:]\\n\\n if endpoint == 'get_with_params':\\n #echo back uri parameters as dict...\\n response_text = str(dict(urlparse.parse_qsl(environ['QUERY_STRING'])))\\n\\n #set status code for response based on request...\\n requested_status = environ['PATH_INFO'][1:]\\n\\n status = self.codes.get(requested_status, '200 OK') # HTTP Status\\n headers = [('Content-type', 'text/plain')] # HTTP Headers\\n start_response(status, headers)\\n #print(environ)\\n #print('pathInfo: {0}'.format(environ.get('PATH_INFO')))\\n #print('queryString: {0}'.format(environ.get('QUERY_STRING')))\\n #print('requestMethod:{0}'.format(environ['REQUEST_METHOD']))\\n # The returned object is going to be printed\\n return response_text\\n\\n threading.Thread.__init__(self)\\n self.httpd = make_server('', 0, application)\\n self.address = self.httpd.server_address\",\n \"def handler(self):\\n msg = self.create_http_request()\\n self.send(msg)\\n\\n if self.file_name == \\\"/\\\":\\n self.file_name = \\\"/index.html\\\"\\n else:\\n self.file_name = HttpClient.create_file_location(self.file_name)\\n\\n if self.http_command == \\\"HEAD\\\":\\n recv_raw, _ = self.recv_header()\\n recv = recv_raw.decode(self.format_body)\\n recv_with_updated_imgs = self.update_images(recv)\\n self.write_to_html_file(recv_with_updated_imgs)\\n elif self.http_command == \\\"PUT\\\":\\n recv_raw = self.recv_all_data()\\n if recv_raw != b'':\\n recv = recv_raw.decode(self.format_body)\\n recv_with_updated_imgs = self.update_images(recv)\\n self.write_to_html_file(recv_with_updated_imgs)\\n elif self.http_command == \\\"POST\\\":\\n recv_raw = self.recv_all_data()\\n if recv_raw != b'':\\n recv = recv_raw.decode(self.format_body)\\n recv_with_updated_imgs = self.update_images(recv)\\n self.write_to_html_file(recv_with_updated_imgs)\\n else: # http_command == \\\"GET\\\" or it is a bad request\\n recv_raw = self.recv_all_data()\\n recv = recv_raw.decode(self.format_body)\\n recv_with_updated_imgs = self.update_images(recv)\\n self.write_to_html_file(recv_with_updated_imgs)\\n\\n self.disconnect()\\n print(\\\"[CONNECTION] Client terminated\\\")\",\n \"def main():\\n host = ''\\n port = 8088\\n HTTPServer((host, port), HandleRequests).serve_forever()\",\n \"async def main():\\n # Create the queue of work\\n work_queue = asyncio.Queue()\\n\\n # Put some work in the queue\\n for url in [\\n \\\"http://google.com\\\",\\n \\\"http://yahoo.com\\\",\\n \\\"http://linkedin.com\\\",\\n \\\"http://apple.com\\\",\\n \\\"http://microsoft.com\\\",\\n \\\"http://facebook.com\\\",\\n \\\"http://twitter.com\\\",\\n ]:\\n await work_queue.put(url)\\n\\n browser = HTTPBrowser(2,\\n work_queue.qsize(),\\n LoadBalancing()\\n )\\n\\n await browser.browser_session(work_queue)\",\n \"def run(self):\\n while True:\\n req = self._requests.get()[1]\\n req.start()\\n logging.info('Running request %s', req)\",\n \"def request_handler(self):\\n\\n size = 1024\\n while True:\\n # accept message from client\\n clientSock, addr = self.socket.accept()\\n self.printLine()\\n print('connect to {}'.format(addr))\\n\\n # print client message content\\n msg = clientSock.recv(size).decode('utf-8')\\n self.printLine()\\n print(\\\"sent message :\\\")\\n print(msg)\\n\\n self.printLine()\\n self._set_fileName(msg)\\n\\n # check for existance of file in the server (with name of file.txt)\\n data, isFileExist = self._send_file_handler()\\n\\n self.printLine()\\n print('data of file :')\\n print(data)\\n\\n # create header for response message\\n if isFileExist:\\n header = self._generate_headers(200)\\n else:\\n header = self._generate_headers(404)\\n\\n response = header.encode() + data.encode()\\n\\n # send response in http protocol\\n clientSock.send(response)\\n\\n # close the signal\\n clientSock.close()\",\n \"def server():\",\n \"def server():\",\n \"def image_server():\\n yield from http_server_thread(ImageHandler)\",\n \"def client():\",\n \"def run(self):\\n while self._num_workers > 0:\\n self.server.handle_request()\\n self._graph = None\",\n \"def run(self):\\n try:\\n # connect method implementation should return a TaskInfo object\\n info = self.connect()\\n except:\\n # if an error has occurred create a TaskHeadError object\\n info = TaskHeadError(self.url.host, 0)\\n finally:\\n self.data_queue.put(info) # put the result in the queue\\n self.ready.set() # and mark the thread as completed\",\n \"def run(self):\\n print('ClientThread[{}] is running!'.format(self.threadID))\\n while True:\\n request = self.receive()\\n try:\\n requestcode = request.split(',')[0]\\n if requestcode == 'SYNCFROM':\\n self.syncToClient()\\n continue\\n elif requestcode == 'SYNCTO':\\n self.syncFromClient()\\n continue\\n elif requestcode == 'GETINDEX':\\n self.sendIndex()\\n continue\\n elif requestcode == 'CLOSE':\\n print('Connection to {}:{} closed'.format(self.ip,self.port))\\n self.tcpsock.close()\\n break\\n elif not request:\\n continue\\n else:\\n print(request, type(request))\\n raise Exception('Unexpected bytes from client.')\\n except KeyboardInterrupt:\\n sys.exit()\\n except Exception as err:\\n traceback.print_exc()\\n continue\\n self.tcpsock.close()\\n print('ClientThread[{}] exiting..'.format(self.threadID))\",\n \"def handle(self):\\n self.request.recv(1024)\\n self.request.sendall(pickle.dumps(self.server.lymphocytes_getter()))\",\n \"def process_thread(self):\",\n \"async def handle_client(self, reader: StreamReader, writer: StreamWriter):\\n peername = writer.transport.get_extra_info(\\\"peername\\\")\\n log.info(\\\"handle_client : %s\\\", peername)\\n\\n try:\\n\\n remote_host, remote_port, req = await parse_http_request_header(\\n reader, writer\\n )\\n\\n remote_reader, remote_writer = await asyncio.open_connection(\\n remote_host, remote_port\\n )\\n if req:\\n log.info(\\\"req: %s\\\", req)\\n remote_writer.write(req)\\n\\n asyncio.create_task(http_channel(remote_reader, writer))\\n\\n asyncio.create_task(http_channel(reader, remote_writer))\\n\\n except Exception as ex:\\n log.exception(ex)\",\n \"def make_request_thread(self, service, request):\\n requestResponse = self.callbacks.makeHttpRequest(service, request)\\n print self.helpers.analyzeRequest(requestResponse).getUrl().toString()\",\n \"def threadget(self, url, *args):\\n\\n self.logger.debug(\\\"Starting a thread to simulate a GET request to %s\\\" % url)\\n api_get = threading.Thread(target=self.get, args=(url, *args,))\\n api_get.start()\",\n \"def client_request(self, evt):\\n threads.deferToThread(self.cli_db.accept, evt)\",\n \"def run(self):\\n to_client_request_thread = threading.Thread(target=self._dispatch_to_client_request, daemon=True)\\n to_client_request_thread.start()\\n\\n from_client_request_thread = threading.Thread(target=self._dispatch_from_client_request, daemon=True)\\n from_client_request_thread.start()\\n\\n from_client_commands_thread = threading.Thread(target=self._from_client_commands, daemon=True)\\n from_client_commands_thread.start()\\n\\n to_client_update_state_thread = threading.Thread(target=self._to_client_update_state, daemon=True)\\n to_client_update_state_thread.start()\\n\\n server_control_thread = threading.Thread(target=self._server_control, daemon=True)\\n server_control_thread.start()\\n\\n # Wait for threads to finish\\n to_client_request_thread.join()\\n from_client_request_thread.join()\\n from_client_commands_thread.join()\\n to_client_update_state_thread.join()\\n server_control_thread.join()\\n \\n # Close server connection\\n self._to_client_request.close()\\n self._from_client_request.close()\",\n \"def run(self):\\n self.initialize()\\n\\n # run the start callback\\n tools.run_callback(\\\"start\\\", {'request': self._request})\\n\\n data = self._request.getData()\\n pyhttp = self._request.getHttp()\\n config = self._request.getConfiguration()\\n\\n # allow anyone else to handle the request at this point\\n handled = tools.run_callback(\\\"handle\\\", \\n {'request': self._request},\\n mappingfunc=lambda x,y:x,\\n donefunc=lambda x:x)\\n\\n if not handled == 1:\\n blosxom_handler(self._request)\\n\\n # do end callback\\n tools.run_callback(\\\"end\\\", {'request': self._request})\",\n \"async def worker(self) -> None:\\n session = aiohttp.ClientSession()\\n while 1:\\n try:\\n data = await self.task_queue.get()\\n await self.add_page_views(url_sub=data, session=session)\\n except Exception as e:\\n print(e)\\n await asyncio.sleep(delay=self.wait_frequency)\",\n \"def run(self):\\n # bind to the port\\n self.serversocket.bind(('0.0.0.0', PORT))\\n print(\\\"Listening on: \\\" + Colors.BOLD + HOST + \\\":\\\" + str(PORT) + Colors.ENDC)\\n print(\\\"... waiting for a connection\\\", file=sys.stderr)\\n try:\\n while True:\\n # queue up to 5 requests\\n self.serversocket.listen(5)\\n clientsocket, addr = self.serversocket.accept()\\n print(\\\"Got a connection from \\\" + Colors.WARNING + \\\"%s\\\" % str(addr) + Colors.ENDC)\\n self.clientsocket.append(clientsocket)\\n newthread = ClientThread(addr, self.clientsocket[-1])\\n newthread.start()\\n finally:\\n for cskt in self.clientsocket:\\n cskt.close()\",\n \"def threading_handler(client_connection, client_address):\\r\\n\\r\\n while True:\\r\\n request_method, request_path, request_proto, request_data = parse_request(client_connection)\\r\\n\\r\\n if not request_data:\\r\\n break\\r\\n\\r\\n collect_request_data(request_path, client_address)\\r\\n write_data(access_entries)\\r\\n\\r\\n if request_method == 'GET':\\r\\n if request_path in allowed_path:\\r\\n requested_file = open(os.getcwd() + request_path, 'rb')\\r\\n data = requested_file.read(os.path.getsize(os.getcwd() + request_path))\\r\\n requested_file.close()\\r\\n request_handler(client_connection, request_path, request_proto, '200', 'OK', data, True)\\r\\n break\\r\\n elif '/www' == request_path or '/' == request_path:\\r\\n request_handler(client_connection, request_path, request_proto, '403', 'Forbidden', '403 Unauthorized',\\r\\n False)\\r\\n break\\r\\n elif '/www/access_entries.html' == request_path:\\r\\n final_tag = construct_access_entries_data()\\r\\n request_handler(client_connection, '/www/access_list.txt', request_proto, '200', 'OK', final_tag, True)\\r\\n break\\r\\n else:\\r\\n request_handler(client_connection, request_path, request_proto, '404', 'Not found', '404 Not Found',\\r\\n False)\\r\\n break\\r\\n else:\\r\\n request_handler(client_connection, request_path, request_proto, '405', 'Method Not Allowed',\\r\\n '405 Method Not Allowed', False)\\r\\n\\r\\n client_connection.close()\\r\\n print (\\\" --- Connection Closed --- \\\", client_address)\",\n \"def handle(self):\\n global log_th\\n sent = 1\\n msg_body = ''\\n get_recv = True\\n get_data = True\\n empty_check = 0\\n # Looping session requests\\n while 1:\\n try:\\n # If enabled sleep feauture\\n if self.sleep_between != 0:\\n time.sleep(self.sleep_between)\\n # If no answer feauture\\n if self.no_answer != 0:\\n time.sleep(1)\\n continue\\n # Changing receive size if receiving data part\\n if sent == 3 or sent == 4:\\n data = self.request.recv(self.data_recv_size)\\n else:\\n data = self.request.recv(self.std_recv_size)\\n if sent != 5:\\n self.command_w_th_inc.write_commands(\\n data=bytes(data).decode().encode('ascii', 'ignore')\\n .decode().rstrip(), qid=self.message_id)\\n # To many empty line received, closed thread\\n if self.func_empty_check(data):\\n if empty_check >= 3:\\n break\\n else:\\n empty_check += 1\\n continue\\n # Logging session requests if steps not equal to data section\\n if sent != 5:\\n log_th.log_info('{} - {} client executed : \\\"{}\\\"'.format(\\n self.message_id, self.client_ip, bytes(data).decode().rstrip()))\\n # Break the loop\\n if self.func_quit(data):\\n break\\n except Exception as ae:\\n log_th.log_warning('{} encounter an error from {} thread : {}'.format(\\n self.client_ip, threading.current_thread().name, str(ae)))\\n break\\n else:\\n try:\\n # Checking the all steps\\n if self.func_rset(data):\\n sent = 2\\n continue\\n if self.func_auth(data):\\n continue\\n if self.func_auth_plain(data):\\n continue\\n if self.func_starttls(data):\\n continue\\n # Starting the sent steps\\n # Ehlo/hello\\n if sent == 1:\\n if self.func_ehlo(data) or self.func_helo(data):\\n sent += 1\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('command not found'))\\n # Mail from, rcpt to, data\\n elif sent == 2:\\n if bytes(data).decode().encode('ascii', 'ignore').decode().rstrip().splitlines().__len__() > 2:\\n get_data = False\\n get_recv = False\\n elif bytes(data).decode().encode('ascii',\\n 'ignore').decode().rstrip().splitlines().__len__() > 1:\\n get_recv = False\\n if self.func_from(data, get_recv):\\n sent += 1\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('mail from'))\\n if not get_recv:\\n if self.func_to(data, get_recv, get_data):\\n sent += 1\\n get_recv = True\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))\\n if not get_data:\\n if self.func_data(data, get_recv, get_data):\\n sent += 1\\n get_data = True\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\\n # rcpt to and data\\n elif sent == 3:\\n if bytes(data).decode().encode('ascii', 'ignore').decode().rstrip().splitlines().__len__() > 1:\\n get_data = False\\n if self.func_to(data, get_recv, get_data):\\n sent += 1\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))\\n if not get_data:\\n if self.func_data(data, get_recv, get_data):\\n sent += 1\\n get_data = True\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\\n # data\\n elif sent == 4:\\n if self.func_to(data, get_recv, get_data):\\n continue\\n if self.func_data(data, get_recv, get_data):\\n sent += 1\\n else:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('data'))\\n # content writing to file (if enabled) and quit statement\\n elif sent == 5:\\n data_list = bytes(data).decode().split('\\\\r\\\\n')\\n for line in data_list:\\n if str(line) == '.':\\n if self.mail_save_enable != 0:\\n out_file = open(self.mail_save_path + '/'\\n + self.message_id + '.eml', 'w')\\n out_file.write(msg_body)\\n out_file.close()\\n self.func_data_ok()\\n sent = 1\\n break\\n else:\\n msg_body += str(line) + '\\\\r\\\\n'\\n except IndexError:\\n if sent == 2:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('mail from'))\\n elif sent == 3:\\n self.func_denied(self.conf_th_ic.get_item(q_key='err-messages').get('rcpt to'))\",\n \"def thread(self, request, *args, **kwargs):\\n thread = self.get_thread()\\n protocol = request.is_secure() and 'wss://' or 'ws://'\\n url = protocol + request.get_host() + settings.WEBSOCKET_URL + 'thread-%i' % thread.id\\n return Response(url)\",\n \"def run(self):\\n httpd = HTTPServer((self.host, self.port), self._Handler)\\n sa = httpd.socket.getsockname()\\n serve_message = \\\"Serving HTTP on {host} port {port} (http://{host}:{port}/) ...\\\"\\n print(serve_message.format(host=sa[0], port=sa[1]))\\n try:\\n httpd.serve_forever()\\n except KeyboardInterrupt:\\n print(\\\"\\\\nKeyboard interrupt received, exiting.\\\")\\n httpd.shutdown()\",\n \"def run(self):\\n self.log_id = 0\\n\\n # all active websockets and their state\\n self.websocks = {}\\n\\n # all active python interpreter sessions\\n self.pysessions = {}\\n\\n if self.DISABLE_REQUESTS_DEBUG_LOGS:\\n disable_requests_debug_logs()\\n\\n self.threadpool = ThreadPool(self.THREADPOOL_WORKERS)\\n\\n self.api = None\\n\\n # tornado app object\\n base_handlers = self.prepare_base_handlers()\\n handlers = self.prepare_handlers()\\n self.template_loader = TemplateLoader([resolve_path(self.TEMPLATE_PATH)])\\n _ = self.prepare_template_loader(self.template_loader)\\n if _ is not None:\\n self.template_loader = _\\n\\n shclass = CustomStaticFileHandler\\n shclass.PATHS.append(resolve_path(self.STATIC_PATH))\\n _ = self.prepare_static_paths(shclass.PATHS)\\n if _ is not None:\\n shclass.PATHS = _\\n\\n self.static_handler_class = shclass\\n\\n self.nav_tabs = [(\\\"Home\\\", \\\"/\\\")]\\n if self.args.debug:\\n self.nav_tabs += [(\\\"Console\\\", \\\"/console\\\"), (\\\"Logs\\\", \\\"/logs\\\")]\\n self.nav_tabs = self.prepare_nav_tabs(self.nav_tabs)\\n\\n settings = {\\n \\\"static_path\\\": \\\"<DUMMY-INEXISTENT-PATH>\\\",\\n \\\"static_handler_class\\\": self.static_handler_class,\\n \\\"template_loader\\\": self.template_loader,\\n \\\"compress_response\\\": True,\\n \\\"debug\\\": self.args.debug,\\n }\\n\\n all_handlers = handlers + base_handlers\\n self.app = self.APP_CLASS(**settings)\\n self.app.add_handlers(self.VIRTUAL_HOST, all_handlers)\\n\\n sys.funcserver = self.app.funcserver = self\\n\\n self.api = self.prepare_api()\\n if self.api is not None and not hasattr(self.api, \\\"log\\\"):\\n self.api.log = self.log\\n\\n if self.args.port != 0:\\n self.app.listen(self.args.port)\\n\\n tornado.ioloop.IOLoop.instance().start()\",\n \"def start(self):\\n threading.Thread(target=self.serve_forever).start()\",\n \"def main():\\n server = ThreadedServer(MasterControllerService, port=5000)\\n server.start()\",\n \"def getServerInThread(data, onlyOnce=False):\\n\\tclass Handler(BaseHTTPServer.BaseHTTPRequestHandler):\\n\\t\\tdef do_GET(self):\\n\\t\\t\\tself.wfile.write(data)\\n\\t\\tdo_POST = do_GET\\n\\t\\n\\tport = 34000\\n\\thttpd = BaseHTTPServer.HTTPServer(('', port), Handler)\\n\\n\\tif onlyOnce:\\n\\t\\tserve = httpd.handle_request\\n\\telse:\\n\\t\\tserve = httpd.serve_forever\\n\\n\\tt = threading.Thread(target=serve)\\n\\tt.setDaemon(True)\\n\\tt.start()\\n\\treturn httpd, t, \\\"http://localhost:%s\\\"%port\",\n \"def run(self, host):\\n\\n # Trying to connect to given host.\\n while self.csrftoken == None:\\n try:\\n self.client.get(self.url)\\n self.csrftoken = self.client.cookies['csrftoken']\\n self.cookies = dict(self.client.cookies)\\n except:\\n pass\\n\\n while True:\\n if self.queue.empty():\\n self.sending = False\\n time.sleep(1)\\n else:\\n self.sending = True\\n\\n # sending operations when queue reached batching size\\n # after about 5 seconds send anyway...\\n qsize = self.queue.qsize()\\n if qsize < BATCH_SIZE and qsize != self.old_qsize:\\n time.sleep(1)\\n self.old_qsize = qsize\\n elif qsize < BATCH_SIZE and qsize == self.old_qsize and self.count < 5:\\n time.sleep(1)\\n self.count += 1\\n else:\\n data_list = []\\n while not self.queue.empty():\\n op = self.queue.get()\\n data_list.append(op)\\n # just for testing\\n # shuffle(data_list)\\n data_dict = dict(list=json.dumps(data_list)) \\n\\n while True:\\n try:\\n self.send_post(data_dict)\\n break\\n except requests.exceptions.RequestException:\\n time.sleep(2)\\n print \\\"[THREAD \\\" + str(host['id']) + \\\"] Can't reach host \\\" + str(host['port'])\\n continue\\n\\n self.count = 0\",\n \"def do_GET(self):\\r\\n self.logger.debug(\\\"SseHTTPRequestHandler(Thread-%s): do_GET, path=%s\\\",\\r\\n threading.current_thread().ident, self.path)\\r\\n threading.current_thread().name = (self.path + \\\"_thread_\\\" +\\r\\n str(threading.current_thread().ident))\\r\\n if self.path == SseHTTPRequestHandler.eventsource_path:\\r\\n self._start_event_stream()\\r\\n else:\\r\\n http.server.SimpleHTTPRequestHandler.do_GET(self)\",\n \"def __image_request_handler(self):\\n self.__logger.info(\\\"Image Request Handling Thread started\\\")\\n ticker = Ticker(2)\\n while self._running:\\n timeout = ticker.end_tick(False)\\n try:\\n task = self.__image_queue.get(True, timeout)\\n except Queue.Empty:\\n ticker.start_tick()\\n continue\\n\\n # There is a task to process\\n ticker.start_tick()\\n source, connection = task\\n\\n # Check if the connection has been closed. If it was,\\n # do not bother processing the request.\\n if not connection.connected():\\n self.__logger.info(\\\"Skipping request for image of source %s\\\" \\\\\\n \\\" because requesting client disconnected\\\" \\\\\\n % source)\\n self.__image_queue.task_done()\\n continue \\n\\n # Obtain new image\\n error = \\\"No image available\\\"\\n image = None\\n mtime = time.time()\\n if source in self.__video_modules:\\n try:\\n mtime, image = self.__get_image(source)\\n except Exception as err:\\n error = \\\"Obtaining image failed: %s\\\" % repr(err)\\n else:\\n error = \\\"Video source %s has not been started\\\" % source\\n\\n if connection.connected():\\n if image:\\n # Valid image was obtained\\n img_str = image.tostring()\\n data = {'name': 'image',\\n 'source': source,\\n 'time': mtime,\\n 'shape': (image.width, image.height),\\n 'depth': image.depth,\\n 'nChannels': image.nChannels}\\n else:\\n # An error occured, notify the vision module\\n self.__logger.info(\\\"Failed to obtain image for source %s. \\\"\\\\\\n \\\" Error message: %s\\\" % (source, error))\\n img_str = \\\"\\\"\\n data = {'name': 'image',\\n 'source': source,\\n 'time': mtime,\\n 'error': error}\\n # Send the data to the vision module.\\n if not connection.sendall(data, img_str):\\n self.__logger.warning(\\\"Failed to send data to client. \\\" \\\\\\n \\\"Probably disconnected\\\")\\n else:\\n self.__logger.info(\\\"Image of source %s obtained but not \\\" \\\\\\n \\\"sending because requesting client \\\" \\\\\\n \\\"disconnected\\\" % source)\\n self.__image_queue.task_done()\\n self.__logger.info(\\\"Image Request Handling Thread ended\\\")\",\n \"def _worker(self, args):\\n pass\",\n \"def run(self):\\n self._connection = self.open_connection()\\n self._connection.ioloop.start()\",\n \"def handle_client(self):\\n e = threading.Event()\\n reg_t = threading.Thread(target=self.handle_reg_client, args=(e,))\\n stream_t = threading.Thread(target=self.handle_stream_client,\\n args=(e,))\\n reg_t.start()\\n stream_t.start()\",\n \"def start(self):\\n if self._params.arq:\\n s = udpsocket.UDPSocket()\\n else:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n try:\\n s.bind(('', self._params.port))\\n s.listen(5)\\n self.check_and_print_debug_message(\\n 'HTTP Server is running on port: ' + str(self._params.port))\\n while True:\\n c, addr = s.accept()\\n Thread(\\n target=self.process_http_request,\\n args=(c, addr)\\n ).start()\\n except Exception as e:\\n self.check_and_print_debug_message(str(e))\\n s.close()\\n finally:\\n self.check_and_print_debug_message(\\n \\\"HTTP Server connection is closed.\\\")\\n s.close()\",\n \"def serve_forever(self):\\n\\t\\tself.serverActive = 1\\n\\t\\twhile self.serverActive:\\n\\t\\t\\trfds = self.procHelper.ReadFDs\\n\\t\\t\\tsockFile = self.socket.fileno()\\n\\t\\t\\trfds.append(sockFile)\\n\\t\\t\\ttry_select = 1\\n\\t\\t\\t#print \\\"HTTP RFDS:\\\", rfds\\n\\t\\t\\tif len(rfds) == 1:\\n\\t\\t\\t\\tprint \\\"RPC-HTTP: We are lost our parents or socket closed.. Exiting Normally !\\\"\\n\\t\\t\\t\\tself.procHelper.exit()\\n\\t\\t\\t\\tprint \\\"Error.. We can't exit :(\\\"\\n\\n\\t\\t\\twhile try_select:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\trds = select.select(rfds, [], [], 3)\\n\\t\\t\\t\\t\\ttry_select = 0\\n\\t\\t\\t\\t\\t#print \\\"Connection from:\\\", rds, rfds\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tprint \\\"Select Exception. Can be a SIGCHLD ?\\\"\\n\\n\\t\\t\\tif rds != None and len(rds) == 1:\\n\\t\\t\\t\\tprint \\\"RPC-HTTP: We are lost our parents or socket closed.. Exiting Normally !\\\"\\n\\t\\t\\t\\tself.procHelper.exit()\\n\\t\\t\\txsock = 0\\n\\t\\t\\tif rds:\\n\\t\\t\\t\\tfor rfd in rds[0]:\\n\\t\\t\\t\\t\\tif rfd == sockFile:\\n\\t\\t\\t\\t\\t\\txsock = rfd\\n\\t\\t\\t\\t\\t\\t#print \\\"HTTP Main Loop: Socket I/O\\\"\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tio = self.procHelper.readFD2io(rfd)\\n\\t\\t\\t\\t\\t\\tsrcpid = self.procHelper.rfd2PID(rfd)\\n\\t\\t\\t\\t\\t\\tppid = self.procHelper.parentRFD()\\n\\t\\t\\t\\t\\t\\tcmd = io.getCommand()\\n\\t\\t\\t\\t\\t\\t#print self.procHelper.myPID, os.getpid(), \\\"HTTP Main Loop: Read a command:\\\", str(cmd)[:20], ppid, srcpid\\n\\t\\t\\t\\t\\t\\tif io.cmdrpoll.poll(1)[0][1] & select.POLLHUP:\\n\\t\\t\\t\\t\\t\\t\\tself.procHelper.removeChild(srcpid)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tpkPid = int(cmd[0])\\n\\t\\t\\t\\t\\t\\t\\tpkTid = int(cmd[1])\\n\\t\\t\\t\\t\\t\\t\\tpkData = cmd[3]\\n\\t\\t\\t\\t\\t\\t\\tcommand = cmd[2]\\n\\t\\t\\t\\t\\t\\t\\t#print \\\"ACCEPT READ FROM PIPE:\\\",rfd, ppid, str(cmd)[:20]\\n\\t\\t\\t\\t\\t\\t\\tif rfd != ppid:\\n\\t\\t\\t\\t\\t\\t\\t\\t#print \\\"Send To Parent:\\\", pkPid, command\\n\\t\\t\\t\\t\\t\\t\\t\\tself.procHelper.checkUpCmd(dir=\\\"P\\\", srcpid=srcpid, cmd=command, pktpid=pkPid, tid=pkTid, data=pkData)\\n\\t\\t\\t\\t\\t\\t\\t\\tif cmd:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.procHelper.sendParentCommand(command, pkPid, pkTid, pkData)\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tif cmd:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.procHelper.checkUpCmd(dir=\\\"C\\\", srcpid=srcpid, cmd=command, pktpid=pkPid, tid=pkTid, data=pkData)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#print \\\"Send To Child:\\\", pkPid, command\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.procHelper.sendCommand(pkPid, command, pkPid, pkTid, pkData)\\n\\t\\t\\t\\tif xsock > 0:\\n\\t\\t\\t\\t\\tself.handle_request()\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i in l:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tx = os.waitpid(i, os.WNOHANG)\\n\\t\\t\\t\\t\\t\\tprint \\\"Child info:\\\", i, x\\n\\t\\t\\t\\t\\t\\tif os.WIFEXITED(x[1]):\\n\\t\\t\\t\\t\\t\\t\\tprint \\\"Child Deleted:\\\", i, x\\n\\t\\t\\t\\t\\t\\t\\tdel l[p]\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tpass\",\n \"def process_request(self, request, client_address):\\n\\t\\t#traceback.print_stack()\\n\\t\\tself.collect_children()\\n\\t\\tchldPID = self.procHelper.makeChild()\\n\\t\\tprint \\\"HTTP New Connection: Go Fork for accept: \\\", self.procHelper.PID2io(chldPID), chldPID\\n\\t\\tparentrpid = os.getpid()\\n\\t\\tpid = os.fork()\\n\\t\\tif pid:\\n\\t\\t\\t# Parent process\\n\\t\\t\\tprint \\\"HTTPD Conn Parent:\\\", self.procHelper.myPID ,chldPID\\n\\t\\t\\tself.procHelper.setIODebug(chldPID, 0, \\\"HTTP->Connection\\\")\\n\\t\\t\\tself.procHelper.initForParent(chldPID)\\n\\t\\t\\tself.procHelper.registerChild(chldPID, self.procHelper.myPID)\\n\\t\\t\\tsigset = self.procHelper\\n\\t\\t\\tself.procHelper.sendCommand(child = chldPID, command = \\\"INTU_MCL\\\", PID = chldPID, TID = 0, data = None)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tselect.select([self.procHelper.PID2rfd(chldPID)], [],[], 0.1)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tpass\\n\\t\\t\\ttp = self.procHelper.readConn(chldPID)\\n\\t\\t\\tpid = int(tp[3])\\n\\t\\t\\tl = sigset.getUserData()\\n\\t\\t\\tif l == None:\\n\\t\\t\\t\\tl = []\\n\\t\\t\\tl.append(pid)\\n\\t\\t\\tsigset.setUserData(l[:])\\n\\t\\t\\tsigchldhandler(0, sigset)\\n\\t\\t\\tif self.active_children is None:\\n\\t\\t\\t\\tself.active_children = []\\n\\t\\t\\tself.active_children.append(pid)\\n\\t\\t\\tself.close_request(request)\\n\\t\\t\\t#print \\\"PID\\\", chldPID, \\\"created ppid:\\\", pid, sigset.getUserData()\\n\\t\\t\\treturn\\n\\t\\telse:\\n\\t\\t\\t# Child process.\\n\\t\\t\\t# This must never return, hence os._exit()!\\n\\t\\t\\tself.procHelper.setIODebug(chldPID, 0, \\\"Connection->HTTP\\\")\\n\\t\\t\\tgloPIO = self.procHelper.PID2io(chldPID)\\n\\t\\t\\tgloPPid = self.procHelper.myPID + 0\\n\\t\\t\\tnew_ph = CHLDHELPER.childHelper(gloPIO, gloPPid, chldPID)\\n\\t\\t\\tnew_ph.setIODebug(chldPID, 0, \\\"Connection->HTTP\\\")\\n\\t\\t\\tnew_ph.initForChild(gloPPid)\\n\\t\\t\\tself.procHelper = new_ph\\n\\t\\t\\tself.procHelper.parentppid = parentrpid\\n\\t\\t\\tprint \\\"HTTPD Conn Child:\\\", chldPID, os.getpid(), self.procHelper.parentppid, self.procHelper.myPID, self.procHelper.gloPPid\\n\\t\\t\\ttry:\\n\\t\\t\\t\\t#print \\\"I am new child:\\\", os.getpid()\\n\\t\\t\\t\\tself.procHelper.waitForParentCmd(timeout = 1)\\n\\t\\t\\t\\tpcmd = self.procHelper.getParentCommand()\\n\\t\\t\\t\\tself.procHelper.sendParentCommand(cmd = \\\"IRSU_PPID\\\", pid = chldPID, tid = 0, data=str(os.getpid()))\\n\\t\\t\\t\\tself.finish_request(request, client_address)\\n\\t\\t\\t\\tos._exit(0)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tself.handle_error(request, client_address)\\n\\t\\t\\t\\tfinally:\\n\\t\\t\\t\\t\\tos._exit(1)\",\n \"def __ServiceClient(self,Client):\\n\\t\\twhile True:\\n\\t\\t\\tDataClient = Client.recv(1024)\\n\\t\\t\\tprint(DataClient)\\n\\t\\t\\t# your source code here\\n\\t\\t\\tmessage = DataClient\\n\\t\\t\\t# data to be sent to api\\n\\t\\t\\tdata = {'message': message}\\n\\t\\t\\t# sending post request and saving response as response object\\n\\t\\t\\tr = requests.post(url = self.API_ENDPOINT, data = data)\\n\\t\\t\\t# extracting response text\\n\\t\\t\\t#pastebin_url = r.text\\n\\t\\t\\t#print(\\\"The pastebin URL is:%s\\\"%pastebin_url)\",\n \"def handle(self):\\n data = self.request.recv(1024)\\n self.request.send(data)\",\n \"def run(self):\\n print(\\\"Client: Started\\\", flush=True)\\n ack = Packet()\\n ack_data = b''\\n\\n request = \\\"download\\\"\\n req_pkt = Packet(0, request)\\n req_packed = req_pkt.pkt_pack()\\n\\n self.client_socket.sendto(req_packed, self.server_addr)\\n\\n ack_data = self.client_socket.recv(self.pkt_size)\\n ack.pkt_unpack(ack_data)\\n\\n self.recv_img(self.img_save_to)\\n\\n ack = Packet()\\n ack_data = b''\\n request = \\\"upload\\\"\\n req_pkt = Packet(0, request)\\n req_packed = req_pkt.pkt_pack()\\n\\n self.client_socket.sendto(req_packed, self.server_addr)\\n\\n ack_data = self.client_socket.recv(self.pkt_size)\\n ack.pkt_unpack(ack_data)\\n\\n self.send_img(self.img_to_send)\\n\\n sleep(5)\\n\\n ack = Packet()\\n ack_data = b''\\n request = \\\"exit\\\"\\n req_pkt = Packet(0, request)\\n req_packed = req_pkt.pkt_pack()\\n\\n self.client_socket.sendto(req_packed, self.server_addr)\\n\\n ack_data = self.client_socket.recv(self.pkt_size)\\n ack.pkt_unpack(ack_data)\\n\\n print(\\\"Client: Exiting...\\\")\\n # close socket when finished\\n self.client_socket.close()\",\n \"async def worker(\\n self, queue: asyncio.Queue, session: aiohttp.ClientSession\\n ) -> None:\\n while True:\\n url = await queue.get()\\n await self.fetch(url, session)\\n queue.task_done()\",\n \"def serve(self):\\n\\t\\tself.keep_running=1\\n\\t\\tif self.debug:\\n\\t\\t\\tprint \\\"server started\\\"\\n\\t\\ttry:\\n\\t\\t\\twhile self.keep_running:\\n\\t\\t\\t\\tself.handle_request()\\n\\t\\tfinally:\\n\\t\\t\\tif self.debug:\\n\\t\\t\\t\\tprint \\\"server finished\\\"\\n\\t\\t\\tself.keep_running=0\\n\\t\\t\\tself.close()\",\n \"def start(self):\\n #Starting the thread able to handle the administrator request\\n t2 = threading.Thread(target=self.handleServer)\\n t2.start()\\n self.ActiveThreads.append(t2)\\n #Listen continously\\n while True:\\n self.server.listen(50)\\n self.Log.log(\\\"Waiting for connections...\\\")\\n #Obtaining the parameters like the socket and the address/port of the incoming connection\\n (conn, (ip,port)) = self.server.accept()\\n #Creating a new thread able to handle the new connection with the client\\n newClient = ClientHandler(conn,ip,port,self.DB,self.Users,self.Log,self.XML);\\n #Starting the new thread\\n newClient.start()\\n #Appending the thread to the list of active threads\\n self.ActiveThreads.append(newClient)\",\n \"def run(self):\\n self.debug(__name__ + \\\".run(): self.threadName=\\\" + str(self.threadName) + \\\"\\\\n\\\")\\n self.debug(__name__ + \\\".run(): self.statusFile=\\\" + str(self.statusFile) + \\\"\\\\n\\\")\\n self.debug(__name__ + \\\".run(): self.recvData=\\\" + str(self.recvData) + \\\"\\\\n\\\")\\n self.debug(__name__ + \\\".run(): self.socketConn=\\\" + str(self.socketConn) + \\\"\\\\n\\\")\\n\\n status = True\\n data = self.getFileData()\\n self.mySocketObj.serverSend(self.socketConn, data)\\n if self.socketConn: self.socketConn.close()\\n # self.updateCounts()\\n self.status = status\\n if status:\\n self.appendMsg(__name__ + \\\".run(): Completed successfully for \\\" + str(self.threadName) + \\\"\\\\n\\\")\\n else:\\n self.appendMsg(__name__ + \\\".run(): Failed for \\\" + str(self.threadName) + \\\"\\\\n\\\")\\n # Endif\",\n \"def _make_thread(self):\\r\\n pass\",\n \"def _process_request(self):\\n if not self._requests:\\n if self._stream:\\n self._stream.close()\\n self._stream = None\\n if self._processing:\\n self._processing = False\\n Engine.instance().stop()\\n return\\n\\n request = self._requests[0]\\n\\n request.append(\\n Engine.instance().defer(request[5], self._request_timeout, request))\\n\\n port = request[2].port\\n if not port:\\n if request[2].scheme.lower() == 'https':\\n port = 443\\n else:\\n port = 80\\n\\n host = \\\"%s:%d\\\" % (request[2].hostname, port)\\n\\n if self._stream:\\n if not self._server == host.lower() or not \\\\\\n self._is_secure == (request[2].scheme.lower() == 'https'):\\n self._stream.end()\\n return\\n\\n if not self._stream:\\n # Store the current server.\\n self._server = host.lower()\\n\\n # Create a Stream, hook into it, and connect.\\n self._stream = Stream()\\n\\n self._stream.on_close = self._on_close\\n self._stream.on_connect = self._on_connect\\n\\n self._is_secure = request[2].scheme.lower() == 'https'\\n if self._is_secure:\\n raise Exception(\\\"SSL has not yet been implemented in this version of Pants.\\\")\\n self._stream.startTLS()\\n\\n self._stream.connect((request[2].hostname, port))\\n return\\n\\n # If we got here, we're connected, and to the right server. Do stuff.\\n self._stream.write('%s %s HTTP/1.1%s' % (request[0], request[8], CRLF))\\n for k, v in request[3].iteritems():\\n self._stream.write('%s: %s%s' % (k, v, CRLF))\\n\\n if request[4]:\\n self._stream.write('%s%s' % (CRLF, request[4]))\\n else:\\n self._stream.write(CRLF)\\n\\n # Now, wait for a response.\\n self._stream.on_read = self._read_headers\\n self._stream.read_delimiter = DOUBLE_CRLF\",\n \"def run(self):\\n self.logger.debug(\\\"Resource manager main thread started\\\")\\n\\n while not self._stop_flag:\\n try:\\n self._handle_requests()\\n self._accept_requests()\\n\\n except Exception as ex:\\n self.logger.exception(\\\"Resource manager failed. \\\"\\n \\\"Reason: %s\\\", ex)\\n\\n self.logger.debug(\\\"Resource manager thread is down\\\")\",\n \"def done(self, *arg, **kw):\\r\\n\\r\\n # ----------------------------------------\\r\\n # persistent connection management\\r\\n # ----------------------------------------\\r\\n\\r\\n # --- BUCKLE UP! ----\\r\\n\\r\\n connection = http_server.get_header(http_server.CONNECTION,self.header)\\r\\n connection = connection.lower()\\r\\n\\r\\n close_it = 0\\r\\n wrap_in_chunking = 0\\r\\n globbing = 1\\r\\n\\r\\n if self.version == '1.0':\\r\\n if connection == 'keep-alive':\\r\\n if not 'Content-Length' in self:\\r\\n close_it = 1\\r\\n else:\\r\\n self['Connection'] = 'Keep-Alive'\\r\\n else:\\r\\n close_it = 1\\r\\n elif self.version == '1.1':\\r\\n if connection == 'close':\\r\\n close_it = 1\\r\\n elif not 'Content-Length' in self:\\r\\n if 'Transfer-Encoding' in self:\\r\\n if not self['Transfer-Encoding'] == 'chunked':\\r\\n close_it = 1\\r\\n elif self.use_chunked:\\r\\n self['Transfer-Encoding'] = 'chunked'\\r\\n wrap_in_chunking = 1\\r\\n # globbing slows down tail -f output, so only use it if\\r\\n # we're not in chunked mode\\r\\n globbing = 0\\r\\n else:\\r\\n close_it = 1\\r\\n elif self.version is None:\\r\\n # Although we don't *really* support http/0.9 (because\\r\\n # we'd have to use \\\\r\\\\n as a terminator, and it would just\\r\\n # yuck up a lot of stuff) it's very common for developers\\r\\n # to not want to type a version number when using telnet\\r\\n # to debug a server.\\r\\n close_it = 1\\r\\n\\r\\n outgoing_header = producers.simple_producer(self.build_reply_header())\\r\\n\\r\\n if close_it:\\r\\n self['Connection'] = 'close'\\r\\n\\r\\n if wrap_in_chunking:\\r\\n outgoing_producer = deferring_chunked_producer(\\r\\n deferring_composite_producer(self.outgoing)\\r\\n )\\r\\n # prepend the header\\r\\n outgoing_producer = deferring_composite_producer(\\r\\n [outgoing_header, outgoing_producer]\\r\\n )\\r\\n else:\\r\\n # prepend the header\\r\\n self.outgoing.insert(0, outgoing_header)\\r\\n outgoing_producer = deferring_composite_producer(self.outgoing)\\r\\n\\r\\n # hook logging into the output\\r\\n outgoing_producer = deferring_hooked_producer(outgoing_producer,\\r\\n self.log)\\r\\n\\r\\n if globbing:\\r\\n outgoing_producer = deferring_globbing_producer(outgoing_producer)\\r\\n\\r\\n self.channel.push_with_producer(outgoing_producer)\\r\\n\\r\\n self.channel.current_request = None\\r\\n\\r\\n if close_it:\\r\\n self.channel.close_when_done()\",\n \"def run(self):\\n ioloop.IOLoop.current().start()\",\n \"def _run(self) -> None:\\n\\n log.debug(\\\"Volumio 2 Web Service client starting ...\\\")\\n log.debug(f\\\"Connecting to Volumio 2 Web Service on {self._server}:{self._port}\\\")\\n\\n with SocketIO(self._server, self._port) as socketIO:\\n log.debug(\\\"Connected to Volumio 2 Web Service\\\")\\n socketIO.on(\\\"pushState\\\", self._on_state_response)\\n socketIO.emit(\\\"GetState\\\", on_GetState_response)\\n\\n # Request initial values\\n socketIO.emit(\\\"getState\\\", \\\"\\\")\\n\\n while not self._stop_event.is_set():\\n # rely on Volumio to push states mostly, but request an update\\n # at a low frequency to get some lacy update.\\n socketIO.wait_for_callbacks(seconds=10)\\n socketIO.emit(\\\"getState\\\", \\\"\\\")\",\n \"def run(self):\\n \\n try:\\n logging.info('Thread:{} starting'.format(self.thread_id))\\n\\n self.crawl_url()\\n self.parse_html()\\n except IOError as e:\\n self.thread_post_processing()\\n logging.error('CrawlUrlError url:{} msg:{}'.format(self.url, e))\\n\\n self.thread_post_processing()\",\n \"def process_request_thread(self):\\n while True:\\n try:\\n request, client_address = self._request_queue.get(\\n timeout=self.timeout_on_get,\\n )\\n except Queue.Empty:\\n # You wouldn't believe how much crap this can end up leaking,\\n # so we clear the exception.\\n sys.exc_clear()\\n if self._shutdown_event.isSet():\\n return\\n continue\\n try:\\n self.finish_request(request, client_address)\\n self.shutdown_request(request)\\n except:\\n self.handle_error(request, client_address)\\n self.shutdown_request(request)\\n self._request_queue.task_done()\",\n \"def proxy_thread(self, conn, client_addr):\\n req = conn.recv(self.config['MAX_REQUEST_LENGTH'])\\n line1 = req.split(b'\\\\n')[0]\\n x = line1.split(b' ')\\n if len(x) > 1:\\n url = x[1]\\n else:\\n return\\n \\n # Check if the file requested is css/js file\\n if bool(self.regex.findall(url.decode())):\\n # send cached version if present\\n if self.cache.retrieve(url.decode()):\\n time_elapsed = time()\\n conn.send(self.cache.retrieve(url.decode()).data)\\n conn.close()\\n time_elapsed = time() - time_elapsed\\n print('Retrieving from cache: ' + url.decode())\\n print('Time taken: {}'.format(time_elapsed))\\n else:\\n self.log(\\\"INFO\\\", client_addr, \\\"Request: \\\" + str(line1))\\n time_elapsed = time()\\n http_pos = url.find(b'://')\\n if http_pos == -1:\\n temp = url\\n else:\\n temp = url[(http_pos + 3):]\\n port_pos = temp.find(b':')\\n webserver_pos = temp.find(b'/')\\n if webserver_pos == -1:\\n webserver_pos = len(temp)\\n\\n webserver = \\\"\\\"\\n port = -1\\n if port_pos == -1 or webserver_pos < port_pos:\\n port = 80\\n webserver = temp[:webserver_pos]\\n else:\\n port = int((temp[port_pos + 1:])[:webserver_pos-port_pos-1])\\n webserver = temp[:port_pos]\\n\\n try:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(self.config['CONNECTION_TIMEOUT'])\\n s.connect((webserver, port))\\n s.sendall(req)\\n while True:\\n data = s.recv(self.config['MAX_REQUEST_LENGTH'])\\n if len(data) > 0:\\n self.cache.add(url.decode(), data)\\n conn.send(data)\\n else:\\n break\\n s.close()\\n conn.close()\\n\\n except socket.error as error_msg:\\n self.log('ERROR', client_addr, error_msg)\\n if s:\\n s.close()\\n if conn:\\n conn.close()\\n self.log(\\\"WARNING\\\", client_addr, \\\"Peer Reset \\\" + str(line1))\\n print('Recieving from website: ' + url.decode())\\n time_elapsed = time() - time_elapsed\\n print('Time Elapsed: {}'.format(time_elapsed))\\n else:\\n http_pos = url.find(b'://')\\n if http_pos == -1:\\n temp = url\\n else:\\n temp = url[(http_pos + 3):]\\n port_pos = temp.find(b':')\\n webserver_pos = temp.find(b'/')\\n if webserver_pos == -1:\\n webserver_pos = len(temp)\\n\\n webserver = \\\"\\\"\\n port = -1\\n if port_pos == -1 or webserver_pos < port_pos:\\n port = 80\\n webserver = temp[:webserver_pos]\\n else:\\n port = int((temp[port_pos + 1:])[:webserver_pos - port_pos - 1])\\n webserver = temp[:port_pos]\\n\\n try:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(self.config['CONNECTION_TIMEOUT'])\\n s.connect((webserver, port))\\n s.sendall(req)\\n while True:\\n data = s.recv(self.config['MAX_REQUEST_LENGTH'])\\n if len(data) > 0:\\n conn.send(data)\\n else:\\n break\\n s.close()\\n conn.close()\\n except socket.error:\\n if s:\\n s.close()\\n if conn:\\n conn.close()\\n self.log(\\\"WARNING\\\", client_addr, \\\"Peer Reset \\\" + str(line1))\",\n \"def __init__(self, client_ident):\\n\\t\\tthreading.Thread.__init__(self, None)\\n\\t\\tself.client_ident\\t\\t= client_ident\\n\\t\\tself.start()\",\n \"def handle(self):\\n\\n global batch_no\\n\\n if self.client_address[0] in workers: # Established worker\\n # self.request is a TCP socket connected to the client\\n print('Worker connected... handling')\\n self.data = recv_all(self.request, bytes_expected)\\n\\n try:\\n network_weight_updates = deque(struct.unpack('{}f'.format(num_weights), self.data))\\n print('\\\\tReceived data unpacked')\\n except:\\n print('\\\\tError unpacking data')\\n else:\\n print('\\\\tTraining master network')\\n nn.train_master(network_weight_updates)\\n batch_no += 1\\n print('\\\\tCompleted batch #{} of {}'.format(batch_no, total_batches))\\n\\n print('\\\\tGetting network weights')\\n network_weights = nn.get_weights()\\n print('\\\\tPacking response')\\n response = struct.pack('{}f'.format(num_weights), *network_weights)\\n print('\\\\tSending back weights')\\n self.request.sendall(response)\\n\\n elif self.client_address[0] in validators: # Established validator\\n print('Validator connected... handling')\\n print('\\\\tGetting network weights')\\n network_weights = nn.get_weights()\\n print('\\\\tPacking response')\\n response = struct.pack('{}f'.format(num_weights), *network_weights)\\n print('\\\\tSending back weights')\\n self.request.sendall(response)\\n\\n else: # New node connected\\n # Initialize client into set of workers\\n print('Contacted by new node...')\\n self.data = self.request.recv(1024).strip()\\n print('\\\\tReceived init string')\\n\\n # New nodes should send b'w' to become a worker, or b'v' to become a validator\\n if self.data == b'w':\\n print('\\\\tNew WORKER node... handling')\\n workers.add(self.client_address[0]) # Add IP to list of workers\\n elif self.data == b'v':\\n print('\\\\tNew VALIDATOR node... handling')\\n validators.add(self.client_address[0]) # Add IP to list of validators\\n else:\\n print('\\\\tUnknown node request! Moving on...')\\n return\\n\\n # Gather current network weights, pack as bytes, and send back to client\\n network_weights = nn.get_weights()\\n response = struct.pack('{}f'.format(num_weights), *network_weights)\\n print('\\\\tSending network weights')\\n self.request.sendall(response)\\n\\n print('\\\\tDONE with node')\\n\\n if batch_no > total_batches:\\n # Done with desired number of batches! Finalize network training\\n save_pkl(nn, 'saved_nn/iris_nn_relu_16n8n_100e_16b_l2.pkl') # Save trained network\\n\\n print('Eval training...')\\n eval_tr = nn.eval_classification(train)\\n print('Eval validation...')\\n eval_v = nn.eval_classification(validation)\\n print('Eval testing...')\\n eval_t = nn.eval_classification(test)\\n\\n print('Batches: {}\\\\tTrain: {:.3f}\\\\tValidation: {:.3f}\\\\tTest: {:.3f}'\\n .format(batch_no, eval_tr, eval_v, eval_t))\\n exit()\",\n \"def run(self):\\r\\n self.rpc_server.serve_forever(0.5)\",\n \"def doctest_BackgroundWorkerThread():\",\n \"def workerProcess(self):\\r\\n\\r\\n if self.postForkCallback:\\r\\n self.postForkCallback()\\r\\n\\r\\n while self.isRunning.value == True:\\r\\n try:\\r\\n client = self.serverTransport.accept()\\r\\n self.serveClient(client)\\r\\n except (KeyboardInterrupt, SystemExit):\\r\\n return 0\\r\\n except Exception, x:\\r\\n logging.exception(x)\",\n \"def main():\\n\\n httpd = BaseHTTPServer.HTTPServer(\\n ('localhost', int(ADDON.getSetting(\\\"port\\\"))),\\n StupidHTTPRequestHandler)\\n httpd_thread = threading.Thread(target=httpd.serve_forever)\\n httpd_thread.start()\\n\\n monitor = xbmc.Monitor()\\n \\n while not monitor.abortRequested():\\n # Sleep/wait for abort for 10 seconds\\n if monitor.waitForAbort(10):\\n # Abort was requested while waiting. We should exit\\n break\\n\\n httpd.shutdown()\\n httpd.server_close()\",\n \"def http_server():\\n #Set up the server socket.\\n server_socket = socket.socket(\\n socket.AF_INET,\\n socket.SOCK_STREAM,\\n socket.IPPROTO_IP)\\n\\n try:\\n #Connect the server socket.\\n server_socket.bind(('127.0.0.1', 50000))\\n server_socket.listen(1)\\n\\n #Loop indefinitely while waiting for connections.\\n\\n while True:\\n try:\\n conn, addr = server_socket.accept()\\n msg = receive_message(conn)\\n uri = parse_request(msg)\\n resource, mimetype = map_uri(uri)\\n\\n except (Error404, ParseException) as err:\\n response = build_response(err.message, 'text/plain', err.code)\\n\\n except:\\n response = build_response(\\\"500 Internal Server Error\\\",\\n 'text/plain', '500')\\n\\n else:\\n response = build_response(resource, mimetype)\\n\\n finally:\\n conn.sendall(response)\\n # conn.shutdown(socket.SHUT_WR)\\n conn.close()\\n\\n finally:\\n #Make sure the socket is closed if we can't continue.\\n server_socket.close()\",\n \"def do_GET(self):\\n server_ip = Setup.parse_options()['ip_address']\\n uri = \\\"http://\\\" + server_ip + self.path\\n response = urllib.urlopen(uri)\\n self.copyfile(response, self.wfile)\\n headers = self.generate_header_dic(self.headers.headers)\\n ip_address = self.client_address[0] # get client iP address\\n if Setup.system_status != 'green':\\n self.process_request(ip_address, headers, self.path)\\n self.process_response(ip_address, response.headers)\",\n \"def main(self):\\n while True:\\n if not self.data_server_command.empty():\\n command_data_server = self.data_server_command.get()\\n if command_data_server[0] == 4:\\n thread.start_new_thread(self.get_file, (command_data_server[1],))\\n else:\\n self.data_server_command_def[command_data_server[0]](command_data_server[1])\",\n \"def do_work(self):\",\n \"def server(conn, address):\\n print(\\\"Client Connection Open\\\")\\n while True:\\n request = server_read(conn)\\n if request:\\n print(request)\\n manage_client(request, conn)\",\n \"def handle_one_request(self):\\n \\n try:\\n \\n self.raw_requestline = self.rfile.readline(65537)\\n \\n if len(self.raw_requestline) > 65536:\\n \\n self.requestline = ''\\n \\n self.request_version = ''\\n \\n self.command = ''\\n \\n self.send_error(414)\\n \\n return\\n \\n if not self.raw_requestline:\\n \\n self.close_connection = 1\\n \\n return\\n \\n if not self.parse_request():\\n \\n # An error code has been sent, just exit\\n \\n return\\n \\n mname = 'do_' + self.command\\n \\n if not hasattr(self, mname):\\n \\n self.send_error(501, \\\"Unsupported method (%r)\\\" % self.command)\\n \\n return\\n \\n method = getattr(self, mname)\\n \\n print \\\"before call do_Get\\\"\\n \\n method()\\n \\n #增加 debug info 及 wfile 判断是否已经 close\\n \\n print \\\"after call do_Get\\\"\\n \\n if not self.wfile.closed:\\n self.wfile.flush() #actually send the response if not already done.\\n \\n print \\\"after wfile.flush()\\\"\\n \\n except socket.timeout, e:\\n \\n #a read or a write timed out. Discard this connection\\n self.log_error(\\\"Request timed out: %r\\\", e)\\n self.close_connection = 1\\n return\",\n \"async def _handler(self, reader, writer):\\n gc.collect()\\n\\n try:\\n req = request(reader)\\n resp = response(writer)\\n # Read HTTP Request with timeout\\n await asyncio.wait_for(self._handle_request(req, resp),\\n self.request_timeout)\\n\\n # OPTIONS method is handled automatically\\n if req.method == b'OPTIONS':\\n resp.add_access_control_headers()\\n # Since we support only HTTP 1.0 - it is important\\n # to tell browser that there is no payload expected\\n # otherwise some webkit based browsers (Chrome)\\n # treat this behavior as an error\\n resp.add_header('Content-Length', '0')\\n await resp._send_headers()\\n return\\n\\n # Ensure that HTTP method is allowed for this path\\n if req.method not in req.params['methods']:\\n raise HTTPException(405)\\n\\n # Handle URL\\n gc.collect()\\n if hasattr(req, '_param'):\\n await req.handler(req, resp, req._param)\\n else:\\n await req.handler(req, resp)\\n # Done here\\n except (asyncio.CancelledError, asyncio.TimeoutError):\\n pass\\n except OSError as e:\\n # Do not send response for connection related errors - too late :)\\n # P.S. code 32 - is possible BROKEN PIPE error (TODO: is it true?)\\n if e.args[0] not in (errno.ECONNABORTED, errno.ECONNRESET, 32):\\n try:\\n await resp.error(500)\\n except Exception as e:\\n log.exc(e, \\\"\\\")\\n except HTTPException as e:\\n try:\\n await resp.error(e.code)\\n except Exception as e:\\n log.exc(e)\\n except Exception as e:\\n # Unhandled expection in user's method\\n log.error(req.path.decode())\\n log.exc(e, \\\"\\\")\\n try:\\n await resp.error(500)\\n # Send exception info if desired\\n if self.debug:\\n sys.print_exception(e, resp.writer.s)\\n except Exception:\\n pass\\n finally:\\n await writer.aclose()\\n # Max concurrency support -\\n # if queue is full schedule resume of TCP server task\\n if len(self.conns) == self.max_concurrency:\\n self.loop.create_task(self._server_coro)\\n # Delete connection, using socket as a key\\n del self.conns[id(writer.s)]\",\n \"def main(self):\\n addr = (self.uri, self.port)\\n try:\\n self.client.connect(addr)\\n except socket.gaierror:\\n print(\\\"[ERROR] not a valid URI. Try again please...\\\")\\n else:\\n print(\\\"[SETUP] client connected to IPv4 address\\\", self.uri, \\\"on port\\\", self.port)\\n self.handler()\",\n \"def run(self):\\n self._server = self._get_server()\\n self._server.serve_forever()\",\n \"def run(self):\\n self.submit()\\n self.start()\",\n \"async def run(self) -> None:\",\n \"async def run(self) -> None:\",\n \"def start(self):\\n #url = '{}://{}:{}/'.format('http',\\n # self.ip,\\n # self.port)\\n #self.service_info = ServiceInfo(\\n # '_webthing._sub._http._tcp.local.',\\n # '{}._http._tcp.local.'.format(self.name),\\n # address=socket.inet_aton(self.ip),\\n # port=self.port,\\n # properties={\\n # 'url': url,\\n # },\\n # server='{}.local.'.format(socket.gethostname()))\\n #self.zeroconf = Zeroconf()\\n #self.zeroconf.register_service(self.service_info)\\n\\n # If WebSocketS used and NOT running in thread, and WebServer IS\\n # running in thread make shure WebServer has enough stack size to\\n # handle also the WebSocket requests.\\n log.info('Starting Web Server')\\n self.server.Start(threaded=srv_run_in_thread, stackSize=8192)\",\n \"def _bg_thread_main(self) -> None:\\n while not self._done:\\n self._run_server_cycle()\",\n \"async def main():\\n await serve_websocket(handle_server, SERVER, PORT, ssl_context=None)\",\n \"def main_thread_http_entry(self, msg):\\n # V/nsHttp HttpBaseChannel::Init [this=c30d000]\\n if msg['message'].startswith('HttpBaseChannel::Init'):\\n match = re.search(r'^HttpBaseChannel::Init \\\\[this=(?P<channel>[\\\\w\\\\d]+)]',\\n msg['message'])\\n if match:\\n self.http['current_channel'] = match.groupdict().get('channel')\\n # D/nsHttp nsHttpChannel::Init [this=c30d000]\\n elif 'current_channel' in self.http and msg['message'].startswith('nsHttpChannel::Init'):\\n del self.http['current_channel']\\n # V/nsHttp uri=http://www.webpagetest.org/?bare=1\\n elif 'current_channel' in self.http and msg['message'].startswith('uri='):\\n match = re.search(r'^uri=(?P<url>[^ \\\\r\\\\n]+)', msg['message'])\\n if match:\\n self.http['channels'][self.http['current_channel']] = \\\\\\n match.groupdict().get('url')\\n # V/nsHttp Creating nsHttpTransaction @0x7f88bb130400\\n elif msg['message'].startswith('Creating nsHttpTransaction '):\\n match = re.search(r'^Creating nsHttpTransaction @(?P<id>[\\\\w\\\\d]+)', msg['message'])\\n if match:\\n self.http['creating_trans_id'] = match.groupdict().get('id')\\n # D/nsHttp nsHttpChannel c30d000 created nsHttpTransaction c138c00\\n elif msg['message'].startswith('nsHttpChannel') and \\\\\\n msg['message'].find(' created nsHttpTransaction ') > -1:\\n match = re.search(r'^nsHttpChannel (?P<channel>[\\\\w\\\\d]+) created '\\\\\\n r'nsHttpTransaction (?P<id>[\\\\w\\\\d]+)', msg['message'])\\n if match:\\n channel = match.groupdict().get('channel')\\n if channel in self.http['channels']:\\n url = self.http['channels'][channel]\\n del self.http['channels'][channel]\\n if 'creating_trans_id' in self.http:\\n trans_id = self.http['creating_trans_id']\\n del self.http['creating_trans_id']\\n else:\\n trans_id = match.groupdict().get('id')\\n # If there is already an existing transaction with the same ID,\\n # move it to a unique ID.\\n if trans_id in self.http['requests']:\\n tmp_request = self.http['requests'][trans_id]\\n del self.http['requests'][trans_id]\\n self.unique_id += 1\\n new_id = '{0}.{1:d}'.format(trans_id, self.unique_id)\\n self.http['requests'][new_id] = tmp_request\\n self.http['requests'][trans_id] = {'url': url,\\n 'request_headers': [],\\n 'response_headers': [],\\n 'status': None,\\n 'bytes_in': 0,\\n 'chunks': []}\\n # D/nsHttp nsHttpTransaction::Init [this=c138c00 caps=21]\\n elif msg['message'].startswith('nsHttpTransaction::Init '):\\n match = re.search(r'^nsHttpTransaction::Init \\\\[this=(?P<id>[\\\\w\\\\d]+)', msg['message'])\\n if match:\\n trans_id = match.groupdict().get('id')\\n self.http['current_transaction'] = trans_id\\n # D/nsHttp nsHttpTransaction c138c00 SetRequestContext c15ba00\\n elif 'current_transaction' in self.http and \\\\\\n msg['message'].startswith('nsHttpTransaction ') and \\\\\\n msg['message'].find(' SetRequestContext ') > -1:\\n del self.http['current_transaction']\\n # I/nsHttp http request [\\n elif 'current_transaction' in self.http and msg['message'] == 'http request [':\\n self.http['request_headers'] = self.http['current_transaction']\\n elif 'request_headers' in self.http and msg['message'] == ']':\\n del self.http['request_headers']\\n # Individual request headers\\n elif 'request_headers' in self.http and msg['message'][0:2] == ' ':\\n trans_id = self.http['request_headers']\\n if trans_id in self.http['requests']:\\n self.http['requests'][trans_id]['request_headers'].append(msg['message'][2:])\",\n \"def handle(self):\\n self.ip = self.client_address[0]\\n self.port = self.client_address[1]\\n self.connection = self.request\\n #self.logged_in = False\\n\\n # Loop that listens for messages from the client\\n while True:\\n received_string = self.connection.recv(4096).strip()\\n if received_string:\\n jsonObject = json.loads(received_string)\\n request = jsonObject.get('request')\\n #print(received_string)\\n #self.handle_data(received_string)\\n if request == 'login':\\n print 'logging in'\\n self.login(jsonObject)\\n elif request == 'logout':\\n self.logout()\\n elif request == 'msg':\\n self.send(jsonObject)\\n elif request == 'names':\\n self.getNames()\\n elif request == 'help':\\n return \\\"geiegohruuhiegr\\\"\\n else:\\n return \\\"you suck\\\"\\n\\n else:\\n print('The client is disconnected.')\\n break \\n # TODO: Add handling of received payload from client\",\n \"async def _main(self):\\n while True:\\n time.sleep(1)\",\n \"def serve_requests(self):\\n while True:\\n self.server_socket.listen(self.request_queue_size)\\n client_connection, client_address = self.server_socket.accept()\\n self.request_handler(client_connection)\",\n \"def run(self):\\n try:\\n self.server = socket(AF_INET, SOCK_STREAM)\\n self.server.bind(self.address)\\n self.server.listen(5) # Allows up to 5 waiting clients\\n\\n while True:\\n self.myView.updateStatus('Waiting for connection ...')\\n client, address = self.server.accept()\\n self.myView.updateStatus('... connected from ' + str(address))\\n handler = ClientHandler(client, self.bank, self.myView)\\n handler.start()\\n\\n except Exception as message:\\n self.myView.updateStatus(message)\\n self.server.close()\\n self.myView.updateStatus(\\\"Server shutting down.\\\")\",\n \"def background_stuff():\\n print \\\"BACKGROUND\\\"\\n\\n\\n\\n # # global socketio\\n\\n # # print(wsClient.url, wsClient.products)\\n # while (wsClient.MessageCount < 30):\\n # print(\\\"\\\\nMessageCount =\\\", \\\"%i \\\\n\\\" % wsClient.MessageCount)\\n # # time.sleep(1)\\n # # socketio.emit('my response', {'data': [\\\"TEST\\\"]}, namespace=\\\"/test\\\", broadcast=True)\\n # #\\n # wsClient.close()\\n #\\n # while True:\\n # time.sleep(1)\\n # t = str(time.clock())\\n # print t\\n\\n def minute_passed(oldepoch):\\n return time.time() - oldepoch >= .1\\n\\n global wsClient\\n\\n # t = time.time()\\n # for i in range(3000):\\n # # while True:\\n # # print time.clock(), t\\n # # if time.clock() > ( t + .1 ):\\n # # show = True #minute_passed(t)\\n # # if show:\\n # # print show, time.time(), t, time.time() - t\\n # t = time.time()\\n # cur_time = str(t)\\n # socketio.emit('message', {'data': cur_time, \\\"msg\\\": wsClient.message['price'] }, namespace=\\\"/test\\\", broadcast=True)\\n\\n # global thread\\n # thread = None\",\n \"def handle(self):\\n thread_name = get_threadname()\\n\\n style_args = {}\\n style_args['label'] = thread_name\\n\\n axis_args = {}\\n axis_args['name'] = 'default'\\n axis_args['x_label'] = ''\\n axis_args['y_label'] = ''\\n\\n # client_init blocks until client sends 'BEGIN'\\n first_value = self._handle_client_init(style_args, axis_args)\\n if type(first_value) != float:\\n if first_value == \\\"RETURN\\\":\\n # done with client\\n return\\n else:\\n print \\\"Unknown status\\\"\\n return\\n\\n self._handle_setup_axis(axis_args)\\n\\n print \\\"Assigning to axis:\\\", axis_args['name']\\n axes = self.server.axes[axis_args['name']]\\n line_name = self._handle_create_line(axes, style_args)\\n self._handle_update_legend(axes)\\n\\n # NOTE: client_read_data will block until client disconnects.\\n self._handle_client_read_data(first_value, line_name)\\n print \\\"Exiting:\\\", thread_name\",\n \"def run(self):\\n try:\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.connect(self.address)\\n\\n #send dummy data\\n sock.sendall(bytes(\\\"Give me\\\", \\\"utf-8\\\"))\\n received = sock.recv(1024)\\n while True:\\n data = sock.recv(1024)\\n if not data: break\\n received += data\\n lymphocytes = pickle.loads(received)\\n self.lymphocytes_setter(lymphocytes)\\n except ConnectionRefusedError:\\n #Don't bother. May be it's better to add more logic to determine\\n #permanent connection errors.\\n pass\\n finally:\\n sock.close()\",\n \"def main():\\n try:\\n # persistent('infected', os.path.abspath(__file__)) # DON'T REMOVE\\n\\n # rename me to client.py\\n # os.rename(__file__, os.getcwd() + \\\"/client1.py\\\")\\n # print os.path.abspath(__file__)\\n global my_socket\\n my_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n my_socket.connect((SERVER_ADDRESS, PORT))\\n\\n handle = Handle()\\n handle.say_hello()\\n print my_socket.getsockname()\\n msg = ''\\n while True:\\n # ----------send data----------\\n if msvcrt.kbhit():\\n char = msvcrt.getch()\\n if char == chr(MY_CHAR):\\n break\\n if msg == '':\\n sys.stdout.write('[Me] ')\\n msg += char\\n sys.stdout.write(char)\\n sys.stdout.flush()\\n if char == chr(CHAR_ENTER):\\n my_socket.send(msg)\\n msg = ''\\n sys.stdout.write('\\\\n')\\n\\n # ----------receive data-------------\\n rlist, wlist, xlist = select.select([my_socket], [my_socket], [], 1)\\n if my_socket in rlist:\\n data = my_socket.recv(MAX_SIZE_RESPONSE)\\n if not data or data == \\\"\\\":\\n my_socket.close()\\n print \\\"Connection with server closed.\\\"\\n return\\n handle.handle_request(data)\\n if msg != '':\\n sys.stdout.write('[Me] ' + msg)\\n sys.stdout.flush()\\n\\n except Exception as e:\\n print e.message\\n my_socket.close()\\n print 'connection closed'\\n return\\n\\n my_socket.close()\\n print 'connection closed'\",\n \"def _worker(self):\\n while True:\\n request = self.queue.get()\\n self.worker(request)\\n self.queue.task_done()\",\n \"def process_request(self, request, client_address):\\r\\n t = self.thread_class(target = self.process_request_thread,\\r\\n args = (request, client_address))\\r\\n if self.daemon_threads:\\r\\n t.setDaemon (1)\\r\\n t.start()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6994863","0.68837047","0.6724171","0.65650207","0.6561778","0.64669704","0.63919336","0.63876474","0.63811946","0.63524044","0.6348768","0.63193417","0.6318059","0.62665766","0.6266232","0.6265155","0.6248112","0.6233416","0.6225023","0.61834395","0.61834395","0.6169332","0.6168503","0.6135096","0.61297274","0.60723007","0.6069672","0.60695297","0.6059198","0.6048037","0.60373145","0.60232526","0.60130686","0.6008499","0.6003302","0.6001352","0.59938157","0.5978662","0.5965577","0.5951893","0.59441096","0.59320986","0.59305847","0.5928787","0.59268147","0.5911027","0.5905872","0.58825266","0.5879967","0.5869474","0.5857926","0.5855451","0.5825319","0.582088","0.58168495","0.58137697","0.5811733","0.5782301","0.5763887","0.57631385","0.57605606","0.57407117","0.57394683","0.57355434","0.5732799","0.5710418","0.5707434","0.57032603","0.5700601","0.569782","0.56840533","0.56836206","0.5683258","0.5682616","0.56819683","0.5681463","0.5677108","0.5659664","0.56479955","0.56425446","0.56407624","0.5601558","0.5600113","0.5590967","0.5588472","0.55857646","0.55857646","0.5585009","0.55765456","0.5574871","0.5571103","0.55665356","0.5558183","0.55521184","0.55502343","0.5550072","0.5548582","0.55438894","0.55416256","0.5539582","0.55382574"],"string":"[\n \"0.6994863\",\n \"0.68837047\",\n \"0.6724171\",\n \"0.65650207\",\n \"0.6561778\",\n \"0.64669704\",\n \"0.63919336\",\n \"0.63876474\",\n \"0.63811946\",\n \"0.63524044\",\n \"0.6348768\",\n \"0.63193417\",\n \"0.6318059\",\n \"0.62665766\",\n \"0.6266232\",\n \"0.6265155\",\n \"0.6248112\",\n \"0.6233416\",\n \"0.6225023\",\n \"0.61834395\",\n \"0.61834395\",\n \"0.6169332\",\n \"0.6168503\",\n \"0.6135096\",\n \"0.61297274\",\n \"0.60723007\",\n \"0.6069672\",\n \"0.60695297\",\n \"0.6059198\",\n \"0.6048037\",\n \"0.60373145\",\n \"0.60232526\",\n \"0.60130686\",\n \"0.6008499\",\n \"0.6003302\",\n \"0.6001352\",\n \"0.59938157\",\n \"0.5978662\",\n \"0.5965577\",\n \"0.5951893\",\n \"0.59441096\",\n \"0.59320986\",\n \"0.59305847\",\n \"0.5928787\",\n \"0.59268147\",\n \"0.5911027\",\n \"0.5905872\",\n \"0.58825266\",\n \"0.5879967\",\n \"0.5869474\",\n \"0.5857926\",\n \"0.5855451\",\n \"0.5825319\",\n \"0.582088\",\n \"0.58168495\",\n \"0.58137697\",\n \"0.5811733\",\n \"0.5782301\",\n \"0.5763887\",\n \"0.57631385\",\n \"0.57605606\",\n \"0.57407117\",\n \"0.57394683\",\n \"0.57355434\",\n \"0.5732799\",\n \"0.5710418\",\n \"0.5707434\",\n \"0.57032603\",\n \"0.5700601\",\n \"0.569782\",\n \"0.56840533\",\n \"0.56836206\",\n \"0.5683258\",\n \"0.5682616\",\n \"0.56819683\",\n \"0.5681463\",\n \"0.5677108\",\n \"0.5659664\",\n \"0.56479955\",\n \"0.56425446\",\n \"0.56407624\",\n \"0.5601558\",\n \"0.5600113\",\n \"0.5590967\",\n \"0.5588472\",\n \"0.55857646\",\n \"0.55857646\",\n \"0.5585009\",\n \"0.55765456\",\n \"0.5574871\",\n \"0.5571103\",\n \"0.55665356\",\n \"0.5558183\",\n \"0.55521184\",\n \"0.55502343\",\n \"0.5550072\",\n \"0.5548582\",\n \"0.55438894\",\n \"0.55416256\",\n \"0.5539582\",\n \"0.55382574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7174,"cells":{"query":{"kind":"string","value":"Performs a single iteration of the algorithm. This includes collecting the data, updating the parameters, and adding the metrics of interest to the logger. Does not update the `curr_iter` attribute."},"document":{"kind":"string","value":"def step(self, snapshot_mode: str, meta_info: dict = None):\n # Save snapshot to save the correct iteration count\n self.save_snapshot()\n\n if self.curr_checkpoint == -1:\n self.train_teachers(snapshot_mode, None)\n self.reached_checkpoint() # setting counter to 0\n\n if self.curr_checkpoint == 0:\n # Sample observations\n ros, rets, all_lengths = self.sample()\n\n # Log current progress\n self.logger.add_value(\"max return\", np.max(rets), 4)\n self.logger.add_value(\"median return\", np.median(rets), 4)\n self.logger.add_value(\"avg return\", np.mean(rets), 4)\n self.logger.add_value(\"min return\", np.min(rets), 4)\n self.logger.add_value(\"std return\", np.std(rets), 4)\n self.logger.add_value(\"std var\", self.expl_strat.std.item(), 4)\n self.logger.add_value(\"avg rollout len\", np.mean(all_lengths), 4)\n self.logger.add_value(\"num total samples\", np.sum(all_lengths))\n\n # Save snapshot data\n self.make_snapshot(snapshot_mode, np.mean(rets), meta_info)\n\n # Update policy and value function\n self.update(rollouts=ros)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def iterate(self, data):\n \n # Append data to self.data\n self.data = np.append(self.data, data)\n \n for i, d in enumerate(data):\n update = self.current*self.likelihood(d)\n self.current = self._normalize(update)\n self.posterior = np.concatenate((self.posterior,[self.current]))\n \n print(str(len(data)) + \" iterations completed!\")\n \n return None","def update():\n global iteration, result\n iteration += 1\n # Stop iterating after max_iterations\n if iteration >= max_iterations:\n timer.stop()\n print \"Output is\", result\n else:\n result = get_next(result)","def _optimization_loop(self, iteration=0):\n self.logger.print_optimization_header()\n\n while iteration < self.iterations:\n try:\n self._execute_experiment()\n except RepeatedExperimentError:\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\n')\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n self.skipped_iterations += 1\n continue\n except StopIteration:\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\n G.log_(f\"Hyperparameter search space has been exhausted\")\n break\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\n self._set_hyperparameter_space()\n continue\n\n self.logger.print_result(\n self.current_hyperparameters_list,\n self.current_score,\n experiment_id=self.current_experiment.experiment_id,\n )\n\n if (\n (self.best_experiment is None) # First evaluation\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\n ):\n self.best_experiment = self.current_experiment.experiment_id\n self.best_score = self.current_score\n\n iteration += 1","def run(self):\n if not self._no_progress and self._verbose:\n from progressbar import ProgressBar\n progress = ProgressBar()\n iter_range = progress(range(self._iters))\n else:\n iter_range = range(self._iters)\n\n if self._no_progress and self._time_iters:\n from time import time\n\n i = 0\n try:\n for i in iter_range:\n if self._verbose and self._no_progress:\n print(\"Iteration \" + repr(i))\n\n if self._no_progress and self._time_iters:\n start = time()\n\n self.iteration += 1\n\n self._forward(self._p_k, self._v_k)\n sigma_k = measure(self._p_k, self._v_k)\n alpha_k = self._rho_k / sigma_k\n if self._double:\n update_m_double(self._m, alpha_k, self._p_k)\n sub_scaled_vector_double(self._residual_k,\n self._residual_k,\n alpha_k, self._v_k)\n else:\n update_m(self._m, alpha_k, self._p_k)\n sub_scaled_vector(self._residual_k, self._residual_k,\n alpha_k, self._v_k)\n self._v_k = gpuarray_copy(self._residual_k)\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\n rho_k_t = np.abs(rho_k_plus_1)\n\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\n or (rho_k_t <= self._absolute_tolerance):\n print(\"Converged.\")\n self.converged = True\n break\n\n if self._double:\n add_scaled_vector_double(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k,\n self._p_k)\n else:\n add_scaled_vector(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k, self._p_k)\n\n self._rho_k = rho_k_plus_1\n\n if self._noisy:\n print(\" Residual=\" + str(rho_k_t))\n\n if self._no_progress and self._time_iters:\n print(\"Elapsed time for iteration \" + str(i) + \": \" +\n str(time() - start) + \" seconds\")\n\n if self._save_images:\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\n self._data.nX2)),\n self._out_dir, i, self._image_format)\n if self._save_matlab:\n save_matlab(self._m.get().reshape(self._data.nX1,\n self._data.nX2),\n self._out_dir, i)\n except KeyboardInterrupt:\n print(\"Reconstruction aborted (CTRL-C) at iteration \" + str(i))\n finally:\n if self._save_images:\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\n self._data.nX2)),\n self._out_dir, \"result\", self._image_format)\n if self._save_matlab:\n save_matlab(self._m.get().reshape(self._data.nX1,\n self._data.nX2),\n self._out_dir, \"result\")\n self.iteration = i+1\n return (self._m.get().reshape(self._data.nX1, self._data.nX2),\n self.iteration)","def process(self, data_itr):\n for data in data_itr:\n self.update(data)\n while True:\n try:\n out = self.next()\n yield out\n except StopIteration:\n break","def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","def _iteration(self, data_loader):\n\n iter_rho = AverageMeter('Iter rho')\n iter_nmse = AverageMeter('Iter nmse')\n iter_loss = AverageMeter('Iter loss')\n iter_time = AverageMeter('Iter time')\n time_tmp = time.time()\n\n for batch_idx, (sparse_gt, raw_gt) in enumerate(data_loader):\n sparse_gt = sparse_gt.to(self.device)\n sparse_pred = self.model(sparse_gt)\n loss = self.criterion(sparse_pred, sparse_gt)\n rho, nmse = evaluator(sparse_pred, sparse_gt, raw_gt)\n\n # Log and visdom update\n iter_loss.update(loss)\n iter_rho.update(rho)\n iter_nmse.update(nmse)\n iter_time.update(time.time() - time_tmp)\n time_tmp = time.time()\n\n # plot progress\n if (batch_idx + 1) % self.print_freq == 0:\n logger.info(f'[{batch_idx + 1}/{len(data_loader)}] '\n f'loss: {iter_loss.avg:.3e} | rho: {iter_rho.avg:.3e} | '\n f'NMSE: {iter_nmse.avg:.3e} | time: {iter_time.avg:.3f}')\n\n logger.info(f'=> Test rho:{iter_rho.avg:.3e} NMSE: {iter_nmse.avg:.3e}\\n')\n\n return iter_loss.avg, iter_rho.avg, iter_nmse.avg","def run_all_iterations(self):\n self.start_time = time.time()\n for _ in xrange(self.iterations):\n self.run_iteration()\n self.elapsed_time = time.time() - self.start_time\n\n self.print_statistics()","def iterate(self, update_func):\n self.pre_loop()\n logger.info('%s: start iterations', self.__class__.__name__)\n with recording_exit_reason(self.datastore):\n for gen_step in range(self.iterations):\n self.post_update(gen_step, update_func(gen_step))\n logger.info('%s: maximum iterations reached', self.__class__.__name__)","def _run_one_iteration(self, iteration):\n statistics = iteration_statistics.IterationStatistics()\n logging.info('Starting iteration %d', iteration)\n num_episodes_train, average_reward_train, average_steps_per_second = (\n self._run_train_phase(statistics))\n active_num_episodes_eval, active_average_reward_eval = self._run_eval_phase(\n statistics, 'active')\n passive_num_episodes_eval, passive_average_reward_eval = (\n self._run_eval_phase(statistics, 'passive'))\n\n self._save_tensorboard_summaries(iteration, num_episodes_train,\n average_reward_train,\n active_num_episodes_eval,\n active_average_reward_eval,\n passive_num_episodes_eval,\n passive_average_reward_eval,\n average_steps_per_second)\n return statistics.data_lists","def iterate(self, update_func):\n if self.disc_param_save_on_error:\n update_func = param_file.wrap_with_save_on_error(\n self.gan.discriminator,\n self.datastore.path('disc_param', 'pre_error.npz'),\n self.datastore.path('disc_param', 'post_error.npz'),\n )(update_func)\n\n self.pre_loop()\n logger.info('%s: start iterations', self.__class__.__name__)\n with recording_exit_reason(self.datastore):\n for gen_step in range(self.iterations):\n self.post_update(gen_step, update_func(gen_step))\n logger.info('%s: maximum iterations reached', self.__class__.__name__)","def _run_iter(self, data):\n\n global_cost = 0\n\n # We want to iterate over data randomly so as not to unintentionally\n # bias the word vector contents\n shuffle(data)\n\n for (v_main, v_context, b_main, b_context, gradsq_W_main, gradsq_W_context,\n gradsq_b_main, gradsq_b_context, cooccurrence) in data:\n weight = (cooccurrence / self._x_max) ** self._alpha if cooccurrence < self._x_max else 1\n\n # Compute inner component of cost function, which is used in\n # both overall cost calculation and in gradient calculation\n #\n # $$ J' = w_i^Tw_j + b_i + b_j - log(X_{ij}) $$\n cost_inner = (v_main.dot(v_context)\n + b_main[0] + b_context[0]\n - log(cooccurrence))\n\n # Compute cost\n #\n # $$ J = f(X_{ij}) (J')^2 $$\n cost = weight * (cost_inner ** 2)\n\n # Add weighted cost to the global cost tracker\n global_cost += 0.5 * cost\n\n # Compute gradients for word vector terms.\n #\n # NB: `main_word` is only a view into `W` (not a copy), so our\n # modifications here will affect the global weight matrix;\n # likewise for context_word, biases, etc.\n grad_main = weight * cost_inner * v_context\n grad_context = weight * cost_inner * v_main\n\n # Compute gradients for bias terms\n grad_bias_main = weight * cost_inner\n grad_bias_context = weight * cost_inner\n\n # Now perform adaptive updates\n v_main -= (self._learning_rate * grad_main / np.sqrt(gradsq_W_main))\n v_context -= (self._learning_rate * grad_context / np.sqrt(gradsq_W_context))\n\n b_main -= (self._learning_rate * grad_bias_main / np.sqrt(gradsq_b_main))\n b_context -= (self._learning_rate * grad_bias_context / np.sqrt(\n gradsq_b_context))\n\n # Update squared gradient sums\n gradsq_W_main += np.square(grad_main)\n gradsq_W_context += np.square(grad_context)\n gradsq_b_main += grad_bias_main ** 2\n gradsq_b_context += grad_bias_context ** 2\n\n return global_cost","def __call__(self, param):\n count = param.nbatch\n if self.last_count > count:\n self.init = False\n self.last_count = count\n\n if self.init:\n if count % self.frequent == 0:\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\n s = ''\n if param.eval_metric is not None:\n name, value = param.eval_metric.get()\n s = \"Epoch[%d] Batch [%d]\\tSpeed: %.2f samples/sec\\tTrain-\" % (param.epoch, count, speed)\n for n, v in zip(name, value):\n s += \"%s=%f,\\t\" % (n, v)\n else:\n s = \"Iter[%d] Batch [%d]\\tSpeed: %.2f samples/sec\" % (param.epoch, count, speed)\n\n logging.info(s)\n print(s)\n self.tic = time.time()\n else:\n self.init = True\n self.tic = time.time()","def evaluate(self):\n iterator = self._iterators['main']\n\n if self.eval_hook:\n self.eval_hook(self)\n\n summary = reporting.DictSummary()\n\n progress = IterationStatus(len(iterator))\n if self._progress_bar:\n pbar = _IteratorProgressBar(iterator=progress)\n\n last_iter = len(iterator) - 1\n with _in_eval_mode(self._targets.values()):\n for idx, batch in enumerate(iterator):\n last_batch = idx == last_iter\n progress.current_position = idx\n observation = {}\n with reporting.report_scope(observation):\n if isinstance(batch, (tuple, list)):\n outs = self.eval_func(*batch)\n elif isinstance(batch, dict):\n outs = self.eval_func(**batch)\n else:\n outs = self.eval_func(batch)\n for metric in self._metrics:\n metric(batch, outs, last_batch)\n summary.add(observation)\n\n if self._progress_bar:\n pbar.update()\n\n if self._progress_bar:\n pbar.close()\n\n return summary.compute_mean()","def report(self, individuals):\n print(f'Iteration: {self.current}')\n self.current += 1","def step(self):\n for p, grad, v, square_grad_avg, delta_x_acc in self.params:\n # Compute the running average of the squared gradients \n square_grad_avg.mul_(self.rho)\n square_grad_avg.addcmul_(grad, grad, value = 1 - self.rho)\n # Compute the RMS of the previous squared gradients (eps to avoid numerical issues later for division)\n std = (square_grad_avg.add_(self.eps)).sqrt_()\n # Compute the accumulated update\n delta_x = ((delta_x_acc.add_(self.eps)).sqrt_()) * grad / std\n # Accumulate the updates\n delta_x_acc.mul_(self.rho)\n delta_x_acc.addcmul_(delta_x, delta_x, value = 1 - self.rho) \n # Update the parameters\n p.add_(delta_x, alpha = - self.lr)","def __call__(self, param):\n count = param.nbatch\n if self.last_count > count:\n self.init = False\n self.last_count = count\n\n if self.init:\n if count % self.frequent == 0:\n # #11504\n try:\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\n except ZeroDivisionError:\n speed = float('inf')\n if param.eval_metric is not None:\n name_value = param.eval_metric.get_name_value()\n if self.auto_reset:\n param.eval_metric.reset()\n msg = 'Epoch[%d] Batch [%d-%d]\\tSpeed: %.2f samples/sec'\n msg += '\\t%s=%f'*len(name_value)\n logging.info(msg, param.epoch, count-self.frequent, count, speed, *sum(name_value, ()))\n else:\n msg = 'Epoch[%d] Batch [0-%d]\\tSpeed: %.2f samples/sec'\n msg += '\\t%s=%f'*len(name_value)\n logging.info(msg, param.epoch, count, speed, *sum(name_value, ()))\n else:\n logging.info(\"Iter[%d] Batch [%d]\\tSpeed: %.2f samples/sec\",\n param.epoch, count, speed)\n self.tic = time.time()\n else:\n self.init = True\n self.tic = time.time()","def __iter__(self):\n self.current_epoch = self.current_epoch + 1\n self.num_samples_yield = -1\n return self","def __iter__(self):\n self.current_epoch = self.current_epoch + 1\n self.num_samples_yield = -1\n return self","def _run(self):\n self._algorithm(self._list, self)","def step(self) -> ResultDict:\n # Do we have to run `self.evaluate()` this iteration?\n # `self.iteration` gets incremented after this function returns,\n # meaning that e. g. the first time this function is called,\n # self.iteration will be 0.\n evaluate_this_iter = (\n self.config.evaluation_interval is not None\n and (self.iteration + 1) % self.config.evaluation_interval == 0\n )\n\n # Results dict for training (and if appolicable: evaluation).\n results: ResultDict = {}\n\n # Parallel eval + training: Kick off evaluation-loop and parallel train() call.\n if evaluate_this_iter and self.config.evaluation_parallel_to_training:\n (\n results,\n train_iter_ctx,\n ) = self._run_one_training_iteration_and_evaluation_in_parallel()\n # - No evaluation necessary, just run the next training iteration.\n # - We have to evaluate in this training iteration, but no parallelism ->\n # evaluate after the training iteration is entirely done.\n else:\n results, train_iter_ctx = self._run_one_training_iteration()\n\n # Sequential: Train (already done above), then evaluate.\n if evaluate_this_iter and not self.config.evaluation_parallel_to_training:\n results.update(self._run_one_evaluation(train_future=None))\n\n # Attach latest available evaluation results to train results,\n # if necessary.\n if not evaluate_this_iter and self.config.always_attach_evaluation_results:\n assert isinstance(\n self.evaluation_metrics, dict\n ), \"Algorithm.evaluate() needs to return a dict.\"\n results.update(self.evaluation_metrics)\n\n if hasattr(self, \"workers\") and isinstance(self.workers, WorkerSet):\n # Sync filters on workers.\n self._sync_filters_if_needed(\n central_worker=self.workers.local_worker(),\n workers=self.workers,\n config=self.config,\n )\n # TODO (avnishn): Remove the execution plan API by q1 2023\n # Collect worker metrics and add combine them with `results`.\n if self.config._disable_execution_plan_api:\n episodes_this_iter = collect_episodes(\n self.workers,\n self._remote_worker_ids_for_metrics(),\n timeout_seconds=self.config.metrics_episode_collection_timeout_s,\n )\n results = self._compile_iteration_results(\n episodes_this_iter=episodes_this_iter,\n step_ctx=train_iter_ctx,\n iteration_results=results,\n )\n\n # Check `env_task_fn` for possible update of the env's task.\n if self.config.env_task_fn is not None:\n if not callable(self.config.env_task_fn):\n raise ValueError(\n \"`env_task_fn` must be None or a callable taking \"\n \"[train_results, env, env_ctx] as args!\"\n )\n\n def fn(env, env_context, task_fn):\n new_task = task_fn(results, env, env_context)\n cur_task = env.get_task()\n if cur_task != new_task:\n env.set_task(new_task)\n\n fn = functools.partial(fn, task_fn=self.config.env_task_fn)\n self.workers.foreach_env_with_context(fn)\n\n return results","def update(self, iteration):\n pass","def iterate_data(dataset,iter_no=5,pixel_mask=None,plot_clear=True,algo=\"FordRollett\",unit_weights=False):\n import overlap\n start_gain = array.ones(len(dataset))\n if unit_weights is True:\n weights = array.ones_like(dataset)\n else:\n weights = 1.0/dataset.var\n # Use weights as the mask\n if pixel_mask is not None:\n weights = weights*pixel_mask\n if algo == \"FordRollett\":\n gain,first_ave,ar,esds,k = overlap.find_gain_fr(dataset,weights,start_gain,pixel_mask=pixel_mask)\n else:\n raise ValueError(\"No such algorithm: %s\" % algo)\n chisquared,residual_map = overlap.get_statistics_fr(gain,first_ave,dataset,dataset.var,pixel_mask)\n old_result = first_ave #store for later\n chisq_history = [chisquared]\n k_history = [k]\n if iter_no > 0: \n no_iters = iter_no\n else:\n no_iters = abs(iter_no)\n for cycle_no in range(no_iters+1):\n esdflag = (cycle_no == no_iters) # need esds as well, and flags the last cycle\n print 'Cycle %d' % cycle_no\n if cycle_no > 3 and iter_no < 0:\n esdflag = (esdflag or (abs(chisq_history[-2]-chisq_history[-1]))<0.005)\n if algo == \"FordRollett\":\n gain,interim_result,ar,esds,k = overlap.find_gain_fr(dataset,weights,gain,arminus1=ar,pixel_mask=pixel_mask,errors=esdflag)\n chisquared,residual_map = overlap.get_statistics_fr(gain,interim_result,dataset,dataset.var,pixel_mask)\n chisq_history.append(chisquared)\n k_history.append(k)\n if esdflag is True:\n break\n print 'Chisquared: ' + `chisq_history`\n print 'K: ' + `k_history`\n print 'Total cycles: %d' % cycle_no\n print 'Maximum shift/error: %f' % max(ar/esds)\n return gain,dataset,interim_result,residual_map,chisq_history,esds,first_ave,weights","def do_iteration(self, mutation_fn, aggression):\n raise NotImplementedError()","def iterate(self):\n raise NotImplementedError()","def __iter__(self):\n self._loop_idx = 0\n self._target_time = time.time()\n return self","def run(self):\n for i,p in enumerate(self.pairs):\n self.forPointPair(i)\n if i % 100000 == 0:\n print('Percentage Processed: ' + str(round(i * 100 / len(self.pairs), 3)) + '. Existing Cluster Labels: ', len(np.unique(self.labels)))","def Optimize(self):\n self._OpenOutputFiles()\n while self.n_iter < self.n_maxiter and not self.is_converged:\n self.n_iter += 1\n self._ChooseStepDirection(self.opt_type)\n self._LineSearch(-1.0 * self.step_dir)\n self._UpdateEnergy()\n self._UpdateGradient()\n self.traj.AppendStep(self.mol)\n self._UpdateCriteria()\n self._CheckConvergence()\n self._PrintStatus()\n self._CloseOutputFiles()","def _report_iteration(self):\n # Call report_iteration_items for a subclass-friendly function\n self._report_iteration_items()\n self._reporter.write_timestamp(self._iteration)\n self._reporter.write_last_iteration(self._iteration)","def oneIteration(self):\n\t\traise NotImplementedError","def log_stats(self):\n while True:\n for stats in self.stats.values():\n stats.log_stats()\n\n yield from asyncio.sleep(stats_delay)","def update(self, itr, algorithm, agent, traj_sample_lists, pol_sample_lists):\n if self._first_update:\n self._output_column_titles(algorithm)\n self._first_update = False\n\n costs = [np.mean(np.sum(algorithm.prev[m].cs, axis=1)) for m in range(algorithm.M)]\n self._update_iteration_data(itr, algorithm, costs, pol_sample_lists)\n self._cost_plotter.update(costs, t=itr)\n if END_EFFECTOR_POINTS in agent.x_data_types:\n self._update_trajectory_visualizations(algorithm, agent,\n traj_sample_lists, pol_sample_lists)\n\n self._fig.canvas.draw()\n self._fig.canvas.flush_events() # Fixes bug in Qt4Agg backend","def run_one_iteration(self, current_state, current_log_pdf):\n # Start the loop over nsamples - this code uses the parallel version of the stretch algorithm\n all_inds = np.arange(self.nchains)\n inds = all_inds % 2\n accept_vec = np.zeros((self.nchains, ))\n # Separate the full ensemble into two sets, use one as a complementary ensemble to the other and vice-versa\n for split in range(2):\n set1 = (inds == split)\n\n # Get current and complementary sets\n sets = [current_state[inds == j01, :] for j01 in range(2)]\n curr_set, comp_set = sets[split], sets[1 - split] # current and complementary sets respectively\n ns, nc = len(curr_set), len(comp_set)\n\n # Sample new state for S1 based on S0\n unif_rvs = Uniform().rvs(nsamples=ns, random_state=self.random_state)\n zz = ((self.scale - 1.) * unif_rvs + 1.) ** 2. / self.scale # sample Z\n factors = (self.dimension - 1.) * np.log(zz) # compute log(Z ** (d - 1))\n multi_rvs = Multinomial(n=1, p=[1. / nc, ] * nc).rvs(nsamples=ns, random_state=self.random_state)\n rint = np.nonzero(multi_rvs)[1] # sample X_{j} from complementary set\n candidates = comp_set[rint, :] - (comp_set[rint, :] - curr_set) * np.tile(\n zz, [1, self.dimension]) # new candidates\n\n # Compute new likelihood, can be done in parallel :)\n logp_candidates = self.evaluate_log_target(candidates)\n\n # Compute acceptance rate\n unif_rvs = Uniform().rvs(nsamples=len(all_inds[set1]), random_state=self.random_state).reshape((-1,))\n for j, f, lpc, candidate, u_rv in zip(\n all_inds[set1], factors, logp_candidates, candidates, unif_rvs):\n accept = np.log(u_rv) < f + lpc - current_log_pdf[j]\n if accept:\n current_state[j] = candidate\n current_log_pdf[j] = lpc\n accept_vec[j] += 1.\n\n # Update the acceptance rate\n self._update_acceptance_rate(accept_vec)\n return current_state, current_log_pdf","def run(self) -> Dict[str, Union[float, str]]:\n try:\n self.is_run = True\n deque(self, maxlen=0) # feed the entire iterator into a zero-length deque\n info = gather_info(\n self.start_time, self.train_collector, self.test_collector,\n self.best_reward, self.best_reward_std\n )\n finally:\n self.is_run = False\n\n return info","def __call__(self, d):\n try:\n with self.statistics_lock:\n self.statistics_data['input']+=1\n if self.batch_size<=1:\n try:\n self.output(self._work(d))\n except Discard:\n with self.statistics_lock:\n self.statistics_data['discarded']+=1\n except Exception, e:\n logging.exception(\"Caught exception, Data discarded\")\n with self.statistics_lock:\n self.statistics_data['discarded']+=1\n finally:\n self.task_done()\n else:\n self.batch_repo.append(d)\n if len(self.batch_repo)>=self.batch_size:\n self._batch_work()\n except:\n pass","def __iter__(self):\n while True:\n if self.stop:\n return\n for item in self.get_next_batch():\n yield item","def __iter__(self):\n return iter((self.train_stats, self.preprocessed_data, self.output_directory))","def callback(self, parameters):\n print(\"\\nFinsished Iteration: {}\".format(self.current_iteration), flush=True)\n print(\"Current Parameters: {}\".format(parameters), flush=True)\n\n # If getting the value history, perform an evaluation with current parameters\n\n print(\"Starting Next Iteration...\", flush=True)\n\n # Update currrent_iteration index and add new blank history\n self.current_iteration += 1","def run(self, data):\n for idx, block in enumerate(data):\n current = float(np.mean(block['current']['value']))\n print(f'{idx}: {current}')","def run(self):\n iteration = 0\n\n while not self._is_converged():\n # (1) Compute current operating point and update last one.\n xs, us, costs = self._compute_operating_point()\n self._last_operating_point = self._current_operating_point\n self._current_operating_point = (xs, us, costs)\n\n # If this is the first time through, then set up reference deviation\n # costs and add to player costs. Otherwise, just update those costs.\n if self._reference_deviation_weight is not None and iteration == 0:\n self._x_reference_cost = ReferenceDeviationCost(xs)\n self._u_reference_costs = [\n ReferenceDeviationCost(ui) for ui in us]\n\n for ii in range(self._num_players):\n self._player_costs[ii].add_cost(\n self._x_reference_cost, \"x\",\n self._reference_deviation_weight)\n self._player_costs[ii].add_cost(\n self._u_reference_costs[ii], ii,\n self._reference_deviation_weight)\n elif self._reference_deviation_weight is not None:\n self._x_reference_cost.reference = self._last_operating_point[0]\n for ii in range(self._num_players):\n self._u_reference_costs[ii].reference = \\\n self._last_operating_point[1][ii]\n\n # Visualization.\n if self._visualizer is not None:\n traj = {\"xs\" : xs}\n for ii in range(self._num_players):\n traj[\"u%ds\" % (ii + 1)] = us[ii]\n\n self._visualizer.add_trajectory(iteration, traj)\n# self._visualizer.plot_controls(1)\n# plt.pause(0.01)\n# plt.clf()\n# self._visualizer.plot_controls(2)\n# plt.pause(0.01)\n# plt.clf()\n self._visualizer.plot()\n plt.pause(0.01)\n plt.clf()\n\n # (2) Linearize about this operating point. Make sure to\n # stack appropriately since we will concatenate state vectors\n # but not control vectors, so that\n # ``` x_{k+1} - xs_k = A_k (x_k - xs_k) +\n # sum_i Bi_k (ui_k - uis_k) ```\n As = []\n Bs = [[] for ii in range(self._num_players)]\n for k in range(self._horizon):\n A, B = self._dynamics.linearize_discrete(\n xs[k], [uis[k] for uis in us])\n As.append(A)\n\n for ii in range(self._num_players):\n Bs[ii].append(B[ii])\n\n # (3) Quadraticize costs.\n Qs = [[] for ii in range(self._num_players)]\n ls = [[] for ii in range(self._num_players)]\n Rs = [[[] for jj in range(self._num_players)]\n for ii in range(self._num_players)]\n for ii in range(self._num_players):\n for k in range(self._horizon):\n _, l, Q, R = self._player_costs[ii].quadraticize(\n xs[k], [uis[k] for uis in us], k)\n\n Qs[ii].append(Q)\n ls[ii].append(l)\n\n for jj in range(self._num_players):\n Rs[ii][jj].append(R[jj])\n\n # (4) Compute feedback Nash equilibrium of the resulting LQ game.\n Ps, alphas = solve_lq_game(As, Bs, Qs, ls, Rs)\n\n # Accumulate total costs for both players.\n total_costs = [sum(costis).item() for costis in costs]\n print(\"Total cost for all players: \", total_costs)\n\n # Log everything.\n if self._logger is not None:\n self._logger.log(\"xs\", xs)\n self._logger.log(\"us\", us)\n self._logger.log(\"total_costs\", total_costs)\n self._logger.dump()\n\n # Update the member variables.\n self._Ps = Ps\n self._alphas = alphas\n\n # (5) Linesearch.\n self._linesearch()\n iteration += 1","def iterate(self):\n for i in range(self.generations):\n sorted_polulation = sorted(self.data, key=lambda item: - item.fitness_function(item))\n print(\n [item.to_string() for item in sorted_polulation[:8]],\n [round(item.fitness_function(item),2) for item in sorted_polulation]\n )\n\n # print([item.to_string() for item in self.data])\n\n self.step()\n print(\"result\")\n sorted_polulation = sorted(self.data, key=lambda item: - item.fitness_function(item))\n print([str(item) for item in sorted_polulation])","def run(self):\n for msr in self.msrs:\n # find state transition matrix\n phi_p, state_prop = self._compute_stm(msr.time)\n\n # use stm to propagate perturbation and covariance\n cov_m = np.matmul(phi_p, np.matmul(self.cov_list[-1],\n np.transpose(phi_p)))\n\n # compute observation deviation, obs_state matrix\n y_i, h_tilde = self._msr_resid(msr, state_prop)\n\n # calculate kalman gain\n k_gain = self._calc_k_gain(cov_m, h_tilde, msr.cov)\n\n # measurement update\n cov_p, state_est = self._measurement_update(y_i,\n h_tilde,\n k_gain,\n cov_m,\n state_prop)\n\n # update the state lists\n self.residuals.append(y_i)\n self.prop_state_list.append(state_est)\n self.estimates.append(state_est)\n self.cov_list.append(cov_p)\n self.times.append(msr.time)","def logging_loop(self, num_gpus):\n # Launch the test worker to get performance metrics\n self.test_worker = self_play.SelfPlay.options(\n num_cpus=0, num_gpus=num_gpus,\n ).remote(\n self.checkpoint,\n self.Game,\n self.config,\n self.config.seed + self.config.num_workers,\n )\n self.test_worker.continuous_self_play.remote(\n self.shared_storage_worker, None, True\n )\n\n # Write everything in TensorBoard\n writer = SummaryWriter(self.config.results_path)\n\n print(\n \"\\nTraining...\\nRun tensorboard --logdir ./results and go to http://localhost:6006/ to see in real time the training performance.\\n\"\n )\n\n # Save hyperparameters to TensorBoard\n hp_table = [\n f\"| {key} | {value} |\" for key, value in self.config.__dict__.items()\n ]\n writer.add_text(\n \"Hyperparameters\",\n \"| Parameter | Value |\\n|-------|-------|\\n\" + \"\\n\".join(hp_table),\n )\n # Save model representation\n writer.add_text(\n \"Model summary\", self.summary,\n )\n # Loop for updating the training performance\n counter = 0\n keys = [\n \"total_reward\",\n \"wormzero_reward\",\n \"opponent_reward\",\n \"episode_length\",\n \"mean_value\",\n \"training_step\",\n \"lr\",\n \"total_loss\",\n \"value_loss\",\n \"policy_loss\",\n \"num_played_games\",\n \"num_played_steps\",\n \"num_reanalysed_games\",\n ]\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\n try:\n while info[\"training_step\"] < self.config.training_steps:\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\n writer.add_scalar(\n \"1.Total_reward/1.Total_reward\", info[\"total_reward\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/2.Mean_value\", info[\"mean_value\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/3.Episode_length\", info[\"episode_length\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/4.WormZero_reward\", info[\"wormzero_reward\"], counter,\n )\n writer.add_scalar(\n \"1.Total_reward/5.Opponent_reward\",\n info[\"opponent_reward\"],\n counter,\n )\n writer.add_scalar(\n \"2.Workers/1.Self_played_games\", info[\"num_played_games\"], counter,\n )\n writer.add_scalar(\n \"2.Workers/2.Training_steps\", info[\"training_step\"], counter\n )\n writer.add_scalar(\n \"2.Workers/3.Self_played_steps\", info[\"num_played_steps\"], counter\n )\n writer.add_scalar(\n \"2.Workers/4.Reanalysed_games\",\n info[\"num_reanalysed_games\"],\n counter,\n )\n writer.add_scalar(\n \"2.Workers/5.Training_steps_per_self_played_step_ratio\",\n info[\"training_step\"] / max(1, info[\"num_played_steps\"]),\n counter,\n )\n writer.add_scalar(\"2.Workers/6.Learning_rate\", info[\"lr\"], counter)\n writer.add_scalar(\n \"3.Loss/1.Total_weighted_loss\", info[\"total_loss\"], counter\n )\n writer.add_scalar(\"3.Loss/Value_loss\", info[\"value_loss\"], counter)\n writer.add_scalar(\"3.Loss/Policy_loss\", info[\"policy_loss\"], counter)\n print(\n f'Last test reward: {info[\"total_reward\"]:.2f}. Training step: {info[\"training_step\"]}/{self.config.training_steps}. Played games: {info[\"num_played_games\"]}. Loss: {info[\"total_loss\"]:.2f}',\n end=\"\\r\",\n )\n counter += 1\n time.sleep(0.5)\n except KeyboardInterrupt:\n pass\n\n self.terminate_workers()\n\n if self.config.save_model:\n # Persist replay buffer to disk\n print(\"\\n\\nPersisting replay buffer games to disk...\")\n pickle.dump(\n {\n \"buffer\": self.replay_buffer,\n \"num_played_games\": self.checkpoint[\"num_played_games\"],\n \"num_played_steps\": self.checkpoint[\"num_played_steps\"],\n \"num_reanalysed_games\": self.checkpoint[\"num_reanalysed_games\"],\n },\n open(os.path.join(self.config.results_path, \"replay_buffer.pkl\"), \"wb\"),\n )","def on_iterate(self, data: Any = None):\n raise NotImplementedError","def _run(self, **params):\n# if softEvidence is None:\n# self.softEvidence = self.mln.softEvidence\n# else:\n# self.softEvidence = softEvidence\n # initialize chains\n chains = MCMCInference.ChainGroup(self)\n for i in range(self.chains):\n chain = GibbsSampler.Chain(self, self.queries)\n chains.chain(chain)\n# if self.softEvidence is not None:\n# chain.setSoftEvidence(self.softEvidence)\n # do Gibbs sampling\n# if verbose and details: print \"sampling...\"\n converged = 0\n steps = 0\n if self.verbose:\n bar = ProgressBar(color='green', steps=self.maxsteps)\n while converged != self.chains and steps < self.maxsteps:\n converged = 0\n steps += 1\n print('STEP {} / {}'.format(steps, self.maxsteps))\n for chain in chains.chains:\n chain.step()\n if self.verbose:\n bar.inc()\n bar.label('%d / %d' % (steps, self.maxsteps))\n# if self.useConvergenceTest:\n# if chain.converged and numSteps >= minSteps:\n# converged += 1\n# if verbose and details:\n# if numSteps % infoInterval == 0:\n# print \"step %d (fraction converged: %.2f)\" % (numSteps, float(converged) / numChains)\n# if numSteps % resultsInterval == 0:\n# chainGroup.getResults()\n# chainGroup.printResults(shortOutput=True)\n # get the results\n return chains.results()[0]","def run(self, iteration_key):\n record_provider = SqlDocumentProvider(iteration_key, self.case_accessor())\n logger = SQLBasedProgressLogger(iteration_key)\n processor = BulkDocProcessor(record_provider, self.doc_processor(self.domain),\n progress_logger=logger)\n processed, skipped = processor.run()\n return processed, skipped, logger.logs","def __call__(self, stage, itr):\n if not self.logWeights:\n weightNames = list(stage.logWeights.keys())\n getLogger(__name__).info(\"Start collecting log weights. \"\n \"Found %s at trajectory %d.\", weightNames, itr)\n self.logWeights = {name: [] for name in weightNames}\n for key, val in stage.logWeights.items():\n self.logWeights[key].append(complex(val))","def run_one_learning_iteration(\n current_beta, current_mu, batch_size, true_data_mean, max_k_updates,\n max_mu_updates, global_iteration, optimal_vals_logliks_mus,\n within_run_vals_logliks):\n count = 0\n while count < max_k_updates:\n count += 1\n updated_beta, value_delta = update_discriminator(\n current_beta, current_mu, batch_size, true_data_mean)\n current_beta = updated_beta\n\n count = 0\n while count < max_mu_updates:\n count += 1\n updated_mu, value_delta = update_generator(current_beta, current_mu,\n batch_size, true_data_mean,\n global_iteration)\n current_mu = updated_mu\n\n val_loglik = compute_val_loglik(current_beta, current_mu, true_data_mean,\n batch_size)\n val_loglik_mu = val_loglik + (current_mu,)\n optimal_vals_logliks_mus.append(val_loglik_mu)\n\n num_within_run_graphs = 30\n within_run_vals_logliks.append([\n compute_val_loglik(current_beta, m, true_data_mean, batch_size) for m\n in np.linspace(-3, true_data_mean + 3, num_within_run_graphs)])\n\n\n print 'UPDATING BETA...\\n beta PRE: {}'.format(current_beta)\n print ' beta POST: {}'.format(updated_beta)\n print 'UPDATING MU...\\n mu PRE: {}'.format(current_mu)\n print ' mu POST: {}'.format(updated_mu)\n return current_beta, current_mu, optimal_vals_logliks_mus, \\\n within_run_vals_logliks","def run(self):\n import time\n\n\n for msr in self.msrs:\n # find state transition matrix\n phi_p, state_prop = self._compute_stm(msr.time)\n\n # use stm to propagate perturbation and covariance\n pert_m = np.matmul(phi_p, self.pert_vec[-1])\n cov_m = np.matmul(phi_p, np.matmul(self.cov_list[-1],\n np.transpose(phi_p)))\n\n\n # compute observation deviation, obs_state matrix\n y_i, h_tilde = self._msr_resid(msr, state_prop)\n\n # calculate kalman gain\n k_gain = self._calc_k_gain(cov_m, h_tilde, msr.cov)\n\n # measurement update\n cov_p, pert_p = self._measurement_update(y_i,\n h_tilde,\n pert_m,\n k_gain,\n cov_m)\n\n # update the state lists\n self.residuals.append(y_i)\n self.prop_state_list.append(state_prop)\n self.estimates.append(np.add(state_prop, np.transpose(pert_p))[0])\n self.cov_list.append(cov_p)\n self.pert_vec.append(pert_p)\n self.times.append(msr.time)","def run_optimization(self):\n # Get batch\n (obs, action, old_logp, old_value, return_, advantage) = self.buffer.eject()\n\n # Train pi\n print(\"-\" * 20 + \"\\nPi Update\" + \"\\n\" + \"-\" * 20)\n (policy_loss, entropy,\n kl_divergence, clipping_fraction, steps) = self.update_actor(obs, action, old_logp, advantage)\n\n # Train value function\n print(\"-\" * 20 + \"\\nValue Function Update\" + \"\\n\" + \"-\" * 20)\n (value_loss,\n explained_variance) = self.update_critic(obs, old_value, return_)\n\n # Logging\n self.update_counter += 1\n self.log_update(policy_loss, entropy, kl_divergence, clipping_fraction,\n value_loss, explained_variance, steps)\n\n # Update learning rate\n self.decay_lr()\n\n # Save current weights (overwrites previous weights)\n self.save_weights()\n\n # Empty scenario counter\n self.scenario_counter = dict.fromkeys(self.scenario_counter, 0)","def eval(self, algorithm: RLAlgorithm, step_metric_values: Dict[str, int]):\n with alf.summary.push_summary_writer(self._summary_writer):\n logging.info(\"Start evaluation\")\n metrics = evaluate(self._env, algorithm,\n self._config.num_eval_episodes)\n common.log_metrics(metrics)\n for metric in metrics:\n metric.gen_summaries(\n train_step=alf.summary.get_global_counter(),\n other_steps=step_metric_values)","def iterate(self, datapoint, reset=False):\n val = self.compute_local_statistic(datapoint)\n return super().iterate(datapoint=val, reset=reset)","def nextIter(self):\n\t\tpass","def __next__(self) -> Union[None, Tuple[int, Dict[str, Any], Dict[str, Any]]]:\n self.epoch += 1\n self.iter_num += 1\n\n if self.iter_num > 1:\n\n # iterator exhaustion check\n if self.epoch > self.max_epoch:\n raise StopIteration\n\n # exit flag 1, when stop_fn succeeds in train_step or test_step\n if self.stop_fn_flag:\n raise StopIteration\n\n # set policy in train mode\n self.policy.train()\n\n epoch_stat: Dict[str, Any] = dict()\n\n if self.show_progress:\n progress = tqdm.tqdm\n else:\n progress = DummyTqdm\n\n # perform n step_per_epoch\n with progress(\n total=self.step_per_epoch, desc=f\"Epoch #{self.epoch}\", **tqdm_config\n ) as t:\n while t.n < t.total and not self.stop_fn_flag:\n data: Dict[str, Any] = dict()\n result: Dict[str, Any] = dict()\n if self.train_collector is not None:\n data, result, self.stop_fn_flag = self.train_step()\n t.update(result[\"n/st\"])\n if self.stop_fn_flag:\n t.set_postfix(**data)\n break\n else:\n assert self.buffer, \"No train_collector or buffer specified\"\n result[\"n/ep\"] = len(self.buffer)\n result[\"n/st\"] = int(self.gradient_step)\n t.update()\n\n self.policy_update_fn(data, result)\n t.set_postfix(**data)\n\n if t.n <= t.total and not self.stop_fn_flag:\n t.update()\n\n # for offline RL\n if self.train_collector is None:\n self.env_step = self.gradient_step * self.batch_size\n\n if not self.stop_fn_flag:\n self.logger.save_data(\n self.epoch, self.env_step, self.gradient_step, self.save_checkpoint_fn\n )\n # test\n if self.test_collector is not None:\n test_stat, self.stop_fn_flag = self.test_step()\n if not self.is_run:\n epoch_stat.update(test_stat)\n\n if not self.is_run:\n epoch_stat.update({k: v.get() for k, v in self.stat.items()})\n epoch_stat[\"gradient_step\"] = self.gradient_step\n epoch_stat.update(\n {\n \"env_step\": self.env_step,\n \"rew\": self.last_rew,\n \"len\": int(self.last_len),\n \"n/ep\": int(result[\"n/ep\"]),\n \"n/st\": int(result[\"n/st\"]),\n }\n )\n info = gather_info(\n self.start_time, self.train_collector, self.test_collector,\n self.best_reward, self.best_reward_std\n )\n return self.epoch, epoch_stat, info\n else:\n return None","def _step(self):\n # Make a minibatch of training data\n num_train = self.X_train.shape[0]\n # random choose the samples\n batch_mask = np.random.choice(num_train, self.batch_size)\n X_batch = self.X_train[batch_mask]\n y_batch = self.y_train[batch_mask]\n\n # Compute loss and gradient\n loss, grads = self.model.loss(X_batch, y_batch)\n self.loss_history.append(loss)\n\n # Perform a parameter update\n for p, w in self.model.params.items():\n dw = grads[p]\n config = self.optim_configs[p]\n next_w, next_config = self.update_rule(w, dw, config)\n self.model.params[p] = next_w\n self.optim_configs[p] = next_config","async def process_reports(self):\n features = [features for (__, features) in self.updates]\n\n # Faster way to deep flatten a list of lists compared to list comprehension\n feature_dataset = list(chain.from_iterable(features))\n\n # Training the model using all the features received from the client\n sampler = all_inclusive.Sampler(feature_dataset)\n self.algorithm.train(feature_dataset, sampler,\n Config().algorithm.cut_layer)\n\n # Test the updated model\n self.accuracy = self.trainer.test(self.testset)\n logging.info('[Server #{:d}] Global model accuracy: {:.2f}%\\n'.format(\n os.getpid(), 100 * self.accuracy))\n\n await self.wrap_up_processing_reports()","def iterate(self):","def _evaluate(self, training_state, val_iter, val_metric):\n val_iter.reset()\n val_metric.reset()\n\n for nbatch, eval_batch in enumerate(val_iter):\n self.module.forward(eval_batch, is_train=False)\n self.module.update_metric(val_metric, eval_batch.label)\n\n for name, val in val_metric.get_name_value():\n logger.info('Checkpoint [%d]\\tValidation-%s=%f', training_state.checkpoint, name, val)\n\n return self.training_monitor.eval_end_callback(training_state.checkpoint, val_metric)","def __iter__(self):\n if not self.loading:\n self.reset_loading()\n self.current_batch_index = 0\n return self","def process(self, accumulator: [FLContext], fl_ctx: FLContext):\n # The model data is in model.params as a dict.\n model = fl_ctx.get_model()\n vars_to_aggregate = [set(item.get_model().params) for item in accumulator]\n vars_to_aggregate = set.union(*vars_to_aggregate)\n\n for v_name in vars_to_aggregate:\n n_local_iters, np_vars = [], []\n for item in accumulator:\n data = item.get_model()\n if v_name not in data.params:\n continue # this item doesn't have the variable from client\n\n # contribution is a protobuf msg\n # it has `n_iter` which represents number of local iterations \n # used to compute this contribution \n acc = item.get_prop('_contribution')\n float_n_iter = np.float(acc.n_iter)\n n_local_iters.append(float_n_iter)\n\n # weighted using local iterations\n weighted_value = proto_to_ndarray(data.params[v_name]) * float_n_iter\n np_vars.append(weighted_value)\n if not n_local_iters:\n continue # didn't receive this variable from any clients\n new_val = np.sum(np_vars, axis=0) / np.sum(n_local_iters)\n new_val += proto_to_ndarray(model.params[v_name])\n\n # Update the params in model using CopyFrom because it is a ProtoBuf structure\n model.params[v_name].CopyFrom(ndarray_to_proto(new_val))\n return False","def apply(self, simulation):\n t = simulation.time\n dt = simulation.timeStep\n if main_rank == 0:\n simulation.printState()\n # OpenCL update\n self.numMethod(self.gpu_field.gpu_data[self.component],\n self.color)\n self.window.widget.updateGL()\n if simulation.currentIteration > 1:\n self.window.label.setText(\n self.labelText + \"t={0:6.2f}, fps={1:6.2f}\".format(\n t + dt,\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\n self.ctime = self.timer.f_timers.values()[0].t","def thread_main(self, sess):\n for x_h, x_m, y in self.data_iterator():\n sess.run(self.enqueue_op, feed_dict={self.x_h:x_h, self.x_m:x_m})","def single_step(self):\n # Make a minibatch of training data by choosing \"batch_size\" elements with replacement\n num_train = self.X_train.shape[0]\n batch_mask = np.random.choice(num_train, self.batch_size) # random choice with replacement\n X_batch = self.X_train[batch_mask]\n y_batch = self.y_train[batch_mask]\n\n # Compute loss and gradient\n loss, grads = self.model.loss(X_batch, y_batch)\n self.latest_loss = loss\n\n # Perform a parameter update based on chosen optimiser\n for p, w in self.model.params.items():\n dw = grads[p] # current gradients\n config = self.optim_configs[p] # moments of gradients and learning rate till previous accuracy() call\n next_w, next_config = optimiser_type(self.optim_type, w, dw, config) # sent to choice of optimising technique\n self.model.params[p] = next_w # model params updated\n self.optim_configs[p] = next_config # # moments of gradients updated","def deepmd_single_process_continue_iter(deepmd_graph_dir: str,\n deepmd_data: Dict,\n iter_index: int,\n need_continue: bool):\n # Training and freezing the model\n deepmd_run(iter_index, deepmd_graph_dir, deepmd_data, need_continue)","def go(self):\n if self.model_initializer is None:\n raise ValueError(\"Experiment guidelines must be set before starting optimization\")\n\n _reporter_params = dict(dict(do_maximize=self.do_maximize), **self.reporter_parameters)\n self.logger = OptimizationReporter([_.name for _ in self.dimensions], **_reporter_params)\n\n self.tested_keys = []\n self._set_hyperparameter_space()\n self._find_similar_experiments()\n\n loop_start_time = datetime.now()\n self._optimization_loop()\n loop_end_time = datetime.now()\n G.log_(f\"Optimization loop completed in {loop_end_time - loop_start_time}\")\n G.log_(f'Best score was {self.best_score} from Experiment \"{self.best_experiment}\"')\n self._clean_up_optimization()","def run(self):\n print('PSO start running...')\n self.init_population()\n self.iterator()\n print(\"Iteration completed.\")\n self.plot_curve()\n print_params(self.GlobalBest_Pos, self.candidate, net=self.net)","def intermediate(self, alg_mod, iter_count, obj_value,\n inf_pr, inf_du, mu, d_norm, regularization_size,\n alpha_du, alpha_pr, ls_trials):\n # TODO: Document the arguments\n pass","def _process(self):\n while True:\n with Timer() as data_timer:\n frame = self._frames_q.get()\n\n with Timer() as agent_timer:\n s, frame_metadata = self._unwrap_frame(frame)\n s = np.expand_dims(s, 0) # batch\n act = self.pred(s)[0][0].argmax()\n put_overwrite(self._actions_q, self._wrap_action(act, frame_metadata))\n\n print('.', end='', flush=True)\n if self.verbose:\n print('Avg data wait time: %.3f' % data_timer.time())\n print('Avg agent neural net eval time: %.3f' % agent_timer.time())","def thread_main(self, sess):\n for x_h, x_m, y in self.data_iterator():\n sess.run(self.enqueue_op, feed_dict={self.x_h:x_h, self.x_m:x_m, self.y:y})","def __iter__(self):\n while True:\n results = self.poll()\n for x in results:\n yield x\n if not results:\n time.sleep(self.poll_delay)","def run(self, obs_data, eps, step, n_samples, logger=sys.stdout, info=False, rng=np.random):\n\n ps = []\n n_accepted = 0\n cur_dist = None\n n_dim = self.cur_ps.size\n\n logger = open(os.devnull, 'w') if logger is None else logger\n\n for i in range(n_samples):\n\n prop_ps = self.cur_ps + step * rng.randn(n_dim)\n prop_data = self.sim_model(prop_ps, rng=rng)\n prop_dist = calc_dist(prop_data, obs_data)\n\n # acceptance / rejection step\n if prop_dist < eps:\n\n prop_log_prior = self.prior.eval(prop_ps, log=True)\n\n if rng.rand() < np.exp(prop_log_prior - self.cur_log_prior):\n\n self.cur_ps = prop_ps\n self.cur_log_prior = prop_log_prior\n cur_dist = prop_dist\n n_accepted += 1\n\n ps.append(self.cur_ps.copy())\n\n logger.write('iter = {0}, dist = {1:.3}, acc rate = {2:.2%}\\n'.format(i, cur_dist, float(n_accepted) / (i+1)))\n\n ps = np.array(ps)\n acc_rate = float(n_accepted) / n_samples\n\n if info:\n return ps, acc_rate\n else:\n return ps","def epoch_logs( self, progress_bar, iteration:int, output: SimpleNamespace, prev_mechanism_weights: List[float], next_mechanism_weights: List[float] ):\n self_uid = self.metagraph.hotkeys.index( self.wallet.hotkey.public_key )\n stake = self.metagraph.S[ self_uid ].item()\n rank = self.metagraph.R[ self_uid ].item()\n incentive = self.metagraph.I[ self_uid ].item()\n info = {\n 'GS': colored('{}'.format(self.global_step), 'red'),\n 'LS': colored('{}'.format(iteration), 'blue'),\n 'Epoch': colored('{}'.format(self.epoch+1), 'green'),\n 'Loss': colored('{:.4f}'.format(self.epoch_loss), 'yellow'),\n 'Best': colored('{:.4f}'.format(self.best_epoch_loss), 'red'),\n 'L-loss': colored('{:.4f}'.format(output.local_target_loss.item()), 'blue'),\n 'R-loss': colored('{:.4f}'.format(output.remote_target_loss.item()), 'green'),\n 'D-loss': colored('{:.4f}'.format(output.distillation_loss.item()), 'yellow'),\n 'nPeers': colored(self.metagraph.n.item(), 'red'),\n 'Stake(\\u03C4)': colored('{:.3f}'.format(stake), 'green'),\n 'Rank(\\u03C4)': colored('{:.3f}'.format(rank), 'blue'),\n 'Incentive(\\u03C4/block)': colored('{:.6f}'.format(incentive), 'yellow'),\n }\n for uid in self.metagraph.uids.tolist():\n if next_mechanism_weights[uid] != 0:\n weight_dif = next_mechanism_weights[uid] - prev_mechanism_weights[uid]\n if weight_dif > 0:\n info[colored(str(uid), 'green')] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'green')\n elif weight_dif == 0:\n info[str(uid)] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'white')\n else:\n info[colored(str(uid), 'red')] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'red')\n\n progress_bar.set_infos( info )\n\n if self.config.neuron.use_tensorboard:\n self.tensorboard.add_scalar('R-loss', output.remote_target_loss.item(), self.global_step)\n self.tensorboard.add_scalar('L-loss', output.local_target_loss.item(), self.global_step)\n self.tensorboard.add_scalar('D-loss', output.distillation_loss.item(), self.global_step)","def loop_nonThreaded():\n nonlocal index, total\n nonlocal d_tree\n nonlocal fn_inputReadCallback\n nonlocal fn_analysisCallback\n nonlocal fn_outputWriteCallback\n nonlocal dret_inputSet\n nonlocal dret_analyze\n nonlocal dret_outputSet\n nonlocal str_desc\n\n b_analyzeStatusHist: bool = False\n b_inputStatusHist: bool = False\n b_outputStatusHist: bool = False\n\n if int(self.verbosityLevel) and self.toConsole():\n iterator = tqdm( self.d_inputTree.items(),\n desc = str_desc)\n else:\n iterator = self.d_inputTree.items()\n\n for path, data in iterator:\n dret_inputSet = {}\n dret_analyze = {}\n dret_outputSet = {}\n # Read (is sometimes skipped) / Analyze / Write (also sometimes skipped)\n if fn_inputReadCallback:\n dret_inputSet = inputSet_read(path, data)\n try:\n b_inputStatusHist = b_inputStatusHist or dret_inputSet['status']\n except:\n pass\n if fn_analysisCallback:\n try:\n dret_analyze = analysis_do(path, d_tree[path], index)\n except:\n dret_analyze['status'] = False\n self.dp.qprint(\"Analysis failed\", comms = 'error')\n try:\n b_analyzeStatusHist = b_analyzeStatusHist or dret_analyze['status']\n except:\n pass\n if fn_outputWriteCallback:\n if 'status' in dret_analyze.keys():\n if dret_analyze['status']:\n dret_outputSet = outputSet_write(path, d_tree[path])\n try:\n b_outputStatusHist = b_outputStatusHist or dret_outputSet['status']\n except:\n pass\n index += 1\n dret_inputSet['status'] = b_inputStatusHist\n dret_analyze['status'] = b_analyzeStatusHist\n dret_outputSet['status'] = b_outputStatusHist\n tree_removeDeadBranches()","def _train_loop(self, X, update_counter, context_mask):\n\n epoch = 0\n prev_activation = np.zeros((self.map_dim, self.data_dim))\n influences, learning_rates = self._param_update(0, len(update_counter))\n\n for idx, x in enumerate(progressbar(X)):\n\n prev_activation = self._example(x, influences, prev_activation=prev_activation)\n\n if idx in update_counter:\n\n epoch += 1\n influences, learning_rate = self._param_update(epoch, len(update_counter))\n\n if idx in context_mask:\n\n prev_activation = np.zeros((self.map_dim, self.data_dim))","def __iter__(self):\n yield from self.calls","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run(self, verbose=True, max_total_iterations=50000):\n self.verbose = verbose\n\n # Upper bounds on number of evaluations\n self.max_total_iterations = max_total_iterations\n\n self.initialise_mean_and_count()\n self.directed_edges = []\n self.active_strategy_profiles = []\n self.initialise_queue()\n\n # Forced initial exploration\n self.forced_exploration()\n\n # Keep evaluating nodes until check method declares that we're finished\n iterations = 0\n edges_resolved_this_round = []\n while self.total_interactions < max_total_iterations:\n # Add nodes to queue\n self.add_to_queue(removed=edges_resolved_this_round)\n\n # Evaluate the nodes and log results\n for v, _ in self.evaluate_strategy_profile():\n if verbose:\n print(v)\n\n # Recompute confidence bounds, eliminate, stop etc.\n edges_resolved_this_round = self.check_confidence()\n\n if not self.edges_remaining:\n break\n iterations += 1\n\n # Fill in missing edges if max iters reached without resolving all edges\n self.compute_graph()\n\n # Compute objects to be returned\n if verbose:\n total_steps = self.compute_total_steps()\n print('\\nTotal steps taken = {}'.format(total_steps))\n results = {}\n results['interactions'] = int(np.sum(self.count[0]))\n graph = self._construct_digraph(self.directed_edges)\n results['graph'] = graph\n return results","def _on_step(self) -> None:\n self._n_calls += 1\n # Account for multiple environments\n # each call to step() corresponds to n_envs transitions\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\n # Copy running stats, see GH issue #996\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\n\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\n self.logger.record(\"rollout/exploration_rate\", self.exploration_rate)","def batch_and_learn(i, lock=threading.Lock()):\n nonlocal step, stats\n timings = prof.Timings()\n while step < flags.total_steps:\n timings.reset()\n batch, agent_state = get_batch(\n flags,\n free_queue,\n full_queue,\n buffers,\n initial_agent_state_buffers,\n timings,\n )\n stats = learn(\n flags, model, learner_model, batch, agent_state, optimizer, scheduler\n )\n timings.time(\"learn\")\n with lock:\n to_log = dict(step=step)\n to_log.update({k: stats[k] for k in stat_keys})\n plogger.log(to_log)\n step += T * B\n\n if i == 0:\n logging.info(\"Batch and learn: %s\", timings.summary())","def iterator(self, *args, **kwargs):\n kwargs['logger'] = self\n return MonitorIterator(*args, **kwargs)","def update_learning_rate(self, it):\n self.scheduler.step()\n for param_group in self.optimizer.param_groups:\n v = param_group['lr']\n self.tb_logger.add_scalar('train/lr', v, it)","def __call__(self):\n if self.numbatches is None:\n pool = self.pooler()\n if self.batchsize is None:\n self.batchsize = self.pooler.nInPool()\n self.numbatches = self.pooler.nInPool()//self.batchsize\n for i in xrange(self.numbatches):\n pool = self.pooler()\n self._reset_batch()\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\n batchinds,keyids = self._samplebalanced(pool)\n elif self.samplemethod == 'uniform':\n batchinds,keyids = self._sampleuniform(pool)\n else:\n batchinds,keyids = self._samplesequential(i)\n batch = self._extractInds(pool,batchinds,keyids)\n for k in batch:\n batch[k][np.isnan(batch[k])] = self.nanreplacement\n yield batch","def iteration(self):\n T = self.generate_T()\n R = self.reproduce(T)\n self.P = self.choose_mi_best(R)\n #print(self.P)","def run(self):\n self._stats.reset_results()\n for row in self._rows:\n self._process_row(row)\n self.__worker.wait()\n self._handle_if_errors()\n return self._stats._results","def batch_and_learn(i, lock=threading.Lock()):\n nonlocal step, stats\n timings = prof.Timings()\n while step < flags.total_steps:\n timings.reset()\n batch, agent_state = get_batch(\n flags,\n free_queue,\n full_queue,\n buffers,\n initial_agent_state_buffers,\n timings,\n )\n stats = learn(\n flags, model, learner_model, batch, agent_state, optimizer, scheduler\n )\n timings.time(\"learn\")\n with lock:\n to_log = dict(step=step)\n to_log.update({k: stats[k] for k in stat_keys})\n plogger.log(to_log)\n step += T #* B # just count the parallel steps \n # end batch_and_learn\n \n if i == 0:\n logging.info(\"Batch and learn: %s\", timings.summary())","def train(self, current_hyper_params):\n train_loss = 0\n train_n_iter = 0\n # Set model to train mode\n self.model.train()\n # Iterate over train data\n print(\"Iterating over training data...\")\n for i, batch in enumerate(tqdm(self.train_loader)):\n loss = self._train_batch(batch)\n # Statistics\n train_loss += loss.item()\n train_n_iter += 1\n self.stats.train_loss_history.append(train_loss / train_n_iter)","def __call__(self, y, *args, **kwargs):\n\t\tself.ylast = y.copy()\n\n\t\tfor k in range(self.params['maxit']):\n\t\t\ty = self.F(y,*args,**kwargs)\n\t\t\tif self.stopit(k, y, self.ylast, self) and k >= self.params['minit']:\n\t\t\t\tbreak\n\t\t\tnp.copyto(self.ylast,y)\n\t\telse:\n\t\t\tException(\"No convergence after %i iterations.\"%k)\n\n\t\tself.stats['niterations'] += k\n\t\tself.stats['ncalls'] += 1\n\t\tself.stats['niterations/call'] = self.stats['niterations']/self.stats['ncalls']\n\t\treturn y","def algorithm_loop(self):","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def _batch_iter(self, source, target, i: int):\n # send data to device\n source = source.to(self.device)\n target = target.to(self.device)\n\n # the result and loss\n result = self.model(source)\n loss = self.criterion(result, target)\n\n # optimization and backward\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n # update the loss\n self.epoch_loss.update(loss.item(), source.size(0))\n\n # print the information\n if self.info:\n print(f\"\\rEpoch: { self.epoch } | Batch: { i } | loss: { self.epoch_loss.avg }\", end=\"\")\n\n # clean the data\n del source, target\n\n return result","def run(self):\n\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient, 2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n x = self.draw_normal()\n gradient = self.cv_gradient(x)\n gradient[np.isnan(gradient)] = 0\n self.change_parameters(self.optim.update(gradient))\n\n if self.printer is True:\n self.print_progress(i, self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, elbo_records","def step(self):\r\n self.datacollector.collect(self)\r\n self.datacollector2.collect(self)\r\n self.datacollector3.collect(self)\r\n self.datacollector4.collect(self)\r\n self.datacollector5.collect(self)\r\n self.datacollector6.collect(self)\r\n self.datacollector7.collect(self)\r\n self.datacollector8.collect(self)\r\n self.datacollector9.collect(self)\r\n self.datacollector10.collect(self)\r\n self.datacollector11.collect(self)\r\n self.datacollector12.collect(self)\r\n self.datacollector13.collect(self)\r\n\r\n self.datacollector14.collect(self)\r\n self.datacollector15.collect(self)\r\n self.datacollector16.collect(self)\r\n self.datacollector17.collect(self)\r\n self.datacollector18.collect(self)\r\n self.datacollector19.collect(self)\r\n self.datacollector20.collect(self)\r\n self.datacollector21.collect(self)\r\n self.datacollector22.collect(self)\r\n self.datacollector23.collect(self)\r\n self.datacollector24.collect(self)\r\n self.datacollector25.collect(self)\r\n self.datacollector26.collect(self)\r\n self.schedule.step()","def run(self, context):\n self.start()\n while not context.is_finished():\n self.update_predictions(context)\n new_results, instances = self.evaluate()\n if new_results:\n self.write_results(new_results, instances)\n self.update_plot(new_results, instances)\n\n if self.plot_file:\n self.visualizer.savefig(self.plot_file)\n if self.show_plot:\n self.visualizer.show()\n self.end()","def _fit(self,\n train_iter: data_io.ParallelBucketSentenceIter,\n val_iter: data_io.ParallelBucketSentenceIter,\n output_folder: str,\n max_params_files_to_keep: int,\n metrics: List[AnyStr],\n max_updates: int,\n checkpoint_frequency: int,\n max_num_not_improved: int,\n min_num_epochs: Optional[int] = None,\n mxmonitor: Optional[mx.monitor.Monitor] = None):\n metric_train = self._create_eval_metric(metrics)\n metric_val = self._create_eval_metric(metrics)\n\n tic = time.time()\n\n training_state_dir = os.path.join(output_folder, C.TRAINING_STATE_DIRNAME)\n if os.path.exists(training_state_dir):\n train_state = self.load_checkpoint(training_state_dir, train_iter)\n else:\n train_state = _TrainingState(\n num_not_improved=0,\n epoch=0,\n checkpoint=0,\n updates=0,\n samples=0\n )\n\n next_data_batch = train_iter.next()\n\n while max_updates == -1 or train_state.updates < max_updates:\n\n # <EcoSys> Added the profiler start and end point.\n import numba.cuda as cuda\n\n if train_state.updates == 501:\n cuda.profile_start()\n if train_state.updates == 511:\n cuda.profile_stop()\n # </EcoSys>\n\n if not train_iter.iter_next():\n train_state.epoch += 1\n train_iter.reset()\n\n # process batch\n batch = next_data_batch\n\n if mxmonitor is not None:\n mxmonitor.tic()\n\n self.module.forward_backward(batch)\n self.module.update()\n\n if mxmonitor is not None:\n results = mxmonitor.toc()\n if results:\n for _, k, v in results:\n logger.info('Monitor: Batch [{:d}] {:s} {:s}'.format(train_state.updates, k, v))\n\n if train_iter.iter_next():\n # pre-fetch next batch\n next_data_batch = train_iter.next()\n self.module.prepare(next_data_batch)\n\n self.module.update_metric(metric_train, batch.label)\n\n self.training_monitor.batch_end_callback(train_state.epoch, train_state.updates, metric_train)\n train_state.updates += 1\n train_state.samples += train_iter.batch_size\n\n if train_state.updates > 0 and train_state.updates % checkpoint_frequency == 0:\n train_state.checkpoint += 1\n self._save_params(output_folder, train_state.checkpoint)\n cleanup_params_files(output_folder, max_params_files_to_keep,\n train_state.checkpoint, self.training_monitor.get_best_checkpoint())\n self.training_monitor.checkpoint_callback(train_state.checkpoint, metric_train)\n\n toc = time.time()\n logger.info(\"Checkpoint [%d]\\tUpdates=%d Epoch=%d Samples=%d Time-cost=%.3f\",\n train_state.checkpoint, train_state.updates, train_state.epoch,\n train_state.samples, (toc - tic))\n tic = time.time()\n\n for name, val in metric_train.get_name_value():\n logger.info('Checkpoint [%d]\\tTrain-%s=%f', train_state.checkpoint, name, val)\n metric_train.reset()\n\n # evaluation on validation set\n has_improved, best_checkpoint = self._evaluate(train_state, val_iter, metric_val)\n if self.lr_scheduler is not None:\n self.lr_scheduler.new_evaluation_result(has_improved)\n\n if has_improved:\n best_path = os.path.join(output_folder, C.PARAMS_BEST_NAME)\n if os.path.lexists(best_path):\n os.remove(best_path)\n actual_best_fname = C.PARAMS_NAME % best_checkpoint\n os.symlink(actual_best_fname, best_path)\n train_state.num_not_improved = 0\n else:\n train_state.num_not_improved += 1\n logger.info(\"Model has not improved for %d checkpoints\", train_state.num_not_improved)\n\n if max_num_not_improved >= 0 and train_state.num_not_improved >= max_num_not_improved:\n logger.info(\"Maximum number of not improved checkpoints (%d) reached: %d\",\n max_num_not_improved, train_state.num_not_improved)\n stop_fit = True\n\n if min_num_epochs is not None and train_state.epoch < min_num_epochs:\n logger.info(\"Minimum number of epochs (%d) not reached yet: %d\",\n min_num_epochs,\n train_state.epoch)\n stop_fit = False\n\n if stop_fit:\n logger.info(\"Stopping fit\")\n self.training_monitor.stop_fit_callback()\n final_training_state_dirname = os.path.join(output_folder, C.TRAINING_STATE_DIRNAME)\n if os.path.exists(final_training_state_dirname):\n shutil.rmtree(final_training_state_dirname)\n break\n\n self._checkpoint(train_state, output_folder, train_iter)\n cleanup_params_files(output_folder, max_params_files_to_keep,\n train_state.checkpoint, self.training_monitor.get_best_checkpoint())","def __iter__(self):\n index = len(self._logrecords)\n while index > 0:\n index -= 1\n yield self._logrecords[index]","def run(self, num_iter: Union[int, None] =None) -> np.ndarray:\n # update iterations\n if num_iter is not None:\n self.num_iter = num_iter\n\n # run once to get the output shape\n out = np.asarray(self.func(**self.get_next_params(), **self._kwargs))\n\n # output container\n result = np.ones((self.num_iter, out.shape[0])) * np.nan\n\n # build input parameter\n params = (({**self.get_next_params(), **self._kwargs},i) for i in range(self.num_iter))\n\n # build the wrapper \n def wrap(tup):\n # unwrap the tuple\n par, i = tup\n \n # save result\n result[i] = self.func(**par)\n\n # create the parallel worker\n parallel_worker = Parallel(n_jobs=self.n_jobs, require='sharedmem')\n\n # create verbose or non verbose parameter generator\n if self._kwargs.get('verbose', False):\n func_gen = (delayed(wrap)(param) for param in params)\n else:\n func_gen = (delayed(wrap)(param) for param in tqdm(params, total=self.num_iter))\n \n # run the job in parallel\n parallel_worker(func_gen)\n\n # finished - return the result\n return result","def step(self, estim: EstimBase) -> None:\n _, results = estim.get_last_results()\n results = [self.to_metrics(res) for res in results]\n self.metrics.extend(results)\n if len(self.metrics) >= len(self.population):\n self.population = self._mating(self.population)\n self.metrics = []","def run(self):\n i = 0\n try:\n for i in range(0, self._iters):\n if self._verbose:\n print(\" Inner CG Iteration \" + repr(i))\n\n self._forward(self._p_k, self._v_k)\n sigma_k = measure(self._p_k, self._v_k)\n alpha_k = self._rho_k / sigma_k\n update_m(self._m, alpha_k, self._p_k)\n sub_scaled_vector(self._residual_k, self._residual_k, alpha_k,\n self._v_k)\n self._v_k = gpuarray_copy(self._residual_k)\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\n rho_k_t = np.abs(rho_k_plus_1)\n\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\n or (rho_k_t <= self._absolute_tolerance):\n if self._verbose:\n print(\"Converged at Iteration \" + str(i) + \".\")\n self.converged = True\n self.iteration = i+1\n return\n\n add_scaled_vector(self._p_k, self._v_k,\n rho_k_plus_1/self._rho_k,\n self._p_k)\n self._rho_k = rho_k_plus_1\n\n if self._verbose >= 3:\n print(\" Residual=\" + repr(rho_k_t))\n except KeyboardInterrupt:\n raise\n finally:\n self.iteration = i+1","def iter_epoch(self):\n\n # set to train mode\n self._set_train()\n\n # start epoch\n for i, (source, target) in enumerate(self.train_dataset):\n self._batch_iter(source, target, i)\n\n if self.info:\n print(f\"\\rEpoch: { self.epoch } | Average loss: { self.epoch_loss.avg }\")\n\n # update epoch and reset the epoch_loss\n self.epoch_loss.reset()\n self.epoch += 1","def after_train_iter(self,\n runner,\n batch_idx: int,\n data_batch: DATA_BATCH = None,\n outputs: Optional[dict] = None) -> None:\n # Print experiment name every n iterations.\n if self.every_n_train_iters(\n runner, self.interval_exp_name) or (self.end_of_epoch(\n runner.train_dataloader, batch_idx)):\n exp_info = f'Exp name: {runner.experiment_name}'\n runner.logger.info(exp_info)\n if self.every_n_inner_iters(batch_idx, self.interval):\n tag, log_str = runner.log_processor.get_log_after_iter(\n runner, batch_idx, 'train')\n elif (self.end_of_epoch(runner.train_dataloader, batch_idx)\n and (not self.ignore_last\n or len(runner.train_dataloader) <= self.interval)):\n # `runner.max_iters` may not be divisible by `self.interval`. if\n # `self.ignore_last==True`, the log of remaining iterations will\n # be recorded (Epoch [4][1000/1007], the logs of 998-1007\n # iterations will be recorded).\n tag, log_str = runner.log_processor.get_log_after_iter(\n runner, batch_idx, 'train')\n else:\n return\n runner.logger.info(log_str)\n runner.visualizer.add_scalars(\n tag, step=runner.iter + 1, file_path=self.json_log_path)"],"string":"[\n \"def iterate(self, data):\\n \\n # Append data to self.data\\n self.data = np.append(self.data, data)\\n \\n for i, d in enumerate(data):\\n update = self.current*self.likelihood(d)\\n self.current = self._normalize(update)\\n self.posterior = np.concatenate((self.posterior,[self.current]))\\n \\n print(str(len(data)) + \\\" iterations completed!\\\")\\n \\n return None\",\n \"def update():\\n global iteration, result\\n iteration += 1\\n # Stop iterating after max_iterations\\n if iteration >= max_iterations:\\n timer.stop()\\n print \\\"Output is\\\", result\\n else:\\n result = get_next(result)\",\n \"def _optimization_loop(self, iteration=0):\\n self.logger.print_optimization_header()\\n\\n while iteration < self.iterations:\\n try:\\n self._execute_experiment()\\n except RepeatedExperimentError:\\n # G.debug_(F'Skipping repeated Experiment: {_ex!s}\\\\n')\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n self.skipped_iterations += 1\\n continue\\n except StopIteration:\\n if len(self.similar_experiments) + len(self.tested_keys) >= self.search_space_size:\\n G.log_(f\\\"Hyperparameter search space has been exhausted\\\")\\n break\\n # G.debug_(f'Re-initializing hyperparameter grid after testing {len(self.tested_keys)} keys')\\n self._set_hyperparameter_space()\\n continue\\n\\n self.logger.print_result(\\n self.current_hyperparameters_list,\\n self.current_score,\\n experiment_id=self.current_experiment.experiment_id,\\n )\\n\\n if (\\n (self.best_experiment is None) # First evaluation\\n or (self.do_maximize and (self.best_score < self.current_score)) # New best max\\n or (not self.do_maximize and (self.best_score > self.current_score)) # New best min\\n ):\\n self.best_experiment = self.current_experiment.experiment_id\\n self.best_score = self.current_score\\n\\n iteration += 1\",\n \"def run(self):\\n if not self._no_progress and self._verbose:\\n from progressbar import ProgressBar\\n progress = ProgressBar()\\n iter_range = progress(range(self._iters))\\n else:\\n iter_range = range(self._iters)\\n\\n if self._no_progress and self._time_iters:\\n from time import time\\n\\n i = 0\\n try:\\n for i in iter_range:\\n if self._verbose and self._no_progress:\\n print(\\\"Iteration \\\" + repr(i))\\n\\n if self._no_progress and self._time_iters:\\n start = time()\\n\\n self.iteration += 1\\n\\n self._forward(self._p_k, self._v_k)\\n sigma_k = measure(self._p_k, self._v_k)\\n alpha_k = self._rho_k / sigma_k\\n if self._double:\\n update_m_double(self._m, alpha_k, self._p_k)\\n sub_scaled_vector_double(self._residual_k,\\n self._residual_k,\\n alpha_k, self._v_k)\\n else:\\n update_m(self._m, alpha_k, self._p_k)\\n sub_scaled_vector(self._residual_k, self._residual_k,\\n alpha_k, self._v_k)\\n self._v_k = gpuarray_copy(self._residual_k)\\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\\n rho_k_t = np.abs(rho_k_plus_1)\\n\\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\\\\n or (rho_k_t <= self._absolute_tolerance):\\n print(\\\"Converged.\\\")\\n self.converged = True\\n break\\n\\n if self._double:\\n add_scaled_vector_double(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k,\\n self._p_k)\\n else:\\n add_scaled_vector(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k, self._p_k)\\n\\n self._rho_k = rho_k_plus_1\\n\\n if self._noisy:\\n print(\\\" Residual=\\\" + str(rho_k_t))\\n\\n if self._no_progress and self._time_iters:\\n print(\\\"Elapsed time for iteration \\\" + str(i) + \\\": \\\" +\\n str(time() - start) + \\\" seconds\\\")\\n\\n if self._save_images:\\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\\n self._data.nX2)),\\n self._out_dir, i, self._image_format)\\n if self._save_matlab:\\n save_matlab(self._m.get().reshape(self._data.nX1,\\n self._data.nX2),\\n self._out_dir, i)\\n except KeyboardInterrupt:\\n print(\\\"Reconstruction aborted (CTRL-C) at iteration \\\" + str(i))\\n finally:\\n if self._save_images:\\n save_image(np.abs(self._m.get().reshape(self._data.nX1,\\n self._data.nX2)),\\n self._out_dir, \\\"result\\\", self._image_format)\\n if self._save_matlab:\\n save_matlab(self._m.get().reshape(self._data.nX1,\\n self._data.nX2),\\n self._out_dir, \\\"result\\\")\\n self.iteration = i+1\\n return (self._m.get().reshape(self._data.nX1, self._data.nX2),\\n self.iteration)\",\n \"def process(self, data_itr):\\n for data in data_itr:\\n self.update(data)\\n while True:\\n try:\\n out = self.next()\\n yield out\\n except StopIteration:\\n break\",\n \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"def _iteration(self, data_loader):\\n\\n iter_rho = AverageMeter('Iter rho')\\n iter_nmse = AverageMeter('Iter nmse')\\n iter_loss = AverageMeter('Iter loss')\\n iter_time = AverageMeter('Iter time')\\n time_tmp = time.time()\\n\\n for batch_idx, (sparse_gt, raw_gt) in enumerate(data_loader):\\n sparse_gt = sparse_gt.to(self.device)\\n sparse_pred = self.model(sparse_gt)\\n loss = self.criterion(sparse_pred, sparse_gt)\\n rho, nmse = evaluator(sparse_pred, sparse_gt, raw_gt)\\n\\n # Log and visdom update\\n iter_loss.update(loss)\\n iter_rho.update(rho)\\n iter_nmse.update(nmse)\\n iter_time.update(time.time() - time_tmp)\\n time_tmp = time.time()\\n\\n # plot progress\\n if (batch_idx + 1) % self.print_freq == 0:\\n logger.info(f'[{batch_idx + 1}/{len(data_loader)}] '\\n f'loss: {iter_loss.avg:.3e} | rho: {iter_rho.avg:.3e} | '\\n f'NMSE: {iter_nmse.avg:.3e} | time: {iter_time.avg:.3f}')\\n\\n logger.info(f'=> Test rho:{iter_rho.avg:.3e} NMSE: {iter_nmse.avg:.3e}\\\\n')\\n\\n return iter_loss.avg, iter_rho.avg, iter_nmse.avg\",\n \"def run_all_iterations(self):\\n self.start_time = time.time()\\n for _ in xrange(self.iterations):\\n self.run_iteration()\\n self.elapsed_time = time.time() - self.start_time\\n\\n self.print_statistics()\",\n \"def iterate(self, update_func):\\n self.pre_loop()\\n logger.info('%s: start iterations', self.__class__.__name__)\\n with recording_exit_reason(self.datastore):\\n for gen_step in range(self.iterations):\\n self.post_update(gen_step, update_func(gen_step))\\n logger.info('%s: maximum iterations reached', self.__class__.__name__)\",\n \"def _run_one_iteration(self, iteration):\\n statistics = iteration_statistics.IterationStatistics()\\n logging.info('Starting iteration %d', iteration)\\n num_episodes_train, average_reward_train, average_steps_per_second = (\\n self._run_train_phase(statistics))\\n active_num_episodes_eval, active_average_reward_eval = self._run_eval_phase(\\n statistics, 'active')\\n passive_num_episodes_eval, passive_average_reward_eval = (\\n self._run_eval_phase(statistics, 'passive'))\\n\\n self._save_tensorboard_summaries(iteration, num_episodes_train,\\n average_reward_train,\\n active_num_episodes_eval,\\n active_average_reward_eval,\\n passive_num_episodes_eval,\\n passive_average_reward_eval,\\n average_steps_per_second)\\n return statistics.data_lists\",\n \"def iterate(self, update_func):\\n if self.disc_param_save_on_error:\\n update_func = param_file.wrap_with_save_on_error(\\n self.gan.discriminator,\\n self.datastore.path('disc_param', 'pre_error.npz'),\\n self.datastore.path('disc_param', 'post_error.npz'),\\n )(update_func)\\n\\n self.pre_loop()\\n logger.info('%s: start iterations', self.__class__.__name__)\\n with recording_exit_reason(self.datastore):\\n for gen_step in range(self.iterations):\\n self.post_update(gen_step, update_func(gen_step))\\n logger.info('%s: maximum iterations reached', self.__class__.__name__)\",\n \"def _run_iter(self, data):\\n\\n global_cost = 0\\n\\n # We want to iterate over data randomly so as not to unintentionally\\n # bias the word vector contents\\n shuffle(data)\\n\\n for (v_main, v_context, b_main, b_context, gradsq_W_main, gradsq_W_context,\\n gradsq_b_main, gradsq_b_context, cooccurrence) in data:\\n weight = (cooccurrence / self._x_max) ** self._alpha if cooccurrence < self._x_max else 1\\n\\n # Compute inner component of cost function, which is used in\\n # both overall cost calculation and in gradient calculation\\n #\\n # $$ J' = w_i^Tw_j + b_i + b_j - log(X_{ij}) $$\\n cost_inner = (v_main.dot(v_context)\\n + b_main[0] + b_context[0]\\n - log(cooccurrence))\\n\\n # Compute cost\\n #\\n # $$ J = f(X_{ij}) (J')^2 $$\\n cost = weight * (cost_inner ** 2)\\n\\n # Add weighted cost to the global cost tracker\\n global_cost += 0.5 * cost\\n\\n # Compute gradients for word vector terms.\\n #\\n # NB: `main_word` is only a view into `W` (not a copy), so our\\n # modifications here will affect the global weight matrix;\\n # likewise for context_word, biases, etc.\\n grad_main = weight * cost_inner * v_context\\n grad_context = weight * cost_inner * v_main\\n\\n # Compute gradients for bias terms\\n grad_bias_main = weight * cost_inner\\n grad_bias_context = weight * cost_inner\\n\\n # Now perform adaptive updates\\n v_main -= (self._learning_rate * grad_main / np.sqrt(gradsq_W_main))\\n v_context -= (self._learning_rate * grad_context / np.sqrt(gradsq_W_context))\\n\\n b_main -= (self._learning_rate * grad_bias_main / np.sqrt(gradsq_b_main))\\n b_context -= (self._learning_rate * grad_bias_context / np.sqrt(\\n gradsq_b_context))\\n\\n # Update squared gradient sums\\n gradsq_W_main += np.square(grad_main)\\n gradsq_W_context += np.square(grad_context)\\n gradsq_b_main += grad_bias_main ** 2\\n gradsq_b_context += grad_bias_context ** 2\\n\\n return global_cost\",\n \"def __call__(self, param):\\n count = param.nbatch\\n if self.last_count > count:\\n self.init = False\\n self.last_count = count\\n\\n if self.init:\\n if count % self.frequent == 0:\\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\\n s = ''\\n if param.eval_metric is not None:\\n name, value = param.eval_metric.get()\\n s = \\\"Epoch[%d] Batch [%d]\\\\tSpeed: %.2f samples/sec\\\\tTrain-\\\" % (param.epoch, count, speed)\\n for n, v in zip(name, value):\\n s += \\\"%s=%f,\\\\t\\\" % (n, v)\\n else:\\n s = \\\"Iter[%d] Batch [%d]\\\\tSpeed: %.2f samples/sec\\\" % (param.epoch, count, speed)\\n\\n logging.info(s)\\n print(s)\\n self.tic = time.time()\\n else:\\n self.init = True\\n self.tic = time.time()\",\n \"def evaluate(self):\\n iterator = self._iterators['main']\\n\\n if self.eval_hook:\\n self.eval_hook(self)\\n\\n summary = reporting.DictSummary()\\n\\n progress = IterationStatus(len(iterator))\\n if self._progress_bar:\\n pbar = _IteratorProgressBar(iterator=progress)\\n\\n last_iter = len(iterator) - 1\\n with _in_eval_mode(self._targets.values()):\\n for idx, batch in enumerate(iterator):\\n last_batch = idx == last_iter\\n progress.current_position = idx\\n observation = {}\\n with reporting.report_scope(observation):\\n if isinstance(batch, (tuple, list)):\\n outs = self.eval_func(*batch)\\n elif isinstance(batch, dict):\\n outs = self.eval_func(**batch)\\n else:\\n outs = self.eval_func(batch)\\n for metric in self._metrics:\\n metric(batch, outs, last_batch)\\n summary.add(observation)\\n\\n if self._progress_bar:\\n pbar.update()\\n\\n if self._progress_bar:\\n pbar.close()\\n\\n return summary.compute_mean()\",\n \"def report(self, individuals):\\n print(f'Iteration: {self.current}')\\n self.current += 1\",\n \"def step(self):\\n for p, grad, v, square_grad_avg, delta_x_acc in self.params:\\n # Compute the running average of the squared gradients \\n square_grad_avg.mul_(self.rho)\\n square_grad_avg.addcmul_(grad, grad, value = 1 - self.rho)\\n # Compute the RMS of the previous squared gradients (eps to avoid numerical issues later for division)\\n std = (square_grad_avg.add_(self.eps)).sqrt_()\\n # Compute the accumulated update\\n delta_x = ((delta_x_acc.add_(self.eps)).sqrt_()) * grad / std\\n # Accumulate the updates\\n delta_x_acc.mul_(self.rho)\\n delta_x_acc.addcmul_(delta_x, delta_x, value = 1 - self.rho) \\n # Update the parameters\\n p.add_(delta_x, alpha = - self.lr)\",\n \"def __call__(self, param):\\n count = param.nbatch\\n if self.last_count > count:\\n self.init = False\\n self.last_count = count\\n\\n if self.init:\\n if count % self.frequent == 0:\\n # #11504\\n try:\\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\\n except ZeroDivisionError:\\n speed = float('inf')\\n if param.eval_metric is not None:\\n name_value = param.eval_metric.get_name_value()\\n if self.auto_reset:\\n param.eval_metric.reset()\\n msg = 'Epoch[%d] Batch [%d-%d]\\\\tSpeed: %.2f samples/sec'\\n msg += '\\\\t%s=%f'*len(name_value)\\n logging.info(msg, param.epoch, count-self.frequent, count, speed, *sum(name_value, ()))\\n else:\\n msg = 'Epoch[%d] Batch [0-%d]\\\\tSpeed: %.2f samples/sec'\\n msg += '\\\\t%s=%f'*len(name_value)\\n logging.info(msg, param.epoch, count, speed, *sum(name_value, ()))\\n else:\\n logging.info(\\\"Iter[%d] Batch [%d]\\\\tSpeed: %.2f samples/sec\\\",\\n param.epoch, count, speed)\\n self.tic = time.time()\\n else:\\n self.init = True\\n self.tic = time.time()\",\n \"def __iter__(self):\\n self.current_epoch = self.current_epoch + 1\\n self.num_samples_yield = -1\\n return self\",\n \"def __iter__(self):\\n self.current_epoch = self.current_epoch + 1\\n self.num_samples_yield = -1\\n return self\",\n \"def _run(self):\\n self._algorithm(self._list, self)\",\n \"def step(self) -> ResultDict:\\n # Do we have to run `self.evaluate()` this iteration?\\n # `self.iteration` gets incremented after this function returns,\\n # meaning that e. g. the first time this function is called,\\n # self.iteration will be 0.\\n evaluate_this_iter = (\\n self.config.evaluation_interval is not None\\n and (self.iteration + 1) % self.config.evaluation_interval == 0\\n )\\n\\n # Results dict for training (and if appolicable: evaluation).\\n results: ResultDict = {}\\n\\n # Parallel eval + training: Kick off evaluation-loop and parallel train() call.\\n if evaluate_this_iter and self.config.evaluation_parallel_to_training:\\n (\\n results,\\n train_iter_ctx,\\n ) = self._run_one_training_iteration_and_evaluation_in_parallel()\\n # - No evaluation necessary, just run the next training iteration.\\n # - We have to evaluate in this training iteration, but no parallelism ->\\n # evaluate after the training iteration is entirely done.\\n else:\\n results, train_iter_ctx = self._run_one_training_iteration()\\n\\n # Sequential: Train (already done above), then evaluate.\\n if evaluate_this_iter and not self.config.evaluation_parallel_to_training:\\n results.update(self._run_one_evaluation(train_future=None))\\n\\n # Attach latest available evaluation results to train results,\\n # if necessary.\\n if not evaluate_this_iter and self.config.always_attach_evaluation_results:\\n assert isinstance(\\n self.evaluation_metrics, dict\\n ), \\\"Algorithm.evaluate() needs to return a dict.\\\"\\n results.update(self.evaluation_metrics)\\n\\n if hasattr(self, \\\"workers\\\") and isinstance(self.workers, WorkerSet):\\n # Sync filters on workers.\\n self._sync_filters_if_needed(\\n central_worker=self.workers.local_worker(),\\n workers=self.workers,\\n config=self.config,\\n )\\n # TODO (avnishn): Remove the execution plan API by q1 2023\\n # Collect worker metrics and add combine them with `results`.\\n if self.config._disable_execution_plan_api:\\n episodes_this_iter = collect_episodes(\\n self.workers,\\n self._remote_worker_ids_for_metrics(),\\n timeout_seconds=self.config.metrics_episode_collection_timeout_s,\\n )\\n results = self._compile_iteration_results(\\n episodes_this_iter=episodes_this_iter,\\n step_ctx=train_iter_ctx,\\n iteration_results=results,\\n )\\n\\n # Check `env_task_fn` for possible update of the env's task.\\n if self.config.env_task_fn is not None:\\n if not callable(self.config.env_task_fn):\\n raise ValueError(\\n \\\"`env_task_fn` must be None or a callable taking \\\"\\n \\\"[train_results, env, env_ctx] as args!\\\"\\n )\\n\\n def fn(env, env_context, task_fn):\\n new_task = task_fn(results, env, env_context)\\n cur_task = env.get_task()\\n if cur_task != new_task:\\n env.set_task(new_task)\\n\\n fn = functools.partial(fn, task_fn=self.config.env_task_fn)\\n self.workers.foreach_env_with_context(fn)\\n\\n return results\",\n \"def update(self, iteration):\\n pass\",\n \"def iterate_data(dataset,iter_no=5,pixel_mask=None,plot_clear=True,algo=\\\"FordRollett\\\",unit_weights=False):\\n import overlap\\n start_gain = array.ones(len(dataset))\\n if unit_weights is True:\\n weights = array.ones_like(dataset)\\n else:\\n weights = 1.0/dataset.var\\n # Use weights as the mask\\n if pixel_mask is not None:\\n weights = weights*pixel_mask\\n if algo == \\\"FordRollett\\\":\\n gain,first_ave,ar,esds,k = overlap.find_gain_fr(dataset,weights,start_gain,pixel_mask=pixel_mask)\\n else:\\n raise ValueError(\\\"No such algorithm: %s\\\" % algo)\\n chisquared,residual_map = overlap.get_statistics_fr(gain,first_ave,dataset,dataset.var,pixel_mask)\\n old_result = first_ave #store for later\\n chisq_history = [chisquared]\\n k_history = [k]\\n if iter_no > 0: \\n no_iters = iter_no\\n else:\\n no_iters = abs(iter_no)\\n for cycle_no in range(no_iters+1):\\n esdflag = (cycle_no == no_iters) # need esds as well, and flags the last cycle\\n print 'Cycle %d' % cycle_no\\n if cycle_no > 3 and iter_no < 0:\\n esdflag = (esdflag or (abs(chisq_history[-2]-chisq_history[-1]))<0.005)\\n if algo == \\\"FordRollett\\\":\\n gain,interim_result,ar,esds,k = overlap.find_gain_fr(dataset,weights,gain,arminus1=ar,pixel_mask=pixel_mask,errors=esdflag)\\n chisquared,residual_map = overlap.get_statistics_fr(gain,interim_result,dataset,dataset.var,pixel_mask)\\n chisq_history.append(chisquared)\\n k_history.append(k)\\n if esdflag is True:\\n break\\n print 'Chisquared: ' + `chisq_history`\\n print 'K: ' + `k_history`\\n print 'Total cycles: %d' % cycle_no\\n print 'Maximum shift/error: %f' % max(ar/esds)\\n return gain,dataset,interim_result,residual_map,chisq_history,esds,first_ave,weights\",\n \"def do_iteration(self, mutation_fn, aggression):\\n raise NotImplementedError()\",\n \"def iterate(self):\\n raise NotImplementedError()\",\n \"def __iter__(self):\\n self._loop_idx = 0\\n self._target_time = time.time()\\n return self\",\n \"def run(self):\\n for i,p in enumerate(self.pairs):\\n self.forPointPair(i)\\n if i % 100000 == 0:\\n print('Percentage Processed: ' + str(round(i * 100 / len(self.pairs), 3)) + '. Existing Cluster Labels: ', len(np.unique(self.labels)))\",\n \"def Optimize(self):\\n self._OpenOutputFiles()\\n while self.n_iter < self.n_maxiter and not self.is_converged:\\n self.n_iter += 1\\n self._ChooseStepDirection(self.opt_type)\\n self._LineSearch(-1.0 * self.step_dir)\\n self._UpdateEnergy()\\n self._UpdateGradient()\\n self.traj.AppendStep(self.mol)\\n self._UpdateCriteria()\\n self._CheckConvergence()\\n self._PrintStatus()\\n self._CloseOutputFiles()\",\n \"def _report_iteration(self):\\n # Call report_iteration_items for a subclass-friendly function\\n self._report_iteration_items()\\n self._reporter.write_timestamp(self._iteration)\\n self._reporter.write_last_iteration(self._iteration)\",\n \"def oneIteration(self):\\n\\t\\traise NotImplementedError\",\n \"def log_stats(self):\\n while True:\\n for stats in self.stats.values():\\n stats.log_stats()\\n\\n yield from asyncio.sleep(stats_delay)\",\n \"def update(self, itr, algorithm, agent, traj_sample_lists, pol_sample_lists):\\n if self._first_update:\\n self._output_column_titles(algorithm)\\n self._first_update = False\\n\\n costs = [np.mean(np.sum(algorithm.prev[m].cs, axis=1)) for m in range(algorithm.M)]\\n self._update_iteration_data(itr, algorithm, costs, pol_sample_lists)\\n self._cost_plotter.update(costs, t=itr)\\n if END_EFFECTOR_POINTS in agent.x_data_types:\\n self._update_trajectory_visualizations(algorithm, agent,\\n traj_sample_lists, pol_sample_lists)\\n\\n self._fig.canvas.draw()\\n self._fig.canvas.flush_events() # Fixes bug in Qt4Agg backend\",\n \"def run_one_iteration(self, current_state, current_log_pdf):\\n # Start the loop over nsamples - this code uses the parallel version of the stretch algorithm\\n all_inds = np.arange(self.nchains)\\n inds = all_inds % 2\\n accept_vec = np.zeros((self.nchains, ))\\n # Separate the full ensemble into two sets, use one as a complementary ensemble to the other and vice-versa\\n for split in range(2):\\n set1 = (inds == split)\\n\\n # Get current and complementary sets\\n sets = [current_state[inds == j01, :] for j01 in range(2)]\\n curr_set, comp_set = sets[split], sets[1 - split] # current and complementary sets respectively\\n ns, nc = len(curr_set), len(comp_set)\\n\\n # Sample new state for S1 based on S0\\n unif_rvs = Uniform().rvs(nsamples=ns, random_state=self.random_state)\\n zz = ((self.scale - 1.) * unif_rvs + 1.) ** 2. / self.scale # sample Z\\n factors = (self.dimension - 1.) * np.log(zz) # compute log(Z ** (d - 1))\\n multi_rvs = Multinomial(n=1, p=[1. / nc, ] * nc).rvs(nsamples=ns, random_state=self.random_state)\\n rint = np.nonzero(multi_rvs)[1] # sample X_{j} from complementary set\\n candidates = comp_set[rint, :] - (comp_set[rint, :] - curr_set) * np.tile(\\n zz, [1, self.dimension]) # new candidates\\n\\n # Compute new likelihood, can be done in parallel :)\\n logp_candidates = self.evaluate_log_target(candidates)\\n\\n # Compute acceptance rate\\n unif_rvs = Uniform().rvs(nsamples=len(all_inds[set1]), random_state=self.random_state).reshape((-1,))\\n for j, f, lpc, candidate, u_rv in zip(\\n all_inds[set1], factors, logp_candidates, candidates, unif_rvs):\\n accept = np.log(u_rv) < f + lpc - current_log_pdf[j]\\n if accept:\\n current_state[j] = candidate\\n current_log_pdf[j] = lpc\\n accept_vec[j] += 1.\\n\\n # Update the acceptance rate\\n self._update_acceptance_rate(accept_vec)\\n return current_state, current_log_pdf\",\n \"def run(self) -> Dict[str, Union[float, str]]:\\n try:\\n self.is_run = True\\n deque(self, maxlen=0) # feed the entire iterator into a zero-length deque\\n info = gather_info(\\n self.start_time, self.train_collector, self.test_collector,\\n self.best_reward, self.best_reward_std\\n )\\n finally:\\n self.is_run = False\\n\\n return info\",\n \"def __call__(self, d):\\n try:\\n with self.statistics_lock:\\n self.statistics_data['input']+=1\\n if self.batch_size<=1:\\n try:\\n self.output(self._work(d))\\n except Discard:\\n with self.statistics_lock:\\n self.statistics_data['discarded']+=1\\n except Exception, e:\\n logging.exception(\\\"Caught exception, Data discarded\\\")\\n with self.statistics_lock:\\n self.statistics_data['discarded']+=1\\n finally:\\n self.task_done()\\n else:\\n self.batch_repo.append(d)\\n if len(self.batch_repo)>=self.batch_size:\\n self._batch_work()\\n except:\\n pass\",\n \"def __iter__(self):\\n while True:\\n if self.stop:\\n return\\n for item in self.get_next_batch():\\n yield item\",\n \"def __iter__(self):\\n return iter((self.train_stats, self.preprocessed_data, self.output_directory))\",\n \"def callback(self, parameters):\\n print(\\\"\\\\nFinsished Iteration: {}\\\".format(self.current_iteration), flush=True)\\n print(\\\"Current Parameters: {}\\\".format(parameters), flush=True)\\n\\n # If getting the value history, perform an evaluation with current parameters\\n\\n print(\\\"Starting Next Iteration...\\\", flush=True)\\n\\n # Update currrent_iteration index and add new blank history\\n self.current_iteration += 1\",\n \"def run(self, data):\\n for idx, block in enumerate(data):\\n current = float(np.mean(block['current']['value']))\\n print(f'{idx}: {current}')\",\n \"def run(self):\\n iteration = 0\\n\\n while not self._is_converged():\\n # (1) Compute current operating point and update last one.\\n xs, us, costs = self._compute_operating_point()\\n self._last_operating_point = self._current_operating_point\\n self._current_operating_point = (xs, us, costs)\\n\\n # If this is the first time through, then set up reference deviation\\n # costs and add to player costs. Otherwise, just update those costs.\\n if self._reference_deviation_weight is not None and iteration == 0:\\n self._x_reference_cost = ReferenceDeviationCost(xs)\\n self._u_reference_costs = [\\n ReferenceDeviationCost(ui) for ui in us]\\n\\n for ii in range(self._num_players):\\n self._player_costs[ii].add_cost(\\n self._x_reference_cost, \\\"x\\\",\\n self._reference_deviation_weight)\\n self._player_costs[ii].add_cost(\\n self._u_reference_costs[ii], ii,\\n self._reference_deviation_weight)\\n elif self._reference_deviation_weight is not None:\\n self._x_reference_cost.reference = self._last_operating_point[0]\\n for ii in range(self._num_players):\\n self._u_reference_costs[ii].reference = \\\\\\n self._last_operating_point[1][ii]\\n\\n # Visualization.\\n if self._visualizer is not None:\\n traj = {\\\"xs\\\" : xs}\\n for ii in range(self._num_players):\\n traj[\\\"u%ds\\\" % (ii + 1)] = us[ii]\\n\\n self._visualizer.add_trajectory(iteration, traj)\\n# self._visualizer.plot_controls(1)\\n# plt.pause(0.01)\\n# plt.clf()\\n# self._visualizer.plot_controls(2)\\n# plt.pause(0.01)\\n# plt.clf()\\n self._visualizer.plot()\\n plt.pause(0.01)\\n plt.clf()\\n\\n # (2) Linearize about this operating point. Make sure to\\n # stack appropriately since we will concatenate state vectors\\n # but not control vectors, so that\\n # ``` x_{k+1} - xs_k = A_k (x_k - xs_k) +\\n # sum_i Bi_k (ui_k - uis_k) ```\\n As = []\\n Bs = [[] for ii in range(self._num_players)]\\n for k in range(self._horizon):\\n A, B = self._dynamics.linearize_discrete(\\n xs[k], [uis[k] for uis in us])\\n As.append(A)\\n\\n for ii in range(self._num_players):\\n Bs[ii].append(B[ii])\\n\\n # (3) Quadraticize costs.\\n Qs = [[] for ii in range(self._num_players)]\\n ls = [[] for ii in range(self._num_players)]\\n Rs = [[[] for jj in range(self._num_players)]\\n for ii in range(self._num_players)]\\n for ii in range(self._num_players):\\n for k in range(self._horizon):\\n _, l, Q, R = self._player_costs[ii].quadraticize(\\n xs[k], [uis[k] for uis in us], k)\\n\\n Qs[ii].append(Q)\\n ls[ii].append(l)\\n\\n for jj in range(self._num_players):\\n Rs[ii][jj].append(R[jj])\\n\\n # (4) Compute feedback Nash equilibrium of the resulting LQ game.\\n Ps, alphas = solve_lq_game(As, Bs, Qs, ls, Rs)\\n\\n # Accumulate total costs for both players.\\n total_costs = [sum(costis).item() for costis in costs]\\n print(\\\"Total cost for all players: \\\", total_costs)\\n\\n # Log everything.\\n if self._logger is not None:\\n self._logger.log(\\\"xs\\\", xs)\\n self._logger.log(\\\"us\\\", us)\\n self._logger.log(\\\"total_costs\\\", total_costs)\\n self._logger.dump()\\n\\n # Update the member variables.\\n self._Ps = Ps\\n self._alphas = alphas\\n\\n # (5) Linesearch.\\n self._linesearch()\\n iteration += 1\",\n \"def iterate(self):\\n for i in range(self.generations):\\n sorted_polulation = sorted(self.data, key=lambda item: - item.fitness_function(item))\\n print(\\n [item.to_string() for item in sorted_polulation[:8]],\\n [round(item.fitness_function(item),2) for item in sorted_polulation]\\n )\\n\\n # print([item.to_string() for item in self.data])\\n\\n self.step()\\n print(\\\"result\\\")\\n sorted_polulation = sorted(self.data, key=lambda item: - item.fitness_function(item))\\n print([str(item) for item in sorted_polulation])\",\n \"def run(self):\\n for msr in self.msrs:\\n # find state transition matrix\\n phi_p, state_prop = self._compute_stm(msr.time)\\n\\n # use stm to propagate perturbation and covariance\\n cov_m = np.matmul(phi_p, np.matmul(self.cov_list[-1],\\n np.transpose(phi_p)))\\n\\n # compute observation deviation, obs_state matrix\\n y_i, h_tilde = self._msr_resid(msr, state_prop)\\n\\n # calculate kalman gain\\n k_gain = self._calc_k_gain(cov_m, h_tilde, msr.cov)\\n\\n # measurement update\\n cov_p, state_est = self._measurement_update(y_i,\\n h_tilde,\\n k_gain,\\n cov_m,\\n state_prop)\\n\\n # update the state lists\\n self.residuals.append(y_i)\\n self.prop_state_list.append(state_est)\\n self.estimates.append(state_est)\\n self.cov_list.append(cov_p)\\n self.times.append(msr.time)\",\n \"def logging_loop(self, num_gpus):\\n # Launch the test worker to get performance metrics\\n self.test_worker = self_play.SelfPlay.options(\\n num_cpus=0, num_gpus=num_gpus,\\n ).remote(\\n self.checkpoint,\\n self.Game,\\n self.config,\\n self.config.seed + self.config.num_workers,\\n )\\n self.test_worker.continuous_self_play.remote(\\n self.shared_storage_worker, None, True\\n )\\n\\n # Write everything in TensorBoard\\n writer = SummaryWriter(self.config.results_path)\\n\\n print(\\n \\\"\\\\nTraining...\\\\nRun tensorboard --logdir ./results and go to http://localhost:6006/ to see in real time the training performance.\\\\n\\\"\\n )\\n\\n # Save hyperparameters to TensorBoard\\n hp_table = [\\n f\\\"| {key} | {value} |\\\" for key, value in self.config.__dict__.items()\\n ]\\n writer.add_text(\\n \\\"Hyperparameters\\\",\\n \\\"| Parameter | Value |\\\\n|-------|-------|\\\\n\\\" + \\\"\\\\n\\\".join(hp_table),\\n )\\n # Save model representation\\n writer.add_text(\\n \\\"Model summary\\\", self.summary,\\n )\\n # Loop for updating the training performance\\n counter = 0\\n keys = [\\n \\\"total_reward\\\",\\n \\\"wormzero_reward\\\",\\n \\\"opponent_reward\\\",\\n \\\"episode_length\\\",\\n \\\"mean_value\\\",\\n \\\"training_step\\\",\\n \\\"lr\\\",\\n \\\"total_loss\\\",\\n \\\"value_loss\\\",\\n \\\"policy_loss\\\",\\n \\\"num_played_games\\\",\\n \\\"num_played_steps\\\",\\n \\\"num_reanalysed_games\\\",\\n ]\\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\\n try:\\n while info[\\\"training_step\\\"] < self.config.training_steps:\\n info = ray.get(self.shared_storage_worker.get_info.remote(keys))\\n writer.add_scalar(\\n \\\"1.Total_reward/1.Total_reward\\\", info[\\\"total_reward\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/2.Mean_value\\\", info[\\\"mean_value\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/3.Episode_length\\\", info[\\\"episode_length\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/4.WormZero_reward\\\", info[\\\"wormzero_reward\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"1.Total_reward/5.Opponent_reward\\\",\\n info[\\\"opponent_reward\\\"],\\n counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/1.Self_played_games\\\", info[\\\"num_played_games\\\"], counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/2.Training_steps\\\", info[\\\"training_step\\\"], counter\\n )\\n writer.add_scalar(\\n \\\"2.Workers/3.Self_played_steps\\\", info[\\\"num_played_steps\\\"], counter\\n )\\n writer.add_scalar(\\n \\\"2.Workers/4.Reanalysed_games\\\",\\n info[\\\"num_reanalysed_games\\\"],\\n counter,\\n )\\n writer.add_scalar(\\n \\\"2.Workers/5.Training_steps_per_self_played_step_ratio\\\",\\n info[\\\"training_step\\\"] / max(1, info[\\\"num_played_steps\\\"]),\\n counter,\\n )\\n writer.add_scalar(\\\"2.Workers/6.Learning_rate\\\", info[\\\"lr\\\"], counter)\\n writer.add_scalar(\\n \\\"3.Loss/1.Total_weighted_loss\\\", info[\\\"total_loss\\\"], counter\\n )\\n writer.add_scalar(\\\"3.Loss/Value_loss\\\", info[\\\"value_loss\\\"], counter)\\n writer.add_scalar(\\\"3.Loss/Policy_loss\\\", info[\\\"policy_loss\\\"], counter)\\n print(\\n f'Last test reward: {info[\\\"total_reward\\\"]:.2f}. Training step: {info[\\\"training_step\\\"]}/{self.config.training_steps}. Played games: {info[\\\"num_played_games\\\"]}. Loss: {info[\\\"total_loss\\\"]:.2f}',\\n end=\\\"\\\\r\\\",\\n )\\n counter += 1\\n time.sleep(0.5)\\n except KeyboardInterrupt:\\n pass\\n\\n self.terminate_workers()\\n\\n if self.config.save_model:\\n # Persist replay buffer to disk\\n print(\\\"\\\\n\\\\nPersisting replay buffer games to disk...\\\")\\n pickle.dump(\\n {\\n \\\"buffer\\\": self.replay_buffer,\\n \\\"num_played_games\\\": self.checkpoint[\\\"num_played_games\\\"],\\n \\\"num_played_steps\\\": self.checkpoint[\\\"num_played_steps\\\"],\\n \\\"num_reanalysed_games\\\": self.checkpoint[\\\"num_reanalysed_games\\\"],\\n },\\n open(os.path.join(self.config.results_path, \\\"replay_buffer.pkl\\\"), \\\"wb\\\"),\\n )\",\n \"def on_iterate(self, data: Any = None):\\n raise NotImplementedError\",\n \"def _run(self, **params):\\n# if softEvidence is None:\\n# self.softEvidence = self.mln.softEvidence\\n# else:\\n# self.softEvidence = softEvidence\\n # initialize chains\\n chains = MCMCInference.ChainGroup(self)\\n for i in range(self.chains):\\n chain = GibbsSampler.Chain(self, self.queries)\\n chains.chain(chain)\\n# if self.softEvidence is not None:\\n# chain.setSoftEvidence(self.softEvidence)\\n # do Gibbs sampling\\n# if verbose and details: print \\\"sampling...\\\"\\n converged = 0\\n steps = 0\\n if self.verbose:\\n bar = ProgressBar(color='green', steps=self.maxsteps)\\n while converged != self.chains and steps < self.maxsteps:\\n converged = 0\\n steps += 1\\n print('STEP {} / {}'.format(steps, self.maxsteps))\\n for chain in chains.chains:\\n chain.step()\\n if self.verbose:\\n bar.inc()\\n bar.label('%d / %d' % (steps, self.maxsteps))\\n# if self.useConvergenceTest:\\n# if chain.converged and numSteps >= minSteps:\\n# converged += 1\\n# if verbose and details:\\n# if numSteps % infoInterval == 0:\\n# print \\\"step %d (fraction converged: %.2f)\\\" % (numSteps, float(converged) / numChains)\\n# if numSteps % resultsInterval == 0:\\n# chainGroup.getResults()\\n# chainGroup.printResults(shortOutput=True)\\n # get the results\\n return chains.results()[0]\",\n \"def run(self, iteration_key):\\n record_provider = SqlDocumentProvider(iteration_key, self.case_accessor())\\n logger = SQLBasedProgressLogger(iteration_key)\\n processor = BulkDocProcessor(record_provider, self.doc_processor(self.domain),\\n progress_logger=logger)\\n processed, skipped = processor.run()\\n return processed, skipped, logger.logs\",\n \"def __call__(self, stage, itr):\\n if not self.logWeights:\\n weightNames = list(stage.logWeights.keys())\\n getLogger(__name__).info(\\\"Start collecting log weights. \\\"\\n \\\"Found %s at trajectory %d.\\\", weightNames, itr)\\n self.logWeights = {name: [] for name in weightNames}\\n for key, val in stage.logWeights.items():\\n self.logWeights[key].append(complex(val))\",\n \"def run_one_learning_iteration(\\n current_beta, current_mu, batch_size, true_data_mean, max_k_updates,\\n max_mu_updates, global_iteration, optimal_vals_logliks_mus,\\n within_run_vals_logliks):\\n count = 0\\n while count < max_k_updates:\\n count += 1\\n updated_beta, value_delta = update_discriminator(\\n current_beta, current_mu, batch_size, true_data_mean)\\n current_beta = updated_beta\\n\\n count = 0\\n while count < max_mu_updates:\\n count += 1\\n updated_mu, value_delta = update_generator(current_beta, current_mu,\\n batch_size, true_data_mean,\\n global_iteration)\\n current_mu = updated_mu\\n\\n val_loglik = compute_val_loglik(current_beta, current_mu, true_data_mean,\\n batch_size)\\n val_loglik_mu = val_loglik + (current_mu,)\\n optimal_vals_logliks_mus.append(val_loglik_mu)\\n\\n num_within_run_graphs = 30\\n within_run_vals_logliks.append([\\n compute_val_loglik(current_beta, m, true_data_mean, batch_size) for m\\n in np.linspace(-3, true_data_mean + 3, num_within_run_graphs)])\\n\\n\\n print 'UPDATING BETA...\\\\n beta PRE: {}'.format(current_beta)\\n print ' beta POST: {}'.format(updated_beta)\\n print 'UPDATING MU...\\\\n mu PRE: {}'.format(current_mu)\\n print ' mu POST: {}'.format(updated_mu)\\n return current_beta, current_mu, optimal_vals_logliks_mus, \\\\\\n within_run_vals_logliks\",\n \"def run(self):\\n import time\\n\\n\\n for msr in self.msrs:\\n # find state transition matrix\\n phi_p, state_prop = self._compute_stm(msr.time)\\n\\n # use stm to propagate perturbation and covariance\\n pert_m = np.matmul(phi_p, self.pert_vec[-1])\\n cov_m = np.matmul(phi_p, np.matmul(self.cov_list[-1],\\n np.transpose(phi_p)))\\n\\n\\n # compute observation deviation, obs_state matrix\\n y_i, h_tilde = self._msr_resid(msr, state_prop)\\n\\n # calculate kalman gain\\n k_gain = self._calc_k_gain(cov_m, h_tilde, msr.cov)\\n\\n # measurement update\\n cov_p, pert_p = self._measurement_update(y_i,\\n h_tilde,\\n pert_m,\\n k_gain,\\n cov_m)\\n\\n # update the state lists\\n self.residuals.append(y_i)\\n self.prop_state_list.append(state_prop)\\n self.estimates.append(np.add(state_prop, np.transpose(pert_p))[0])\\n self.cov_list.append(cov_p)\\n self.pert_vec.append(pert_p)\\n self.times.append(msr.time)\",\n \"def run_optimization(self):\\n # Get batch\\n (obs, action, old_logp, old_value, return_, advantage) = self.buffer.eject()\\n\\n # Train pi\\n print(\\\"-\\\" * 20 + \\\"\\\\nPi Update\\\" + \\\"\\\\n\\\" + \\\"-\\\" * 20)\\n (policy_loss, entropy,\\n kl_divergence, clipping_fraction, steps) = self.update_actor(obs, action, old_logp, advantage)\\n\\n # Train value function\\n print(\\\"-\\\" * 20 + \\\"\\\\nValue Function Update\\\" + \\\"\\\\n\\\" + \\\"-\\\" * 20)\\n (value_loss,\\n explained_variance) = self.update_critic(obs, old_value, return_)\\n\\n # Logging\\n self.update_counter += 1\\n self.log_update(policy_loss, entropy, kl_divergence, clipping_fraction,\\n value_loss, explained_variance, steps)\\n\\n # Update learning rate\\n self.decay_lr()\\n\\n # Save current weights (overwrites previous weights)\\n self.save_weights()\\n\\n # Empty scenario counter\\n self.scenario_counter = dict.fromkeys(self.scenario_counter, 0)\",\n \"def eval(self, algorithm: RLAlgorithm, step_metric_values: Dict[str, int]):\\n with alf.summary.push_summary_writer(self._summary_writer):\\n logging.info(\\\"Start evaluation\\\")\\n metrics = evaluate(self._env, algorithm,\\n self._config.num_eval_episodes)\\n common.log_metrics(metrics)\\n for metric in metrics:\\n metric.gen_summaries(\\n train_step=alf.summary.get_global_counter(),\\n other_steps=step_metric_values)\",\n \"def iterate(self, datapoint, reset=False):\\n val = self.compute_local_statistic(datapoint)\\n return super().iterate(datapoint=val, reset=reset)\",\n \"def nextIter(self):\\n\\t\\tpass\",\n \"def __next__(self) -> Union[None, Tuple[int, Dict[str, Any], Dict[str, Any]]]:\\n self.epoch += 1\\n self.iter_num += 1\\n\\n if self.iter_num > 1:\\n\\n # iterator exhaustion check\\n if self.epoch > self.max_epoch:\\n raise StopIteration\\n\\n # exit flag 1, when stop_fn succeeds in train_step or test_step\\n if self.stop_fn_flag:\\n raise StopIteration\\n\\n # set policy in train mode\\n self.policy.train()\\n\\n epoch_stat: Dict[str, Any] = dict()\\n\\n if self.show_progress:\\n progress = tqdm.tqdm\\n else:\\n progress = DummyTqdm\\n\\n # perform n step_per_epoch\\n with progress(\\n total=self.step_per_epoch, desc=f\\\"Epoch #{self.epoch}\\\", **tqdm_config\\n ) as t:\\n while t.n < t.total and not self.stop_fn_flag:\\n data: Dict[str, Any] = dict()\\n result: Dict[str, Any] = dict()\\n if self.train_collector is not None:\\n data, result, self.stop_fn_flag = self.train_step()\\n t.update(result[\\\"n/st\\\"])\\n if self.stop_fn_flag:\\n t.set_postfix(**data)\\n break\\n else:\\n assert self.buffer, \\\"No train_collector or buffer specified\\\"\\n result[\\\"n/ep\\\"] = len(self.buffer)\\n result[\\\"n/st\\\"] = int(self.gradient_step)\\n t.update()\\n\\n self.policy_update_fn(data, result)\\n t.set_postfix(**data)\\n\\n if t.n <= t.total and not self.stop_fn_flag:\\n t.update()\\n\\n # for offline RL\\n if self.train_collector is None:\\n self.env_step = self.gradient_step * self.batch_size\\n\\n if not self.stop_fn_flag:\\n self.logger.save_data(\\n self.epoch, self.env_step, self.gradient_step, self.save_checkpoint_fn\\n )\\n # test\\n if self.test_collector is not None:\\n test_stat, self.stop_fn_flag = self.test_step()\\n if not self.is_run:\\n epoch_stat.update(test_stat)\\n\\n if not self.is_run:\\n epoch_stat.update({k: v.get() for k, v in self.stat.items()})\\n epoch_stat[\\\"gradient_step\\\"] = self.gradient_step\\n epoch_stat.update(\\n {\\n \\\"env_step\\\": self.env_step,\\n \\\"rew\\\": self.last_rew,\\n \\\"len\\\": int(self.last_len),\\n \\\"n/ep\\\": int(result[\\\"n/ep\\\"]),\\n \\\"n/st\\\": int(result[\\\"n/st\\\"]),\\n }\\n )\\n info = gather_info(\\n self.start_time, self.train_collector, self.test_collector,\\n self.best_reward, self.best_reward_std\\n )\\n return self.epoch, epoch_stat, info\\n else:\\n return None\",\n \"def _step(self):\\n # Make a minibatch of training data\\n num_train = self.X_train.shape[0]\\n # random choose the samples\\n batch_mask = np.random.choice(num_train, self.batch_size)\\n X_batch = self.X_train[batch_mask]\\n y_batch = self.y_train[batch_mask]\\n\\n # Compute loss and gradient\\n loss, grads = self.model.loss(X_batch, y_batch)\\n self.loss_history.append(loss)\\n\\n # Perform a parameter update\\n for p, w in self.model.params.items():\\n dw = grads[p]\\n config = self.optim_configs[p]\\n next_w, next_config = self.update_rule(w, dw, config)\\n self.model.params[p] = next_w\\n self.optim_configs[p] = next_config\",\n \"async def process_reports(self):\\n features = [features for (__, features) in self.updates]\\n\\n # Faster way to deep flatten a list of lists compared to list comprehension\\n feature_dataset = list(chain.from_iterable(features))\\n\\n # Training the model using all the features received from the client\\n sampler = all_inclusive.Sampler(feature_dataset)\\n self.algorithm.train(feature_dataset, sampler,\\n Config().algorithm.cut_layer)\\n\\n # Test the updated model\\n self.accuracy = self.trainer.test(self.testset)\\n logging.info('[Server #{:d}] Global model accuracy: {:.2f}%\\\\n'.format(\\n os.getpid(), 100 * self.accuracy))\\n\\n await self.wrap_up_processing_reports()\",\n \"def iterate(self):\",\n \"def _evaluate(self, training_state, val_iter, val_metric):\\n val_iter.reset()\\n val_metric.reset()\\n\\n for nbatch, eval_batch in enumerate(val_iter):\\n self.module.forward(eval_batch, is_train=False)\\n self.module.update_metric(val_metric, eval_batch.label)\\n\\n for name, val in val_metric.get_name_value():\\n logger.info('Checkpoint [%d]\\\\tValidation-%s=%f', training_state.checkpoint, name, val)\\n\\n return self.training_monitor.eval_end_callback(training_state.checkpoint, val_metric)\",\n \"def __iter__(self):\\n if not self.loading:\\n self.reset_loading()\\n self.current_batch_index = 0\\n return self\",\n \"def process(self, accumulator: [FLContext], fl_ctx: FLContext):\\n # The model data is in model.params as a dict.\\n model = fl_ctx.get_model()\\n vars_to_aggregate = [set(item.get_model().params) for item in accumulator]\\n vars_to_aggregate = set.union(*vars_to_aggregate)\\n\\n for v_name in vars_to_aggregate:\\n n_local_iters, np_vars = [], []\\n for item in accumulator:\\n data = item.get_model()\\n if v_name not in data.params:\\n continue # this item doesn't have the variable from client\\n\\n # contribution is a protobuf msg\\n # it has `n_iter` which represents number of local iterations \\n # used to compute this contribution \\n acc = item.get_prop('_contribution')\\n float_n_iter = np.float(acc.n_iter)\\n n_local_iters.append(float_n_iter)\\n\\n # weighted using local iterations\\n weighted_value = proto_to_ndarray(data.params[v_name]) * float_n_iter\\n np_vars.append(weighted_value)\\n if not n_local_iters:\\n continue # didn't receive this variable from any clients\\n new_val = np.sum(np_vars, axis=0) / np.sum(n_local_iters)\\n new_val += proto_to_ndarray(model.params[v_name])\\n\\n # Update the params in model using CopyFrom because it is a ProtoBuf structure\\n model.params[v_name].CopyFrom(ndarray_to_proto(new_val))\\n return False\",\n \"def apply(self, simulation):\\n t = simulation.time\\n dt = simulation.timeStep\\n if main_rank == 0:\\n simulation.printState()\\n # OpenCL update\\n self.numMethod(self.gpu_field.gpu_data[self.component],\\n self.color)\\n self.window.widget.updateGL()\\n if simulation.currentIteration > 1:\\n self.window.label.setText(\\n self.labelText + \\\"t={0:6.2f}, fps={1:6.2f}\\\".format(\\n t + dt,\\n 1. / (self.timer.f_timers.values()[0].t - self.ctime)))\\n self.ctime = self.timer.f_timers.values()[0].t\",\n \"def thread_main(self, sess):\\n for x_h, x_m, y in self.data_iterator():\\n sess.run(self.enqueue_op, feed_dict={self.x_h:x_h, self.x_m:x_m})\",\n \"def single_step(self):\\n # Make a minibatch of training data by choosing \\\"batch_size\\\" elements with replacement\\n num_train = self.X_train.shape[0]\\n batch_mask = np.random.choice(num_train, self.batch_size) # random choice with replacement\\n X_batch = self.X_train[batch_mask]\\n y_batch = self.y_train[batch_mask]\\n\\n # Compute loss and gradient\\n loss, grads = self.model.loss(X_batch, y_batch)\\n self.latest_loss = loss\\n\\n # Perform a parameter update based on chosen optimiser\\n for p, w in self.model.params.items():\\n dw = grads[p] # current gradients\\n config = self.optim_configs[p] # moments of gradients and learning rate till previous accuracy() call\\n next_w, next_config = optimiser_type(self.optim_type, w, dw, config) # sent to choice of optimising technique\\n self.model.params[p] = next_w # model params updated\\n self.optim_configs[p] = next_config # # moments of gradients updated\",\n \"def deepmd_single_process_continue_iter(deepmd_graph_dir: str,\\n deepmd_data: Dict,\\n iter_index: int,\\n need_continue: bool):\\n # Training and freezing the model\\n deepmd_run(iter_index, deepmd_graph_dir, deepmd_data, need_continue)\",\n \"def go(self):\\n if self.model_initializer is None:\\n raise ValueError(\\\"Experiment guidelines must be set before starting optimization\\\")\\n\\n _reporter_params = dict(dict(do_maximize=self.do_maximize), **self.reporter_parameters)\\n self.logger = OptimizationReporter([_.name for _ in self.dimensions], **_reporter_params)\\n\\n self.tested_keys = []\\n self._set_hyperparameter_space()\\n self._find_similar_experiments()\\n\\n loop_start_time = datetime.now()\\n self._optimization_loop()\\n loop_end_time = datetime.now()\\n G.log_(f\\\"Optimization loop completed in {loop_end_time - loop_start_time}\\\")\\n G.log_(f'Best score was {self.best_score} from Experiment \\\"{self.best_experiment}\\\"')\\n self._clean_up_optimization()\",\n \"def run(self):\\n print('PSO start running...')\\n self.init_population()\\n self.iterator()\\n print(\\\"Iteration completed.\\\")\\n self.plot_curve()\\n print_params(self.GlobalBest_Pos, self.candidate, net=self.net)\",\n \"def intermediate(self, alg_mod, iter_count, obj_value,\\n inf_pr, inf_du, mu, d_norm, regularization_size,\\n alpha_du, alpha_pr, ls_trials):\\n # TODO: Document the arguments\\n pass\",\n \"def _process(self):\\n while True:\\n with Timer() as data_timer:\\n frame = self._frames_q.get()\\n\\n with Timer() as agent_timer:\\n s, frame_metadata = self._unwrap_frame(frame)\\n s = np.expand_dims(s, 0) # batch\\n act = self.pred(s)[0][0].argmax()\\n put_overwrite(self._actions_q, self._wrap_action(act, frame_metadata))\\n\\n print('.', end='', flush=True)\\n if self.verbose:\\n print('Avg data wait time: %.3f' % data_timer.time())\\n print('Avg agent neural net eval time: %.3f' % agent_timer.time())\",\n \"def thread_main(self, sess):\\n for x_h, x_m, y in self.data_iterator():\\n sess.run(self.enqueue_op, feed_dict={self.x_h:x_h, self.x_m:x_m, self.y:y})\",\n \"def __iter__(self):\\n while True:\\n results = self.poll()\\n for x in results:\\n yield x\\n if not results:\\n time.sleep(self.poll_delay)\",\n \"def run(self, obs_data, eps, step, n_samples, logger=sys.stdout, info=False, rng=np.random):\\n\\n ps = []\\n n_accepted = 0\\n cur_dist = None\\n n_dim = self.cur_ps.size\\n\\n logger = open(os.devnull, 'w') if logger is None else logger\\n\\n for i in range(n_samples):\\n\\n prop_ps = self.cur_ps + step * rng.randn(n_dim)\\n prop_data = self.sim_model(prop_ps, rng=rng)\\n prop_dist = calc_dist(prop_data, obs_data)\\n\\n # acceptance / rejection step\\n if prop_dist < eps:\\n\\n prop_log_prior = self.prior.eval(prop_ps, log=True)\\n\\n if rng.rand() < np.exp(prop_log_prior - self.cur_log_prior):\\n\\n self.cur_ps = prop_ps\\n self.cur_log_prior = prop_log_prior\\n cur_dist = prop_dist\\n n_accepted += 1\\n\\n ps.append(self.cur_ps.copy())\\n\\n logger.write('iter = {0}, dist = {1:.3}, acc rate = {2:.2%}\\\\n'.format(i, cur_dist, float(n_accepted) / (i+1)))\\n\\n ps = np.array(ps)\\n acc_rate = float(n_accepted) / n_samples\\n\\n if info:\\n return ps, acc_rate\\n else:\\n return ps\",\n \"def epoch_logs( self, progress_bar, iteration:int, output: SimpleNamespace, prev_mechanism_weights: List[float], next_mechanism_weights: List[float] ):\\n self_uid = self.metagraph.hotkeys.index( self.wallet.hotkey.public_key )\\n stake = self.metagraph.S[ self_uid ].item()\\n rank = self.metagraph.R[ self_uid ].item()\\n incentive = self.metagraph.I[ self_uid ].item()\\n info = {\\n 'GS': colored('{}'.format(self.global_step), 'red'),\\n 'LS': colored('{}'.format(iteration), 'blue'),\\n 'Epoch': colored('{}'.format(self.epoch+1), 'green'),\\n 'Loss': colored('{:.4f}'.format(self.epoch_loss), 'yellow'),\\n 'Best': colored('{:.4f}'.format(self.best_epoch_loss), 'red'),\\n 'L-loss': colored('{:.4f}'.format(output.local_target_loss.item()), 'blue'),\\n 'R-loss': colored('{:.4f}'.format(output.remote_target_loss.item()), 'green'),\\n 'D-loss': colored('{:.4f}'.format(output.distillation_loss.item()), 'yellow'),\\n 'nPeers': colored(self.metagraph.n.item(), 'red'),\\n 'Stake(\\\\u03C4)': colored('{:.3f}'.format(stake), 'green'),\\n 'Rank(\\\\u03C4)': colored('{:.3f}'.format(rank), 'blue'),\\n 'Incentive(\\\\u03C4/block)': colored('{:.6f}'.format(incentive), 'yellow'),\\n }\\n for uid in self.metagraph.uids.tolist():\\n if next_mechanism_weights[uid] != 0:\\n weight_dif = next_mechanism_weights[uid] - prev_mechanism_weights[uid]\\n if weight_dif > 0:\\n info[colored(str(uid), 'green')] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'green')\\n elif weight_dif == 0:\\n info[str(uid)] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'white')\\n else:\\n info[colored(str(uid), 'red')] = colored('{:.4f}'.format(next_mechanism_weights[uid]), 'red')\\n\\n progress_bar.set_infos( info )\\n\\n if self.config.neuron.use_tensorboard:\\n self.tensorboard.add_scalar('R-loss', output.remote_target_loss.item(), self.global_step)\\n self.tensorboard.add_scalar('L-loss', output.local_target_loss.item(), self.global_step)\\n self.tensorboard.add_scalar('D-loss', output.distillation_loss.item(), self.global_step)\",\n \"def loop_nonThreaded():\\n nonlocal index, total\\n nonlocal d_tree\\n nonlocal fn_inputReadCallback\\n nonlocal fn_analysisCallback\\n nonlocal fn_outputWriteCallback\\n nonlocal dret_inputSet\\n nonlocal dret_analyze\\n nonlocal dret_outputSet\\n nonlocal str_desc\\n\\n b_analyzeStatusHist: bool = False\\n b_inputStatusHist: bool = False\\n b_outputStatusHist: bool = False\\n\\n if int(self.verbosityLevel) and self.toConsole():\\n iterator = tqdm( self.d_inputTree.items(),\\n desc = str_desc)\\n else:\\n iterator = self.d_inputTree.items()\\n\\n for path, data in iterator:\\n dret_inputSet = {}\\n dret_analyze = {}\\n dret_outputSet = {}\\n # Read (is sometimes skipped) / Analyze / Write (also sometimes skipped)\\n if fn_inputReadCallback:\\n dret_inputSet = inputSet_read(path, data)\\n try:\\n b_inputStatusHist = b_inputStatusHist or dret_inputSet['status']\\n except:\\n pass\\n if fn_analysisCallback:\\n try:\\n dret_analyze = analysis_do(path, d_tree[path], index)\\n except:\\n dret_analyze['status'] = False\\n self.dp.qprint(\\\"Analysis failed\\\", comms = 'error')\\n try:\\n b_analyzeStatusHist = b_analyzeStatusHist or dret_analyze['status']\\n except:\\n pass\\n if fn_outputWriteCallback:\\n if 'status' in dret_analyze.keys():\\n if dret_analyze['status']:\\n dret_outputSet = outputSet_write(path, d_tree[path])\\n try:\\n b_outputStatusHist = b_outputStatusHist or dret_outputSet['status']\\n except:\\n pass\\n index += 1\\n dret_inputSet['status'] = b_inputStatusHist\\n dret_analyze['status'] = b_analyzeStatusHist\\n dret_outputSet['status'] = b_outputStatusHist\\n tree_removeDeadBranches()\",\n \"def _train_loop(self, X, update_counter, context_mask):\\n\\n epoch = 0\\n prev_activation = np.zeros((self.map_dim, self.data_dim))\\n influences, learning_rates = self._param_update(0, len(update_counter))\\n\\n for idx, x in enumerate(progressbar(X)):\\n\\n prev_activation = self._example(x, influences, prev_activation=prev_activation)\\n\\n if idx in update_counter:\\n\\n epoch += 1\\n influences, learning_rate = self._param_update(epoch, len(update_counter))\\n\\n if idx in context_mask:\\n\\n prev_activation = np.zeros((self.map_dim, self.data_dim))\",\n \"def __iter__(self):\\n yield from self.calls\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run(self, verbose=True, max_total_iterations=50000):\\n self.verbose = verbose\\n\\n # Upper bounds on number of evaluations\\n self.max_total_iterations = max_total_iterations\\n\\n self.initialise_mean_and_count()\\n self.directed_edges = []\\n self.active_strategy_profiles = []\\n self.initialise_queue()\\n\\n # Forced initial exploration\\n self.forced_exploration()\\n\\n # Keep evaluating nodes until check method declares that we're finished\\n iterations = 0\\n edges_resolved_this_round = []\\n while self.total_interactions < max_total_iterations:\\n # Add nodes to queue\\n self.add_to_queue(removed=edges_resolved_this_round)\\n\\n # Evaluate the nodes and log results\\n for v, _ in self.evaluate_strategy_profile():\\n if verbose:\\n print(v)\\n\\n # Recompute confidence bounds, eliminate, stop etc.\\n edges_resolved_this_round = self.check_confidence()\\n\\n if not self.edges_remaining:\\n break\\n iterations += 1\\n\\n # Fill in missing edges if max iters reached without resolving all edges\\n self.compute_graph()\\n\\n # Compute objects to be returned\\n if verbose:\\n total_steps = self.compute_total_steps()\\n print('\\\\nTotal steps taken = {}'.format(total_steps))\\n results = {}\\n results['interactions'] = int(np.sum(self.count[0]))\\n graph = self._construct_digraph(self.directed_edges)\\n results['graph'] = graph\\n return results\",\n \"def _on_step(self) -> None:\\n self._n_calls += 1\\n # Account for multiple environments\\n # each call to step() corresponds to n_envs transitions\\n if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:\\n polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\\n # Copy running stats, see GH issue #996\\n polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)\\n\\n self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\\n self.logger.record(\\\"rollout/exploration_rate\\\", self.exploration_rate)\",\n \"def batch_and_learn(i, lock=threading.Lock()):\\n nonlocal step, stats\\n timings = prof.Timings()\\n while step < flags.total_steps:\\n timings.reset()\\n batch, agent_state = get_batch(\\n flags,\\n free_queue,\\n full_queue,\\n buffers,\\n initial_agent_state_buffers,\\n timings,\\n )\\n stats = learn(\\n flags, model, learner_model, batch, agent_state, optimizer, scheduler\\n )\\n timings.time(\\\"learn\\\")\\n with lock:\\n to_log = dict(step=step)\\n to_log.update({k: stats[k] for k in stat_keys})\\n plogger.log(to_log)\\n step += T * B\\n\\n if i == 0:\\n logging.info(\\\"Batch and learn: %s\\\", timings.summary())\",\n \"def iterator(self, *args, **kwargs):\\n kwargs['logger'] = self\\n return MonitorIterator(*args, **kwargs)\",\n \"def update_learning_rate(self, it):\\n self.scheduler.step()\\n for param_group in self.optimizer.param_groups:\\n v = param_group['lr']\\n self.tb_logger.add_scalar('train/lr', v, it)\",\n \"def __call__(self):\\n if self.numbatches is None:\\n pool = self.pooler()\\n if self.batchsize is None:\\n self.batchsize = self.pooler.nInPool()\\n self.numbatches = self.pooler.nInPool()//self.batchsize\\n for i in xrange(self.numbatches):\\n pool = self.pooler()\\n self._reset_batch()\\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\\n batchinds,keyids = self._samplebalanced(pool)\\n elif self.samplemethod == 'uniform':\\n batchinds,keyids = self._sampleuniform(pool)\\n else:\\n batchinds,keyids = self._samplesequential(i)\\n batch = self._extractInds(pool,batchinds,keyids)\\n for k in batch:\\n batch[k][np.isnan(batch[k])] = self.nanreplacement\\n yield batch\",\n \"def iteration(self):\\n T = self.generate_T()\\n R = self.reproduce(T)\\n self.P = self.choose_mi_best(R)\\n #print(self.P)\",\n \"def run(self):\\n self._stats.reset_results()\\n for row in self._rows:\\n self._process_row(row)\\n self.__worker.wait()\\n self._handle_if_errors()\\n return self._stats._results\",\n \"def batch_and_learn(i, lock=threading.Lock()):\\n nonlocal step, stats\\n timings = prof.Timings()\\n while step < flags.total_steps:\\n timings.reset()\\n batch, agent_state = get_batch(\\n flags,\\n free_queue,\\n full_queue,\\n buffers,\\n initial_agent_state_buffers,\\n timings,\\n )\\n stats = learn(\\n flags, model, learner_model, batch, agent_state, optimizer, scheduler\\n )\\n timings.time(\\\"learn\\\")\\n with lock:\\n to_log = dict(step=step)\\n to_log.update({k: stats[k] for k in stat_keys})\\n plogger.log(to_log)\\n step += T #* B # just count the parallel steps \\n # end batch_and_learn\\n \\n if i == 0:\\n logging.info(\\\"Batch and learn: %s\\\", timings.summary())\",\n \"def train(self, current_hyper_params):\\n train_loss = 0\\n train_n_iter = 0\\n # Set model to train mode\\n self.model.train()\\n # Iterate over train data\\n print(\\\"Iterating over training data...\\\")\\n for i, batch in enumerate(tqdm(self.train_loader)):\\n loss = self._train_batch(batch)\\n # Statistics\\n train_loss += loss.item()\\n train_n_iter += 1\\n self.stats.train_loss_history.append(train_loss / train_n_iter)\",\n \"def __call__(self, y, *args, **kwargs):\\n\\t\\tself.ylast = y.copy()\\n\\n\\t\\tfor k in range(self.params['maxit']):\\n\\t\\t\\ty = self.F(y,*args,**kwargs)\\n\\t\\t\\tif self.stopit(k, y, self.ylast, self) and k >= self.params['minit']:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tnp.copyto(self.ylast,y)\\n\\t\\telse:\\n\\t\\t\\tException(\\\"No convergence after %i iterations.\\\"%k)\\n\\n\\t\\tself.stats['niterations'] += k\\n\\t\\tself.stats['ncalls'] += 1\\n\\t\\tself.stats['niterations/call'] = self.stats['niterations']/self.stats['ncalls']\\n\\t\\treturn y\",\n \"def algorithm_loop(self):\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def _batch_iter(self, source, target, i: int):\\n # send data to device\\n source = source.to(self.device)\\n target = target.to(self.device)\\n\\n # the result and loss\\n result = self.model(source)\\n loss = self.criterion(result, target)\\n\\n # optimization and backward\\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\\n\\n # update the loss\\n self.epoch_loss.update(loss.item(), source.size(0))\\n\\n # print the information\\n if self.info:\\n print(f\\\"\\\\rEpoch: { self.epoch } | Batch: { i } | loss: { self.epoch_loss.avg }\\\", end=\\\"\\\")\\n\\n # clean the data\\n del source, target\\n\\n return result\",\n \"def run(self):\\n\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient, 2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n x = self.draw_normal()\\n gradient = self.cv_gradient(x)\\n gradient[np.isnan(gradient)] = 0\\n self.change_parameters(self.optim.update(gradient))\\n\\n if self.printer is True:\\n self.print_progress(i, self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, elbo_records\",\n \"def step(self):\\r\\n self.datacollector.collect(self)\\r\\n self.datacollector2.collect(self)\\r\\n self.datacollector3.collect(self)\\r\\n self.datacollector4.collect(self)\\r\\n self.datacollector5.collect(self)\\r\\n self.datacollector6.collect(self)\\r\\n self.datacollector7.collect(self)\\r\\n self.datacollector8.collect(self)\\r\\n self.datacollector9.collect(self)\\r\\n self.datacollector10.collect(self)\\r\\n self.datacollector11.collect(self)\\r\\n self.datacollector12.collect(self)\\r\\n self.datacollector13.collect(self)\\r\\n\\r\\n self.datacollector14.collect(self)\\r\\n self.datacollector15.collect(self)\\r\\n self.datacollector16.collect(self)\\r\\n self.datacollector17.collect(self)\\r\\n self.datacollector18.collect(self)\\r\\n self.datacollector19.collect(self)\\r\\n self.datacollector20.collect(self)\\r\\n self.datacollector21.collect(self)\\r\\n self.datacollector22.collect(self)\\r\\n self.datacollector23.collect(self)\\r\\n self.datacollector24.collect(self)\\r\\n self.datacollector25.collect(self)\\r\\n self.datacollector26.collect(self)\\r\\n self.schedule.step()\",\n \"def run(self, context):\\n self.start()\\n while not context.is_finished():\\n self.update_predictions(context)\\n new_results, instances = self.evaluate()\\n if new_results:\\n self.write_results(new_results, instances)\\n self.update_plot(new_results, instances)\\n\\n if self.plot_file:\\n self.visualizer.savefig(self.plot_file)\\n if self.show_plot:\\n self.visualizer.show()\\n self.end()\",\n \"def _fit(self,\\n train_iter: data_io.ParallelBucketSentenceIter,\\n val_iter: data_io.ParallelBucketSentenceIter,\\n output_folder: str,\\n max_params_files_to_keep: int,\\n metrics: List[AnyStr],\\n max_updates: int,\\n checkpoint_frequency: int,\\n max_num_not_improved: int,\\n min_num_epochs: Optional[int] = None,\\n mxmonitor: Optional[mx.monitor.Monitor] = None):\\n metric_train = self._create_eval_metric(metrics)\\n metric_val = self._create_eval_metric(metrics)\\n\\n tic = time.time()\\n\\n training_state_dir = os.path.join(output_folder, C.TRAINING_STATE_DIRNAME)\\n if os.path.exists(training_state_dir):\\n train_state = self.load_checkpoint(training_state_dir, train_iter)\\n else:\\n train_state = _TrainingState(\\n num_not_improved=0,\\n epoch=0,\\n checkpoint=0,\\n updates=0,\\n samples=0\\n )\\n\\n next_data_batch = train_iter.next()\\n\\n while max_updates == -1 or train_state.updates < max_updates:\\n\\n # <EcoSys> Added the profiler start and end point.\\n import numba.cuda as cuda\\n\\n if train_state.updates == 501:\\n cuda.profile_start()\\n if train_state.updates == 511:\\n cuda.profile_stop()\\n # </EcoSys>\\n\\n if not train_iter.iter_next():\\n train_state.epoch += 1\\n train_iter.reset()\\n\\n # process batch\\n batch = next_data_batch\\n\\n if mxmonitor is not None:\\n mxmonitor.tic()\\n\\n self.module.forward_backward(batch)\\n self.module.update()\\n\\n if mxmonitor is not None:\\n results = mxmonitor.toc()\\n if results:\\n for _, k, v in results:\\n logger.info('Monitor: Batch [{:d}] {:s} {:s}'.format(train_state.updates, k, v))\\n\\n if train_iter.iter_next():\\n # pre-fetch next batch\\n next_data_batch = train_iter.next()\\n self.module.prepare(next_data_batch)\\n\\n self.module.update_metric(metric_train, batch.label)\\n\\n self.training_monitor.batch_end_callback(train_state.epoch, train_state.updates, metric_train)\\n train_state.updates += 1\\n train_state.samples += train_iter.batch_size\\n\\n if train_state.updates > 0 and train_state.updates % checkpoint_frequency == 0:\\n train_state.checkpoint += 1\\n self._save_params(output_folder, train_state.checkpoint)\\n cleanup_params_files(output_folder, max_params_files_to_keep,\\n train_state.checkpoint, self.training_monitor.get_best_checkpoint())\\n self.training_monitor.checkpoint_callback(train_state.checkpoint, metric_train)\\n\\n toc = time.time()\\n logger.info(\\\"Checkpoint [%d]\\\\tUpdates=%d Epoch=%d Samples=%d Time-cost=%.3f\\\",\\n train_state.checkpoint, train_state.updates, train_state.epoch,\\n train_state.samples, (toc - tic))\\n tic = time.time()\\n\\n for name, val in metric_train.get_name_value():\\n logger.info('Checkpoint [%d]\\\\tTrain-%s=%f', train_state.checkpoint, name, val)\\n metric_train.reset()\\n\\n # evaluation on validation set\\n has_improved, best_checkpoint = self._evaluate(train_state, val_iter, metric_val)\\n if self.lr_scheduler is not None:\\n self.lr_scheduler.new_evaluation_result(has_improved)\\n\\n if has_improved:\\n best_path = os.path.join(output_folder, C.PARAMS_BEST_NAME)\\n if os.path.lexists(best_path):\\n os.remove(best_path)\\n actual_best_fname = C.PARAMS_NAME % best_checkpoint\\n os.symlink(actual_best_fname, best_path)\\n train_state.num_not_improved = 0\\n else:\\n train_state.num_not_improved += 1\\n logger.info(\\\"Model has not improved for %d checkpoints\\\", train_state.num_not_improved)\\n\\n if max_num_not_improved >= 0 and train_state.num_not_improved >= max_num_not_improved:\\n logger.info(\\\"Maximum number of not improved checkpoints (%d) reached: %d\\\",\\n max_num_not_improved, train_state.num_not_improved)\\n stop_fit = True\\n\\n if min_num_epochs is not None and train_state.epoch < min_num_epochs:\\n logger.info(\\\"Minimum number of epochs (%d) not reached yet: %d\\\",\\n min_num_epochs,\\n train_state.epoch)\\n stop_fit = False\\n\\n if stop_fit:\\n logger.info(\\\"Stopping fit\\\")\\n self.training_monitor.stop_fit_callback()\\n final_training_state_dirname = os.path.join(output_folder, C.TRAINING_STATE_DIRNAME)\\n if os.path.exists(final_training_state_dirname):\\n shutil.rmtree(final_training_state_dirname)\\n break\\n\\n self._checkpoint(train_state, output_folder, train_iter)\\n cleanup_params_files(output_folder, max_params_files_to_keep,\\n train_state.checkpoint, self.training_monitor.get_best_checkpoint())\",\n \"def __iter__(self):\\n index = len(self._logrecords)\\n while index > 0:\\n index -= 1\\n yield self._logrecords[index]\",\n \"def run(self, num_iter: Union[int, None] =None) -> np.ndarray:\\n # update iterations\\n if num_iter is not None:\\n self.num_iter = num_iter\\n\\n # run once to get the output shape\\n out = np.asarray(self.func(**self.get_next_params(), **self._kwargs))\\n\\n # output container\\n result = np.ones((self.num_iter, out.shape[0])) * np.nan\\n\\n # build input parameter\\n params = (({**self.get_next_params(), **self._kwargs},i) for i in range(self.num_iter))\\n\\n # build the wrapper \\n def wrap(tup):\\n # unwrap the tuple\\n par, i = tup\\n \\n # save result\\n result[i] = self.func(**par)\\n\\n # create the parallel worker\\n parallel_worker = Parallel(n_jobs=self.n_jobs, require='sharedmem')\\n\\n # create verbose or non verbose parameter generator\\n if self._kwargs.get('verbose', False):\\n func_gen = (delayed(wrap)(param) for param in params)\\n else:\\n func_gen = (delayed(wrap)(param) for param in tqdm(params, total=self.num_iter))\\n \\n # run the job in parallel\\n parallel_worker(func_gen)\\n\\n # finished - return the result\\n return result\",\n \"def step(self, estim: EstimBase) -> None:\\n _, results = estim.get_last_results()\\n results = [self.to_metrics(res) for res in results]\\n self.metrics.extend(results)\\n if len(self.metrics) >= len(self.population):\\n self.population = self._mating(self.population)\\n self.metrics = []\",\n \"def run(self):\\n i = 0\\n try:\\n for i in range(0, self._iters):\\n if self._verbose:\\n print(\\\" Inner CG Iteration \\\" + repr(i))\\n\\n self._forward(self._p_k, self._v_k)\\n sigma_k = measure(self._p_k, self._v_k)\\n alpha_k = self._rho_k / sigma_k\\n update_m(self._m, alpha_k, self._p_k)\\n sub_scaled_vector(self._residual_k, self._residual_k, alpha_k,\\n self._v_k)\\n self._v_k = gpuarray_copy(self._residual_k)\\n rho_k_plus_1 = measure(self._v_k, self._residual_k)\\n rho_k_t = np.abs(rho_k_plus_1)\\n\\n if (rho_k_t / self._rho_0 <= self._relative_tolerance) \\\\\\n or (rho_k_t <= self._absolute_tolerance):\\n if self._verbose:\\n print(\\\"Converged at Iteration \\\" + str(i) + \\\".\\\")\\n self.converged = True\\n self.iteration = i+1\\n return\\n\\n add_scaled_vector(self._p_k, self._v_k,\\n rho_k_plus_1/self._rho_k,\\n self._p_k)\\n self._rho_k = rho_k_plus_1\\n\\n if self._verbose >= 3:\\n print(\\\" Residual=\\\" + repr(rho_k_t))\\n except KeyboardInterrupt:\\n raise\\n finally:\\n self.iteration = i+1\",\n \"def iter_epoch(self):\\n\\n # set to train mode\\n self._set_train()\\n\\n # start epoch\\n for i, (source, target) in enumerate(self.train_dataset):\\n self._batch_iter(source, target, i)\\n\\n if self.info:\\n print(f\\\"\\\\rEpoch: { self.epoch } | Average loss: { self.epoch_loss.avg }\\\")\\n\\n # update epoch and reset the epoch_loss\\n self.epoch_loss.reset()\\n self.epoch += 1\",\n \"def after_train_iter(self,\\n runner,\\n batch_idx: int,\\n data_batch: DATA_BATCH = None,\\n outputs: Optional[dict] = None) -> None:\\n # Print experiment name every n iterations.\\n if self.every_n_train_iters(\\n runner, self.interval_exp_name) or (self.end_of_epoch(\\n runner.train_dataloader, batch_idx)):\\n exp_info = f'Exp name: {runner.experiment_name}'\\n runner.logger.info(exp_info)\\n if self.every_n_inner_iters(batch_idx, self.interval):\\n tag, log_str = runner.log_processor.get_log_after_iter(\\n runner, batch_idx, 'train')\\n elif (self.end_of_epoch(runner.train_dataloader, batch_idx)\\n and (not self.ignore_last\\n or len(runner.train_dataloader) <= self.interval)):\\n # `runner.max_iters` may not be divisible by `self.interval`. if\\n # `self.ignore_last==True`, the log of remaining iterations will\\n # be recorded (Epoch [4][1000/1007], the logs of 998-1007\\n # iterations will be recorded).\\n tag, log_str = runner.log_processor.get_log_after_iter(\\n runner, batch_idx, 'train')\\n else:\\n return\\n runner.logger.info(log_str)\\n runner.visualizer.add_scalars(\\n tag, step=runner.iter + 1, file_path=self.json_log_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67205477","0.6347677","0.634362","0.61698365","0.61429197","0.6141512","0.60966563","0.599339","0.5989898","0.59228843","0.59223664","0.5900693","0.5892551","0.58662176","0.58467567","0.584372","0.582183","0.58217317","0.58217317","0.5800747","0.5751619","0.57142967","0.56881905","0.56487346","0.5622366","0.5618461","0.5608925","0.5597325","0.55813277","0.5576631","0.5558902","0.55454737","0.5533819","0.55317396","0.5528168","0.55112815","0.5511232","0.5506743","0.55048734","0.5502616","0.5493754","0.5489712","0.5488822","0.5488179","0.5478177","0.54706573","0.54671204","0.54570675","0.5456217","0.5453276","0.54389894","0.5436288","0.5424637","0.5419588","0.541848","0.5416497","0.5416142","0.5413724","0.5411625","0.5402948","0.53970325","0.5396287","0.53938156","0.5390968","0.5388772","0.5384174","0.5374141","0.5370007","0.53694546","0.5362715","0.5361494","0.53478336","0.53444016","0.53345597","0.5325954","0.53240687","0.53240687","0.5309258","0.52990717","0.5294978","0.52925766","0.5289717","0.52856946","0.52817446","0.5278964","0.5269137","0.5253607","0.5244458","0.52414775","0.52402353","0.5238802","0.5238032","0.5235822","0.5234841","0.5234079","0.5233999","0.5225841","0.52242386","0.52235025","0.5223041","0.5218089"],"string":"[\n \"0.67205477\",\n \"0.6347677\",\n \"0.634362\",\n \"0.61698365\",\n \"0.61429197\",\n \"0.6141512\",\n \"0.60966563\",\n \"0.599339\",\n \"0.5989898\",\n \"0.59228843\",\n \"0.59223664\",\n \"0.5900693\",\n \"0.5892551\",\n \"0.58662176\",\n \"0.58467567\",\n \"0.584372\",\n \"0.582183\",\n \"0.58217317\",\n \"0.58217317\",\n \"0.5800747\",\n \"0.5751619\",\n \"0.57142967\",\n \"0.56881905\",\n \"0.56487346\",\n \"0.5622366\",\n \"0.5618461\",\n \"0.5608925\",\n \"0.5597325\",\n \"0.55813277\",\n \"0.5576631\",\n \"0.5558902\",\n \"0.55454737\",\n \"0.5533819\",\n \"0.55317396\",\n \"0.5528168\",\n \"0.55112815\",\n \"0.5511232\",\n \"0.5506743\",\n \"0.55048734\",\n \"0.5502616\",\n \"0.5493754\",\n \"0.5489712\",\n \"0.5488822\",\n \"0.5488179\",\n \"0.5478177\",\n \"0.54706573\",\n \"0.54671204\",\n \"0.54570675\",\n \"0.5456217\",\n \"0.5453276\",\n \"0.54389894\",\n \"0.5436288\",\n \"0.5424637\",\n \"0.5419588\",\n \"0.541848\",\n \"0.5416497\",\n \"0.5416142\",\n \"0.5413724\",\n \"0.5411625\",\n \"0.5402948\",\n \"0.53970325\",\n \"0.5396287\",\n \"0.53938156\",\n \"0.5390968\",\n \"0.5388772\",\n \"0.5384174\",\n \"0.5374141\",\n \"0.5370007\",\n \"0.53694546\",\n \"0.5362715\",\n \"0.5361494\",\n \"0.53478336\",\n \"0.53444016\",\n \"0.53345597\",\n \"0.5325954\",\n \"0.53240687\",\n \"0.53240687\",\n \"0.5309258\",\n \"0.52990717\",\n \"0.5294978\",\n \"0.52925766\",\n \"0.5289717\",\n \"0.52856946\",\n \"0.52817446\",\n \"0.5278964\",\n \"0.5269137\",\n \"0.5253607\",\n \"0.5244458\",\n \"0.52414775\",\n \"0.52402353\",\n \"0.5238802\",\n \"0.5238032\",\n \"0.5235822\",\n \"0.5234841\",\n \"0.5234079\",\n \"0.5233999\",\n \"0.5225841\",\n \"0.52242386\",\n \"0.52235025\",\n \"0.5223041\",\n \"0.5218089\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7175,"cells":{"query":{"kind":"string","value":"Samples observations from several samplers."},"document":{"kind":"string","value":"def sample(self) -> Tuple[List[List[StepSequence]], np.array, np.array]:\n ros = []\n rets = []\n all_lengths = []\n for sampler in self.samplers:\n samples = sampler.sample()\n ros.append(samples)\n rets.extend([sample.undiscounted_return() for sample in samples])\n all_lengths.extend([sample.length for sample in samples])\n\n return ros, np.array(rets), np.array(all_lengths)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def samples(self):\n pass","def load_samplers(self):\n for sampler in self.gltf.samplers:\n # Use a sane default sampler if the sampler data is empty\n # Samplers can simply just be json data: \"{}\"\n if sampler.minFilter is sampler.magFilter is None:\n self.samplers.append(\n self.ctx.sampler(\n filter=(moderngl.LINEAR_MIPMAP_LINEAR, moderngl.LINEAR),\n repeat_x=False,\n repeat_y=False,\n anisotropy=16.0,\n )\n )\n else:\n self.samplers.append(\n self.ctx.sampler(\n filter=(sampler.minFilter, sampler.magFilter),\n repeat_x=sampler.wrapS in [REPEAT, MIRRORED_REPEAT],\n repeat_y=sampler.wrapT in [REPEAT, MIRRORED_REPEAT],\n anisotropy=16.0,\n )\n )","def draw_samplers(self):\n raise NotImplementedError(\" The draw_samplers() method has not been implemented \")","def set_samplers(self, samplers):\n self._derived_properties[\"samplers\"] = samplers\n self.nsamplers = len(self.samplers)","def sample(self, num_samples, **kwargs):\n pass","def build_sampler(self, params):\n\n def sampler(logits_1, logits_2, key):\n q_kwargs = dict(transpose=True) if self.use_transpose else {}\n x = self.model.bind(params).sample(logits_1, key)\n y = self.model.bind(params).sample(logits_2, key, **q_kwargs)\n return jnp.zeros([10, 10]).at[x, y].set(1.)\n\n return sampler","def sampler(self, *args, **kwargs):\n\n return (samples_subgraphs ** 2).tolist()","def create_samples(self, skills_sample_fraction=1.0, users_sample_fraction=1.0):\n # Sampling\n self.sample_skills_to_be_covered(skills_sample_fraction)\n self.sample_users(users_sample_fraction)","def __call__(self, samples_number):\n self.sampler.sample(samples_number)","def run(self, repetitions, **kwargs):\n\t\tself.sampler.sample(repetitions, **kwargs)","def _sampler(self, value, sample_args):\n valid = []\n for filter_, sampler in _FILTERS_AND_SAMPLERS:\n if filter_(value):\n valid.append(sampler)\n if not valid:\n raise ValueError('No valid samplers found: value={} sample_args={}'\n .format(value, sample_args))\n return random.choice(valid)","def get_samplers(test, count):\n samplers = []\n for i in range(count):\n create = require(test.next_call_of(\"vkCreateSampler\"))\n require_equal(VK_SUCCESS, int(create.return_val))\n require_not_equal(0, create.int_device)\n require_not_equal(0, create.hex_pSampler)\n sampler = little_endian_bytes_to_int(require(create.get_write_data(\n create.hex_pSampler, NON_DISPATCHABLE_HANDLE_SIZE)))\n require_not_equal(0, sampler)\n samplers.append(sampler)\n return samplers","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def samples(self, gp, Y_metadata=None, samples=1):\n raise NotImplementedError(\"\"\"May be possible to use MCMC with user-tuning, see\n MCMC_pdf_samples in likelihood.py and write samples function\n using this, beware this is a simple implementation\n of Metropolis and will not work well for all likelihoods\"\"\")","def sample(self,p0=None,nsamp=None): \r\n raise NotImplementedError('Need to implement sample function')","def samples(self, gp):\r\n raise NotImplementedError","def _generate(self, **kwargs):\n N = self.parameter_schema['N']\n parameter_count = len(self._parameter_names)\n common_override_kwargs = {}\n override_kwargs = self._sampler_overrides(common_override_kwargs)\n if kwargs:\n kwargs.update(override_kwargs)\n else:\n kwargs = override_kwargs\n __import__(\"SALib.sample\", fromlist=[self.sampler_class])\n sampler = getattr(SALib.sample, self.sampler_class)\n problem = self.parameter_schema[\"problem\"]\n self._samples = sampler.sample(problem, N, **kwargs)\n self._samples = numpy.unique(self._samples, axis=0)\n super()._generate()","def run_model_sampler(Y, latent_dim, n_iter):\n\tF_sample = []\n\tloading_sample = []\n\tvariance_sample = []\n\ttrace_sample = []\n\tmse_history = []\n\tF = initiate_factors(Y, latent_dim)\n\tfor i in tqdm(range(n_iter)):\n\t\tF, loading_matrix, Y_variance, gp_traces, mse = gibbs_sampling(F, Y)\n\t\tF_sample.append(F)\n\t\tloading_sample.append(loading_matrix)\n\t\tvariance_sample.append(Y_variance)\n\t\ttrace_sample.append(gp_traces)\n\t\tmse_history.append(mse)\n\treturn F_sample, loading_sample, variance_sample, trace_sample, mse_history","def samples(self, samples):\n\n self._samples = samples","def set_samplers(self):\n\n if self.length_scale_indexes is None:\n ignore_index = None\n if not self.noise or self.data.get('var_noise') is not None:\n ignore_index = [0, 1]\n\n slice_parameters = {\n 'max_steps_out': self.max_steps_out,\n 'component_wise': True,\n }\n self.slice_samplers.append(SliceSampling(\n wrapper_log_prob, range(self.dimension_parameters), ignore_index=ignore_index,\n **slice_parameters))\n else:\n slice_parameters = {\n 'max_steps_out': self.max_steps_out,\n 'component_wise': False,\n }\n indexes = [i for i in range(self.dimension_parameters) if i not in\n self.length_scale_indexes]\n ignore_index = None\n if not self.noise or self.data.get('var_noise') is not None:\n ignore_index = [0, 1]\n\n if ORNSTEIN_KERNEL in self.type_kernel:\n if ignore_index is None:\n ignore_index = []\n ignore_index += [2]\n\n if len(indexes) != len(ignore_index):\n self.slice_samplers.append(\n SliceSampling(\n wrapper_log_prob, indexes, ignore_index=ignore_index, **slice_parameters))\n\n slice_parameters['component_wise'] = True\n self.slice_samplers.append(SliceSampling(wrapper_log_prob, self.length_scale_indexes,\n **slice_parameters))\n\n if self.start_point_sampler is not None and len(self.start_point_sampler) > 0:\n if len(self.samples_parameters) == 0:\n self.samples_parameters.append(np.array(self.start_point_sampler))\n else:\n self.samples_parameters = []\n self.samples_parameters.append(self.get_value_parameters_model)\n if self.n_burning > 0:\n parameters = self.sample_parameters(float(self.n_burning) / (self.thinning + 1))\n self.samples_parameters = []\n self.samples_parameters.append(parameters[-1])\n self.start_point_sampler = parameters[-1]\n else:\n self.start_point_sampler = self.get_value_parameters_model","def sample_streams(self, sample_streams):\n\n self._sample_streams = sample_streams","def sample(self):\n\n # pick sample type according to probability\n samplers = [\"unif\", \"geo\", \"diverse\"]\n sample_idx = np.random.multinomial(\n 1, [self.unif_prob, self.geo_prob, self.diverse_prob])\n idx = np.argmax(sample_idx)\n sampler = samplers[idx]\n\n if sampler == \"unif\":\n return self.unif_sampler()\n if sampler == \"geo\":\n return self.geo_sampler()\n if sampler == \"diverse\":\n return self.diverse_sampler()","def sample(self):\r\n raise NotImplementedError","def initSamples(self):\n # Define this (note random.uniform is helpful here!)\n for i in range(self.numParticles):\n # Create particles uniformly and generate same weights for all particles.\n particle = random.uniform(self.minValue, self.maxValue)\n self.samples.append(particle)\n self.weights.append(1/self.numParticles)","def samples_set(self):\n self.get_samples_set(self.samples_db)\n self.choose_samples(self.chosen_samples_db, self.chosen_hashes)","def sample(self, *args, **kwargs):","def create_samples(self):\n for s_id in range(len(self.data[\"sample\"])):\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\n self.data[\"label\"][s_id]))","def samples(self):\n if not self._samples:\n self._samples = defaultdict(list)\n blobs = self.blobs()\n sequencing = self._get_entities('sequencing')\n for s in self._get_entities('sample'):\n s = sample_factory(s, workspace=self, blobs=blobs, sequencing=sequencing, avro_path=self.avro_path)\n self._samples[s.subject_id].append(s)\n if s.missing_sequence:\n self.missing_sequence = s.missing_sequence\n return self._samples","def create_samples(self):\n self._samples = self.load_samples()\n self.modify_samples()","def initialise_sampler(self):\n raise NotImplementedError","def test_validation_correct_samplers():\n samplers = [\n {'type': 'MultiStateSampler', 'locality': 3},\n {'type': 'ReplicaExchangeSampler'},\n # MCMCMove 'single' is defined in get_template_script().\n {'type': 'SAMSSampler', 'mcmc_moves': 'single'},\n {'type': 'ReplicaExchangeSampler', 'number_of_iterations': 5, 'replica_mixing_scheme': 'swap-neighbors'},\n {'type': 'ReplicaExchangeSampler', 'number_of_iterations': 5, 'replica_mixing_scheme': None}\n ]\n exp_builder = ExperimentBuilder(get_template_script())\n for sampler in samplers:\n script = {'samplers': {'sampler1': sampler}}\n yield exp_builder._validate_samplers, script","def sample(self):\n raise NotImplementedError","def sample(self):\n raise NotImplementedError","def samples(self, groups=None):\n groups = check_parameters_for_validity(\n groups, \"groups\", self.groups(), self.groups()\n )\n all_samples = []\n\n if \"train\" in groups:\n all_samples.extend(self.background_model_samples())\n groups.remove(\"train\")\n\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\n\n # Add znorm samples. Returning znorm samples for one group of dev or\n # eval is enough because they are duplicated.\n for grp in groups:\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\n break\n\n # Add tnorm samples.\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\n\n return all_samples","def sample(self, verbose=False):\n\t\tfor i in range(self.nsamp): self.subsample(verbose)\n\t\treturn self.amps, self.pos, self.irads","def sample(self, observation):\n raise NotImplementedError","def __call__(self, params):\r\n return self.sample(params)","def score_samples(self, X):\n ...","def oversampling(models, X_train, y_train):\n print(\"SMOTE...\")\n sm = over_sampling.SMOTE(random_state=42, n_jobs=2)\n X_train, y_train = sm.fit_sample(X_train, y_train)\n for m in models:\n nobs = len(X_train[y_train == m])\n\n return X_train, y_train","def sample(self):\n logger.info(\"%s: collect sensor data\", self.__class__.__name__)\n samples = []\n self._fetch_data(samples)\n return samples","def easy_sample(self, num, **kwargs):\n return self.preprocess(self.sample(num, **kwargs), **kwargs)","def sampling(X_train, y_train, X_test, y_test, sampling_instances, model_instances, func):\n\n metrics = []\n # go through all sampling methods\n for sampling_instance in sampling_instances:\n if sampling_instance is not None:\n print('fitting sampling '+ str(sampling_instances.index(sampling_instance) + 1) + ' on ' +\n str(len(sampling_instances)), \" : \", type(sampling_instance).__name__)\n X_train1, y_train1 = sampling_instance.fit_resample(X=X_train, y=y_train)\n else:\n print('fitting sampling '+ str(sampling_instances.index(sampling_instance) + 1) + ' on ' +\n str(len(sampling_instances)), \" : \", type(sampling_instance).__name__)\n X_train1, y_train1 = X_train, y_train\n\n # Go through all models\n for model_instance in model_instances:\n print('fitting model ' + str(model_instances.index(model_instance) + 1) + ' on ' +\n str(len(model_instances)), \" : \", type(model_instance).__name__)\n model_instance.fit(X_train1, y_train1)\n metrics.append(func(y_test, model_instance.predict(X_test)))\n\n models = [type(model).__name__ for model in model_instances]\n methods = [type(sampling).__name__ for sampling in sampling_instances]\n index = [model + '_' + method for model in models for method in methods]\n\n #Dry run of compute metrics with return_index=True to get indexes\n columns = func(y_test, y_test, average='weighted', return_index=True)\n metrics = pd.DataFrame(metrics, columns=columns, index=index)\n\n return metrics","def sample(self):","def sample(self, like_params):\n\t\traise NotImplementedError","def _sample_likelihood_params(self):\r\n self._sample_omega()\r\n self._sample_beta()\r\n self._sample_r()","def double_sampling(X_train, y_train, X_test, y_test, sampling_instances1, sampling_instances2, model_instances, func):\n\n list_of_df_metrics = []\n\n for sampling_instance1 in sampling_instances1:\n print('First Round:')\n if sampling_instance1 is not None:\n print('fitting sampling of 1st round '+ str(sampling_instances1.index(sampling_instance1) + 1) + ' over ' + str(len(sampling_instances1)) + ': ' + type(sampling_instance1).__name__)\n X_train_1st_round, y_train1st_round = sampling_instance1.fit_resample(X=X_train, y=y_train)\n print('Second Round:')\n else:\n print('No 1st round Sampling methods applied')\n X_train_1st_round, y_train1st_round = X_train, y_train\n\n df_metrics = sampling(X_train_1st_round, y_train1st_round, X_test, y_test, sampling_instances2, model_instances, func)\n\n list_of_df_metrics.append(df_metrics)\n\n df_metrics_all = pd.concat(list_of_df_metrics, keys=[type(x).__name__ for x in sampling_instances1], names=['First Sampling Round'])\n\n return df_metrics_all","def sample(self):\n return [agent_observation_space.sample() for agent_observation_space in self._agents_observation_space]","def list_samples(arn=None, nextToken=None):\n pass","def score_samples(self, x):\n raise NotImplementedError","def test_sampling1 ():\n cpus = list(range(C.N_PARALLEL))\n affinity = dict(cuda_idx=C.CUDA_IDX, workers_cpus=cpus)\n agent_ = findOptimalAgent(reward=None)\n agent = CategoricalPgAgent(AcrobotNet, \n initial_model_state_dict=agent_.state_dict())\n s0 = np.array([1, 0, 1/np.sqrt(2), 1/np.sqrt(2), 4, 2], dtype=np.float)\n sampler = SerialSampler(\n EnvCls=rlpyt_make,\n env_kwargs=dict(id=C.ENV, reward=None, internalStateFn=C.INTERNAL_STATE_FN, s0=s0),\n batch_T=500,\n batch_B=16,\n max_decorrelation_steps=0,\n )\n sampler.initialize(\n agent=agent,\n affinity=affinity,\n seed=0\n )\n _, traj_info = sampler.obtain_samples(0)\n print(np.mean([t['DiscountedReturn'] for t in traj_info]))","def get_samples(**project_filters):\n p = get_project(**project_filters)\n return Sample.query.filter_by(_project_id=p.id).all()","def sample(self, detections):\n\n raise NotImplementedError","def get_samples(self, min_samples):\n raise NotImplementedError","def sample(self, processor, batch_size=None):\n pass","def samples(\n self,\n pcollection_ids: Optional[Iterable[str]] = None\n ) -> beam_fn_api_pb2.SampleDataResponse:\n ret = beam_fn_api_pb2.SampleDataResponse()\n\n with self._samplers_lock:\n samplers = self._samplers.copy()\n\n for pcoll_id in samplers:\n if pcollection_ids and pcoll_id not in pcollection_ids:\n continue\n\n samples = samplers[pcoll_id].flush()\n if samples:\n ret.element_samples[pcoll_id].elements.extend(samples)\n\n return ret","def mksampler(x):\n if hasattr(x, \"__call__\"):\n return x\n elif type(x) is list:\n # NB: disjoint ranges can be given as nested lists, e.g. [(1,2), (4,5)]\n if len(x) == 2 and type(x[0]) in (int,float) and type(x[1]) in (int,float):\n #print \"MKSAMPLER: Casting %s to UniformSampler\" % str(x)\n return UniformSampler(*x)\n elif len(x) > 2 or (len(x) > 0 and type(x[0]) not in (int,float)):\n #print \"MKSAMPLER: Casting %s to DisjointUniformSampler\" % str(x)\n return DisjointUniformSampler(x)\n if len(x) < 2:\n raise Exception(\"Supplied list could not be converted to a continuous sampler\")\n elif type(x) is tuple:\n #print \"MKSAMPLER: Casting %s to CyclicSeqSampler\" % str(x)\n return CyclicSeqSampler(*x)\n elif type(x) is set:\n #print \"MKSAMPLER: Casting %s to RandomSeqSampler\" % str(x)\n return RandomSeqSampler(*x)\n else:\n #print \"MKSAMPLER: Casting %s to ConstSampler\" % str(x)\n return ConstSampler(x)","def sample_survey(self, **kwargs):","def _compute_samples(self, samples):\n return samples","def sample_stats(self, analytes=None, filt=True,\n stat_fns=[np.nanmean, np.nanstd],\n eachtrace=True):\n if analytes is None:\n analytes = self.analytes\n elif isinstance(analytes, str):\n analytes = [analytes]\n\n self.stats = {}\n self.stats_calced = [f.__name__ for f in stat_fns]\n\n # calculate stats for each sample\n for s in self.samples:\n if self.srm_identifier not in s:\n self.data_dict[s].sample_stats(analytes, filt=filt,\n stat_fns=stat_fns,\n eachtrace=eachtrace)\n\n self.stats[s] = self.data_dict[s].stats","def samples(self):\n if self._samples:\n return self._samples\n if SAMPLE_DF_KEY not in self or self[SAMPLE_DF_KEY] is None:\n _LOGGER.debug(\"No samples are defined\")\n return []","def apply(recorders, args) -> None:\n operation = _get(args)\n for recorder in recorders:\n recorder.apply_sample(operation)","def initialize_samplers(\n self,\n transform_id: str,\n descriptor: beam_fn_api_pb2.ProcessBundleDescriptor,\n coder_factory) -> List[OutputSampler]:\n transform_proto = descriptor.transforms[transform_id]\n with self._samplers_lock:\n if transform_id in self._samplers_by_output:\n return self._samplers_by_output[transform_id]\n\n # Initialize the samplers.\n for pcoll_id in transform_proto.outputs.values():\n # Only initialize new PCollections.\n if pcoll_id in self._samplers:\n continue\n\n # Create the sampler with the corresponding coder.\n coder_id = descriptor.pcollections[pcoll_id].coder_id\n coder = coder_factory(coder_id)\n sampler = OutputSampler(\n coder, self._max_samples, self._sample_every_sec)\n self._samplers[pcoll_id] = sampler\n\n # Next update the lookup table for ElementSamplers for a given PTransform.\n # Operations look up the ElementSampler for an output based on the index\n # of the tag in the PTransform's outputs. The following code intializes\n # the array with ElementSamplers in the correct indices.\n outputs = transform_proto.outputs\n samplers = [self._samplers[pcoll_id] for pcoll_id in outputs.values()]\n self._samplers_by_output[transform_id] = samplers\n\n return samplers","def sample(self, batchsize, *args, **kwargs):\n raise NotImplementedError","def search_samples():\n r = req('GET', SUB_API + 'search/samples', params=apply_search_filters())\n samples = []\n for sample in demisto.get(r.json(), 'data.items'):\n samples.append({\n 'ID': demisto.get(sample, 'result'),\n 'Details': demisto.get(sample, 'details')\n })\n demisto.results({\n 'Type': entryTypes['note'],\n 'EntryContext': {'ThreatGrid.Sample': samples},\n 'HumanReadable': tableToMarkdown('ThreatGrid - Sample Search', samples, ['Result', 'Details']),\n 'ContentsFormat': formats['json'],\n 'Contents': r.json()\n })","def _generate_distribution_samples(self, set_count, parameter_count):\n self._samples = numpy.zeros((set_count, parameter_count))\n for i, distribution in enumerate(self.parameter_distributions.values()):\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])","def samples(self):\n return self._values[:self.nsamples]","def __init__(self, samples):\n self.samples = samples","def test_sampler_building(self):\n with mmtools.utils.temporary_directory() as tmp_dir:\n template_script = self.get_implicit_template_script(tmp_dir)\n template_script['options']['resume_setup'] = True\n default_number_of_iterations = template_script['options']['default_number_of_iterations']\n\n # Add tested samplers.\n template_script['samplers'] = {\n 'my-sampler1': {\n 'type': 'ReplicaExchangeSampler',\n 'number_of_iterations': 9,\n 'replica_mixing_scheme': 'swap-neighbors',\n },\n 'my-sampler2': {\n 'type': 'MultiStateSampler',\n 'locality': 5\n }\n }\n\n def check_default_number_of_iterations(phase, sampler_description):\n if 'number_of_iterations' not in sampler_description:\n assert phase.sampler.number_of_iterations == default_number_of_iterations\n\n # Test that options are passed to the sampler correctly.\n for sampler_id, sampler_description in template_script['samplers'].items():\n template_script['experiments']['sampler'] = sampler_id\n constructor_description = template_script['samplers'][sampler_id]\n yield (self.check_constructor, template_script, constructor_description,\n 'sampler', None, check_default_number_of_iterations)","def _samples(self):\n finite_types = \\\n [QuiverMutationType(t) for t in [['A', 1], ['A', 5], ['B', 2], ['B', 5],\n ['C', 3], ['C', 5], ['D', 2], ['D', 5],\n [\"E\", 6], [\"E\", 7], [\"E\", 8], [\"F\", 4],\n [\"G\", 2]]]\n affine_types = \\\n [QuiverMutationType(t) for t in [['A', [1,1], 1], ['A', [4,5], 1], ['D', 4, 1], ['BB', 5, 1]]]\n elliptic_types = \\\n [QuiverMutationType(t) for t in [['E', 6, [1,1]], ['E', 7, [1,1]]]]\n mutation_finite_types = \\\n [QuiverMutationType(t) for t in [['R2',(1,5)], ['R2',(3,5)]]]\n mutation_infinite_types = \\\n [QuiverMutationType(t) for t in [['E',10], ['BE',5], ['GR',(3,10)], ['T',(3,3,4)]]]\n\n return finite_types + affine_types + elliptic_types + mutation_finite_types + mutation_infinite_types","def sample(self):\n raise NotImplementedError(\"Override me!\")","def samples(self):\n return self._samples","def generate_bootstrap_samples(num_samples, test_universe, test_set_sizes):\n for sample_idx, sample_size in zip(range(num_samples), cycle(test_set_sizes)):\n yield random.sample(test_universe, sample_size)","def initial_samples(lb, ub, method, numSamp):\r\n if not len(lb) == len(ub):\r\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\r\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\r\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\r\n for case in Switch(method):\r\n if case('random'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = lb + (ub - lb) * rand(len(lb))\r\n\r\n break\r\n if case('rand-wor'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\r\n\r\n break\r\n if case('nolh'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = range(q)\r\n if r != 0:\r\n remove = range(dim - r, dim)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-rp'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = random.sample(range(q), q)\r\n if r != 0:\r\n remove = random.sample(range(q - 1), r)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-cdr'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf, remove = get_cdr_permutations(len(ub))\r\n if remove != []:\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('lhc'):\r\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\r\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\r\n ])\r\n break\r\n if case():\r\n print 'Somehow you evaded my assert statement - good job!',\r\n print ' However, you still need to use a valid method string.'\r\n\r\n return s","def collect_samples(self):\n self.replay_buffer = self.collect_initial_batch(\n self.replay_buffer, self.acm_pre_train_samples\n )","def sample(self, data, chains=2, iter=1024, warmup=None, **kwargs):\n\n kwds = self._validate_stan_inputs(\n data=data, chains=chains, iter=iter, warmup=warmup, **kwargs)\n return self._model.sampling(**kwds)","def sample(self,burnin,thinning,samples,append=True):\n\n\t\tif hasattr(self, 'sampled_topics') and append == True:\n\n\t\t\tsampled_topics = samplers_lda.sampler(self.docid,self.tokens,self.sampled_topics[self.samples-1,:],\n\t\t\t\t\t\t\t\t\tself.N,self.V,self.K,self.D,self.alpha,self.beta,\n\t\t\t\t\t\t\t\t\tburnin,thinning,samples)\n\n\t\t\ttt_temp = self.tt_comp(sampled_topics)\n\t\t\tdt_temp = self.dt_comp(sampled_topics)\n\n\t\t\tself.sampled_topics = np.concatenate((self.sampled_topics,sampled_topics))\n\t\t\tself.tt = np.concatenate((self.tt,tt_temp),axis=2)\n\t\t\tself.dt = np.concatenate((self.dt,dt_temp),axis=2)\n\n\t\t\tself.samples = self.samples + samples\n\n\t\telse:\n\n\t\t\tself.samples = samples\n\n\t\t\tself.sampled_topics = samplers_lda.sampler(self.docid,self.tokens,self.topic_seed,\n\t\t\t\t\t\t\t\t\t\tself.N,self.V,self.K,self.D,self.alpha,self.beta,\n\t\t\t\t\t\t\t\t\t\tburnin,thinning,self.samples)\n\n\t\t\tself.tt = self.tt_comp(self.sampled_topics)\n\t\t\tself.dt = self.dt_comp(self.sampled_topics)","def get_samples(self):\n \n # determine correct string to split the data on\n split = {\n 'v1.0-trainval': {True: 'train', False: 'val'},\n 'v1.0-mini': {True: 'mini_train', False: 'mini_val'},\n }[nusc.version][self.is_train]\n\n # filters the appropriate scene names\n scenes = create_splits_scenes()[split]\n \n # collects samples with matching scene names\n samples = [samp for samp in nusc.sample if\n nusc.get('scene', samp['scene_token'])['name'] in scenes]\n\n return samples","def sampler_to_iterator(dataset, sampler):\n for sample in sampler:\n if isinstance(sample, (list, tuple)):\n # yield a batch\n yield [dataset[i] for i in sample]\n else:\n # yield a single example\n yield dataset[sample]","def sample(self, x):","def __iter__(self):\n for sample in self.samples:\n yield sample","def get_samples_list(self):\n return self.samples_list","def sample_input_domain(num_samples):\n s1 = np.random.random(num_samples) * 10\n s2 = np.random.random(num_samples) * 2 - 5\n s3 = np.random.random(num_samples)\n s4 = np.random.random(num_samples) * 30 + 20\n return s1, s2, s3, s4","def samples(self, u=None):\n roots = [u]\n if u is None:\n roots = self.roots\n for root in roots:\n yield from self._sample_generator(root)","def sample(self, num, **kwargs):\n raise NotImplementedError(f'Should be implemented in derived class!')","def take_samples(self, num_samples: int) -> List:\n if num_samples > len(self.samples):\n return random.sample(self.samples, len(self.samples))\n return random.sample(self.samples, num_samples)","def start_sampler_threads(self, _sender, **_unused_msg):\n pass\n # try:\n # for sampler in traverse_nested(sender.get_sampler()):\n # if sampler is None:\n # continue\n # sampler.run_threads(self.num_threads)\n # tf.logging.info('filling queues (this can take a few minutes).')\n # except (NameError, TypeError, AttributeError, IndexError):\n # tf.logging.fatal(\n # \"samplers not running, pop_batch_op operations \"\n # \"are blocked.\")\n # raise","def evaluate_samples(self, samples, eval_key=None, missing=None):\n pass","def sample(self, state, model_args, model_kwargs):\n raise NotImplementedError","def draw_samples(nsubj, ngroups, split_method='default'):\n if split_method == 'default':\n if nsubj > 10 * ngroups:\n samples = split_group(nsubj, ngroups)\n else:\n samples = bootstrap_group(nsubj, ngroups)\n elif split_method == 'bootstrap':\n samples = bootstrap_group(nsubj, ngroups)\n elif split_method == '':\n samples = split_group(nsubj, ngroups)\n else:\n raise ValueError('unknown splitting method')\n\n return samples","def fixture_samples(sample_single) -> Iterator[dict]:\n _samples = []\n sample_id = sample_single[\"sample_id\"]\n for number in range(3):\n sample = copy.deepcopy(sample_single)\n sample[\"sample_id\"] = \"_\".join([sample_id, str(number)])\n _samples.append(sample)\n return _samples","def test_get_all_samples(self):\n self.login()\n\n page_size = 20\n\n # hit the API endpoint for both pages\n for page in range(1, 3):\n\n data = {'page': page,\n 'page_size': page_size}\n response = self.client.get(reverse('searchsamples'), data, format='json')\n\n expected = Sample.objects.all().order_by(\"-received\")\n\n paginator = Paginator(expected, page_size)\n res = paginator.page(page)\n\n # format queryset into json for returning\n serializer = SampleSerializer(res, many=True)\n\n context = {\n 'data': serializer.data,\n 'more': (page == 1)\n }\n\n self.assertEqual(response.json(), context)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def input(self, *args, **kwargs):\n return lambda wildcards: self.samples.map(*args, file=\"samples/all/runs/{sample_run}/samples.csv\", **wildcards, **kwargs)","def sample(self, count):\n batch = deepcopy(random.sample(self.buffer, count))\n batch = list(zip(*batch))\n args, kwargs = batch[:-1], batch[-1]\n args = [np.stack(arr) for arr in args]\n out = dict(zip(self.keywords, list(zip(*[d.values() for d in kwargs]))))\n for k, v in out.items():\n out[k] = np.stack(v)\n return (*args, out)","def getSamplesList(self):\n return self.sample_names","async def _samples(cls, responses: SourceResponses) -> AsyncIterator[Samples]:\n rows = await cls.__parse_csv(responses)\n samples = [row for row in rows if not row[\"responseMessage\"].startswith(\"Number of samples in transaction\")]\n labels = {sample[\"label\"] for sample in samples}\n for label in sorted(labels):\n yield [sample for sample in samples if sample[\"label\"] == label]","def test_sample(system_generator):\n\n name, test = system_generator()\n print(name)\n\n w_F, w_R, N_k = test.sample([10, 8], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([1, 1], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([10, 0], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([0, 5], mode=\"wFwR\")","def generate_samples(self):\n self.analytic_probability()","def data_source():\n dataset = [0.1, 0.2, 0.3, 0.4, 0.5]\n while True:\n time.sleep(2)\n yield random.choice(dataset)","def _sample_synthetic(self, X):\n n_samples = X.shape[0]\n self.y = np.concatenate((np.ones(n_samples), np.zeros(n_samples)))\n \n random_state = _forest.check_random_state(self.random_state) \n\n X_synth = np.asarray([np.apply_along_axis(random_state.choice, 0, X) for _ in range(n_samples)])\n self.X = np.concatenate((X, X_synth))\n\n return self.X, self.y","def get_data_and_model_samples(self):\n model_samples = (\n self.net_.sample_fantasy(\n x=self.model_samples_[-1],\n num_mc_steps=self.num_sample_mc_steps,\n beta=self.sample_beta,\n mc_dynamics=self.sampler,\n )\n .detach()\n .cpu()\n .numpy()\n )\n data_sample_ixs = torch.randint(\n 0, self.samples.shape[0], size=(model_samples.shape[0],)\n )\n data_samples = self.samples[data_sample_ixs, ...]\n return data_samples, model_samples"],"string":"[\n \"def samples(self):\\n pass\",\n \"def load_samplers(self):\\n for sampler in self.gltf.samplers:\\n # Use a sane default sampler if the sampler data is empty\\n # Samplers can simply just be json data: \\\"{}\\\"\\n if sampler.minFilter is sampler.magFilter is None:\\n self.samplers.append(\\n self.ctx.sampler(\\n filter=(moderngl.LINEAR_MIPMAP_LINEAR, moderngl.LINEAR),\\n repeat_x=False,\\n repeat_y=False,\\n anisotropy=16.0,\\n )\\n )\\n else:\\n self.samplers.append(\\n self.ctx.sampler(\\n filter=(sampler.minFilter, sampler.magFilter),\\n repeat_x=sampler.wrapS in [REPEAT, MIRRORED_REPEAT],\\n repeat_y=sampler.wrapT in [REPEAT, MIRRORED_REPEAT],\\n anisotropy=16.0,\\n )\\n )\",\n \"def draw_samplers(self):\\n raise NotImplementedError(\\\" The draw_samplers() method has not been implemented \\\")\",\n \"def set_samplers(self, samplers):\\n self._derived_properties[\\\"samplers\\\"] = samplers\\n self.nsamplers = len(self.samplers)\",\n \"def sample(self, num_samples, **kwargs):\\n pass\",\n \"def build_sampler(self, params):\\n\\n def sampler(logits_1, logits_2, key):\\n q_kwargs = dict(transpose=True) if self.use_transpose else {}\\n x = self.model.bind(params).sample(logits_1, key)\\n y = self.model.bind(params).sample(logits_2, key, **q_kwargs)\\n return jnp.zeros([10, 10]).at[x, y].set(1.)\\n\\n return sampler\",\n \"def sampler(self, *args, **kwargs):\\n\\n return (samples_subgraphs ** 2).tolist()\",\n \"def create_samples(self, skills_sample_fraction=1.0, users_sample_fraction=1.0):\\n # Sampling\\n self.sample_skills_to_be_covered(skills_sample_fraction)\\n self.sample_users(users_sample_fraction)\",\n \"def __call__(self, samples_number):\\n self.sampler.sample(samples_number)\",\n \"def run(self, repetitions, **kwargs):\\n\\t\\tself.sampler.sample(repetitions, **kwargs)\",\n \"def _sampler(self, value, sample_args):\\n valid = []\\n for filter_, sampler in _FILTERS_AND_SAMPLERS:\\n if filter_(value):\\n valid.append(sampler)\\n if not valid:\\n raise ValueError('No valid samplers found: value={} sample_args={}'\\n .format(value, sample_args))\\n return random.choice(valid)\",\n \"def get_samplers(test, count):\\n samplers = []\\n for i in range(count):\\n create = require(test.next_call_of(\\\"vkCreateSampler\\\"))\\n require_equal(VK_SUCCESS, int(create.return_val))\\n require_not_equal(0, create.int_device)\\n require_not_equal(0, create.hex_pSampler)\\n sampler = little_endian_bytes_to_int(require(create.get_write_data(\\n create.hex_pSampler, NON_DISPATCHABLE_HANDLE_SIZE)))\\n require_not_equal(0, sampler)\\n samplers.append(sampler)\\n return samplers\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def samples(self, gp, Y_metadata=None, samples=1):\\n raise NotImplementedError(\\\"\\\"\\\"May be possible to use MCMC with user-tuning, see\\n MCMC_pdf_samples in likelihood.py and write samples function\\n using this, beware this is a simple implementation\\n of Metropolis and will not work well for all likelihoods\\\"\\\"\\\")\",\n \"def sample(self,p0=None,nsamp=None): \\r\\n raise NotImplementedError('Need to implement sample function')\",\n \"def samples(self, gp):\\r\\n raise NotImplementedError\",\n \"def _generate(self, **kwargs):\\n N = self.parameter_schema['N']\\n parameter_count = len(self._parameter_names)\\n common_override_kwargs = {}\\n override_kwargs = self._sampler_overrides(common_override_kwargs)\\n if kwargs:\\n kwargs.update(override_kwargs)\\n else:\\n kwargs = override_kwargs\\n __import__(\\\"SALib.sample\\\", fromlist=[self.sampler_class])\\n sampler = getattr(SALib.sample, self.sampler_class)\\n problem = self.parameter_schema[\\\"problem\\\"]\\n self._samples = sampler.sample(problem, N, **kwargs)\\n self._samples = numpy.unique(self._samples, axis=0)\\n super()._generate()\",\n \"def run_model_sampler(Y, latent_dim, n_iter):\\n\\tF_sample = []\\n\\tloading_sample = []\\n\\tvariance_sample = []\\n\\ttrace_sample = []\\n\\tmse_history = []\\n\\tF = initiate_factors(Y, latent_dim)\\n\\tfor i in tqdm(range(n_iter)):\\n\\t\\tF, loading_matrix, Y_variance, gp_traces, mse = gibbs_sampling(F, Y)\\n\\t\\tF_sample.append(F)\\n\\t\\tloading_sample.append(loading_matrix)\\n\\t\\tvariance_sample.append(Y_variance)\\n\\t\\ttrace_sample.append(gp_traces)\\n\\t\\tmse_history.append(mse)\\n\\treturn F_sample, loading_sample, variance_sample, trace_sample, mse_history\",\n \"def samples(self, samples):\\n\\n self._samples = samples\",\n \"def set_samplers(self):\\n\\n if self.length_scale_indexes is None:\\n ignore_index = None\\n if not self.noise or self.data.get('var_noise') is not None:\\n ignore_index = [0, 1]\\n\\n slice_parameters = {\\n 'max_steps_out': self.max_steps_out,\\n 'component_wise': True,\\n }\\n self.slice_samplers.append(SliceSampling(\\n wrapper_log_prob, range(self.dimension_parameters), ignore_index=ignore_index,\\n **slice_parameters))\\n else:\\n slice_parameters = {\\n 'max_steps_out': self.max_steps_out,\\n 'component_wise': False,\\n }\\n indexes = [i for i in range(self.dimension_parameters) if i not in\\n self.length_scale_indexes]\\n ignore_index = None\\n if not self.noise or self.data.get('var_noise') is not None:\\n ignore_index = [0, 1]\\n\\n if ORNSTEIN_KERNEL in self.type_kernel:\\n if ignore_index is None:\\n ignore_index = []\\n ignore_index += [2]\\n\\n if len(indexes) != len(ignore_index):\\n self.slice_samplers.append(\\n SliceSampling(\\n wrapper_log_prob, indexes, ignore_index=ignore_index, **slice_parameters))\\n\\n slice_parameters['component_wise'] = True\\n self.slice_samplers.append(SliceSampling(wrapper_log_prob, self.length_scale_indexes,\\n **slice_parameters))\\n\\n if self.start_point_sampler is not None and len(self.start_point_sampler) > 0:\\n if len(self.samples_parameters) == 0:\\n self.samples_parameters.append(np.array(self.start_point_sampler))\\n else:\\n self.samples_parameters = []\\n self.samples_parameters.append(self.get_value_parameters_model)\\n if self.n_burning > 0:\\n parameters = self.sample_parameters(float(self.n_burning) / (self.thinning + 1))\\n self.samples_parameters = []\\n self.samples_parameters.append(parameters[-1])\\n self.start_point_sampler = parameters[-1]\\n else:\\n self.start_point_sampler = self.get_value_parameters_model\",\n \"def sample_streams(self, sample_streams):\\n\\n self._sample_streams = sample_streams\",\n \"def sample(self):\\n\\n # pick sample type according to probability\\n samplers = [\\\"unif\\\", \\\"geo\\\", \\\"diverse\\\"]\\n sample_idx = np.random.multinomial(\\n 1, [self.unif_prob, self.geo_prob, self.diverse_prob])\\n idx = np.argmax(sample_idx)\\n sampler = samplers[idx]\\n\\n if sampler == \\\"unif\\\":\\n return self.unif_sampler()\\n if sampler == \\\"geo\\\":\\n return self.geo_sampler()\\n if sampler == \\\"diverse\\\":\\n return self.diverse_sampler()\",\n \"def sample(self):\\r\\n raise NotImplementedError\",\n \"def initSamples(self):\\n # Define this (note random.uniform is helpful here!)\\n for i in range(self.numParticles):\\n # Create particles uniformly and generate same weights for all particles.\\n particle = random.uniform(self.minValue, self.maxValue)\\n self.samples.append(particle)\\n self.weights.append(1/self.numParticles)\",\n \"def samples_set(self):\\n self.get_samples_set(self.samples_db)\\n self.choose_samples(self.chosen_samples_db, self.chosen_hashes)\",\n \"def sample(self, *args, **kwargs):\",\n \"def create_samples(self):\\n for s_id in range(len(self.data[\\\"sample\\\"])):\\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\\n self.data[\\\"label\\\"][s_id]))\",\n \"def samples(self):\\n if not self._samples:\\n self._samples = defaultdict(list)\\n blobs = self.blobs()\\n sequencing = self._get_entities('sequencing')\\n for s in self._get_entities('sample'):\\n s = sample_factory(s, workspace=self, blobs=blobs, sequencing=sequencing, avro_path=self.avro_path)\\n self._samples[s.subject_id].append(s)\\n if s.missing_sequence:\\n self.missing_sequence = s.missing_sequence\\n return self._samples\",\n \"def create_samples(self):\\n self._samples = self.load_samples()\\n self.modify_samples()\",\n \"def initialise_sampler(self):\\n raise NotImplementedError\",\n \"def test_validation_correct_samplers():\\n samplers = [\\n {'type': 'MultiStateSampler', 'locality': 3},\\n {'type': 'ReplicaExchangeSampler'},\\n # MCMCMove 'single' is defined in get_template_script().\\n {'type': 'SAMSSampler', 'mcmc_moves': 'single'},\\n {'type': 'ReplicaExchangeSampler', 'number_of_iterations': 5, 'replica_mixing_scheme': 'swap-neighbors'},\\n {'type': 'ReplicaExchangeSampler', 'number_of_iterations': 5, 'replica_mixing_scheme': None}\\n ]\\n exp_builder = ExperimentBuilder(get_template_script())\\n for sampler in samplers:\\n script = {'samplers': {'sampler1': sampler}}\\n yield exp_builder._validate_samplers, script\",\n \"def sample(self):\\n raise NotImplementedError\",\n \"def sample(self):\\n raise NotImplementedError\",\n \"def samples(self, groups=None):\\n groups = check_parameters_for_validity(\\n groups, \\\"groups\\\", self.groups(), self.groups()\\n )\\n all_samples = []\\n\\n if \\\"train\\\" in groups:\\n all_samples.extend(self.background_model_samples())\\n groups.remove(\\\"train\\\")\\n\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.references(grp)))\\n all_samples.extend(_sample_sets_to_samples(self.probes(grp)))\\n\\n # Add znorm samples. Returning znorm samples for one group of dev or\\n # eval is enough because they are duplicated.\\n for grp in groups:\\n all_samples.extend(_sample_sets_to_samples(self.zprobes(grp)))\\n break\\n\\n # Add tnorm samples.\\n all_samples.extend(_sample_sets_to_samples(self.treferences()))\\n\\n return all_samples\",\n \"def sample(self, verbose=False):\\n\\t\\tfor i in range(self.nsamp): self.subsample(verbose)\\n\\t\\treturn self.amps, self.pos, self.irads\",\n \"def sample(self, observation):\\n raise NotImplementedError\",\n \"def __call__(self, params):\\r\\n return self.sample(params)\",\n \"def score_samples(self, X):\\n ...\",\n \"def oversampling(models, X_train, y_train):\\n print(\\\"SMOTE...\\\")\\n sm = over_sampling.SMOTE(random_state=42, n_jobs=2)\\n X_train, y_train = sm.fit_sample(X_train, y_train)\\n for m in models:\\n nobs = len(X_train[y_train == m])\\n\\n return X_train, y_train\",\n \"def sample(self):\\n logger.info(\\\"%s: collect sensor data\\\", self.__class__.__name__)\\n samples = []\\n self._fetch_data(samples)\\n return samples\",\n \"def easy_sample(self, num, **kwargs):\\n return self.preprocess(self.sample(num, **kwargs), **kwargs)\",\n \"def sampling(X_train, y_train, X_test, y_test, sampling_instances, model_instances, func):\\n\\n metrics = []\\n # go through all sampling methods\\n for sampling_instance in sampling_instances:\\n if sampling_instance is not None:\\n print('fitting sampling '+ str(sampling_instances.index(sampling_instance) + 1) + ' on ' +\\n str(len(sampling_instances)), \\\" : \\\", type(sampling_instance).__name__)\\n X_train1, y_train1 = sampling_instance.fit_resample(X=X_train, y=y_train)\\n else:\\n print('fitting sampling '+ str(sampling_instances.index(sampling_instance) + 1) + ' on ' +\\n str(len(sampling_instances)), \\\" : \\\", type(sampling_instance).__name__)\\n X_train1, y_train1 = X_train, y_train\\n\\n # Go through all models\\n for model_instance in model_instances:\\n print('fitting model ' + str(model_instances.index(model_instance) + 1) + ' on ' +\\n str(len(model_instances)), \\\" : \\\", type(model_instance).__name__)\\n model_instance.fit(X_train1, y_train1)\\n metrics.append(func(y_test, model_instance.predict(X_test)))\\n\\n models = [type(model).__name__ for model in model_instances]\\n methods = [type(sampling).__name__ for sampling in sampling_instances]\\n index = [model + '_' + method for model in models for method in methods]\\n\\n #Dry run of compute metrics with return_index=True to get indexes\\n columns = func(y_test, y_test, average='weighted', return_index=True)\\n metrics = pd.DataFrame(metrics, columns=columns, index=index)\\n\\n return metrics\",\n \"def sample(self):\",\n \"def sample(self, like_params):\\n\\t\\traise NotImplementedError\",\n \"def _sample_likelihood_params(self):\\r\\n self._sample_omega()\\r\\n self._sample_beta()\\r\\n self._sample_r()\",\n \"def double_sampling(X_train, y_train, X_test, y_test, sampling_instances1, sampling_instances2, model_instances, func):\\n\\n list_of_df_metrics = []\\n\\n for sampling_instance1 in sampling_instances1:\\n print('First Round:')\\n if sampling_instance1 is not None:\\n print('fitting sampling of 1st round '+ str(sampling_instances1.index(sampling_instance1) + 1) + ' over ' + str(len(sampling_instances1)) + ': ' + type(sampling_instance1).__name__)\\n X_train_1st_round, y_train1st_round = sampling_instance1.fit_resample(X=X_train, y=y_train)\\n print('Second Round:')\\n else:\\n print('No 1st round Sampling methods applied')\\n X_train_1st_round, y_train1st_round = X_train, y_train\\n\\n df_metrics = sampling(X_train_1st_round, y_train1st_round, X_test, y_test, sampling_instances2, model_instances, func)\\n\\n list_of_df_metrics.append(df_metrics)\\n\\n df_metrics_all = pd.concat(list_of_df_metrics, keys=[type(x).__name__ for x in sampling_instances1], names=['First Sampling Round'])\\n\\n return df_metrics_all\",\n \"def sample(self):\\n return [agent_observation_space.sample() for agent_observation_space in self._agents_observation_space]\",\n \"def list_samples(arn=None, nextToken=None):\\n pass\",\n \"def score_samples(self, x):\\n raise NotImplementedError\",\n \"def test_sampling1 ():\\n cpus = list(range(C.N_PARALLEL))\\n affinity = dict(cuda_idx=C.CUDA_IDX, workers_cpus=cpus)\\n agent_ = findOptimalAgent(reward=None)\\n agent = CategoricalPgAgent(AcrobotNet, \\n initial_model_state_dict=agent_.state_dict())\\n s0 = np.array([1, 0, 1/np.sqrt(2), 1/np.sqrt(2), 4, 2], dtype=np.float)\\n sampler = SerialSampler(\\n EnvCls=rlpyt_make,\\n env_kwargs=dict(id=C.ENV, reward=None, internalStateFn=C.INTERNAL_STATE_FN, s0=s0),\\n batch_T=500,\\n batch_B=16,\\n max_decorrelation_steps=0,\\n )\\n sampler.initialize(\\n agent=agent,\\n affinity=affinity,\\n seed=0\\n )\\n _, traj_info = sampler.obtain_samples(0)\\n print(np.mean([t['DiscountedReturn'] for t in traj_info]))\",\n \"def get_samples(**project_filters):\\n p = get_project(**project_filters)\\n return Sample.query.filter_by(_project_id=p.id).all()\",\n \"def sample(self, detections):\\n\\n raise NotImplementedError\",\n \"def get_samples(self, min_samples):\\n raise NotImplementedError\",\n \"def sample(self, processor, batch_size=None):\\n pass\",\n \"def samples(\\n self,\\n pcollection_ids: Optional[Iterable[str]] = None\\n ) -> beam_fn_api_pb2.SampleDataResponse:\\n ret = beam_fn_api_pb2.SampleDataResponse()\\n\\n with self._samplers_lock:\\n samplers = self._samplers.copy()\\n\\n for pcoll_id in samplers:\\n if pcollection_ids and pcoll_id not in pcollection_ids:\\n continue\\n\\n samples = samplers[pcoll_id].flush()\\n if samples:\\n ret.element_samples[pcoll_id].elements.extend(samples)\\n\\n return ret\",\n \"def mksampler(x):\\n if hasattr(x, \\\"__call__\\\"):\\n return x\\n elif type(x) is list:\\n # NB: disjoint ranges can be given as nested lists, e.g. [(1,2), (4,5)]\\n if len(x) == 2 and type(x[0]) in (int,float) and type(x[1]) in (int,float):\\n #print \\\"MKSAMPLER: Casting %s to UniformSampler\\\" % str(x)\\n return UniformSampler(*x)\\n elif len(x) > 2 or (len(x) > 0 and type(x[0]) not in (int,float)):\\n #print \\\"MKSAMPLER: Casting %s to DisjointUniformSampler\\\" % str(x)\\n return DisjointUniformSampler(x)\\n if len(x) < 2:\\n raise Exception(\\\"Supplied list could not be converted to a continuous sampler\\\")\\n elif type(x) is tuple:\\n #print \\\"MKSAMPLER: Casting %s to CyclicSeqSampler\\\" % str(x)\\n return CyclicSeqSampler(*x)\\n elif type(x) is set:\\n #print \\\"MKSAMPLER: Casting %s to RandomSeqSampler\\\" % str(x)\\n return RandomSeqSampler(*x)\\n else:\\n #print \\\"MKSAMPLER: Casting %s to ConstSampler\\\" % str(x)\\n return ConstSampler(x)\",\n \"def sample_survey(self, **kwargs):\",\n \"def _compute_samples(self, samples):\\n return samples\",\n \"def sample_stats(self, analytes=None, filt=True,\\n stat_fns=[np.nanmean, np.nanstd],\\n eachtrace=True):\\n if analytes is None:\\n analytes = self.analytes\\n elif isinstance(analytes, str):\\n analytes = [analytes]\\n\\n self.stats = {}\\n self.stats_calced = [f.__name__ for f in stat_fns]\\n\\n # calculate stats for each sample\\n for s in self.samples:\\n if self.srm_identifier not in s:\\n self.data_dict[s].sample_stats(analytes, filt=filt,\\n stat_fns=stat_fns,\\n eachtrace=eachtrace)\\n\\n self.stats[s] = self.data_dict[s].stats\",\n \"def samples(self):\\n if self._samples:\\n return self._samples\\n if SAMPLE_DF_KEY not in self or self[SAMPLE_DF_KEY] is None:\\n _LOGGER.debug(\\\"No samples are defined\\\")\\n return []\",\n \"def apply(recorders, args) -> None:\\n operation = _get(args)\\n for recorder in recorders:\\n recorder.apply_sample(operation)\",\n \"def initialize_samplers(\\n self,\\n transform_id: str,\\n descriptor: beam_fn_api_pb2.ProcessBundleDescriptor,\\n coder_factory) -> List[OutputSampler]:\\n transform_proto = descriptor.transforms[transform_id]\\n with self._samplers_lock:\\n if transform_id in self._samplers_by_output:\\n return self._samplers_by_output[transform_id]\\n\\n # Initialize the samplers.\\n for pcoll_id in transform_proto.outputs.values():\\n # Only initialize new PCollections.\\n if pcoll_id in self._samplers:\\n continue\\n\\n # Create the sampler with the corresponding coder.\\n coder_id = descriptor.pcollections[pcoll_id].coder_id\\n coder = coder_factory(coder_id)\\n sampler = OutputSampler(\\n coder, self._max_samples, self._sample_every_sec)\\n self._samplers[pcoll_id] = sampler\\n\\n # Next update the lookup table for ElementSamplers for a given PTransform.\\n # Operations look up the ElementSampler for an output based on the index\\n # of the tag in the PTransform's outputs. The following code intializes\\n # the array with ElementSamplers in the correct indices.\\n outputs = transform_proto.outputs\\n samplers = [self._samplers[pcoll_id] for pcoll_id in outputs.values()]\\n self._samplers_by_output[transform_id] = samplers\\n\\n return samplers\",\n \"def sample(self, batchsize, *args, **kwargs):\\n raise NotImplementedError\",\n \"def search_samples():\\n r = req('GET', SUB_API + 'search/samples', params=apply_search_filters())\\n samples = []\\n for sample in demisto.get(r.json(), 'data.items'):\\n samples.append({\\n 'ID': demisto.get(sample, 'result'),\\n 'Details': demisto.get(sample, 'details')\\n })\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'EntryContext': {'ThreatGrid.Sample': samples},\\n 'HumanReadable': tableToMarkdown('ThreatGrid - Sample Search', samples, ['Result', 'Details']),\\n 'ContentsFormat': formats['json'],\\n 'Contents': r.json()\\n })\",\n \"def _generate_distribution_samples(self, set_count, parameter_count):\\n self._samples = numpy.zeros((set_count, parameter_count))\\n for i, distribution in enumerate(self.parameter_distributions.values()):\\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])\",\n \"def samples(self):\\n return self._values[:self.nsamples]\",\n \"def __init__(self, samples):\\n self.samples = samples\",\n \"def test_sampler_building(self):\\n with mmtools.utils.temporary_directory() as tmp_dir:\\n template_script = self.get_implicit_template_script(tmp_dir)\\n template_script['options']['resume_setup'] = True\\n default_number_of_iterations = template_script['options']['default_number_of_iterations']\\n\\n # Add tested samplers.\\n template_script['samplers'] = {\\n 'my-sampler1': {\\n 'type': 'ReplicaExchangeSampler',\\n 'number_of_iterations': 9,\\n 'replica_mixing_scheme': 'swap-neighbors',\\n },\\n 'my-sampler2': {\\n 'type': 'MultiStateSampler',\\n 'locality': 5\\n }\\n }\\n\\n def check_default_number_of_iterations(phase, sampler_description):\\n if 'number_of_iterations' not in sampler_description:\\n assert phase.sampler.number_of_iterations == default_number_of_iterations\\n\\n # Test that options are passed to the sampler correctly.\\n for sampler_id, sampler_description in template_script['samplers'].items():\\n template_script['experiments']['sampler'] = sampler_id\\n constructor_description = template_script['samplers'][sampler_id]\\n yield (self.check_constructor, template_script, constructor_description,\\n 'sampler', None, check_default_number_of_iterations)\",\n \"def _samples(self):\\n finite_types = \\\\\\n [QuiverMutationType(t) for t in [['A', 1], ['A', 5], ['B', 2], ['B', 5],\\n ['C', 3], ['C', 5], ['D', 2], ['D', 5],\\n [\\\"E\\\", 6], [\\\"E\\\", 7], [\\\"E\\\", 8], [\\\"F\\\", 4],\\n [\\\"G\\\", 2]]]\\n affine_types = \\\\\\n [QuiverMutationType(t) for t in [['A', [1,1], 1], ['A', [4,5], 1], ['D', 4, 1], ['BB', 5, 1]]]\\n elliptic_types = \\\\\\n [QuiverMutationType(t) for t in [['E', 6, [1,1]], ['E', 7, [1,1]]]]\\n mutation_finite_types = \\\\\\n [QuiverMutationType(t) for t in [['R2',(1,5)], ['R2',(3,5)]]]\\n mutation_infinite_types = \\\\\\n [QuiverMutationType(t) for t in [['E',10], ['BE',5], ['GR',(3,10)], ['T',(3,3,4)]]]\\n\\n return finite_types + affine_types + elliptic_types + mutation_finite_types + mutation_infinite_types\",\n \"def sample(self):\\n raise NotImplementedError(\\\"Override me!\\\")\",\n \"def samples(self):\\n return self._samples\",\n \"def generate_bootstrap_samples(num_samples, test_universe, test_set_sizes):\\n for sample_idx, sample_size in zip(range(num_samples), cycle(test_set_sizes)):\\n yield random.sample(test_universe, sample_size)\",\n \"def initial_samples(lb, ub, method, numSamp):\\r\\n if not len(lb) == len(ub):\\r\\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\\r\\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\\r\\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\\r\\n for case in Switch(method):\\r\\n if case('random'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = lb + (ub - lb) * rand(len(lb))\\r\\n\\r\\n break\\r\\n if case('rand-wor'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\\r\\n\\r\\n break\\r\\n if case('nolh'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = range(q)\\r\\n if r != 0:\\r\\n remove = range(dim - r, dim)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-rp'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = random.sample(range(q), q)\\r\\n if r != 0:\\r\\n remove = random.sample(range(q - 1), r)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-cdr'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf, remove = get_cdr_permutations(len(ub))\\r\\n if remove != []:\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('lhc'):\\r\\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\\r\\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case():\\r\\n print 'Somehow you evaded my assert statement - good job!',\\r\\n print ' However, you still need to use a valid method string.'\\r\\n\\r\\n return s\",\n \"def collect_samples(self):\\n self.replay_buffer = self.collect_initial_batch(\\n self.replay_buffer, self.acm_pre_train_samples\\n )\",\n \"def sample(self, data, chains=2, iter=1024, warmup=None, **kwargs):\\n\\n kwds = self._validate_stan_inputs(\\n data=data, chains=chains, iter=iter, warmup=warmup, **kwargs)\\n return self._model.sampling(**kwds)\",\n \"def sample(self,burnin,thinning,samples,append=True):\\n\\n\\t\\tif hasattr(self, 'sampled_topics') and append == True:\\n\\n\\t\\t\\tsampled_topics = samplers_lda.sampler(self.docid,self.tokens,self.sampled_topics[self.samples-1,:],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.N,self.V,self.K,self.D,self.alpha,self.beta,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tburnin,thinning,samples)\\n\\n\\t\\t\\ttt_temp = self.tt_comp(sampled_topics)\\n\\t\\t\\tdt_temp = self.dt_comp(sampled_topics)\\n\\n\\t\\t\\tself.sampled_topics = np.concatenate((self.sampled_topics,sampled_topics))\\n\\t\\t\\tself.tt = np.concatenate((self.tt,tt_temp),axis=2)\\n\\t\\t\\tself.dt = np.concatenate((self.dt,dt_temp),axis=2)\\n\\n\\t\\t\\tself.samples = self.samples + samples\\n\\n\\t\\telse:\\n\\n\\t\\t\\tself.samples = samples\\n\\n\\t\\t\\tself.sampled_topics = samplers_lda.sampler(self.docid,self.tokens,self.topic_seed,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.N,self.V,self.K,self.D,self.alpha,self.beta,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tburnin,thinning,self.samples)\\n\\n\\t\\t\\tself.tt = self.tt_comp(self.sampled_topics)\\n\\t\\t\\tself.dt = self.dt_comp(self.sampled_topics)\",\n \"def get_samples(self):\\n \\n # determine correct string to split the data on\\n split = {\\n 'v1.0-trainval': {True: 'train', False: 'val'},\\n 'v1.0-mini': {True: 'mini_train', False: 'mini_val'},\\n }[nusc.version][self.is_train]\\n\\n # filters the appropriate scene names\\n scenes = create_splits_scenes()[split]\\n \\n # collects samples with matching scene names\\n samples = [samp for samp in nusc.sample if\\n nusc.get('scene', samp['scene_token'])['name'] in scenes]\\n\\n return samples\",\n \"def sampler_to_iterator(dataset, sampler):\\n for sample in sampler:\\n if isinstance(sample, (list, tuple)):\\n # yield a batch\\n yield [dataset[i] for i in sample]\\n else:\\n # yield a single example\\n yield dataset[sample]\",\n \"def sample(self, x):\",\n \"def __iter__(self):\\n for sample in self.samples:\\n yield sample\",\n \"def get_samples_list(self):\\n return self.samples_list\",\n \"def sample_input_domain(num_samples):\\n s1 = np.random.random(num_samples) * 10\\n s2 = np.random.random(num_samples) * 2 - 5\\n s3 = np.random.random(num_samples)\\n s4 = np.random.random(num_samples) * 30 + 20\\n return s1, s2, s3, s4\",\n \"def samples(self, u=None):\\n roots = [u]\\n if u is None:\\n roots = self.roots\\n for root in roots:\\n yield from self._sample_generator(root)\",\n \"def sample(self, num, **kwargs):\\n raise NotImplementedError(f'Should be implemented in derived class!')\",\n \"def take_samples(self, num_samples: int) -> List:\\n if num_samples > len(self.samples):\\n return random.sample(self.samples, len(self.samples))\\n return random.sample(self.samples, num_samples)\",\n \"def start_sampler_threads(self, _sender, **_unused_msg):\\n pass\\n # try:\\n # for sampler in traverse_nested(sender.get_sampler()):\\n # if sampler is None:\\n # continue\\n # sampler.run_threads(self.num_threads)\\n # tf.logging.info('filling queues (this can take a few minutes).')\\n # except (NameError, TypeError, AttributeError, IndexError):\\n # tf.logging.fatal(\\n # \\\"samplers not running, pop_batch_op operations \\\"\\n # \\\"are blocked.\\\")\\n # raise\",\n \"def evaluate_samples(self, samples, eval_key=None, missing=None):\\n pass\",\n \"def sample(self, state, model_args, model_kwargs):\\n raise NotImplementedError\",\n \"def draw_samples(nsubj, ngroups, split_method='default'):\\n if split_method == 'default':\\n if nsubj > 10 * ngroups:\\n samples = split_group(nsubj, ngroups)\\n else:\\n samples = bootstrap_group(nsubj, ngroups)\\n elif split_method == 'bootstrap':\\n samples = bootstrap_group(nsubj, ngroups)\\n elif split_method == '':\\n samples = split_group(nsubj, ngroups)\\n else:\\n raise ValueError('unknown splitting method')\\n\\n return samples\",\n \"def fixture_samples(sample_single) -> Iterator[dict]:\\n _samples = []\\n sample_id = sample_single[\\\"sample_id\\\"]\\n for number in range(3):\\n sample = copy.deepcopy(sample_single)\\n sample[\\\"sample_id\\\"] = \\\"_\\\".join([sample_id, str(number)])\\n _samples.append(sample)\\n return _samples\",\n \"def test_get_all_samples(self):\\n self.login()\\n\\n page_size = 20\\n\\n # hit the API endpoint for both pages\\n for page in range(1, 3):\\n\\n data = {'page': page,\\n 'page_size': page_size}\\n response = self.client.get(reverse('searchsamples'), data, format='json')\\n\\n expected = Sample.objects.all().order_by(\\\"-received\\\")\\n\\n paginator = Paginator(expected, page_size)\\n res = paginator.page(page)\\n\\n # format queryset into json for returning\\n serializer = SampleSerializer(res, many=True)\\n\\n context = {\\n 'data': serializer.data,\\n 'more': (page == 1)\\n }\\n\\n self.assertEqual(response.json(), context)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def input(self, *args, **kwargs):\\n return lambda wildcards: self.samples.map(*args, file=\\\"samples/all/runs/{sample_run}/samples.csv\\\", **wildcards, **kwargs)\",\n \"def sample(self, count):\\n batch = deepcopy(random.sample(self.buffer, count))\\n batch = list(zip(*batch))\\n args, kwargs = batch[:-1], batch[-1]\\n args = [np.stack(arr) for arr in args]\\n out = dict(zip(self.keywords, list(zip(*[d.values() for d in kwargs]))))\\n for k, v in out.items():\\n out[k] = np.stack(v)\\n return (*args, out)\",\n \"def getSamplesList(self):\\n return self.sample_names\",\n \"async def _samples(cls, responses: SourceResponses) -> AsyncIterator[Samples]:\\n rows = await cls.__parse_csv(responses)\\n samples = [row for row in rows if not row[\\\"responseMessage\\\"].startswith(\\\"Number of samples in transaction\\\")]\\n labels = {sample[\\\"label\\\"] for sample in samples}\\n for label in sorted(labels):\\n yield [sample for sample in samples if sample[\\\"label\\\"] == label]\",\n \"def test_sample(system_generator):\\n\\n name, test = system_generator()\\n print(name)\\n\\n w_F, w_R, N_k = test.sample([10, 8], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([1, 1], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([10, 0], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([0, 5], mode=\\\"wFwR\\\")\",\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def data_source():\\n dataset = [0.1, 0.2, 0.3, 0.4, 0.5]\\n while True:\\n time.sleep(2)\\n yield random.choice(dataset)\",\n \"def _sample_synthetic(self, X):\\n n_samples = X.shape[0]\\n self.y = np.concatenate((np.ones(n_samples), np.zeros(n_samples)))\\n \\n random_state = _forest.check_random_state(self.random_state) \\n\\n X_synth = np.asarray([np.apply_along_axis(random_state.choice, 0, X) for _ in range(n_samples)])\\n self.X = np.concatenate((X, X_synth))\\n\\n return self.X, self.y\",\n \"def get_data_and_model_samples(self):\\n model_samples = (\\n self.net_.sample_fantasy(\\n x=self.model_samples_[-1],\\n num_mc_steps=self.num_sample_mc_steps,\\n beta=self.sample_beta,\\n mc_dynamics=self.sampler,\\n )\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n data_sample_ixs = torch.randint(\\n 0, self.samples.shape[0], size=(model_samples.shape[0],)\\n )\\n data_samples = self.samples[data_sample_ixs, ...]\\n return data_samples, model_samples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67290366","0.66936","0.66508645","0.6528074","0.6460033","0.6458007","0.63837016","0.6380185","0.6372922","0.63533103","0.63098216","0.6204721","0.6148462","0.6148462","0.6144332","0.61341447","0.6125732","0.61021185","0.60895056","0.6007772","0.59698457","0.59687287","0.5955888","0.5945422","0.59383196","0.59179974","0.5884791","0.5877266","0.5874533","0.5870259","0.5854025","0.5853452","0.58378696","0.58378696","0.583063","0.5822746","0.58157647","0.5802257","0.5797433","0.57915926","0.5777567","0.5775868","0.5760964","0.5736618","0.5733723","0.5733204","0.5729045","0.5727251","0.57087076","0.57070976","0.57025886","0.5697704","0.56973517","0.5683021","0.5682155","0.5674125","0.5672556","0.5669079","0.56660926","0.56433195","0.5636014","0.56104857","0.5608157","0.56035674","0.5602729","0.5598635","0.55825317","0.5573346","0.55727774","0.55612874","0.5558158","0.5556689","0.5553553","0.55454963","0.55421335","0.55309516","0.55295664","0.5527838","0.55243254","0.5522784","0.55189306","0.5510126","0.54993385","0.54962176","0.54871476","0.54864854","0.5477058","0.5468625","0.54684466","0.5464737","0.5458703","0.54506695","0.5449402","0.5437978","0.54365534","0.5436068","0.5434799","0.5429538","0.54253066","0.5422303","0.5422026"],"string":"[\n \"0.67290366\",\n \"0.66936\",\n \"0.66508645\",\n \"0.6528074\",\n \"0.6460033\",\n \"0.6458007\",\n \"0.63837016\",\n \"0.6380185\",\n \"0.6372922\",\n \"0.63533103\",\n \"0.63098216\",\n \"0.6204721\",\n \"0.6148462\",\n \"0.6148462\",\n \"0.6144332\",\n \"0.61341447\",\n \"0.6125732\",\n \"0.61021185\",\n \"0.60895056\",\n \"0.6007772\",\n \"0.59698457\",\n \"0.59687287\",\n \"0.5955888\",\n \"0.5945422\",\n \"0.59383196\",\n \"0.59179974\",\n \"0.5884791\",\n \"0.5877266\",\n \"0.5874533\",\n \"0.5870259\",\n \"0.5854025\",\n \"0.5853452\",\n \"0.58378696\",\n \"0.58378696\",\n \"0.583063\",\n \"0.5822746\",\n \"0.58157647\",\n \"0.5802257\",\n \"0.5797433\",\n \"0.57915926\",\n \"0.5777567\",\n \"0.5775868\",\n \"0.5760964\",\n \"0.5736618\",\n \"0.5733723\",\n \"0.5733204\",\n \"0.5729045\",\n \"0.5727251\",\n \"0.57087076\",\n \"0.57070976\",\n \"0.57025886\",\n \"0.5697704\",\n \"0.56973517\",\n \"0.5683021\",\n \"0.5682155\",\n \"0.5674125\",\n \"0.5672556\",\n \"0.5669079\",\n \"0.56660926\",\n \"0.56433195\",\n \"0.5636014\",\n \"0.56104857\",\n \"0.5608157\",\n \"0.56035674\",\n \"0.5602729\",\n \"0.5598635\",\n \"0.55825317\",\n \"0.5573346\",\n \"0.55727774\",\n \"0.55612874\",\n \"0.5558158\",\n \"0.5556689\",\n \"0.5553553\",\n \"0.55454963\",\n \"0.55421335\",\n \"0.55309516\",\n \"0.55295664\",\n \"0.5527838\",\n \"0.55243254\",\n \"0.5522784\",\n \"0.55189306\",\n \"0.5510126\",\n \"0.54993385\",\n \"0.54962176\",\n \"0.54871476\",\n \"0.54864854\",\n \"0.5477058\",\n \"0.5468625\",\n \"0.54684466\",\n \"0.5464737\",\n \"0.5458703\",\n \"0.54506695\",\n \"0.5449402\",\n \"0.5437978\",\n \"0.54365534\",\n \"0.5436068\",\n \"0.5434799\",\n \"0.5429538\",\n \"0.54253066\",\n \"0.5422303\",\n \"0.5422026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7176,"cells":{"query":{"kind":"string","value":"Update the policy's (and value functions') parameters based on the collected rollout data."},"document":{"kind":"string","value":"def update(self, *args: Any, **kwargs: Any):\n obss = []\n losses = []\n for t in range(self.num_teachers):\n concat_ros = StepSequence.concat(kwargs[\"rollouts\"][t])\n concat_ros.torch(data_type=to.get_default_dtype())\n obss.append(concat_ros.get_data_values(\"observations\")[: self.min_steps])\n\n # Train student\n for epoch in range(self.num_epochs):\n self.optimizer.zero_grad()\n\n loss = 0\n for t_idx, teacher in enumerate(self.teacher_policies):\n s_dist = self.expl_strat.action_dist_at(self.policy(obss[t_idx]))\n s_act = s_dist.sample()\n t_dist = self.teacher_expl_strats[t_idx].action_dist_at(teacher(obss[t_idx]))\n\n l = self.teacher_weights[t_idx] * self.criterion(t_dist.log_prob(s_act), s_dist.log_prob(s_act))\n loss += l\n losses.append([t_idx, l.item()])\n print(f\"Epoch {epoch} Loss: {loss.item()}\")\n loss.backward()\n self.optimizer.step()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def policy_update_fn(self, data: Dict[str, Any], result: Dict[str, Any]) -> None:","def update_policy(self, *args, **kwargs):\r\n pass","def update_policy(self):\n pass","def update_Policy(self,inputpolicy):\n \n policyob = self.SD_Map.retrieve_ob(inputpolicy)\n policyob.values[-1] = self.PolicyDicts[inputpolicy][self.translate(self.policy_option_vars[inputpolicy].get(),\n input_language = self.language,\n output_language = 'english')]","def update_policy(self):\n self.trainer_metrics.start_policy_update_timer(\n number_experiences=len(self.training_buffer.update_buffer[\"actions\"]),\n mean_return=float(np.mean(self.cumulative_returns_since_policy_update)),\n )\n self.cumulative_returns_since_policy_update = []\n n_sequences = max(\n int(self.trainer_parameters[\"batch_size\"] / self.policy.sequence_length), 1\n )\n value_total, policy_total = [], []\n advantages = self.training_buffer.update_buffer[\"advantages\"].get_batch()\n self.training_buffer.update_buffer[\"advantages\"].set(\n (advantages - advantages.mean()) / (advantages.std() + 1e-10)\n )\n num_epoch = self.trainer_parameters[\"num_epoch\"]\n for _ in range(num_epoch):\n self.training_buffer.update_buffer.shuffle()\n buffer = self.training_buffer.update_buffer\n for l in range(\n len(self.training_buffer.update_buffer[\"actions\"]) // n_sequences\n ):\n start = l * n_sequences\n end = (l + 1) * n_sequences\n run_out = self.policy.update(\n buffer.make_mini_batch(start, end), n_sequences\n )\n value_total.append(run_out[\"value_loss\"])\n policy_total.append(np.abs(run_out[\"policy_loss\"]))\n self.stats[\"Losses/Value Loss\"].append(np.mean(value_total))\n self.stats[\"Losses/Policy Loss\"].append(np.mean(policy_total))\n for _, reward_signal in self.policy.reward_signals.items():\n update_stats = reward_signal.update(\n self.training_buffer.update_buffer, n_sequences\n )\n for stat, val in update_stats.items():\n self.stats[stat].append(val)\n if self.policy.bc_module:\n update_stats = self.policy.bc_module.update()\n for stat, val in update_stats.items():\n self.stats[stat].append(val)\n self.training_buffer.reset_update_buffer()\n self.trainer_metrics.end_policy_update()","def updateData(self, *args):\n # if self.move_next_option == \"R\":\n # self.restSampling()\n # elif self.move_next_option == \"A\":\n # self.addExtra()\n # else:\n # self.continueReview()\n for name, value in self.parameter_inputs.items():\n self.parameters[name] = value.value\n # directly change the value of class variables\n logMsg((\"update settings: \", self.ml_classifier_cls, name, value.value))\n setattr(self.ml_classifier_cls, name, value.value)\n\n pass","def update_policy_values(policy_lookup, board, state, player, action, next_state, reward):\n\t\n\t# compute total expected reward including future rewards\n\tif board.check_end():\n\t\texpected = reward\n\telse:\n\t\tif player == 1:\n\t\t\texpected = reward + discount * min_value(policy_lookup, next_state, 2)\n\t\telif player == 2:\n\t\t\texpected = reward + discount * max_value(policy_lookup, next_state, 1)\n\t# get current policy action value\n\tpolicy_value = get_policy_value(policy_lookup, state, player, action)\n\t# update policy action value\n\tpolicy_lookup[(state, player)][action] += learning_rate * (expected - policy_value)","def _run_policy(env, policy, num_rollouts):\n start_states, final_states, goal_states, actions, paths = [], [], [], [], []\n\n for i in range(num_rollouts):\n path = rollout(\n env,\n policy,\n max_path_length=100,\n animated=False,\n )\n obs = path[\"observations\"]\n acts = path[\"actions\"]\n\n goal_idx = np.argmax(obs[0, 2:])\n start_x, start_y = obs[0, 0], obs[0, 1]\n acts_x, acts_y = acts[:, 0], acts[:, 1]\n final_x, final_y = obs[len(obs) - 1, 0], obs[len(obs) - 1, 1]\n goal = env.goals[goal_idx]\n goal_x, goal_y = goal[0], goal[1]\n\n start_states.append(np.array([start_x, start_y]))\n final_states.append(np.array([final_x, final_y]))\n goal_states.append(np.array([goal_x, goal_y]))\n actions.append(np.array([acts_x, acts_y]))\n paths.append(path)\n\n return dict(\n start_states=np.array(start_states),\n final_states=np.array(final_states),\n goal_states=np.array(goal_states),\n actions=np.array(actions),\n paths=paths,\n env=env\n )","def update_policy(self, policy, inverse_policy=None):\n self.make_T_policy_matrix(policy)\n self.inverse_dynamics_by_time = dict()\n self.policy = policy\n self.inverse_policy = inverse_policy","def update_parameters(self, ob_no, hidden, ac_na, fixed_log_probs, q_n, adv_n):\n self.update_critic(ob_no, hidden, q_n)\n self.update_policy(ob_no, hidden, ac_na, fixed_log_probs, adv_n)","def policies(self, policies):\n\n self._policies = policies","def update_parameters(self):\n self.alignment_factor = rospy.get_param('/dyn_reconf/alignment_factor')\n self.cohesion_factor = rospy.get_param('/dyn_reconf/cohesion_factor')\n self.separation_factor = rospy.get_param('/dyn_reconf/separation_factor')\n self.avoid_factor = rospy.get_param('/dyn_reconf/avoid_factor')\n self.max_speed = rospy.get_param('/dyn_reconf/max_speed')\n self.max_force = rospy.get_param('/dyn_reconf/max_force')\n self.friction = rospy.get_param('/dyn_reconf/friction')\n self.crowd_radius = rospy.get_param('/dyn_reconf/crowd_radius')\n self.search_radius = rospy.get_param('/dyn_reconf/search_radius')\n\n rospy.loginfo(rospy.get_caller_id() + \" -> Parameters updated\")\n if DEBUG:\n print('alignment_factor: ', self.alignment_factor)\n print('cohesion_factor: ', self.cohesion_factor)\n print('separation_factor: ', self.separation_factor)\n print('avoid_factor: ', self.avoid_factor)\n print('max_speed: ', self.max_speed)\n print('max_force: ', self.max_force)\n print('friction: ', self.friction)\n print('crowd_radius: ', self.crowd_radius)\n print('search_radius: ', self.search_radius)","def apply_policy(cls, metadata, policy):\r\n for attr, value in policy.iteritems():\r\n attr = cls._translate(attr)\r\n if attr not in cls.fields:\r\n # Store unknown attributes coming from policy.json\r\n # in such a way that they will export to xml unchanged\r\n metadata['xml_attributes'][attr] = value\r\n else:\r\n metadata[attr] = value","def policy_update(self):\n mini_batch = random.sample(self.data_buffer, self.batch_size)\n state_batch = [data[0] for data in mini_batch]\n mcts_probs_batch = [data[1] for data in mini_batch]\n winner_batch = [data[2] for data in mini_batch]\n loss, entropy = self.policy_value_net.train_step(\n state_batch,\n mcts_probs_batch,\n winner_batch,\n self.learn_rate)\n return loss, entropy","def update(self):\n with torch.no_grad():\n self.preprocess_rollout()\n \n # DEEP-RL TUTORIALS: КОСТЫЛЬ\n #self.advantages = self.returns[:-1] - self.values[:-1]\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\n \n # going through rollout several (config.epochs) times:\n for epoch in range(self.config.epochs):\n # TODO: drop last = False? What if there is 1 sample?\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\n \n for indices in sampler:\n # retrieving new batch as part of rollout\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\n \n # calculating current value, action_log_prob, entropy\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \n self.entropy_b = dist.entropy()#.sum(dim=-1)\n \n # performing step\n self.gradient_ascent_step()","def _gen_policy_params(self, state: State) -> Tensor:\n ...","def update_policy(self):\n n_sequences = max(int(self.trainer_parameters['batch_size'] / self.policy.sequence_length), 1)\n value_total, policy_total, forward_total, inverse_total = [], [], [], []\n advantages = self.training_buffer.update_buffer['advantages'].get_batch()\n self.training_buffer.update_buffer['advantages'].set(\n (advantages - advantages.mean()) / (advantages.std() + 1e-10))\n num_epoch = self.trainer_parameters['num_epoch']\n for k in range(num_epoch):\n self.training_buffer.update_buffer.shuffle()\n buffer = self.training_buffer.update_buffer\n for l in range(len(self.training_buffer.update_buffer['actions']) // n_sequences):\n start = l * n_sequences\n end = (l + 1) * n_sequences\n run_out = self.policy.update(buffer.make_mini_batch(start, end), n_sequences)\n value_total.append(run_out['value_loss'])\n policy_total.append(np.abs(run_out['policy_loss']))\n if self.use_curiosity:\n inverse_total.append(run_out['inverse_loss'])\n forward_total.append(run_out['forward_loss'])\n self.stats['value_loss'].append(np.mean(value_total))\n self.stats['policy_loss'].append(np.mean(policy_total))\n if self.use_curiosity:\n self.stats['forward_loss'].append(np.mean(forward_total))\n self.stats['inverse_loss'].append(np.mean(inverse_total))\n self.training_buffer.reset_update_buffer()","def update_parameters(self, timestamp, inputs):\n pass","def update(self):\n\n # get states, actions, rewards and total timesteps from memory\n states, actions, R, T = self.memory.get()\n n_ep = len(R)\n\n # compute value estimates for the states\n v = self.critic(states)\n\n # compute advantages (using GAE) and rewards to go\n A, rtg = utils.gae_rtg((R, v, T), self.gam, self.lam)\n\n # store the initial version of both the policy and the log probs of the\n # actions for later comparison with the future versions (needed for PPO)\n policy_old = copy.deepcopy(self.policy)\n log_probs_old = policy_old(states).log_prob(actions)\n\n # sample from a batch of experiences\n # (\"_\" subscript indicates \"sampled from\")\n for (v_, A_, rtg_, log_probs_old_), i in utils.sample_batch((v, A, rtg, log_probs_old), self.batch_size, self.policy_updates):\n log_probs_ = self.policy(states).log_prob(actions)[i]\n\n # estimate ratio between the new log probs and the old ones\n r_ = torch.exp(log_probs_ - log_probs_old_)\n\n l_1 = r_ * A_\n l_2 = torch.clamp(r_, 1-self.eps, 1+self.eps) * A_\n\n # TODO: implement entropy\n # TODO: merge policy and critic\n\n # surragate loss function for PPO\n l_clip = -torch.mean(torch.min(l_1, l_2))\n\n # update the policy\n self.policy_optimizer.zero_grad()\n l_clip.backward(retain_graph=True)\n self.policy_optimizer.step()\n\n # sample a batch of value estimates and the corresponding rewards to go\n # to update the value function.\n for (v_, rtg_), _ in utils.sample_batch((v, rtg), self.batch_size, self.v_updates):\n # compute the loss\n critic_loss = F.mse_loss(v_, rtg_)\n\n # update the critic\n self.critic_optimizer.zero_grad()\n critic_loss.backward(retain_graph=True)\n self.critic_optimizer.step()\n\n # clear the memory. PPO is an On-Policy method so we don't need these\n # memories anymore\n self.memory.clear()\n\n # return the loss of the value function for display\n return F.mse_loss(v, rtg)","def apply_policy(self, policy, method):\n action, optimal_value, move = policy(self, method)\n return action, optimal_value, move","def UpdatePolicy(self, request, global_params=None):\n config = self.GetMethodConfig('UpdatePolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","def update_policy(self):\n self._sess.run(self._hard_copy_to_target_op);","def update_policy(self):\n # this is update_policy \n # sample batch of 32 from the memory\n batch_of_samples = self.replay_memory.sample(batch_size=32)\n current_state_samples = batch_of_samples['current_state_samples']\n next_state_samples = batch_of_samples['next_state_samples']\n #print type(current_state_samples[0])\n #print current_state_samples\n\n # fetch stuff we need from samples 32*84*84*4\n current_state_images = np.zeros([1, 84, 84, 4])\n #print current_state_samples\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\n\n next_state_images = np.zeros([1, 84, 84, 4])\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\n\n # preprocess\n current_state_images = self.preprocessor.process_batch(current_state_images)\n next_state_images = self.preprocessor.process_batch(next_state_images)\n # print \"current_state_images {} max {} \".format(current_state_images.shape, np.max(current_state_images))\n #print current_state_images.shape\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\n\n # targets\n y_targets_all = q_current #1*num_actions\n #print y_targets_all.shape # [1,6]\n idx = 0 \n last_sample = current_state_samples[-1]\n if last_sample.is_terminal:\n y_targets_all[idx, last_sample.action] = last_sample.reward\n else:\n if self.mode == 'vanilla':\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\n if self.mode == 'double': \n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \n\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\n\n with tf.name_scope('summaries'):\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\n\n if not (self.iter_ctr % self.log_loss_every_nth):\n self.dump_train_loss(loss)\n\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\n # # copy weights\n # print \"Iter {} Updating target Q network\".format(self.iter_ctr)\n # self.target_q_network.set_weights(self.q_network.get_weights())\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\n # for i in range(len(self.target_q_network.trainable_weights))]","def update_parameters(self):\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\n self.update_gamma()\n self.update_gamma0()\n self.update_lambda()\n self.update_nu()\n if self.sample_xi:\n self.update_xi()","def update_values(self, to_update):\n for key, value in kwargs.iteritems():\n self.params[key] = value\n # update the possibly dependent parameters\n self.set_filenames()","def ppo_update(policy_model, policy_optimizer, baseline_model, baseline_optimizer, baseline_criterion,\n ppo_epoch, minibatch_size, obs_n_tensor, log_prob_n_old_tensor, action_n_tensor, rewards_n, mask_n,\n gamma, lam, update_policy=True, update_baseline=True):\n\n ## get number of data\n n_data = obs_n_tensor.shape[0]\n ## get baseline estimations\n baseline_n = baseline_model(obs_n_tensor).detach() ## between -1 and 1\n ## get q values, used for scaling baseline values\n q_n = get_reward_to_go(rewards_n, mask_n, gamma)\n q_n = Tensor(q_n).reshape(-1, 1)\n q_n_mean = q_n.mean()\n q_n_std = q_n.std() + 1e-2\n ## get scaled baseline values\n baseline_n_scaled = baseline_n * q_n_std + q_n_mean\n\n ## get advantage and scaled baseline targets\n ## the adv returned is normalized, baseline_target_scaled is not normalized\n adv_n, baseline_target_n_scaled = get_gae_advantage(rewards_n, baseline_n_scaled, mask_n, gamma, lam)\n adv_n = adv_n.detach()\n\n ## get baseline targets\n baseline_target_n = ((baseline_target_n_scaled - q_n_mean) / q_n_std).detach() ## target now is normalized\n\n ## NOTE MAKE SURE YOU DETACH THINGS OTHERWISE YOU WILL HAVE VERY STRANGE ERRORS\n\n ## for each training iteration, do some epoches\n for i_epoch in range(ppo_epoch):\n ## ppo: shuffle data\n shuffle_indexes = torch.randperm(n_data)\n obs_n_tensor = obs_n_tensor[shuffle_indexes]\n adv_n = adv_n[shuffle_indexes]\n log_prob_n_old_tensor = log_prob_n_old_tensor[shuffle_indexes]\n action_n_tensor = action_n_tensor[shuffle_indexes]\n baseline_target_n = baseline_target_n[shuffle_indexes]\n\n ## after shuffle data, we do the minibatch ppo update\n n_minibatch = int(n_data / minibatch_size)\n for i_minibatch in range(n_minibatch):\n ## get minibatch\n istart = i_minibatch * minibatch_size\n iend = (i_minibatch + 1) * minibatch_size\n obs_m = obs_n_tensor[istart:iend]\n adv_m = adv_n[istart:iend]\n log_prob_old_m = log_prob_n_old_tensor[istart:iend]\n action_m = action_n_tensor[istart:iend]\n baseline_target_m = baseline_target_n[istart:iend]\n\n ## update baseline\n if update_baseline:\n baseline_m = baseline_model(obs_m)\n baseline_optimizer.zero_grad()\n baseline_loss = baseline_criterion(baseline_m, baseline_target_m)\n baseline_loss.backward()\n baseline_optimizer.step()\n\n ## update policy\n ## we need the new policy's log probs so that we can calculate importance sampling term\n if update_policy:\n mu, log_sigma = policy_model(obs_m)\n normal_dist = Normal(mu, log_sigma.exp())\n log_prob_new_m = normal_dist.log_prob(action_m)\n log_prob_new_m = torch.sum(log_prob_new_m, dim=1).reshape(-1, 1)\n ## now we get the importance sampling weight term\n is_term_m = (log_prob_new_m - log_prob_old_m).exp()\n\n ## compute the clipped surrogate\n epsilon = 0.2\n first_term = is_term_m * adv_m\n clipped_term = is_term_m.clamp(1 - epsilon, 1 + epsilon) * adv_m\n obj_term = torch.min(first_term, clipped_term)\n obj_sum = obj_term.sum()\n policy_loss = -obj_sum / n_data\n policy_optimizer.zero_grad()\n policy_loss.backward()\n policy_optimizer.step()","def apply_parameters(self):\n n_bins = int(self.record_length / self.bin_width)\n time_bins = self.bin_width * np.arange(n_bins)\n \n\n self.tau = np.arange(self.tau_start, self.tau_end+self.tau_delta, self.tau_delta)\n sequence = self.generate_sequence()\n self.n_laser = find_laser_pulses(sequence)\n \n FC.Configure(self.record_length, self.bin_width, self.n_laser)\n\n if self.keep_data and sequence == self.sequence and np.all(time_bins == self.time_bins): # if the sequence and time_bins are the same as previous, keep existing data\n self.old_count_data = self.count_data.copy()\n else:\n self.old_count_data = np.zeros_like(FC.GetData())\n \n self.sequence = sequence\n self.time_bins = time_bins\n self.n_bins = n_bins\n \n self.MW_source = {'mw':mw, 'mw2':mw2}[self.mw_source]","def optimize_policy(self, all_samples_data, log=True):\n raise NotImplementedError","def update_policy(self, req):\n\n with self.mutex:\n\n # Apply all gradients\n for gradient in req.gradients:\n\n self.global_policy.zero_grad()\n\n # Transfer gradient to global policy\n self.transfer_gradient(gradient)\n\n # Clip gradient\n torch.nn.utils.clip_grad_norm_(self.global_policy.parameters(),\n 1.0)\n # torch.nn.utils.clip_grad_value_(self.global_policy.parameters(),\n # 1.0)\n\n # Apply gradient\n self.optimizer.step()\n self.iteration_counter += 1\n\n self._log_iteration(req)\n\n if (self.save_path and\n self.save_steps > 0 and\n self.iteration_counter >= self.save_steps * self.i):\n self.save_model()\n self.i += 1\n\n weights = self.encode_weights()\n cov = max(1.0 - self.iteration_counter /\n float(self.exploration), 0.05)\n covariance = [cov, cov]\n\n return UpdateGlobalPolicyResponse(weights, covariance)","def defineUpdateOperations(self):\n self.updated_value = tf.placeholder(shape=[1, self.network.action_size], dtype=tf.float32)\n self.loss = tf.reduce_sum(tf.square(self.updated_value - self.network.policyLayer))\n self.trainer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate)\n\n self.updateModel = self.trainer.minimize(self.loss)","def callback(self, rollout):\n assert len(self._t_switch)==len(self._scale)==len(self._k_star)\n # Log rollout statistics\n if self._ro_with_policy or self._unfinished_mix(len(rollout)):\n if self._unfinished_mix(len(rollout)):\n del self._k_star[-1]\n del self._t_switch[-1]\n del self._scale[-1]\n self._ind_ro_pol.append(self._n_ro)\n self._n_samples_ro_pol+=len(rollout)\n if self._ro_by_n_samples:\n self._ro_with_policy = self._n_samples_ro_pol<self._n_samples_ro_mix\n else:\n self._ro_with_policy = False\n else:\n self._ind_ro_mix.append(self._n_ro)\n self._n_samples_ro_mix+=len(rollout)\n self._ro_with_policy = True\n\n # unlock so `pi` can be called again\n self._locked =False\n self._n_ro+=1","def update_policy(env, policy, V, discount_factor):\n\n for state in range(env.nS):\n # for a given state compute state-action value.\n action_values = one_step_lookahead(env, state, V, discount_factor)\n\n # choose the action which maximizes the state-action value.\n policy[state] = np.argmax(action_values)\n\n return policy","def refresh(self):\n self._policies = self._get_policies()","def _update_params(self):\n pass","def handle_wps_update(self, data):\n\n self.jobs = data","def update_params(self):\n pass","def update_parameters(parameters, grads, learning_rate):\n pass","def update_policy(self):\n self.optimizer.step()\n self.optimizer.zero_grad()","def update_params(self, learning_rate):\n\t\t#######################################################################\n\t\t# ** START OF YOUR CODE **\n\t\t#######################################################################\n\t\tself._W = self._W - learning_rate * self._grad_W_current\n\t\tself._b = self._b - learning_rate * self._grad_b_current\n\t\t#######################################################################\n\t\t# ** END OF YOUR CODE **\n\t\t#######################################################################","def update_params(self):\n \n # read parameters from context broker\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\n self.params[attr] = float(self.ORION_CB.get_entity_attribute_value(entity_name=self.params['name'], attribute_name=attr))\n self.params['reverse_act'] = str(self.ORION_CB.get_entity_attribute_value(entity_name=self.params['name'], attribute_name='reverse_act')) \n \n # update PID parameters\n self.PID.Kp = self.params['Kp']\n self.PID.Ti = self.params['Ti']\n self.PID.Td = self.params['Td']\n self.PID.lim_low = self.params['lim_low']\n self.PID.lim_high = self.params['lim_high']\n self.PID.reverse_act = eval(eval(self.params['reverse_act']))","def _apply_params(self):\n config = self.get_startup_config()\n # Pass true to _set_params so we know these are startup values\n self._set_params(config, True)","def updatedata(self, force_update=False):\n\n self._update_counter = self._update_counter + 1\n logging.debug('ParameterViewer: update values')\n for instrument_name in self._instrumentnames:\n instr = self._instruments[self._instrumentnames.index(instrument_name)]\n parameters = {}\n\n try:\n parameters = instr.parameters\n except AttributeError as ex:\n # instrument was removed\n print('instrument was removed, stopping ParameterViewer')\n # logging.exception(ex)\n self._timer.stop()\n\n parameter_names = sorted(parameters.keys())\n\n si = sys.getswitchinterval()\n\n for parameter_name in parameter_names:\n # hack to make this semi thread-safe\n\n for field_name in self._fields:\n if field_name == 'Value':\n sys.setswitchinterval(100)\n value = parameters[parameter_name].get_latest()\n sys.setswitchinterval(si)\n self.update_field_signal.emit(instrument_name, parameter_name, field_name, value, force_update)\n else:\n if self._update_counter % 20 == 1 or 1:\n sys.setswitchinterval(100)\n value = getattr(parameters[parameter_name], field_name, '')\n sys.setswitchinterval(si)\n self.update_field_signal.emit(instrument_name, parameter_name,\n field_name, value, force_update)\n\n for callback_function in self.callbacklist:\n try:\n callback_function()\n except Exception as ex:\n logging.debug('update function failed')\n logging.exception(str(ex))","def update_with_evaluation(param_object, evaluation_dict, verbose):\n if evaluation_dict: # evaluates true if dict is not empty and the value is not None\n for key, value in evaluation_dict.items():\n try:\n setattr(param_object, key, value)\n TellUser.debug('attribute (' + param_object.name + ': ' + key + ') set: ' + str(value))\n except KeyError:\n TellUser.debug('No attribute ' + param_object.name + ': ' + key)","def updateParameters(self, parameters):","def pre_network_policy_update(self, resource_id, resource_dict):\n pass","def _modify_schedule_policy_properties(self):\n request_json = {\n 'taskInfo':\n {\n 'taskOperation': 1,\n 'associations': self._associations,\n 'task': self._task_json,\n \"appGroup\":\n {\n \"appGroups\": self._app_groups if self._app_groups else [],\n },\n 'subTasks': self._subtasks\n }\n }\n\n flag, response = self._commcell_object._cvpysdk_object.make_request(\n 'PUT', self._MODIFY_SCHEDULE_POLICY, request_json\n )\n output = self._process_schedule_policy_update_response(flag, response)\n self.refresh()\n\n if output[0]:\n return\n\n o_str = 'Failed to update properties of Schedule Policy\\nError: \"{0}\"'\n raise SDKException('Schedules', '102', o_str.format(output[2]))","def __setitem__(self, i, v):\n # The policy function can't be modified","def set_policy (self, policy = None, args = (), policy_cleanup = None):\n if policy == self.policy:\n # same policy; might want to change args/cleanup function, though\n self._policy_args = args\n if policy is not None and not isinstance(policy, basestring):\n self._policy_cleanup = policy_cleanup\n return\n # perform cleanup for current policy, if any\n if isinstance(self.policy, basestring):\n # built-in\n try:\n POLICY_CLEANUP[self.policy](self)\n except AttributeError:\n pass\n elif self.policy is not None and self._policy_cleanup is not None:\n # custom\n self._policy_cleanup(self)\n del self._policy_cleanup\n # set new policy\n self.policy = policy\n if policy is None:\n # if disabling scrolling, clean up some attributes we won't need\n try:\n del self._scroll_fn, self._policy_args\n except AttributeError:\n pass\n else:\n self._policy_args = args if args else ()\n if isinstance(policy, basestring):\n # built-in\n self._scroll_fn = POLICY_SCROLL[policy]\n else:\n # custom\n self._scroll_fn = policy\n self._policy_cleanup = policy_cleanup","def _update_embedding_param(self):\n for layer, ids in self._tls._unique_ids_all_layers.items():\n value = self._get_embedding_variable(layer).numpy()\n self._update_embedding_func(layer, ids, value)\n\n for slot in self._allowed_slot_names:\n value = self._get_slot_variable(layer, slot).numpy()\n slot_table_name = get_slot_table_name(layer, slot)\n self._update_embedding_func(slot_table_name, ids, value)","def update_policy(ranger_url, policy_id, policy_data, admin_username_password):\n\n url = format(\"{ranger_url}/service/public/v2/api/policy/{policy_id}\")\n\n base_64_string = base64.encodestring(admin_username_password).replace('\\n', '')\n\n request = urllib2.Request(url, json.dumps(policy_data))\n request.get_method = lambda: 'PUT'\n request.add_header('Content-Type', 'application/json')\n request.add_header('Accept', 'application/json')\n request.add_header('Authorization', format('Basic {base_64_string}'))\n\n try:\n result = openurl(request, timeout=20)\n response_code = result.getcode()\n if response_code == 200:\n Logger.info(format(\"Successfully updated policy in Ranger Admin\"))\n return response_code\n else:\n Logger.error(format(\"Unable to update policy in Ranger Admin\"))\n return None\n except urllib2.HTTPError as e:\n raise Fail(\"HTTPError while updating policy Reason = \" + str(e.code))\n except urllib2.URLError as e:\n raise Fail(\"URLError while updating policy. Reason = \" + str(e.reason))\n except TimeoutError:\n raise Fail(\"Connection timeout error while updating policy\")\n except Exception as err:\n raise Fail(format(\"Error while updating policy. Reason = {err}\"))","def _init_actor_update(self):\n\n actions = self._policy.actions([self._observations_ph])\n log_pis = self._policy.log_pis([self._observations_ph], actions)\n\n assert log_pis.shape.as_list() == [None, 1]\n\n log_alpha = self._log_alpha = tf.get_variable(\n 'log_alpha',\n dtype=tf.float32,\n initializer=0.0)\n alpha = tf.exp(log_alpha)\n\n if isinstance(self._target_entropy, Number):\n alpha_loss = -tf.reduce_mean(\n log_alpha * tf.stop_gradient(log_pis + self._target_entropy))\n\n self._alpha_optimizer = tf.train.AdamOptimizer(\n self._policy_lr, name='alpha_optimizer')\n self._alpha_train_op = self._alpha_optimizer.minimize(\n loss=alpha_loss, var_list=[log_alpha])\n\n self._training_ops.update({\n 'temperature_alpha': self._alpha_train_op\n })\n\n self._alpha = alpha\n\n if self._action_prior == 'normal':\n policy_prior = tf.contrib.distributions.MultivariateNormalDiag(\n loc=tf.zeros(self._action_shape),\n scale_diag=tf.ones(self._action_shape))\n policy_prior_log_probs = policy_prior.log_prob(actions)\n elif self._action_prior == 'uniform':\n policy_prior_log_probs = 0.0\n\n Q_log_targets = tuple(\n Q([self._observations_ph, actions])\n for Q in self._Qs)\n min_Q_log_target = tf.reduce_min(Q_log_targets, axis=0)\n\n if self._reparameterize:\n policy_kl_losses = (\n alpha * log_pis\n - min_Q_log_target\n - policy_prior_log_probs)\n else:\n raise NotImplementedError\n\n assert policy_kl_losses.shape.as_list() == [None, 1]\n\n policy_loss = tf.reduce_mean(policy_kl_losses)\n\n self._policy_optimizer = tf.train.AdamOptimizer(\n learning_rate=self._policy_lr,\n name=\"policy_optimizer\")\n policy_train_op = tf.contrib.layers.optimize_loss(\n policy_loss,\n self.global_step,\n learning_rate=self._policy_lr,\n optimizer=self._policy_optimizer,\n variables=self._policy.trainable_variables,\n increment_global_step=False,\n summaries=(\n \"loss\", \"gradients\", \"gradient_norm\", \"global_gradient_norm\"\n ) if self._tf_summaries else ())\n\n self._training_ops.update({'policy_train_op': policy_train_op})","def updateParameters(self):\n\n return","def variational_update(self):\n with self.elbo_check('update_p_allele_swap'):\n self.model.update_p_allele_swap()\n\n with self.elbo_check('p_cn'):\n self.model.update_p_cn()\n\n with self.elbo_check('p_breakpoint'):\n self.model.update_p_breakpoint()\n\n with self.elbo_check('p_outlier_total'):\n self.model.update_p_outlier_total()\n\n with self.elbo_check('p_outlier_allele'):\n self.model.update_p_outlier_allele()","def log_update(self, policy_loss, entropy, kl_divergence, clipping_fraction,\n value_loss, explained_variance, steps):\n\n # Diagnostics\n self.writer.add_scalar(\"Diagnostics/Policy/PolicyLoss\",\n policy_loss,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Policy/Entropy\",\n entropy,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Policy/KLDivergence\",\n kl_divergence,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Policy/ClipFraction\",\n clipping_fraction,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Value/ValueLoss\",\n value_loss,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Value/ValueEstimate\",\n np.mean(self.buffer.values),\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Value/ExplainedVariance\",\n explained_variance,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Info/LearningRate\",\n self.lr_pi,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Info/TotalTimesteps\",\n self.update_counter * self.batch_size,\n self.update_counter)\n self.writer.add_scalar(\"Diagnostics/Info/KLDivCoef\",\n self.kl_coef,\n self.update_counter)\n # Training Episodes\n self.writer.add_scalar(\"Training/Episodes/PolicyGradientSteps\",\n steps,\n self.update_counter)\n mean_frames = np.mean(self.buffer.episode_lengths)\n std_frames = np.std(self.buffer.episode_lengths)\n self.writer.add_scalar(\"Training/Episodes/Mean_Frames\",\n mean_frames,\n self.update_counter)\n self.writer.add_scalar(\"Training/Episodes/Std_Frames\",\n std_frames,\n self.update_counter)\n self.writer.add_histogram(\"Training/Episodes/Frames\",\n np.array(self.buffer.episode_lengths),\n self.update_counter)\n mean_reward = np.mean(self.buffer.episode_rewards)\n std_reward = np.std(self.buffer.episode_rewards)\n rews_per_frame = np.array(self.buffer.episode_rewards) / \\\n np.array(self.buffer.episode_lengths, dtype=np.float)\n self.writer.add_scalar(\"Training/Episodes/Mean_Reward\",\n mean_reward,\n self.update_counter)\n self.writer.add_scalar(\"Training/Episodes/Std_Reward\",\n std_reward,\n self.update_counter)\n self.writer.add_scalar(\"Training/Episodes/Reward_per_Frame_Mean\",\n np.mean(rews_per_frame),\n self.update_counter)\n self.writer.add_scalar(\"Training/Episodes/Reward_per_Frame_Std\",\n np.std(rews_per_frame),\n self.update_counter)\n self.writer.add_histogram(\"Training/Episodes/Rewards\",\n np.array(self.buffer.episode_rewards),\n self.update_counter)\n self.writer.add_histogram(\"Training/Episodes/Rewards_per_Frame\",\n rews_per_frame,\n self.update_counter)\n actions = np.array(self.buffer.actions)\n self.writer.add_histogram(\"Training/Action/DeltaVel\",\n actions[:, 0],\n self.update_counter)\n self.writer.add_histogram(\"Training/Action/DeltaLat\",\n actions[:, 1],\n self.update_counter)\n self.writer.add_histogram(\"Training/Values\",\n np.array(self.buffer.values),\n self.update_counter)\n self.writer.add_histogram(\"Training/Avantages\",\n np.array(self.buffer.advantages),\n self.update_counter)\n self.writer.add_histogram(\"Training/GradNorms\",\n np.array(self.grad_norms),\n self.update_counter)\n self.writer.add_histogram(\"Training/Ratio/Ratio\",\n np.array(self.ratios).flatten(),\n self.update_counter)\n self.writer.add_histogram(\"Training/Ratio/ClippedRatio\",\n np.array(self.clipped_ratios).flatten(),\n self.update_counter)\n\n self.writer.flush()\n\n print(\"-\" * 30)\n print(\"PPO Optimization\")\n print(\"Policy_Loss: {}\\t\\t\".format(policy_loss))\n print(\"Value_Loss: {}\\t\\t\".format(value_loss))\n print(\"Entropy: {}\\t\\t\".format(entropy))\n print(\"Lr_pi: {}\\t\\t\".format(self.lr_pi))\n print(\"Lr_vf: {}\\t\\t\".format(self.lr_vf))\n print(\"KL_Divergence: {}\\t\\t\".format(kl_divergence))\n print(\"Clip_Fraction: {}\\t\\t\".format(clipping_fraction))\n print(\"Exp_Variance: {}\\t\\t\".format(explained_variance))\n print(\"Mean_Reward: {}\\t\\t\".format(mean_reward))\n print(\"Std_Reward: {}\\t\\t\".format(std_reward))\n print(\"Mean_Frames: {}\\t\\t\".format(mean_frames))\n print(\"Std_Frames: {}\\t\\t\".format(std_frames))\n print(\"Mean_Reward_per_frame: {}\\t\\t\".format(np.mean(rews_per_frame)))\n print(\"Std_Reward_per_frame: {}\\t\\t\".format(np.std(rews_per_frame)))\n print(\"Optimization steps: {}\\t\\t\". format(self.update_counter))\n print(\"-\" * 30)","def update_l7_policy(request, **kwargs):\n data = request.DATA\n l7_policy_id = data['l7policy'].get('id')\n\n conn = get_sdk_connection(request)\n l7_policy = conn.load_balancer.update_l7_policy(\n action=data['l7policy']['action'],\n admin_state_up=data['l7policy'].get('admin_state_up'),\n description=data['l7policy'].get('description'),\n l7_policy=l7_policy_id,\n name=data['l7policy'].get('name'),\n position=data['l7policy'].get('position'),\n redirect_pool_id=data['l7policy'].get('redirect_pool_id'),\n redirect_url=data['l7policy'].get('redirect_url'),\n )\n\n return _get_sdk_object_dict(l7_policy)","def update_parameters(updates):\r\n for (key, val) in updates.items():\r\n par[key] = val\r\n print('Updating:', key, '-->', val)\r\n update_dependencies()","def rollout(pi, env):\n rollout = defaultdict(list)\n t = 0\n ob = (env.reset())\n features = pi.get_initial_features()\n for _ in range(2000):\n rets = pi.compute(ob, features)\n ac, value, features = rets[0], rets[1], rets[2:]\n rollout[\"obs\"].append(ob)\n rollout[\"vs\"].append(value)\n rollout[\"actions\"].append(ac)\n ob, rew, done, _ = env.step(ac)\n rollout[\"rs\"].append(rew)\n rollout[\"features\"].append(features)\n t += 1\n if done:\n break\n print(\"Cur policy: \", len(rollout[\"obs\"]))\n rollout[\"r\"] = 0\n rollout[\"terminal\"] = True\n return rollout","def _update_params(self, perf_params, loop_info):\n for vartype in list(perf_params.keys()):\n for var in perf_params[vartype]:\n self.tspec_params['performance_params'][var] = \\\n self.indent + 'param %s[] = %s;\\t#%s\\n' % (var, repr(default_perf_params[vartype]), vartype)\n\n #loop_info.vars: set of input vars","def _update(self):\n self.all_params = {}\n self._update_experiment_params()\n self._update_preprocessing_params()\n self._update_model_params()","def _update_parameter(self, dWxh, dbh, dWhy, dby):\n # Add code to update all the weights and biases here","def update_generate_params(self,inps,trgs,preds):\n batch_size = np.shape(trgs)[0]\n\n self.delta_weight_h_to_v = self.learning_rate / batch_size * np.transpose(trgs) @ (inps - preds)\n self.delta_bias_v = self.learning_rate * np.mean(inps - preds)\n \n self.weight_h_to_v += self.delta_weight_h_to_v\n self.bias_v += self.delta_bias_v \n \n return","def replace_namespaced_policy(self, body, namespace, name, **kwargs):\n\n all_params = ['body', 'namespace', 'name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method replace_namespaced_policy\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'body' is set\n if ('body' not in params) or (params['body'] is None):\n raise ValueError(\"Missing the required parameter `body` when calling `replace_namespaced_policy`\")\n # verify the required parameter 'namespace' is set\n if ('namespace' not in params) or (params['namespace'] is None):\n raise ValueError(\"Missing the required parameter `namespace` when calling `replace_namespaced_policy`\")\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `replace_namespaced_policy`\")\n\n resource_path = '/oapi/v1/namespaces/{namespace}/policies/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'namespace' in params:\n path_params['namespace'] = params['namespace']\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'PUT',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1Policy',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def refresh(self):\n self._get_schedule_policy_properties()","def pre_update(self, **values):\r\n pass","def post_network_policy_update(self, resource_id, resource_dict):\n pass","def update_service_access_policies(DomainName=None, AccessPolicies=None):\n pass","def _policy_improvement(self) -> Tuple[np.ndarray, np.ndarray]:\n # Start with a (random) policy\n policy = np.zeros([self.state_dim, self.action_dim])\n V = np.zeros([self.state_dim])\n #random init the policy\n for s in range(self.state_dim):\n policy[s,0] = 0.0\n policy[s,1] = 0.0\n policy[s,2] = 1.0\n\n V = self._policy_eval(policy)\n\n policy_stable = False\n dr = 0.9\n\n while (policy_stable != True):\n policy_stable = True\n for s in self.mdp._state_dict:\n old_action = (policy[self.mdp._state_dict[s]]).tolist()\n action_dict = {}\n for a in self.mdp._action_dict:\n temp = 0.0\n for next_s in self.mdp._state_dict:\n p = self.mdp.P[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\n r = self.mdp.R[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\n Vs = V[self.mdp._state_dict[next_s]]\n temp = temp + p * (r + dr * Vs)\n action_dict[self.mdp._action_dict[a]]= temp \n max_act = max(action_dict.values())\n V[self.mdp._state_dict[s]] = max_act\n res = [t for t,v in action_dict.items() if v == max_act][0]\n for opt in range(self.action_dim):\n if opt == res:\n policy[self.mdp._state_dict[s],opt] = 1.0\n else:\n policy[self.mdp._state_dict[s],opt] = 0.0\n if (old_action - policy[self.mdp._state_dict[s]]).any() == True:\n \n policy_stable = False\n if policy_stable == False:\n V = self._policy_eval(policy)\n \n return policy, V","def em_update_params(self):\n for name in self.likelihood_params:\n with self.elbo_check(name):\n self.update_param(name)","def update_multiple_parameters(self, detuning=None, lamb_dicke=None,\n base_rabi=None):\n if detuning is not None:\n self.__detuning = detuning\n if lamb_dicke is not None:\n self.__lamb_dicke = lamb_dicke\n if base_rabi is not None:\n self.__base_rabi = base_rabi\n if detuning is None and lamb_dicke is None and base_rabi is None:\n return\n self.__update_prefactors()","def policy_improvement(P, nS, nA, value_from_policy, gamma=0.9):\n\n new_policy = np.ones([nS, nA]) / nA\n\t############################\n\t# YOUR IMPLEMENTATION HERE #\n #iteration_policy=new_policy\n for state in range(nS):\n #current_policy=new_policy[state] \n action_policy = np.zeros(nA) \n for action in range(nA):\n for p,nextS,r,boolean_v in P[state][action]:\n action_policy[action] += p*( r + gamma* value_from_policy[nextS])\n #print(action_policy)\n updated_policy=np.zeros(nA)\n updated_policy[np.argmax(action_policy)]= 1\n #print(updated_policy) \n new_policy[state]=updated_policy\n \n \t############################\n return new_policy","def initialize_policies(self, policy_collection, options):","def put(self, request, l7_policy_id):\n kwargs = {'l7_policy_id': l7_policy_id}\n update_l7_policy(request, **kwargs)","def update_data(self, **kwargs):\n self.source_data = self.get_dict()\n for c in self.callbacks[\"update_data\"]:\n c()","def update_lr(self, *args, **kwargs):\n raise NotImplementedError","def update_lr(self, *args, **kwargs):\n raise NotImplementedError","def patch_namespaced_policy(self, body, namespace, name, **kwargs):\n\n all_params = ['body', 'namespace', 'name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method patch_namespaced_policy\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'body' is set\n if ('body' not in params) or (params['body'] is None):\n raise ValueError(\"Missing the required parameter `body` when calling `patch_namespaced_policy`\")\n # verify the required parameter 'namespace' is set\n if ('namespace' not in params) or (params['namespace'] is None):\n raise ValueError(\"Missing the required parameter `namespace` when calling `patch_namespaced_policy`\")\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `patch_namespaced_policy`\")\n\n resource_path = '/oapi/v1/namespaces/{namespace}/policies/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'namespace' in params:\n path_params['namespace'] = params['namespace']\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['application/json-patch+json', 'application/merge-patch+json', 'application/strategic-merge-patch+json'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'PATCH',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1Policy',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def rollout_policy_fn(board):\n # rollout randomly\n action_probs = np.random.rand(len(board.availables))\n return zip(board.availables, action_probs)","def test_update_policy(self):\n self.ports[1].qos_policy_id = self.qos_policies[0].id\n self.ports[1].update()\n self.ports[2].qos_policy_id = self.qos_policies[1].id\n self.ports[2].update()\n self.ports[4].qos_policy_id = self.qos_policies[0].id\n self.ports[4].update()\n self.ports[5].qos_policy_id = self.qos_policies[1].id\n self.ports[5].update()\n self.networks[1].qos_policy_id = self.qos_policies[0].id\n self.networks[1].update()\n self.fips[0].qos_policy_id = self.qos_policies[0].id\n self.fips[0].update()\n self.fips[1].qos_policy_id = self.qos_policies[1].id\n self.fips[1].update()\n self._update_router_qos(self.ctx, self.routers[0].id,\n self.qos_policies[0].id)\n self._update_router_qos(self.ctx, self.routers[1].id,\n self.qos_policies[1].id)\n mock_qos_rules = mock.Mock()\n with mock.patch.object(self.qos_driver, '_qos_rules',\n return_value=mock_qos_rules), \\\n mock.patch.object(self.qos_driver, 'update_floatingip') as \\\n mock_update_fip, \\\n mock.patch.object(self.qos_driver, 'update_router') as \\\n mock_update_router:\n self.qos_driver.update_policy(self.ctx, self.qos_policies[0])\n # Ports updated from \"update_port\": self.ports[1], self.ports[4]\n updated_ports = [self.ports[1], self.ports[4]]\n calls = [mock.call(self.txn, port.id, port.network_id,\n self.qos_policies[0].id, mock_qos_rules,\n lsp=None, port_deleted=False)\n for port in updated_ports]\n # Port updated from \"update_network\": self.ports[3]\n calls.append(mock.call(self.txn, self.ports[3].id,\n self.ports[3].network_id,\n self.qos_policies[0].id, mock_qos_rules))\n\n # We can't ensure the call order because we are not enforcing any order\n # when retrieving the port and the network list.\n self.mock_rules.assert_has_calls(calls, any_order=True)\n with db_api.CONTEXT_READER.using(self.ctx):\n fip = self.qos_driver._plugin_l3.get_floatingip(self.ctx,\n self.fips[0].id)\n mock_update_fip.assert_called_once_with(self.txn, fip)\n\n with db_api.CONTEXT_READER.using(self.ctx):\n router = self.qos_driver._plugin_l3.get_router(self.ctx,\n self.routers[0].id)\n mock_update_router.assert_called_once_with(self.txn, router)","def update_params(self, learning_rate):\n\t\t#######################################################################\n\t\t# ** START OF YOUR CODE **\n\t\t#######################################################################\n\n\t\tfor layer in self._layers:\n\t\t\tlayer.update_params(learning_rate)\n\n\t\t#######################################################################\n\t\t# ** END OF YOUR CODE **\n\t\t#######################################################################","def _policy_eval(self, policy: np.ndarray) -> np.ndarray:\n V = np.zeros(self.state_dim)\n diff = 1.0\n dr = 0.9\n while (diff >= self.theta):\n diff = 0.0\n for s in self.mdp._state_dict:\n old = V[self.mdp._state_dict[s]]\n temp = 0.0\n for opt in range(self.action_dim):\n if policy[self.mdp._state_dict[s],opt] == 1.0: \n for next_s in self.mdp._state_dict:\n p = self.mdp.P[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\n r = self.mdp.R[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\n Vs = V[self.mdp._state_dict[next_s]]\n temp = temp + p * (r + dr * Vs)\n V[self.mdp._state_dict[s]] = temp\n diff = max(diff,abs(old - V[self.mdp._state_dict[s]]))\n return V","def gen_policy_rollout(self, observations, dynam_model):\n # initialize statespace array in pytorch tensor -> need that good good gradient!\n # raise NotImplementedError(\"Need to implement the rollouts with tensors, or maybe not for gradients\")\n\n # for sampling the state progression\n norm_dist = torch.distributions.Normal(0, 1)\n\n # for storing the costs and gradients\n costs = torch.Tensor()\n baselines = torch.Tensor()\n log_probabilities = torch.Tensor()\n states = torch.Tensor()\n ''' \n # for all of these values, think a row as an updating time series for each particle\n costs = torch.zeros((self.P, self.T))\n baselines = torch.zeros((self.P, self.T))\n probabilities = torch.zeros((self.P, self.T))\n\n # This is what we would do for the states, but it's more efficient to concatenate them\n states = torch.Tensor((self.P,self.T, self.n_in))\n '''\n\n # Change in state vs raw state key\n pred_key = dynam_model.change_state_list\n # print(pred_key)\n\n # TODO: Generate initial states for each particle based on the distribution of data it was trained on\n bound_up = torch.Tensor(np.max(observations, 0))\n bound_low = torch.Tensor(np.min(observations, 0))\n\n obs_dist = torch.distributions.uniform.Uniform(bound_low, bound_up)\n\n # iterate through each particle for the states and probabilites for gradient\n for p in range(self.P):\n\n # Choose the dynamics model from the ensemble\n num_ens = dynam_model.E\n if num_ens == 0:\n model = dynam_model\n else:\n model_idx = random.randint(0, num_ens - 1)\n model = dynam_model.networks[model_idx]\n\n num_obs = np.shape(observations)[1]\n # x0 = torch.Tensor(observations[random.randint(0,num_obs),:])\n x0 = obs_dist.sample()\n\n # TODO: Normalize the states before passing into the NN\n\n state_mat = x0.view((1, 1, -1))\n # print(state_mat)\n # state_mat = x0.unsqueeze(0).unsqueeze(0) # takes a (n_in) vector to a (1,1,n_in) Tensor\n # torch.cat((), axis = 1) to cat the times\n # torch.cat((), axis = 0) to cat the particle\n # is there a way to do this without unsqueeze? Seems like the most efficient way\n log_prob_vect = torch.Tensor([1]) # states the column with 1 for concat'ing\n\n for t in range(self.T):\n # generate action from the policy\n action = self.forward(state_mat[0, t, :])\n # print(action)\n # quit()\n\n # forward pass current state to generate distribution values from dynamics model\n means, var = model.distribution(state_mat[0, t, :], action)\n # print(var)\n # sample the next state from the means and variances of the state transition probabilities\n vals = var * norm_dist.sample((1, self.n_in)) + means\n # need to account for the fact that some states are change in and some are raw here\n\n # batch mode prob calc\n log_probs = -.5 * torch.abs(vals - means) / var\n # log_probs = -.5*torch.abs(vals - means)/(var**2)\n\n # for s in range(self.n_in):\n # # sample predicted new state for each element\n # val = var[s]*np.random.normal()+means[0] # sample from the scaled gaussian with y = sigma*x + mu\n\n # # calculate probability of this state for each sub state\n # p = -.5*(val-means[0])/var[0]\n # states = torch.cat((states, state), 0)\n # probabilities = torch.cat((probabilities, p), 0)\n\n # reduce the probabilities vector to get a single probability of the state transition\n log_prob = torch.sum((log_probs), 1)\n log_prob_vect = torch.cat((log_prob_vect, log_prob))\n\n state = torch.Tensor(vals).view((1, 1, -1))\n\n state_mat = torch.cat((state_mat, state),\n 1) # appends the currnt state to the current particle, without overwriting the otherone\n\n # print(state_mat)\n # calculates costs\n # idea ~ calculate the cost of each each element and then do an cumulative sum for the costs\n # use torch.cumsum\n c_list = []\n for state in state_mat.squeeze():\n c_row = self.cost_fnc(state)\n c_list.append(c_row)\n c_list = torch.stack(c_list)\n\n # note we calc the cum sum on the flipped tensor, then flip costs back\n cost_cum = torch.cumsum(torch.flip(c_list, [0]), 0)\n # Assembles the arrays for the current particle\n\n # costs were given above\n costs = torch.cat((costs, torch.flip(cost_cum, [0]).view(1, -1)), 0)\n\n # update the states array for each particle\n states = torch.cat((states, state_mat), 0)\n\n # concatenates the vector of prob at each time to the 2d array\n log_probabilities = torch.cat(\n (log_probabilities, log_prob_vect.view((1, -1))), 0)\n\n # calculates baselines as the leave one out mean for each particle at each time\n costs_summed = torch.sum(costs, 0)\n costs_summed_exp = costs_summed.expand_as(costs)\n costs_leave_one_out = costs_summed_exp - costs\n baselines = costs_leave_one_out / (self.P - 1)\n\n # freezes gradients on costs and baselines\n # these two lines of code actually do nothing, but are for clarification\n # costs.requires_grad_(requires_grad=False)\n # baselines.requires_grad_(requires_grad=False)\n # . detach() is another way to ensure this\n \"\"\"\n RuntimeError: you can only change requires_grad flags of leaf variables. If you want to use a \n computed variable in a subgraph that doesn't require differentiation use var_no_grad = var.detach().\n \"\"\"\n costs_d = costs.detach()\n baselines_d = baselines.detach()\n # costs_d = costs.requires_grad_(False)\n # baselines_d = baselines.requires_grad_(False)\n # print(baselines)\n # print(probabilities)\n # print(costs)\n # print(log_probabilities)\n return states, log_probabilities, costs_d, baselines_d","def test_function_values(self):\n\n self.param_dict.add_paramdictval(\n FunctionParamDictVal(\n \"fn_foo\",\n self.pick_byte2,\n lambda x : str(x),\n direct_access=True,\n startup_param=True,\n value=1,\n visibility=ParameterDictVisibility.READ_WRITE)\n )\n self.param_dict.add_paramdictval(\n FunctionParamDictVal(\n \"fn_bar\",\n lambda x : bool(x&2), # bit map example\n lambda x : str(x),\n direct_access=True,\n startup_param=True,\n value=False,\n visibility=ParameterDictVisibility.READ_WRITE)\n )\n \n # check defaults just to be safe\n val = self.param_dict.get(\"fn_foo\")\n self.assertEqual(val, 1)\n val = self.param_dict.get(\"fn_bar\")\n self.assertEqual(val, False)\n \n result = self.param_dict.update(1005) # just change first in list\n val = self.param_dict.get(\"fn_foo\")\n self.assertEqual(val, 3)\n val = self.param_dict.get(\"fn_bar\")\n self.assertEqual(val, False)\n \n # fn_bar does not get updated here\n result = self.param_dict.update_many(1205)\n self.assertEqual(result['fn_foo'], True)\n self.assertEqual(len(result), 1)\n val = self.param_dict.get(\"fn_foo\")\n self.assertEqual(val, 4)\n val = self.param_dict.get(\"fn_bar\")\n self.assertEqual(val, False)\n \n # both are updated now\n result = self.param_dict.update_many(6)\n self.assertEqual(result['fn_foo'], True)\n self.assertEqual(result['fn_bar'], True)\n self.assertEqual(len(result), 2)\n \n val = self.param_dict.get(\"fn_foo\")\n self.assertEqual(val, 0)\n val = self.param_dict.get(\"fn_bar\")\n self.assertEqual(val, True)","def eval_policy(env, policy, obs_key, goal_key, num_rollouts, limit, pos=None, goal=None, filename=None):\n from tqdm import trange\n from ml_logger import logger\n\n logger.summary_cache.clear()\n if filename:\n frames = []\n\n for _ in trange(num_rollouts, desc=\"evaluate\"):\n\n obs, done = env.reset(), False\n if pos is not None or goal is not None:\n obs = env.unwrapped.reset(pos, goal)\n # d2goal = l2(obs['x'], obs['goal'])\n i, R = 0, 0\n while not done:\n if filename:\n _ = np.concatenate([obs[obs_key], obs[goal_key]], axis=-1)[0]\n frames.append(_) # , obs['goal_img']\n action = policy(obs[obs_key], obs[goal_key])\n obs, reward, done, info = env.step(action)\n R += reward\n if info['success'] or limit and i >= limit:\n logger.store_metrics( # d2goal=d2goal,\n success=info['success'])\n break\n i += 1\n\n if filename:\n logger.log_video(np.array(frames), key=filename)\n\n logger.print(f'has finished evaluation.')","def _update_parameters(self, curr_state, reward, next_state):\n phi = self._features.vector(curr_state)\n phi_dash = self._features.vector(next_state)\n\n self._A += np.outer(phi, (phi - self._gamma * phi_dash))\n self._b += reward * phi","def hard_update(self,target, source):\n\t\tfor target_param, param in zip(target.parameters(), source.parameters()):\n\t\t\t\ttarget_param.data.copy_(param.data)","def updateParameters(self, parameters):\r\n\t\tin_wikiplace_IRI = parameters[0]\r\n\t\tin_relation_degree = parameters[1]\r\n\t\tin_first_property_dir = parameters[2]\r\n\t\tin_first_property = parameters[3]\r\n\t\tin_second_property_dir = parameters[4]\r\n\t\tin_second_property = parameters[5]\r\n\t\tin_third_property_dir = parameters[6]\r\n\t\tin_third_property = parameters[7]\r\n\t\tin_fourth_property_dir = parameters[8]\r\n\t\tin_fourth_property = parameters[9]\r\n\t\tout_location = parameters[10]\r\n\t\tout_table_name = parameters[11]\r\n\t\tout_points_name = parameters[12]\r\n\r\n\t\t\r\n\r\n\t\tif in_relation_degree.altered:\r\n\t\t\trelationDegree = int(in_relation_degree.valueAsText)\r\n\t\t\tif relationDegree == 1:\r\n\t\t\t\tin_first_property.enabled = True\r\n\t\t\t\tin_first_property_dir.enabled = True\r\n\t\t\t\tin_second_property.enabled = False\r\n\t\t\t\tin_second_property_dir.enabled = False\r\n\t\t\t\tin_third_property.enabled = False\r\n\t\t\t\tin_third_property_dir.enabled = False\r\n\t\t\t\tin_fourth_property.enabled = False\r\n\t\t\t\tin_fourth_property_dir.enabled = False\r\n\t\t\telif relationDegree == 2:\r\n\t\t\t\tin_first_property.enabled = True\r\n\t\t\t\tin_first_property_dir.enabled = True\r\n\t\t\t\tin_second_property.enabled = True\r\n\t\t\t\tin_second_property_dir.enabled = True\r\n\t\t\t\tin_third_property.enabled = False\r\n\t\t\t\tin_third_property_dir.enabled = False\r\n\t\t\t\tin_fourth_property.enabled = False\r\n\t\t\t\tin_fourth_property_dir.enabled = False\r\n\t\t\telif relationDegree == 3:\r\n\t\t\t\tin_first_property.enabled = True\r\n\t\t\t\tin_first_property_dir.enabled = True\r\n\t\t\t\tin_second_property.enabled = True\r\n\t\t\t\tin_second_property_dir.enabled = True\r\n\t\t\t\tin_third_property.enabled = True\r\n\t\t\t\tin_third_property_dir.enabled = True\r\n\t\t\t\tin_fourth_property.enabled = False\r\n\t\t\t\tin_fourth_property_dir.enabled = False\r\n\t\t\telif relationDegree == 4:\r\n\t\t\t\tin_first_property.enabled = True\r\n\t\t\t\tin_first_property_dir.enabled = True\r\n\t\t\t\tin_second_property.enabled = True\r\n\t\t\t\tin_second_property_dir.enabled = True\r\n\t\t\t\tin_third_property.enabled = True\r\n\t\t\t\tin_third_property_dir.enabled = True\r\n\t\t\t\tin_fourth_property.enabled = True\r\n\t\t\t\tin_fourth_property_dir.enabled = True\r\n\t\t\r\n\t\t\tif in_wikiplace_IRI.value:\r\n\t\t\t\tinputFeatureClassName = in_wikiplace_IRI.valueAsText\r\n\t\t\t\tlastIndexOFGDB = inputFeatureClassName.rfind(\"\\\\\")\r\n\t\t\t\tfeatureClassName = inputFeatureClassName[(lastIndexOFGDB+1):]\r\n\t\t\t\tcurrentWorkspace = inputFeatureClassName[:lastIndexOFGDB]\r\n\r\n\t\t\t\tarcpy.env.workspace = currentWorkspace\r\n\t\t\t\tout_location.value = currentWorkspace\r\n\r\n\t\t\t\tout_table_name.value = featureClassName + \"PathQueryTripleStore\"\r\n\r\n\t\t\t\tout_points_name.value = featureClassName + \"PathQueryLocation\"\r\n\r\n\r\n\t\t\t\toutLocation = out_location.valueAsText\r\n\t\t\t\toutTableName = out_table_name.valueAsText\r\n\t\t\t\toutputTableName = os.path.join(outLocation,outTableName)\r\n\t\t\t\tif arcpy.Exists(outputTableName):\r\n\t\t\t\t\tarcpy.AddError(\"The output table already exists in current workspace!\")\r\n\t\t\t\t\traise arcpy.ExecuteError\r\n\r\n\t\t\t\toutFeatureClassName = out_points_name.valueAsText\r\n\t\t\t\toutputFeatureClassName = os.path.join(outLocation,outFeatureClassName)\r\n\t\t\t\tif arcpy.Exists(outputFeatureClassName):\r\n\t\t\t\t\tarcpy.AddError(\"The output Feature Class already exists in current workspace!\")\r\n\t\t\t\t\traise arcpy.ExecuteError\r\n\r\n\r\n\t\t\t\t# get all the IRI from input point feature class of wikidata places\r\n\t\t\t\tinplaceIRIList = []\r\n\t\t\t\tcursor = arcpy.SearchCursor(inputFeatureClassName)\r\n\t\t\t\tfor row in cursor:\r\n\t\t\t\t\tinplaceIRIList.append(row.getValue(\"URL\"))\r\n\r\n\r\n\r\n\t\t\t\t# get the first property URL list and label list\r\n\t\t\t\tif in_first_property_dir.value:\r\n\t\t\t\t\tfristDirection = in_first_property_dir.valueAsText\r\n\t\t\t\t\t# get the first property URL list\r\n\t\t\t\t\tfirstPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection], [\"\", \"\", \"\"])\r\n\t\t\t\t\tfirstPropertyURLList = []\r\n\t\t\t\t\tfor jsonItem in firstPropertyURLListJsonBindingObject:\r\n\t\t\t\t\t\tfirstPropertyURLList.append(jsonItem[\"p1\"][\"value\"])\r\n\r\n\t\t\t\t\tfirstPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(firstPropertyURLList)\r\n\t\t\t\t\t# firstPropertyLabelJSON = firstPropertyLabelJSONObj[\"results\"][\"bindings\"]\r\n\r\n\t\t\t\t\t# get the first property label list\r\n\t\t\t\t\tfirstPropertyURLList = []\r\n\t\t\t\t\tfirstPropertyLabelList = []\r\n\t\t\t\t\tfor jsonItem in firstPropertyLabelJSON:\r\n\t\t\t\t\t\tpropertyURL = jsonItem[\"p\"][\"value\"]\r\n\t\t\t\t\t\tfirstPropertyURLList.append(propertyURL)\r\n\t\t\t\t\t\tpropertyName = jsonItem[\"propertyLabel\"][\"value\"]\r\n\t\t\t\t\t\tfirstPropertyLabelList.append(propertyName)\r\n\r\n\t\t\t\t\tRelFinder.firstPropertyLabelURLDict = dict(zip(firstPropertyLabelList, firstPropertyURLList))\r\n\r\n\t\t\t\t\tin_first_property.filter.list = firstPropertyLabelList\r\n\r\n\t\t\t\t\t# get the second property URL list and label list\r\n\t\t\t\t\tif in_second_property_dir.value:\r\n\t\t\t\t\t\tfristDirection = in_first_property_dir.valueAsText\r\n\t\t\t\t\t\tfirstProperty = in_first_property.valueAsText\r\n\r\n\t\t\t\t\t\tif firstProperty == None:\r\n\t\t\t\t\t\t\tfirstProperty = \"\"\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\r\n\r\n\t\t\t\t\t\tsecondDirection = in_second_property_dir.valueAsText\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# get the second property URL list\r\n\t\t\t\t\t\tsecondPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection], [firstProperty, \"\", \"\"])\r\n\t\t\t\t\t\tsecondPropertyURLList = []\r\n\t\t\t\t\t\tfor jsonItem in secondPropertyURLListJsonBindingObject:\r\n\t\t\t\t\t\t\tsecondPropertyURLList.append(jsonItem[\"p2\"][\"value\"])\r\n\r\n\t\t\t\t\t\tsecondPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(secondPropertyURLList)\r\n\t\t\t\t\t\t# secondPropertyLabelJSON = secondPropertyLabelJSONObj[\"results\"][\"bindings\"]\r\n\r\n\t\t\t\t\t\t# get the second property label list\r\n\t\t\t\t\t\tsecondPropertyURLList = []\r\n\t\t\t\t\t\tsecondPropertyLabelList = []\r\n\t\t\t\t\t\tfor jsonItem in secondPropertyLabelJSON:\r\n\t\t\t\t\t\t\tpropertyURL = jsonItem[\"p\"][\"value\"]\r\n\t\t\t\t\t\t\tsecondPropertyURLList.append(propertyURL)\r\n\t\t\t\t\t\t\tpropertyName = jsonItem[\"propertyLabel\"][\"value\"]\r\n\t\t\t\t\t\t\tsecondPropertyLabelList.append(propertyName)\r\n\r\n\t\t\t\t\t\tRelFinder.secondPropertyLabelURLDict = dict(zip(secondPropertyLabelList, secondPropertyURLList))\r\n\r\n\t\t\t\t\t\tin_second_property.filter.list = secondPropertyLabelList\r\n\r\n\t\t\t\t\t\t# get the third property URL list and label list\r\n\t\t\t\t\t\tif in_third_property_dir.value:\r\n\t\t\t\t\t\t\tfristDirection = in_first_property_dir.valueAsText\r\n\t\t\t\t\t\t\tfirstProperty = in_first_property.valueAsText\r\n\r\n\t\t\t\t\t\t\tsecondDirection = in_second_property_dir.valueAsText\r\n\t\t\t\t\t\t\tsecondProperty = in_second_property.valueAsText\r\n\r\n\t\t\t\t\t\t\tif firstProperty == None:\r\n\t\t\t\t\t\t\t\tfirstProperty = \"\"\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\r\n\t\t\t\t\t\t\tif secondProperty == None:\r\n\t\t\t\t\t\t\t\tsecondProperty = \"\"\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tsecondProperty = RelFinder.secondPropertyLabelURLDict[secondProperty]\r\n\r\n\t\t\t\t\t\t\tthirdDirection = in_third_property_dir.valueAsText\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t# get the third property URL list\r\n\t\t\t\t\t\t\tthirdPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection, thirdDirection], [firstProperty, secondProperty, \"\"])\r\n\t\t\t\t\t\t\tthirdPropertyURLList = []\r\n\t\t\t\t\t\t\tfor jsonItem in thirdPropertyURLListJsonBindingObject:\r\n\t\t\t\t\t\t\t\tthirdPropertyURLList.append(jsonItem[\"p3\"][\"value\"])\r\n\r\n\t\t\t\t\t\t\tthirdPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(thirdPropertyURLList)\r\n\t\t\t\t\t\t\t# thirdPropertyLabelJSON = thirdPropertyLabelJSONObj[\"results\"][\"bindings\"]\r\n\r\n\t\t\t\t\t\t\t# get the third property label list\r\n\t\t\t\t\t\t\tthirdPropertyURLList = []\r\n\t\t\t\t\t\t\tthirdPropertyLabelList = []\r\n\t\t\t\t\t\t\tfor jsonItem in thirdPropertyLabelJSON:\r\n\t\t\t\t\t\t\t\tpropertyURL = jsonItem[\"p\"][\"value\"]\r\n\t\t\t\t\t\t\t\tthirdPropertyURLList.append(propertyURL)\r\n\t\t\t\t\t\t\t\tpropertyName = jsonItem[\"propertyLabel\"][\"value\"]\r\n\t\t\t\t\t\t\t\tthirdPropertyLabelList.append(propertyName)\r\n\r\n\t\t\t\t\t\t\tRelFinder.thirdPropertyLabelURLDict = dict(zip(thirdPropertyLabelList, thirdPropertyURLList))\r\n\r\n\t\t\t\t\t\t\tin_third_property.filter.list = thirdPropertyLabelList\r\n\r\n\t\t\t\t\t\t\t# get the fourth property URL list and label list\r\n\t\t\t\t\t\t\tif in_fourth_property_dir.value:\r\n\t\t\t\t\t\t\t\tfristDirection = in_first_property_dir.valueAsText\r\n\t\t\t\t\t\t\t\tfirstProperty = in_first_property.valueAsText\r\n\r\n\t\t\t\t\t\t\t\tsecondDirection = in_second_property_dir.valueAsText\r\n\t\t\t\t\t\t\t\tsecondProperty = in_second_property.valueAsText\r\n\r\n\t\t\t\t\t\t\t\tthirdDirection = in_third_property_dir.valueAsText\r\n\t\t\t\t\t\t\t\tthirdProperty = in_third_property.valueAsText\r\n\r\n\t\t\t\t\t\t\t\tif firstProperty == None:\r\n\t\t\t\t\t\t\t\t\tfirstProperty = \"\"\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\r\n\t\t\t\t\t\t\t\tif secondProperty == None:\r\n\t\t\t\t\t\t\t\t\tsecondProperty = \"\"\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\tsecondProperty = RelFinder.secondPropertyLabelURLDict[secondProperty]\r\n\t\t\t\t\t\t\t\tif thirdProperty == None:\r\n\t\t\t\t\t\t\t\t\tthirdProperty = \"\"\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\tthirdProperty = RelFinder.thirdPropertyLabelURLDict[thirdProperty]\r\n\r\n\t\t\t\t\t\t\t\tfourthDirection = in_fourth_property_dir.valueAsText\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t# get the fourth property URL list\r\n\t\t\t\t\t\t\t\tfourthPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection, thirdDirection, fourthDirection], [firstProperty, secondProperty, thirdProperty])\r\n\t\t\t\t\t\t\t\tfourthPropertyURLList = []\r\n\t\t\t\t\t\t\t\tfor jsonItem in fourthPropertyURLListJsonBindingObject:\r\n\t\t\t\t\t\t\t\t\tfourthPropertyURLList.append(jsonItem[\"p4\"][\"value\"])\r\n\r\n\t\t\t\t\t\t\t\tfourthPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(fourthPropertyURLList)\r\n\t\t\t\t\t\t\t\t# fourthPropertyLabelJSON = fourthPropertyLabelJSONObj[\"results\"][\"bindings\"]\r\n\r\n\t\t\t\t\t\t\t\t# get the fourth property label list\r\n\t\t\t\t\t\t\t\tfourthPropertyURLList = []\r\n\t\t\t\t\t\t\t\tfourthPropertyLabelList = []\r\n\t\t\t\t\t\t\t\tfor jsonItem in fourthPropertyLabelJSON:\r\n\t\t\t\t\t\t\t\t\tpropertyURL = jsonItem[\"p\"][\"value\"]\r\n\t\t\t\t\t\t\t\t\tfourthPropertyURLList.append(propertyURL)\r\n\t\t\t\t\t\t\t\t\tpropertyName = jsonItem[\"propertyLabel\"][\"value\"]\r\n\t\t\t\t\t\t\t\t\tfourthPropertyLabelList.append(propertyName)\r\n\r\n\t\t\t\t\t\t\t\tRelFinder.fourthPropertyLabelURLDict = dict(zip(fourthPropertyLabelList, fourthPropertyURLList))\r\n\r\n\t\t\t\t\t\t\t\tin_fourth_property.filter.list = fourthPropertyLabelList\r\n\r\n\r\n\t\treturn","def update_parameters(parameters, grads, learning_rate=0.01):\n # Retrieve each parameter from the dictionary \"parameters\"\n ### START CODE HERE ### (≈ 4 lines of code)\n W1 = parameters[\"W1\"]\n b1 = parameters[\"b1\"]\n W2 = parameters[\"W2\"]\n b2 = parameters[\"b2\"]\n W3 = parameters[\"W3\"]\n b3 = parameters[\"b3\"]\n ### END CODE HERE ###\n\n # Retrieve each gradient from the dictionary \"grads\"\n ### START CODE HERE ### (≈ 4 lines of code)\n dW1 = grads[\"dW1\"]\n db1 = grads[\"db1\"]\n dW2 = grads[\"dW2\"]\n db2 = grads[\"db2\"]\n dW3 = grads[\"dW3\"]\n db3 = grads[\"db3\"]\n ## END CODE HERE ###\n\n # Update rule for each parameter\n ### START CODE HERE ### (≈ 4 lines of code)\n W1 = W1 - (learning_rate * dW1)\n b1 = b1 - (learning_rate * db1)\n W2 = W2 - (learning_rate * dW2)\n b2 = b2 - (learning_rate * db2)\n W3 = W3 - (learning_rate * dW3)\n b3 = b3 - (learning_rate * db3)\n ### END CODE HERE ###\n\n parameters = {\"W1\": W1,\n \"b1\": b1,\n \"W2\": W2,\n \"b2\": b2,\n \"W3\": W3,\n \"b3\": b3}\n\n return parameters","def update_recognize_params(self,inps,trgs,preds):\n batch_size = np.shape(trgs)[0]\n self.delta_weight_v_to_h = self.learning_rate/batch_size * np.transpose(trgs) @ (inps - preds)\n self.delta_bias_h = self.learning_rate * np.mean(inps - preds)\n\n self.weight_v_to_h += self.delta_weight_v_to_h\n self.bias_h += self.delta_bias_h\n \n return","def update():\n\n # Get last new x value as last x value + 1\n x_n0 = data_source.data['x'][-1]\n x_n1 = x_n0 + 0.1\n\n # Assign a new y value\n y_n1 = param_source.data['amp_sine'][0] * np.sin(x_n1) +\\\n param_source.data['amp_rand'][0] * np.random.rand(1)\n\n # Get old last average and use to calculate new average\n avg_n1 = _get_new_avg(data_source,\n y_n1,\n param_source.data['rollover'][0])\n\n # Make a dict of data to add on to the end of the source\n additional_data = dict(x=[x_n1], y=[y_n1], avg=[avg_n1])\n\n # Stream the new data with a rollover value of 10\n data_source.stream(additional_data,\n rollover=param_source.data['rollover'][0])\n\n # logger.debug(param_source.data['update_delay'][0])","def update_parameters(self, learning_rate):\n for i in range(self.L - 1):\n self.W[i] -= learning_rate * self.dW[i]\n self.b[i] -= learning_rate * self.db[i]","def _update_params(self):\n _load = not self.san_interface.runmode\n params={}\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\n params['iosched']=str(self.iosched)\n if ('readahead' in self._updatedattr or _load) and self.readahead :\n params['readahead']=self.readahead\n if params:\n for pt in self.paths():\n pt.provider.set_dev_params(pt,params)","def _update_parameters(self, delta):\n if delta is not None:\n self.SGD.update_with_L1_regularization(self.variables, delta, self.L1)","def rollout_policy_fn(board):\n # rollout randomly\n # action_probs = np.random.rand(len(board.availables))\n action_probs = np.random.rand(len(board.availables))\n return zip(board.availables, action_probs)","def updateInstrumentParameterValue(tablews, paramdict):\n paramnames = paramdict.keys()\n for parname in paramnames: \n parvalue = paramdict[parname]\n\t# print \"%s = %f\" % (parname, parvalue)\n\tif parname.count(\"Chi2\") == 0:\n\t # Only update parameters nothing to do with chi2\n UpdatePeakParameterTableValue(InputWorkspace=tablews,\n\t\tColumn='Value',\n ParameterNames=[parname],\n\t\tNewFloatValue=parvalue)\n\n return","def _update_params(self):\n raise NotImplementedException()","def update_filter_params(self, fh):\n (self.data_timestamp, self.framerate,\n self.l, self.d, self.gamma,\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])","def update(self):\n ## Initialize\n self.domain.update()\n self.var = self.domain.var.copy()\n self.out = []\n\n ## Construct var and out, respecting DAG properties\n for fun in self.functions:\n self.var = list(set(self.var).union(set(fun.var).difference(set(self.out))))\n\n self.out = list(set(self.out).union(set(fun.out)))\n\n try:\n self.var_rand = list(self.density.marginals.keys())\n except AttributeError:\n self.var_rand = []\n self.var_det = list(set(self.var).difference(self.var_rand))\n\n ## TODO parameters\n\n ## Convenience constants\n self.n_var = len(self.var)\n self.n_var_rand = len(self.var_rand)\n self.n_var_det = len(self.var_det)\n self.n_out = len(self.out)","def update_throughput(self, throughput):\n\n # Update the throughput values.\n throughput_new = []\n for throughput_n in throughput: # Loop over orders.\n\n if callable(throughput_n):\n\n # Throughput was given as a callable function.\n throughput_new.append(throughput_n(self.wave_grid))\n\n elif throughput_n.shape == self.wave_grid.shape:\n\n # Throughput was given as an array.\n throughput_new.append(throughput_n)\n\n else:\n msg = 'Throughputs must be given as callable or arrays matching the extraction grid.'\n log.critical(msg)\n raise ValueError(msg)\n\n # Set the attribute to the new values.\n self.throughput = throughput_new\n\n return","def update_by_config(self, policy_enabling_map):\n self.enabled_policies = []\n\n for policy_name, policy_config in policy_enabling_map.items():\n if not self._is_policy_exists(policy_name):\n self._warn_unexistent_policy(policy_name)\n continue\n\n if policy_config['enabled']:\n enabled_policy = self._get_policy(policy_name)\n self.enabled_policies.append(enabled_policy)","def applyFuncOnValues(self, func):\r\n self._value = func(self._value)"],"string":"[\n \"def policy_update_fn(self, data: Dict[str, Any], result: Dict[str, Any]) -> None:\",\n \"def update_policy(self, *args, **kwargs):\\r\\n pass\",\n \"def update_policy(self):\\n pass\",\n \"def update_Policy(self,inputpolicy):\\n \\n policyob = self.SD_Map.retrieve_ob(inputpolicy)\\n policyob.values[-1] = self.PolicyDicts[inputpolicy][self.translate(self.policy_option_vars[inputpolicy].get(),\\n input_language = self.language,\\n output_language = 'english')]\",\n \"def update_policy(self):\\n self.trainer_metrics.start_policy_update_timer(\\n number_experiences=len(self.training_buffer.update_buffer[\\\"actions\\\"]),\\n mean_return=float(np.mean(self.cumulative_returns_since_policy_update)),\\n )\\n self.cumulative_returns_since_policy_update = []\\n n_sequences = max(\\n int(self.trainer_parameters[\\\"batch_size\\\"] / self.policy.sequence_length), 1\\n )\\n value_total, policy_total = [], []\\n advantages = self.training_buffer.update_buffer[\\\"advantages\\\"].get_batch()\\n self.training_buffer.update_buffer[\\\"advantages\\\"].set(\\n (advantages - advantages.mean()) / (advantages.std() + 1e-10)\\n )\\n num_epoch = self.trainer_parameters[\\\"num_epoch\\\"]\\n for _ in range(num_epoch):\\n self.training_buffer.update_buffer.shuffle()\\n buffer = self.training_buffer.update_buffer\\n for l in range(\\n len(self.training_buffer.update_buffer[\\\"actions\\\"]) // n_sequences\\n ):\\n start = l * n_sequences\\n end = (l + 1) * n_sequences\\n run_out = self.policy.update(\\n buffer.make_mini_batch(start, end), n_sequences\\n )\\n value_total.append(run_out[\\\"value_loss\\\"])\\n policy_total.append(np.abs(run_out[\\\"policy_loss\\\"]))\\n self.stats[\\\"Losses/Value Loss\\\"].append(np.mean(value_total))\\n self.stats[\\\"Losses/Policy Loss\\\"].append(np.mean(policy_total))\\n for _, reward_signal in self.policy.reward_signals.items():\\n update_stats = reward_signal.update(\\n self.training_buffer.update_buffer, n_sequences\\n )\\n for stat, val in update_stats.items():\\n self.stats[stat].append(val)\\n if self.policy.bc_module:\\n update_stats = self.policy.bc_module.update()\\n for stat, val in update_stats.items():\\n self.stats[stat].append(val)\\n self.training_buffer.reset_update_buffer()\\n self.trainer_metrics.end_policy_update()\",\n \"def updateData(self, *args):\\n # if self.move_next_option == \\\"R\\\":\\n # self.restSampling()\\n # elif self.move_next_option == \\\"A\\\":\\n # self.addExtra()\\n # else:\\n # self.continueReview()\\n for name, value in self.parameter_inputs.items():\\n self.parameters[name] = value.value\\n # directly change the value of class variables\\n logMsg((\\\"update settings: \\\", self.ml_classifier_cls, name, value.value))\\n setattr(self.ml_classifier_cls, name, value.value)\\n\\n pass\",\n \"def update_policy_values(policy_lookup, board, state, player, action, next_state, reward):\\n\\t\\n\\t# compute total expected reward including future rewards\\n\\tif board.check_end():\\n\\t\\texpected = reward\\n\\telse:\\n\\t\\tif player == 1:\\n\\t\\t\\texpected = reward + discount * min_value(policy_lookup, next_state, 2)\\n\\t\\telif player == 2:\\n\\t\\t\\texpected = reward + discount * max_value(policy_lookup, next_state, 1)\\n\\t# get current policy action value\\n\\tpolicy_value = get_policy_value(policy_lookup, state, player, action)\\n\\t# update policy action value\\n\\tpolicy_lookup[(state, player)][action] += learning_rate * (expected - policy_value)\",\n \"def _run_policy(env, policy, num_rollouts):\\n start_states, final_states, goal_states, actions, paths = [], [], [], [], []\\n\\n for i in range(num_rollouts):\\n path = rollout(\\n env,\\n policy,\\n max_path_length=100,\\n animated=False,\\n )\\n obs = path[\\\"observations\\\"]\\n acts = path[\\\"actions\\\"]\\n\\n goal_idx = np.argmax(obs[0, 2:])\\n start_x, start_y = obs[0, 0], obs[0, 1]\\n acts_x, acts_y = acts[:, 0], acts[:, 1]\\n final_x, final_y = obs[len(obs) - 1, 0], obs[len(obs) - 1, 1]\\n goal = env.goals[goal_idx]\\n goal_x, goal_y = goal[0], goal[1]\\n\\n start_states.append(np.array([start_x, start_y]))\\n final_states.append(np.array([final_x, final_y]))\\n goal_states.append(np.array([goal_x, goal_y]))\\n actions.append(np.array([acts_x, acts_y]))\\n paths.append(path)\\n\\n return dict(\\n start_states=np.array(start_states),\\n final_states=np.array(final_states),\\n goal_states=np.array(goal_states),\\n actions=np.array(actions),\\n paths=paths,\\n env=env\\n )\",\n \"def update_policy(self, policy, inverse_policy=None):\\n self.make_T_policy_matrix(policy)\\n self.inverse_dynamics_by_time = dict()\\n self.policy = policy\\n self.inverse_policy = inverse_policy\",\n \"def update_parameters(self, ob_no, hidden, ac_na, fixed_log_probs, q_n, adv_n):\\n self.update_critic(ob_no, hidden, q_n)\\n self.update_policy(ob_no, hidden, ac_na, fixed_log_probs, adv_n)\",\n \"def policies(self, policies):\\n\\n self._policies = policies\",\n \"def update_parameters(self):\\n self.alignment_factor = rospy.get_param('/dyn_reconf/alignment_factor')\\n self.cohesion_factor = rospy.get_param('/dyn_reconf/cohesion_factor')\\n self.separation_factor = rospy.get_param('/dyn_reconf/separation_factor')\\n self.avoid_factor = rospy.get_param('/dyn_reconf/avoid_factor')\\n self.max_speed = rospy.get_param('/dyn_reconf/max_speed')\\n self.max_force = rospy.get_param('/dyn_reconf/max_force')\\n self.friction = rospy.get_param('/dyn_reconf/friction')\\n self.crowd_radius = rospy.get_param('/dyn_reconf/crowd_radius')\\n self.search_radius = rospy.get_param('/dyn_reconf/search_radius')\\n\\n rospy.loginfo(rospy.get_caller_id() + \\\" -> Parameters updated\\\")\\n if DEBUG:\\n print('alignment_factor: ', self.alignment_factor)\\n print('cohesion_factor: ', self.cohesion_factor)\\n print('separation_factor: ', self.separation_factor)\\n print('avoid_factor: ', self.avoid_factor)\\n print('max_speed: ', self.max_speed)\\n print('max_force: ', self.max_force)\\n print('friction: ', self.friction)\\n print('crowd_radius: ', self.crowd_radius)\\n print('search_radius: ', self.search_radius)\",\n \"def apply_policy(cls, metadata, policy):\\r\\n for attr, value in policy.iteritems():\\r\\n attr = cls._translate(attr)\\r\\n if attr not in cls.fields:\\r\\n # Store unknown attributes coming from policy.json\\r\\n # in such a way that they will export to xml unchanged\\r\\n metadata['xml_attributes'][attr] = value\\r\\n else:\\r\\n metadata[attr] = value\",\n \"def policy_update(self):\\n mini_batch = random.sample(self.data_buffer, self.batch_size)\\n state_batch = [data[0] for data in mini_batch]\\n mcts_probs_batch = [data[1] for data in mini_batch]\\n winner_batch = [data[2] for data in mini_batch]\\n loss, entropy = self.policy_value_net.train_step(\\n state_batch,\\n mcts_probs_batch,\\n winner_batch,\\n self.learn_rate)\\n return loss, entropy\",\n \"def update(self):\\n with torch.no_grad():\\n self.preprocess_rollout()\\n \\n # DEEP-RL TUTORIALS: КОСТЫЛЬ\\n #self.advantages = self.returns[:-1] - self.values[:-1]\\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\\n \\n # going through rollout several (config.epochs) times:\\n for epoch in range(self.config.epochs):\\n # TODO: drop last = False? What if there is 1 sample?\\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\\n \\n for indices in sampler:\\n # retrieving new batch as part of rollout\\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\\n \\n # calculating current value, action_log_prob, entropy\\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \\n self.entropy_b = dist.entropy()#.sum(dim=-1)\\n \\n # performing step\\n self.gradient_ascent_step()\",\n \"def _gen_policy_params(self, state: State) -> Tensor:\\n ...\",\n \"def update_policy(self):\\n n_sequences = max(int(self.trainer_parameters['batch_size'] / self.policy.sequence_length), 1)\\n value_total, policy_total, forward_total, inverse_total = [], [], [], []\\n advantages = self.training_buffer.update_buffer['advantages'].get_batch()\\n self.training_buffer.update_buffer['advantages'].set(\\n (advantages - advantages.mean()) / (advantages.std() + 1e-10))\\n num_epoch = self.trainer_parameters['num_epoch']\\n for k in range(num_epoch):\\n self.training_buffer.update_buffer.shuffle()\\n buffer = self.training_buffer.update_buffer\\n for l in range(len(self.training_buffer.update_buffer['actions']) // n_sequences):\\n start = l * n_sequences\\n end = (l + 1) * n_sequences\\n run_out = self.policy.update(buffer.make_mini_batch(start, end), n_sequences)\\n value_total.append(run_out['value_loss'])\\n policy_total.append(np.abs(run_out['policy_loss']))\\n if self.use_curiosity:\\n inverse_total.append(run_out['inverse_loss'])\\n forward_total.append(run_out['forward_loss'])\\n self.stats['value_loss'].append(np.mean(value_total))\\n self.stats['policy_loss'].append(np.mean(policy_total))\\n if self.use_curiosity:\\n self.stats['forward_loss'].append(np.mean(forward_total))\\n self.stats['inverse_loss'].append(np.mean(inverse_total))\\n self.training_buffer.reset_update_buffer()\",\n \"def update_parameters(self, timestamp, inputs):\\n pass\",\n \"def update(self):\\n\\n # get states, actions, rewards and total timesteps from memory\\n states, actions, R, T = self.memory.get()\\n n_ep = len(R)\\n\\n # compute value estimates for the states\\n v = self.critic(states)\\n\\n # compute advantages (using GAE) and rewards to go\\n A, rtg = utils.gae_rtg((R, v, T), self.gam, self.lam)\\n\\n # store the initial version of both the policy and the log probs of the\\n # actions for later comparison with the future versions (needed for PPO)\\n policy_old = copy.deepcopy(self.policy)\\n log_probs_old = policy_old(states).log_prob(actions)\\n\\n # sample from a batch of experiences\\n # (\\\"_\\\" subscript indicates \\\"sampled from\\\")\\n for (v_, A_, rtg_, log_probs_old_), i in utils.sample_batch((v, A, rtg, log_probs_old), self.batch_size, self.policy_updates):\\n log_probs_ = self.policy(states).log_prob(actions)[i]\\n\\n # estimate ratio between the new log probs and the old ones\\n r_ = torch.exp(log_probs_ - log_probs_old_)\\n\\n l_1 = r_ * A_\\n l_2 = torch.clamp(r_, 1-self.eps, 1+self.eps) * A_\\n\\n # TODO: implement entropy\\n # TODO: merge policy and critic\\n\\n # surragate loss function for PPO\\n l_clip = -torch.mean(torch.min(l_1, l_2))\\n\\n # update the policy\\n self.policy_optimizer.zero_grad()\\n l_clip.backward(retain_graph=True)\\n self.policy_optimizer.step()\\n\\n # sample a batch of value estimates and the corresponding rewards to go\\n # to update the value function.\\n for (v_, rtg_), _ in utils.sample_batch((v, rtg), self.batch_size, self.v_updates):\\n # compute the loss\\n critic_loss = F.mse_loss(v_, rtg_)\\n\\n # update the critic\\n self.critic_optimizer.zero_grad()\\n critic_loss.backward(retain_graph=True)\\n self.critic_optimizer.step()\\n\\n # clear the memory. PPO is an On-Policy method so we don't need these\\n # memories anymore\\n self.memory.clear()\\n\\n # return the loss of the value function for display\\n return F.mse_loss(v, rtg)\",\n \"def apply_policy(self, policy, method):\\n action, optimal_value, move = policy(self, method)\\n return action, optimal_value, move\",\n \"def UpdatePolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('UpdatePolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def update_policy(self):\\n self._sess.run(self._hard_copy_to_target_op);\",\n \"def update_policy(self):\\n # this is update_policy \\n # sample batch of 32 from the memory\\n batch_of_samples = self.replay_memory.sample(batch_size=32)\\n current_state_samples = batch_of_samples['current_state_samples']\\n next_state_samples = batch_of_samples['next_state_samples']\\n #print type(current_state_samples[0])\\n #print current_state_samples\\n\\n # fetch stuff we need from samples 32*84*84*4\\n current_state_images = np.zeros([1, 84, 84, 4])\\n #print current_state_samples\\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\\n\\n next_state_images = np.zeros([1, 84, 84, 4])\\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\\n\\n # preprocess\\n current_state_images = self.preprocessor.process_batch(current_state_images)\\n next_state_images = self.preprocessor.process_batch(next_state_images)\\n # print \\\"current_state_images {} max {} \\\".format(current_state_images.shape, np.max(current_state_images))\\n #print current_state_images.shape\\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\\n\\n # targets\\n y_targets_all = q_current #1*num_actions\\n #print y_targets_all.shape # [1,6]\\n idx = 0 \\n last_sample = current_state_samples[-1]\\n if last_sample.is_terminal:\\n y_targets_all[idx, last_sample.action] = last_sample.reward\\n else:\\n if self.mode == 'vanilla':\\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\\n if self.mode == 'double': \\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \\n\\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\\n\\n with tf.name_scope('summaries'):\\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\\n\\n if not (self.iter_ctr % self.log_loss_every_nth):\\n self.dump_train_loss(loss)\\n\\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\\n # # copy weights\\n # print \\\"Iter {} Updating target Q network\\\".format(self.iter_ctr)\\n # self.target_q_network.set_weights(self.q_network.get_weights())\\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\\\\n # for i in range(len(self.target_q_network.trainable_weights))]\",\n \"def update_parameters(self):\\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\\n self.update_gamma()\\n self.update_gamma0()\\n self.update_lambda()\\n self.update_nu()\\n if self.sample_xi:\\n self.update_xi()\",\n \"def update_values(self, to_update):\\n for key, value in kwargs.iteritems():\\n self.params[key] = value\\n # update the possibly dependent parameters\\n self.set_filenames()\",\n \"def ppo_update(policy_model, policy_optimizer, baseline_model, baseline_optimizer, baseline_criterion,\\n ppo_epoch, minibatch_size, obs_n_tensor, log_prob_n_old_tensor, action_n_tensor, rewards_n, mask_n,\\n gamma, lam, update_policy=True, update_baseline=True):\\n\\n ## get number of data\\n n_data = obs_n_tensor.shape[0]\\n ## get baseline estimations\\n baseline_n = baseline_model(obs_n_tensor).detach() ## between -1 and 1\\n ## get q values, used for scaling baseline values\\n q_n = get_reward_to_go(rewards_n, mask_n, gamma)\\n q_n = Tensor(q_n).reshape(-1, 1)\\n q_n_mean = q_n.mean()\\n q_n_std = q_n.std() + 1e-2\\n ## get scaled baseline values\\n baseline_n_scaled = baseline_n * q_n_std + q_n_mean\\n\\n ## get advantage and scaled baseline targets\\n ## the adv returned is normalized, baseline_target_scaled is not normalized\\n adv_n, baseline_target_n_scaled = get_gae_advantage(rewards_n, baseline_n_scaled, mask_n, gamma, lam)\\n adv_n = adv_n.detach()\\n\\n ## get baseline targets\\n baseline_target_n = ((baseline_target_n_scaled - q_n_mean) / q_n_std).detach() ## target now is normalized\\n\\n ## NOTE MAKE SURE YOU DETACH THINGS OTHERWISE YOU WILL HAVE VERY STRANGE ERRORS\\n\\n ## for each training iteration, do some epoches\\n for i_epoch in range(ppo_epoch):\\n ## ppo: shuffle data\\n shuffle_indexes = torch.randperm(n_data)\\n obs_n_tensor = obs_n_tensor[shuffle_indexes]\\n adv_n = adv_n[shuffle_indexes]\\n log_prob_n_old_tensor = log_prob_n_old_tensor[shuffle_indexes]\\n action_n_tensor = action_n_tensor[shuffle_indexes]\\n baseline_target_n = baseline_target_n[shuffle_indexes]\\n\\n ## after shuffle data, we do the minibatch ppo update\\n n_minibatch = int(n_data / minibatch_size)\\n for i_minibatch in range(n_minibatch):\\n ## get minibatch\\n istart = i_minibatch * minibatch_size\\n iend = (i_minibatch + 1) * minibatch_size\\n obs_m = obs_n_tensor[istart:iend]\\n adv_m = adv_n[istart:iend]\\n log_prob_old_m = log_prob_n_old_tensor[istart:iend]\\n action_m = action_n_tensor[istart:iend]\\n baseline_target_m = baseline_target_n[istart:iend]\\n\\n ## update baseline\\n if update_baseline:\\n baseline_m = baseline_model(obs_m)\\n baseline_optimizer.zero_grad()\\n baseline_loss = baseline_criterion(baseline_m, baseline_target_m)\\n baseline_loss.backward()\\n baseline_optimizer.step()\\n\\n ## update policy\\n ## we need the new policy's log probs so that we can calculate importance sampling term\\n if update_policy:\\n mu, log_sigma = policy_model(obs_m)\\n normal_dist = Normal(mu, log_sigma.exp())\\n log_prob_new_m = normal_dist.log_prob(action_m)\\n log_prob_new_m = torch.sum(log_prob_new_m, dim=1).reshape(-1, 1)\\n ## now we get the importance sampling weight term\\n is_term_m = (log_prob_new_m - log_prob_old_m).exp()\\n\\n ## compute the clipped surrogate\\n epsilon = 0.2\\n first_term = is_term_m * adv_m\\n clipped_term = is_term_m.clamp(1 - epsilon, 1 + epsilon) * adv_m\\n obj_term = torch.min(first_term, clipped_term)\\n obj_sum = obj_term.sum()\\n policy_loss = -obj_sum / n_data\\n policy_optimizer.zero_grad()\\n policy_loss.backward()\\n policy_optimizer.step()\",\n \"def apply_parameters(self):\\n n_bins = int(self.record_length / self.bin_width)\\n time_bins = self.bin_width * np.arange(n_bins)\\n \\n\\n self.tau = np.arange(self.tau_start, self.tau_end+self.tau_delta, self.tau_delta)\\n sequence = self.generate_sequence()\\n self.n_laser = find_laser_pulses(sequence)\\n \\n FC.Configure(self.record_length, self.bin_width, self.n_laser)\\n\\n if self.keep_data and sequence == self.sequence and np.all(time_bins == self.time_bins): # if the sequence and time_bins are the same as previous, keep existing data\\n self.old_count_data = self.count_data.copy()\\n else:\\n self.old_count_data = np.zeros_like(FC.GetData())\\n \\n self.sequence = sequence\\n self.time_bins = time_bins\\n self.n_bins = n_bins\\n \\n self.MW_source = {'mw':mw, 'mw2':mw2}[self.mw_source]\",\n \"def optimize_policy(self, all_samples_data, log=True):\\n raise NotImplementedError\",\n \"def update_policy(self, req):\\n\\n with self.mutex:\\n\\n # Apply all gradients\\n for gradient in req.gradients:\\n\\n self.global_policy.zero_grad()\\n\\n # Transfer gradient to global policy\\n self.transfer_gradient(gradient)\\n\\n # Clip gradient\\n torch.nn.utils.clip_grad_norm_(self.global_policy.parameters(),\\n 1.0)\\n # torch.nn.utils.clip_grad_value_(self.global_policy.parameters(),\\n # 1.0)\\n\\n # Apply gradient\\n self.optimizer.step()\\n self.iteration_counter += 1\\n\\n self._log_iteration(req)\\n\\n if (self.save_path and\\n self.save_steps > 0 and\\n self.iteration_counter >= self.save_steps * self.i):\\n self.save_model()\\n self.i += 1\\n\\n weights = self.encode_weights()\\n cov = max(1.0 - self.iteration_counter /\\n float(self.exploration), 0.05)\\n covariance = [cov, cov]\\n\\n return UpdateGlobalPolicyResponse(weights, covariance)\",\n \"def defineUpdateOperations(self):\\n self.updated_value = tf.placeholder(shape=[1, self.network.action_size], dtype=tf.float32)\\n self.loss = tf.reduce_sum(tf.square(self.updated_value - self.network.policyLayer))\\n self.trainer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate)\\n\\n self.updateModel = self.trainer.minimize(self.loss)\",\n \"def callback(self, rollout):\\n assert len(self._t_switch)==len(self._scale)==len(self._k_star)\\n # Log rollout statistics\\n if self._ro_with_policy or self._unfinished_mix(len(rollout)):\\n if self._unfinished_mix(len(rollout)):\\n del self._k_star[-1]\\n del self._t_switch[-1]\\n del self._scale[-1]\\n self._ind_ro_pol.append(self._n_ro)\\n self._n_samples_ro_pol+=len(rollout)\\n if self._ro_by_n_samples:\\n self._ro_with_policy = self._n_samples_ro_pol<self._n_samples_ro_mix\\n else:\\n self._ro_with_policy = False\\n else:\\n self._ind_ro_mix.append(self._n_ro)\\n self._n_samples_ro_mix+=len(rollout)\\n self._ro_with_policy = True\\n\\n # unlock so `pi` can be called again\\n self._locked =False\\n self._n_ro+=1\",\n \"def update_policy(env, policy, V, discount_factor):\\n\\n for state in range(env.nS):\\n # for a given state compute state-action value.\\n action_values = one_step_lookahead(env, state, V, discount_factor)\\n\\n # choose the action which maximizes the state-action value.\\n policy[state] = np.argmax(action_values)\\n\\n return policy\",\n \"def refresh(self):\\n self._policies = self._get_policies()\",\n \"def _update_params(self):\\n pass\",\n \"def handle_wps_update(self, data):\\n\\n self.jobs = data\",\n \"def update_params(self):\\n pass\",\n \"def update_parameters(parameters, grads, learning_rate):\\n pass\",\n \"def update_policy(self):\\n self.optimizer.step()\\n self.optimizer.zero_grad()\",\n \"def update_params(self, learning_rate):\\n\\t\\t#######################################################################\\n\\t\\t# ** START OF YOUR CODE **\\n\\t\\t#######################################################################\\n\\t\\tself._W = self._W - learning_rate * self._grad_W_current\\n\\t\\tself._b = self._b - learning_rate * self._grad_b_current\\n\\t\\t#######################################################################\\n\\t\\t# ** END OF YOUR CODE **\\n\\t\\t#######################################################################\",\n \"def update_params(self):\\n \\n # read parameters from context broker\\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\\n self.params[attr] = float(self.ORION_CB.get_entity_attribute_value(entity_name=self.params['name'], attribute_name=attr))\\n self.params['reverse_act'] = str(self.ORION_CB.get_entity_attribute_value(entity_name=self.params['name'], attribute_name='reverse_act')) \\n \\n # update PID parameters\\n self.PID.Kp = self.params['Kp']\\n self.PID.Ti = self.params['Ti']\\n self.PID.Td = self.params['Td']\\n self.PID.lim_low = self.params['lim_low']\\n self.PID.lim_high = self.params['lim_high']\\n self.PID.reverse_act = eval(eval(self.params['reverse_act']))\",\n \"def _apply_params(self):\\n config = self.get_startup_config()\\n # Pass true to _set_params so we know these are startup values\\n self._set_params(config, True)\",\n \"def updatedata(self, force_update=False):\\n\\n self._update_counter = self._update_counter + 1\\n logging.debug('ParameterViewer: update values')\\n for instrument_name in self._instrumentnames:\\n instr = self._instruments[self._instrumentnames.index(instrument_name)]\\n parameters = {}\\n\\n try:\\n parameters = instr.parameters\\n except AttributeError as ex:\\n # instrument was removed\\n print('instrument was removed, stopping ParameterViewer')\\n # logging.exception(ex)\\n self._timer.stop()\\n\\n parameter_names = sorted(parameters.keys())\\n\\n si = sys.getswitchinterval()\\n\\n for parameter_name in parameter_names:\\n # hack to make this semi thread-safe\\n\\n for field_name in self._fields:\\n if field_name == 'Value':\\n sys.setswitchinterval(100)\\n value = parameters[parameter_name].get_latest()\\n sys.setswitchinterval(si)\\n self.update_field_signal.emit(instrument_name, parameter_name, field_name, value, force_update)\\n else:\\n if self._update_counter % 20 == 1 or 1:\\n sys.setswitchinterval(100)\\n value = getattr(parameters[parameter_name], field_name, '')\\n sys.setswitchinterval(si)\\n self.update_field_signal.emit(instrument_name, parameter_name,\\n field_name, value, force_update)\\n\\n for callback_function in self.callbacklist:\\n try:\\n callback_function()\\n except Exception as ex:\\n logging.debug('update function failed')\\n logging.exception(str(ex))\",\n \"def update_with_evaluation(param_object, evaluation_dict, verbose):\\n if evaluation_dict: # evaluates true if dict is not empty and the value is not None\\n for key, value in evaluation_dict.items():\\n try:\\n setattr(param_object, key, value)\\n TellUser.debug('attribute (' + param_object.name + ': ' + key + ') set: ' + str(value))\\n except KeyError:\\n TellUser.debug('No attribute ' + param_object.name + ': ' + key)\",\n \"def updateParameters(self, parameters):\",\n \"def pre_network_policy_update(self, resource_id, resource_dict):\\n pass\",\n \"def _modify_schedule_policy_properties(self):\\n request_json = {\\n 'taskInfo':\\n {\\n 'taskOperation': 1,\\n 'associations': self._associations,\\n 'task': self._task_json,\\n \\\"appGroup\\\":\\n {\\n \\\"appGroups\\\": self._app_groups if self._app_groups else [],\\n },\\n 'subTasks': self._subtasks\\n }\\n }\\n\\n flag, response = self._commcell_object._cvpysdk_object.make_request(\\n 'PUT', self._MODIFY_SCHEDULE_POLICY, request_json\\n )\\n output = self._process_schedule_policy_update_response(flag, response)\\n self.refresh()\\n\\n if output[0]:\\n return\\n\\n o_str = 'Failed to update properties of Schedule Policy\\\\nError: \\\"{0}\\\"'\\n raise SDKException('Schedules', '102', o_str.format(output[2]))\",\n \"def __setitem__(self, i, v):\\n # The policy function can't be modified\",\n \"def set_policy (self, policy = None, args = (), policy_cleanup = None):\\n if policy == self.policy:\\n # same policy; might want to change args/cleanup function, though\\n self._policy_args = args\\n if policy is not None and not isinstance(policy, basestring):\\n self._policy_cleanup = policy_cleanup\\n return\\n # perform cleanup for current policy, if any\\n if isinstance(self.policy, basestring):\\n # built-in\\n try:\\n POLICY_CLEANUP[self.policy](self)\\n except AttributeError:\\n pass\\n elif self.policy is not None and self._policy_cleanup is not None:\\n # custom\\n self._policy_cleanup(self)\\n del self._policy_cleanup\\n # set new policy\\n self.policy = policy\\n if policy is None:\\n # if disabling scrolling, clean up some attributes we won't need\\n try:\\n del self._scroll_fn, self._policy_args\\n except AttributeError:\\n pass\\n else:\\n self._policy_args = args if args else ()\\n if isinstance(policy, basestring):\\n # built-in\\n self._scroll_fn = POLICY_SCROLL[policy]\\n else:\\n # custom\\n self._scroll_fn = policy\\n self._policy_cleanup = policy_cleanup\",\n \"def _update_embedding_param(self):\\n for layer, ids in self._tls._unique_ids_all_layers.items():\\n value = self._get_embedding_variable(layer).numpy()\\n self._update_embedding_func(layer, ids, value)\\n\\n for slot in self._allowed_slot_names:\\n value = self._get_slot_variable(layer, slot).numpy()\\n slot_table_name = get_slot_table_name(layer, slot)\\n self._update_embedding_func(slot_table_name, ids, value)\",\n \"def update_policy(ranger_url, policy_id, policy_data, admin_username_password):\\n\\n url = format(\\\"{ranger_url}/service/public/v2/api/policy/{policy_id}\\\")\\n\\n base_64_string = base64.encodestring(admin_username_password).replace('\\\\n', '')\\n\\n request = urllib2.Request(url, json.dumps(policy_data))\\n request.get_method = lambda: 'PUT'\\n request.add_header('Content-Type', 'application/json')\\n request.add_header('Accept', 'application/json')\\n request.add_header('Authorization', format('Basic {base_64_string}'))\\n\\n try:\\n result = openurl(request, timeout=20)\\n response_code = result.getcode()\\n if response_code == 200:\\n Logger.info(format(\\\"Successfully updated policy in Ranger Admin\\\"))\\n return response_code\\n else:\\n Logger.error(format(\\\"Unable to update policy in Ranger Admin\\\"))\\n return None\\n except urllib2.HTTPError as e:\\n raise Fail(\\\"HTTPError while updating policy Reason = \\\" + str(e.code))\\n except urllib2.URLError as e:\\n raise Fail(\\\"URLError while updating policy. Reason = \\\" + str(e.reason))\\n except TimeoutError:\\n raise Fail(\\\"Connection timeout error while updating policy\\\")\\n except Exception as err:\\n raise Fail(format(\\\"Error while updating policy. Reason = {err}\\\"))\",\n \"def _init_actor_update(self):\\n\\n actions = self._policy.actions([self._observations_ph])\\n log_pis = self._policy.log_pis([self._observations_ph], actions)\\n\\n assert log_pis.shape.as_list() == [None, 1]\\n\\n log_alpha = self._log_alpha = tf.get_variable(\\n 'log_alpha',\\n dtype=tf.float32,\\n initializer=0.0)\\n alpha = tf.exp(log_alpha)\\n\\n if isinstance(self._target_entropy, Number):\\n alpha_loss = -tf.reduce_mean(\\n log_alpha * tf.stop_gradient(log_pis + self._target_entropy))\\n\\n self._alpha_optimizer = tf.train.AdamOptimizer(\\n self._policy_lr, name='alpha_optimizer')\\n self._alpha_train_op = self._alpha_optimizer.minimize(\\n loss=alpha_loss, var_list=[log_alpha])\\n\\n self._training_ops.update({\\n 'temperature_alpha': self._alpha_train_op\\n })\\n\\n self._alpha = alpha\\n\\n if self._action_prior == 'normal':\\n policy_prior = tf.contrib.distributions.MultivariateNormalDiag(\\n loc=tf.zeros(self._action_shape),\\n scale_diag=tf.ones(self._action_shape))\\n policy_prior_log_probs = policy_prior.log_prob(actions)\\n elif self._action_prior == 'uniform':\\n policy_prior_log_probs = 0.0\\n\\n Q_log_targets = tuple(\\n Q([self._observations_ph, actions])\\n for Q in self._Qs)\\n min_Q_log_target = tf.reduce_min(Q_log_targets, axis=0)\\n\\n if self._reparameterize:\\n policy_kl_losses = (\\n alpha * log_pis\\n - min_Q_log_target\\n - policy_prior_log_probs)\\n else:\\n raise NotImplementedError\\n\\n assert policy_kl_losses.shape.as_list() == [None, 1]\\n\\n policy_loss = tf.reduce_mean(policy_kl_losses)\\n\\n self._policy_optimizer = tf.train.AdamOptimizer(\\n learning_rate=self._policy_lr,\\n name=\\\"policy_optimizer\\\")\\n policy_train_op = tf.contrib.layers.optimize_loss(\\n policy_loss,\\n self.global_step,\\n learning_rate=self._policy_lr,\\n optimizer=self._policy_optimizer,\\n variables=self._policy.trainable_variables,\\n increment_global_step=False,\\n summaries=(\\n \\\"loss\\\", \\\"gradients\\\", \\\"gradient_norm\\\", \\\"global_gradient_norm\\\"\\n ) if self._tf_summaries else ())\\n\\n self._training_ops.update({'policy_train_op': policy_train_op})\",\n \"def updateParameters(self):\\n\\n return\",\n \"def variational_update(self):\\n with self.elbo_check('update_p_allele_swap'):\\n self.model.update_p_allele_swap()\\n\\n with self.elbo_check('p_cn'):\\n self.model.update_p_cn()\\n\\n with self.elbo_check('p_breakpoint'):\\n self.model.update_p_breakpoint()\\n\\n with self.elbo_check('p_outlier_total'):\\n self.model.update_p_outlier_total()\\n\\n with self.elbo_check('p_outlier_allele'):\\n self.model.update_p_outlier_allele()\",\n \"def log_update(self, policy_loss, entropy, kl_divergence, clipping_fraction,\\n value_loss, explained_variance, steps):\\n\\n # Diagnostics\\n self.writer.add_scalar(\\\"Diagnostics/Policy/PolicyLoss\\\",\\n policy_loss,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Policy/Entropy\\\",\\n entropy,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Policy/KLDivergence\\\",\\n kl_divergence,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Policy/ClipFraction\\\",\\n clipping_fraction,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Value/ValueLoss\\\",\\n value_loss,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Value/ValueEstimate\\\",\\n np.mean(self.buffer.values),\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Value/ExplainedVariance\\\",\\n explained_variance,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Info/LearningRate\\\",\\n self.lr_pi,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Info/TotalTimesteps\\\",\\n self.update_counter * self.batch_size,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Diagnostics/Info/KLDivCoef\\\",\\n self.kl_coef,\\n self.update_counter)\\n # Training Episodes\\n self.writer.add_scalar(\\\"Training/Episodes/PolicyGradientSteps\\\",\\n steps,\\n self.update_counter)\\n mean_frames = np.mean(self.buffer.episode_lengths)\\n std_frames = np.std(self.buffer.episode_lengths)\\n self.writer.add_scalar(\\\"Training/Episodes/Mean_Frames\\\",\\n mean_frames,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Training/Episodes/Std_Frames\\\",\\n std_frames,\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Episodes/Frames\\\",\\n np.array(self.buffer.episode_lengths),\\n self.update_counter)\\n mean_reward = np.mean(self.buffer.episode_rewards)\\n std_reward = np.std(self.buffer.episode_rewards)\\n rews_per_frame = np.array(self.buffer.episode_rewards) / \\\\\\n np.array(self.buffer.episode_lengths, dtype=np.float)\\n self.writer.add_scalar(\\\"Training/Episodes/Mean_Reward\\\",\\n mean_reward,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Training/Episodes/Std_Reward\\\",\\n std_reward,\\n self.update_counter)\\n self.writer.add_scalar(\\\"Training/Episodes/Reward_per_Frame_Mean\\\",\\n np.mean(rews_per_frame),\\n self.update_counter)\\n self.writer.add_scalar(\\\"Training/Episodes/Reward_per_Frame_Std\\\",\\n np.std(rews_per_frame),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Episodes/Rewards\\\",\\n np.array(self.buffer.episode_rewards),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Episodes/Rewards_per_Frame\\\",\\n rews_per_frame,\\n self.update_counter)\\n actions = np.array(self.buffer.actions)\\n self.writer.add_histogram(\\\"Training/Action/DeltaVel\\\",\\n actions[:, 0],\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Action/DeltaLat\\\",\\n actions[:, 1],\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Values\\\",\\n np.array(self.buffer.values),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Avantages\\\",\\n np.array(self.buffer.advantages),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/GradNorms\\\",\\n np.array(self.grad_norms),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Ratio/Ratio\\\",\\n np.array(self.ratios).flatten(),\\n self.update_counter)\\n self.writer.add_histogram(\\\"Training/Ratio/ClippedRatio\\\",\\n np.array(self.clipped_ratios).flatten(),\\n self.update_counter)\\n\\n self.writer.flush()\\n\\n print(\\\"-\\\" * 30)\\n print(\\\"PPO Optimization\\\")\\n print(\\\"Policy_Loss: {}\\\\t\\\\t\\\".format(policy_loss))\\n print(\\\"Value_Loss: {}\\\\t\\\\t\\\".format(value_loss))\\n print(\\\"Entropy: {}\\\\t\\\\t\\\".format(entropy))\\n print(\\\"Lr_pi: {}\\\\t\\\\t\\\".format(self.lr_pi))\\n print(\\\"Lr_vf: {}\\\\t\\\\t\\\".format(self.lr_vf))\\n print(\\\"KL_Divergence: {}\\\\t\\\\t\\\".format(kl_divergence))\\n print(\\\"Clip_Fraction: {}\\\\t\\\\t\\\".format(clipping_fraction))\\n print(\\\"Exp_Variance: {}\\\\t\\\\t\\\".format(explained_variance))\\n print(\\\"Mean_Reward: {}\\\\t\\\\t\\\".format(mean_reward))\\n print(\\\"Std_Reward: {}\\\\t\\\\t\\\".format(std_reward))\\n print(\\\"Mean_Frames: {}\\\\t\\\\t\\\".format(mean_frames))\\n print(\\\"Std_Frames: {}\\\\t\\\\t\\\".format(std_frames))\\n print(\\\"Mean_Reward_per_frame: {}\\\\t\\\\t\\\".format(np.mean(rews_per_frame)))\\n print(\\\"Std_Reward_per_frame: {}\\\\t\\\\t\\\".format(np.std(rews_per_frame)))\\n print(\\\"Optimization steps: {}\\\\t\\\\t\\\". format(self.update_counter))\\n print(\\\"-\\\" * 30)\",\n \"def update_l7_policy(request, **kwargs):\\n data = request.DATA\\n l7_policy_id = data['l7policy'].get('id')\\n\\n conn = get_sdk_connection(request)\\n l7_policy = conn.load_balancer.update_l7_policy(\\n action=data['l7policy']['action'],\\n admin_state_up=data['l7policy'].get('admin_state_up'),\\n description=data['l7policy'].get('description'),\\n l7_policy=l7_policy_id,\\n name=data['l7policy'].get('name'),\\n position=data['l7policy'].get('position'),\\n redirect_pool_id=data['l7policy'].get('redirect_pool_id'),\\n redirect_url=data['l7policy'].get('redirect_url'),\\n )\\n\\n return _get_sdk_object_dict(l7_policy)\",\n \"def update_parameters(updates):\\r\\n for (key, val) in updates.items():\\r\\n par[key] = val\\r\\n print('Updating:', key, '-->', val)\\r\\n update_dependencies()\",\n \"def rollout(pi, env):\\n rollout = defaultdict(list)\\n t = 0\\n ob = (env.reset())\\n features = pi.get_initial_features()\\n for _ in range(2000):\\n rets = pi.compute(ob, features)\\n ac, value, features = rets[0], rets[1], rets[2:]\\n rollout[\\\"obs\\\"].append(ob)\\n rollout[\\\"vs\\\"].append(value)\\n rollout[\\\"actions\\\"].append(ac)\\n ob, rew, done, _ = env.step(ac)\\n rollout[\\\"rs\\\"].append(rew)\\n rollout[\\\"features\\\"].append(features)\\n t += 1\\n if done:\\n break\\n print(\\\"Cur policy: \\\", len(rollout[\\\"obs\\\"]))\\n rollout[\\\"r\\\"] = 0\\n rollout[\\\"terminal\\\"] = True\\n return rollout\",\n \"def _update_params(self, perf_params, loop_info):\\n for vartype in list(perf_params.keys()):\\n for var in perf_params[vartype]:\\n self.tspec_params['performance_params'][var] = \\\\\\n self.indent + 'param %s[] = %s;\\\\t#%s\\\\n' % (var, repr(default_perf_params[vartype]), vartype)\\n\\n #loop_info.vars: set of input vars\",\n \"def _update(self):\\n self.all_params = {}\\n self._update_experiment_params()\\n self._update_preprocessing_params()\\n self._update_model_params()\",\n \"def _update_parameter(self, dWxh, dbh, dWhy, dby):\\n # Add code to update all the weights and biases here\",\n \"def update_generate_params(self,inps,trgs,preds):\\n batch_size = np.shape(trgs)[0]\\n\\n self.delta_weight_h_to_v = self.learning_rate / batch_size * np.transpose(trgs) @ (inps - preds)\\n self.delta_bias_v = self.learning_rate * np.mean(inps - preds)\\n \\n self.weight_h_to_v += self.delta_weight_h_to_v\\n self.bias_v += self.delta_bias_v \\n \\n return\",\n \"def replace_namespaced_policy(self, body, namespace, name, **kwargs):\\n\\n all_params = ['body', 'namespace', 'name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method replace_namespaced_policy\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'body' is set\\n if ('body' not in params) or (params['body'] is None):\\n raise ValueError(\\\"Missing the required parameter `body` when calling `replace_namespaced_policy`\\\")\\n # verify the required parameter 'namespace' is set\\n if ('namespace' not in params) or (params['namespace'] is None):\\n raise ValueError(\\\"Missing the required parameter `namespace` when calling `replace_namespaced_policy`\\\")\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `replace_namespaced_policy`\\\")\\n\\n resource_path = '/oapi/v1/namespaces/{namespace}/policies/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'namespace' in params:\\n path_params['namespace'] = params['namespace']\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'body' in params:\\n body_params = params['body']\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'PUT',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1Policy',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def refresh(self):\\n self._get_schedule_policy_properties()\",\n \"def pre_update(self, **values):\\r\\n pass\",\n \"def post_network_policy_update(self, resource_id, resource_dict):\\n pass\",\n \"def update_service_access_policies(DomainName=None, AccessPolicies=None):\\n pass\",\n \"def _policy_improvement(self) -> Tuple[np.ndarray, np.ndarray]:\\n # Start with a (random) policy\\n policy = np.zeros([self.state_dim, self.action_dim])\\n V = np.zeros([self.state_dim])\\n #random init the policy\\n for s in range(self.state_dim):\\n policy[s,0] = 0.0\\n policy[s,1] = 0.0\\n policy[s,2] = 1.0\\n\\n V = self._policy_eval(policy)\\n\\n policy_stable = False\\n dr = 0.9\\n\\n while (policy_stable != True):\\n policy_stable = True\\n for s in self.mdp._state_dict:\\n old_action = (policy[self.mdp._state_dict[s]]).tolist()\\n action_dict = {}\\n for a in self.mdp._action_dict:\\n temp = 0.0\\n for next_s in self.mdp._state_dict:\\n p = self.mdp.P[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\\n r = self.mdp.R[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\\n Vs = V[self.mdp._state_dict[next_s]]\\n temp = temp + p * (r + dr * Vs)\\n action_dict[self.mdp._action_dict[a]]= temp \\n max_act = max(action_dict.values())\\n V[self.mdp._state_dict[s]] = max_act\\n res = [t for t,v in action_dict.items() if v == max_act][0]\\n for opt in range(self.action_dim):\\n if opt == res:\\n policy[self.mdp._state_dict[s],opt] = 1.0\\n else:\\n policy[self.mdp._state_dict[s],opt] = 0.0\\n if (old_action - policy[self.mdp._state_dict[s]]).any() == True:\\n \\n policy_stable = False\\n if policy_stable == False:\\n V = self._policy_eval(policy)\\n \\n return policy, V\",\n \"def em_update_params(self):\\n for name in self.likelihood_params:\\n with self.elbo_check(name):\\n self.update_param(name)\",\n \"def update_multiple_parameters(self, detuning=None, lamb_dicke=None,\\n base_rabi=None):\\n if detuning is not None:\\n self.__detuning = detuning\\n if lamb_dicke is not None:\\n self.__lamb_dicke = lamb_dicke\\n if base_rabi is not None:\\n self.__base_rabi = base_rabi\\n if detuning is None and lamb_dicke is None and base_rabi is None:\\n return\\n self.__update_prefactors()\",\n \"def policy_improvement(P, nS, nA, value_from_policy, gamma=0.9):\\n\\n new_policy = np.ones([nS, nA]) / nA\\n\\t############################\\n\\t# YOUR IMPLEMENTATION HERE #\\n #iteration_policy=new_policy\\n for state in range(nS):\\n #current_policy=new_policy[state] \\n action_policy = np.zeros(nA) \\n for action in range(nA):\\n for p,nextS,r,boolean_v in P[state][action]:\\n action_policy[action] += p*( r + gamma* value_from_policy[nextS])\\n #print(action_policy)\\n updated_policy=np.zeros(nA)\\n updated_policy[np.argmax(action_policy)]= 1\\n #print(updated_policy) \\n new_policy[state]=updated_policy\\n \\n \\t############################\\n return new_policy\",\n \"def initialize_policies(self, policy_collection, options):\",\n \"def put(self, request, l7_policy_id):\\n kwargs = {'l7_policy_id': l7_policy_id}\\n update_l7_policy(request, **kwargs)\",\n \"def update_data(self, **kwargs):\\n self.source_data = self.get_dict()\\n for c in self.callbacks[\\\"update_data\\\"]:\\n c()\",\n \"def update_lr(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def update_lr(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def patch_namespaced_policy(self, body, namespace, name, **kwargs):\\n\\n all_params = ['body', 'namespace', 'name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method patch_namespaced_policy\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'body' is set\\n if ('body' not in params) or (params['body'] is None):\\n raise ValueError(\\\"Missing the required parameter `body` when calling `patch_namespaced_policy`\\\")\\n # verify the required parameter 'namespace' is set\\n if ('namespace' not in params) or (params['namespace'] is None):\\n raise ValueError(\\\"Missing the required parameter `namespace` when calling `patch_namespaced_policy`\\\")\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `patch_namespaced_policy`\\\")\\n\\n resource_path = '/oapi/v1/namespaces/{namespace}/policies/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'namespace' in params:\\n path_params['namespace'] = params['namespace']\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'body' in params:\\n body_params = params['body']\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['application/json-patch+json', 'application/merge-patch+json', 'application/strategic-merge-patch+json'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'PATCH',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1Policy',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def rollout_policy_fn(board):\\n # rollout randomly\\n action_probs = np.random.rand(len(board.availables))\\n return zip(board.availables, action_probs)\",\n \"def test_update_policy(self):\\n self.ports[1].qos_policy_id = self.qos_policies[0].id\\n self.ports[1].update()\\n self.ports[2].qos_policy_id = self.qos_policies[1].id\\n self.ports[2].update()\\n self.ports[4].qos_policy_id = self.qos_policies[0].id\\n self.ports[4].update()\\n self.ports[5].qos_policy_id = self.qos_policies[1].id\\n self.ports[5].update()\\n self.networks[1].qos_policy_id = self.qos_policies[0].id\\n self.networks[1].update()\\n self.fips[0].qos_policy_id = self.qos_policies[0].id\\n self.fips[0].update()\\n self.fips[1].qos_policy_id = self.qos_policies[1].id\\n self.fips[1].update()\\n self._update_router_qos(self.ctx, self.routers[0].id,\\n self.qos_policies[0].id)\\n self._update_router_qos(self.ctx, self.routers[1].id,\\n self.qos_policies[1].id)\\n mock_qos_rules = mock.Mock()\\n with mock.patch.object(self.qos_driver, '_qos_rules',\\n return_value=mock_qos_rules), \\\\\\n mock.patch.object(self.qos_driver, 'update_floatingip') as \\\\\\n mock_update_fip, \\\\\\n mock.patch.object(self.qos_driver, 'update_router') as \\\\\\n mock_update_router:\\n self.qos_driver.update_policy(self.ctx, self.qos_policies[0])\\n # Ports updated from \\\"update_port\\\": self.ports[1], self.ports[4]\\n updated_ports = [self.ports[1], self.ports[4]]\\n calls = [mock.call(self.txn, port.id, port.network_id,\\n self.qos_policies[0].id, mock_qos_rules,\\n lsp=None, port_deleted=False)\\n for port in updated_ports]\\n # Port updated from \\\"update_network\\\": self.ports[3]\\n calls.append(mock.call(self.txn, self.ports[3].id,\\n self.ports[3].network_id,\\n self.qos_policies[0].id, mock_qos_rules))\\n\\n # We can't ensure the call order because we are not enforcing any order\\n # when retrieving the port and the network list.\\n self.mock_rules.assert_has_calls(calls, any_order=True)\\n with db_api.CONTEXT_READER.using(self.ctx):\\n fip = self.qos_driver._plugin_l3.get_floatingip(self.ctx,\\n self.fips[0].id)\\n mock_update_fip.assert_called_once_with(self.txn, fip)\\n\\n with db_api.CONTEXT_READER.using(self.ctx):\\n router = self.qos_driver._plugin_l3.get_router(self.ctx,\\n self.routers[0].id)\\n mock_update_router.assert_called_once_with(self.txn, router)\",\n \"def update_params(self, learning_rate):\\n\\t\\t#######################################################################\\n\\t\\t# ** START OF YOUR CODE **\\n\\t\\t#######################################################################\\n\\n\\t\\tfor layer in self._layers:\\n\\t\\t\\tlayer.update_params(learning_rate)\\n\\n\\t\\t#######################################################################\\n\\t\\t# ** END OF YOUR CODE **\\n\\t\\t#######################################################################\",\n \"def _policy_eval(self, policy: np.ndarray) -> np.ndarray:\\n V = np.zeros(self.state_dim)\\n diff = 1.0\\n dr = 0.9\\n while (diff >= self.theta):\\n diff = 0.0\\n for s in self.mdp._state_dict:\\n old = V[self.mdp._state_dict[s]]\\n temp = 0.0\\n for opt in range(self.action_dim):\\n if policy[self.mdp._state_dict[s],opt] == 1.0: \\n for next_s in self.mdp._state_dict:\\n p = self.mdp.P[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\\n r = self.mdp.R[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\\n Vs = V[self.mdp._state_dict[next_s]]\\n temp = temp + p * (r + dr * Vs)\\n V[self.mdp._state_dict[s]] = temp\\n diff = max(diff,abs(old - V[self.mdp._state_dict[s]]))\\n return V\",\n \"def gen_policy_rollout(self, observations, dynam_model):\\n # initialize statespace array in pytorch tensor -> need that good good gradient!\\n # raise NotImplementedError(\\\"Need to implement the rollouts with tensors, or maybe not for gradients\\\")\\n\\n # for sampling the state progression\\n norm_dist = torch.distributions.Normal(0, 1)\\n\\n # for storing the costs and gradients\\n costs = torch.Tensor()\\n baselines = torch.Tensor()\\n log_probabilities = torch.Tensor()\\n states = torch.Tensor()\\n ''' \\n # for all of these values, think a row as an updating time series for each particle\\n costs = torch.zeros((self.P, self.T))\\n baselines = torch.zeros((self.P, self.T))\\n probabilities = torch.zeros((self.P, self.T))\\n\\n # This is what we would do for the states, but it's more efficient to concatenate them\\n states = torch.Tensor((self.P,self.T, self.n_in))\\n '''\\n\\n # Change in state vs raw state key\\n pred_key = dynam_model.change_state_list\\n # print(pred_key)\\n\\n # TODO: Generate initial states for each particle based on the distribution of data it was trained on\\n bound_up = torch.Tensor(np.max(observations, 0))\\n bound_low = torch.Tensor(np.min(observations, 0))\\n\\n obs_dist = torch.distributions.uniform.Uniform(bound_low, bound_up)\\n\\n # iterate through each particle for the states and probabilites for gradient\\n for p in range(self.P):\\n\\n # Choose the dynamics model from the ensemble\\n num_ens = dynam_model.E\\n if num_ens == 0:\\n model = dynam_model\\n else:\\n model_idx = random.randint(0, num_ens - 1)\\n model = dynam_model.networks[model_idx]\\n\\n num_obs = np.shape(observations)[1]\\n # x0 = torch.Tensor(observations[random.randint(0,num_obs),:])\\n x0 = obs_dist.sample()\\n\\n # TODO: Normalize the states before passing into the NN\\n\\n state_mat = x0.view((1, 1, -1))\\n # print(state_mat)\\n # state_mat = x0.unsqueeze(0).unsqueeze(0) # takes a (n_in) vector to a (1,1,n_in) Tensor\\n # torch.cat((), axis = 1) to cat the times\\n # torch.cat((), axis = 0) to cat the particle\\n # is there a way to do this without unsqueeze? Seems like the most efficient way\\n log_prob_vect = torch.Tensor([1]) # states the column with 1 for concat'ing\\n\\n for t in range(self.T):\\n # generate action from the policy\\n action = self.forward(state_mat[0, t, :])\\n # print(action)\\n # quit()\\n\\n # forward pass current state to generate distribution values from dynamics model\\n means, var = model.distribution(state_mat[0, t, :], action)\\n # print(var)\\n # sample the next state from the means and variances of the state transition probabilities\\n vals = var * norm_dist.sample((1, self.n_in)) + means\\n # need to account for the fact that some states are change in and some are raw here\\n\\n # batch mode prob calc\\n log_probs = -.5 * torch.abs(vals - means) / var\\n # log_probs = -.5*torch.abs(vals - means)/(var**2)\\n\\n # for s in range(self.n_in):\\n # # sample predicted new state for each element\\n # val = var[s]*np.random.normal()+means[0] # sample from the scaled gaussian with y = sigma*x + mu\\n\\n # # calculate probability of this state for each sub state\\n # p = -.5*(val-means[0])/var[0]\\n # states = torch.cat((states, state), 0)\\n # probabilities = torch.cat((probabilities, p), 0)\\n\\n # reduce the probabilities vector to get a single probability of the state transition\\n log_prob = torch.sum((log_probs), 1)\\n log_prob_vect = torch.cat((log_prob_vect, log_prob))\\n\\n state = torch.Tensor(vals).view((1, 1, -1))\\n\\n state_mat = torch.cat((state_mat, state),\\n 1) # appends the currnt state to the current particle, without overwriting the otherone\\n\\n # print(state_mat)\\n # calculates costs\\n # idea ~ calculate the cost of each each element and then do an cumulative sum for the costs\\n # use torch.cumsum\\n c_list = []\\n for state in state_mat.squeeze():\\n c_row = self.cost_fnc(state)\\n c_list.append(c_row)\\n c_list = torch.stack(c_list)\\n\\n # note we calc the cum sum on the flipped tensor, then flip costs back\\n cost_cum = torch.cumsum(torch.flip(c_list, [0]), 0)\\n # Assembles the arrays for the current particle\\n\\n # costs were given above\\n costs = torch.cat((costs, torch.flip(cost_cum, [0]).view(1, -1)), 0)\\n\\n # update the states array for each particle\\n states = torch.cat((states, state_mat), 0)\\n\\n # concatenates the vector of prob at each time to the 2d array\\n log_probabilities = torch.cat(\\n (log_probabilities, log_prob_vect.view((1, -1))), 0)\\n\\n # calculates baselines as the leave one out mean for each particle at each time\\n costs_summed = torch.sum(costs, 0)\\n costs_summed_exp = costs_summed.expand_as(costs)\\n costs_leave_one_out = costs_summed_exp - costs\\n baselines = costs_leave_one_out / (self.P - 1)\\n\\n # freezes gradients on costs and baselines\\n # these two lines of code actually do nothing, but are for clarification\\n # costs.requires_grad_(requires_grad=False)\\n # baselines.requires_grad_(requires_grad=False)\\n # . detach() is another way to ensure this\\n \\\"\\\"\\\"\\n RuntimeError: you can only change requires_grad flags of leaf variables. If you want to use a \\n computed variable in a subgraph that doesn't require differentiation use var_no_grad = var.detach().\\n \\\"\\\"\\\"\\n costs_d = costs.detach()\\n baselines_d = baselines.detach()\\n # costs_d = costs.requires_grad_(False)\\n # baselines_d = baselines.requires_grad_(False)\\n # print(baselines)\\n # print(probabilities)\\n # print(costs)\\n # print(log_probabilities)\\n return states, log_probabilities, costs_d, baselines_d\",\n \"def test_function_values(self):\\n\\n self.param_dict.add_paramdictval(\\n FunctionParamDictVal(\\n \\\"fn_foo\\\",\\n self.pick_byte2,\\n lambda x : str(x),\\n direct_access=True,\\n startup_param=True,\\n value=1,\\n visibility=ParameterDictVisibility.READ_WRITE)\\n )\\n self.param_dict.add_paramdictval(\\n FunctionParamDictVal(\\n \\\"fn_bar\\\",\\n lambda x : bool(x&2), # bit map example\\n lambda x : str(x),\\n direct_access=True,\\n startup_param=True,\\n value=False,\\n visibility=ParameterDictVisibility.READ_WRITE)\\n )\\n \\n # check defaults just to be safe\\n val = self.param_dict.get(\\\"fn_foo\\\")\\n self.assertEqual(val, 1)\\n val = self.param_dict.get(\\\"fn_bar\\\")\\n self.assertEqual(val, False)\\n \\n result = self.param_dict.update(1005) # just change first in list\\n val = self.param_dict.get(\\\"fn_foo\\\")\\n self.assertEqual(val, 3)\\n val = self.param_dict.get(\\\"fn_bar\\\")\\n self.assertEqual(val, False)\\n \\n # fn_bar does not get updated here\\n result = self.param_dict.update_many(1205)\\n self.assertEqual(result['fn_foo'], True)\\n self.assertEqual(len(result), 1)\\n val = self.param_dict.get(\\\"fn_foo\\\")\\n self.assertEqual(val, 4)\\n val = self.param_dict.get(\\\"fn_bar\\\")\\n self.assertEqual(val, False)\\n \\n # both are updated now\\n result = self.param_dict.update_many(6)\\n self.assertEqual(result['fn_foo'], True)\\n self.assertEqual(result['fn_bar'], True)\\n self.assertEqual(len(result), 2)\\n \\n val = self.param_dict.get(\\\"fn_foo\\\")\\n self.assertEqual(val, 0)\\n val = self.param_dict.get(\\\"fn_bar\\\")\\n self.assertEqual(val, True)\",\n \"def eval_policy(env, policy, obs_key, goal_key, num_rollouts, limit, pos=None, goal=None, filename=None):\\n from tqdm import trange\\n from ml_logger import logger\\n\\n logger.summary_cache.clear()\\n if filename:\\n frames = []\\n\\n for _ in trange(num_rollouts, desc=\\\"evaluate\\\"):\\n\\n obs, done = env.reset(), False\\n if pos is not None or goal is not None:\\n obs = env.unwrapped.reset(pos, goal)\\n # d2goal = l2(obs['x'], obs['goal'])\\n i, R = 0, 0\\n while not done:\\n if filename:\\n _ = np.concatenate([obs[obs_key], obs[goal_key]], axis=-1)[0]\\n frames.append(_) # , obs['goal_img']\\n action = policy(obs[obs_key], obs[goal_key])\\n obs, reward, done, info = env.step(action)\\n R += reward\\n if info['success'] or limit and i >= limit:\\n logger.store_metrics( # d2goal=d2goal,\\n success=info['success'])\\n break\\n i += 1\\n\\n if filename:\\n logger.log_video(np.array(frames), key=filename)\\n\\n logger.print(f'has finished evaluation.')\",\n \"def _update_parameters(self, curr_state, reward, next_state):\\n phi = self._features.vector(curr_state)\\n phi_dash = self._features.vector(next_state)\\n\\n self._A += np.outer(phi, (phi - self._gamma * phi_dash))\\n self._b += reward * phi\",\n \"def hard_update(self,target, source):\\n\\t\\tfor target_param, param in zip(target.parameters(), source.parameters()):\\n\\t\\t\\t\\ttarget_param.data.copy_(param.data)\",\n \"def updateParameters(self, parameters):\\r\\n\\t\\tin_wikiplace_IRI = parameters[0]\\r\\n\\t\\tin_relation_degree = parameters[1]\\r\\n\\t\\tin_first_property_dir = parameters[2]\\r\\n\\t\\tin_first_property = parameters[3]\\r\\n\\t\\tin_second_property_dir = parameters[4]\\r\\n\\t\\tin_second_property = parameters[5]\\r\\n\\t\\tin_third_property_dir = parameters[6]\\r\\n\\t\\tin_third_property = parameters[7]\\r\\n\\t\\tin_fourth_property_dir = parameters[8]\\r\\n\\t\\tin_fourth_property = parameters[9]\\r\\n\\t\\tout_location = parameters[10]\\r\\n\\t\\tout_table_name = parameters[11]\\r\\n\\t\\tout_points_name = parameters[12]\\r\\n\\r\\n\\t\\t\\r\\n\\r\\n\\t\\tif in_relation_degree.altered:\\r\\n\\t\\t\\trelationDegree = int(in_relation_degree.valueAsText)\\r\\n\\t\\t\\tif relationDegree == 1:\\r\\n\\t\\t\\t\\tin_first_property.enabled = True\\r\\n\\t\\t\\t\\tin_first_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_second_property.enabled = False\\r\\n\\t\\t\\t\\tin_second_property_dir.enabled = False\\r\\n\\t\\t\\t\\tin_third_property.enabled = False\\r\\n\\t\\t\\t\\tin_third_property_dir.enabled = False\\r\\n\\t\\t\\t\\tin_fourth_property.enabled = False\\r\\n\\t\\t\\t\\tin_fourth_property_dir.enabled = False\\r\\n\\t\\t\\telif relationDegree == 2:\\r\\n\\t\\t\\t\\tin_first_property.enabled = True\\r\\n\\t\\t\\t\\tin_first_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_second_property.enabled = True\\r\\n\\t\\t\\t\\tin_second_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_third_property.enabled = False\\r\\n\\t\\t\\t\\tin_third_property_dir.enabled = False\\r\\n\\t\\t\\t\\tin_fourth_property.enabled = False\\r\\n\\t\\t\\t\\tin_fourth_property_dir.enabled = False\\r\\n\\t\\t\\telif relationDegree == 3:\\r\\n\\t\\t\\t\\tin_first_property.enabled = True\\r\\n\\t\\t\\t\\tin_first_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_second_property.enabled = True\\r\\n\\t\\t\\t\\tin_second_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_third_property.enabled = True\\r\\n\\t\\t\\t\\tin_third_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_fourth_property.enabled = False\\r\\n\\t\\t\\t\\tin_fourth_property_dir.enabled = False\\r\\n\\t\\t\\telif relationDegree == 4:\\r\\n\\t\\t\\t\\tin_first_property.enabled = True\\r\\n\\t\\t\\t\\tin_first_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_second_property.enabled = True\\r\\n\\t\\t\\t\\tin_second_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_third_property.enabled = True\\r\\n\\t\\t\\t\\tin_third_property_dir.enabled = True\\r\\n\\t\\t\\t\\tin_fourth_property.enabled = True\\r\\n\\t\\t\\t\\tin_fourth_property_dir.enabled = True\\r\\n\\t\\t\\r\\n\\t\\t\\tif in_wikiplace_IRI.value:\\r\\n\\t\\t\\t\\tinputFeatureClassName = in_wikiplace_IRI.valueAsText\\r\\n\\t\\t\\t\\tlastIndexOFGDB = inputFeatureClassName.rfind(\\\"\\\\\\\\\\\")\\r\\n\\t\\t\\t\\tfeatureClassName = inputFeatureClassName[(lastIndexOFGDB+1):]\\r\\n\\t\\t\\t\\tcurrentWorkspace = inputFeatureClassName[:lastIndexOFGDB]\\r\\n\\r\\n\\t\\t\\t\\tarcpy.env.workspace = currentWorkspace\\r\\n\\t\\t\\t\\tout_location.value = currentWorkspace\\r\\n\\r\\n\\t\\t\\t\\tout_table_name.value = featureClassName + \\\"PathQueryTripleStore\\\"\\r\\n\\r\\n\\t\\t\\t\\tout_points_name.value = featureClassName + \\\"PathQueryLocation\\\"\\r\\n\\r\\n\\r\\n\\t\\t\\t\\toutLocation = out_location.valueAsText\\r\\n\\t\\t\\t\\toutTableName = out_table_name.valueAsText\\r\\n\\t\\t\\t\\toutputTableName = os.path.join(outLocation,outTableName)\\r\\n\\t\\t\\t\\tif arcpy.Exists(outputTableName):\\r\\n\\t\\t\\t\\t\\tarcpy.AddError(\\\"The output table already exists in current workspace!\\\")\\r\\n\\t\\t\\t\\t\\traise arcpy.ExecuteError\\r\\n\\r\\n\\t\\t\\t\\toutFeatureClassName = out_points_name.valueAsText\\r\\n\\t\\t\\t\\toutputFeatureClassName = os.path.join(outLocation,outFeatureClassName)\\r\\n\\t\\t\\t\\tif arcpy.Exists(outputFeatureClassName):\\r\\n\\t\\t\\t\\t\\tarcpy.AddError(\\\"The output Feature Class already exists in current workspace!\\\")\\r\\n\\t\\t\\t\\t\\traise arcpy.ExecuteError\\r\\n\\r\\n\\r\\n\\t\\t\\t\\t# get all the IRI from input point feature class of wikidata places\\r\\n\\t\\t\\t\\tinplaceIRIList = []\\r\\n\\t\\t\\t\\tcursor = arcpy.SearchCursor(inputFeatureClassName)\\r\\n\\t\\t\\t\\tfor row in cursor:\\r\\n\\t\\t\\t\\t\\tinplaceIRIList.append(row.getValue(\\\"URL\\\"))\\r\\n\\r\\n\\r\\n\\r\\n\\t\\t\\t\\t# get the first property URL list and label list\\r\\n\\t\\t\\t\\tif in_first_property_dir.value:\\r\\n\\t\\t\\t\\t\\tfristDirection = in_first_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t# get the first property URL list\\r\\n\\t\\t\\t\\t\\tfirstPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection], [\\\"\\\", \\\"\\\", \\\"\\\"])\\r\\n\\t\\t\\t\\t\\tfirstPropertyURLList = []\\r\\n\\t\\t\\t\\t\\tfor jsonItem in firstPropertyURLListJsonBindingObject:\\r\\n\\t\\t\\t\\t\\t\\tfirstPropertyURLList.append(jsonItem[\\\"p1\\\"][\\\"value\\\"])\\r\\n\\r\\n\\t\\t\\t\\t\\tfirstPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(firstPropertyURLList)\\r\\n\\t\\t\\t\\t\\t# firstPropertyLabelJSON = firstPropertyLabelJSONObj[\\\"results\\\"][\\\"bindings\\\"]\\r\\n\\r\\n\\t\\t\\t\\t\\t# get the first property label list\\r\\n\\t\\t\\t\\t\\tfirstPropertyURLList = []\\r\\n\\t\\t\\t\\t\\tfirstPropertyLabelList = []\\r\\n\\t\\t\\t\\t\\tfor jsonItem in firstPropertyLabelJSON:\\r\\n\\t\\t\\t\\t\\t\\tpropertyURL = jsonItem[\\\"p\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\tfirstPropertyURLList.append(propertyURL)\\r\\n\\t\\t\\t\\t\\t\\tpropertyName = jsonItem[\\\"propertyLabel\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\tfirstPropertyLabelList.append(propertyName)\\r\\n\\r\\n\\t\\t\\t\\t\\tRelFinder.firstPropertyLabelURLDict = dict(zip(firstPropertyLabelList, firstPropertyURLList))\\r\\n\\r\\n\\t\\t\\t\\t\\tin_first_property.filter.list = firstPropertyLabelList\\r\\n\\r\\n\\t\\t\\t\\t\\t# get the second property URL list and label list\\r\\n\\t\\t\\t\\t\\tif in_second_property_dir.value:\\r\\n\\t\\t\\t\\t\\t\\tfristDirection = in_first_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\tfirstProperty = in_first_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tif firstProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\tfirstProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tsecondDirection = in_second_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t\\t# get the second property URL list\\r\\n\\t\\t\\t\\t\\t\\tsecondPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection], [firstProperty, \\\"\\\", \\\"\\\"])\\r\\n\\t\\t\\t\\t\\t\\tsecondPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\tfor jsonItem in secondPropertyURLListJsonBindingObject:\\r\\n\\t\\t\\t\\t\\t\\t\\tsecondPropertyURLList.append(jsonItem[\\\"p2\\\"][\\\"value\\\"])\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tsecondPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(secondPropertyURLList)\\r\\n\\t\\t\\t\\t\\t\\t# secondPropertyLabelJSON = secondPropertyLabelJSONObj[\\\"results\\\"][\\\"bindings\\\"]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# get the second property label list\\r\\n\\t\\t\\t\\t\\t\\tsecondPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\tsecondPropertyLabelList = []\\r\\n\\t\\t\\t\\t\\t\\tfor jsonItem in secondPropertyLabelJSON:\\r\\n\\t\\t\\t\\t\\t\\t\\tpropertyURL = jsonItem[\\\"p\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\tsecondPropertyURLList.append(propertyURL)\\r\\n\\t\\t\\t\\t\\t\\t\\tpropertyName = jsonItem[\\\"propertyLabel\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\tsecondPropertyLabelList.append(propertyName)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tRelFinder.secondPropertyLabelURLDict = dict(zip(secondPropertyLabelList, secondPropertyURLList))\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tin_second_property.filter.list = secondPropertyLabelList\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t# get the third property URL list and label list\\r\\n\\t\\t\\t\\t\\t\\tif in_third_property_dir.value:\\r\\n\\t\\t\\t\\t\\t\\t\\tfristDirection = in_first_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\tfirstProperty = in_first_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tsecondDirection = in_second_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\tsecondProperty = in_second_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tif firstProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfirstProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\\r\\n\\t\\t\\t\\t\\t\\t\\tif secondProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tsecondProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tsecondProperty = RelFinder.secondPropertyLabelURLDict[secondProperty]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdDirection = in_third_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t\\t\\t# get the third property URL list\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection, thirdDirection], [firstProperty, secondProperty, \\\"\\\"])\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\t\\tfor jsonItem in thirdPropertyURLListJsonBindingObject:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tthirdPropertyURLList.append(jsonItem[\\\"p3\\\"][\\\"value\\\"])\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(thirdPropertyURLList)\\r\\n\\t\\t\\t\\t\\t\\t\\t# thirdPropertyLabelJSON = thirdPropertyLabelJSONObj[\\\"results\\\"][\\\"bindings\\\"]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t# get the third property label list\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\t\\tthirdPropertyLabelList = []\\r\\n\\t\\t\\t\\t\\t\\t\\tfor jsonItem in thirdPropertyLabelJSON:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tpropertyURL = jsonItem[\\\"p\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tthirdPropertyURLList.append(propertyURL)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tpropertyName = jsonItem[\\\"propertyLabel\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tthirdPropertyLabelList.append(propertyName)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tRelFinder.thirdPropertyLabelURLDict = dict(zip(thirdPropertyLabelList, thirdPropertyURLList))\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\tin_third_property.filter.list = thirdPropertyLabelList\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t# get the fourth property URL list and label list\\r\\n\\t\\t\\t\\t\\t\\t\\tif in_fourth_property_dir.value:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfristDirection = in_first_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfirstProperty = in_first_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tsecondDirection = in_second_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tsecondProperty = in_second_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tthirdDirection = in_third_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tthirdProperty = in_third_property.valueAsText\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tif firstProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfirstProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfirstProperty = RelFinder.firstPropertyLabelURLDict[firstProperty]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tif secondProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tsecondProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tsecondProperty = RelFinder.secondPropertyLabelURLDict[secondProperty]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tif thirdProperty == None:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tthirdProperty = \\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tthirdProperty = RelFinder.thirdPropertyLabelURLDict[thirdProperty]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthDirection = in_fourth_property_dir.valueAsText\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t# get the fourth property URL list\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyURLListJsonBindingObject = SPARQLQuery.relFinderCommonPropertyQuery(inplaceIRIList, relationDegree, [fristDirection, secondDirection, thirdDirection, fourthDirection], [firstProperty, secondProperty, thirdProperty])\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfor jsonItem in fourthPropertyURLListJsonBindingObject:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyURLList.append(jsonItem[\\\"p4\\\"][\\\"value\\\"])\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyLabelJSON = SPARQLQuery.locationCommonPropertyLabelQuery(fourthPropertyURLList)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t# fourthPropertyLabelJSON = fourthPropertyLabelJSONObj[\\\"results\\\"][\\\"bindings\\\"]\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t# get the fourth property label list\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyURLList = []\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyLabelList = []\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tfor jsonItem in fourthPropertyLabelJSON:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tpropertyURL = jsonItem[\\\"p\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyURLList.append(propertyURL)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tpropertyName = jsonItem[\\\"propertyLabel\\\"][\\\"value\\\"]\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfourthPropertyLabelList.append(propertyName)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tRelFinder.fourthPropertyLabelURLDict = dict(zip(fourthPropertyLabelList, fourthPropertyURLList))\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tin_fourth_property.filter.list = fourthPropertyLabelList\\r\\n\\r\\n\\r\\n\\t\\treturn\",\n \"def update_parameters(parameters, grads, learning_rate=0.01):\\n # Retrieve each parameter from the dictionary \\\"parameters\\\"\\n ### START CODE HERE ### (≈ 4 lines of code)\\n W1 = parameters[\\\"W1\\\"]\\n b1 = parameters[\\\"b1\\\"]\\n W2 = parameters[\\\"W2\\\"]\\n b2 = parameters[\\\"b2\\\"]\\n W3 = parameters[\\\"W3\\\"]\\n b3 = parameters[\\\"b3\\\"]\\n ### END CODE HERE ###\\n\\n # Retrieve each gradient from the dictionary \\\"grads\\\"\\n ### START CODE HERE ### (≈ 4 lines of code)\\n dW1 = grads[\\\"dW1\\\"]\\n db1 = grads[\\\"db1\\\"]\\n dW2 = grads[\\\"dW2\\\"]\\n db2 = grads[\\\"db2\\\"]\\n dW3 = grads[\\\"dW3\\\"]\\n db3 = grads[\\\"db3\\\"]\\n ## END CODE HERE ###\\n\\n # Update rule for each parameter\\n ### START CODE HERE ### (≈ 4 lines of code)\\n W1 = W1 - (learning_rate * dW1)\\n b1 = b1 - (learning_rate * db1)\\n W2 = W2 - (learning_rate * dW2)\\n b2 = b2 - (learning_rate * db2)\\n W3 = W3 - (learning_rate * dW3)\\n b3 = b3 - (learning_rate * db3)\\n ### END CODE HERE ###\\n\\n parameters = {\\\"W1\\\": W1,\\n \\\"b1\\\": b1,\\n \\\"W2\\\": W2,\\n \\\"b2\\\": b2,\\n \\\"W3\\\": W3,\\n \\\"b3\\\": b3}\\n\\n return parameters\",\n \"def update_recognize_params(self,inps,trgs,preds):\\n batch_size = np.shape(trgs)[0]\\n self.delta_weight_v_to_h = self.learning_rate/batch_size * np.transpose(trgs) @ (inps - preds)\\n self.delta_bias_h = self.learning_rate * np.mean(inps - preds)\\n\\n self.weight_v_to_h += self.delta_weight_v_to_h\\n self.bias_h += self.delta_bias_h\\n \\n return\",\n \"def update():\\n\\n # Get last new x value as last x value + 1\\n x_n0 = data_source.data['x'][-1]\\n x_n1 = x_n0 + 0.1\\n\\n # Assign a new y value\\n y_n1 = param_source.data['amp_sine'][0] * np.sin(x_n1) +\\\\\\n param_source.data['amp_rand'][0] * np.random.rand(1)\\n\\n # Get old last average and use to calculate new average\\n avg_n1 = _get_new_avg(data_source,\\n y_n1,\\n param_source.data['rollover'][0])\\n\\n # Make a dict of data to add on to the end of the source\\n additional_data = dict(x=[x_n1], y=[y_n1], avg=[avg_n1])\\n\\n # Stream the new data with a rollover value of 10\\n data_source.stream(additional_data,\\n rollover=param_source.data['rollover'][0])\\n\\n # logger.debug(param_source.data['update_delay'][0])\",\n \"def update_parameters(self, learning_rate):\\n for i in range(self.L - 1):\\n self.W[i] -= learning_rate * self.dW[i]\\n self.b[i] -= learning_rate * self.db[i]\",\n \"def _update_params(self):\\n _load = not self.san_interface.runmode\\n params={}\\n if ('iosched' in self._updatedattr or _load) and self.iosched<>IoSchedType.default:\\n params['iosched']=str(self.iosched)\\n if ('readahead' in self._updatedattr or _load) and self.readahead :\\n params['readahead']=self.readahead\\n if params:\\n for pt in self.paths():\\n pt.provider.set_dev_params(pt,params)\",\n \"def _update_parameters(self, delta):\\n if delta is not None:\\n self.SGD.update_with_L1_regularization(self.variables, delta, self.L1)\",\n \"def rollout_policy_fn(board):\\n # rollout randomly\\n # action_probs = np.random.rand(len(board.availables))\\n action_probs = np.random.rand(len(board.availables))\\n return zip(board.availables, action_probs)\",\n \"def updateInstrumentParameterValue(tablews, paramdict):\\n paramnames = paramdict.keys()\\n for parname in paramnames: \\n parvalue = paramdict[parname]\\n\\t# print \\\"%s = %f\\\" % (parname, parvalue)\\n\\tif parname.count(\\\"Chi2\\\") == 0:\\n\\t # Only update parameters nothing to do with chi2\\n UpdatePeakParameterTableValue(InputWorkspace=tablews,\\n\\t\\tColumn='Value',\\n ParameterNames=[parname],\\n\\t\\tNewFloatValue=parvalue)\\n\\n return\",\n \"def _update_params(self):\\n raise NotImplementedException()\",\n \"def update_filter_params(self, fh):\\n (self.data_timestamp, self.framerate,\\n self.l, self.d, self.gamma,\\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])\",\n \"def update(self):\\n ## Initialize\\n self.domain.update()\\n self.var = self.domain.var.copy()\\n self.out = []\\n\\n ## Construct var and out, respecting DAG properties\\n for fun in self.functions:\\n self.var = list(set(self.var).union(set(fun.var).difference(set(self.out))))\\n\\n self.out = list(set(self.out).union(set(fun.out)))\\n\\n try:\\n self.var_rand = list(self.density.marginals.keys())\\n except AttributeError:\\n self.var_rand = []\\n self.var_det = list(set(self.var).difference(self.var_rand))\\n\\n ## TODO parameters\\n\\n ## Convenience constants\\n self.n_var = len(self.var)\\n self.n_var_rand = len(self.var_rand)\\n self.n_var_det = len(self.var_det)\\n self.n_out = len(self.out)\",\n \"def update_throughput(self, throughput):\\n\\n # Update the throughput values.\\n throughput_new = []\\n for throughput_n in throughput: # Loop over orders.\\n\\n if callable(throughput_n):\\n\\n # Throughput was given as a callable function.\\n throughput_new.append(throughput_n(self.wave_grid))\\n\\n elif throughput_n.shape == self.wave_grid.shape:\\n\\n # Throughput was given as an array.\\n throughput_new.append(throughput_n)\\n\\n else:\\n msg = 'Throughputs must be given as callable or arrays matching the extraction grid.'\\n log.critical(msg)\\n raise ValueError(msg)\\n\\n # Set the attribute to the new values.\\n self.throughput = throughput_new\\n\\n return\",\n \"def update_by_config(self, policy_enabling_map):\\n self.enabled_policies = []\\n\\n for policy_name, policy_config in policy_enabling_map.items():\\n if not self._is_policy_exists(policy_name):\\n self._warn_unexistent_policy(policy_name)\\n continue\\n\\n if policy_config['enabled']:\\n enabled_policy = self._get_policy(policy_name)\\n self.enabled_policies.append(enabled_policy)\",\n \"def applyFuncOnValues(self, func):\\r\\n self._value = func(self._value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73782724","0.7067951","0.66169596","0.63102925","0.60266966","0.5975819","0.595031","0.5920189","0.5888775","0.58857965","0.5820289","0.5794157","0.57780397","0.5770066","0.5761156","0.57414526","0.5721823","0.5698898","0.56790006","0.56590545","0.56160057","0.56153065","0.5565208","0.5556312","0.5517219","0.54992104","0.54901123","0.54857236","0.54672176","0.5462786","0.545099","0.5439098","0.54196477","0.5411225","0.53655124","0.5348262","0.53421843","0.5337477","0.5331581","0.53118527","0.5305816","0.52816814","0.527974","0.52783436","0.52560455","0.52547145","0.5254095","0.5253944","0.5249747","0.5246404","0.52357113","0.522655","0.52248305","0.5221231","0.52193296","0.5208372","0.5202038","0.52006006","0.5200223","0.5198469","0.51870227","0.5185032","0.5178441","0.5176815","0.517646","0.5173889","0.5173757","0.517247","0.5163767","0.5162832","0.51529115","0.51507014","0.51481485","0.5147096","0.5147096","0.5143221","0.5142306","0.51294446","0.5128907","0.5096138","0.5095267","0.5094718","0.5094446","0.5093303","0.5087175","0.5081736","0.50812024","0.50739354","0.5071112","0.5069899","0.5069716","0.5064084","0.50622696","0.5056564","0.50534225","0.50533885","0.5046644","0.50452745","0.50401545","0.5036533"],"string":"[\n \"0.73782724\",\n \"0.7067951\",\n \"0.66169596\",\n \"0.63102925\",\n \"0.60266966\",\n \"0.5975819\",\n \"0.595031\",\n \"0.5920189\",\n \"0.5888775\",\n \"0.58857965\",\n \"0.5820289\",\n \"0.5794157\",\n \"0.57780397\",\n \"0.5770066\",\n \"0.5761156\",\n \"0.57414526\",\n \"0.5721823\",\n \"0.5698898\",\n \"0.56790006\",\n \"0.56590545\",\n \"0.56160057\",\n \"0.56153065\",\n \"0.5565208\",\n \"0.5556312\",\n \"0.5517219\",\n \"0.54992104\",\n \"0.54901123\",\n \"0.54857236\",\n \"0.54672176\",\n \"0.5462786\",\n \"0.545099\",\n \"0.5439098\",\n \"0.54196477\",\n \"0.5411225\",\n \"0.53655124\",\n \"0.5348262\",\n \"0.53421843\",\n \"0.5337477\",\n \"0.5331581\",\n \"0.53118527\",\n \"0.5305816\",\n \"0.52816814\",\n \"0.527974\",\n \"0.52783436\",\n \"0.52560455\",\n \"0.52547145\",\n \"0.5254095\",\n \"0.5253944\",\n \"0.5249747\",\n \"0.5246404\",\n \"0.52357113\",\n \"0.522655\",\n \"0.52248305\",\n \"0.5221231\",\n \"0.52193296\",\n \"0.5208372\",\n \"0.5202038\",\n \"0.52006006\",\n \"0.5200223\",\n \"0.5198469\",\n \"0.51870227\",\n \"0.5185032\",\n \"0.5178441\",\n \"0.5176815\",\n \"0.517646\",\n \"0.5173889\",\n \"0.5173757\",\n \"0.517247\",\n \"0.5163767\",\n \"0.5162832\",\n \"0.51529115\",\n \"0.51507014\",\n \"0.51481485\",\n \"0.5147096\",\n \"0.5147096\",\n \"0.5143221\",\n \"0.5142306\",\n \"0.51294446\",\n \"0.5128907\",\n \"0.5096138\",\n \"0.5095267\",\n \"0.5094718\",\n \"0.5094446\",\n \"0.5093303\",\n \"0.5087175\",\n \"0.5081736\",\n \"0.50812024\",\n \"0.50739354\",\n \"0.5071112\",\n \"0.5069899\",\n \"0.5069716\",\n \"0.5064084\",\n \"0.50622696\",\n \"0.5056564\",\n \"0.50534225\",\n \"0.50533885\",\n \"0.5046644\",\n \"0.50452745\",\n \"0.50401545\",\n \"0.5036533\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5152639"},"document_rank":{"kind":"string","value":"71"}}},{"rowIdx":7177,"cells":{"query":{"kind":"string","value":"Creates random environments of the given type."},"document":{"kind":"string","value":"def set_random_envs(self):\n self.randomizer.randomize(num_samples=self.num_teachers)\n params = self.randomizer.get_params(fmt=\"dict\", dtype=\"numpy\")\n\n for e in range(self.num_teachers):\n self.teacher_envs.append(deepcopy(self.env_real))\n print({key: value[e] for key, value in params.items()})\n self.teacher_envs[e].domain_param = {key: value[e] for key, value in params.items()}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_environment(seed, task_horizon):\n # Load the gym environment.\n environment = CartPoleEnv()\n environment = gym_wrappers.TimeLimit(environment, task_horizon)\n environment.seed(seed)\n environment = wrappers.GymWrapper(environment)\n environment = wrappers.SinglePrecisionWrapper(environment)\n return environment","def _make_environment(\n self, problem_type, curriculum_sample, wrapper_type, backend_type=None):\n if backend_type is None:\n backend_type = FLAGS.backend\n return dm_construction.get_environment(\n problem_type,\n unity_environment=self._unity_envs[backend_type],\n wrapper_type=wrapper_type,\n curriculum_sample=curriculum_sample)","def get_env_class(environment_type):\n if environment_type == \"vanilla\":\n return city.CityGridEnv\n elif environment_type == \"distraction\":\n return city.DistractionGridEnv\n elif environment_type == \"map\":\n return city.MapGridEnv\n elif environment_type == \"cooking\":\n return cooking.CookingGridEnv\n elif environment_type == \"miniworld_sign\":\n # Dependencies on OpenGL, so only load if absolutely necessary\n from envs.miniworld import sign\n return sign.MiniWorldSign\n else:\n raise ValueError(\n \"Unsupported environment type: {}\".format(environment_type))","def make_env(env_id, rand, seed=0):\n def _init():\n env = gym.make(env_id)\n # env.unwrapped\n env.seed(seed + rand)\n return env\n set_global_seeds(seed)\n return _init","def generate(self, site_type='random', arg='random'):\n size = entities.world['size']\n if site_type == 'random':\n if randint(1,3) == 1:\n site_type = 'adventure'\n else:\n site_type = 'resource'\n elif site_type in ref.material_type_dct.keys():\n self.resource = site_type\n site_type = 'resource'\n terrain_list = None\n if arg == 'random':\n terrain_list = [x for x in ref.terrain_dct.keys() if type(x) == int]\n elif arg in ref.terrain_type_list:\n terrain_list = [\n x for x in ref.terrain_dct.keys() if ref.terrain_dct[x]['terrain type'] == arg\n ]\n x = randint(0, size-1)\n y = randint(0, size-1)\n terrain_type = entities.world['grid'][y][x]\n site_locations = [s.location for s in entities.sites['object list']]\n while terrain_type not in terrain_list or [x,y] in site_locations:\n x = randint(0, size-1)\n y = randint(0, size-1)\n terrain_type = entities.world['grid'][y][x]\n\n self.location = [x,y]\n self.structure = Structure().generate(\n ref.terrain_dct[terrain_type]['terrain type'], site_type\n )\n if self.resource == None:\n if 'resource type' in ref.structure_type_dct[\n self.structure.structure_type\n ].keys():\n resource_type = ref.structure_type_dct[\n self.structure.structure_type]['resource type'\n ]\n resource_possibilities = []\n for possible_material in [\n x for x in ref.material_class_dct[resource_type][\n 'types'] if 'rarity' in ref.material_type_dct[x].keys()\n ]:\n for x in xrange(ref.rarity_dct[\n ref.material_type_dct[possible_material]['rarity']\n ]):\n resource_possibilities.append(possible_material)\n self.resource = choice(resource_possibilities)\n #resources measured in grams\n if self.resource != None:\n self.harvestable = randint(100000, 1500000)\n try:\n entities.town['object'].resources[\n ref.material_type_dct[self.resource]['class']][\n self.resource]['harvestable'] += self.harvestable\n except KeyError:\n pass\n #NOTE: These numbers suitable for metal, may not be for other materials\n #NOTE: Mine production should be ~1kg pure metal per day per miner.\n #NOTE: IRL mine has ~43500kg before producing much less.\n \n self.set_site_id()\n return self","def _generate_raw_environments(self, num, seed):","def make_env(rank, env_conf, seed=0):\n def _init():\n env = RedGymEnv(env_conf)\n env.seed(seed + rank)\n return env\n set_random_seed(seed)\n return _init","def _create_environment(config):\n if isinstance(config.env, str):\n env = gym.make(config.env)\n else:\n env = config.env()\n if config.max_length:\n env = tools.wrappers.LimitDuration(env, config.max_length)\n env = tools.wrappers.RangeNormalize(env)\n env = tools.wrappers.ClipAction(env)\n env = tools.wrappers.ConvertTo32Bit(env)\n return env","def init_environment(env_name):\n env = gym.make(env_name)\n discrete = False\n if type(env.action_space) is gym.spaces.Discrete:\n discrete = True\n else:\n env = NormalizedActions(env)\n return env, discrete","def generate_environment(dataset, tmpdir):\n\n print(\">>> Test environment:\")\n print(\"dataset:\", dataset)\n print(\"tmpdir:\", tmpdir)\n\n generate_test_environment(tmpdir, dataset)\n\n return { 'dataset': dataset, 'tmpdir': tmpdir }","def create_environment(game_name, seed, is_test=False):\n\n # Note, you may want to use a level cache to speed of compilation of\n # environment maps. See the documentation for the Python interface of DeepMind\n # Lab.\n config = {\n \"width\": FLAGS.width,\n \"height\": FLAGS.height,\n \"logLevel\": \"WARN\",\n \"n_input_frames\": FLAGS.history,\n \"show_display\": FLAGS.display,\n \"save_video\": FLAGS.save_video,\n }\n if is_test:\n config[\"allowHoldOutLevels\"] = \"true\"\n # Mixer seed for evalution, see\n # https://github.com/deepmind/lab/blob/master/docs/users/python_api.md\n config[\"mixerSeed\"] = 0x600D5EED\n\n p = py_process.PyProcess(\n environments.PyProcessAtari,\n game_name,\n config,\n FLAGS.num_action_repeats,\n seed,\n )\n return environments.FlowEnvironment(p.proxy)","def make_env(rank, agents, seed=10000):\n\tdef _init():\n\t\tenv = TradingGameEnv.TradingGameEnv(player_count = NUM_PLAYERS, other_agent_list = agents,\n\t\t\tseq_per_day = SEQ_PER_DAY, cards_per_suit = CARDS_PER_SUIT, player_hand_count = HAND_COUNT,\n\t\t\trandom_seq = True, self_play = SELF_PLAY, policy_type = POLICY_TYPE, self_copy_freq = SELF_COPY_FREQ,\n\t\t\tobs_transaction_history_size=TRANSACTION_HISTORY_SIZE)\n\t\tenv.seed(seed + rank)\n\t\treturn env\n\tset_global_seeds(seed)\n\treturn _init","def make_env(\n\t\tdomain_name,\n\t\ttask_name,\n\t\tseed=0,\n\t\tepisode_length=1000,\n\t\tframe_stack=3,\n\t\taction_repeat=4,\n\t\timage_size=100,\n\t\tmode='original'\n\t):\n\tassert mode in {'original', 'color_easy', 'color_hard', 'video_easy', 'video_hard'}, \\\n\t\tf'specified mode \"{mode}\" is not supported'\n\n\tenv = dmc2gym.make(\n\t\tdomain_name=domain_name,\n\t\ttask_name=task_name,\n\t\tseed=seed,\n\t\tvisualize_reward=False,\n\t\tfrom_pixels=True,\n\t\theight=image_size,\n\t\twidth=image_size,\n\t\tepisode_length=episode_length,\n\t\tframe_skip=action_repeat\n\t)\n\tenv = VideoWrapper(env, mode, seed)\n\tenv = FrameStack(env, frame_stack)\n\tenv = RandomizationWrapper(env, mode, seed)\n\n\treturn env","def generate_random_value(self, type):\n generators = {\n str: lambda: self.generate_random_string(20, uppercase=True, punctuations=True),\n int: lambda: random.randrange(100000),\n float: lambda: random.random() * 100000.0,\n bool: lambda: bool(random.getrandbits(1)),\n list: lambda: self.generate_random_list_or_string(),\n dict: lambda: self.generate_random_dict_or_string()\n }\n generator = generators[type]\n return generator()","def make_env(stack=True, scale_rew=True, frame_wrapper=WarpFrame, reward_type=None, env_idx=-1):\n print(env_idx)\n if env_idx == -1:\n idx = random.randint(0, 12)\n elif env_idx < 0 or env_idx > 12:\n print(\"index of environment {} out of range [0, 12]\".format(str(env_idx)))\n exit(1)\n else:\n idx = env_idx\n print(\"select level: \", idx)\n game, state = train_level[idx]\n # game, state = ['SonicTheHedgehog-Genesis', 'GreenHillZone.Act1']\n\n print(game, state)\n env = make(game=game, state=state)\n\n return wrap_env(env, stack, scale_rew, frame_wrapper, reward_type)","def create_environment(cls, full_config):\n\n config = full_config['template']['devops_settings']\n environment = cls.create(config['env_name'])\n\n # create groups and drivers\n groups = config['groups']\n environment.add_groups(groups)\n\n # create address pools\n address_pools = config['address_pools']\n environment.add_address_pools(address_pools)\n\n # process group items\n for group_data in groups:\n group = environment.get_group(name=group_data['name'])\n\n # add l2_network_devices\n group.add_l2_network_devices(\n group_data.get('l2_network_devices', {}))\n\n # add network_pools\n group.add_network_pools(\n group_data.get('network_pools', {}))\n\n # Connect nodes to already created networks\n for group_data in groups:\n group = environment.get_group(name=group_data['name'])\n\n # add group volumes\n group.add_volumes(\n group_data.get('group_volumes', []))\n\n # add nodes\n group.add_nodes(\n group_data.get('nodes', []))\n\n return environment","def buildWeatherFromType(type, environment, turns=-1, forever=True):\r\n weatherClass = WeatherFactory.weatherTypeDictionary[type]\r\n return weatherClass(overCallbackFunction=environment.clearWeather, turns=turns, forever=forever)","def generate(self, terrain_type='random', site_type='random'):\n if terrain_type == 'random' and site_type == 'random':\n self.structure_type = choice(ref.structure_type_dct.keys())\n elif site_type == 'random':\n self.structure_type = choice(ref.structure_class_dct[terrain_type])\n elif terrain_type == 'random':\n self.structure_type = choice(ref.site_type_dct[site_type])\n else:\n self.structure_type = choice(\n [x for x in ref.site_type_dct[site_type] if ref.structure_type_dct[\n x]['class'] == terrain_type]\n )\n if self.get_attribute('site type') == 'resource':\n self.workers = 0\n else:\n self.workers = []\n self.monsters = []\n self.worker_capacity = self.get_attribute('worker capacity')\n self.time_until_harvest = self.get_attribute('time per harvest')\n return self","def init():\n env = Environment(5, 5, 20, [10, 20, 10, 5])\n return env","def get_environment_class_by_name(environment_type):\n for cls in util.iter_subclasses(Environment):\n if cls.tool_name == environment_type:\n return cls\n raise EnvironmentUnavailable(\n f\"Unknown environment type '{environment_type}'\")","def _init_seeding(cls, seed_type=int(SeedType.NONE), seeds=None):\n seed_type = int(seed_type)\n \n if seed_type == (SeedType.NONE):\n assert seeds is None, \"Seed type set to NONE, therefore seed cannot be set.\"\n elif seed_type == (SeedType.CONSTANT):\n assert seeds is not None, \"Seed set to constant seed, so seed must be specified.\"\n cls._seed_generator = [int(x) for x in seeds.split(\",\") if x]\n elif seed_type == (SeedType.GENERATED):\n assert seeds is not None, \"Seed set to generated seed, so initial seed must be specified.\"\n cls._seed_generator = Random(int(seeds))\n elif seed_type == (SeedType.SPECIFIED):\n cls._seed_generator = ([[str(x) for x in s.split(\",\") if x] for s in seeds.split(\"-\") if s])\n else:\n raise TypeError(\"Seed type {} not supported\".format(seed_type))\n \n cls._seed_type = seed_type","def easy(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None, **kwargs):\n\n physics = Physics.from_xml_string(*get_model_and_assets())\n task = Cloth(randomize_gains=False, random=random, **kwargs)\n environment_kwargs = environment_kwargs or {}\n return control.Environment(\n physics, task, time_limit=time_limit,n_frame_skip=1,special_task=True, **environment_kwargs)","def generate_environment(timestep=3600,\n year_timer=2017,\n year_co2=2017,\n try_path=None,\n location=(51.529086, 6.944689),\n altitude=55,\n new_try=False):\n\n # Create environment\n timer = time.TimerExtended(timestep=timestep, year=year_timer)\n\n weather = weath.Weather(timer, useTRY=True, pathTRY=try_path,\n location=location, altitude=altitude,\n new_try=new_try)\n\n market = germanmarket.GermanMarket()\n co2em = co2.Emissions(year=year_co2)\n\n environment = env.EnvironmentExtended(timer=timer,\n weather=weather,\n prices=market,\n location=location,\n co2em=co2em)\n\n return environment","def by_type(environments):\n types = {}\n for env in environments:\n et = env.environmentType\n options = types.setdefault(et.id, set())\n options.add(env.id)\n return types","def get_environment():\n return GenericGymEnv(id=\"real-time-gym-v1\", gym_kwargs={\"config\": CONFIG_DICT})","def make_halide_env(env_id, seed):\n logger.log('random_seed: %s'%seed)\n set_global_seeds(seed)\n env = gym.make(env_id)\n env = Monitor(env, logger.get_dir(), info_keywords=('best_exec', 'best_schedule'))\n env.seed(seed)\n return env","def make_env(local_env, rank, seed=0):\n\n def _init():\n local_env.seed(seed + rank)\n return local_env\n\n set_random_seed(seed)\n return _init","def make_env(local_env, rank, seed=0):\n\n def _init():\n local_env.seed(seed + rank)\n return local_env\n\n set_random_seed(seed)\n return _init","def generate_random_toy() -> Toy:\n dimensions = round(uniform(5, 100), 2)\n rooms_number = randint(1, 5)\n return SantaWorkShop(dimensions, rooms_number, 5)","def make_robotics_env(env_id, seed, rank=0):\n set_global_seeds(seed)\n env = gym.make(env_id)\n env = FlattenDictWrapper(env, ['observation', 'desired_goal'])\n env = Monitor(\n env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),\n info_keywords=('is_success',))\n env.seed(seed)\n return env","def random(ctx, type=\"str\", nchars=\"\", special_chars=string.punctuation):\n\n pool_lookup = {\n \"str\": string.ascii_letters + string.digits + \"-_\",\n \"int\": string.digits,\n \"loweralpha\": string.ascii_lowercase,\n \"upperalpha\": string.ascii_uppercase,\n \"loweralphanum\": string.ascii_lowercase + string.digits,\n \"upperalphanum\": string.ascii_uppercase + string.digits,\n \"special\": string.ascii_letters + string.digits + special_chars,\n }\n\n default_nchars_lookup = {\n \"str\": 43,\n \"int\": 16,\n }\n\n # get pool of given type\n pool = pool_lookup.get(type, None)\n if not pool:\n raise RefError(\n \"{}: unknown random type used. Choose one of {}\".format(type, [key for key in pool_lookup])\n )\n\n # get default value for nchars if nchars is not specified\n if not nchars:\n nchars = default_nchars_lookup.get(type, 8)\n else:\n # check input for nchars\n try:\n nchars = int(nchars)\n except ValueError:\n raise RefError(f\"Ref error: eval_func: {nchars} cannot be converted into integer.\")\n\n # check if any special characters are specified without using type special\n if type != \"special\" and special_chars != string.punctuation:\n raise RefError(\n \"Ref error: eval_func: {} has no option to use special characters. Use type special instead, i.e. ||random:special:{}\".format(\n type, special_chars\n )\n )\n\n # check if pool is valid, eliminates duplicates\n allowed_pool = string.ascii_letters + string.digits + string.punctuation\n pool = \"\".join(set(pool).intersection(allowed_pool))\n\n # generate string based on given pool\n generated_str = \"\".join(secrets.choice(pool) for i in range(nchars))\n\n # set ctx.data to generated string\n ctx.data = generated_str","def environments_of(groups):\n types = {}\n for group in groups:\n for env in group.environments:\n et = env.environmentType\n envs = types.setdefault((et.id, et.name), set())\n envs.add((env.id, env.name))\n return types","def generate_random_human_from_environment(environment,\n generate_appearance=False):\n appearance = None\n if generate_appearance:\n appearance = \\\n HumanAppearance.generate_rand_human_appearance(HumanAppearance)\n configs = HumanConfigs.generate_random_human_config(environment)\n return Human.generate_human(appearance, configs)","def run_seed(self, mode):\n # Clear data from tables\n # clear_data()\n if mode == MODE_CLEAR:\n return\n # industry = create_industy()\n # structure_type, structure_type1 = create_structure_type()\n # structure, structure2 = create_structure(structure_type)\n # stock = create_stock(industry, structure)\n # create_price_list(stock)\n # create_news(stock, structure, structure2)\n # create_analysis(structure)\n # create_market_indices()\n create_section_group()","def make_env(env_name):\n \n env = gym.make(env_name) \n return env","def createEnvironment(self, _):\r\n if self._namespaces:\r\n raise InternalError('The environment can have only one namespace '\r\n 'at a time.')\r\n\r\n environment = Environment(self)\r\n return self._avatar.callRemote('setupNamespace', environment)","def _create_local_env_from_spec(\n env_spec: study_pb2.EnvironmentSpec) -> environment.Environment:\n global HAS_PROCO\n env_type = env_spec.WhichOneof('type')\n\n if env_type == 'procgen':\n from rlds_creator.envs import procgen_env\n return procgen_env.ProcgenEnvironment(env_spec)\n elif env_type == 'atari':\n from rlds_creator.envs import atari_env\n return atari_env.AtariEnvironment(env_spec)\n elif env_type == 'dmlab':\n from rlds_creator.envs import dmlab_env\n return dmlab_env.DmLabEnvironment(env_spec)\n elif env_type == 'net_hack':\n from rlds_creator.envs import net_hack_env\n return net_hack_env.NetHackEnvironment(env_spec)\n elif env_type == 'robodesk':\n from rlds_creator.envs import robodesk_env\n return robodesk_env.RoboDeskEnvironment(env_spec)\n elif env_type == 'robosuite':\n from rlds_creator.envs import robosuite_env\n return robosuite_env.RobosuiteEnvironment(env_spec)\n else:\n raise ValueError('Unsupported environment spec.')","def create_environment(loader):\n loaders = (loader, PackageLoader('pynuts', 'templates'))\n environment = Environment(\n loader=ChoiceLoader(loaders), extensions=[ShowOnMatch])\n environment.globals.update({'url_for': flask.url_for})\n environment.filters['data'] = filters.data\n environment.filters['base64'] = filters.base64\n return environment","def make_env(num_steps, stack=True, scale_rew=True):\n env = retro.make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1')\n env = gym.wrappers.TimeLimit(env, max_episode_steps=num_steps)\n env = SonicDiscretizer(env)\n env = AllowBacktracking(env)\n if scale_rew:\n env = RewardScaler(env, REWARD_RATE)\n env = WarpFrame(env)\n if stack:\n env = FrameStack(env, NUM_STATES)\n return env","def env_init(self, env_info={}):\n self.dealer_sticks = env_info['dealer_sticks']\n self.random = np.random.RandomState(env_info['seed'])\n self.current_state = None","def build_randomization(\n cls, constants: CType, parameters: PType\n ) -> EnvRandomization:\n return EnvRandomization(\n parameter_randomizer=cls.build_parameter_randomizer(constants, parameters),\n observation_randomizer=EnvObservationRandomizer(\n cls.build_observation_randomizers(constants)\n ),\n action_randomizer=EnvActionRandomizer(\n cls.build_action_randomizers(constants)\n ),\n simulation_randomizer=EnvSimulationRandomizer(\n cls.build_simulation_randomizers(constants)\n ),\n )","def generate_environment(self):\n try:\n if self._environment is None:\n self._environment = Environment.fromfilepath(self._environmentName,\n self._configuration.environment_file_path)\n except Exception:\n raise","def setup_env(config_path=BENCHMARK_CONF_PATH, env_type=None):\n check_arg_not_blank(config_path, 'config_path')\n \n conf = BenchmarkConfig(config_path=config_path)\n _setup_fabric_env(conf)\n\n if not env_type or env_type == 'client':\n execute(_setup_clients, conf, hosts=_config_hosts_or_cli_hosts(conf.client_conf))\n\n if not env_type or env_type == 'server':\n execute(_setup_servers, conf, hosts=_config_hosts_or_cli_hosts(conf.server_conf))","def generate_E_random(number_obeservations, number_environments):\n E = np.random.randint(0, number_environments, (number_obeservations,1))\n return E","def make_environment(manager: 'Manager') -> None:\n global environment\n environment = FlexGetEnvironment(\n undefined=StrictUndefined,\n loader=ChoiceLoader(\n [\n PackageLoader('flexget'),\n FileSystemLoader(os.path.join(manager.config_base, 'templates')),\n ]\n ),\n extensions=['jinja2.ext.loopcontrols'],\n )\n environment.template_class = FlexGetTemplate\n for name, filt in list(globals().items()):\n if name.startswith('filter_'):\n environment.filters[name.split('_', 1)[1]] = filt\n for name, test in list(globals().items()):\n if name.startswith('is_'):\n environment.tests[name.split('_', 1)[1]] = test","def create_data_generators(shuffle=True, novelty_type='normal', item_to_include='None',\n scale_level=1):\n\n total_noi_i = 10 # Number of processed images from one environemnt i\n noe = 1 # Numer of environments\n n_p = 32 # Patch size, patch --> n_p x n_p\n\n novelty = novelty_type\n datasets = []\n\n for i in range(noe):\n\n # Load only images of the environment which includes images of the stated novel item.\n if item_to_include is not None and novelty == 'novel_item':\n dataset_env_i = PolycraftDatasetWithSpecificItem(\n nov_type=novelty, noi=total_noi_i, env_idx=i, p_size=n_p, scale_factor=scale_level,\n item_name=item_to_include)\n datasets.append(dataset_env_i)\n # We only process the one environment with the item (maybe change this\n # if we have more than one environement per novel_item!?)\n break\n\n # No specific item given which should be included.\n else:\n dataset_env_i = PolycraftDatasetNoSpecificItem(\n nov_type=novelty, noi=total_noi_i, env_idx=i, p_size=n_p, scale_factor=scale_level)\n datasets.append(dataset_env_i)\n\n final_dataset = ConcatDataset(datasets)\n\n total_noi = len(final_dataset) # Total number of processed images from all datasets\n\n if(total_noi < 7):\n print('Number of samples too small for splitting dataset in training-/valid-/test set.')\n\n train_noi = int(0.7 * total_noi) # Number of images used for training (70 %)\n valid_noi = int(0.15 * total_noi) # Number of images used for validation (15 %)\n test_noi = total_noi - train_noi - valid_noi # Number of images used for testing (15 %)\n train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split(\n final_dataset, [train_noi, valid_noi, test_noi])\n\n train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)\n valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=True)\n test_loader = DataLoader(test_dataset, batch_size=1, shuffle=True)\n\n return train_loader, valid_loader, test_loader","def generate_random_roles():\n\n role_names = set()\n roles_size = 0\n while roles_size != 3:\n role_name = ''\n for i in range(3):\n role_name += random.choice(['0', '1'])\n if role_name not in role_names:\n role_names.add(role_name)\n roles_size += 1\n\n for role_name in role_names:\n delete_access = ActionTypes.DELETE.value if role_name[0] == '1' else ''\n write_access = ActionTypes.WRITE.value if role_name[1] == '1' else ''\n read_access = ActionTypes.READ.value if role_name[2] == '1' else ''\n\n allowed_actions = [access for access in (delete_access, write_access, read_access) if access]\n Role(role_name, allowed_actions)","def main():\n # parse arguments from the command line (argparse validates arguments)\n args = _get_args()\n # build the environment with the given ID\n env = gym.make(args.env)\n # play the environment with the given mode\n if args.mode == 'human':\n play_human(env)\n else:\n play_random(env, args.steps)","def generate_hosts(quantity, host_type):\n assert type(quantity) == int\n assert host_type in ['source', 'host']\n for n in range(0, quantity, 1):\n if host_type == 'host':\n yield {'hostname': 'host-{}'.format(uuid.uuid4()),\n 'lastReceived': random.randint(1154394061 * 1000, 1505330380 * 1000)}\n elif host_type == 'source':\n yield {'sourcePath': \".\".join(map(str, (random.randint(0, 254) for _ in range(4)))),\n 'lastReceived': random.randint(1154394061 * 1000, 1505330380 * 1000)}","def env_generator(\n env_name: str, max_episode_steps: int, env_wrappers: List[gym.Wrapper] = None\n) -> Callable:\n\n def _thunk(rank: int):\n env = gym.make(env_name)\n env.seed(777 + rank + 1)\n env, _ = set_env(env, max_episode_steps, env_wrappers)\n return env\n\n return _thunk","def build_net(env, seeds, model_type, hidden_size, noise_std, action_type):\r\n torch.manual_seed(seeds[0])\r\n if action_type == gym.spaces.box.Box:\r\n \taction_space = env.action_space.shape[0] \r\n else:\r\n action_space = env.action_space.n\r\n if model_type == \"ff\":\r\n net = model.Model_FF(env.observation_space.shape[0], action_space, hidden_size, action_type)\r\n elif model_type == \"cnn\":\r\n net = model.Model_CNN1D(env.observation_space.shape[0], action_space, hidden_size)\r\n else:\r\n net = model.Model_LSTM(env.observation_space.shape[0], action_space, hidden_size, action_type)\r\n for seed in seeds[1:]:\r\n net = mutate(net, seed, noise_std, copy_net=False)\r\n\r\n return net","def make_vec_env(env_id, env_type, num_env, seed, wrapper_kwargs=None, start_index=0, reward_scale=1.0, gamestate=None):\n if wrapper_kwargs is None: wrapper_kwargs = {}\n mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0\n seed = seed + 10000 * mpi_rank if seed is not None else None\n def make_thunk(rank):\n return lambda: make_env(\n env_id=env_id,\n env_type=env_type,\n subrank = rank,\n seed=seed,\n reward_scale=reward_scale,\n gamestate=gamestate,\n wrapper_kwargs=wrapper_kwargs\n )\n\n set_global_seeds(seed)\n if num_env > 1:\n return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])\n else:\n return DummyVecEnv([make_thunk(start_index)])","def __init__(self, config_type='random'):\n switcher = {\n 'random': self._create_random_config,\n 'generic_class': self._create_generic_class_config,\n 'generic_class_diff': self._create_generic_class_diff_config,\n 'generic_class_orient': self._create_generic_class_orient_config,\n 'random_sequence_baseline': self._create_random_sequence_baseline_config,\n 'playground': self._create_playground_config,\n }\n self.create_config = switcher.get(config_type, None)\n\n logging.info('Setting config type to {}'.format(config_type))\n if self.create_config is None:\n logging.warning('Invalid config type {}. Setting config type to the default random config.'\n .format(config_type))\n self.create_config = self._create_random_config","def create_random(self):\n number_of_layers = random.choice(self.parameter_choices['number_of_layers'])\n neurons_per_layer = []\n dropout_per_layer = []\n self.network['number_of_layers'] = number_of_layers\n\n for i in range(number_of_layers):\n neurons_per_layer.append(random.choice(self.parameter_choices['neurons_per_layer']))\n dropout_per_layer.append(random.choice(self.parameter_choices['dropout_per_layer']))\n\n self.network['neurons_per_layer'] = neurons_per_layer\n self.network['dropout_per_layer'] = dropout_per_layer\n self.network['optimizer'] = random.choice(self.parameter_choices['optimizer'])\n self.network['activation'] = random.choice(self.parameter_choices['activation'])","def make_planet():\n dice = random.randint(0, 100)\n if dice < 60:\n status = 'nopressure'\n else:\n status = random.choice(list(constants.STATUS.keys()))\n\n return Planet(\n name=random.choice(constants.NAMES),\n x=random.randint(constants.XMIN, constants.MAXWIDTH - 1),\n y=random.randint(constants.YMIN, constants.MAXHEIGHT - 1),\n system_size=random.choice(list(constants.SYSTEMSIZE.keys())),\n tech_level=random.choice(list(constants.TECHLEVEL.keys())),\n regim=random.choice(list(constants.REGIM.keys())),\n special=random.choice(list(constants.SPECIALRESOURCES.keys())),\n status=status,\n price_slip={},\n shipyard=[])","def create_test_env(\n env_id: str,\n n_envs: int = 1,\n stats_path: Optional[str] = None,\n seed: int = 0,\n log_dir: Optional[str] = None,\n should_render: bool = True,\n hyperparams: Optional[Dict[str, Any]] = None,\n env_kwargs: Optional[Dict[str, Any]] = None,\n) -> VecEnv:\n # Create the environment and wrap it if necessary\n env_wrapper = get_wrapper_class(hyperparams)\n\n hyperparams = {} if hyperparams is None else hyperparams\n\n if \"env_wrapper\" in hyperparams.keys():\n del hyperparams[\"env_wrapper\"]\n\n vec_env_kwargs = {}\n vec_env_cls = DummyVecEnv\n if n_envs > 1 or \"Bullet\" in env_id:\n # HACK: force SubprocVecEnv for Bullet env\n # as Pybullet envs does not follow gym.render() interface\n vec_env_cls = SubprocVecEnv\n # start_method = 'spawn' for thread safe\n\n env = make_vec_env(\n env_id,\n n_envs=n_envs,\n monitor_dir=log_dir,\n seed=seed,\n wrapper_class=env_wrapper,\n env_kwargs=env_kwargs,\n vec_env_cls=vec_env_cls,\n vec_env_kwargs=vec_env_kwargs,\n )\n\n # Load saved stats for normalizing input and rewards\n # And optionally stack frames\n if stats_path is not None:\n if hyperparams[\"normalize\"]:\n print(\"Loading running average\")\n print(f\"with params: {hyperparams['normalize_kwargs']}\")\n path_ = os.path.join(stats_path, \"vecnormalize.pkl\")\n if os.path.exists(path_):\n env = VecNormalize.load(path_, env)\n # Deactivate training and reward normalization\n env.training = False\n env.norm_reward = False\n else:\n raise ValueError(f\"VecNormalize stats {path_} not found\")\n\n n_stack = hyperparams.get(\"frame_stack\", 0)\n if n_stack > 0:\n print(f\"Stacking {n_stack} frames\")\n env = VecFrameStack(env, n_stack)\n return env","def env_creator(env_config):\n return CartPoleBTEnv(\n goal_state=env_config['goal_state'],\n disturbances=env_config['disturbances'],\n initial_state=env_config['initial_state'],\n initial_state_variance=env_config['initial_state_variance']\n )","def make_envs(env_gen: Callable, n_envs: int = 8) -> SubprocVecEnv:\n envs = [env_gen(i) for i in range(n_envs)]\n subproc_env = SubprocVecEnv(envs)\n return subproc_env","async def test_create_engine_uses_robot_type(\n decoy: Decoy, robot_type: RobotType, deck_type: pe_types.DeckType\n) -> None:\n # TODO(mc, 2021-06-11): to make these test more effective and valuable, we\n # should pass in some sort of actual, valid HardwareAPI instead of a mock\n hardware_api = decoy.mock(cls=API)\n subject = EngineStore(\n hardware_api=hardware_api, robot_type=robot_type, deck_type=deck_type\n )\n\n await subject.create(run_id=\"run-id\", labware_offsets=[], protocol=None)\n\n assert subject.engine.state_view.config.robot_type == robot_type","def generate_test_environment(tmpdir, dataset):\n\n # Overwrite settings with test settings\n generate_test_settings(tmpdir, dataset)\n\n # Generate the archive files\n for usage in ['train', 'test']:\n \n for dstype in ['images', 'labels']:\n \n dataset_type = usage + '.' + dstype\n \n mnist_dataset = 'datasets.mnist.' + dataset_type\n filepath = get_setting(mnist_dataset)\n\n test_dataset = dataset + '.' + dataset_type\n generate_test_dataset_archive(filepath, test_dataset)","def set_single_environment(cls, products, values):\n env = values[\"name\"]\n for i in range(len(products)):\n products[i][\"environments\"] = [env]\n return products","def __create_test_environment(self):\n os.chdir(self.wd)\n temp_dir = tempfile.gettempdir()\n self.test_root = os.path.join(temp_dir, \"test-grpc\")\n print(\"Creating testing environment in {}\".format(self.test_root))\n if os.path.exists(self.test_root):\n # delete any previous environment\n shutil.rmtree(self.test_root)\n # create root directory\n os.makedirs(self.test_root)\n def copy_app(name):\n app_root = os.path.join(self.test_root, name)\n os.makedirs(app_root)\n filename = \"grpc-{}\".format(name)\n src = os.path.join(self.args.bin, filename)\n dst = os.path.join(app_root, filename)\n shutil.copy(src, dst)\n return dst\n # copy client and server into the new test environment\n self.server_path = copy_app(\"server\")\n self.client_path = copy_app(\"client\")","def _get_or_create_environment_objects(openshift_api=settings.OPENSHIFT_API):\n\n choices = {}\n\n for k in openshift_api.keys():\n obj, created = Environment.objects.get_or_create(environment=k)\n choices[k] = obj\n logger.info('{}, {}'.format(obj, created))\n\n return choices","def make_env(self, env_id, seed, logger_dir=None, reward_scale=1.0, mpi_rank=0, subrank=0, info_keywords=()):\n scenario = scenarios.load('{}.py'.format(env_id)).Scenario()\n world = scenario.make_world()\n env_dict = {\n \"world\": world,\n 'reset_callback': scenario.reset_world,\n 'reward_callback': scenario.reward, \n 'observation_callback': scenario.observation,\n 'info_callback': None,\n 'done_callback': scenario.done, \n 'shared_viewer': True\n }\n env = gym.make('MultiAgent-v0', **env_dict)\n env.seed(seed + subrank if seed is not None else None)\n env = Monitor(env,\n logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),\n allow_early_resets=True,\n info_keywords=info_keywords)\n env = ClipActionsWrapper(env)\n if reward_scale != 1.0:\n from baselines.common.retro_wrappers import RewardScaler\n env = RewardScaler(env, reward_scale)\n return env","def make_environment(\n evaluation: bool = False,\n task: str = 'MountainCarContinuous-v0') -> dm_env.Environment:\n del evaluation\n\n # Load the gym environment.\n environment = gym.make(task)\n\n # Make sure the environment obeys the dm_env.Environment interface.\n environment = wrappers.GymWrapper(environment)\n # Clip the action returned by the agent to the environment spec.\n environment = wrappers.CanonicalSpecWrapper(environment, clip=True)\n environment = wrappers.SinglePrecisionWrapper(environment)\n\n return environment","def _create_spacecrafts(self):\n self.spacecraft = MPDSpacecraft.create_random(self.space, )\n self.space.spacecrafts.append(self.spacecraft)\n\n vehicles_type = utils.class_from_path(self.config[\"other_spacecrafts_type\"])\n for _ in range(self.config[\"spacecrafts_count\"]):\n self.road.spacecrafts.append(spacecrafts_type.create_random(self.space))","def conda_create_environment(name, python='3'):\n conda = '{0}/bin/conda'.format(utils.home('apps', 'miniconda'))\n\n run('{conda} create --name {name} python={python} --yes'.format(\n name=name,\n conda=conda,\n python=python))","def build_env(env_id, inst=0, **all_args):\n env_keys = ['dt', 'timing']\n if env_id == 'RDM-v0':\n env_keys.append('stimEv')\n env_args = {x: all_args[x] for x in env_keys}\n\n if all_args['combine']:\n env1 = gym.make(env_id, **env_args)\n env_args['timing'] = all_args['timing2']\n env2 = gym.make(all_args['env2'], **env_args)\n env = combine.combine(env1, env2, delay=all_args['delay'])\n else:\n env = gym.make(env_id, **env_args)\n if all_args['trial_hist']:\n env = trial_hist.TrialHistory(env, rep_prob=all_args['rep_prob'],\n block_dur=all_args['bl_dur'],\n blk_ch_prob=all_args['blk_ch_prob'])\n elif all_args['side_bias']:\n env = side_bias.SideBias(env, prob=all_args['rep_prob'],\n block_dur=all_args['bl_dur'])\n if all_args['catch_trials']:\n env = catch_trials.CatchTrials(env,\n catch_prob=all_args['catch_prob'],\n stim_th=all_args['stim_th'])\n if all_args['reaction_time']:\n env = reaction_time.ReactionTime(env)\n if all_args['pass_reward']:\n env = pass_reward.PassReward(env)\n if all_args['pass_action']:\n env = pass_action.PassAction(env)\n env = manage_data.manage_data(env, inst=inst, plt_tr=all_args['figs'],\n folder=all_args['save_path'])\n return env","def testrandom(self):\n for i in range(100):\n WeaponAbility()","def randomize(self):\n for network in self.networks.values():\n network.database = []\n self.env = Environment(self.networks)","def make_mujoco_env(env_id, seed):\n rank = MPI.COMM_WORLD.Get_rank()\n set_global_seeds(seed + 10000 * rank)\n env = gym.make(env_id)\n env = Monitor(env, os.path.join(logger.get_dir(), str(rank)))\n env.seed(seed)\n return env","def _create_random_ca(cls):\n random_ca = entity.CustomAttribute()\n random_ca.ca_type = random.choice(AttributesTypes.ALL_TYPES)\n random_ca.title = cls._generate_title(random_ca.ca_type)\n random_ca.definition_type = random.choice(objects.all_objects)\n return random_ca","def draw_random_setup(types_available, team, game_dim):\n\n nr_pieces = len(types_available)-1\n types_available = [type_ for type_ in types_available if not type_ == 0]\n if game_dim == 5:\n row_offset = 2\n elif game_dim == 7:\n row_offset = 3\n else:\n row_offset = 4\n setup_agent = np.empty((row_offset, game_dim), dtype=object)\n if team == 0:\n flag_positions = [(game_dim-1, j) for j in range(game_dim)]\n flag_choice = np.random.choice(range(len(flag_positions)), 1)[0]\n flag_pos = game_dim-1 - flag_positions[flag_choice][0], game_dim-1 - flag_positions[flag_choice][1]\n setup_agent[flag_pos] = pieces.Piece(0, 0, flag_positions[flag_choice])\n\n types_draw = np.random.choice(types_available, nr_pieces, replace=False)\n positions_agent_0 = [(i, j) for i in range(game_dim-row_offset, game_dim) for j in range(game_dim)]\n positions_agent_0.remove(flag_positions[flag_choice])\n\n for idx in range(nr_pieces):\n pos = positions_agent_0[idx]\n setup_agent[(game_dim-1 - pos[0], game_dim-1 - pos[1])] = pieces.Piece(types_draw[idx], 0, pos)\n elif team == 1:\n flag_positions = [(0, j) for j in range(game_dim)]\n flag_choice = np.random.choice(range(len(flag_positions)), 1)[0]\n setup_agent[flag_positions[flag_choice]] = pieces.Piece(0, 1, flag_positions[flag_choice])\n\n types_draw = np.random.choice(types_available, nr_pieces, replace=False)\n positions_agent_1 = [(i, j) for i in range(row_offset) for j in range(game_dim)]\n positions_agent_1.remove(flag_positions[flag_choice])\n\n for idx in range(nr_pieces):\n pos = positions_agent_1[idx]\n setup_agent[pos] = pieces.Piece(types_draw[idx], 1, pos)\n return setup_agent","def create_universe():\n # Universe is a large list, planets and others things are separated using\n # type() and isinstance(object, ClassInfo)\n univers = []\n captain = Captain()\n captain.ship = Ship()\n captain.account = BankAccount(captain.name, [])\n planetes = create_planetes()\n for planete in planetes:\n if planete.homeworld:\n captain.homeworld = planete\n captain.location = planete\n if planete.tech_level > 5:\n planete.shipyard = populate_shipyard()\n\n univers.append(captain)\n univers.extend(planetes)\n\n return univers","def random_placement(area):\n\n area.create_houses(True)\n\n for house in area.houses:\n place_house(area, house)","def __init__(self, env, random_seed=None):\n self.env = env \n self.RandomState = np.random.RandomState(random_seed)","def initialize_random_number_generator(self,question_type):\n\t\tself.generator.seed(self.generate_index(self.magic, self.level, self.problem_id, question_type))","def _active_environment(self, tuple_type):\n return","def create_environment(args):\n env.username = args.user\n env.password = args.password\n env.service_url = args.service_url\n env.quiet = args.quiet\n env.verbose = args.verbose\n env.manifest = args.manifest\n env.debug = args.debug\n env.always_confirm = args.yes\n env.args = args\n env.api = ravello.RavelloClient(env.username, env.password, env.service_url)","def network_factory(\n environment_spec: specs.EnvironmentSpec,\n agent_types: Dict[ma_types.AgentID, ma_types.GenericAgent],\n init_network_fn: Optional[ma_types.InitNetworkFn] = None\n) -> ma_types.MultiAgentNetworks:\n init_fn = init_network_fn or init_default_network\n networks = {}\n for agent_id, agent_type in agent_types.items():\n single_agent_spec = ma_utils.get_agent_spec(environment_spec, agent_id)\n networks[agent_id] = init_fn(agent_type, single_agent_spec)\n return networks","def role_batch():\n return [\n RoleFactory(\n scopes=ScopeFactory.create_batch(randint(0, 3), type=choice(('odp', 'client'))),\n is_collection_role=n in (1, 2) or randint(0, 1),\n )\n for n in range(randint(3, 5))\n ]","def _random_mode(self):\n modes = ((1, 2, 3, 4, 5),\n (1, 2, 4, 5),\n (2, 3, 4),\n (1, 3, 5),\n (1, 5),\n (2, 4),\n (3,),\n ())\n return random.choice(modes)","def random_player(env_name, render=False):\n\n # Make\n env = gym.make(env_name)\n\n n_game = 0\n\n # For each game\n while True:\n\n # Reset\n env.reset()\n done = False\n if render:\n env.render()\n\n # Until the end\n while not done:\n\n # Random agent moves\n action = env.action_space.sample()\n\n # Environment moves\n observation, reward, done, info = env.step(action)\n\n # Result\n if render:\n env.render()\n yield observation\n\n n_game += 1\n\n # Leave\n env.close()","def testrandom(self):\n for i in range(100):\n AmuletAbility()","def jobtype_factory(jobtype_toolkit):\n\n jobtype_toolkits = {'aimless_shooting': jobtype.AimlessShooting(),\n 'committor_analysis': jobtype.CommittorAnalysis(),\n 'equilibrium_path_sampling': jobtype.EquilibriumPathSampling(),\n 'find_ts': jobtype.FindTS(),\n 'umbrella_sampling': jobtype.UmbrellaSampling()}\n\n if jobtype_toolkit not in jobtype_toolkits.keys():\n raise ValueError('unsupported JobType name: ' + jobtype_toolkit)\n\n return jobtype_toolkits[jobtype_toolkit]","def seed_model(model):\n if model == 'all':\n seed_all()\n elif model == 'client':\n seed_client()\n elif model == 'comment':\n seed_comment()\n elif model == 'staff':\n seed_staff()\n elif model == 'request':\n seed_request()","def construct_envs(\n config,\n env_class,\n workers_ignore_signals=False,\n):\n\n num_processes = config.NUM_ENVIRONMENTS\n configs = []\n env_classes = [env_class for _ in range(num_processes)]\n dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)\n scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES\n if \"*\" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:\n scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)\n\n if num_processes > 1:\n if len(scenes) == 0:\n raise RuntimeError(\n \"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes\"\n )\n\n if len(scenes) < num_processes:\n scenes = scenes * num_processes\n\n random.shuffle(scenes)\n\n scene_splits = [[] for _ in range(num_processes)]\n for idx, scene in enumerate(scenes):\n scene_splits[idx % len(scene_splits)].append(scene)\n\n assert sum(map(len, scene_splits)) == len(scenes)\n\n for i in range(num_processes):\n proc_config = config.clone()\n proc_config.defrost()\n\n task_config = proc_config.TASK_CONFIG\n task_config.SEED = task_config.SEED + i\n if len(scenes) > 0:\n task_config.DATASET.CONTENT_SCENES = scene_splits[i]\n\n task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (\n config.SIMULATOR_GPU_ID\n )\n\n task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS\n\n proc_config.freeze()\n configs.append(proc_config)\n\n envs = habitat.ThreadedVectorEnv(\n make_env_fn=make_env_fn,\n env_fn_args=tuple(zip(configs, env_classes)),\n workers_ignore_signals=workers_ignore_signals,\n )\n return envs","def create_aiida_project_environment(self):\n try:\n self.create_folder_structure()\n self.build_python_environment()\n self.install_packages_from_index()\n except Exception:\n self.exit_on_exception()\n raise\n self.create_spec_entry()","def generate_random_individual():\n genotype = []\n ### Your code here\n return {'genotype': genotype, 'fitness': None }","def base_env(*args, **kwargs):\n try:\n # regular gym\n env = gym.make(*args, **kwargs)\n except:\n try:\n # gym retro\n env = retro.make(*args, **kwargs)\n except:\n # gym-super-mario-bros\n env = gym_super_mario_bros.make(*args, **kwargs)\n env.recognized = None\n return env","def get_rng(environ, experiment, swabid):\n r = Random()\n r.seed(experiment.seed_strategy(environ, experiment, swabid))\n return r","def usertype_add(request, simulation):\n # Create new distributions with good defaults.\n alphaTI = Distribution(type='NONE', mean=10)\n alphaTP = Distribution(type='NONE', mean=15)\n beta = Distribution(type='NONE', mean=5)\n delta = Distribution(type='NONE', mean=10)\n departureMu = Distribution(type='NONE', mean=2)\n gamma = Distribution(type='NONE', mean=20)\n modeMu = Distribution(type='NONE', mean=5)\n penaltyTP = Distribution(type='NONE', mean=2)\n routeMu = Distribution(type='NONE', mean=10)\n # Default value for t star is average arrival at middle of period and\n # uniform distribution over half of the period.\n mid_time = (simulation.startTime + simulation.lastRecord) / 2\n length = simulation.lastRecord - simulation.startTime\n tstar = Distribution(type='UNIFORM', mean=mid_time, std=length / (4 * sqrt(3)))\n # Save the distributions to generate ids.\n alphaTI.save()\n alphaTP.save()\n beta.save()\n delta.save()\n departureMu.save()\n gamma.save()\n modeMu.save()\n penaltyTP.save()\n routeMu.save()\n tstar.save()\n # Create the new user type.\n usertype = UserType()\n usertype.alphaTI = alphaTI\n usertype.alphaTP = alphaTP\n usertype.beta = beta\n usertype.delta = delta\n usertype.departureMu = departureMu\n usertype.gamma = gamma\n usertype.modeMu = modeMu\n usertype.penaltyTP = penaltyTP\n usertype.routeMu = routeMu\n usertype.tstar = tstar\n # Set user_id to user_id of previous usertype + 1.\n usertypes = get_query('usertype', simulation)\n if usertypes.exists():\n usertype.user_id = usertypes.last().user_id + 1\n else:\n usertype.user_id = 1\n usertype.save()\n # Create a demand segment and a matrix for the user type.\n matrix = Matrices()\n matrix.save()\n demandsegment = DemandSegment()\n demandsegment.usertype = usertype\n demandsegment.matrix = matrix\n demandsegment.save()\n demandsegment.demand.add(simulation.scenario.demand)\n # Return the view to edit the new user type.\n return HttpResponseRedirect(\n reverse('metro:usertype_edit', args=(simulation.id, demandsegment.id,))\n )","def generate_deck(suits=4, type_cards=13):\n cards = []\n for suite in range(suits):\n for type_card in range(1, type_cards+1):\n # Setting the key-value pair for every card\n if (type_card == 1):\n cards.append({'A':type_cards+1})\n elif (type_card == 11):\n cards.append({'J': type_card})\n elif (type_card == 12):\n cards.append({'Q': type_card})\n elif (type_card == 13):\n cards.append({'K': type_card})\n else:\n cards.append({type_card:type_card})\n # Randomize the set of cards in the deck\n random.shuffle(cards)\n return cards","def setup(env, NUM_TRACKS, landtime, t_inter):\n # Create the airport\n airport = Airport(env, NUM_TRACKS, landtime)\n\n # Create 4 initial planes\n for i in range(1):\n env.process(plane(env, 'Aviao %d' % i, airport))\n\n # Create more planes while the simulation is running\n while True:\n yield env.timeout(random.randint(t_inter-2, t_inter+2))\n# yield env.timeout(random.expovariate(1.0 / t_inter))\n i += 1\n env.process(plane(env, 'Aviao %d' % i, airport))","def generate(count):\n return unpack_random_animals(generate_animals_randomly(count))","def get_env_type ( base_name ) :\n return base_name.split( '-', 1 )[ 0 ]","def init_from_env(\n *,\n device_type: T.Optional[str] = None,\n dist_init_method_type: T.Literal[\"env\", \"tcp\", \"file\"] = \"env\",\n pg_backend: T.Optional[str] = None,\n pg_timeout: timedelta = default_pg_timeout,\n float32_matmul_precision: str = \"high\",\n) -> torch.device:\n device = torch.device(\"cpu\") if device_type == \"cpu\" else get_device_from_env()\n\n if device_type is not None and device.type != device_type:\n raise RuntimeError(\n f\"Device type is specified to {device_type} but got {device.type} from env\"\n )\n\n if _check_dist_env():\n if not torch.distributed.is_available():\n _log.warning(\n \"torch.distributed is not available. Skipping initializing the process group.\"\n )\n return device\n if torch.distributed.is_initialized():\n _log.warning(\n \"torch.distributed is already initialized. Skipping initializing the process group.\"\n )\n return device\n pg_backend = (\n pg_backend\n if pg_backend is not None\n else get_process_group_backend_from_device(device)\n )\n init_method: Optional[str] = None\n if dist_init_method_type == \"tcp\":\n init_method = get_tcp_init_method()\n elif dist_init_method_type == \"file\":\n init_method = get_file_init_method()\n torch.distributed.init_process_group(\n init_method=init_method, backend=pg_backend, timeout=pg_timeout\n )\n maybe_enable_tf32(float32_matmul_precision)\n return device","def init_gym(env_name):\n env = gym.make(env_name)\n obs_dim = env.observation_space.shape[0]\n act_dim = env.action_space.shape[0]\n\n return env, obs_dim, act_dim","def init_gym(env_name):\n env = gym.make(env_name)\n obs_dim = env.observation_space.shape[0]\n act_dim = env.action_space.shape[0]\n\n return env, obs_dim, act_dim","def do_create_environment(csp: CloudProviderInterface, environment_id=None):\n\n environment = Environments.get(environment_id)\n\n with claim_for_update(environment) as environment:\n\n if environment.cloud_id is not None:\n app.logger.warning(\n \"Environment cloud ID %s already present.\", environment.cloud_id\n )\n return\n\n parent_id = environment.application.cloud_id\n tenant_id = environment.portfolio.csp_data[\"tenant_id\"]\n\n log_do_create_environment(environment.portfolio.id, parent_id, tenant_id)\n\n payload = EnvironmentCSPPayload(\n tenant_id=tenant_id, display_name=environment.name, parent_id=parent_id\n )\n env_result = csp.create_environment(payload)\n Environments.update(environment, new_data={\"cloud_id\": env_result.id})\n\n app.logger.info(\"Created environment %s\", env_result.name)\n async_result = create_subscription.delay(environment_id=environment.id)\n app.logger.info(\n \"Attempting to create subscription for environment %s [Task ID: %s])\",\n env_result.name,\n async_result.task_id,\n )"],"string":"[\n \"def make_environment(seed, task_horizon):\\n # Load the gym environment.\\n environment = CartPoleEnv()\\n environment = gym_wrappers.TimeLimit(environment, task_horizon)\\n environment.seed(seed)\\n environment = wrappers.GymWrapper(environment)\\n environment = wrappers.SinglePrecisionWrapper(environment)\\n return environment\",\n \"def _make_environment(\\n self, problem_type, curriculum_sample, wrapper_type, backend_type=None):\\n if backend_type is None:\\n backend_type = FLAGS.backend\\n return dm_construction.get_environment(\\n problem_type,\\n unity_environment=self._unity_envs[backend_type],\\n wrapper_type=wrapper_type,\\n curriculum_sample=curriculum_sample)\",\n \"def get_env_class(environment_type):\\n if environment_type == \\\"vanilla\\\":\\n return city.CityGridEnv\\n elif environment_type == \\\"distraction\\\":\\n return city.DistractionGridEnv\\n elif environment_type == \\\"map\\\":\\n return city.MapGridEnv\\n elif environment_type == \\\"cooking\\\":\\n return cooking.CookingGridEnv\\n elif environment_type == \\\"miniworld_sign\\\":\\n # Dependencies on OpenGL, so only load if absolutely necessary\\n from envs.miniworld import sign\\n return sign.MiniWorldSign\\n else:\\n raise ValueError(\\n \\\"Unsupported environment type: {}\\\".format(environment_type))\",\n \"def make_env(env_id, rand, seed=0):\\n def _init():\\n env = gym.make(env_id)\\n # env.unwrapped\\n env.seed(seed + rand)\\n return env\\n set_global_seeds(seed)\\n return _init\",\n \"def generate(self, site_type='random', arg='random'):\\n size = entities.world['size']\\n if site_type == 'random':\\n if randint(1,3) == 1:\\n site_type = 'adventure'\\n else:\\n site_type = 'resource'\\n elif site_type in ref.material_type_dct.keys():\\n self.resource = site_type\\n site_type = 'resource'\\n terrain_list = None\\n if arg == 'random':\\n terrain_list = [x for x in ref.terrain_dct.keys() if type(x) == int]\\n elif arg in ref.terrain_type_list:\\n terrain_list = [\\n x for x in ref.terrain_dct.keys() if ref.terrain_dct[x]['terrain type'] == arg\\n ]\\n x = randint(0, size-1)\\n y = randint(0, size-1)\\n terrain_type = entities.world['grid'][y][x]\\n site_locations = [s.location for s in entities.sites['object list']]\\n while terrain_type not in terrain_list or [x,y] in site_locations:\\n x = randint(0, size-1)\\n y = randint(0, size-1)\\n terrain_type = entities.world['grid'][y][x]\\n\\n self.location = [x,y]\\n self.structure = Structure().generate(\\n ref.terrain_dct[terrain_type]['terrain type'], site_type\\n )\\n if self.resource == None:\\n if 'resource type' in ref.structure_type_dct[\\n self.structure.structure_type\\n ].keys():\\n resource_type = ref.structure_type_dct[\\n self.structure.structure_type]['resource type'\\n ]\\n resource_possibilities = []\\n for possible_material in [\\n x for x in ref.material_class_dct[resource_type][\\n 'types'] if 'rarity' in ref.material_type_dct[x].keys()\\n ]:\\n for x in xrange(ref.rarity_dct[\\n ref.material_type_dct[possible_material]['rarity']\\n ]):\\n resource_possibilities.append(possible_material)\\n self.resource = choice(resource_possibilities)\\n #resources measured in grams\\n if self.resource != None:\\n self.harvestable = randint(100000, 1500000)\\n try:\\n entities.town['object'].resources[\\n ref.material_type_dct[self.resource]['class']][\\n self.resource]['harvestable'] += self.harvestable\\n except KeyError:\\n pass\\n #NOTE: These numbers suitable for metal, may not be for other materials\\n #NOTE: Mine production should be ~1kg pure metal per day per miner.\\n #NOTE: IRL mine has ~43500kg before producing much less.\\n \\n self.set_site_id()\\n return self\",\n \"def _generate_raw_environments(self, num, seed):\",\n \"def make_env(rank, env_conf, seed=0):\\n def _init():\\n env = RedGymEnv(env_conf)\\n env.seed(seed + rank)\\n return env\\n set_random_seed(seed)\\n return _init\",\n \"def _create_environment(config):\\n if isinstance(config.env, str):\\n env = gym.make(config.env)\\n else:\\n env = config.env()\\n if config.max_length:\\n env = tools.wrappers.LimitDuration(env, config.max_length)\\n env = tools.wrappers.RangeNormalize(env)\\n env = tools.wrappers.ClipAction(env)\\n env = tools.wrappers.ConvertTo32Bit(env)\\n return env\",\n \"def init_environment(env_name):\\n env = gym.make(env_name)\\n discrete = False\\n if type(env.action_space) is gym.spaces.Discrete:\\n discrete = True\\n else:\\n env = NormalizedActions(env)\\n return env, discrete\",\n \"def generate_environment(dataset, tmpdir):\\n\\n print(\\\">>> Test environment:\\\")\\n print(\\\"dataset:\\\", dataset)\\n print(\\\"tmpdir:\\\", tmpdir)\\n\\n generate_test_environment(tmpdir, dataset)\\n\\n return { 'dataset': dataset, 'tmpdir': tmpdir }\",\n \"def create_environment(game_name, seed, is_test=False):\\n\\n # Note, you may want to use a level cache to speed of compilation of\\n # environment maps. See the documentation for the Python interface of DeepMind\\n # Lab.\\n config = {\\n \\\"width\\\": FLAGS.width,\\n \\\"height\\\": FLAGS.height,\\n \\\"logLevel\\\": \\\"WARN\\\",\\n \\\"n_input_frames\\\": FLAGS.history,\\n \\\"show_display\\\": FLAGS.display,\\n \\\"save_video\\\": FLAGS.save_video,\\n }\\n if is_test:\\n config[\\\"allowHoldOutLevels\\\"] = \\\"true\\\"\\n # Mixer seed for evalution, see\\n # https://github.com/deepmind/lab/blob/master/docs/users/python_api.md\\n config[\\\"mixerSeed\\\"] = 0x600D5EED\\n\\n p = py_process.PyProcess(\\n environments.PyProcessAtari,\\n game_name,\\n config,\\n FLAGS.num_action_repeats,\\n seed,\\n )\\n return environments.FlowEnvironment(p.proxy)\",\n \"def make_env(rank, agents, seed=10000):\\n\\tdef _init():\\n\\t\\tenv = TradingGameEnv.TradingGameEnv(player_count = NUM_PLAYERS, other_agent_list = agents,\\n\\t\\t\\tseq_per_day = SEQ_PER_DAY, cards_per_suit = CARDS_PER_SUIT, player_hand_count = HAND_COUNT,\\n\\t\\t\\trandom_seq = True, self_play = SELF_PLAY, policy_type = POLICY_TYPE, self_copy_freq = SELF_COPY_FREQ,\\n\\t\\t\\tobs_transaction_history_size=TRANSACTION_HISTORY_SIZE)\\n\\t\\tenv.seed(seed + rank)\\n\\t\\treturn env\\n\\tset_global_seeds(seed)\\n\\treturn _init\",\n \"def make_env(\\n\\t\\tdomain_name,\\n\\t\\ttask_name,\\n\\t\\tseed=0,\\n\\t\\tepisode_length=1000,\\n\\t\\tframe_stack=3,\\n\\t\\taction_repeat=4,\\n\\t\\timage_size=100,\\n\\t\\tmode='original'\\n\\t):\\n\\tassert mode in {'original', 'color_easy', 'color_hard', 'video_easy', 'video_hard'}, \\\\\\n\\t\\tf'specified mode \\\"{mode}\\\" is not supported'\\n\\n\\tenv = dmc2gym.make(\\n\\t\\tdomain_name=domain_name,\\n\\t\\ttask_name=task_name,\\n\\t\\tseed=seed,\\n\\t\\tvisualize_reward=False,\\n\\t\\tfrom_pixels=True,\\n\\t\\theight=image_size,\\n\\t\\twidth=image_size,\\n\\t\\tepisode_length=episode_length,\\n\\t\\tframe_skip=action_repeat\\n\\t)\\n\\tenv = VideoWrapper(env, mode, seed)\\n\\tenv = FrameStack(env, frame_stack)\\n\\tenv = RandomizationWrapper(env, mode, seed)\\n\\n\\treturn env\",\n \"def generate_random_value(self, type):\\n generators = {\\n str: lambda: self.generate_random_string(20, uppercase=True, punctuations=True),\\n int: lambda: random.randrange(100000),\\n float: lambda: random.random() * 100000.0,\\n bool: lambda: bool(random.getrandbits(1)),\\n list: lambda: self.generate_random_list_or_string(),\\n dict: lambda: self.generate_random_dict_or_string()\\n }\\n generator = generators[type]\\n return generator()\",\n \"def make_env(stack=True, scale_rew=True, frame_wrapper=WarpFrame, reward_type=None, env_idx=-1):\\n print(env_idx)\\n if env_idx == -1:\\n idx = random.randint(0, 12)\\n elif env_idx < 0 or env_idx > 12:\\n print(\\\"index of environment {} out of range [0, 12]\\\".format(str(env_idx)))\\n exit(1)\\n else:\\n idx = env_idx\\n print(\\\"select level: \\\", idx)\\n game, state = train_level[idx]\\n # game, state = ['SonicTheHedgehog-Genesis', 'GreenHillZone.Act1']\\n\\n print(game, state)\\n env = make(game=game, state=state)\\n\\n return wrap_env(env, stack, scale_rew, frame_wrapper, reward_type)\",\n \"def create_environment(cls, full_config):\\n\\n config = full_config['template']['devops_settings']\\n environment = cls.create(config['env_name'])\\n\\n # create groups and drivers\\n groups = config['groups']\\n environment.add_groups(groups)\\n\\n # create address pools\\n address_pools = config['address_pools']\\n environment.add_address_pools(address_pools)\\n\\n # process group items\\n for group_data in groups:\\n group = environment.get_group(name=group_data['name'])\\n\\n # add l2_network_devices\\n group.add_l2_network_devices(\\n group_data.get('l2_network_devices', {}))\\n\\n # add network_pools\\n group.add_network_pools(\\n group_data.get('network_pools', {}))\\n\\n # Connect nodes to already created networks\\n for group_data in groups:\\n group = environment.get_group(name=group_data['name'])\\n\\n # add group volumes\\n group.add_volumes(\\n group_data.get('group_volumes', []))\\n\\n # add nodes\\n group.add_nodes(\\n group_data.get('nodes', []))\\n\\n return environment\",\n \"def buildWeatherFromType(type, environment, turns=-1, forever=True):\\r\\n weatherClass = WeatherFactory.weatherTypeDictionary[type]\\r\\n return weatherClass(overCallbackFunction=environment.clearWeather, turns=turns, forever=forever)\",\n \"def generate(self, terrain_type='random', site_type='random'):\\n if terrain_type == 'random' and site_type == 'random':\\n self.structure_type = choice(ref.structure_type_dct.keys())\\n elif site_type == 'random':\\n self.structure_type = choice(ref.structure_class_dct[terrain_type])\\n elif terrain_type == 'random':\\n self.structure_type = choice(ref.site_type_dct[site_type])\\n else:\\n self.structure_type = choice(\\n [x for x in ref.site_type_dct[site_type] if ref.structure_type_dct[\\n x]['class'] == terrain_type]\\n )\\n if self.get_attribute('site type') == 'resource':\\n self.workers = 0\\n else:\\n self.workers = []\\n self.monsters = []\\n self.worker_capacity = self.get_attribute('worker capacity')\\n self.time_until_harvest = self.get_attribute('time per harvest')\\n return self\",\n \"def init():\\n env = Environment(5, 5, 20, [10, 20, 10, 5])\\n return env\",\n \"def get_environment_class_by_name(environment_type):\\n for cls in util.iter_subclasses(Environment):\\n if cls.tool_name == environment_type:\\n return cls\\n raise EnvironmentUnavailable(\\n f\\\"Unknown environment type '{environment_type}'\\\")\",\n \"def _init_seeding(cls, seed_type=int(SeedType.NONE), seeds=None):\\n seed_type = int(seed_type)\\n \\n if seed_type == (SeedType.NONE):\\n assert seeds is None, \\\"Seed type set to NONE, therefore seed cannot be set.\\\"\\n elif seed_type == (SeedType.CONSTANT):\\n assert seeds is not None, \\\"Seed set to constant seed, so seed must be specified.\\\"\\n cls._seed_generator = [int(x) for x in seeds.split(\\\",\\\") if x]\\n elif seed_type == (SeedType.GENERATED):\\n assert seeds is not None, \\\"Seed set to generated seed, so initial seed must be specified.\\\"\\n cls._seed_generator = Random(int(seeds))\\n elif seed_type == (SeedType.SPECIFIED):\\n cls._seed_generator = ([[str(x) for x in s.split(\\\",\\\") if x] for s in seeds.split(\\\"-\\\") if s])\\n else:\\n raise TypeError(\\\"Seed type {} not supported\\\".format(seed_type))\\n \\n cls._seed_type = seed_type\",\n \"def easy(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None, **kwargs):\\n\\n physics = Physics.from_xml_string(*get_model_and_assets())\\n task = Cloth(randomize_gains=False, random=random, **kwargs)\\n environment_kwargs = environment_kwargs or {}\\n return control.Environment(\\n physics, task, time_limit=time_limit,n_frame_skip=1,special_task=True, **environment_kwargs)\",\n \"def generate_environment(timestep=3600,\\n year_timer=2017,\\n year_co2=2017,\\n try_path=None,\\n location=(51.529086, 6.944689),\\n altitude=55,\\n new_try=False):\\n\\n # Create environment\\n timer = time.TimerExtended(timestep=timestep, year=year_timer)\\n\\n weather = weath.Weather(timer, useTRY=True, pathTRY=try_path,\\n location=location, altitude=altitude,\\n new_try=new_try)\\n\\n market = germanmarket.GermanMarket()\\n co2em = co2.Emissions(year=year_co2)\\n\\n environment = env.EnvironmentExtended(timer=timer,\\n weather=weather,\\n prices=market,\\n location=location,\\n co2em=co2em)\\n\\n return environment\",\n \"def by_type(environments):\\n types = {}\\n for env in environments:\\n et = env.environmentType\\n options = types.setdefault(et.id, set())\\n options.add(env.id)\\n return types\",\n \"def get_environment():\\n return GenericGymEnv(id=\\\"real-time-gym-v1\\\", gym_kwargs={\\\"config\\\": CONFIG_DICT})\",\n \"def make_halide_env(env_id, seed):\\n logger.log('random_seed: %s'%seed)\\n set_global_seeds(seed)\\n env = gym.make(env_id)\\n env = Monitor(env, logger.get_dir(), info_keywords=('best_exec', 'best_schedule'))\\n env.seed(seed)\\n return env\",\n \"def make_env(local_env, rank, seed=0):\\n\\n def _init():\\n local_env.seed(seed + rank)\\n return local_env\\n\\n set_random_seed(seed)\\n return _init\",\n \"def make_env(local_env, rank, seed=0):\\n\\n def _init():\\n local_env.seed(seed + rank)\\n return local_env\\n\\n set_random_seed(seed)\\n return _init\",\n \"def generate_random_toy() -> Toy:\\n dimensions = round(uniform(5, 100), 2)\\n rooms_number = randint(1, 5)\\n return SantaWorkShop(dimensions, rooms_number, 5)\",\n \"def make_robotics_env(env_id, seed, rank=0):\\n set_global_seeds(seed)\\n env = gym.make(env_id)\\n env = FlattenDictWrapper(env, ['observation', 'desired_goal'])\\n env = Monitor(\\n env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),\\n info_keywords=('is_success',))\\n env.seed(seed)\\n return env\",\n \"def random(ctx, type=\\\"str\\\", nchars=\\\"\\\", special_chars=string.punctuation):\\n\\n pool_lookup = {\\n \\\"str\\\": string.ascii_letters + string.digits + \\\"-_\\\",\\n \\\"int\\\": string.digits,\\n \\\"loweralpha\\\": string.ascii_lowercase,\\n \\\"upperalpha\\\": string.ascii_uppercase,\\n \\\"loweralphanum\\\": string.ascii_lowercase + string.digits,\\n \\\"upperalphanum\\\": string.ascii_uppercase + string.digits,\\n \\\"special\\\": string.ascii_letters + string.digits + special_chars,\\n }\\n\\n default_nchars_lookup = {\\n \\\"str\\\": 43,\\n \\\"int\\\": 16,\\n }\\n\\n # get pool of given type\\n pool = pool_lookup.get(type, None)\\n if not pool:\\n raise RefError(\\n \\\"{}: unknown random type used. Choose one of {}\\\".format(type, [key for key in pool_lookup])\\n )\\n\\n # get default value for nchars if nchars is not specified\\n if not nchars:\\n nchars = default_nchars_lookup.get(type, 8)\\n else:\\n # check input for nchars\\n try:\\n nchars = int(nchars)\\n except ValueError:\\n raise RefError(f\\\"Ref error: eval_func: {nchars} cannot be converted into integer.\\\")\\n\\n # check if any special characters are specified without using type special\\n if type != \\\"special\\\" and special_chars != string.punctuation:\\n raise RefError(\\n \\\"Ref error: eval_func: {} has no option to use special characters. Use type special instead, i.e. ||random:special:{}\\\".format(\\n type, special_chars\\n )\\n )\\n\\n # check if pool is valid, eliminates duplicates\\n allowed_pool = string.ascii_letters + string.digits + string.punctuation\\n pool = \\\"\\\".join(set(pool).intersection(allowed_pool))\\n\\n # generate string based on given pool\\n generated_str = \\\"\\\".join(secrets.choice(pool) for i in range(nchars))\\n\\n # set ctx.data to generated string\\n ctx.data = generated_str\",\n \"def environments_of(groups):\\n types = {}\\n for group in groups:\\n for env in group.environments:\\n et = env.environmentType\\n envs = types.setdefault((et.id, et.name), set())\\n envs.add((env.id, env.name))\\n return types\",\n \"def generate_random_human_from_environment(environment,\\n generate_appearance=False):\\n appearance = None\\n if generate_appearance:\\n appearance = \\\\\\n HumanAppearance.generate_rand_human_appearance(HumanAppearance)\\n configs = HumanConfigs.generate_random_human_config(environment)\\n return Human.generate_human(appearance, configs)\",\n \"def run_seed(self, mode):\\n # Clear data from tables\\n # clear_data()\\n if mode == MODE_CLEAR:\\n return\\n # industry = create_industy()\\n # structure_type, structure_type1 = create_structure_type()\\n # structure, structure2 = create_structure(structure_type)\\n # stock = create_stock(industry, structure)\\n # create_price_list(stock)\\n # create_news(stock, structure, structure2)\\n # create_analysis(structure)\\n # create_market_indices()\\n create_section_group()\",\n \"def make_env(env_name):\\n \\n env = gym.make(env_name) \\n return env\",\n \"def createEnvironment(self, _):\\r\\n if self._namespaces:\\r\\n raise InternalError('The environment can have only one namespace '\\r\\n 'at a time.')\\r\\n\\r\\n environment = Environment(self)\\r\\n return self._avatar.callRemote('setupNamespace', environment)\",\n \"def _create_local_env_from_spec(\\n env_spec: study_pb2.EnvironmentSpec) -> environment.Environment:\\n global HAS_PROCO\\n env_type = env_spec.WhichOneof('type')\\n\\n if env_type == 'procgen':\\n from rlds_creator.envs import procgen_env\\n return procgen_env.ProcgenEnvironment(env_spec)\\n elif env_type == 'atari':\\n from rlds_creator.envs import atari_env\\n return atari_env.AtariEnvironment(env_spec)\\n elif env_type == 'dmlab':\\n from rlds_creator.envs import dmlab_env\\n return dmlab_env.DmLabEnvironment(env_spec)\\n elif env_type == 'net_hack':\\n from rlds_creator.envs import net_hack_env\\n return net_hack_env.NetHackEnvironment(env_spec)\\n elif env_type == 'robodesk':\\n from rlds_creator.envs import robodesk_env\\n return robodesk_env.RoboDeskEnvironment(env_spec)\\n elif env_type == 'robosuite':\\n from rlds_creator.envs import robosuite_env\\n return robosuite_env.RobosuiteEnvironment(env_spec)\\n else:\\n raise ValueError('Unsupported environment spec.')\",\n \"def create_environment(loader):\\n loaders = (loader, PackageLoader('pynuts', 'templates'))\\n environment = Environment(\\n loader=ChoiceLoader(loaders), extensions=[ShowOnMatch])\\n environment.globals.update({'url_for': flask.url_for})\\n environment.filters['data'] = filters.data\\n environment.filters['base64'] = filters.base64\\n return environment\",\n \"def make_env(num_steps, stack=True, scale_rew=True):\\n env = retro.make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1')\\n env = gym.wrappers.TimeLimit(env, max_episode_steps=num_steps)\\n env = SonicDiscretizer(env)\\n env = AllowBacktracking(env)\\n if scale_rew:\\n env = RewardScaler(env, REWARD_RATE)\\n env = WarpFrame(env)\\n if stack:\\n env = FrameStack(env, NUM_STATES)\\n return env\",\n \"def env_init(self, env_info={}):\\n self.dealer_sticks = env_info['dealer_sticks']\\n self.random = np.random.RandomState(env_info['seed'])\\n self.current_state = None\",\n \"def build_randomization(\\n cls, constants: CType, parameters: PType\\n ) -> EnvRandomization:\\n return EnvRandomization(\\n parameter_randomizer=cls.build_parameter_randomizer(constants, parameters),\\n observation_randomizer=EnvObservationRandomizer(\\n cls.build_observation_randomizers(constants)\\n ),\\n action_randomizer=EnvActionRandomizer(\\n cls.build_action_randomizers(constants)\\n ),\\n simulation_randomizer=EnvSimulationRandomizer(\\n cls.build_simulation_randomizers(constants)\\n ),\\n )\",\n \"def generate_environment(self):\\n try:\\n if self._environment is None:\\n self._environment = Environment.fromfilepath(self._environmentName,\\n self._configuration.environment_file_path)\\n except Exception:\\n raise\",\n \"def setup_env(config_path=BENCHMARK_CONF_PATH, env_type=None):\\n check_arg_not_blank(config_path, 'config_path')\\n \\n conf = BenchmarkConfig(config_path=config_path)\\n _setup_fabric_env(conf)\\n\\n if not env_type or env_type == 'client':\\n execute(_setup_clients, conf, hosts=_config_hosts_or_cli_hosts(conf.client_conf))\\n\\n if not env_type or env_type == 'server':\\n execute(_setup_servers, conf, hosts=_config_hosts_or_cli_hosts(conf.server_conf))\",\n \"def generate_E_random(number_obeservations, number_environments):\\n E = np.random.randint(0, number_environments, (number_obeservations,1))\\n return E\",\n \"def make_environment(manager: 'Manager') -> None:\\n global environment\\n environment = FlexGetEnvironment(\\n undefined=StrictUndefined,\\n loader=ChoiceLoader(\\n [\\n PackageLoader('flexget'),\\n FileSystemLoader(os.path.join(manager.config_base, 'templates')),\\n ]\\n ),\\n extensions=['jinja2.ext.loopcontrols'],\\n )\\n environment.template_class = FlexGetTemplate\\n for name, filt in list(globals().items()):\\n if name.startswith('filter_'):\\n environment.filters[name.split('_', 1)[1]] = filt\\n for name, test in list(globals().items()):\\n if name.startswith('is_'):\\n environment.tests[name.split('_', 1)[1]] = test\",\n \"def create_data_generators(shuffle=True, novelty_type='normal', item_to_include='None',\\n scale_level=1):\\n\\n total_noi_i = 10 # Number of processed images from one environemnt i\\n noe = 1 # Numer of environments\\n n_p = 32 # Patch size, patch --> n_p x n_p\\n\\n novelty = novelty_type\\n datasets = []\\n\\n for i in range(noe):\\n\\n # Load only images of the environment which includes images of the stated novel item.\\n if item_to_include is not None and novelty == 'novel_item':\\n dataset_env_i = PolycraftDatasetWithSpecificItem(\\n nov_type=novelty, noi=total_noi_i, env_idx=i, p_size=n_p, scale_factor=scale_level,\\n item_name=item_to_include)\\n datasets.append(dataset_env_i)\\n # We only process the one environment with the item (maybe change this\\n # if we have more than one environement per novel_item!?)\\n break\\n\\n # No specific item given which should be included.\\n else:\\n dataset_env_i = PolycraftDatasetNoSpecificItem(\\n nov_type=novelty, noi=total_noi_i, env_idx=i, p_size=n_p, scale_factor=scale_level)\\n datasets.append(dataset_env_i)\\n\\n final_dataset = ConcatDataset(datasets)\\n\\n total_noi = len(final_dataset) # Total number of processed images from all datasets\\n\\n if(total_noi < 7):\\n print('Number of samples too small for splitting dataset in training-/valid-/test set.')\\n\\n train_noi = int(0.7 * total_noi) # Number of images used for training (70 %)\\n valid_noi = int(0.15 * total_noi) # Number of images used for validation (15 %)\\n test_noi = total_noi - train_noi - valid_noi # Number of images used for testing (15 %)\\n train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split(\\n final_dataset, [train_noi, valid_noi, test_noi])\\n\\n train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)\\n valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=True)\\n test_loader = DataLoader(test_dataset, batch_size=1, shuffle=True)\\n\\n return train_loader, valid_loader, test_loader\",\n \"def generate_random_roles():\\n\\n role_names = set()\\n roles_size = 0\\n while roles_size != 3:\\n role_name = ''\\n for i in range(3):\\n role_name += random.choice(['0', '1'])\\n if role_name not in role_names:\\n role_names.add(role_name)\\n roles_size += 1\\n\\n for role_name in role_names:\\n delete_access = ActionTypes.DELETE.value if role_name[0] == '1' else ''\\n write_access = ActionTypes.WRITE.value if role_name[1] == '1' else ''\\n read_access = ActionTypes.READ.value if role_name[2] == '1' else ''\\n\\n allowed_actions = [access for access in (delete_access, write_access, read_access) if access]\\n Role(role_name, allowed_actions)\",\n \"def main():\\n # parse arguments from the command line (argparse validates arguments)\\n args = _get_args()\\n # build the environment with the given ID\\n env = gym.make(args.env)\\n # play the environment with the given mode\\n if args.mode == 'human':\\n play_human(env)\\n else:\\n play_random(env, args.steps)\",\n \"def generate_hosts(quantity, host_type):\\n assert type(quantity) == int\\n assert host_type in ['source', 'host']\\n for n in range(0, quantity, 1):\\n if host_type == 'host':\\n yield {'hostname': 'host-{}'.format(uuid.uuid4()),\\n 'lastReceived': random.randint(1154394061 * 1000, 1505330380 * 1000)}\\n elif host_type == 'source':\\n yield {'sourcePath': \\\".\\\".join(map(str, (random.randint(0, 254) for _ in range(4)))),\\n 'lastReceived': random.randint(1154394061 * 1000, 1505330380 * 1000)}\",\n \"def env_generator(\\n env_name: str, max_episode_steps: int, env_wrappers: List[gym.Wrapper] = None\\n) -> Callable:\\n\\n def _thunk(rank: int):\\n env = gym.make(env_name)\\n env.seed(777 + rank + 1)\\n env, _ = set_env(env, max_episode_steps, env_wrappers)\\n return env\\n\\n return _thunk\",\n \"def build_net(env, seeds, model_type, hidden_size, noise_std, action_type):\\r\\n torch.manual_seed(seeds[0])\\r\\n if action_type == gym.spaces.box.Box:\\r\\n \\taction_space = env.action_space.shape[0] \\r\\n else:\\r\\n action_space = env.action_space.n\\r\\n if model_type == \\\"ff\\\":\\r\\n net = model.Model_FF(env.observation_space.shape[0], action_space, hidden_size, action_type)\\r\\n elif model_type == \\\"cnn\\\":\\r\\n net = model.Model_CNN1D(env.observation_space.shape[0], action_space, hidden_size)\\r\\n else:\\r\\n net = model.Model_LSTM(env.observation_space.shape[0], action_space, hidden_size, action_type)\\r\\n for seed in seeds[1:]:\\r\\n net = mutate(net, seed, noise_std, copy_net=False)\\r\\n\\r\\n return net\",\n \"def make_vec_env(env_id, env_type, num_env, seed, wrapper_kwargs=None, start_index=0, reward_scale=1.0, gamestate=None):\\n if wrapper_kwargs is None: wrapper_kwargs = {}\\n mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0\\n seed = seed + 10000 * mpi_rank if seed is not None else None\\n def make_thunk(rank):\\n return lambda: make_env(\\n env_id=env_id,\\n env_type=env_type,\\n subrank = rank,\\n seed=seed,\\n reward_scale=reward_scale,\\n gamestate=gamestate,\\n wrapper_kwargs=wrapper_kwargs\\n )\\n\\n set_global_seeds(seed)\\n if num_env > 1:\\n return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])\\n else:\\n return DummyVecEnv([make_thunk(start_index)])\",\n \"def __init__(self, config_type='random'):\\n switcher = {\\n 'random': self._create_random_config,\\n 'generic_class': self._create_generic_class_config,\\n 'generic_class_diff': self._create_generic_class_diff_config,\\n 'generic_class_orient': self._create_generic_class_orient_config,\\n 'random_sequence_baseline': self._create_random_sequence_baseline_config,\\n 'playground': self._create_playground_config,\\n }\\n self.create_config = switcher.get(config_type, None)\\n\\n logging.info('Setting config type to {}'.format(config_type))\\n if self.create_config is None:\\n logging.warning('Invalid config type {}. Setting config type to the default random config.'\\n .format(config_type))\\n self.create_config = self._create_random_config\",\n \"def create_random(self):\\n number_of_layers = random.choice(self.parameter_choices['number_of_layers'])\\n neurons_per_layer = []\\n dropout_per_layer = []\\n self.network['number_of_layers'] = number_of_layers\\n\\n for i in range(number_of_layers):\\n neurons_per_layer.append(random.choice(self.parameter_choices['neurons_per_layer']))\\n dropout_per_layer.append(random.choice(self.parameter_choices['dropout_per_layer']))\\n\\n self.network['neurons_per_layer'] = neurons_per_layer\\n self.network['dropout_per_layer'] = dropout_per_layer\\n self.network['optimizer'] = random.choice(self.parameter_choices['optimizer'])\\n self.network['activation'] = random.choice(self.parameter_choices['activation'])\",\n \"def make_planet():\\n dice = random.randint(0, 100)\\n if dice < 60:\\n status = 'nopressure'\\n else:\\n status = random.choice(list(constants.STATUS.keys()))\\n\\n return Planet(\\n name=random.choice(constants.NAMES),\\n x=random.randint(constants.XMIN, constants.MAXWIDTH - 1),\\n y=random.randint(constants.YMIN, constants.MAXHEIGHT - 1),\\n system_size=random.choice(list(constants.SYSTEMSIZE.keys())),\\n tech_level=random.choice(list(constants.TECHLEVEL.keys())),\\n regim=random.choice(list(constants.REGIM.keys())),\\n special=random.choice(list(constants.SPECIALRESOURCES.keys())),\\n status=status,\\n price_slip={},\\n shipyard=[])\",\n \"def create_test_env(\\n env_id: str,\\n n_envs: int = 1,\\n stats_path: Optional[str] = None,\\n seed: int = 0,\\n log_dir: Optional[str] = None,\\n should_render: bool = True,\\n hyperparams: Optional[Dict[str, Any]] = None,\\n env_kwargs: Optional[Dict[str, Any]] = None,\\n) -> VecEnv:\\n # Create the environment and wrap it if necessary\\n env_wrapper = get_wrapper_class(hyperparams)\\n\\n hyperparams = {} if hyperparams is None else hyperparams\\n\\n if \\\"env_wrapper\\\" in hyperparams.keys():\\n del hyperparams[\\\"env_wrapper\\\"]\\n\\n vec_env_kwargs = {}\\n vec_env_cls = DummyVecEnv\\n if n_envs > 1 or \\\"Bullet\\\" in env_id:\\n # HACK: force SubprocVecEnv for Bullet env\\n # as Pybullet envs does not follow gym.render() interface\\n vec_env_cls = SubprocVecEnv\\n # start_method = 'spawn' for thread safe\\n\\n env = make_vec_env(\\n env_id,\\n n_envs=n_envs,\\n monitor_dir=log_dir,\\n seed=seed,\\n wrapper_class=env_wrapper,\\n env_kwargs=env_kwargs,\\n vec_env_cls=vec_env_cls,\\n vec_env_kwargs=vec_env_kwargs,\\n )\\n\\n # Load saved stats for normalizing input and rewards\\n # And optionally stack frames\\n if stats_path is not None:\\n if hyperparams[\\\"normalize\\\"]:\\n print(\\\"Loading running average\\\")\\n print(f\\\"with params: {hyperparams['normalize_kwargs']}\\\")\\n path_ = os.path.join(stats_path, \\\"vecnormalize.pkl\\\")\\n if os.path.exists(path_):\\n env = VecNormalize.load(path_, env)\\n # Deactivate training and reward normalization\\n env.training = False\\n env.norm_reward = False\\n else:\\n raise ValueError(f\\\"VecNormalize stats {path_} not found\\\")\\n\\n n_stack = hyperparams.get(\\\"frame_stack\\\", 0)\\n if n_stack > 0:\\n print(f\\\"Stacking {n_stack} frames\\\")\\n env = VecFrameStack(env, n_stack)\\n return env\",\n \"def env_creator(env_config):\\n return CartPoleBTEnv(\\n goal_state=env_config['goal_state'],\\n disturbances=env_config['disturbances'],\\n initial_state=env_config['initial_state'],\\n initial_state_variance=env_config['initial_state_variance']\\n )\",\n \"def make_envs(env_gen: Callable, n_envs: int = 8) -> SubprocVecEnv:\\n envs = [env_gen(i) for i in range(n_envs)]\\n subproc_env = SubprocVecEnv(envs)\\n return subproc_env\",\n \"async def test_create_engine_uses_robot_type(\\n decoy: Decoy, robot_type: RobotType, deck_type: pe_types.DeckType\\n) -> None:\\n # TODO(mc, 2021-06-11): to make these test more effective and valuable, we\\n # should pass in some sort of actual, valid HardwareAPI instead of a mock\\n hardware_api = decoy.mock(cls=API)\\n subject = EngineStore(\\n hardware_api=hardware_api, robot_type=robot_type, deck_type=deck_type\\n )\\n\\n await subject.create(run_id=\\\"run-id\\\", labware_offsets=[], protocol=None)\\n\\n assert subject.engine.state_view.config.robot_type == robot_type\",\n \"def generate_test_environment(tmpdir, dataset):\\n\\n # Overwrite settings with test settings\\n generate_test_settings(tmpdir, dataset)\\n\\n # Generate the archive files\\n for usage in ['train', 'test']:\\n \\n for dstype in ['images', 'labels']:\\n \\n dataset_type = usage + '.' + dstype\\n \\n mnist_dataset = 'datasets.mnist.' + dataset_type\\n filepath = get_setting(mnist_dataset)\\n\\n test_dataset = dataset + '.' + dataset_type\\n generate_test_dataset_archive(filepath, test_dataset)\",\n \"def set_single_environment(cls, products, values):\\n env = values[\\\"name\\\"]\\n for i in range(len(products)):\\n products[i][\\\"environments\\\"] = [env]\\n return products\",\n \"def __create_test_environment(self):\\n os.chdir(self.wd)\\n temp_dir = tempfile.gettempdir()\\n self.test_root = os.path.join(temp_dir, \\\"test-grpc\\\")\\n print(\\\"Creating testing environment in {}\\\".format(self.test_root))\\n if os.path.exists(self.test_root):\\n # delete any previous environment\\n shutil.rmtree(self.test_root)\\n # create root directory\\n os.makedirs(self.test_root)\\n def copy_app(name):\\n app_root = os.path.join(self.test_root, name)\\n os.makedirs(app_root)\\n filename = \\\"grpc-{}\\\".format(name)\\n src = os.path.join(self.args.bin, filename)\\n dst = os.path.join(app_root, filename)\\n shutil.copy(src, dst)\\n return dst\\n # copy client and server into the new test environment\\n self.server_path = copy_app(\\\"server\\\")\\n self.client_path = copy_app(\\\"client\\\")\",\n \"def _get_or_create_environment_objects(openshift_api=settings.OPENSHIFT_API):\\n\\n choices = {}\\n\\n for k in openshift_api.keys():\\n obj, created = Environment.objects.get_or_create(environment=k)\\n choices[k] = obj\\n logger.info('{}, {}'.format(obj, created))\\n\\n return choices\",\n \"def make_env(self, env_id, seed, logger_dir=None, reward_scale=1.0, mpi_rank=0, subrank=0, info_keywords=()):\\n scenario = scenarios.load('{}.py'.format(env_id)).Scenario()\\n world = scenario.make_world()\\n env_dict = {\\n \\\"world\\\": world,\\n 'reset_callback': scenario.reset_world,\\n 'reward_callback': scenario.reward, \\n 'observation_callback': scenario.observation,\\n 'info_callback': None,\\n 'done_callback': scenario.done, \\n 'shared_viewer': True\\n }\\n env = gym.make('MultiAgent-v0', **env_dict)\\n env.seed(seed + subrank if seed is not None else None)\\n env = Monitor(env,\\n logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),\\n allow_early_resets=True,\\n info_keywords=info_keywords)\\n env = ClipActionsWrapper(env)\\n if reward_scale != 1.0:\\n from baselines.common.retro_wrappers import RewardScaler\\n env = RewardScaler(env, reward_scale)\\n return env\",\n \"def make_environment(\\n evaluation: bool = False,\\n task: str = 'MountainCarContinuous-v0') -> dm_env.Environment:\\n del evaluation\\n\\n # Load the gym environment.\\n environment = gym.make(task)\\n\\n # Make sure the environment obeys the dm_env.Environment interface.\\n environment = wrappers.GymWrapper(environment)\\n # Clip the action returned by the agent to the environment spec.\\n environment = wrappers.CanonicalSpecWrapper(environment, clip=True)\\n environment = wrappers.SinglePrecisionWrapper(environment)\\n\\n return environment\",\n \"def _create_spacecrafts(self):\\n self.spacecraft = MPDSpacecraft.create_random(self.space, )\\n self.space.spacecrafts.append(self.spacecraft)\\n\\n vehicles_type = utils.class_from_path(self.config[\\\"other_spacecrafts_type\\\"])\\n for _ in range(self.config[\\\"spacecrafts_count\\\"]):\\n self.road.spacecrafts.append(spacecrafts_type.create_random(self.space))\",\n \"def conda_create_environment(name, python='3'):\\n conda = '{0}/bin/conda'.format(utils.home('apps', 'miniconda'))\\n\\n run('{conda} create --name {name} python={python} --yes'.format(\\n name=name,\\n conda=conda,\\n python=python))\",\n \"def build_env(env_id, inst=0, **all_args):\\n env_keys = ['dt', 'timing']\\n if env_id == 'RDM-v0':\\n env_keys.append('stimEv')\\n env_args = {x: all_args[x] for x in env_keys}\\n\\n if all_args['combine']:\\n env1 = gym.make(env_id, **env_args)\\n env_args['timing'] = all_args['timing2']\\n env2 = gym.make(all_args['env2'], **env_args)\\n env = combine.combine(env1, env2, delay=all_args['delay'])\\n else:\\n env = gym.make(env_id, **env_args)\\n if all_args['trial_hist']:\\n env = trial_hist.TrialHistory(env, rep_prob=all_args['rep_prob'],\\n block_dur=all_args['bl_dur'],\\n blk_ch_prob=all_args['blk_ch_prob'])\\n elif all_args['side_bias']:\\n env = side_bias.SideBias(env, prob=all_args['rep_prob'],\\n block_dur=all_args['bl_dur'])\\n if all_args['catch_trials']:\\n env = catch_trials.CatchTrials(env,\\n catch_prob=all_args['catch_prob'],\\n stim_th=all_args['stim_th'])\\n if all_args['reaction_time']:\\n env = reaction_time.ReactionTime(env)\\n if all_args['pass_reward']:\\n env = pass_reward.PassReward(env)\\n if all_args['pass_action']:\\n env = pass_action.PassAction(env)\\n env = manage_data.manage_data(env, inst=inst, plt_tr=all_args['figs'],\\n folder=all_args['save_path'])\\n return env\",\n \"def testrandom(self):\\n for i in range(100):\\n WeaponAbility()\",\n \"def randomize(self):\\n for network in self.networks.values():\\n network.database = []\\n self.env = Environment(self.networks)\",\n \"def make_mujoco_env(env_id, seed):\\n rank = MPI.COMM_WORLD.Get_rank()\\n set_global_seeds(seed + 10000 * rank)\\n env = gym.make(env_id)\\n env = Monitor(env, os.path.join(logger.get_dir(), str(rank)))\\n env.seed(seed)\\n return env\",\n \"def _create_random_ca(cls):\\n random_ca = entity.CustomAttribute()\\n random_ca.ca_type = random.choice(AttributesTypes.ALL_TYPES)\\n random_ca.title = cls._generate_title(random_ca.ca_type)\\n random_ca.definition_type = random.choice(objects.all_objects)\\n return random_ca\",\n \"def draw_random_setup(types_available, team, game_dim):\\n\\n nr_pieces = len(types_available)-1\\n types_available = [type_ for type_ in types_available if not type_ == 0]\\n if game_dim == 5:\\n row_offset = 2\\n elif game_dim == 7:\\n row_offset = 3\\n else:\\n row_offset = 4\\n setup_agent = np.empty((row_offset, game_dim), dtype=object)\\n if team == 0:\\n flag_positions = [(game_dim-1, j) for j in range(game_dim)]\\n flag_choice = np.random.choice(range(len(flag_positions)), 1)[0]\\n flag_pos = game_dim-1 - flag_positions[flag_choice][0], game_dim-1 - flag_positions[flag_choice][1]\\n setup_agent[flag_pos] = pieces.Piece(0, 0, flag_positions[flag_choice])\\n\\n types_draw = np.random.choice(types_available, nr_pieces, replace=False)\\n positions_agent_0 = [(i, j) for i in range(game_dim-row_offset, game_dim) for j in range(game_dim)]\\n positions_agent_0.remove(flag_positions[flag_choice])\\n\\n for idx in range(nr_pieces):\\n pos = positions_agent_0[idx]\\n setup_agent[(game_dim-1 - pos[0], game_dim-1 - pos[1])] = pieces.Piece(types_draw[idx], 0, pos)\\n elif team == 1:\\n flag_positions = [(0, j) for j in range(game_dim)]\\n flag_choice = np.random.choice(range(len(flag_positions)), 1)[0]\\n setup_agent[flag_positions[flag_choice]] = pieces.Piece(0, 1, flag_positions[flag_choice])\\n\\n types_draw = np.random.choice(types_available, nr_pieces, replace=False)\\n positions_agent_1 = [(i, j) for i in range(row_offset) for j in range(game_dim)]\\n positions_agent_1.remove(flag_positions[flag_choice])\\n\\n for idx in range(nr_pieces):\\n pos = positions_agent_1[idx]\\n setup_agent[pos] = pieces.Piece(types_draw[idx], 1, pos)\\n return setup_agent\",\n \"def create_universe():\\n # Universe is a large list, planets and others things are separated using\\n # type() and isinstance(object, ClassInfo)\\n univers = []\\n captain = Captain()\\n captain.ship = Ship()\\n captain.account = BankAccount(captain.name, [])\\n planetes = create_planetes()\\n for planete in planetes:\\n if planete.homeworld:\\n captain.homeworld = planete\\n captain.location = planete\\n if planete.tech_level > 5:\\n planete.shipyard = populate_shipyard()\\n\\n univers.append(captain)\\n univers.extend(planetes)\\n\\n return univers\",\n \"def random_placement(area):\\n\\n area.create_houses(True)\\n\\n for house in area.houses:\\n place_house(area, house)\",\n \"def __init__(self, env, random_seed=None):\\n self.env = env \\n self.RandomState = np.random.RandomState(random_seed)\",\n \"def initialize_random_number_generator(self,question_type):\\n\\t\\tself.generator.seed(self.generate_index(self.magic, self.level, self.problem_id, question_type))\",\n \"def _active_environment(self, tuple_type):\\n return\",\n \"def create_environment(args):\\n env.username = args.user\\n env.password = args.password\\n env.service_url = args.service_url\\n env.quiet = args.quiet\\n env.verbose = args.verbose\\n env.manifest = args.manifest\\n env.debug = args.debug\\n env.always_confirm = args.yes\\n env.args = args\\n env.api = ravello.RavelloClient(env.username, env.password, env.service_url)\",\n \"def network_factory(\\n environment_spec: specs.EnvironmentSpec,\\n agent_types: Dict[ma_types.AgentID, ma_types.GenericAgent],\\n init_network_fn: Optional[ma_types.InitNetworkFn] = None\\n) -> ma_types.MultiAgentNetworks:\\n init_fn = init_network_fn or init_default_network\\n networks = {}\\n for agent_id, agent_type in agent_types.items():\\n single_agent_spec = ma_utils.get_agent_spec(environment_spec, agent_id)\\n networks[agent_id] = init_fn(agent_type, single_agent_spec)\\n return networks\",\n \"def role_batch():\\n return [\\n RoleFactory(\\n scopes=ScopeFactory.create_batch(randint(0, 3), type=choice(('odp', 'client'))),\\n is_collection_role=n in (1, 2) or randint(0, 1),\\n )\\n for n in range(randint(3, 5))\\n ]\",\n \"def _random_mode(self):\\n modes = ((1, 2, 3, 4, 5),\\n (1, 2, 4, 5),\\n (2, 3, 4),\\n (1, 3, 5),\\n (1, 5),\\n (2, 4),\\n (3,),\\n ())\\n return random.choice(modes)\",\n \"def random_player(env_name, render=False):\\n\\n # Make\\n env = gym.make(env_name)\\n\\n n_game = 0\\n\\n # For each game\\n while True:\\n\\n # Reset\\n env.reset()\\n done = False\\n if render:\\n env.render()\\n\\n # Until the end\\n while not done:\\n\\n # Random agent moves\\n action = env.action_space.sample()\\n\\n # Environment moves\\n observation, reward, done, info = env.step(action)\\n\\n # Result\\n if render:\\n env.render()\\n yield observation\\n\\n n_game += 1\\n\\n # Leave\\n env.close()\",\n \"def testrandom(self):\\n for i in range(100):\\n AmuletAbility()\",\n \"def jobtype_factory(jobtype_toolkit):\\n\\n jobtype_toolkits = {'aimless_shooting': jobtype.AimlessShooting(),\\n 'committor_analysis': jobtype.CommittorAnalysis(),\\n 'equilibrium_path_sampling': jobtype.EquilibriumPathSampling(),\\n 'find_ts': jobtype.FindTS(),\\n 'umbrella_sampling': jobtype.UmbrellaSampling()}\\n\\n if jobtype_toolkit not in jobtype_toolkits.keys():\\n raise ValueError('unsupported JobType name: ' + jobtype_toolkit)\\n\\n return jobtype_toolkits[jobtype_toolkit]\",\n \"def seed_model(model):\\n if model == 'all':\\n seed_all()\\n elif model == 'client':\\n seed_client()\\n elif model == 'comment':\\n seed_comment()\\n elif model == 'staff':\\n seed_staff()\\n elif model == 'request':\\n seed_request()\",\n \"def construct_envs(\\n config,\\n env_class,\\n workers_ignore_signals=False,\\n):\\n\\n num_processes = config.NUM_ENVIRONMENTS\\n configs = []\\n env_classes = [env_class for _ in range(num_processes)]\\n dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)\\n scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES\\n if \\\"*\\\" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:\\n scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)\\n\\n if num_processes > 1:\\n if len(scenes) == 0:\\n raise RuntimeError(\\n \\\"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes\\\"\\n )\\n\\n if len(scenes) < num_processes:\\n scenes = scenes * num_processes\\n\\n random.shuffle(scenes)\\n\\n scene_splits = [[] for _ in range(num_processes)]\\n for idx, scene in enumerate(scenes):\\n scene_splits[idx % len(scene_splits)].append(scene)\\n\\n assert sum(map(len, scene_splits)) == len(scenes)\\n\\n for i in range(num_processes):\\n proc_config = config.clone()\\n proc_config.defrost()\\n\\n task_config = proc_config.TASK_CONFIG\\n task_config.SEED = task_config.SEED + i\\n if len(scenes) > 0:\\n task_config.DATASET.CONTENT_SCENES = scene_splits[i]\\n\\n task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (\\n config.SIMULATOR_GPU_ID\\n )\\n\\n task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS\\n\\n proc_config.freeze()\\n configs.append(proc_config)\\n\\n envs = habitat.ThreadedVectorEnv(\\n make_env_fn=make_env_fn,\\n env_fn_args=tuple(zip(configs, env_classes)),\\n workers_ignore_signals=workers_ignore_signals,\\n )\\n return envs\",\n \"def create_aiida_project_environment(self):\\n try:\\n self.create_folder_structure()\\n self.build_python_environment()\\n self.install_packages_from_index()\\n except Exception:\\n self.exit_on_exception()\\n raise\\n self.create_spec_entry()\",\n \"def generate_random_individual():\\n genotype = []\\n ### Your code here\\n return {'genotype': genotype, 'fitness': None }\",\n \"def base_env(*args, **kwargs):\\n try:\\n # regular gym\\n env = gym.make(*args, **kwargs)\\n except:\\n try:\\n # gym retro\\n env = retro.make(*args, **kwargs)\\n except:\\n # gym-super-mario-bros\\n env = gym_super_mario_bros.make(*args, **kwargs)\\n env.recognized = None\\n return env\",\n \"def get_rng(environ, experiment, swabid):\\n r = Random()\\n r.seed(experiment.seed_strategy(environ, experiment, swabid))\\n return r\",\n \"def usertype_add(request, simulation):\\n # Create new distributions with good defaults.\\n alphaTI = Distribution(type='NONE', mean=10)\\n alphaTP = Distribution(type='NONE', mean=15)\\n beta = Distribution(type='NONE', mean=5)\\n delta = Distribution(type='NONE', mean=10)\\n departureMu = Distribution(type='NONE', mean=2)\\n gamma = Distribution(type='NONE', mean=20)\\n modeMu = Distribution(type='NONE', mean=5)\\n penaltyTP = Distribution(type='NONE', mean=2)\\n routeMu = Distribution(type='NONE', mean=10)\\n # Default value for t star is average arrival at middle of period and\\n # uniform distribution over half of the period.\\n mid_time = (simulation.startTime + simulation.lastRecord) / 2\\n length = simulation.lastRecord - simulation.startTime\\n tstar = Distribution(type='UNIFORM', mean=mid_time, std=length / (4 * sqrt(3)))\\n # Save the distributions to generate ids.\\n alphaTI.save()\\n alphaTP.save()\\n beta.save()\\n delta.save()\\n departureMu.save()\\n gamma.save()\\n modeMu.save()\\n penaltyTP.save()\\n routeMu.save()\\n tstar.save()\\n # Create the new user type.\\n usertype = UserType()\\n usertype.alphaTI = alphaTI\\n usertype.alphaTP = alphaTP\\n usertype.beta = beta\\n usertype.delta = delta\\n usertype.departureMu = departureMu\\n usertype.gamma = gamma\\n usertype.modeMu = modeMu\\n usertype.penaltyTP = penaltyTP\\n usertype.routeMu = routeMu\\n usertype.tstar = tstar\\n # Set user_id to user_id of previous usertype + 1.\\n usertypes = get_query('usertype', simulation)\\n if usertypes.exists():\\n usertype.user_id = usertypes.last().user_id + 1\\n else:\\n usertype.user_id = 1\\n usertype.save()\\n # Create a demand segment and a matrix for the user type.\\n matrix = Matrices()\\n matrix.save()\\n demandsegment = DemandSegment()\\n demandsegment.usertype = usertype\\n demandsegment.matrix = matrix\\n demandsegment.save()\\n demandsegment.demand.add(simulation.scenario.demand)\\n # Return the view to edit the new user type.\\n return HttpResponseRedirect(\\n reverse('metro:usertype_edit', args=(simulation.id, demandsegment.id,))\\n )\",\n \"def generate_deck(suits=4, type_cards=13):\\n cards = []\\n for suite in range(suits):\\n for type_card in range(1, type_cards+1):\\n # Setting the key-value pair for every card\\n if (type_card == 1):\\n cards.append({'A':type_cards+1})\\n elif (type_card == 11):\\n cards.append({'J': type_card})\\n elif (type_card == 12):\\n cards.append({'Q': type_card})\\n elif (type_card == 13):\\n cards.append({'K': type_card})\\n else:\\n cards.append({type_card:type_card})\\n # Randomize the set of cards in the deck\\n random.shuffle(cards)\\n return cards\",\n \"def setup(env, NUM_TRACKS, landtime, t_inter):\\n # Create the airport\\n airport = Airport(env, NUM_TRACKS, landtime)\\n\\n # Create 4 initial planes\\n for i in range(1):\\n env.process(plane(env, 'Aviao %d' % i, airport))\\n\\n # Create more planes while the simulation is running\\n while True:\\n yield env.timeout(random.randint(t_inter-2, t_inter+2))\\n# yield env.timeout(random.expovariate(1.0 / t_inter))\\n i += 1\\n env.process(plane(env, 'Aviao %d' % i, airport))\",\n \"def generate(count):\\n return unpack_random_animals(generate_animals_randomly(count))\",\n \"def get_env_type ( base_name ) :\\n return base_name.split( '-', 1 )[ 0 ]\",\n \"def init_from_env(\\n *,\\n device_type: T.Optional[str] = None,\\n dist_init_method_type: T.Literal[\\\"env\\\", \\\"tcp\\\", \\\"file\\\"] = \\\"env\\\",\\n pg_backend: T.Optional[str] = None,\\n pg_timeout: timedelta = default_pg_timeout,\\n float32_matmul_precision: str = \\\"high\\\",\\n) -> torch.device:\\n device = torch.device(\\\"cpu\\\") if device_type == \\\"cpu\\\" else get_device_from_env()\\n\\n if device_type is not None and device.type != device_type:\\n raise RuntimeError(\\n f\\\"Device type is specified to {device_type} but got {device.type} from env\\\"\\n )\\n\\n if _check_dist_env():\\n if not torch.distributed.is_available():\\n _log.warning(\\n \\\"torch.distributed is not available. Skipping initializing the process group.\\\"\\n )\\n return device\\n if torch.distributed.is_initialized():\\n _log.warning(\\n \\\"torch.distributed is already initialized. Skipping initializing the process group.\\\"\\n )\\n return device\\n pg_backend = (\\n pg_backend\\n if pg_backend is not None\\n else get_process_group_backend_from_device(device)\\n )\\n init_method: Optional[str] = None\\n if dist_init_method_type == \\\"tcp\\\":\\n init_method = get_tcp_init_method()\\n elif dist_init_method_type == \\\"file\\\":\\n init_method = get_file_init_method()\\n torch.distributed.init_process_group(\\n init_method=init_method, backend=pg_backend, timeout=pg_timeout\\n )\\n maybe_enable_tf32(float32_matmul_precision)\\n return device\",\n \"def init_gym(env_name):\\n env = gym.make(env_name)\\n obs_dim = env.observation_space.shape[0]\\n act_dim = env.action_space.shape[0]\\n\\n return env, obs_dim, act_dim\",\n \"def init_gym(env_name):\\n env = gym.make(env_name)\\n obs_dim = env.observation_space.shape[0]\\n act_dim = env.action_space.shape[0]\\n\\n return env, obs_dim, act_dim\",\n \"def do_create_environment(csp: CloudProviderInterface, environment_id=None):\\n\\n environment = Environments.get(environment_id)\\n\\n with claim_for_update(environment) as environment:\\n\\n if environment.cloud_id is not None:\\n app.logger.warning(\\n \\\"Environment cloud ID %s already present.\\\", environment.cloud_id\\n )\\n return\\n\\n parent_id = environment.application.cloud_id\\n tenant_id = environment.portfolio.csp_data[\\\"tenant_id\\\"]\\n\\n log_do_create_environment(environment.portfolio.id, parent_id, tenant_id)\\n\\n payload = EnvironmentCSPPayload(\\n tenant_id=tenant_id, display_name=environment.name, parent_id=parent_id\\n )\\n env_result = csp.create_environment(payload)\\n Environments.update(environment, new_data={\\\"cloud_id\\\": env_result.id})\\n\\n app.logger.info(\\\"Created environment %s\\\", env_result.name)\\n async_result = create_subscription.delay(environment_id=environment.id)\\n app.logger.info(\\n \\\"Attempting to create subscription for environment %s [Task ID: %s])\\\",\\n env_result.name,\\n async_result.task_id,\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.639185","0.61963034","0.6018439","0.5951477","0.58627504","0.5845492","0.57870317","0.57843524","0.5763606","0.56360656","0.56098664","0.5605386","0.55746096","0.55565745","0.55424","0.5505138","0.5489955","0.5485594","0.5475609","0.54569376","0.54431057","0.5383052","0.5377942","0.537035","0.533368","0.5313675","0.5278804","0.5278804","0.52732605","0.52709585","0.52593523","0.52563876","0.52329487","0.52316195","0.5218797","0.52102125","0.5194515","0.51866895","0.5166156","0.515835","0.51398325","0.5135437","0.5132706","0.51324636","0.5131158","0.51213187","0.5096718","0.509515","0.50815076","0.507846","0.50758123","0.5074542","0.50720716","0.505994","0.5058852","0.5054973","0.50504345","0.50494015","0.5032212","0.50084466","0.49937198","0.49893355","0.4988699","0.49881414","0.49587047","0.49550214","0.4952673","0.4946545","0.49359736","0.49356723","0.49349436","0.49067375","0.48907775","0.48858875","0.488026","0.48751807","0.48678333","0.48659298","0.48584455","0.48572108","0.4855854","0.48518696","0.48496425","0.4840201","0.48333257","0.48306638","0.4829679","0.4815217","0.48084435","0.47800606","0.4769437","0.47504744","0.47403175","0.47369665","0.47293139","0.47227347","0.47162747","0.47113392","0.47113392","0.47046828"],"string":"[\n \"0.639185\",\n \"0.61963034\",\n \"0.6018439\",\n \"0.5951477\",\n \"0.58627504\",\n \"0.5845492\",\n \"0.57870317\",\n \"0.57843524\",\n \"0.5763606\",\n \"0.56360656\",\n \"0.56098664\",\n \"0.5605386\",\n \"0.55746096\",\n \"0.55565745\",\n \"0.55424\",\n \"0.5505138\",\n \"0.5489955\",\n \"0.5485594\",\n \"0.5475609\",\n \"0.54569376\",\n \"0.54431057\",\n \"0.5383052\",\n \"0.5377942\",\n \"0.537035\",\n \"0.533368\",\n \"0.5313675\",\n \"0.5278804\",\n \"0.5278804\",\n \"0.52732605\",\n \"0.52709585\",\n \"0.52593523\",\n \"0.52563876\",\n \"0.52329487\",\n \"0.52316195\",\n \"0.5218797\",\n \"0.52102125\",\n \"0.5194515\",\n \"0.51866895\",\n \"0.5166156\",\n \"0.515835\",\n \"0.51398325\",\n \"0.5135437\",\n \"0.5132706\",\n \"0.51324636\",\n \"0.5131158\",\n \"0.51213187\",\n \"0.5096718\",\n \"0.509515\",\n \"0.50815076\",\n \"0.507846\",\n \"0.50758123\",\n \"0.5074542\",\n \"0.50720716\",\n \"0.505994\",\n \"0.5058852\",\n \"0.5054973\",\n \"0.50504345\",\n \"0.50494015\",\n \"0.5032212\",\n \"0.50084466\",\n \"0.49937198\",\n \"0.49893355\",\n \"0.4988699\",\n \"0.49881414\",\n \"0.49587047\",\n \"0.49550214\",\n \"0.4952673\",\n \"0.4946545\",\n \"0.49359736\",\n \"0.49356723\",\n \"0.49349436\",\n \"0.49067375\",\n \"0.48907775\",\n \"0.48858875\",\n \"0.488026\",\n \"0.48751807\",\n \"0.48678333\",\n \"0.48659298\",\n \"0.48584455\",\n \"0.48572108\",\n \"0.4855854\",\n \"0.48518696\",\n \"0.48496425\",\n \"0.4840201\",\n \"0.48333257\",\n \"0.48306638\",\n \"0.4829679\",\n \"0.4815217\",\n \"0.48084435\",\n \"0.47800606\",\n \"0.4769437\",\n \"0.47504744\",\n \"0.47403175\",\n \"0.47369665\",\n \"0.47293139\",\n \"0.47227347\",\n \"0.47162747\",\n \"0.47113392\",\n \"0.47113392\",\n \"0.47046828\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5628647"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":7178,"cells":{"query":{"kind":"string","value":"Recursively load all teachers that can be found in the current experiment's directory."},"document":{"kind":"string","value":"def load_teachers(self):\n # Get the experiment's directory to load from\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\n self.load_teacher_experiment(ex_dir)\n if len(self.teacher_policies) < self.num_teachers:\n print(\n f\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\"\n )\n self.load_teachers()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_teacher_experiment(self, exp: Experiment):\n _, _, extra = load_experiment(exp)\n self.unpack_teachers(extra)","def preload_all_problems(self):\n for _, _, filenames in os.walk(self.problemDir):\n for filename in filenames:\n if filename[-3:] == \".py\" and filename != \"__init__.py\":\n self.load_problem_file(filename[0:-3])","def loadallskills(self):\r\n for skill in os.listdir( os.path.join( es.getAddonPath( info.basename ), \"skills\" )):\r\n es.load(\"%s/skills/%s\" % (info.basename, skill))","def load_training():\n for can in candidates:\n trainings[can] = []\n for subdir, dirs, files in os.walk(os.path.join(corpus_dir, can)):\n for doc in files:\n trainings[can].append(doc)","def _load_dirs(self):\n rootdirs = self._docset.get_compounds(xml.Directory,\n lambda x: x.get_parent() is None)\n for dirdoc in rootdirs:\n self._load_dir(dirdoc, None)","def load_test_users():\n return [load_test_angel(), load_test_troublemaker(), load_test_rebel()]","def load_fixtures(self):\n for fixture_dir in settings.FIXTURE_DIRS:\n fixture_dir = os.path.join(fixture_dir, self.filesystem_name)\n for (root, dirs, files) in os.walk(fixture_dir):\n for file in files:\n full_file_path = os.path.join(root, *dirs, file)\n with open(full_file_path, 'rb') as f:\n self.save(os.path.relpath(full_file_path, fixture_dir), f)","def _load_module_recursive(self, dir) :\t\n\t\tfor filepath in os.listdir(dir) :\n\t\t\tfullpath = os.path.join(dir, filepath)\n\n\t\t\tif os.path.isdir(fullpath) :\n\t\t\t\tself._load_module_recursive(fullpath)\n\n\t\t\telif os.path.splitext(filepath)[1] == '.py' :\n\t\t\t\tutils.load_module(fullpath, self.settings.ROOT_PATH)","def __init__(self, *paths, **kwargs):\n trajectories = load_trajectories(*paths, **kwargs)\n super().__init__(trajectories, **kwargs)","def load_data(self) -> None:\n self.paths: List[str] = []\n self.durations: List[float] = []\n self.transcriptions: List[str] = []\n\n def raise_(err):\n \"\"\"raises error if problem during os.walk\"\"\"\n raise err\n\n for subset in self.subsets:\n subset_path = os.path.join(self.root, self.base_dir, subset)\n for root, dirs, files in os.walk(subset_path, onerror=raise_):\n if not files:\n continue\n matches = fnmatch.filter(files, \"*.trans.txt\")\n assert len(matches) == 1, \"> 1 transcription file found\"\n self._parse_transcription_file(root, matches[0])\n\n self._sort_by_duration()","def load_data_from_files(self):\n # separated method to allow mock easier\n logger.info(\"Loading data...\")\n parent = Path(__file__).parent\n path = parent / \"resources\" / \"scores.txt\"\n self.scorer.load_from_file(path)\n path = parent / \"resources\" / \"american-english-large.txt\"\n self.trie.load_from_file(path)\n path = parent / \"resources\" / \"reels.txt\"\n self.reels = Reel.get_from_file(path)\n logger.info(\"Data loaded!\")","def discover_examples():\n root = './examples'\n for filename in os.listdir(root):\n if os.path.splitext(filename)[1] == '.py':\n yield os.path.join(root, filename)","def loadTrie(self):\n for file in self._gram_files:\n trie_file = getTrieFile(os.path.basename(file), self._pickle_dir)\n with open(trie_file, 'rb') as fd:\n self._tries.append(pickle.load(fd))","def prune_teachers(self):\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]","def _load_trials(self) -> hyperopt.Trials:\n if os.path.isfile(self.trials_path):\n trials = pickle.load(open(self.trials_path, \"rb\"))\n else:\n trials = hyperopt.Trials()\n return trials","def imdb_load():\n for root, dirs, filenames in os.walk(os.path.dirname(__file__) + \"/imdb\"):\n for file_name in filenames:\n if file_name.find(\".json\") > 0:\n Movie.imdb_load_file(os.path.dirname(__file__) + \"/imdb/\" + file_name)\n return Movie.__movies","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def load_all_files(self):\n\t\tself.get_rankings()\n\t\tself.get_partition()\n\t\tself.__load_factors()\n\t\tself.get_document_associations()\n\t\tself.get_term_associations()","def _load_templates(cls):\n if cls._raw_templates is None:\n cls._raw_templates = fetch_rrlyrae_templates()","def load_tests(loader, suite, pattern):\n tests = loader.discover(start_dir=here.parents[0], pattern=pattern)\n suite.addTests(tests)\n return suite","def get_teachers(self):\n query = Teacher.all().order('teacher')\n return query.fetch()","def iter_dir(tree, path):\n for f in os.listdir(path):\n if os.path.isfile(path + '/' + f + '/__init__.py'):\n tree[f] = None\n elif os.path.isdir(path + '/' + f):\n tree[f] = {}\n SnakeWM.iter_dir(tree[f], path + '/' + f)","def get_test_examples(self, data_dir):\n \n raise NotImplementedError()","def loadFiles():\n all_chapters = []\n for name in names:\n f = open(file_path + name, \"r\", encoding=\"utf-8\")\n html_file = f.read()\n f.close()\n chap_text = extractText(html_file)\n new_text = cleanText(chap_text)\n all_chapters.append(new_text)\n concatenated_chapters = \" \".join(all_chapters)\n return concatenated_chapters","def loadDirectory(self, dirname):\r\n cachelist=os.listdir(dirname)\r\n testlist=fnmatch.filter(cachelist,'*.hdf5')\r\n \r\n for file_ in testlist:\r\n print(\"Using {0}\".format(file_))\r\n \r\n files = [h5py.File(os.path.join(dirname, fn),'r') for fn in testlist]\r\n return files","def tubs_from_directory(tub_dir, verbose=False):\n tubs = []\n count = 0\n root_path = Path(tub_dir)\n for item in root_path.iterdir():\n if item.is_dir():\n try:\n t = Tub(str(item),read_only=True)\n count += len(t)\n except FileNotFoundError as ex:\n continue\n except ValueError as ex:\n # In case the catalog file is empty\n continue\n tubs.append(t)\n if verbose:\n print( f\"Loaded {count} records.\" )\n\n return tubs","def preload_all(self):\n for tp in self.tps:\n for f in self.featurefiles + self.maskfiles:\n file = os.path.join(tp, f)\n print('preloading {}'.format(file))\n self.load(file, lazy=False)","def update_treemakers():\n global treemakers\n treemakers = {}\n for module_filename in glob(os.path.join(hax.hax_dir + '/treemakers/*.py')):\n module_name = os.path.splitext(os.path.basename(module_filename))[0]\n if module_name.startswith('_'):\n continue\n\n # Import the module, after which we can do hax.treemakers.blah\n __import__('hax.treemakers.%s' % module_name, globals=globals())\n\n # Now get all the treemakers defined in the module\n for tm_name, tm in inspect.getmembers(getattr(hax.treemakers, module_name),\n lambda x: type(x) == type and issubclass(x, TreeMaker)):\n if tm_name == 'TreeMaker':\n # This one is the base class; we get it because we did from ... import TreeMaker at the top of the file\n continue\n if tm_name in treemakers:\n raise ValueError(\"Two treemakers named %s!\" % tm_name)\n treemakers[tm_name] = tm","def find_user_templates(self):\n\n # a list to store file names in\n local_templates = []\n\n # loop through the directory content\n for name in os.listdir(self._template_directory):\n # check to see if it is a directory and not in the database\n if (os.path.isdir(os.path.join(self._template_directory, name)) and\n name not in self._templates):\n # add it to the list\n local_templates.append(name)\n\n return local_templates","def LoadTeacherModels(lang):\n\n # load Trained teacher model parameters\n log_dir = 'data/logs'\n with open(log_dir + '/' + lang + '_model_params', 'rb') as fp:\n params = pickle.load(fp)\n\n model_args = params['args']\n\n if model_args.use_colab is None:\n OUTPUT_DIR = 'ckpts/' + model_args.lang\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\n else:\n from google.colab import drive\n\n drive.mount('/content/gdrive')\n OUTPUT_DIR = '/content/gdrive/My Drive/ckpts/' + model_args.lang\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\n\n if model_args.enc_type == 'gat' and model_args.dec_type == 'transformer':\n models = {}\n OUTPUT_DIR += '/' + model_args.enc_type + '_' + model_args.dec_type\n\n # Load the vocabs\n with open('vocabs/' + model_args.model + '/' +\n lang + '/' + model_args.opt + '_src_vocab', 'rb') as fp:\n src_vocab = pickle.load(fp)\n # loading the target vocab\n model_args.sentencepiece = 'False'\n if model_args.sentencepiece == 'True':\n sp = spm.SentencePieceProcessor()\n sp.load('vocabs/' + model_args.model + '/' +\n lang + '/' + 'train_tgt.model')\n tgt_vocab = sp\n else:\n tgt_vocab = src_vocab\n\n print('Loaded ' + lang + ' Parameters..')\n model = TransGAT(params['args'], params['src_vocab_size'], src_vocab,\n params['tgt_vocab_size'], tgt_vocab)\n # Load the latest checkpoints\n optimizer = tf.train.AdamOptimizer(beta1=0.9, beta2=0.98,\n epsilon=1e-9)\n\n ckpt = tf.train.Checkpoint(\n model=model,\n optimizer=optimizer\n )\n\n ckpt_manager = tf.train.CheckpointManager(ckpt, OUTPUT_DIR, max_to_keep=5)\n if ckpt_manager.latest_checkpoint:\n ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n\n print('Loaded ' + lang + ' Teacher model !')\n\n return model","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def run_load(rootpath):\n global CSV_PATH\n CSV_PATH = rootpath+'/csv_files/'\n load_movies_details()\n load_movies_cast()\n load_movies_reviews()","def load():\n root = Path(__file__).parent\n for path in root.iterdir():\n if path.is_dir() and not path.name.startswith(\"_\"):\n subject = (path / \"subject.txt\").read_text()\n txt = (path / \"body.txt\").read_text()\n html = path / \"body.html\"\n if html.exists():\n html = html.read_text()\n else:\n html = None\n attachment = None\n pymodule = path / \"__init__.py\"\n if pymodule.exists():\n pymodule = importlib.import_module(f\"egapro.emails.{path.name}\")\n attachment = pymodule.attachment\n globals()[path.name] = Email(subject, txt, html, attachment)","def load_templates(self):\n TemplateHandler.templates = []\n for template in os.listdir(TemplateHandler.templates_path):\n template_config = self.load_template_conf(template)\n if template_config is None:\n continue\n TemplateHandler.templates.append(template_config)","def unpack_teachers(self, extra: dict):\n self.teacher_policies.extend(extra[\"teacher_policies\"])\n self.teacher_envs.extend(extra[\"teacher_envs\"])\n self.teacher_expl_strats.extend(extra[\"teacher_expl_strats\"])\n self.teacher_critics.extend(extra[\"teacher_critics\"])\n self.teacher_ex_dirs.extend(extra[\"teacher_ex_dirs\"])","def LoadAllSubElements(self, recursive=False):\n pass","def load_templates(self):\n\n self.templates = []\n\n if os.path.exists(\"question_templates.txt\"):\n for line in open(\"question_templates.txt\", \"r\"):\n self.templates.append(line.replace(\"\\n\", \"\"))","def load(datasets, treemakers='Basics', force_reload=False):\n if isinstance(datasets, str):\n datasets = [datasets]\n if isinstance(treemakers, (type, str)):\n treemakers = [treemakers]\n\n combined_dataframes = []\n\n for treemaker in treemakers:\n\n dataframes = []\n for dataset in datasets:\n minitree_path = get(dataset, treemaker, force_reload=force_reload)\n new_df = pd.DataFrame.from_records(root_numpy.root2array(minitree_path).view(np.recarray)) \n dataframes.append(new_df)\n\n # Concatenate mini-trees of this type for all datasets\n combined_dataframes.append(pd.concat(dataframes))\n\n # Concatenate mini-trees of all types\n if not len(combined_dataframes):\n raise RuntimeError(\"No data was extracted? What's going on??\")\n return pd.concat(combined_dataframes, axis=1)","def load_tamper(self, dataset_dir, subset):\n # Add classes. We have one class.\n # Naming the dataset nucleus, and the class nucleus\n self.add_class(\"tampers\", 1, \"tampers\")\n\n # Which subset?\n # \"val\": use hard-coded list above\n # \"train\": use data from stage1_train minus the hard-coded list above\n # else: use the data from the specified sub-directory\n # assert subset in [\"train\", \"val\", \"stage1_train\", \"stage1_test\", \"stage2_test\"]\n # subset_dir = \"stage1_train\" if subset in [\"train\", \"val\"] else subset\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\n if subset == \"val\" or subset == \"test\":\n image_ids = next(os.walk(dataset_dir))[2]\n else:\n # Get image ids from directory names\n image_ids = next(os.walk(dataset_dir))[2]\n \n\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # dirnew_splicing = '/data/tamper'\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\n\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n \n # dircopy_move = '/data/gy/ca2att/train3'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\n\n # dirnew_sp = '/data/gy/c2newsp/train'\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\n\n # Add images\n for image_id in image_ids:\n self.add_image(\n \"tampers\",\n image_id=image_id[:-4],\n path=os.path.join(dataset_dir, image_id))\n\n # for image_id in image_ids_copy_move:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dircopy_move, 'images', image_id))\n\n # for image_id in image_ids_new_splicing:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_splicing, 'images', image_id))\n\n # # for image_id in image_ids_txt_sp:\n # # self.add_image(\n # # \"tampers\",\n # # image_id=image_id[:-4],\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\n\n # for image_id in image_ids_new_sp:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_sp, 'images', image_id))","def _find_fixtures(self, start_dir):\r\n fixtures = []\r\n def _find(arg, dirname, names):\r\n if (dirname.endswith('fixtures')) and (dirname.find('unit_test')==-1):\r\n for name in names:\r\n if (name.endswith(FIXTUERS_EXT)) and (name.find('initial_data')==-1):\r\n fixtures.append(name.replace(FIXTUERS_EXT, ''))\r\n os.path.walk(start_dir, _find, None)\r\n \r\n return fixtures","def _load(self, directory):\n pass","def load_all_from_path(self, path):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\t#111: handle expanded paths\n\t\tpath = os.path.abspath(path)\n\t\t#http://stackoverflow.com/questions/301134/dynamic-module-import-in-python\n\t\tif os.path.abspath(path) == self.shutit_main_dir:\n\t\t\treturn\n\t\tif not os.path.exists(path):\n\t\t\treturn\n\t\tif os.path.exists(path + '/STOPBUILD') and not self.build['ignorestop']:\n\t\t\tself.log('Ignoring directory: ' + path + ' as it has a STOPBUILD file in it. Pass --ignorestop to shutit run to override.',level=logging.DEBUG)\n\t\t\treturn\n\t\tfor sub in glob.glob(os.path.join(path, '*')):\n\t\t\tsubpath = os.path.join(path, sub)\n\t\t\tif os.path.isfile(subpath):\n\t\t\t\tself.load_mod_from_file(subpath)\n\t\t\telif os.path.isdir(subpath):\n\t\t\t\tself.load_all_from_path(subpath)","def load_experiment(self):\n load_dir = select_dir(os.getcwd())\n if load_dir is not None:\n if os.path.isfile(os.path.join(load_dir, 'conf', 'config')):\n self.load_main(load_dir)\n else:\n msg_window('missing conf/config file, not experiment directory')\n return\n\n if self.t is None:\n self.t = Tabs(self)\n self.vbox.addWidget(self.t)\n self.t.clear_configs()\n self.t.load_conf(load_dir)\n\n self.set_experiment(True)\n else:\n msg_window('please select valid conf directory')","def load_tree_files(tree_dir):\r\n tree_file_names = os.listdir(tree_dir)\r\n # ignore invisible files like .DS_Store\r\n tree_file_names = [fname for fname in tree_file_names if not\r\n fname.startswith('.')]\r\n\r\n # try to warn user if using multiple types of trees {\r\n try:\r\n base_names = []\r\n for fname in tree_file_names:\r\n base_names.append(qiime.parse.parse_rarefaction_fname(fname)[0])\r\n except ValueError:\r\n pass\r\n else:\r\n if len(set(base_names)) > 1:\r\n warnstr = \"\"\"\r\nwarning: trees are named differently, please be sure you're not\r\ncomparing trees generated in different manners, unless you're quite sure\r\nthat's what you intend to do. types: \"\"\" + str(set(base_names)) + \"\"\"\r\ncontinuing anyway...\"\"\"\r\n warn(warnstr)\r\n # }\r\n trees = []\r\n for fname in tree_file_names:\r\n try:\r\n f = open(os.path.join(tree_dir, fname), 'U')\r\n tree = parse_newick(f, PhyloNode)\r\n tree.filepath = fname\r\n trees.append(tree)\r\n f.close()\r\n except IOError as err:\r\n sys.stderr.write('error loading tree ' + fname + '\\n')\r\n exit(1)\r\n if len(trees) == 0:\r\n raise RuntimeError('Error: no trees loaded' +\r\n ', check that tree directory has has valid trees')\r\n return trees","def find_main_tasks(self):\n for role_path in self.role_paths:\n for root, dirnames, filenames in os.walk(role_path):\n # WHAT OTHER DIRECTORIES SHOULD I LOOK IN???\n # handlers/main.yml??\n for filename in fnmatch.filter(filenames, '*.yml'):\n # Absolute path to file\n fullpath = os.path.join(root, filename)\n m = re.match(\"^.*?/tasks/main.yml$\", fullpath)\n if m:\n self.main_tasks.append(fullpath)","def load_specifications(specification_dir):\n assert E(specification_dir), \"Specification directory {} does not exist\".format(specification_dir)\n\n specification_jsons = glob.glob(J(specification_dir, '*.json'))\n\n logger.info(\"Loading experiment specificaitons...\")\n if not specification_jsons:\n logger.warning(\"Could not find any experiment specifications in {}\".format(specification_dir))\n\n specs = []\n for spec_path in specification_jsons:\n with open(spec_path, 'r') as f:\n specs.append(json.load(f))\n logger.info(\"Found {} experiment specifications\".format(len(specs)))\n\n return specs","def test_examples():\n tests = [d for d in listdir(ex) if path.isdir(path.join(ex, d))]\n for d in tests:\n yield check_examples, d","def get_all_best_trials(self, data_indir):\n directoryFiles = glob.glob(data_indir + '\\\\*\\\\')\n bestTrials = []\n for file in directoryFiles:\n l = self.get_best_trial(file)\n bestTrials.append(l)\n return bestTrials","def getImmediateSubdirectories(dir):","def load_tests(self):\n base_path = os.path.split(os.path.dirname(\n os.path.abspath(valet.__file__)))[0]\n test_dir = \"valet/tests/tempest\"\n full_test_dir = os.path.join(base_path, test_dir)\n return full_test_dir, base_path","def file_loader(self):\n\n for folder in self.config[\"data_folders\"]:\n f = os.path.join(folder, self.data_file)\n yield jsonlist.load_file(f)","def load_people(self, file_path):\n pass","def _load_personas(self, names, is_custom=False):\n names = names or [path.stem for path in\n self.persona_dir[is_custom].iterdir()\n if path.is_dir()]\n for name in names:\n try:\n self.update_persona_dicts(self.process_name(name),\n is_custom=is_custom)\n except:\n warnings.warn(f'Could not load files for {name}.')","def load_all_experiments(cls, apps=settings.INSTALLED_APPS):\n if not cls.__loaded:\n cls.__loaded = True\n for app_name in apps:\n application_path = os.path.dirname(sys.modules[app_name].__file__)\n application_experiment_file_path = (\n application_path +\n ExperimentLoader.APPLICATION_RELATIVE_EXPERIMENT_FILE)\n if os.access(application_experiment_file_path, os.F_OK):\n ExperimentLoader.load_experiments(application_experiment_file_path)","def load_training_data(vocab, directory):\n top_level = os.listdir(directory)\n dataset = []\n for d in top_level:\n if d[-1] == '/':\n label = d[:-1]\n subdir = d\n else:\n label = d\n subdir = d+\"/\"\n files = os.listdir(directory+subdir)\n for f in files:\n bow = create_bow(vocab, directory+subdir+f)\n dataset.append({'label': label, 'bow': bow})\n return dataset","def load_trajs(trajects, topology, PELE_order=True):\n trajectories = [x for x in glob.glob(trajects)]\n topologies = [x for x in glob.glob(topology)]\n if len(topologies) > 1:\n topology_extension = len(topologies[0].split(\".\")[-1])+1\n topologies = sorted(topologies, key=lambda x: int(x.split(\"_\")[-1][:-topology_extension]))\n if PELE_order:\n topologies = topologies[1:] + topologies[:1]\n extension_len = len(trajectories[0].split(\".\")[-1])+1\n trajectories = sorted(trajectories, key=lambda x: int(x.split(\"_\")[-1][:-extension_len]))\n for file_pair in zip(trajectories, itertools.cycle(topologies)):\n yield file_pair","def load_directory(self, parser, path):\n call_back = parser.call_back\n parser.call_back = None\n if call_back is not None:\n call_back('Finding files...')\n call_back(0, 0)\n file_tuples = []\n for root, subdirs, files in os.walk(path, followlinks=True):\n for filename in files:\n if parser.stop_check is not None and parser.stop_check():\n return\n if not parser.match_extension(filename):\n continue\n file_tuples.append((root, filename))\n if len(file_tuples) == 0:\n raise (ParseError(\n 'No files in the specified directory matched the parser. '\n 'Please check to make sure you have the correct parser.'))\n if call_back is not None:\n call_back('Parsing types...')\n call_back(0, len(file_tuples))\n cur = 0\n speakers = set()\n types = defaultdict(set)\n type_headers = None\n token_headers = None\n subannotations = None\n could_not_parse = {}\n for i, t in enumerate(file_tuples):\n if parser.stop_check is not None and parser.stop_check():\n return\n if call_back is not None:\n call_back('Parsing types from file {} of {}...'.format(i + 1, len(file_tuples)))\n call_back(i)\n root, filename = t\n path = os.path.join(root, filename)\n try:\n information = parser.parse_information(path, self.corpus_name)\n if not information['type_headers']:\n raise ParseError('There was an issue using this parser to parse the file {}.'.format(path))\n speakers.update(information['speakers'])\n type_headers = information['type_headers']\n token_headers = information['token_headers']\n subannotations = information['subannotations']\n except ParseError as e:\n could_not_parse[path] = str(e)\n continue\n for k, v in information['types'].items():\n types[k].update(v)\n if could_not_parse:\n error_template = '{}: {}'\n errors = [error_template.format(k, v) for k,v in could_not_parse.items()]\n raise ParseError('There were issues parsing the following files with {} parser: {}'.format(\n parser.name, '\\n\\n'.join(errors)))\n if call_back is not None:\n call_back('Importing types...')\n self.initialize_import(speakers, token_headers, subannotations)\n self.add_types(types, type_headers)\n\n if call_back is not None:\n call_back('Parsing files...')\n call_back(0, len(file_tuples))\n cur = 0\n for i, t in enumerate(file_tuples):\n if parser.stop_check is not None and parser.stop_check():\n return\n root, filename = t\n name = os.path.splitext(filename)[0]\n if call_back is not None:\n call_back('Parsing file {} of {} ({})...'.format(i + 1, len(file_tuples), name))\n call_back(i)\n path = os.path.join(root, filename)\n try:\n data = parser.parse_discourse(path)\n except ParseError:\n continue\n self.add_discourse(data)\n self.finalize_import(speakers, token_headers, parser.hierarchy, call_back, parser.stop_check)\n parser.call_back = call_back","def load_all(self, root_dir, file_list=None, pattern=None):\n # each file name corresponds to another date. Also tools (A, B) and others.\n\n # Select paths for training and evaluation\n if file_list is None:\n data_paths = glob.glob(os.path.join(root_dir, '*')) # list of all paths\n else:\n data_paths = [os.path.join(root_dir, p) for p in file_list]\n if len(data_paths) == 0:\n raise Exception('No files found using: {}'.format(os.path.join(root_dir, '*')))\n\n if pattern is None:\n # by default evaluate on\n selected_paths = data_paths\n else:\n selected_paths = list(filter(lambda x: re.search(pattern, x), data_paths))\n\n input_paths = [p for p in selected_paths if os.path.isfile(p) and p.endswith('.csv')]\n if len(input_paths) == 0:\n raise Exception(\"No .csv files found using pattern: '{}'\".format(pattern))\n\n if self.n_proc > 1:\n # Load in parallel\n _n_proc = min(self.n_proc, len(input_paths)) # no more than file_names needed here\n logger.info(\"Loading {} datasets files using {} parallel processes ...\".format(len(input_paths), _n_proc))\n with Pool(processes=_n_proc) as pool:\n all_df = pd.concat(pool.map(WeldData.load_single, input_paths))\n else: # read 1 file at a time\n all_df = pd.concat(WeldData.load_single(path) for path in input_paths)\n\n return all_df","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def load_users(everyone):\n if user_list.loaded:\n return\n for user in iteritems(everyone):\n user_list.load(user[1])","def loadFolder(self, path):\n for file_name in os.listdir(path):\n if (file_name.split(\".\")[-1] == \"txt\"):\n file_path = path + \"/\" + file_name\n self.loadFile(file_path)","def load_data(self):\n for set_name in self.image_dir_path:\n if self.verbose:\n print('\\n> Loading data files for the set: ' + set_name)\n\n # image dir\n image_dir = os.path.join(self.data_path, self.image_dir_path[set_name])\n\n # annotation file path\n annot_filepath = os.path.join(self.data_path, self.annotation_path[set_name])\n\n if 'test' in set_name:\n yield load_data_test(set_name, image_dir, annot_filepath, self.verbose)\n else:\n yield self.load_data_trainval(set_name, image_dir, annot_filepath)","def check_samples_structure(directory: Path) -> None:\n if not directory.is_dir():\n raise ValueError(f\"{directory} is not a directory\")\n\n for pyfile in directory.rglob('*.py'):\n if pyfile.is_file():\n try:\n scan_sample_directory(pyfile.parent)\n except Exception:\n traceback.print_exc()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def load_tests(loader, filter):\n suite = unittest.TestSuite()\n for r, d, f in os.walk(test_path):\n module_path = os.path.relpath(r, os.path.dirname(test_path)).replace(os.path.sep, \".\")\n for file in f:\n filename, ext = os.path.splitext(file)\n if ext == \".py\" and not file.startswith(\"_\"):\n if not filter or module_path + \".\" + filename in filter:\n print(\"[ADD ] \" + module_path + \".\" + filename)\n test = loader.loadTestsFromName(module_path + \".\" + filename)\n suite.addTest(test)\n else:\n print(\"[SKIP] \" + module_path + \".\" + filename)\n return suite","def all_present_experiments(self):\n return _yield_subdir_names(self.exp_configs)","def train_teacher (nb_teachers, teacher_id):\n # Load the dataset\n X_train, X_test, y_train, y_test = models.get_dataset()\n\n print(X_train.shape)\n print(y_train.shape)\n print(X_test.shape)\n print(y_test.shape)\n \n # Retrieve subset of data for this teacher\n data, labels = partition.partition_dataset(X_train,\n y_train,\n nb_teachers,\n teacher_id)\n\n print(\"Length of training data: \" + str(len(labels)))\n\n # Define teacher checkpoint filename and full path\n\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\n \n # Perform teacher training need to modify \n \n\n # Create teacher model\n model, opt = models.create_two_layer_mlp(46) # num of cols\n model.compile(loss='binary_crossentropy',\n optimizer=\"Adam\",\n metrics=['accuracy'])\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\n\n #modify\n model_json = model.to_json()\n with open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n# serialize weights to HDF5\n model.save_weights(filename2)\n print(\"Saved model to disk\")\n return True","def get_all_model_dirs(experiment_dir):\r\n model_dirs = glob.glob(os.path.join(experiment_dir,\"*\"), recursive=False)\r\n return None if len(model_dirs) == 0 else model_dirs","def load_all_playbooks_from_directory(self, path=None):\n if path is None:\n path = core.config.paths.workflows_path\n for playbook in locate_playbooks_in_directory(path):\n self.load_playbook_from_file(os.path.join(path, playbook))","def scan_fixtures(path):\n results = list()\n for root, dirs, files in os.walk(path):\n relative_path = root.replace(path + \"/\", \"\")\n if relative_path.startswith(\"static\") or relative_path.startswith(\"theme\"):\n continue\n\n for f in files:\n if not f.endswith(\".json\"):\n continue\n\n app_name = os.path.basename(os.path.dirname(relative_path))\n\n results.append((app_name, f, relative_path))\n\n return results","def generate_teachers(G, school_type, N_classes, family_member_counter, \n\t\t\t\t\t family_counter, teacher_p_adults, teacher_p_children):\n\tN_teachers = get_N_teachers(school_type, N_classes)\n\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\n\tG.add_nodes_from(teacher_nodes)\n\t\n\tfor t in teacher_nodes:\n\t\tfamily_nodes = [t]\n\t\t# draw a random number of children and adults for the family\n\t\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\n\t\t\n\t\tages = list(ages)\n\t\tfor adult in range(N_adults - 1):\n\t\t\tages.append(20.5) # default age for adults\n\t\t\n\t\t# add the family member nodes and their attributes to the graph\n\t\tfor age in ages:\n\t\t\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\n\t\t\tfamily_nodes.append(family_member_ID)\n\t\t\tG.add_node(family_member_ID)\n\t\t\tfamily_member_counter += 1\n\t\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t\t{family_member_ID:{'type':'family_member',\n\t\t\t\t\t\t\t\t\t\t 'age':age,\n\t\t\t\t\t\t\t\t\t\t 'family':family_counter,\n\t\t\t\t\t\t\t\t\t\t 'unit':'family'}})\n\t\t\t\t\t\n\t\t# finally, also set the teacher's node attributes\n\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t{t:{'type':'teacher', \n\t\t\t\t\t\t# Note: 20.5 is the age at which\n\t\t\t\t\t\t# the symptom and transmission risk\n\t\t\t\t\t\t# is that of an adult\n\t\t\t\t\t\t'age':20.5,\n\t\t\t\t\t\t'unit':'faculty_room',\n\t\t\t\t\t\t'family':family_counter}})\n\t\tfamily_counter += 1","def load_generic_trfiles_fi(stimuli, subject, root=\"data/trfiles\"):\n trdict = dict()\n\n for stimulus in stimuli:\n try:\n fname = \"{0}_{1}.report\".format(stimulus, subject)\n trf = TRFile(os.path.join(root, fname))\n trdict[stimulus] = [trf]\n except Exception, e:\n print e\n\n return trdict","def load(self):\n\n if not os.path.isdir(self.path):\n raise TreeError('Not a directory: {}'.format(self.path))\n\n self.log.debug('{} load tree'.format(self.path))\n start = int(time.mktime(time.localtime()))\n\n super(Tree, self).load()\n self.paths = {}\n self.empty_dirs = []\n self.relative_dirs = []\n\n for (root, dirs, files) in os.walk(self.path, topdown=True):\n if os.path.basename(root) in IGNORED_TREE_FOLDER_NAMES:\n continue\n\n if files:\n self.files.extend((root, filename) for filename in files if filename != '')\n for filename in files:\n self.paths[os.path.join(root, filename)] = True\n\n elif not dirs:\n self.empty_dirs.append(root)\n\n self.relative_dirs = set(self.relative_path(x[0]) for x in self.files)\n self.files.sort(key=self.__cmp_file_path__())\n\n stop = int(time.mktime(time.localtime()))\n self.log.debug('loaded {:d} files in {:d} seconds'.format(\n len(self.files),\n (stop-start)\n ))","def ReadRecipesFromDirectory(self, path: str) -> None:\n for file_path in glob.glob(os.path.join(path, '*.json')):\n self.ReadRecipeFromFile(file_path)","def _load_files(self):\n for filedoc in self._docset.get_files():\n path = filedoc.get_path()\n if not path:\n # In case of only partially loaded file information,\n # the path information is not set for unloaded files.\n continue\n if not os.path.isabs(path):\n path = os.path.join(self._source_root, path)\n extension = os.path.splitext(path)[1]\n # We don't care about Markdown files that only produce pages\n # (and fail the directory check below).\n if extension == '.md':\n continue\n dirdoc = filedoc.get_directory()\n if not dirdoc:\n self._reporter.xml_assert(filedoc.get_xml_path(),\n \"file is not in any directory in Doxygen\")\n continue\n relpath = self._get_rel_path(path)\n fileobj = self._files.get(relpath)\n if not fileobj:\n fileobj = File(path, relpath, self._docmap[dirdoc])\n self._files[relpath] = fileobj\n fileobj.set_doc_xml(filedoc, self)\n self._docmap[filedoc] = fileobj","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def __init__(self, theme_dir):\n\n if not os.path.isdir(theme_dir): \n print(\"%s not a valid directory, please check!\" % theme_dir, file=sys.stderr)\n sys.exit(1)\n for dirname, dirnames, filenames in os.walk(theme_dir):\n for subdirname in dirnames:\n full_path = os.path.join(dirname, subdirname)\n self.theme_dir.append(full_path)\n print(\"read theme %s\" % full_path, file=sys.stdout)\n print(\"all themes loaded!\", file=sys.stdout)","def load_files(self):\n # Needs to be implemented by child class\n raise NotImplementedError","def get_each_loader(data_path, batch_size, trn_negnum, shuffle=True, num_workers=0):\n \n dataset = ML_Dataset(data_path, trn_negnum)\n \n if data_path.endswith('trn') == True:\n collate = dataset.train_collate\n else:\n collate = test_collate\n\n data_loader = data.DataLoader(dataset=dataset,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers,\n collate_fn=collate)\n\n return data_loader","def _recurse_load_profile(self, text, profile_path):\n try:\n inherited_profile = Profile()\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\n profile_parser = _ProfileParser(text)\n # Iterate the includes and call recursive to get the profile and variables\n # from parent profiles\n for include in profile_parser.includes:\n # Recursion !!\n profile = self._load_profile(include, cwd)\n inherited_profile.compose_profile(profile)\n\n # Current profile before update with parents (but parent variables already applied)\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\n inherited_profile)\n return inherited_profile\n except ConanException:\n raise\n except Exception as exc:\n raise ConanException(\"Error parsing the profile text file: %s\" % str(exc))","def load_all_templates(dataset, template_dir: str) -> Dict[str, NexusTemplate]:\n template_set = {\n template_name\n for template_name in os.listdir(template_dir)\n if not template_name.endswith(\".json\")\n }\n template_set.add(\"linear\")\n\n template_ord = []\n for template_name in TEMPLATE_PREFERRED_ORDER:\n try:\n template_set.remove(template_name)\n except KeyError:\n pass\n else:\n template_ord.append(template_name)\n template_ord.extend(sorted(template_set))\n\n return {\n template_name: load_template(dataset, template_dir, template_name)\n for template_name in template_ord\n }","def load_bodies(directory):\n\n files = glob.glob(os.path.join(directory, \"*.json\"))\n bodies = {}\n for file in files:\n print(\"Loading body \" + file)\n with open(file) as data_file:\n internal_name = splitext(basename(file))[0]\n bodies[internal_name] = load_body(json.load(data_file))\n for key in bodies:\n body = bodies[key]\n if body.parent_internal_name is not None:\n body.parent = bodies[body.parent_internal_name]\n del body.parent_internal_name\n\n for body in bodies.values():\n print(\"Executing post_init for \" + body.name)\n body.post_init()\n\n return bodies.values()","def load_data_from_dir(self,\n dir_list=[],\n exclude=[]):\n dir_list_ = dir_list[:]\n\n if len(dir_list) == 0:\n eprint(\"CANNOT load data generator with an empty list of directories: {}\".format(dir_list))\n return\n\n for directory in dir_list_:\n if not os.path.isdir(directory):\n eprint(\"\\t\\t {}: {} is not a directory\".format(self.load_data_from_dir.__name__, directory))\n return\n\n # Read Data from current directory\n while dir_list_:\n # Pop first directory name and create dataloader if its a valid folder\n current_dir = dir_list_.pop(0)\n valid_dir = True\n for name in exclude:\n if name in current_dir and valid_dir:\n valid_dir = False\n data_file = current_dir + \"/data.mat\"\n if os.path.isfile(data_file) and \"takktile_\" in current_dir and valid_dir:\n self.dataloaders.append(takktile_dataloader(data_dir=current_dir,\n config=self.config,\n augment=self.augment))\n\n # Find all child directories of current directory and recursively load them\n data_dirs = [os.path.join(current_dir, o) for o in os.listdir(current_dir)\n if os.path.isdir(os.path.join(current_dir, o))]\n for d in data_dirs:\n dir_list_.append(d)\n\n self.num_dl = len(self.dataloaders)\n\n if self.transform_type:\n self.__calculate_data_transforms()\n\n # Create Eval Data\n if self.create_eval_data:\n self.eval_len = (self.__len__())//10\n self.create_eval_data = False\n\n # Calculate class number and ratios\n # Also calculate class diffs\n if not self.config['label_type'] == 'value':\n self.__class_nums = self.dataloaders[0].get_data_class_numbers(self.__get_data_idx(0))\n for i, dl in enumerate(self.dataloaders[1:]):\n self.__class_nums += dl.get_data_class_numbers(self.__get_data_idx(i+1))\n self.__class_ratios = self.__class_nums / float(np.mean(self.__class_nums))\n self.__class_diff = np.max(self.__class_nums) - self.__class_nums\n self.__class_diff = [d if n > 0 else 0 for n,d in zip(self.__class_nums, self.__class_diff)]\n\n # Reset and prepare data\n self.on_epoch_end()","def _load_dyne(self):\n if not self._hub._dscan:\n self._hub._scan_dynamic()\n for path in self._hub._dynamic.get(self._dyne_name, {}).get(\"paths\", []):\n self._dirs.append(path)","def loadFiles(root=\"data/TAIWAN_RAW_DATA/ADHD\"):\n\tdata_rt = [] # realtime.csv\n\tdata_trial = [] # trialdata.csv\n\tdata_id = [] # caseid/subjectid\n\tRealTime = \"A2RealTime_\"\n\tTrialData = \"A2TrialData_\"\n\tfolder_list = os.listdir(root) # list of subfolders in the root\n\tfor folders in folder_list:\n\t\tfolders_path = os.path.join(root,folders)\n\t\tif folders.find(\"pass\") != -1:\n\t\t\tcontinue\n\t\t\t\n\t\ttry:\n\t\t\tdata_rt.append(pd.read_csv(os.path.join\n\t\t\t\t\t\t\t\t (folders_path,\n\t\t\t\t\t\t\t\t RealTime+folders[3:]+\".csv\")))\n\t\t\tdata_trial.append(pd.read_csv(os.path.join\n\t\t\t\t\t\t\t\t\t (folders_path,\n\t\t\t\t\t\t\t\t\t TrialData+folders[3:]+\".csv\")))\n\t\t\tdata_id.append(int(folders.split('_')[1]))\n\t\texcept:\n\t\t\tprint(os.path.join(folders_path,TrialData+folders[3:]+\".csv\"))\n\t\t\t\n\treturn data_rt,data_trial,data_id,folder_list","def load_files(directory):\n import os\n import re\n\n files = dict()\n\n for file in os.scandir(directory):\n if re.search(\".txt$\", file.name):\n with open(file.path, \"r\", encoding=\"utf8\") as f:\n # re.sub(\".txt$\", \"\", file.name)\n files[file.name] = f.read()\n\n return files","def load_test_subjects_names(self):\n files = os.listdir(os.path.join(self.db_path, self.test_batch))\n for f in files:\n if f.startswith('test-volume'):\n s_name = str.split(str.split(f, '.')[0], '-')[-1]\n self.testing_subjects.append(s_name)\n self.n_test = len(self.testing_subjects)","def setUp(self):\n cltk_data_dir = '~/cltk_data/sanskrit/model/sanskrit_models_cltk'\n INDIC_RESOURCES_PATH = os.path.expanduser(cltk_data_dir)\n\n resources_present = os.path.isdir(INDIC_RESOURCES_PATH)\n if not resources_present:\n corpus_importer = CorpusImporter('sanskrit')\n corpus_importer.import_corpus('sanskrit_models_cltk')"],"string":"[\n \"def load_teacher_experiment(self, exp: Experiment):\\n _, _, extra = load_experiment(exp)\\n self.unpack_teachers(extra)\",\n \"def preload_all_problems(self):\\n for _, _, filenames in os.walk(self.problemDir):\\n for filename in filenames:\\n if filename[-3:] == \\\".py\\\" and filename != \\\"__init__.py\\\":\\n self.load_problem_file(filename[0:-3])\",\n \"def loadallskills(self):\\r\\n for skill in os.listdir( os.path.join( es.getAddonPath( info.basename ), \\\"skills\\\" )):\\r\\n es.load(\\\"%s/skills/%s\\\" % (info.basename, skill))\",\n \"def load_training():\\n for can in candidates:\\n trainings[can] = []\\n for subdir, dirs, files in os.walk(os.path.join(corpus_dir, can)):\\n for doc in files:\\n trainings[can].append(doc)\",\n \"def _load_dirs(self):\\n rootdirs = self._docset.get_compounds(xml.Directory,\\n lambda x: x.get_parent() is None)\\n for dirdoc in rootdirs:\\n self._load_dir(dirdoc, None)\",\n \"def load_test_users():\\n return [load_test_angel(), load_test_troublemaker(), load_test_rebel()]\",\n \"def load_fixtures(self):\\n for fixture_dir in settings.FIXTURE_DIRS:\\n fixture_dir = os.path.join(fixture_dir, self.filesystem_name)\\n for (root, dirs, files) in os.walk(fixture_dir):\\n for file in files:\\n full_file_path = os.path.join(root, *dirs, file)\\n with open(full_file_path, 'rb') as f:\\n self.save(os.path.relpath(full_file_path, fixture_dir), f)\",\n \"def _load_module_recursive(self, dir) :\\t\\n\\t\\tfor filepath in os.listdir(dir) :\\n\\t\\t\\tfullpath = os.path.join(dir, filepath)\\n\\n\\t\\t\\tif os.path.isdir(fullpath) :\\n\\t\\t\\t\\tself._load_module_recursive(fullpath)\\n\\n\\t\\t\\telif os.path.splitext(filepath)[1] == '.py' :\\n\\t\\t\\t\\tutils.load_module(fullpath, self.settings.ROOT_PATH)\",\n \"def __init__(self, *paths, **kwargs):\\n trajectories = load_trajectories(*paths, **kwargs)\\n super().__init__(trajectories, **kwargs)\",\n \"def load_data(self) -> None:\\n self.paths: List[str] = []\\n self.durations: List[float] = []\\n self.transcriptions: List[str] = []\\n\\n def raise_(err):\\n \\\"\\\"\\\"raises error if problem during os.walk\\\"\\\"\\\"\\n raise err\\n\\n for subset in self.subsets:\\n subset_path = os.path.join(self.root, self.base_dir, subset)\\n for root, dirs, files in os.walk(subset_path, onerror=raise_):\\n if not files:\\n continue\\n matches = fnmatch.filter(files, \\\"*.trans.txt\\\")\\n assert len(matches) == 1, \\\"> 1 transcription file found\\\"\\n self._parse_transcription_file(root, matches[0])\\n\\n self._sort_by_duration()\",\n \"def load_data_from_files(self):\\n # separated method to allow mock easier\\n logger.info(\\\"Loading data...\\\")\\n parent = Path(__file__).parent\\n path = parent / \\\"resources\\\" / \\\"scores.txt\\\"\\n self.scorer.load_from_file(path)\\n path = parent / \\\"resources\\\" / \\\"american-english-large.txt\\\"\\n self.trie.load_from_file(path)\\n path = parent / \\\"resources\\\" / \\\"reels.txt\\\"\\n self.reels = Reel.get_from_file(path)\\n logger.info(\\\"Data loaded!\\\")\",\n \"def discover_examples():\\n root = './examples'\\n for filename in os.listdir(root):\\n if os.path.splitext(filename)[1] == '.py':\\n yield os.path.join(root, filename)\",\n \"def loadTrie(self):\\n for file in self._gram_files:\\n trie_file = getTrieFile(os.path.basename(file), self._pickle_dir)\\n with open(trie_file, 'rb') as fd:\\n self._tries.append(pickle.load(fd))\",\n \"def prune_teachers(self):\\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]\",\n \"def _load_trials(self) -> hyperopt.Trials:\\n if os.path.isfile(self.trials_path):\\n trials = pickle.load(open(self.trials_path, \\\"rb\\\"))\\n else:\\n trials = hyperopt.Trials()\\n return trials\",\n \"def imdb_load():\\n for root, dirs, filenames in os.walk(os.path.dirname(__file__) + \\\"/imdb\\\"):\\n for file_name in filenames:\\n if file_name.find(\\\".json\\\") > 0:\\n Movie.imdb_load_file(os.path.dirname(__file__) + \\\"/imdb/\\\" + file_name)\\n return Movie.__movies\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def load_all_files(self):\\n\\t\\tself.get_rankings()\\n\\t\\tself.get_partition()\\n\\t\\tself.__load_factors()\\n\\t\\tself.get_document_associations()\\n\\t\\tself.get_term_associations()\",\n \"def _load_templates(cls):\\n if cls._raw_templates is None:\\n cls._raw_templates = fetch_rrlyrae_templates()\",\n \"def load_tests(loader, suite, pattern):\\n tests = loader.discover(start_dir=here.parents[0], pattern=pattern)\\n suite.addTests(tests)\\n return suite\",\n \"def get_teachers(self):\\n query = Teacher.all().order('teacher')\\n return query.fetch()\",\n \"def iter_dir(tree, path):\\n for f in os.listdir(path):\\n if os.path.isfile(path + '/' + f + '/__init__.py'):\\n tree[f] = None\\n elif os.path.isdir(path + '/' + f):\\n tree[f] = {}\\n SnakeWM.iter_dir(tree[f], path + '/' + f)\",\n \"def get_test_examples(self, data_dir):\\n \\n raise NotImplementedError()\",\n \"def loadFiles():\\n all_chapters = []\\n for name in names:\\n f = open(file_path + name, \\\"r\\\", encoding=\\\"utf-8\\\")\\n html_file = f.read()\\n f.close()\\n chap_text = extractText(html_file)\\n new_text = cleanText(chap_text)\\n all_chapters.append(new_text)\\n concatenated_chapters = \\\" \\\".join(all_chapters)\\n return concatenated_chapters\",\n \"def loadDirectory(self, dirname):\\r\\n cachelist=os.listdir(dirname)\\r\\n testlist=fnmatch.filter(cachelist,'*.hdf5')\\r\\n \\r\\n for file_ in testlist:\\r\\n print(\\\"Using {0}\\\".format(file_))\\r\\n \\r\\n files = [h5py.File(os.path.join(dirname, fn),'r') for fn in testlist]\\r\\n return files\",\n \"def tubs_from_directory(tub_dir, verbose=False):\\n tubs = []\\n count = 0\\n root_path = Path(tub_dir)\\n for item in root_path.iterdir():\\n if item.is_dir():\\n try:\\n t = Tub(str(item),read_only=True)\\n count += len(t)\\n except FileNotFoundError as ex:\\n continue\\n except ValueError as ex:\\n # In case the catalog file is empty\\n continue\\n tubs.append(t)\\n if verbose:\\n print( f\\\"Loaded {count} records.\\\" )\\n\\n return tubs\",\n \"def preload_all(self):\\n for tp in self.tps:\\n for f in self.featurefiles + self.maskfiles:\\n file = os.path.join(tp, f)\\n print('preloading {}'.format(file))\\n self.load(file, lazy=False)\",\n \"def update_treemakers():\\n global treemakers\\n treemakers = {}\\n for module_filename in glob(os.path.join(hax.hax_dir + '/treemakers/*.py')):\\n module_name = os.path.splitext(os.path.basename(module_filename))[0]\\n if module_name.startswith('_'):\\n continue\\n\\n # Import the module, after which we can do hax.treemakers.blah\\n __import__('hax.treemakers.%s' % module_name, globals=globals())\\n\\n # Now get all the treemakers defined in the module\\n for tm_name, tm in inspect.getmembers(getattr(hax.treemakers, module_name),\\n lambda x: type(x) == type and issubclass(x, TreeMaker)):\\n if tm_name == 'TreeMaker':\\n # This one is the base class; we get it because we did from ... import TreeMaker at the top of the file\\n continue\\n if tm_name in treemakers:\\n raise ValueError(\\\"Two treemakers named %s!\\\" % tm_name)\\n treemakers[tm_name] = tm\",\n \"def find_user_templates(self):\\n\\n # a list to store file names in\\n local_templates = []\\n\\n # loop through the directory content\\n for name in os.listdir(self._template_directory):\\n # check to see if it is a directory and not in the database\\n if (os.path.isdir(os.path.join(self._template_directory, name)) and\\n name not in self._templates):\\n # add it to the list\\n local_templates.append(name)\\n\\n return local_templates\",\n \"def LoadTeacherModels(lang):\\n\\n # load Trained teacher model parameters\\n log_dir = 'data/logs'\\n with open(log_dir + '/' + lang + '_model_params', 'rb') as fp:\\n params = pickle.load(fp)\\n\\n model_args = params['args']\\n\\n if model_args.use_colab is None:\\n OUTPUT_DIR = 'ckpts/' + model_args.lang\\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\\n else:\\n from google.colab import drive\\n\\n drive.mount('/content/gdrive')\\n OUTPUT_DIR = '/content/gdrive/My Drive/ckpts/' + model_args.lang\\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\\n\\n if model_args.enc_type == 'gat' and model_args.dec_type == 'transformer':\\n models = {}\\n OUTPUT_DIR += '/' + model_args.enc_type + '_' + model_args.dec_type\\n\\n # Load the vocabs\\n with open('vocabs/' + model_args.model + '/' +\\n lang + '/' + model_args.opt + '_src_vocab', 'rb') as fp:\\n src_vocab = pickle.load(fp)\\n # loading the target vocab\\n model_args.sentencepiece = 'False'\\n if model_args.sentencepiece == 'True':\\n sp = spm.SentencePieceProcessor()\\n sp.load('vocabs/' + model_args.model + '/' +\\n lang + '/' + 'train_tgt.model')\\n tgt_vocab = sp\\n else:\\n tgt_vocab = src_vocab\\n\\n print('Loaded ' + lang + ' Parameters..')\\n model = TransGAT(params['args'], params['src_vocab_size'], src_vocab,\\n params['tgt_vocab_size'], tgt_vocab)\\n # Load the latest checkpoints\\n optimizer = tf.train.AdamOptimizer(beta1=0.9, beta2=0.98,\\n epsilon=1e-9)\\n\\n ckpt = tf.train.Checkpoint(\\n model=model,\\n optimizer=optimizer\\n )\\n\\n ckpt_manager = tf.train.CheckpointManager(ckpt, OUTPUT_DIR, max_to_keep=5)\\n if ckpt_manager.latest_checkpoint:\\n ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\\n\\n print('Loaded ' + lang + ' Teacher model !')\\n\\n return model\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def run_load(rootpath):\\n global CSV_PATH\\n CSV_PATH = rootpath+'/csv_files/'\\n load_movies_details()\\n load_movies_cast()\\n load_movies_reviews()\",\n \"def load():\\n root = Path(__file__).parent\\n for path in root.iterdir():\\n if path.is_dir() and not path.name.startswith(\\\"_\\\"):\\n subject = (path / \\\"subject.txt\\\").read_text()\\n txt = (path / \\\"body.txt\\\").read_text()\\n html = path / \\\"body.html\\\"\\n if html.exists():\\n html = html.read_text()\\n else:\\n html = None\\n attachment = None\\n pymodule = path / \\\"__init__.py\\\"\\n if pymodule.exists():\\n pymodule = importlib.import_module(f\\\"egapro.emails.{path.name}\\\")\\n attachment = pymodule.attachment\\n globals()[path.name] = Email(subject, txt, html, attachment)\",\n \"def load_templates(self):\\n TemplateHandler.templates = []\\n for template in os.listdir(TemplateHandler.templates_path):\\n template_config = self.load_template_conf(template)\\n if template_config is None:\\n continue\\n TemplateHandler.templates.append(template_config)\",\n \"def unpack_teachers(self, extra: dict):\\n self.teacher_policies.extend(extra[\\\"teacher_policies\\\"])\\n self.teacher_envs.extend(extra[\\\"teacher_envs\\\"])\\n self.teacher_expl_strats.extend(extra[\\\"teacher_expl_strats\\\"])\\n self.teacher_critics.extend(extra[\\\"teacher_critics\\\"])\\n self.teacher_ex_dirs.extend(extra[\\\"teacher_ex_dirs\\\"])\",\n \"def LoadAllSubElements(self, recursive=False):\\n pass\",\n \"def load_templates(self):\\n\\n self.templates = []\\n\\n if os.path.exists(\\\"question_templates.txt\\\"):\\n for line in open(\\\"question_templates.txt\\\", \\\"r\\\"):\\n self.templates.append(line.replace(\\\"\\\\n\\\", \\\"\\\"))\",\n \"def load(datasets, treemakers='Basics', force_reload=False):\\n if isinstance(datasets, str):\\n datasets = [datasets]\\n if isinstance(treemakers, (type, str)):\\n treemakers = [treemakers]\\n\\n combined_dataframes = []\\n\\n for treemaker in treemakers:\\n\\n dataframes = []\\n for dataset in datasets:\\n minitree_path = get(dataset, treemaker, force_reload=force_reload)\\n new_df = pd.DataFrame.from_records(root_numpy.root2array(minitree_path).view(np.recarray)) \\n dataframes.append(new_df)\\n\\n # Concatenate mini-trees of this type for all datasets\\n combined_dataframes.append(pd.concat(dataframes))\\n\\n # Concatenate mini-trees of all types\\n if not len(combined_dataframes):\\n raise RuntimeError(\\\"No data was extracted? What's going on??\\\")\\n return pd.concat(combined_dataframes, axis=1)\",\n \"def load_tamper(self, dataset_dir, subset):\\n # Add classes. We have one class.\\n # Naming the dataset nucleus, and the class nucleus\\n self.add_class(\\\"tampers\\\", 1, \\\"tampers\\\")\\n\\n # Which subset?\\n # \\\"val\\\": use hard-coded list above\\n # \\\"train\\\": use data from stage1_train minus the hard-coded list above\\n # else: use the data from the specified sub-directory\\n # assert subset in [\\\"train\\\", \\\"val\\\", \\\"stage1_train\\\", \\\"stage1_test\\\", \\\"stage2_test\\\"]\\n # subset_dir = \\\"stage1_train\\\" if subset in [\\\"train\\\", \\\"val\\\"] else subset\\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\\n if subset == \\\"val\\\" or subset == \\\"test\\\":\\n image_ids = next(os.walk(dataset_dir))[2]\\n else:\\n # Get image ids from directory names\\n image_ids = next(os.walk(dataset_dir))[2]\\n \\n\\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # dirnew_splicing = '/data/tamper'\\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\\n\\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n \\n # dircopy_move = '/data/gy/ca2att/train3'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\\n\\n # dirnew_sp = '/data/gy/c2newsp/train'\\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\\n\\n # Add images\\n for image_id in image_ids:\\n self.add_image(\\n \\\"tampers\\\",\\n image_id=image_id[:-4],\\n path=os.path.join(dataset_dir, image_id))\\n\\n # for image_id in image_ids_copy_move:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dircopy_move, 'images', image_id))\\n\\n # for image_id in image_ids_new_splicing:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_splicing, 'images', image_id))\\n\\n # # for image_id in image_ids_txt_sp:\\n # # self.add_image(\\n # # \\\"tampers\\\",\\n # # image_id=image_id[:-4],\\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\\n\\n # for image_id in image_ids_new_sp:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_sp, 'images', image_id))\",\n \"def _find_fixtures(self, start_dir):\\r\\n fixtures = []\\r\\n def _find(arg, dirname, names):\\r\\n if (dirname.endswith('fixtures')) and (dirname.find('unit_test')==-1):\\r\\n for name in names:\\r\\n if (name.endswith(FIXTUERS_EXT)) and (name.find('initial_data')==-1):\\r\\n fixtures.append(name.replace(FIXTUERS_EXT, ''))\\r\\n os.path.walk(start_dir, _find, None)\\r\\n \\r\\n return fixtures\",\n \"def _load(self, directory):\\n pass\",\n \"def load_all_from_path(self, path):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\t#111: handle expanded paths\\n\\t\\tpath = os.path.abspath(path)\\n\\t\\t#http://stackoverflow.com/questions/301134/dynamic-module-import-in-python\\n\\t\\tif os.path.abspath(path) == self.shutit_main_dir:\\n\\t\\t\\treturn\\n\\t\\tif not os.path.exists(path):\\n\\t\\t\\treturn\\n\\t\\tif os.path.exists(path + '/STOPBUILD') and not self.build['ignorestop']:\\n\\t\\t\\tself.log('Ignoring directory: ' + path + ' as it has a STOPBUILD file in it. Pass --ignorestop to shutit run to override.',level=logging.DEBUG)\\n\\t\\t\\treturn\\n\\t\\tfor sub in glob.glob(os.path.join(path, '*')):\\n\\t\\t\\tsubpath = os.path.join(path, sub)\\n\\t\\t\\tif os.path.isfile(subpath):\\n\\t\\t\\t\\tself.load_mod_from_file(subpath)\\n\\t\\t\\telif os.path.isdir(subpath):\\n\\t\\t\\t\\tself.load_all_from_path(subpath)\",\n \"def load_experiment(self):\\n load_dir = select_dir(os.getcwd())\\n if load_dir is not None:\\n if os.path.isfile(os.path.join(load_dir, 'conf', 'config')):\\n self.load_main(load_dir)\\n else:\\n msg_window('missing conf/config file, not experiment directory')\\n return\\n\\n if self.t is None:\\n self.t = Tabs(self)\\n self.vbox.addWidget(self.t)\\n self.t.clear_configs()\\n self.t.load_conf(load_dir)\\n\\n self.set_experiment(True)\\n else:\\n msg_window('please select valid conf directory')\",\n \"def load_tree_files(tree_dir):\\r\\n tree_file_names = os.listdir(tree_dir)\\r\\n # ignore invisible files like .DS_Store\\r\\n tree_file_names = [fname for fname in tree_file_names if not\\r\\n fname.startswith('.')]\\r\\n\\r\\n # try to warn user if using multiple types of trees {\\r\\n try:\\r\\n base_names = []\\r\\n for fname in tree_file_names:\\r\\n base_names.append(qiime.parse.parse_rarefaction_fname(fname)[0])\\r\\n except ValueError:\\r\\n pass\\r\\n else:\\r\\n if len(set(base_names)) > 1:\\r\\n warnstr = \\\"\\\"\\\"\\r\\nwarning: trees are named differently, please be sure you're not\\r\\ncomparing trees generated in different manners, unless you're quite sure\\r\\nthat's what you intend to do. types: \\\"\\\"\\\" + str(set(base_names)) + \\\"\\\"\\\"\\r\\ncontinuing anyway...\\\"\\\"\\\"\\r\\n warn(warnstr)\\r\\n # }\\r\\n trees = []\\r\\n for fname in tree_file_names:\\r\\n try:\\r\\n f = open(os.path.join(tree_dir, fname), 'U')\\r\\n tree = parse_newick(f, PhyloNode)\\r\\n tree.filepath = fname\\r\\n trees.append(tree)\\r\\n f.close()\\r\\n except IOError as err:\\r\\n sys.stderr.write('error loading tree ' + fname + '\\\\n')\\r\\n exit(1)\\r\\n if len(trees) == 0:\\r\\n raise RuntimeError('Error: no trees loaded' +\\r\\n ', check that tree directory has has valid trees')\\r\\n return trees\",\n \"def find_main_tasks(self):\\n for role_path in self.role_paths:\\n for root, dirnames, filenames in os.walk(role_path):\\n # WHAT OTHER DIRECTORIES SHOULD I LOOK IN???\\n # handlers/main.yml??\\n for filename in fnmatch.filter(filenames, '*.yml'):\\n # Absolute path to file\\n fullpath = os.path.join(root, filename)\\n m = re.match(\\\"^.*?/tasks/main.yml$\\\", fullpath)\\n if m:\\n self.main_tasks.append(fullpath)\",\n \"def load_specifications(specification_dir):\\n assert E(specification_dir), \\\"Specification directory {} does not exist\\\".format(specification_dir)\\n\\n specification_jsons = glob.glob(J(specification_dir, '*.json'))\\n\\n logger.info(\\\"Loading experiment specificaitons...\\\")\\n if not specification_jsons:\\n logger.warning(\\\"Could not find any experiment specifications in {}\\\".format(specification_dir))\\n\\n specs = []\\n for spec_path in specification_jsons:\\n with open(spec_path, 'r') as f:\\n specs.append(json.load(f))\\n logger.info(\\\"Found {} experiment specifications\\\".format(len(specs)))\\n\\n return specs\",\n \"def test_examples():\\n tests = [d for d in listdir(ex) if path.isdir(path.join(ex, d))]\\n for d in tests:\\n yield check_examples, d\",\n \"def get_all_best_trials(self, data_indir):\\n directoryFiles = glob.glob(data_indir + '\\\\\\\\*\\\\\\\\')\\n bestTrials = []\\n for file in directoryFiles:\\n l = self.get_best_trial(file)\\n bestTrials.append(l)\\n return bestTrials\",\n \"def getImmediateSubdirectories(dir):\",\n \"def load_tests(self):\\n base_path = os.path.split(os.path.dirname(\\n os.path.abspath(valet.__file__)))[0]\\n test_dir = \\\"valet/tests/tempest\\\"\\n full_test_dir = os.path.join(base_path, test_dir)\\n return full_test_dir, base_path\",\n \"def file_loader(self):\\n\\n for folder in self.config[\\\"data_folders\\\"]:\\n f = os.path.join(folder, self.data_file)\\n yield jsonlist.load_file(f)\",\n \"def load_people(self, file_path):\\n pass\",\n \"def _load_personas(self, names, is_custom=False):\\n names = names or [path.stem for path in\\n self.persona_dir[is_custom].iterdir()\\n if path.is_dir()]\\n for name in names:\\n try:\\n self.update_persona_dicts(self.process_name(name),\\n is_custom=is_custom)\\n except:\\n warnings.warn(f'Could not load files for {name}.')\",\n \"def load_all_experiments(cls, apps=settings.INSTALLED_APPS):\\n if not cls.__loaded:\\n cls.__loaded = True\\n for app_name in apps:\\n application_path = os.path.dirname(sys.modules[app_name].__file__)\\n application_experiment_file_path = (\\n application_path +\\n ExperimentLoader.APPLICATION_RELATIVE_EXPERIMENT_FILE)\\n if os.access(application_experiment_file_path, os.F_OK):\\n ExperimentLoader.load_experiments(application_experiment_file_path)\",\n \"def load_training_data(vocab, directory):\\n top_level = os.listdir(directory)\\n dataset = []\\n for d in top_level:\\n if d[-1] == '/':\\n label = d[:-1]\\n subdir = d\\n else:\\n label = d\\n subdir = d+\\\"/\\\"\\n files = os.listdir(directory+subdir)\\n for f in files:\\n bow = create_bow(vocab, directory+subdir+f)\\n dataset.append({'label': label, 'bow': bow})\\n return dataset\",\n \"def load_trajs(trajects, topology, PELE_order=True):\\n trajectories = [x for x in glob.glob(trajects)]\\n topologies = [x for x in glob.glob(topology)]\\n if len(topologies) > 1:\\n topology_extension = len(topologies[0].split(\\\".\\\")[-1])+1\\n topologies = sorted(topologies, key=lambda x: int(x.split(\\\"_\\\")[-1][:-topology_extension]))\\n if PELE_order:\\n topologies = topologies[1:] + topologies[:1]\\n extension_len = len(trajectories[0].split(\\\".\\\")[-1])+1\\n trajectories = sorted(trajectories, key=lambda x: int(x.split(\\\"_\\\")[-1][:-extension_len]))\\n for file_pair in zip(trajectories, itertools.cycle(topologies)):\\n yield file_pair\",\n \"def load_directory(self, parser, path):\\n call_back = parser.call_back\\n parser.call_back = None\\n if call_back is not None:\\n call_back('Finding files...')\\n call_back(0, 0)\\n file_tuples = []\\n for root, subdirs, files in os.walk(path, followlinks=True):\\n for filename in files:\\n if parser.stop_check is not None and parser.stop_check():\\n return\\n if not parser.match_extension(filename):\\n continue\\n file_tuples.append((root, filename))\\n if len(file_tuples) == 0:\\n raise (ParseError(\\n 'No files in the specified directory matched the parser. '\\n 'Please check to make sure you have the correct parser.'))\\n if call_back is not None:\\n call_back('Parsing types...')\\n call_back(0, len(file_tuples))\\n cur = 0\\n speakers = set()\\n types = defaultdict(set)\\n type_headers = None\\n token_headers = None\\n subannotations = None\\n could_not_parse = {}\\n for i, t in enumerate(file_tuples):\\n if parser.stop_check is not None and parser.stop_check():\\n return\\n if call_back is not None:\\n call_back('Parsing types from file {} of {}...'.format(i + 1, len(file_tuples)))\\n call_back(i)\\n root, filename = t\\n path = os.path.join(root, filename)\\n try:\\n information = parser.parse_information(path, self.corpus_name)\\n if not information['type_headers']:\\n raise ParseError('There was an issue using this parser to parse the file {}.'.format(path))\\n speakers.update(information['speakers'])\\n type_headers = information['type_headers']\\n token_headers = information['token_headers']\\n subannotations = information['subannotations']\\n except ParseError as e:\\n could_not_parse[path] = str(e)\\n continue\\n for k, v in information['types'].items():\\n types[k].update(v)\\n if could_not_parse:\\n error_template = '{}: {}'\\n errors = [error_template.format(k, v) for k,v in could_not_parse.items()]\\n raise ParseError('There were issues parsing the following files with {} parser: {}'.format(\\n parser.name, '\\\\n\\\\n'.join(errors)))\\n if call_back is not None:\\n call_back('Importing types...')\\n self.initialize_import(speakers, token_headers, subannotations)\\n self.add_types(types, type_headers)\\n\\n if call_back is not None:\\n call_back('Parsing files...')\\n call_back(0, len(file_tuples))\\n cur = 0\\n for i, t in enumerate(file_tuples):\\n if parser.stop_check is not None and parser.stop_check():\\n return\\n root, filename = t\\n name = os.path.splitext(filename)[0]\\n if call_back is not None:\\n call_back('Parsing file {} of {} ({})...'.format(i + 1, len(file_tuples), name))\\n call_back(i)\\n path = os.path.join(root, filename)\\n try:\\n data = parser.parse_discourse(path)\\n except ParseError:\\n continue\\n self.add_discourse(data)\\n self.finalize_import(speakers, token_headers, parser.hierarchy, call_back, parser.stop_check)\\n parser.call_back = call_back\",\n \"def load_all(self, root_dir, file_list=None, pattern=None):\\n # each file name corresponds to another date. Also tools (A, B) and others.\\n\\n # Select paths for training and evaluation\\n if file_list is None:\\n data_paths = glob.glob(os.path.join(root_dir, '*')) # list of all paths\\n else:\\n data_paths = [os.path.join(root_dir, p) for p in file_list]\\n if len(data_paths) == 0:\\n raise Exception('No files found using: {}'.format(os.path.join(root_dir, '*')))\\n\\n if pattern is None:\\n # by default evaluate on\\n selected_paths = data_paths\\n else:\\n selected_paths = list(filter(lambda x: re.search(pattern, x), data_paths))\\n\\n input_paths = [p for p in selected_paths if os.path.isfile(p) and p.endswith('.csv')]\\n if len(input_paths) == 0:\\n raise Exception(\\\"No .csv files found using pattern: '{}'\\\".format(pattern))\\n\\n if self.n_proc > 1:\\n # Load in parallel\\n _n_proc = min(self.n_proc, len(input_paths)) # no more than file_names needed here\\n logger.info(\\\"Loading {} datasets files using {} parallel processes ...\\\".format(len(input_paths), _n_proc))\\n with Pool(processes=_n_proc) as pool:\\n all_df = pd.concat(pool.map(WeldData.load_single, input_paths))\\n else: # read 1 file at a time\\n all_df = pd.concat(WeldData.load_single(path) for path in input_paths)\\n\\n return all_df\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def load_users(everyone):\\n if user_list.loaded:\\n return\\n for user in iteritems(everyone):\\n user_list.load(user[1])\",\n \"def loadFolder(self, path):\\n for file_name in os.listdir(path):\\n if (file_name.split(\\\".\\\")[-1] == \\\"txt\\\"):\\n file_path = path + \\\"/\\\" + file_name\\n self.loadFile(file_path)\",\n \"def load_data(self):\\n for set_name in self.image_dir_path:\\n if self.verbose:\\n print('\\\\n> Loading data files for the set: ' + set_name)\\n\\n # image dir\\n image_dir = os.path.join(self.data_path, self.image_dir_path[set_name])\\n\\n # annotation file path\\n annot_filepath = os.path.join(self.data_path, self.annotation_path[set_name])\\n\\n if 'test' in set_name:\\n yield load_data_test(set_name, image_dir, annot_filepath, self.verbose)\\n else:\\n yield self.load_data_trainval(set_name, image_dir, annot_filepath)\",\n \"def check_samples_structure(directory: Path) -> None:\\n if not directory.is_dir():\\n raise ValueError(f\\\"{directory} is not a directory\\\")\\n\\n for pyfile in directory.rglob('*.py'):\\n if pyfile.is_file():\\n try:\\n scan_sample_directory(pyfile.parent)\\n except Exception:\\n traceback.print_exc()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def load_tests(loader, filter):\\n suite = unittest.TestSuite()\\n for r, d, f in os.walk(test_path):\\n module_path = os.path.relpath(r, os.path.dirname(test_path)).replace(os.path.sep, \\\".\\\")\\n for file in f:\\n filename, ext = os.path.splitext(file)\\n if ext == \\\".py\\\" and not file.startswith(\\\"_\\\"):\\n if not filter or module_path + \\\".\\\" + filename in filter:\\n print(\\\"[ADD ] \\\" + module_path + \\\".\\\" + filename)\\n test = loader.loadTestsFromName(module_path + \\\".\\\" + filename)\\n suite.addTest(test)\\n else:\\n print(\\\"[SKIP] \\\" + module_path + \\\".\\\" + filename)\\n return suite\",\n \"def all_present_experiments(self):\\n return _yield_subdir_names(self.exp_configs)\",\n \"def train_teacher (nb_teachers, teacher_id):\\n # Load the dataset\\n X_train, X_test, y_train, y_test = models.get_dataset()\\n\\n print(X_train.shape)\\n print(y_train.shape)\\n print(X_test.shape)\\n print(y_test.shape)\\n \\n # Retrieve subset of data for this teacher\\n data, labels = partition.partition_dataset(X_train,\\n y_train,\\n nb_teachers,\\n teacher_id)\\n\\n print(\\\"Length of training data: \\\" + str(len(labels)))\\n\\n # Define teacher checkpoint filename and full path\\n\\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\\n \\n # Perform teacher training need to modify \\n \\n\\n # Create teacher model\\n model, opt = models.create_two_layer_mlp(46) # num of cols\\n model.compile(loss='binary_crossentropy',\\n optimizer=\\\"Adam\\\",\\n metrics=['accuracy'])\\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\\n\\n #modify\\n model_json = model.to_json()\\n with open(\\\"model.json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n# serialize weights to HDF5\\n model.save_weights(filename2)\\n print(\\\"Saved model to disk\\\")\\n return True\",\n \"def get_all_model_dirs(experiment_dir):\\r\\n model_dirs = glob.glob(os.path.join(experiment_dir,\\\"*\\\"), recursive=False)\\r\\n return None if len(model_dirs) == 0 else model_dirs\",\n \"def load_all_playbooks_from_directory(self, path=None):\\n if path is None:\\n path = core.config.paths.workflows_path\\n for playbook in locate_playbooks_in_directory(path):\\n self.load_playbook_from_file(os.path.join(path, playbook))\",\n \"def scan_fixtures(path):\\n results = list()\\n for root, dirs, files in os.walk(path):\\n relative_path = root.replace(path + \\\"/\\\", \\\"\\\")\\n if relative_path.startswith(\\\"static\\\") or relative_path.startswith(\\\"theme\\\"):\\n continue\\n\\n for f in files:\\n if not f.endswith(\\\".json\\\"):\\n continue\\n\\n app_name = os.path.basename(os.path.dirname(relative_path))\\n\\n results.append((app_name, f, relative_path))\\n\\n return results\",\n \"def generate_teachers(G, school_type, N_classes, family_member_counter, \\n\\t\\t\\t\\t\\t family_counter, teacher_p_adults, teacher_p_children):\\n\\tN_teachers = get_N_teachers(school_type, N_classes)\\n\\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\\n\\tG.add_nodes_from(teacher_nodes)\\n\\t\\n\\tfor t in teacher_nodes:\\n\\t\\tfamily_nodes = [t]\\n\\t\\t# draw a random number of children and adults for the family\\n\\t\\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\\n\\t\\t\\n\\t\\tages = list(ages)\\n\\t\\tfor adult in range(N_adults - 1):\\n\\t\\t\\tages.append(20.5) # default age for adults\\n\\t\\t\\n\\t\\t# add the family member nodes and their attributes to the graph\\n\\t\\tfor age in ages:\\n\\t\\t\\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\\n\\t\\t\\tfamily_nodes.append(family_member_ID)\\n\\t\\t\\tG.add_node(family_member_ID)\\n\\t\\t\\tfamily_member_counter += 1\\n\\t\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t\\t{family_member_ID:{'type':'family_member',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'age':age,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'family':family_counter,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'unit':'family'}})\\n\\t\\t\\t\\t\\t\\n\\t\\t# finally, also set the teacher's node attributes\\n\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t{t:{'type':'teacher', \\n\\t\\t\\t\\t\\t\\t# Note: 20.5 is the age at which\\n\\t\\t\\t\\t\\t\\t# the symptom and transmission risk\\n\\t\\t\\t\\t\\t\\t# is that of an adult\\n\\t\\t\\t\\t\\t\\t'age':20.5,\\n\\t\\t\\t\\t\\t\\t'unit':'faculty_room',\\n\\t\\t\\t\\t\\t\\t'family':family_counter}})\\n\\t\\tfamily_counter += 1\",\n \"def load_generic_trfiles_fi(stimuli, subject, root=\\\"data/trfiles\\\"):\\n trdict = dict()\\n\\n for stimulus in stimuli:\\n try:\\n fname = \\\"{0}_{1}.report\\\".format(stimulus, subject)\\n trf = TRFile(os.path.join(root, fname))\\n trdict[stimulus] = [trf]\\n except Exception, e:\\n print e\\n\\n return trdict\",\n \"def load(self):\\n\\n if not os.path.isdir(self.path):\\n raise TreeError('Not a directory: {}'.format(self.path))\\n\\n self.log.debug('{} load tree'.format(self.path))\\n start = int(time.mktime(time.localtime()))\\n\\n super(Tree, self).load()\\n self.paths = {}\\n self.empty_dirs = []\\n self.relative_dirs = []\\n\\n for (root, dirs, files) in os.walk(self.path, topdown=True):\\n if os.path.basename(root) in IGNORED_TREE_FOLDER_NAMES:\\n continue\\n\\n if files:\\n self.files.extend((root, filename) for filename in files if filename != '')\\n for filename in files:\\n self.paths[os.path.join(root, filename)] = True\\n\\n elif not dirs:\\n self.empty_dirs.append(root)\\n\\n self.relative_dirs = set(self.relative_path(x[0]) for x in self.files)\\n self.files.sort(key=self.__cmp_file_path__())\\n\\n stop = int(time.mktime(time.localtime()))\\n self.log.debug('loaded {:d} files in {:d} seconds'.format(\\n len(self.files),\\n (stop-start)\\n ))\",\n \"def ReadRecipesFromDirectory(self, path: str) -> None:\\n for file_path in glob.glob(os.path.join(path, '*.json')):\\n self.ReadRecipeFromFile(file_path)\",\n \"def _load_files(self):\\n for filedoc in self._docset.get_files():\\n path = filedoc.get_path()\\n if not path:\\n # In case of only partially loaded file information,\\n # the path information is not set for unloaded files.\\n continue\\n if not os.path.isabs(path):\\n path = os.path.join(self._source_root, path)\\n extension = os.path.splitext(path)[1]\\n # We don't care about Markdown files that only produce pages\\n # (and fail the directory check below).\\n if extension == '.md':\\n continue\\n dirdoc = filedoc.get_directory()\\n if not dirdoc:\\n self._reporter.xml_assert(filedoc.get_xml_path(),\\n \\\"file is not in any directory in Doxygen\\\")\\n continue\\n relpath = self._get_rel_path(path)\\n fileobj = self._files.get(relpath)\\n if not fileobj:\\n fileobj = File(path, relpath, self._docmap[dirdoc])\\n self._files[relpath] = fileobj\\n fileobj.set_doc_xml(filedoc, self)\\n self._docmap[filedoc] = fileobj\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def __init__(self, theme_dir):\\n\\n if not os.path.isdir(theme_dir): \\n print(\\\"%s not a valid directory, please check!\\\" % theme_dir, file=sys.stderr)\\n sys.exit(1)\\n for dirname, dirnames, filenames in os.walk(theme_dir):\\n for subdirname in dirnames:\\n full_path = os.path.join(dirname, subdirname)\\n self.theme_dir.append(full_path)\\n print(\\\"read theme %s\\\" % full_path, file=sys.stdout)\\n print(\\\"all themes loaded!\\\", file=sys.stdout)\",\n \"def load_files(self):\\n # Needs to be implemented by child class\\n raise NotImplementedError\",\n \"def get_each_loader(data_path, batch_size, trn_negnum, shuffle=True, num_workers=0):\\n \\n dataset = ML_Dataset(data_path, trn_negnum)\\n \\n if data_path.endswith('trn') == True:\\n collate = dataset.train_collate\\n else:\\n collate = test_collate\\n\\n data_loader = data.DataLoader(dataset=dataset,\\n batch_size=batch_size,\\n shuffle=shuffle,\\n num_workers=num_workers,\\n collate_fn=collate)\\n\\n return data_loader\",\n \"def _recurse_load_profile(self, text, profile_path):\\n try:\\n inherited_profile = Profile()\\n cwd = os.path.dirname(os.path.abspath(profile_path)) if profile_path else None\\n profile_parser = _ProfileParser(text)\\n # Iterate the includes and call recursive to get the profile and variables\\n # from parent profiles\\n for include in profile_parser.includes:\\n # Recursion !!\\n profile = self._load_profile(include, cwd)\\n inherited_profile.compose_profile(profile)\\n\\n # Current profile before update with parents (but parent variables already applied)\\n inherited_profile = _ProfileValueParser.get_profile(profile_parser.profile_text,\\n inherited_profile)\\n return inherited_profile\\n except ConanException:\\n raise\\n except Exception as exc:\\n raise ConanException(\\\"Error parsing the profile text file: %s\\\" % str(exc))\",\n \"def load_all_templates(dataset, template_dir: str) -> Dict[str, NexusTemplate]:\\n template_set = {\\n template_name\\n for template_name in os.listdir(template_dir)\\n if not template_name.endswith(\\\".json\\\")\\n }\\n template_set.add(\\\"linear\\\")\\n\\n template_ord = []\\n for template_name in TEMPLATE_PREFERRED_ORDER:\\n try:\\n template_set.remove(template_name)\\n except KeyError:\\n pass\\n else:\\n template_ord.append(template_name)\\n template_ord.extend(sorted(template_set))\\n\\n return {\\n template_name: load_template(dataset, template_dir, template_name)\\n for template_name in template_ord\\n }\",\n \"def load_bodies(directory):\\n\\n files = glob.glob(os.path.join(directory, \\\"*.json\\\"))\\n bodies = {}\\n for file in files:\\n print(\\\"Loading body \\\" + file)\\n with open(file) as data_file:\\n internal_name = splitext(basename(file))[0]\\n bodies[internal_name] = load_body(json.load(data_file))\\n for key in bodies:\\n body = bodies[key]\\n if body.parent_internal_name is not None:\\n body.parent = bodies[body.parent_internal_name]\\n del body.parent_internal_name\\n\\n for body in bodies.values():\\n print(\\\"Executing post_init for \\\" + body.name)\\n body.post_init()\\n\\n return bodies.values()\",\n \"def load_data_from_dir(self,\\n dir_list=[],\\n exclude=[]):\\n dir_list_ = dir_list[:]\\n\\n if len(dir_list) == 0:\\n eprint(\\\"CANNOT load data generator with an empty list of directories: {}\\\".format(dir_list))\\n return\\n\\n for directory in dir_list_:\\n if not os.path.isdir(directory):\\n eprint(\\\"\\\\t\\\\t {}: {} is not a directory\\\".format(self.load_data_from_dir.__name__, directory))\\n return\\n\\n # Read Data from current directory\\n while dir_list_:\\n # Pop first directory name and create dataloader if its a valid folder\\n current_dir = dir_list_.pop(0)\\n valid_dir = True\\n for name in exclude:\\n if name in current_dir and valid_dir:\\n valid_dir = False\\n data_file = current_dir + \\\"/data.mat\\\"\\n if os.path.isfile(data_file) and \\\"takktile_\\\" in current_dir and valid_dir:\\n self.dataloaders.append(takktile_dataloader(data_dir=current_dir,\\n config=self.config,\\n augment=self.augment))\\n\\n # Find all child directories of current directory and recursively load them\\n data_dirs = [os.path.join(current_dir, o) for o in os.listdir(current_dir)\\n if os.path.isdir(os.path.join(current_dir, o))]\\n for d in data_dirs:\\n dir_list_.append(d)\\n\\n self.num_dl = len(self.dataloaders)\\n\\n if self.transform_type:\\n self.__calculate_data_transforms()\\n\\n # Create Eval Data\\n if self.create_eval_data:\\n self.eval_len = (self.__len__())//10\\n self.create_eval_data = False\\n\\n # Calculate class number and ratios\\n # Also calculate class diffs\\n if not self.config['label_type'] == 'value':\\n self.__class_nums = self.dataloaders[0].get_data_class_numbers(self.__get_data_idx(0))\\n for i, dl in enumerate(self.dataloaders[1:]):\\n self.__class_nums += dl.get_data_class_numbers(self.__get_data_idx(i+1))\\n self.__class_ratios = self.__class_nums / float(np.mean(self.__class_nums))\\n self.__class_diff = np.max(self.__class_nums) - self.__class_nums\\n self.__class_diff = [d if n > 0 else 0 for n,d in zip(self.__class_nums, self.__class_diff)]\\n\\n # Reset and prepare data\\n self.on_epoch_end()\",\n \"def _load_dyne(self):\\n if not self._hub._dscan:\\n self._hub._scan_dynamic()\\n for path in self._hub._dynamic.get(self._dyne_name, {}).get(\\\"paths\\\", []):\\n self._dirs.append(path)\",\n \"def loadFiles(root=\\\"data/TAIWAN_RAW_DATA/ADHD\\\"):\\n\\tdata_rt = [] # realtime.csv\\n\\tdata_trial = [] # trialdata.csv\\n\\tdata_id = [] # caseid/subjectid\\n\\tRealTime = \\\"A2RealTime_\\\"\\n\\tTrialData = \\\"A2TrialData_\\\"\\n\\tfolder_list = os.listdir(root) # list of subfolders in the root\\n\\tfor folders in folder_list:\\n\\t\\tfolders_path = os.path.join(root,folders)\\n\\t\\tif folders.find(\\\"pass\\\") != -1:\\n\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tdata_rt.append(pd.read_csv(os.path.join\\n\\t\\t\\t\\t\\t\\t\\t\\t (folders_path,\\n\\t\\t\\t\\t\\t\\t\\t\\t RealTime+folders[3:]+\\\".csv\\\")))\\n\\t\\t\\tdata_trial.append(pd.read_csv(os.path.join\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t (folders_path,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t TrialData+folders[3:]+\\\".csv\\\")))\\n\\t\\t\\tdata_id.append(int(folders.split('_')[1]))\\n\\t\\texcept:\\n\\t\\t\\tprint(os.path.join(folders_path,TrialData+folders[3:]+\\\".csv\\\"))\\n\\t\\t\\t\\n\\treturn data_rt,data_trial,data_id,folder_list\",\n \"def load_files(directory):\\n import os\\n import re\\n\\n files = dict()\\n\\n for file in os.scandir(directory):\\n if re.search(\\\".txt$\\\", file.name):\\n with open(file.path, \\\"r\\\", encoding=\\\"utf8\\\") as f:\\n # re.sub(\\\".txt$\\\", \\\"\\\", file.name)\\n files[file.name] = f.read()\\n\\n return files\",\n \"def load_test_subjects_names(self):\\n files = os.listdir(os.path.join(self.db_path, self.test_batch))\\n for f in files:\\n if f.startswith('test-volume'):\\n s_name = str.split(str.split(f, '.')[0], '-')[-1]\\n self.testing_subjects.append(s_name)\\n self.n_test = len(self.testing_subjects)\",\n \"def setUp(self):\\n cltk_data_dir = '~/cltk_data/sanskrit/model/sanskrit_models_cltk'\\n INDIC_RESOURCES_PATH = os.path.expanduser(cltk_data_dir)\\n\\n resources_present = os.path.isdir(INDIC_RESOURCES_PATH)\\n if not resources_present:\\n corpus_importer = CorpusImporter('sanskrit')\\n corpus_importer.import_corpus('sanskrit_models_cltk')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66310847","0.59174895","0.5706464","0.56006503","0.5449008","0.5424291","0.54034406","0.53829217","0.5337041","0.53273314","0.53095233","0.5307464","0.52964854","0.5285679","0.52551943","0.5226202","0.5178625","0.5178625","0.51691693","0.5146963","0.51436335","0.513781","0.5132956","0.51197994","0.50782996","0.50781155","0.506339","0.50611657","0.5055892","0.5051056","0.5050649","0.50433254","0.50433254","0.50433254","0.50433254","0.50433254","0.50409627","0.5033975","0.50261146","0.5023396","0.5007168","0.5001379","0.49969667","0.49958917","0.49942803","0.49821046","0.49758598","0.49732223","0.494691","0.49390018","0.49357983","0.49355197","0.49330914","0.49328667","0.4930095","0.4906435","0.49035755","0.4901464","0.48974264","0.48872483","0.4879129","0.4870847","0.48698303","0.48635843","0.48635843","0.48608184","0.48558706","0.4855671","0.4855337","0.4854289","0.48472106","0.48457322","0.48439473","0.48437947","0.48397505","0.4828225","0.48127553","0.48119432","0.4803304","0.47946241","0.4792878","0.47553888","0.47553888","0.47553888","0.47553888","0.47553888","0.47553888","0.47553888","0.47531518","0.47527847","0.47478","0.47461495","0.47439265","0.47400233","0.47327346","0.4725157","0.47183686","0.47133836","0.47104988","0.47043326"],"string":"[\n \"0.66310847\",\n \"0.59174895\",\n \"0.5706464\",\n \"0.56006503\",\n \"0.5449008\",\n \"0.5424291\",\n \"0.54034406\",\n \"0.53829217\",\n \"0.5337041\",\n \"0.53273314\",\n \"0.53095233\",\n \"0.5307464\",\n \"0.52964854\",\n \"0.5285679\",\n \"0.52551943\",\n \"0.5226202\",\n \"0.5178625\",\n \"0.5178625\",\n \"0.51691693\",\n \"0.5146963\",\n \"0.51436335\",\n \"0.513781\",\n \"0.5132956\",\n \"0.51197994\",\n \"0.50782996\",\n \"0.50781155\",\n \"0.506339\",\n \"0.50611657\",\n \"0.5055892\",\n \"0.5051056\",\n \"0.5050649\",\n \"0.50433254\",\n \"0.50433254\",\n \"0.50433254\",\n \"0.50433254\",\n \"0.50433254\",\n \"0.50409627\",\n \"0.5033975\",\n \"0.50261146\",\n \"0.5023396\",\n \"0.5007168\",\n \"0.5001379\",\n \"0.49969667\",\n \"0.49958917\",\n \"0.49942803\",\n \"0.49821046\",\n \"0.49758598\",\n \"0.49732223\",\n \"0.494691\",\n \"0.49390018\",\n \"0.49357983\",\n \"0.49355197\",\n \"0.49330914\",\n \"0.49328667\",\n \"0.4930095\",\n \"0.4906435\",\n \"0.49035755\",\n \"0.4901464\",\n \"0.48974264\",\n \"0.48872483\",\n \"0.4879129\",\n \"0.4870847\",\n \"0.48698303\",\n \"0.48635843\",\n \"0.48635843\",\n \"0.48608184\",\n \"0.48558706\",\n \"0.4855671\",\n \"0.4855337\",\n \"0.4854289\",\n \"0.48472106\",\n \"0.48457322\",\n \"0.48439473\",\n \"0.48437947\",\n \"0.48397505\",\n \"0.4828225\",\n \"0.48127553\",\n \"0.48119432\",\n \"0.4803304\",\n \"0.47946241\",\n \"0.4792878\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47553888\",\n \"0.47531518\",\n \"0.47527847\",\n \"0.47478\",\n \"0.47461495\",\n \"0.47439265\",\n \"0.47400233\",\n \"0.47327346\",\n \"0.4725157\",\n \"0.47183686\",\n \"0.47133836\",\n \"0.47104988\",\n \"0.47043326\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7492254"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7179,"cells":{"query":{"kind":"string","value":"Load teachers from PDDRTeachers experiment."},"document":{"kind":"string","value":"def load_teacher_experiment(self, exp: Experiment):\n _, _, extra = load_experiment(exp)\n self.unpack_teachers(extra)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_teachers(self):\n # Get the experiment's directory to load from\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\n self.load_teacher_experiment(ex_dir)\n if len(self.teacher_policies) < self.num_teachers:\n print(\n f\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\"\n )\n self.load_teachers()","def load_tamper(self, dataset_dir, subset):\n # Add classes. We have one class.\n # Naming the dataset nucleus, and the class nucleus\n self.add_class(\"tampers\", 1, \"tampers\")\n\n # Which subset?\n # \"val\": use hard-coded list above\n # \"train\": use data from stage1_train minus the hard-coded list above\n # else: use the data from the specified sub-directory\n # assert subset in [\"train\", \"val\", \"stage1_train\", \"stage1_test\", \"stage2_test\"]\n # subset_dir = \"stage1_train\" if subset in [\"train\", \"val\"] else subset\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\n if subset == \"val\" or subset == \"test\":\n image_ids = next(os.walk(dataset_dir))[2]\n else:\n # Get image ids from directory names\n image_ids = next(os.walk(dataset_dir))[2]\n \n\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # dirnew_splicing = '/data/tamper'\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\n\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n \n # dircopy_move = '/data/gy/ca2att/train3'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\n\n # dirnew_sp = '/data/gy/c2newsp/train'\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\n\n # Add images\n for image_id in image_ids:\n self.add_image(\n \"tampers\",\n image_id=image_id[:-4],\n path=os.path.join(dataset_dir, image_id))\n\n # for image_id in image_ids_copy_move:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dircopy_move, 'images', image_id))\n\n # for image_id in image_ids_new_splicing:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_splicing, 'images', image_id))\n\n # # for image_id in image_ids_txt_sp:\n # # self.add_image(\n # # \"tampers\",\n # # image_id=image_id[:-4],\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\n\n # for image_id in image_ids_new_sp:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_sp, 'images', image_id))","def test_pytd_teacher(self):\n defaults = parser_defaults.copy()\n defaults['datatype'] = 'train:stream'\n defaults['image_mode'] = 'ascii'\n\n with testing_utils.capture_output():\n # Get processed act from agent\n parser = display_setup_args()\n defaults['pytorch_teacher_dataset'] = 'flickr30k'\n del defaults['pytorch_teacher_task']\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n teacher = create_task_agent_from_taskname(opt)[0]\n pytorch_teacher_act = teacher.act()\n\n parser = display_setup_args()\n defaults['task'] = 'flickr30k'\n del defaults['pytorch_teacher_dataset']\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n teacher = create_task_agent_from_taskname(opt)[0]\n regular_teacher_act = teacher.act()\n\n keys = set(pytorch_teacher_act.keys()).intersection(\n set(regular_teacher_act.keys()))\n self.assertTrue(len(keys) != 0)\n for key in keys:\n self.assertTrue(pytorch_teacher_act[key] == regular_teacher_act[key],\n 'PytorchDataTeacher does not have the same value '\n 'as regular teacher for act key: {}'.format(key))","def train_teacher (nb_teachers, teacher_id):\n # Load the dataset\n X_train, X_test, y_train, y_test = models.get_dataset()\n\n print(X_train.shape)\n print(y_train.shape)\n print(X_test.shape)\n print(y_test.shape)\n \n # Retrieve subset of data for this teacher\n data, labels = partition.partition_dataset(X_train,\n y_train,\n nb_teachers,\n teacher_id)\n\n print(\"Length of training data: \" + str(len(labels)))\n\n # Define teacher checkpoint filename and full path\n\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\n \n # Perform teacher training need to modify \n \n\n # Create teacher model\n model, opt = models.create_two_layer_mlp(46) # num of cols\n model.compile(loss='binary_crossentropy',\n optimizer=\"Adam\",\n metrics=['accuracy'])\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\n\n #modify\n model_json = model.to_json()\n with open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n# serialize weights to HDF5\n model.save_weights(filename2)\n print(\"Saved model to disk\")\n return True","def unpack_teachers(self, extra: dict):\n self.teacher_policies.extend(extra[\"teacher_policies\"])\n self.teacher_envs.extend(extra[\"teacher_envs\"])\n self.teacher_expl_strats.extend(extra[\"teacher_expl_strats\"])\n self.teacher_critics.extend(extra[\"teacher_critics\"])\n self.teacher_ex_dirs.extend(extra[\"teacher_ex_dirs\"])","def get_test_loader(id_list = './data/sample_submission.csv', root_dir = './data/test/'):\n data = HumanProteinDataset(id_list, root_dir, transform = transforms.Compose([\n Rescale((256, 256)), \n ToTensor()\n ]))\n\n indices = np.arange(len(data))\n dataloader_test = DataLoader(data, batch_size=10, num_workers=5)\n\n return dataloader_test","def prune_teachers(self):\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]","def _load_trials(self) -> hyperopt.Trials:\n if os.path.isfile(self.trials_path):\n trials = pickle.load(open(self.trials_path, \"rb\"))\n else:\n trials = hyperopt.Trials()\n return trials","def load_primers(tsv_filename):\n answer = []\n with open(tsv_filename) as handle:\n for line in handle:\n if line.startswith(\"#\"):\n continue\n parts = line.rstrip(\"\\n\").split(\"\\t\")\n if len(parts) == 2:\n left, right = parts\n name = f\"P{len(answer)}\"\n else:\n name, left, right = parts[:3]\n answer.append((name, left, right))\n return answer","def get_teacher_assign():\n assignment_data = query_db(\n \"SELECT assignments.id, assignments.name, assignments.due_date \"\n \"FROM assignments JOIN topics ON assignments.topic_id=topics.id \"\n \"JOIN classes ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n assignments = []\n for assignment in assignment_data:\n assignment_dict_teach = {}\n assignment_dict_teach[\"id\"] = assignment[0]\n assignment_dict_teach[\"name\"] = assignment[1]\n assignment_dict_teach[\"due_date\"] = assignment[2]\n assignments.append(assignment_dict_teach)\n return assignments","def load_people(self, file_path):\n pass","def get_train(self, even=None):\n\n #self.images, self.labels, self.traces = trace_data.get_my_teacher()\n _, self.images, self.labels, self.traces, _ = trace_data.get_my_teacher()\n #print(self.labels)\n self.length = len(self.images)\n self.create_teacher()","def LoadTeacherModels(lang):\n\n # load Trained teacher model parameters\n log_dir = 'data/logs'\n with open(log_dir + '/' + lang + '_model_params', 'rb') as fp:\n params = pickle.load(fp)\n\n model_args = params['args']\n\n if model_args.use_colab is None:\n OUTPUT_DIR = 'ckpts/' + model_args.lang\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\n else:\n from google.colab import drive\n\n drive.mount('/content/gdrive')\n OUTPUT_DIR = '/content/gdrive/My Drive/ckpts/' + model_args.lang\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\n\n if model_args.enc_type == 'gat' and model_args.dec_type == 'transformer':\n models = {}\n OUTPUT_DIR += '/' + model_args.enc_type + '_' + model_args.dec_type\n\n # Load the vocabs\n with open('vocabs/' + model_args.model + '/' +\n lang + '/' + model_args.opt + '_src_vocab', 'rb') as fp:\n src_vocab = pickle.load(fp)\n # loading the target vocab\n model_args.sentencepiece = 'False'\n if model_args.sentencepiece == 'True':\n sp = spm.SentencePieceProcessor()\n sp.load('vocabs/' + model_args.model + '/' +\n lang + '/' + 'train_tgt.model')\n tgt_vocab = sp\n else:\n tgt_vocab = src_vocab\n\n print('Loaded ' + lang + ' Parameters..')\n model = TransGAT(params['args'], params['src_vocab_size'], src_vocab,\n params['tgt_vocab_size'], tgt_vocab)\n # Load the latest checkpoints\n optimizer = tf.train.AdamOptimizer(beta1=0.9, beta2=0.98,\n epsilon=1e-9)\n\n ckpt = tf.train.Checkpoint(\n model=model,\n optimizer=optimizer\n )\n\n ckpt_manager = tf.train.CheckpointManager(ckpt, OUTPUT_DIR, max_to_keep=5)\n if ckpt_manager.latest_checkpoint:\n ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\n\n print('Loaded ' + lang + ' Teacher model !')\n\n return model","def get_teachers(self):\n query = Teacher.all().order('teacher')\n return query.fetch()","def load_topics():\n\n print \"Importing topics...\"\n\n # Delete all rows in table, so if we need to run this a second time,\n # we won't be trying to add duplicate retailers\n Topic.query.delete()\n\n # Read CSV file\n with open(\"seed_data/topics.csv\") as source_file:\n example_data = list(csv.reader(source_file))\n\n # skip header row for populating db\n for list_item in example_data[1:]:\n topic = Topic(topic_title=list_item[1])\n\n # Add the current retailer to the session\n db.session.add(topic)\n\n # Commit the db.session changes to the database\n db.session.commit()","def ensemble_preds(dataset, nb_teachers, stdnt_data):\n\n # Compute shape of array that will hold probabilities produced by each\n # teacher, for each training point, and each output class\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\n\n # Create array that will hold result\n result = np.zeros(result_shape, dtype=np.float32)\n\n # Get predictions from each teacher\n for teacher_id in xrange(nb_teachers):\n # Compute path of checkpoint file for teacher model with ID teacher_id\n if FLAGS.deeper:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\n else:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\n\n # Get predictions on our training data and store in result array\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\n\n # This can take a while when there are a lot of teachers so output status\n print(\"Computed Teacher \" + str(teacher_id) + \" softmax predictions\")\n\n return result","def get_each_loader(data_path, batch_size, trn_negnum, shuffle=True, num_workers=0):\n \n dataset = ML_Dataset(data_path, trn_negnum)\n \n if data_path.endswith('trn') == True:\n collate = dataset.train_collate\n else:\n collate = test_collate\n\n data_loader = data.DataLoader(dataset=dataset,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers,\n collate_fn=collate)\n\n return data_loader","def read_triplets(seed_candidates):\n if \"pickle\" in seed_candidates:\n if \"*\" in seed_candidates:\n all_files = glob.glob(seed_candidates)\n new_data = []\n for file_name in all_files:\n with open(file_name, 'rb') as f:\n data = pickle.load(f)\n for dd in data:\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\n else:\n with open(seed_candidates, 'rb') as f:\n data = pickle.load(f)\n new_data = []\n for dd in data:\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\n # idx = int(dd[0][10:])\n # new_data.append((idx, dd[1], dd[2], dd[3]))\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\n else:\n column_names = ['evtid', 'h1', 'h2', 'h3']\n if \"*\" in seed_candidates:\n all_files = glob.glob(seed_candidates)\n new_data = []\n for file_name in all_files:\n df_seed_tmp = pd.read_csv(file_name, header=None, names=column_names,)\n new_data.append(df_seed_tmp)\n df_seed = pd.concat(new_data)\n else:\n df_seed = pd.read_csv(seed_candidates, header=None,\n names=column_names)\n return df_seed","def test_pyt_multitask(self):\n\n def run_display_test(defaults, ep_and_ex_counts):\n with testing_utils.capture_output() as f:\n parser = display_setup_args()\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n display_data(opt)\n str_output = f.getvalue()\n self.assertTrue(\n '[ loaded {} episodes with a total of {} examples ]'.format(\n ep_and_ex_counts[0], ep_and_ex_counts[1]\n ) in str_output,\n 'PytorchDataTeacher multitasking failed with '\n 'following args: {}'.format(opt)\n )\n\n task1 = 'babi:task1k:1'\n task2 = 'babi:task1k:2'\n dataset1 = 'flickr30k'\n dataset2 = 'vqa_v1'\n\n # Expected example and episode counts\n eps_and_exs_counts = [\n (1800, 1800),\n (1080, 1800),\n (29900, 29900),\n (29180, 29900),\n (277349, 277349)\n ]\n defaults = parser_defaults.copy()\n\n # 1.\n defaults['pytorch_teacher_task'] = '{},{}'.format(task1, task2)\n run_display_test(defaults, eps_and_exs_counts[0])\n\n # 2.\n defaults['pytorch_teacher_task'] = task1\n defaults['task'] = task2\n run_display_test(defaults, eps_and_exs_counts[1])\n\n # 3.\n del defaults['task']\n defaults['pytorch_teacher_dataset'] = dataset1\n run_display_test(defaults, eps_and_exs_counts[2])\n\n # 4.\n del defaults['pytorch_teacher_task']\n defaults['task'] = task1\n run_display_test(defaults, eps_and_exs_counts[3])\n\n # 5.\n del defaults['task']\n defaults['pytorch_teacher_dataset'] = '{},{}'.format(dataset1, dataset2)\n run_display_test(defaults, eps_and_exs_counts[4])","def load_ptb_dataset(name='ptb', path='raw_data'):\n path = os.path.join(path, name)\n logging.info(\"Load or Download Penn TreeBank (PTB) dataset > {}\".format(path))\n\n # Maybe dowload and uncompress tar, or load exsisting files\n maybe_download_and_extract(PTB_FILENAME, path, PTB_URL, extract=True)\n\n data_path = os.path.join(path, 'simple-examples', 'data')\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id = nlp.build_vocab(nlp.read_words(train_path))\n\n train_data = nlp.words_to_word_ids(nlp.read_words(train_path), word_to_id)\n valid_data = nlp.words_to_word_ids(nlp.read_words(valid_path), word_to_id)\n test_data = nlp.words_to_word_ids(nlp.read_words(test_path), word_to_id)\n vocab_size = len(word_to_id)\n\n # logging.info(nlp.read_words(train_path)) # ... 'according', 'to', 'mr.', '<unk>', '<eos>']\n # logging.info(train_data) # ... 214, 5, 23, 1, 2]\n # logging.info(word_to_id) # ... 'beyond': 1295, 'anti-nuclear': 9599, 'trouble': 1520, '<eos>': 2 ... }\n # logging.info(vocabulary) # 10000\n # exit()\n return train_data, valid_data, test_data, vocab_size","def loadTrainer(path):\n\tray.shutdown()\n\tray.init()\n\tconfig = createConfig()\n\ttrainer = dqn.DQNTrainer(config=config, env=HiLoPricingEnv)\n\ttrainer.restore(path)\n\treturn trainer","def get_data_loaders():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+5):]\n \n #history_complete.append(history)\n if len(persona) == 4:\n if len(history) > (len(persona)+3):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def TestTeacher(self, model_name, **kwargs):\n batch_size = kwargs.pop(\"batch_size\", 256)\n model_save_path = kwargs.pop(\"model_save_path\", \"./checkpoints/teacher/\")\n record_save_path = kwargs.pop(\"record_save_path\", \"./records/teacher\")\n verbose = kwargs.pop(\"verbose\", False)\n\n # Do some check\n if not os.path.exists(model_save_path):\n raise RuntimeError(\"No pretrained model exists in '{}'\".format(model_save_path))\n if not os.path.exists(record_save_path):\n os.makedirs(record_save_path)\n model_save_path = os.path.join(model_save_path, \"{}.ckpt\".format(model_name))\n\n tf.reset_default_graph()\n\n # Get dataset\n test_data, test_label = self.data_manager.test_data, self.data_manager.test_label\n num_test_data = test_data.shape[0]\n\n X = tf.placeholder(test_data.dtype, shape=[None]+list(test_data.shape[1:]), name=\"input_data\")\n y = tf.placeholder(test_label.dtype, shape=[None]+list(test_label.shape[1:]), name=\"input_label\")\n is_train = tf.placeholder(tf.bool, name=\"is_train\")\n\n dataset = tf.data.Dataset.from_tensor_slices((X, y))\n batched_dataset = dataset.batch(batch_size)\n \n iterator = batched_dataset.make_initializable_iterator()\n batch_data, batch_label = iterator.get_next()\n\n # Get the teacher model\n with tf.variable_scope('teacher_model'):\n logits, probs = self.teacher_model(batch_data, is_train=is_train)\n result = tf.argmax(logits, axis=1)\n correct_num = tf.reduce_sum(tf.cast(tf.equal(result, tf.argmax(batch_label, axis=1)), tf.float32))\n saver = tf.train.Saver()\n\n # Test process\n with tf.Session() as sess:\n sess.run(iterator.initializer, feed_dict={X:test_data, y:test_label})\n saver.restore(sess, model_save_path)\n total_correct_cnt = 0\n while True:\n try:\n right_num = sess.run([correct_num], feed_dict={is_train:False})\n total_correct_cnt += right_num[0]\n except tf.errors.OutOfRangeError:\n acc = total_correct_cnt * 1.0 / num_test_data\n if verbose:\n print(\"Test accuracy: {}\".format(acc))\n break\n acc_hist = [acc]\n self._writeRecord(record_save_path, \"{}_test_accuracy\".format(model_name), acc_hist)","def load_examples_data(dataset_name):\n dataset_name = dataset_name.strip().lower()\n if dataset_name.lower() not in ['pokemon', 'hanzi', 'animals', 'nsfw', 'simpsons', 'horse2zebra', 'people',\n 'autodrive', 'superresolution', 'anpr', 'beauty','antisproofing','facelandmarks','dogs-vs-cats','chinese']:\n raise ValueError('Not a valid dataset_name.')\n dataset_name = 'examples_' + dataset_name\n dirname = os.path.join(_trident_dir, dataset_name)\n if not os.path.exists(dirname):\n try:\n os.makedirs(dirname)\n except OSError:\n # Except permission denied and potential race conditions\n # in multi-threaded environments.\n pass\n is_internet_ok = is_connected()\n if dataset_name == 'examples_pokemon':\n is_download=download_file_from_google_drive('1U-xc54fX9j9BcidvRa0ow6qjssMlSF2A', dirname, 'pokemon.tar')\n tar_file_path = os.path.join(dirname, 'pokemon.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n extract_path = os.path.join(dirname, 'pokemon')\n dataset = load_folder_images(dataset_name, extract_path, folder_as_label=False)\n print('get pokemon images :{0}'.format(len(dataset)))\n return dataset\n\n\n elif dataset_name == 'examples_hanzi':\n download_file_from_google_drive('13UEzSG0az113gpRPKPyKrIE2HDaA2P4H', dirname, 'hanzi.tar')\n tar_file_path = os.path.join(dirname, 'hanzi.tar')\n extract_path = os.path.join(dirname, 'hanzi')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n dataset = load_folder_images(dataset_name, os.path.join(dirname, 'train'), folder_as_label=True,\n object_type=ObjectType.gray)\n\n dataset_test = load_folder_images(dataset_name, os.path.join(dirname, 'test'), folder_as_label=True,\n object_type=ObjectType.gray)\n\n dataset.testdata = dataset_test.traindata\n dataset.class_names['zh-cn'] = dataset.class_names['en-us']\n return dataset\n\n elif dataset_name == 'examples_animals':\n download_file_from_google_drive('19Cjq8OO6qd9k9TMZxlPjDpejDOdiHJoW', dirname, 'animals.tar')\n tar_file_path = os.path.join(dirname, 'animals.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n dataset = load_folder_images(dataset_name, dirname, folder_as_label=True)\n return dataset\n elif dataset_name == 'examples_nsfw':\n tar_file_path = os.path.join(dirname, 'nsfw.tar')\n if os.path.exists(tar_file_path) and get_file_create_time(tar_file_path)<datetime.datetime(2021, 2, 20, 0, 0, 0).timestamp():\n os.remove(tar_file_path)\n if os.path.exists(os.path.join(dirname,'porn_detection_data.pkl')):\n os.remove(os.path.join(dirname,'porn_detection_data.pkl'))\n _delete_h(dirname)\n download_file_from_google_drive('1EXpV2QUrSFJ7zJn8NqtqFl1k6HvXsUzp', dirname, 'nsfw.tar')\n\n extract_path = os.path.join(dirname, 'nsfw')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n folders = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']\n data=unpickle(os.path.join(dirname,'porn_detection_data.pkl'))\n\n trainData = []\n testData = []\n trainLabel = []\n testLabel = []\n for n in range(5):\n folder=folders[n]\n trainData.extend(data[folder]['train'])\n trainLabel.extend([n]*len(data[folder]['train']))\n testData.extend(data[folder]['test'])\n testLabel.extend([n] * len(data[folder]['test']))\n\n trainarray = ImageDataset(trainData,object_type=ObjectType.rgb)\n trainlabel = LabelDataset(trainLabel,object_type=ObjectType.classification_label)\n train_iter = Iterator(data=trainarray, label=trainlabel)\n\n testarray = ImageDataset(testData,object_type=ObjectType.rgb)\n testlabel = LabelDataset(testLabel,object_type=ObjectType.classification_label)\n test_iter = Iterator(data=testarray, label=testlabel)\n print('training images: {0} test images:{1}'.format(len(trainarray), len(testarray)))\n\n dataset = DataProvider(dataset_name, traindata=train_iter, testdata=test_iter)\n dataset.binding_class_names(['drawing', 'hentai', 'neutral', 'porn', 'sexy'], 'en-us')\n dataset.binding_class_names(['绘画', '色情漫画', '中性', '色情', '性感'], 'zh-cn')\n dataset.binding_class_names(['繪畫', '色情漫畫', '中性', '色情', '性感'], 'zh-tw')\n dataset.scenario = 'train'\n return dataset\n elif dataset_name == 'examples_simpsons':\n download_file_from_google_drive('1hGNFbfBv3EZ4nx4Qod6PtSYzO8H4QIxC', dirname, 'simpsons.tar')\n tar_file_path = os.path.join(dirname, 'simpsons.tar')\n extract_path = os.path.join(dirname, 'simpsons')\n extract_archive(tar_file_path, extract_path, archive_format='tar')\n data_provider = load_folder_images(dataset_name, extract_path, folder_as_label=False)\n data_provider.traindata.unpair = RandomNoiseDataset(shape=(100), random_mode='normal')\n print('get simpsons images :{0}'.format(len(data_provider.traindata.data.items)))\n return data_provider\n elif dataset_name == 'examples_horse2zebra':\n download_file_from_google_drive('1pqj-T90Vh4wVNBV09kYZWgVPsZUA2f7U', dirname, 'horse2zebra.tar')\n tar_file_path = os.path.join(dirname, 'horse2zebra.tar')\n extract_path = os.path.join(dirname, 'horse2zebra')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n trainA = ImageDataset(list_images(os.path.join(dirname, 'trainA')), object_type=ObjectType.rgb,\n get_image_mode=GetImageMode.processed)\n trainB = ImageDataset(list_images(os.path.join(dirname, 'trainB')), object_type=ObjectType.rgb,\n get_image_mode=GetImageMode.processed)\n testA = ImageDataset(list_images(os.path.join(dirname, 'testA')), object_type=ObjectType.rgb,\n get_image_mode=GetImageMode.processed)\n testB = ImageDataset(list_images(os.path.join(dirname, 'testB')), object_type=ObjectType.rgb,\n get_image_mode=GetImageMode.processed)\n train_iter = Iterator(data=trainA, unpair=trainB)\n test_iter = Iterator(data=testA, unpair=testB)\n dataset = DataProvider(dataset_name, traindata=train_iter, testdata=test_iter)\n print('get horse2zebra images :{0}'.format(len(dataset)))\n return dataset\n elif dataset_name == 'examples_people':\n download_file_from_google_drive('1H7mJJfWpmXpRxurMZQqY4N_UXWLbQ2pT', dirname, 'people.tar')\n tar_file_path = os.path.join(dirname, 'people.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n imgs = glob.glob(os.path.join(dirname, 'imgs', '*.*g'))\n masks = glob.glob(os.path.join(dirname, 'masks', '*.png'))\n imgs=list(sorted(imgs))\n masks = list(sorted(masks))\n # make_dir_if_need(os.path.join(dirname, 'trimap'))\n # for i in range(len(masks)):\n # mask=mask2array(masks[i])\n # trimap=mask2trimap(mask)\n # save_mask(trimap,masks[i].replace('masks','trimap'))\n # print('trimap',len(masks))\n\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb)\n mskdata = MaskDataset(masks=masks, object_type=ObjectType.binary_mask)\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=mskdata))\n print('get people images :{0}'.format(len(dataset)))\n return dataset\n elif dataset_name == 'examples_autodrive':\n download_file_from_google_drive('1JqPPeHqhWLqnI6bD8nuHcVx-Y56oIZMK', dirname, 'autodrive.tar')\n tar_file_path = os.path.join(dirname, 'autodrive.tar')\n extract_path = os.path.join(dirname, 'autodrive')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n imgs = glob.glob(os.path.join(dirname, 'images', '*.*g'))\n masks = glob.glob(os.path.join(dirname, 'masks', '*.png'))\n imgs = list(sorted(imgs))\n masks = list(sorted(masks))\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb,symbol='image')\n mskdata = MaskDataset(masks=masks, object_type=ObjectType.color_mask,symbol='mask')\n\n def parse_code(l):\n if len(l.strip().split(\"\\t\")) == 2:\n a, b = l.replace('\\t\\t', '\\t').strip().split(\"\\t\")\n return tuple(int(i) for i in b.split(' ')), a\n\n label_codes, label_names = zip(\n *[parse_code(l) for l in open(os.path.join(dirname, \"label_colors.txt\")).readlines()])\n for i in range(len(label_codes)):\n mskdata.palette[label_names[i]] = label_codes[i]\n\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=mskdata))\n print('get autodrive images :{0}'.format(len(dataset)))\n return dataset\n elif dataset_name == 'examples_superresolution':\n download_file_from_google_drive('1v1uoymrWI_MLSiGvSGW7tWJYSnzzXpEQ', dirname, 'superresolution.tar')\n tar_file_path = os.path.join(dirname, 'superresolution.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n imgs = glob.glob(os.path.join(dirname, '*.*g'))\n imgs.extend(glob.glob(os.path.join(dirname, '*.bmp')))\n imgs = list(sorted(imgs))\n\n print('get super resolution images :{0}'.format(len(imgs)))\n\n imgdata = ImageDataset(images=imgs * 2, object_type=ObjectType.rgb, symbol='lr')\n labeldata = ImageDataset(images=imgs * 2, object_type=ObjectType.rgb, symbol='hr')\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=labeldata))\n return dataset\n elif dataset_name == 'examples_beauty':\n download_file_from_google_drive('1aJhxN9IqsxuayhRTm-gmxk6PiLe5wm9X', dirname, 'beauty.tar')\n tar_file_path = os.path.join(dirname, 'beauty.tar')\n\n extract_archive(tar_file_path, dirname, archive_format='tar')\n # 讀取圖片數據\n images_dict = {}\n with open(os.path.join(dirname, 'images_dict.pkl'), 'rb') as fp:\n images_dict = pickle.load(fp)\n\n f = open(os.path.join(dirname, 'All_Ratings.txt'), encoding='utf-8-sig').readlines()\n imgs = []\n landmarks = []\n ratings = []\n for row in f:\n data = row.strip().split('\\t')\n if 'images\\\\' + data[0] in images_dict:\n img = images_dict['images\\\\' + data[0]][0]\n img = img.transpose([2, 0, 1])[::-1].transpose([1, 2, 0])\n imgs.append(img)\n landmark = images_dict['images\\\\' + data[0]][1].astype(np.float32)\n landmarks.append(landmark)\n rating = (float(data[1])) / 5.00\n ratings.append(rating)\n print('{0} faces loaded...'.format(len(imgs)))\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb, symbol='faces')\n landmarkdata = LandmarkDataset(landmarks=landmarks, object_type=ObjectType.landmarks, symbol='target_landmarks')\n labeldata = LabelDataset(data=ratings,object_type=ObjectType.classification_label, symbol='target_beauty')\n data_provider = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=Dataset.zip(landmarkdata,labeldata)))\n return data_provider\n\n elif dataset_name == 'examples_facelandmarks':\n download_file_from_google_drive('1GtswQBAHPa_bXaB4tW2uOOQ8Lxfz2L5B', dirname, 'ibug_300W.tar')\n tar_file_path = os.path.join(dirname, 'ibug_300W.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n root_dir=os.path.join(dirname, 'ibug_300W_large_face_landmark_dataset')\n image_paths = {}\n landmarks = {}\n crops = {}\n\n for mode in ['train','test']:\n make_dir_if_need(os.path.join(dirname, 'crops',mode))\n tree = ElementTree.parse(os.path.join(root_dir, 'labels_ibug_300W_{0}.xml'.format(mode)))\n root = tree.getroot()\n image_paths[mode]=[]\n landmarks[mode] = []\n crops[mode] = []\n\n offset=5\n for j in tqdm(range(len(root[2]))):\n try:\n filename=root[2][j]\n landmark = []\n for num in range(68):\n x_coordinate = int(filename[0][num].attrib['x'])\n y_coordinate = int(filename[0][num].attrib['y'])\n landmark.append([x_coordinate, y_coordinate])\n landmark=np.asarray(landmark)\n\n crop = filename[0].attrib\n for k in crop.keys():\n crop[k] = int(crop[k]) if isinstance(crop[k], str) else crop[k]\n for k in crop.keys():\n if k=='top' and int(landmark[:,1].min())<int(crop[k]):\n crop[k] = int( landmark[:,1].min())\n crop[ 'height']+=crop[k]-int(landmark[:,1].min())\n elif k=='left' and int(landmark[:,0].min())<int(crop[k]):\n crop[k] = int( landmark[:,0].min())\n crop['width']+= crop[k] - int(landmark[:, 0].min())\n elif k == 'width' and int(landmark[:, 0].max()-landmark[:, 0].min()) > int(crop[k]):\n crop[k] = int(landmark[:, 0].max()-landmark[:, 0].min())\n elif k == 'height' and int(landmark[:, 1].max()-landmark[:, 1].min()) > int(crop[k]):\n crop[k] = int(landmark[:, 1].max()-landmark[:, 1].min())\n\n crop['left']-=offset\n crop['top'] -= offset\n crop['width'] += 2*offset\n crop['height'] += 2*offset\n\n\n landmark[:,0]-=crop['left']\n landmark[:, 1] -= crop['top']\n\n\n if not os.path.exists(os.path.join(dirname, 'crops', mode, '{0}.png'.format(j))):\n im=image2array(os.path.join(root_dir, filename.attrib['file']))\n if im.ndim==2:\n im=cv2.cvtColor(im,cv2.COLOR_GRAY2RGB)\n im=im[crop['top']:min(crop['top']+crop['height'],im.shape[0]),crop['left']:min(crop['left']+crop['width'],im.shape[1]),:]\n\n if max(im.shape[:2])/max(min(im.shape[:2]),0)<=5:\n\n array2image(im).save(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\n image_paths[mode].append(os.path.join(dirname, 'crops', mode, '{0}.png'.format(j)))\n crops[mode].append(crop)\n landmarks[mode].append(landmark)\n del im\n else:\n #im = image2array(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\n image_paths[mode].append(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\n crops[mode].append(crop)\n landmarks[mode].append(landmark)\n\n if j%100==0:\n gc.collect()\n except Exception as e:\n pass\n\n print('ibug 300w train dataset: images: {0} landmarks:{1} \\n'.format(len(image_paths['train']),len(landmarks['train'])))\n print('ibug 300w test dataset: images: {0} landmarks:{1} \\n'.format(len(image_paths['test']), len(landmarks['test'])))\n imdata=ImageDataset(images=image_paths['train'],symbol='faces',object_type=ObjectType.rgb)\n landmarkdata = LandmarkDataset(landmarks=landmarks['train'], symbol='landmarks',object_type=ObjectType.landmarks)\n imtestdata = ImageDataset(images=image_paths['test'], symbol='faces',object_type=ObjectType.rgb)\n landmarktestdata = LandmarkDataset(landmarks=landmarks['test'], symbol='landmarks',object_type=ObjectType.landmarks)\n data_provider=DataProvider(traindata=Iterator(data=imdata,label=landmarkdata),testdata=Iterator(data=imtestdata,label=landmarktestdata))\n return data_provider\n\n elif dataset_name == 'examples_antisproofing':\n download_file_from_google_drive('1e7Zjn2MHNCvA5gXdJUECzY8NjK4KVpa7', dirname, 'antisproofing.tar')\n tar_file_path = os.path.join(dirname, 'antisproofing.tar')\n make_dir_if_need(os.path.join(dirname, 'antisproofing'))\n extract_archive(tar_file_path, dirname, archive_format='tar')\n data_provider = load_folder_images(dataset_name,os.path.join(dirname, 'antisproofing'))\n return data_provider\n elif dataset_name == 'examples_anpr':\n download_file_from_google_drive('1uGBd8tXlP0TZAXNgrR6H0jl5MXj7VPbN', dirname, 'anpr.tar')\n tar_file_path = os.path.join(dirname, 'anpr.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n imgs = glob.glob(os.path.join(dirname, '*.*g'))\n imgs = list(sorted(imgs))\n\n # CCPD (Chinese City Parking Dataset, ECCV) and PDRC (license Plate Detection and Recognition Challenge)\n # https://github.com/detectRecog/CCPD\n provinces = [\"皖\", \"沪\", \"津\", \"渝\", \"冀\", \"晋\", \"蒙\", \"辽\", \"吉\", \"黑\", \"苏\", \"浙\", \"京\", \"闽\", \"赣\", \"鲁\", \"豫\", \"鄂\", \"湘\", \"粤\",\n \"桂\", \"琼\", \"川\", \"贵\", \"云\", \"藏\", \"陕\", \"甘\", \"青\", \"宁\", \"新\", \"警\", \"学\", \"O\"]\n alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',\n 'W', 'X', 'Y', 'Z', 'O']\n ads = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W',\n 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'O']\n\n def lp2char(lp):\n cols = lp.split('_')\n charstring = ''\n for i in range(len(cols)):\n if i == 0:\n charstring += provinces[int(cols[i])]\n elif i == 1:\n charstring += alphabets[int(cols[i])]\n else:\n charstring += ads[int(cols[i])]\n return charstring\n\n width = 720\n height = 1160\n for im_path in imgs:\n lbl = im_path.split('/')[-1].rsplit('.', 1)[0].split('-')[-3]\n charstring = lp2char(lbl)\n iname = im_path.rsplit('/', 1)[-1].rsplit('.', 1)[0].split('-')\n [leftUp, rightDown] = [[int(eel) for eel in el.split('&')] for el in iname[2].split('_')]\n box = [leftUp[0], leftUp[1], rightDown[0], rightDown[1]]\n ori_w, ori_h = [float(int(el)) for el in [width, height]]\n new_labels = [(leftUp[0] + rightDown[0]) / (2 * ori_w), (leftUp[1] + rightDown[1]) / (2 * ori_h),\n (rightDown[0] - leftUp[0]) / ori_w, (rightDown[1] - leftUp[1]) / ori_h]\n download_file_from_google_drive('1e7Zjn2MHNCvA5gXdJUECzY8NjK4KVpa7', dirname, 'antisproofing.tar')\n tar_file_path = os.path.join(dirname, 'antisproofing.tar')\n make_dir_if_need(os.path.join(dirname, 'antisproofing'))\n extract_archive(tar_file_path, dirname, archive_format='tar')\n data_provider = load_folder_images(dataset_name, os.path.join(dirname, 'antisproofing'))\n return data_provider\n\n\n\n elif dataset_name == 'examples_dogs-vs-cats':\n download_file_from_google_drive('10czW0On7eIXkPP-MuQ-IRxMWdTizWjNC', dirname, 'dogs-vs-cats.tar')\n tar_file_path = os.path.join(dirname, 'dogs-vs-cats.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n data_provider = load_folder_images(dataset_name, dirname)\n return data_provider\n elif dataset_name == 'examples_chinese':\n to_half=ToHalfWidth()\n to_sc=ChineseConvert(convert_to='simplified')\n download_file_from_google_drive('1yzRzXpLuhSUxnixqCgpbdTk16ajnTEWF', dirname, 'chinese.tar')\n tar_file_path = os.path.join(dirname, 'chinese.tar')\n extract_archive(tar_file_path, dirname, archive_format='tar')\n\n as_train = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'as_training.utf8'), encoding='utf-8-sig').read().strip().replace('\\u3000' ,'|'))).splitlines()\n cityu_train =remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'cityu_training.utf8'), encoding='utf-8-sig').read().strip().replace(' ','|'))).splitlines()\n\n as_test = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'as_testing_gold.utf8'), encoding='utf-8-sig').read().strip().replace('\\u3000', '|'))).splitlines()\n cityu_test = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'cityu_test_gold.utf8'), encoding='utf-8-sig').read().strip().replace(' ', '|'))).splitlines()\n\n\n data = as_train + cityu_train # 把兩個語料合併\n test_data=as_test + cityu_test # 把兩個語料合併\n\n\n raw_data_train = [row.strip('\\n').strip('\\r') for row in data] # 移除分行字元\n raw_data_test = [row.strip('\\n').strip('\\r') for row in test_data] # 移除分行字元\n\n process_data_train=[]\n process_seg_label_train = []\n process_simplifided_label_train = []\n process_traditional_label_train = []\n\n tmp_data_train = []\n tmp_seg_label_train = []\n tmp_simplifided_label_train = []\n tmp_pronunce_label_train = []\n for k in tqdm(range(len(raw_data_train))):\n row=raw_data_train[k]\n if row.startswith('∥'):\n row=row[1:]\n words=row.replace('||','|').split('|')\n for k in range(len(words)):\n\n word = words[k]\n\n for i in range(len(word)):\n tmp_data_train.append(word[i])\n #tmp_simplifided_label_train.append(to_half(to_sc(word[i])))\n #轉換為BMES\n\n if i==0 and len(word)>1: #B 是一個詞的開始\n tmp_seg_label_train.append('B')\n elif i==len(word)-1 and len(word)>=2 and tmp_seg_label_train[-1] in ['B','M']: #E 是一個詞的結束\n tmp_seg_label_train.append('E')\n elif len(word)==1 and i==0: #S 自己就是一個單詞\n tmp_seg_label_train.append('S')\n elif len(word)>=3 and tmp_seg_label_train[-1] in ['B','M']: #M 是一個詞的中間\n tmp_seg_label_train.append('M')\n\n if len(tmp_seg_label_train)>0 and tmp_seg_label_train[-1] in ['E','S']:\n if len(word) > 1 and (is_alphabet(word) or is_punctuation(word)) and k+1<len(words):\n if word in [ '。','﹖']:\n pass\n\n elif random.random() < 0.6 or is_alphabet(word):\n tmp_data_train.append(' ')\n tmp_seg_label_train.append('S')\n\n if (k+1<len(raw_data_train) and not raw_data_train[k+1].startswith( '」')) and words[-1] in [ '。','﹖']:\n #process_traditional_label_train.append(tmp_data_train)\n\n tmp_data_train=to_half(''.join(tmp_data_train))\n tmp_seg_label_train = ''.join(tmp_seg_label_train)\n # if len(tmp_data_train)!=len(tmp_seg_label_train):\n # print('')\n tmp_simplifided_label_train =to_sc(tmp_data_train)\n\n process_data_train.append(tmp_data_train)\n process_seg_label_train.append(tmp_seg_label_train)\n process_simplifided_label_train.append(tmp_simplifided_label_train)\n tmp_data_train = []\n tmp_seg_label_train = []\n tmp_simplifided_label_train = []\n tmp_pronunce_label_train = []\n # else:\n # tmp_data_train.append('\\n')\n # tmp_simplifided_label_train.append('\\n')\n # tmp_seg_label_train.append('\\n')\n corpus=process_data_train\n seg_corpus=process_seg_label_train\n simplifided_corpus =process_simplifided_label_train\n\n process_data_test = []\n process_seg_label_test = []\n process_simplifided_label_test = []\n process_traditional_label_test = []\n print('generate test labels')\n tmp_data_test = []\n tmp_seg_label_test = []\n tmp_simplifided_label_test = []\n tmp_pronunce_label_test = []\n for k in tqdm(range(len(raw_data_test))):\n row=raw_data_test[k]\n if row.startswith('∥'):\n row=row[1:]\n words = row.replace('||', '|').split('|')\n for k in range(len(words)):\n\n word = words[k]\n\n for i in range(len(word)):\n tmp_data_test.append(word[i])\n # tmp_simplifided_label_test.append(to_half(to_sc(word[i])))\n # 轉換為BMES\n\n if i == 0 and len(word) > 1: # B 是一個詞的開始\n tmp_seg_label_test.append('B')\n elif i == len(word) - 1 and len(word) >= 2 and tmp_seg_label_test[-1] in ['B', 'M']: # E 是一個詞的結束\n tmp_seg_label_test.append('E')\n elif len(word) == 1 and i == 0: # S 自己就是一個單詞\n tmp_seg_label_test.append('S')\n elif len(word) >= 3 and tmp_seg_label_test[-1] in ['B', 'M']: # M 是一個詞的中間\n tmp_seg_label_test.append('M')\n\n if len(tmp_seg_label_test) > 0 and tmp_seg_label_test[-1] in ['E', 'S'] and k+1<len(words):\n if len(word) > 1 and (is_alphabet(word) or is_punctuation(word)):\n if word in ['。', '﹖']:\n pass\n elif random.random() < 0.6 or is_alphabet(word):\n tmp_data_test.append(' ')\n tmp_seg_label_test.append('S')\n\n if (k + 1 < len(raw_data_test) and not raw_data_test[k + 1].startswith('」')) and words[-1] in ['。', '﹖']:\n # process_traditional_label_test.append(tmp_data_test)\n\n tmp_data_test = to_half(''.join(tmp_data_test))\n tmp_seg_label_test = ''.join(tmp_seg_label_test)\n # if len(tmp_data_test)!=len(tmp_seg_label_test):\n # print('')\n tmp_simplifided_label_test = to_sc(tmp_data_test)\n\n process_data_test.append(tmp_data_test)\n process_seg_label_test.append(tmp_seg_label_test)\n process_simplifided_label_test.append(tmp_simplifided_label_test)\n tmp_data_test = []\n tmp_seg_label_test = []\n tmp_simplifided_label_test = []\n tmp_pronunce_label_test = []\n # else:\n # tmp_data_test.append('\\n')\n # tmp_simplifided_label_test.append('\\n')\n # tmp_seg_label_test.append('\\n')\n test_corpus = process_data_test\n test_seg_corpus = process_seg_label_test\n test_simplifided_corpus = process_simplifided_label_test\n\n\n data=TextSequenceDataset(corpus=corpus,sequence_length=64,sequence_start_at='section_start',object_type=ObjectType.corpus,symbol='input')\n seg_label = TextSequenceDataset(corpus=seg_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='seg_label')\n simplifided_label = TextSequenceDataset(corpus=simplifided_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='simplified_label')\n traditional_label = TextSequenceDataset(corpus= copy.deepcopy(corpus), sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='traditional_label')\n\n data_test=TextSequenceDataset(corpus=test_corpus,sequence_length=64,sequence_start_at='section_start',object_type=ObjectType.corpus,symbol='input')\n seg_test_label = TextSequenceDataset(corpus=test_seg_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='seg_label')\n simplifided_test_label = TextSequenceDataset(corpus=test_simplifided_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='simplified_label')\n traditional_test_label = TextSequenceDataset(corpus= copy.deepcopy(test_corpus), sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='traditional_label')\n\n\n chars = list(sorted(set(list( ''.join(corpus) +bpmf_phonetic+'\\n\\r\\t∥'+ ''.join(simplifided_corpus)+''.join(test_data)))))\n chars.insert(0, '[CLS]')\n chars.insert(1, '[SEP]')\n chars.insert(2, '[UNK]')\n chars.insert(3, '[PAD]')\n chars.insert(4, '[MASK]')\n\n data.vocabs =data_test.vocabs=simplifided_label.vocabs=simplifided_test_label.vocabs = chars\n data.text2index=data_test.text2index =simplifided_label.text2index=simplifided_test_label.text2index = dict((c, i) for i, c in enumerate(chars))\n data.index2text =data_test.index2text =simplifided_label.index2text=simplifided_test_label.index2text= dict((i, c) for i, c in enumerate(chars))\n traditional_label = copy.deepcopy(data)\n traditional_test_label = copy.deepcopy(data_test)\n traditional_label.object_type =traditional_test_label.object_type = ObjectType.sequence_label\n traditional_label.symbol =traditional_test_label.symbol = 'traditional_label'\n\n mask_label = copy.deepcopy(data)\n mask_test_label = copy.deepcopy(data_test)\n #mask_label.object_type =mask_test_label.object_type= ObjectType.corpus\n mask_label.symbol = mask_test_label.symbol = 'mask_label'\n\n\n\n nextword=copy.deepcopy(data)\n nextword_test = copy.deepcopy(data_test)\n nextword.object_type=nextword_test.object_type=ObjectType.sequence_label\n nextword.symbol=nextword_test.symbol='nextword_label'\n nextword.sequence_offset=nextword_test.sequence_offset=1\n\n label=ZipDataset(seg_label,nextword,simplifided_label,traditional_label,mask_label)\n label_test = ZipDataset(seg_test_label, nextword_test, simplifided_test_label, traditional_test_label, mask_test_label)\n provider=TextSequenceDataProvider(\n traindata=Iterator(data=data,label=label),\n testdata=Iterator(data=data_test,label=label_test))\n return provider\n #,sample_filter=lambda x:x[0][-1]==3\n else:\n return None","def load_data_from_files(self):\n # separated method to allow mock easier\n logger.info(\"Loading data...\")\n parent = Path(__file__).parent\n path = parent / \"resources\" / \"scores.txt\"\n self.scorer.load_from_file(path)\n path = parent / \"resources\" / \"american-english-large.txt\"\n self.trie.load_from_file(path)\n path = parent / \"resources\" / \"reels.txt\"\n self.reels = Reel.get_from_file(path)\n logger.info(\"Data loaded!\")","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\r\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n\t\traise NotImplementedError()","def get_all_profesors(self) -> List[Teacher]:\n self.cursor.execute(\n f\"SELECT * FROM {self.table_name}\")\n \n teachers = []\n for teacher in self.cursor.fetchall():\n teacher_parsed = list(teacher[0:8]) + [json.loads(t) for t in teacher[8:]]\n teachers.append(Teacher.parse_tuple(teacher_parsed))\n \n return teachers","def load_testset_predictions(all, weighted=False):\n if weighted:\n return load_data_from_CSV('results/testset_results_weighted_{}.csv'.format(all))\n else:\n return load_data_from_CSV('results/testset_results_{}.csv'.format(all))","def load_dataset():\n\n\n train_dd_loader = DailyDialogLoader(PATH_TO_TRAIN_DATA, load=False)\n train_dataloader = DataLoader(train_dd_loader, batch_size=16, shuffle=True, num_workers=0,\n collate_fn=PadCollate())\n\n test_dd_loader = DailyDialogLoader(PATH_TO_TEST_DATA, load=True)\n test_dataloader = DataLoader(test_dd_loader, batch_size=1, shuffle=False, num_workers=0,\n collate_fn=PadCollate())\n\n assert train_dd_loader.vocabulary.n_words == test_dd_loader.vocabulary.n_words\n\n return train_dd_loader, train_dataloader, test_dataloader","def loadTrips(analyzer, tripsfile):\n tripsfile = cf.data_dir + tripsfile\n input_file = csv.DictReader(open(tripsfile, encoding=\"utf-8\"),\n delimiter=\",\")\n for trip in input_file:\n model.addTrip(analyzer, trip)\n return analyzer","def get_teacher(self, **fields):\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\n teacher_fields = {}\n for key, value in fields.items():\n if key in existing_fields:\n teacher_fields[key] = value\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\n # Expect single value if search by unique fields, list if by non-unique\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None","def load_data(self):\n with open('data/fordTrain.csv') as f:\n data = csv.reader(f, delimiter=',')\n train = [x for i, x in enumerate(data) if i > 0] \n # Extract features and target variable separately\n trainx = [x[3:] for x in train]\n trainy = [x[2] for x in train]\n\n with open('data/fordTest.csv') as f:\n data = csv.reader(f, delimiter=',')\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \n\n with open('data/Solution.csv') as f:\n data = csv.reader(f, delimiter=',')\n testy = [x[2] for i, x in enumerate(data) if i > 0] \n\n # Extract features and target variable, convert to numpy array\n trainx = np.asarray(trainx, dtype=np.float32)\n trainy = np.asarray(trainy, dtype=np.int8)\n testx = np.asarray(testx, dtype=np.float32)\n testy = np.asarray(testy, dtype=np.int8)\n\n # Return training and test sets\n trainSet = Dataset(trainx, trainy)\n testSet = Dataset(testx, testy)\n return trainSet, testSet","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def load_train_subjects_names(self):\n for tf in self.train_db_batches:\n files = ns.natsorted(os.listdir(os.path.join(self.db_path, tf)))\n for f in files:\n if f.startswith('volume'):\n s_name = str.split(str.split(f, '.')[0], '-')[-1]\n self.training_subjects.append(s_name)\n np.random.seed(1)\n np.random.shuffle(self.training_subjects)\n self.n_train = len(self.training_subjects)","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n \n raise NotImplementedError()","def get_data_loader(batch_size=10, num_workers=2):\n \n data_loader = torch.utils.data.DataLoader(dataset=TempuckeyDataSet(),\n batch_size=batch_size,\n shuffle=True,\n pin_memory=True,\n num_workers=num_workers,\n collate_fn=collate)\n return data_loader","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def get_train_examples(self, data_dir):\n raise NotImplementedError()","def preload_all(self):\n for tp in self.tps:\n for f in self.featurefiles + self.maskfiles:\n file = os.path.join(tp, f)\n print('preloading {}'.format(file))\n self.load(file, lazy=False)","def get_test_loader(test_dataset,\n batch_size,\n num_workers=4,\n pin_memory=False):\n data_loader = torchutils.DataLoader(\n test_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)\n return data_loader","def load_pedestrian(self, dataset_dir, subset):\n # Add classes. We have only one class to add.\n self.add_class(\"pedestrian\", 1, \"pedestrian\")\n\n # Train or validation dataset?\n assert subset in [\"train\", \"val\"]\n dataset_dir = os.path.join(dataset_dir, subset)\n print(dataset_dir)\n # Load annotations\n # VGG Image Annotator (up to version 1.6) saves each image in the form:\n # { 'filename': '28503151_5b5b7ec140_b.jpg',\n # 'regions': {\n # '0': {\n # 'region_attributes': {},\n # 'shape_attributes': {\n # 'all_points_x': [...],\n # 'all_points_y': [...],\n # 'name': 'polygon'}},\n # ... more regions ...\n # },\n # 'size': 100202\n # }\n # We mostly care about the x and y coordinates of each region\n # Note: In VIA 2.0, regions was changed from a dict to a list.\n annotations = json.load(open(os.path.join(dataset_dir, \"via_region_data.json\")))\n annotations = list(annotations.values()) # don't need the dict keys\n\n # The VIA tool saves images in the JSON even if they don't have any\n # annotations. Skip unannotated images.\n annotations = [a for a in annotations if a['regions']]\n\n # Add images\n for a in annotations:\n # Get the x, y coordinaets of points of the polygons that make up\n # the outline of each object instance. These are stores in the\n # shape_attributes (see json format above)\n # The if condition is needed to support VIA versions 1.x and 2.x.\n if type(a['regions']) is dict:\n polygons = [r['shape_attributes'] for r in a['regions'].values()]\n else:\n polygons = [r['shape_attributes'] for r in a['regions']] \n\n # load_mask() needs the image size to convert polygons to masks.\n # Unfortunately, VIA doesn't include it in JSON, so we must read\n # the image. This is only managable since the dataset is tiny.\n image_path = os.path.join(dataset_dir, a['filename'])\n image = skimage.io.imread(image_path)\n height, width = image.shape[:2]\n\n self.add_image(\n \"pedestrian\",\n image_id=a['filename'], # use file name as a unique image id\n path=image_path,\n width=width, height=height,\n polygons=polygons)","def train(self, train_loader):\n pass","def load_premiership_teams():\n # list of PremTeams to add\n team_list = [\n {'name': 'Arsenal', 'code': 'ARS', 'is_prem': True},\n {'name': 'Aston Villa', 'code': 'AVL', 'is_prem': True},\n {'name': 'Brighton and Hove Albion', 'code': 'BTN', 'is_prem': True},\n {'name': 'Brentford', 'code': 'BRE', 'is_prem': True},\n {'name': 'Bournemouth', 'code': 'BOU', 'is_prem': False},\n {'name': 'Burnley', 'code': 'BUR', 'is_prem': True},\n {'name': 'Cardiff City', 'code': 'CAR', 'is_prem': False},\n {'name': 'Chelsea', 'code': 'CHE', 'is_prem': True},\n {'name': 'Crystal Palace', 'code': 'CRY', 'is_prem': True},\n {'name': 'Everton', 'code': 'EVE', 'is_prem': True},\n {'name': 'Fulham', 'code': 'FUL', 'is_prem': False},\n {'name': 'Hull', 'code': 'HUL', 'is_prem': False},\n {'name': 'Huddersfield Town', 'code': 'HUD', 'is_prem': False},\n {'name': 'Leeds United', 'code': 'LEE', 'is_prem': True},\n {'name': 'Leicester City', 'code': 'LEI', 'is_prem': True},\n {'name': 'Liverpool', 'code': 'LIV', 'is_prem': True},\n {'name': 'Manchester City', 'code': 'MCY', 'is_prem': True},\n {'name': 'Manchester United', 'code': 'MUN', 'is_prem': True},\n {'name': 'Middlesbrough', 'code': 'MID', 'is_prem': False},\n {'name': 'Newcastle United', 'code': 'NEW', 'is_prem': True},\n {'name': 'Norwich City', 'code': 'NOR', 'is_prem': True},\n {'name': 'Queens Park Rangers', 'code': 'QPR', 'is_prem': False},\n {'name': 'Sheffield United', 'code': 'SHF', 'is_prem': False},\n {'name': 'Southampton', 'code': 'SOT', 'is_prem': True},\n {'name': 'Stoke City', 'code': 'STO', 'is_prem': False},\n {'name': 'Sunderland', 'code': 'SUN', 'is_prem': False},\n {'name': 'Swansea City', 'code': 'SWA', 'is_prem': False},\n {'name': 'Tottenham Hotspur', 'code': 'TOT', 'is_prem': True},\n {'name': 'Watford', 'code': 'WAT', 'is_prem': True},\n {'name': 'West Bromwich Albion', 'code': 'WBA', 'is_prem': False},\n {'name': 'West Ham United', 'code': 'WHM', 'is_prem': True},\n {'name': 'Wolverhampton Wanderers', 'code': 'WLV', 'is_prem': True},\n ]\n\n for team in team_list:\n print(PremTeam.objects.update_or_create(\n name=team['name'],\n code=team['code'],\n defaults={'is_prem': team['is_prem']}\n ))\n # print(pt, created)","def load_data(batch_size=batch_size):\n trainset = LibriSpeechDataset(training_set, int(LIBRISPEECH_SAMPLING_RATE * n_seconds))\n testset = LibriSpeechDataset(validation_set, int(LIBRISPEECH_SAMPLING_RATE * n_seconds), stochastic=False)\n\n train_loader = DataLoader(trainset, batch_size=batch_size, num_workers=1, shuffle=True, drop_last=True)\n test_loader = DataLoader(testset, batch_size=1, num_workers=1, drop_last=True)\n\n return train_loader, test_loader","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def get_test_examples(self, data_dir):\n raise NotImplementedError()","def load_meetings():\n\n print \"Importing meetings...\"\n\n # Delete all rows in table, so if we need to run this a second time,\n # we won't be trying to add duplicate retailers\n Meeting.query.delete()\n\n # Read CSV file\n with open(\"seed_data/meetings.csv\") as source_file:\n example_data = list(csv.reader(source_file))\n\n # skip header row for populating db\n for list_item in example_data[1:]:\n meeting = Meeting(meeting_title=list_item[1],\n meeting_time=list_item[2],\n attendees=list_item[3],\n length=list_item[4],\n topic_id=list_item[5])\n\n # Add the current retailer to the session\n db.session.add(meeting)\n\n # Commit the db.session changes to the database\n db.session.commit()","def load_trial_data(filelist_path, feature_dir, **kwargs):\n filelist = readlines_and_split_spaces(filelist_path)\n\n\n if (len(filelist[0]) == 4 and (\"spoof\" in filelist[0] or \"target\" in filelist[0])):\n # Four items per line: speaker_id, utterance, system, target/nontarget/spoof\n # ASVSpoof2019 logical access trial list\n return _load_trial_data_asvspoof(filelist, feature_dir, **kwargs)\n elif len(filelist[0]) == 3 and filelist[0][0] in (\"0\", \"1\"):\n # Three items per line, and first is 0 or 1 (target or nontarget)\n return _load_trial_data_voxceleb(filelist, feature_dir)\n else:\n raise RuntimeError(\"Unknown filelist type (file {})\".format(filelist_path))","def test_practitioner_6(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f002-pv.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_6(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_6(inst2)","def PapersFile(filename, researchers=None):\n try:\n with open(filename, \"r\") as file:\n data = json.load(file)\n except FileNotFoundError:\n data = {}\n return Papers(data, filename=filename, researchers=researchers)","def test_practitioner_4(base_settings):\n filename = (\n base_settings[\"unittest_data_dir\"] / \"practitioner-example-xcda-author.json\"\n )\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_4(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_4(inst2)","def test_practitioner_7(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_7(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_7(inst2)","def teacher(self):\n if \"teacher\" in self._prop_dict:\n if isinstance(self._prop_dict[\"teacher\"], OneDriveObjectBase):\n return self._prop_dict[\"teacher\"]\n else :\n self._prop_dict[\"teacher\"] = EducationTeacher(self._prop_dict[\"teacher\"])\n return self._prop_dict[\"teacher\"]\n\n return None","def test_train_on_the_fly(self):\r\n input_seqs_file = NamedTemporaryFile(\r\n prefix='RdpTaxonAssignerTest_', suffix='.fasta')\r\n input_seqs_file.write(test_seq_coll.to_fasta())\r\n input_seqs_file.seek(0)\r\n\r\n exp_assignments = rdp_trained_test1_expected_dict\r\n\r\n app = RdpTaxonAssigner({\r\n 'id_to_taxonomy_fp': self.id_to_taxonomy_file.name,\r\n 'reference_sequences_fp': self.reference_seqs_file.name,\r\n })\r\n obs_assignments = app(self.tmp_seq_filepath)\r\n\r\n key = 'X67228 some description'\r\n self.assertEqual(obs_assignments[key], exp_assignments[key])","def load(self):\n #self.df = read_file(\"../data/yelp_academic_dataset_user.json\") #Full Data.\n self.df = read_file(\"../data/user300.json\") #For local machine.\n #self.get_friend_list()\n #self.save_friend_nodes()","def load_tas_lookup():\n logger.info('Loading TAS')\n load_tas()","def setup(self):\n # TODO check if need both dataset together\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \n tokenizer=self.tokenizer, vocab=\"bert\")\n self.vocabulary = self.train_dataset.vocabulary\n\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\n tokenizer=self.tokenizer, vocab=self.vocabulary)\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)","def load_data():\n print(\"PARSING TRAIN\")\n ys_train, x_train, ids_train = load_pickle_data(\"ys_train\"), load_pickle_data(\"x_train\"), load_pickle_data(\n \"ids_train\")\n if ys_train is None or x_train is None or ids_train is None:\n ys_train, x_train, ids_train = load_csv_data(\"{}/train.csv\".format(DATA_DIR))\n dump_pickle_data(ys_train, \"ys_train\")\n dump_pickle_data(x_train, \"x_train\")\n dump_pickle_data(ids_train, \"ids_train\")\n\n print(\"PARSING TEST\")\n x_test, ids_test = load_pickle_data(\"x_test\"), load_pickle_data(\"ids_test\")\n if x_test is None or ids_test is None:\n _, x_test, ids_test = load_csv_data(\"{}/test.csv\".format(DATA_DIR))\n dump_pickle_data(x_test, \"x_test\")\n dump_pickle_data(ids_test, \"ids_test\")\n\n return ys_train, x_train, ids_train, x_test, ids_test","def get_data_loaders_4sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"]\n #history_complete.append(history)\n if len(history_splitted) > (len(persona)-1):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def fixture_example_data():\n import_example_data()","def get_data_loaders(args, tokenizer):\n personachat = get_dataset(tokenizer, args.dataset_path, args.dataset_cache, args.train_lang)\n _ = personachat.pop(\"test\", None)\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": [], \"valid\": []}\n\n if args.train_lang in [\"En\", \"Fr\", \"It\", \"Id\", \"Jp\", \"Ko\", \"Zh\"]: #monolingual data\n for dataset_name, dataset in personachat.items():\n for dial in dataset[args.train_lang]: #dial: {\"persona\":[], \"history\":[], \"response\":str}\n instance = build_input_from_segments(dial[\"persona\"], dial[\"history\"][-args.max_turns:], dial[\"response\"], tokenizer, lm_labels = True)\n datasets[dataset_name].append(instance) \n else: #multilingual data\n for dataset_name, dataset in personachat.items():\n for lang, dials in dataset.items():\n for dial in dials: #dial: {\"persona\":[], \"history\":[], \"response\":str}\n instance = build_input_from_segments(dial[\"persona\"], dial[\"history\"][-args.max_turns:], dial[\"response\"], tokenizer, lang_id=\"<{}>\".format(lang.lower()), lm_labels = True)\n datasets[dataset_name].append(instance) #all langs together\n\n\n logger.info(\"Build train and validation dataloaders\")\n train_dataset = DatasetTrain(datasets[\"train\"])\n valid_dataset = DatasetTrain(datasets[\"valid\"])\n\n #logger.info(\"Build train and validation dataloaders\")\n #train_dataset, valid_dataset = TensorDataset(*tensor_datasets[\"train\"]), TensorDataset(*tensor_datasets[\"valid\"])\n train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None\n valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None\n train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, shuffle=(not args.distributed), collate_fn=collate_fn)\n valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.valid_batch_size, shuffle=False, collate_fn=collate_fn)\n\n # logger.info(\"Train dataset (Batch, Candidates, Seq length): {}\".format(train_dataset.tensors[0].shape))\n # #logger.info(\"Train dataset (Batch, Candidates, Seq length): {}\".format(train_dataset.tensors[1].shape))\n # logger.info(\"Valid dataset (Batch, Candidates, Seq length): {}\".format(valid_dataset.tensors[0].shape))\n logger.info(\"Train dataset length: {}\".format(len(train_dataset)))\n logger.info(\"Valid dataset length: {}\".format(len(valid_dataset)))\n return train_loader, valid_loader, train_sampler, valid_sampler","def load_trec(loc=DATA_DIR):\n train, test = [], []\n with open(os.path.join(loc, 'TREC', 'train_5500.label'), 'rb') as f:\n for line in f:\n train.append(line.strip())\n with open(os.path.join(loc, 'TREC', 'TREC_10.label'), 'rb') as f:\n for line in f:\n test.append(line.strip())\n return train, test","def load_extractor_engines(reload=False):\r\n\r\n global youtube_dl, youtube_dlc\r\n\r\n # youtube-dl ----------------------------------------------------------------------------------------------------\r\n start = time.time()\r\n\r\n if reload and youtube_dl:\r\n importlib.reload(youtube_dl)\r\n else:\r\n import youtube_dl\r\n\r\n config.youtube_dl_version = youtube_dl.version.__version__\r\n\r\n # calculate loading time\r\n load_time = time.time() - start\r\n log(f'youtube_dl version: {config.youtube_dl_version}, load_time= {int(load_time)} seconds')\r\n\r\n # youtube-dlc ----------------------------------------------------------------------------------------------------\r\n start = time.time()\r\n\r\n if reload and youtube_dlc:\r\n importlib.reload(youtube_dlc)\r\n else:\r\n import youtube_dlc\r\n\r\n config.youtube_dlc_version = youtube_dlc.version.__version__\r\n\r\n # calculate loading time\r\n load_time = time.time() - start\r\n log(f'youtube_dlc version: {config.youtube_dlc_version}, load_time= {int(load_time)} seconds')\r\n\r\n # set interrupt / kill switch\r\n set_interrupt_switch()\r\n\r\n # set default extractor\r\n set_default_extractor()\r\n\r\n # get a random user agent and update headers\r\n if not config.custom_user_agent:\r\n config.http_headers['User-Agent'] = youtube_dl.utils.random_user_agent()","def test_practitioner_1(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f203-jvg.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_1(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_1(inst2)","def _load_training_data(self):\n self._save_training_data()","def load(tweets_file, rtt_file):\n try:\n tw_df = pd.read_csv(tweets_file)\n rtt_df = pd.read_csv(rtt_file)\n except:\n print('one or several files were not found')\n sys.exit()\n\n return tw_df, rtt_df","def test_practitioner_9(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f204-ce.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_9(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_9(inst2)","def load_inst(self):\n self.sanity_check()\n\n fname_pub_auth_all = ''.join([self.config.dir_data, '/',\n self.config.fname_pub_auth_all, '_',\n self.config.experiment_id, '.pk'])\n fname_pub_auth_top = ''.join([self.config.dir_data, '/',\n self.config.fname_pub_auth_top, '_',\n self.config.experiment_id, '.pk'])\n fname_pub_inst_all = ''.join([self.config.dir_data, '/',\n self.config.fname_pub_inst_all, '_',\n self.config.experiment_id, '.pk'])\n fname_pub_inst_top = ''.join([self.config.dir_data, '/',\n self.config.fname_pub_inst_top, '_',\n self.config.experiment_id, '.pk'])\n self.pub_auth_all = pickle.load(open(fname_pub_auth_all, 'rb'))\n self.pub_auth_top = pickle.load(open(fname_pub_auth_top, 'rb'))\n self.pub_inst_all = pickle.load(open(fname_pub_inst_all, 'rb'))\n self.pub_inst_top = pickle.load(open(fname_pub_inst_top, 'rb'))\n\n fname_pub_history = ''.join([self.config.dir_data, '/history_',\n self.config.experiment_id, '.pk'])\n self.history = pickle.load(open(fname_pub_history, 'rb'))\n\n fname_pub_staff = ''.join([self.config.dir_data, '/staff_',\n self.config.experiment_id, '.pk'])\n self.staff = pickle.load(open(fname_pub_staff, 'rb'))","def load_campers():\n\n for row in open(\"static/campers.csv\"):\n row = row.rstrip()\n\n email, password, first_name, last_name, camper_photo, camper_photo_url = row.split(\",\")\n\n camper = Camper(\n camper_email=email,\n password=password,\n first_name=first_name,\n last_name=last_name,\n camper_photo=camper_photo,\n camper_photo_url=camper_photo_url)\n\n db.session.add(camper)\n\n db.session.commit()","def get_teacher(teacher_account_id):\n query = 'SELECT * FROM teacher JOIN person ON teacher.teacher_account_id=person.account_id WHERE teacher.teacher_account_id=%s;'\n args = (teacher_account_id,)\n return database.connection.get_data(query, args)","def load_passengers(self, passengers):\r\n if len(passengers) > self.available_capacity():\r\n raise Exception(\"The elevator cannot handle this many passengers \"\r\n \"and will be over capacity\")\r\n\r\n self.passengers += passengers\r\n\r\n self._update_destinations()","def load_data(y_name='Species'):\n train_path = tf.keras.utils.get_file(args.TRAIN_URL.split('/')[-1], args.TRAIN_URL)\n test_path = tf.keras.utils.get_file(args.TEST_URL.split('/')[-1], args.TEST_URL)\n\n train = pd.read_csv(train_path, names=args.CSV_COLUMN_NAMES, header=0)\n train_x, train_y = train, train.pop(y_name)\n\n test = pd.read_csv(test_path, names=args.CSV_COLUMN_NAMES, header=0)\n test_x, test_y = test, test.pop(y_name)\n\n return (train_x, train_y), (test_x, test_y)","def test_pert_file(self):\n path, case = os.path.split(self.ieee14)\n\n # --- with pert file ---\n ss = andes.run('ieee14.raw', pert='pert.py',\n input_path=path, no_output=True, default_config=True,\n )\n ss.TDS.init()\n self.assertIsNotNone(ss.TDS.callpert)\n\n # --- without pert file ---\n ss = andes.run('ieee14.raw',\n input_path=path, no_output=True, default_config=True,\n )\n ss.TDS.init()\n self.assertIsNone(ss.TDS.callpert)","def load_all_traj():\n pdb='/bpti/bpti-prot/bpti-prot.pdb'\n dcd = lambda x: '/bpti/bpti-prot/bpti-prot-%02d.dcd' % x\n tr = []\n for i in range(11):\n print ('loading ', i)\n start = dt.datetime.now()\n tr.append(md.load(DCD_ALL(i), top=PDB_ALL))\n end = dt.datetime.now()\n print((end-start).total_seconds())\n return tr"],"string":"[\n \"def load_teachers(self):\\n # Get the experiment's directory to load from\\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\\n self.load_teacher_experiment(ex_dir)\\n if len(self.teacher_policies) < self.num_teachers:\\n print(\\n f\\\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\\\"\\n )\\n self.load_teachers()\",\n \"def load_tamper(self, dataset_dir, subset):\\n # Add classes. We have one class.\\n # Naming the dataset nucleus, and the class nucleus\\n self.add_class(\\\"tampers\\\", 1, \\\"tampers\\\")\\n\\n # Which subset?\\n # \\\"val\\\": use hard-coded list above\\n # \\\"train\\\": use data from stage1_train minus the hard-coded list above\\n # else: use the data from the specified sub-directory\\n # assert subset in [\\\"train\\\", \\\"val\\\", \\\"stage1_train\\\", \\\"stage1_test\\\", \\\"stage2_test\\\"]\\n # subset_dir = \\\"stage1_train\\\" if subset in [\\\"train\\\", \\\"val\\\"] else subset\\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\\n if subset == \\\"val\\\" or subset == \\\"test\\\":\\n image_ids = next(os.walk(dataset_dir))[2]\\n else:\\n # Get image ids from directory names\\n image_ids = next(os.walk(dataset_dir))[2]\\n \\n\\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # dirnew_splicing = '/data/tamper'\\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\\n\\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n \\n # dircopy_move = '/data/gy/ca2att/train3'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\\n\\n # dirnew_sp = '/data/gy/c2newsp/train'\\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\\n\\n # Add images\\n for image_id in image_ids:\\n self.add_image(\\n \\\"tampers\\\",\\n image_id=image_id[:-4],\\n path=os.path.join(dataset_dir, image_id))\\n\\n # for image_id in image_ids_copy_move:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dircopy_move, 'images', image_id))\\n\\n # for image_id in image_ids_new_splicing:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_splicing, 'images', image_id))\\n\\n # # for image_id in image_ids_txt_sp:\\n # # self.add_image(\\n # # \\\"tampers\\\",\\n # # image_id=image_id[:-4],\\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\\n\\n # for image_id in image_ids_new_sp:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_sp, 'images', image_id))\",\n \"def test_pytd_teacher(self):\\n defaults = parser_defaults.copy()\\n defaults['datatype'] = 'train:stream'\\n defaults['image_mode'] = 'ascii'\\n\\n with testing_utils.capture_output():\\n # Get processed act from agent\\n parser = display_setup_args()\\n defaults['pytorch_teacher_dataset'] = 'flickr30k'\\n del defaults['pytorch_teacher_task']\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n teacher = create_task_agent_from_taskname(opt)[0]\\n pytorch_teacher_act = teacher.act()\\n\\n parser = display_setup_args()\\n defaults['task'] = 'flickr30k'\\n del defaults['pytorch_teacher_dataset']\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n teacher = create_task_agent_from_taskname(opt)[0]\\n regular_teacher_act = teacher.act()\\n\\n keys = set(pytorch_teacher_act.keys()).intersection(\\n set(regular_teacher_act.keys()))\\n self.assertTrue(len(keys) != 0)\\n for key in keys:\\n self.assertTrue(pytorch_teacher_act[key] == regular_teacher_act[key],\\n 'PytorchDataTeacher does not have the same value '\\n 'as regular teacher for act key: {}'.format(key))\",\n \"def train_teacher (nb_teachers, teacher_id):\\n # Load the dataset\\n X_train, X_test, y_train, y_test = models.get_dataset()\\n\\n print(X_train.shape)\\n print(y_train.shape)\\n print(X_test.shape)\\n print(y_test.shape)\\n \\n # Retrieve subset of data for this teacher\\n data, labels = partition.partition_dataset(X_train,\\n y_train,\\n nb_teachers,\\n teacher_id)\\n\\n print(\\\"Length of training data: \\\" + str(len(labels)))\\n\\n # Define teacher checkpoint filename and full path\\n\\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\\n \\n # Perform teacher training need to modify \\n \\n\\n # Create teacher model\\n model, opt = models.create_two_layer_mlp(46) # num of cols\\n model.compile(loss='binary_crossentropy',\\n optimizer=\\\"Adam\\\",\\n metrics=['accuracy'])\\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\\n\\n #modify\\n model_json = model.to_json()\\n with open(\\\"model.json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n# serialize weights to HDF5\\n model.save_weights(filename2)\\n print(\\\"Saved model to disk\\\")\\n return True\",\n \"def unpack_teachers(self, extra: dict):\\n self.teacher_policies.extend(extra[\\\"teacher_policies\\\"])\\n self.teacher_envs.extend(extra[\\\"teacher_envs\\\"])\\n self.teacher_expl_strats.extend(extra[\\\"teacher_expl_strats\\\"])\\n self.teacher_critics.extend(extra[\\\"teacher_critics\\\"])\\n self.teacher_ex_dirs.extend(extra[\\\"teacher_ex_dirs\\\"])\",\n \"def get_test_loader(id_list = './data/sample_submission.csv', root_dir = './data/test/'):\\n data = HumanProteinDataset(id_list, root_dir, transform = transforms.Compose([\\n Rescale((256, 256)), \\n ToTensor()\\n ]))\\n\\n indices = np.arange(len(data))\\n dataloader_test = DataLoader(data, batch_size=10, num_workers=5)\\n\\n return dataloader_test\",\n \"def prune_teachers(self):\\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]\",\n \"def _load_trials(self) -> hyperopt.Trials:\\n if os.path.isfile(self.trials_path):\\n trials = pickle.load(open(self.trials_path, \\\"rb\\\"))\\n else:\\n trials = hyperopt.Trials()\\n return trials\",\n \"def load_primers(tsv_filename):\\n answer = []\\n with open(tsv_filename) as handle:\\n for line in handle:\\n if line.startswith(\\\"#\\\"):\\n continue\\n parts = line.rstrip(\\\"\\\\n\\\").split(\\\"\\\\t\\\")\\n if len(parts) == 2:\\n left, right = parts\\n name = f\\\"P{len(answer)}\\\"\\n else:\\n name, left, right = parts[:3]\\n answer.append((name, left, right))\\n return answer\",\n \"def get_teacher_assign():\\n assignment_data = query_db(\\n \\\"SELECT assignments.id, assignments.name, assignments.due_date \\\"\\n \\\"FROM assignments JOIN topics ON assignments.topic_id=topics.id \\\"\\n \\\"JOIN classes ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n assignments = []\\n for assignment in assignment_data:\\n assignment_dict_teach = {}\\n assignment_dict_teach[\\\"id\\\"] = assignment[0]\\n assignment_dict_teach[\\\"name\\\"] = assignment[1]\\n assignment_dict_teach[\\\"due_date\\\"] = assignment[2]\\n assignments.append(assignment_dict_teach)\\n return assignments\",\n \"def load_people(self, file_path):\\n pass\",\n \"def get_train(self, even=None):\\n\\n #self.images, self.labels, self.traces = trace_data.get_my_teacher()\\n _, self.images, self.labels, self.traces, _ = trace_data.get_my_teacher()\\n #print(self.labels)\\n self.length = len(self.images)\\n self.create_teacher()\",\n \"def LoadTeacherModels(lang):\\n\\n # load Trained teacher model parameters\\n log_dir = 'data/logs'\\n with open(log_dir + '/' + lang + '_model_params', 'rb') as fp:\\n params = pickle.load(fp)\\n\\n model_args = params['args']\\n\\n if model_args.use_colab is None:\\n OUTPUT_DIR = 'ckpts/' + model_args.lang\\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\\n else:\\n from google.colab import drive\\n\\n drive.mount('/content/gdrive')\\n OUTPUT_DIR = '/content/gdrive/My Drive/ckpts/' + model_args.lang\\n if not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\\n\\n if model_args.enc_type == 'gat' and model_args.dec_type == 'transformer':\\n models = {}\\n OUTPUT_DIR += '/' + model_args.enc_type + '_' + model_args.dec_type\\n\\n # Load the vocabs\\n with open('vocabs/' + model_args.model + '/' +\\n lang + '/' + model_args.opt + '_src_vocab', 'rb') as fp:\\n src_vocab = pickle.load(fp)\\n # loading the target vocab\\n model_args.sentencepiece = 'False'\\n if model_args.sentencepiece == 'True':\\n sp = spm.SentencePieceProcessor()\\n sp.load('vocabs/' + model_args.model + '/' +\\n lang + '/' + 'train_tgt.model')\\n tgt_vocab = sp\\n else:\\n tgt_vocab = src_vocab\\n\\n print('Loaded ' + lang + ' Parameters..')\\n model = TransGAT(params['args'], params['src_vocab_size'], src_vocab,\\n params['tgt_vocab_size'], tgt_vocab)\\n # Load the latest checkpoints\\n optimizer = tf.train.AdamOptimizer(beta1=0.9, beta2=0.98,\\n epsilon=1e-9)\\n\\n ckpt = tf.train.Checkpoint(\\n model=model,\\n optimizer=optimizer\\n )\\n\\n ckpt_manager = tf.train.CheckpointManager(ckpt, OUTPUT_DIR, max_to_keep=5)\\n if ckpt_manager.latest_checkpoint:\\n ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()\\n\\n print('Loaded ' + lang + ' Teacher model !')\\n\\n return model\",\n \"def get_teachers(self):\\n query = Teacher.all().order('teacher')\\n return query.fetch()\",\n \"def load_topics():\\n\\n print \\\"Importing topics...\\\"\\n\\n # Delete all rows in table, so if we need to run this a second time,\\n # we won't be trying to add duplicate retailers\\n Topic.query.delete()\\n\\n # Read CSV file\\n with open(\\\"seed_data/topics.csv\\\") as source_file:\\n example_data = list(csv.reader(source_file))\\n\\n # skip header row for populating db\\n for list_item in example_data[1:]:\\n topic = Topic(topic_title=list_item[1])\\n\\n # Add the current retailer to the session\\n db.session.add(topic)\\n\\n # Commit the db.session changes to the database\\n db.session.commit()\",\n \"def ensemble_preds(dataset, nb_teachers, stdnt_data):\\n\\n # Compute shape of array that will hold probabilities produced by each\\n # teacher, for each training point, and each output class\\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\\n\\n # Create array that will hold result\\n result = np.zeros(result_shape, dtype=np.float32)\\n\\n # Get predictions from each teacher\\n for teacher_id in xrange(nb_teachers):\\n # Compute path of checkpoint file for teacher model with ID teacher_id\\n if FLAGS.deeper:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\\n else:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\\n\\n # Get predictions on our training data and store in result array\\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\\n\\n # This can take a while when there are a lot of teachers so output status\\n print(\\\"Computed Teacher \\\" + str(teacher_id) + \\\" softmax predictions\\\")\\n\\n return result\",\n \"def get_each_loader(data_path, batch_size, trn_negnum, shuffle=True, num_workers=0):\\n \\n dataset = ML_Dataset(data_path, trn_negnum)\\n \\n if data_path.endswith('trn') == True:\\n collate = dataset.train_collate\\n else:\\n collate = test_collate\\n\\n data_loader = data.DataLoader(dataset=dataset,\\n batch_size=batch_size,\\n shuffle=shuffle,\\n num_workers=num_workers,\\n collate_fn=collate)\\n\\n return data_loader\",\n \"def read_triplets(seed_candidates):\\n if \\\"pickle\\\" in seed_candidates:\\n if \\\"*\\\" in seed_candidates:\\n all_files = glob.glob(seed_candidates)\\n new_data = []\\n for file_name in all_files:\\n with open(file_name, 'rb') as f:\\n data = pickle.load(f)\\n for dd in data:\\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\\n else:\\n with open(seed_candidates, 'rb') as f:\\n data = pickle.load(f)\\n new_data = []\\n for dd in data:\\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\\n # idx = int(dd[0][10:])\\n # new_data.append((idx, dd[1], dd[2], dd[3]))\\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\\n else:\\n column_names = ['evtid', 'h1', 'h2', 'h3']\\n if \\\"*\\\" in seed_candidates:\\n all_files = glob.glob(seed_candidates)\\n new_data = []\\n for file_name in all_files:\\n df_seed_tmp = pd.read_csv(file_name, header=None, names=column_names,)\\n new_data.append(df_seed_tmp)\\n df_seed = pd.concat(new_data)\\n else:\\n df_seed = pd.read_csv(seed_candidates, header=None,\\n names=column_names)\\n return df_seed\",\n \"def test_pyt_multitask(self):\\n\\n def run_display_test(defaults, ep_and_ex_counts):\\n with testing_utils.capture_output() as f:\\n parser = display_setup_args()\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n display_data(opt)\\n str_output = f.getvalue()\\n self.assertTrue(\\n '[ loaded {} episodes with a total of {} examples ]'.format(\\n ep_and_ex_counts[0], ep_and_ex_counts[1]\\n ) in str_output,\\n 'PytorchDataTeacher multitasking failed with '\\n 'following args: {}'.format(opt)\\n )\\n\\n task1 = 'babi:task1k:1'\\n task2 = 'babi:task1k:2'\\n dataset1 = 'flickr30k'\\n dataset2 = 'vqa_v1'\\n\\n # Expected example and episode counts\\n eps_and_exs_counts = [\\n (1800, 1800),\\n (1080, 1800),\\n (29900, 29900),\\n (29180, 29900),\\n (277349, 277349)\\n ]\\n defaults = parser_defaults.copy()\\n\\n # 1.\\n defaults['pytorch_teacher_task'] = '{},{}'.format(task1, task2)\\n run_display_test(defaults, eps_and_exs_counts[0])\\n\\n # 2.\\n defaults['pytorch_teacher_task'] = task1\\n defaults['task'] = task2\\n run_display_test(defaults, eps_and_exs_counts[1])\\n\\n # 3.\\n del defaults['task']\\n defaults['pytorch_teacher_dataset'] = dataset1\\n run_display_test(defaults, eps_and_exs_counts[2])\\n\\n # 4.\\n del defaults['pytorch_teacher_task']\\n defaults['task'] = task1\\n run_display_test(defaults, eps_and_exs_counts[3])\\n\\n # 5.\\n del defaults['task']\\n defaults['pytorch_teacher_dataset'] = '{},{}'.format(dataset1, dataset2)\\n run_display_test(defaults, eps_and_exs_counts[4])\",\n \"def load_ptb_dataset(name='ptb', path='raw_data'):\\n path = os.path.join(path, name)\\n logging.info(\\\"Load or Download Penn TreeBank (PTB) dataset > {}\\\".format(path))\\n\\n # Maybe dowload and uncompress tar, or load exsisting files\\n maybe_download_and_extract(PTB_FILENAME, path, PTB_URL, extract=True)\\n\\n data_path = os.path.join(path, 'simple-examples', 'data')\\n train_path = os.path.join(data_path, \\\"ptb.train.txt\\\")\\n valid_path = os.path.join(data_path, \\\"ptb.valid.txt\\\")\\n test_path = os.path.join(data_path, \\\"ptb.test.txt\\\")\\n\\n word_to_id = nlp.build_vocab(nlp.read_words(train_path))\\n\\n train_data = nlp.words_to_word_ids(nlp.read_words(train_path), word_to_id)\\n valid_data = nlp.words_to_word_ids(nlp.read_words(valid_path), word_to_id)\\n test_data = nlp.words_to_word_ids(nlp.read_words(test_path), word_to_id)\\n vocab_size = len(word_to_id)\\n\\n # logging.info(nlp.read_words(train_path)) # ... 'according', 'to', 'mr.', '<unk>', '<eos>']\\n # logging.info(train_data) # ... 214, 5, 23, 1, 2]\\n # logging.info(word_to_id) # ... 'beyond': 1295, 'anti-nuclear': 9599, 'trouble': 1520, '<eos>': 2 ... }\\n # logging.info(vocabulary) # 10000\\n # exit()\\n return train_data, valid_data, test_data, vocab_size\",\n \"def loadTrainer(path):\\n\\tray.shutdown()\\n\\tray.init()\\n\\tconfig = createConfig()\\n\\ttrainer = dqn.DQNTrainer(config=config, env=HiLoPricingEnv)\\n\\ttrainer.restore(path)\\n\\treturn trainer\",\n \"def get_data_loaders():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+5):]\\n \\n #history_complete.append(history)\\n if len(persona) == 4:\\n if len(history) > (len(persona)+3):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def TestTeacher(self, model_name, **kwargs):\\n batch_size = kwargs.pop(\\\"batch_size\\\", 256)\\n model_save_path = kwargs.pop(\\\"model_save_path\\\", \\\"./checkpoints/teacher/\\\")\\n record_save_path = kwargs.pop(\\\"record_save_path\\\", \\\"./records/teacher\\\")\\n verbose = kwargs.pop(\\\"verbose\\\", False)\\n\\n # Do some check\\n if not os.path.exists(model_save_path):\\n raise RuntimeError(\\\"No pretrained model exists in '{}'\\\".format(model_save_path))\\n if not os.path.exists(record_save_path):\\n os.makedirs(record_save_path)\\n model_save_path = os.path.join(model_save_path, \\\"{}.ckpt\\\".format(model_name))\\n\\n tf.reset_default_graph()\\n\\n # Get dataset\\n test_data, test_label = self.data_manager.test_data, self.data_manager.test_label\\n num_test_data = test_data.shape[0]\\n\\n X = tf.placeholder(test_data.dtype, shape=[None]+list(test_data.shape[1:]), name=\\\"input_data\\\")\\n y = tf.placeholder(test_label.dtype, shape=[None]+list(test_label.shape[1:]), name=\\\"input_label\\\")\\n is_train = tf.placeholder(tf.bool, name=\\\"is_train\\\")\\n\\n dataset = tf.data.Dataset.from_tensor_slices((X, y))\\n batched_dataset = dataset.batch(batch_size)\\n \\n iterator = batched_dataset.make_initializable_iterator()\\n batch_data, batch_label = iterator.get_next()\\n\\n # Get the teacher model\\n with tf.variable_scope('teacher_model'):\\n logits, probs = self.teacher_model(batch_data, is_train=is_train)\\n result = tf.argmax(logits, axis=1)\\n correct_num = tf.reduce_sum(tf.cast(tf.equal(result, tf.argmax(batch_label, axis=1)), tf.float32))\\n saver = tf.train.Saver()\\n\\n # Test process\\n with tf.Session() as sess:\\n sess.run(iterator.initializer, feed_dict={X:test_data, y:test_label})\\n saver.restore(sess, model_save_path)\\n total_correct_cnt = 0\\n while True:\\n try:\\n right_num = sess.run([correct_num], feed_dict={is_train:False})\\n total_correct_cnt += right_num[0]\\n except tf.errors.OutOfRangeError:\\n acc = total_correct_cnt * 1.0 / num_test_data\\n if verbose:\\n print(\\\"Test accuracy: {}\\\".format(acc))\\n break\\n acc_hist = [acc]\\n self._writeRecord(record_save_path, \\\"{}_test_accuracy\\\".format(model_name), acc_hist)\",\n \"def load_examples_data(dataset_name):\\n dataset_name = dataset_name.strip().lower()\\n if dataset_name.lower() not in ['pokemon', 'hanzi', 'animals', 'nsfw', 'simpsons', 'horse2zebra', 'people',\\n 'autodrive', 'superresolution', 'anpr', 'beauty','antisproofing','facelandmarks','dogs-vs-cats','chinese']:\\n raise ValueError('Not a valid dataset_name.')\\n dataset_name = 'examples_' + dataset_name\\n dirname = os.path.join(_trident_dir, dataset_name)\\n if not os.path.exists(dirname):\\n try:\\n os.makedirs(dirname)\\n except OSError:\\n # Except permission denied and potential race conditions\\n # in multi-threaded environments.\\n pass\\n is_internet_ok = is_connected()\\n if dataset_name == 'examples_pokemon':\\n is_download=download_file_from_google_drive('1U-xc54fX9j9BcidvRa0ow6qjssMlSF2A', dirname, 'pokemon.tar')\\n tar_file_path = os.path.join(dirname, 'pokemon.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n extract_path = os.path.join(dirname, 'pokemon')\\n dataset = load_folder_images(dataset_name, extract_path, folder_as_label=False)\\n print('get pokemon images :{0}'.format(len(dataset)))\\n return dataset\\n\\n\\n elif dataset_name == 'examples_hanzi':\\n download_file_from_google_drive('13UEzSG0az113gpRPKPyKrIE2HDaA2P4H', dirname, 'hanzi.tar')\\n tar_file_path = os.path.join(dirname, 'hanzi.tar')\\n extract_path = os.path.join(dirname, 'hanzi')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n dataset = load_folder_images(dataset_name, os.path.join(dirname, 'train'), folder_as_label=True,\\n object_type=ObjectType.gray)\\n\\n dataset_test = load_folder_images(dataset_name, os.path.join(dirname, 'test'), folder_as_label=True,\\n object_type=ObjectType.gray)\\n\\n dataset.testdata = dataset_test.traindata\\n dataset.class_names['zh-cn'] = dataset.class_names['en-us']\\n return dataset\\n\\n elif dataset_name == 'examples_animals':\\n download_file_from_google_drive('19Cjq8OO6qd9k9TMZxlPjDpejDOdiHJoW', dirname, 'animals.tar')\\n tar_file_path = os.path.join(dirname, 'animals.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n dataset = load_folder_images(dataset_name, dirname, folder_as_label=True)\\n return dataset\\n elif dataset_name == 'examples_nsfw':\\n tar_file_path = os.path.join(dirname, 'nsfw.tar')\\n if os.path.exists(tar_file_path) and get_file_create_time(tar_file_path)<datetime.datetime(2021, 2, 20, 0, 0, 0).timestamp():\\n os.remove(tar_file_path)\\n if os.path.exists(os.path.join(dirname,'porn_detection_data.pkl')):\\n os.remove(os.path.join(dirname,'porn_detection_data.pkl'))\\n _delete_h(dirname)\\n download_file_from_google_drive('1EXpV2QUrSFJ7zJn8NqtqFl1k6HvXsUzp', dirname, 'nsfw.tar')\\n\\n extract_path = os.path.join(dirname, 'nsfw')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n folders = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']\\n data=unpickle(os.path.join(dirname,'porn_detection_data.pkl'))\\n\\n trainData = []\\n testData = []\\n trainLabel = []\\n testLabel = []\\n for n in range(5):\\n folder=folders[n]\\n trainData.extend(data[folder]['train'])\\n trainLabel.extend([n]*len(data[folder]['train']))\\n testData.extend(data[folder]['test'])\\n testLabel.extend([n] * len(data[folder]['test']))\\n\\n trainarray = ImageDataset(trainData,object_type=ObjectType.rgb)\\n trainlabel = LabelDataset(trainLabel,object_type=ObjectType.classification_label)\\n train_iter = Iterator(data=trainarray, label=trainlabel)\\n\\n testarray = ImageDataset(testData,object_type=ObjectType.rgb)\\n testlabel = LabelDataset(testLabel,object_type=ObjectType.classification_label)\\n test_iter = Iterator(data=testarray, label=testlabel)\\n print('training images: {0} test images:{1}'.format(len(trainarray), len(testarray)))\\n\\n dataset = DataProvider(dataset_name, traindata=train_iter, testdata=test_iter)\\n dataset.binding_class_names(['drawing', 'hentai', 'neutral', 'porn', 'sexy'], 'en-us')\\n dataset.binding_class_names(['绘画', '色情漫画', '中性', '色情', '性感'], 'zh-cn')\\n dataset.binding_class_names(['繪畫', '色情漫畫', '中性', '色情', '性感'], 'zh-tw')\\n dataset.scenario = 'train'\\n return dataset\\n elif dataset_name == 'examples_simpsons':\\n download_file_from_google_drive('1hGNFbfBv3EZ4nx4Qod6PtSYzO8H4QIxC', dirname, 'simpsons.tar')\\n tar_file_path = os.path.join(dirname, 'simpsons.tar')\\n extract_path = os.path.join(dirname, 'simpsons')\\n extract_archive(tar_file_path, extract_path, archive_format='tar')\\n data_provider = load_folder_images(dataset_name, extract_path, folder_as_label=False)\\n data_provider.traindata.unpair = RandomNoiseDataset(shape=(100), random_mode='normal')\\n print('get simpsons images :{0}'.format(len(data_provider.traindata.data.items)))\\n return data_provider\\n elif dataset_name == 'examples_horse2zebra':\\n download_file_from_google_drive('1pqj-T90Vh4wVNBV09kYZWgVPsZUA2f7U', dirname, 'horse2zebra.tar')\\n tar_file_path = os.path.join(dirname, 'horse2zebra.tar')\\n extract_path = os.path.join(dirname, 'horse2zebra')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n trainA = ImageDataset(list_images(os.path.join(dirname, 'trainA')), object_type=ObjectType.rgb,\\n get_image_mode=GetImageMode.processed)\\n trainB = ImageDataset(list_images(os.path.join(dirname, 'trainB')), object_type=ObjectType.rgb,\\n get_image_mode=GetImageMode.processed)\\n testA = ImageDataset(list_images(os.path.join(dirname, 'testA')), object_type=ObjectType.rgb,\\n get_image_mode=GetImageMode.processed)\\n testB = ImageDataset(list_images(os.path.join(dirname, 'testB')), object_type=ObjectType.rgb,\\n get_image_mode=GetImageMode.processed)\\n train_iter = Iterator(data=trainA, unpair=trainB)\\n test_iter = Iterator(data=testA, unpair=testB)\\n dataset = DataProvider(dataset_name, traindata=train_iter, testdata=test_iter)\\n print('get horse2zebra images :{0}'.format(len(dataset)))\\n return dataset\\n elif dataset_name == 'examples_people':\\n download_file_from_google_drive('1H7mJJfWpmXpRxurMZQqY4N_UXWLbQ2pT', dirname, 'people.tar')\\n tar_file_path = os.path.join(dirname, 'people.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n imgs = glob.glob(os.path.join(dirname, 'imgs', '*.*g'))\\n masks = glob.glob(os.path.join(dirname, 'masks', '*.png'))\\n imgs=list(sorted(imgs))\\n masks = list(sorted(masks))\\n # make_dir_if_need(os.path.join(dirname, 'trimap'))\\n # for i in range(len(masks)):\\n # mask=mask2array(masks[i])\\n # trimap=mask2trimap(mask)\\n # save_mask(trimap,masks[i].replace('masks','trimap'))\\n # print('trimap',len(masks))\\n\\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb)\\n mskdata = MaskDataset(masks=masks, object_type=ObjectType.binary_mask)\\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=mskdata))\\n print('get people images :{0}'.format(len(dataset)))\\n return dataset\\n elif dataset_name == 'examples_autodrive':\\n download_file_from_google_drive('1JqPPeHqhWLqnI6bD8nuHcVx-Y56oIZMK', dirname, 'autodrive.tar')\\n tar_file_path = os.path.join(dirname, 'autodrive.tar')\\n extract_path = os.path.join(dirname, 'autodrive')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n imgs = glob.glob(os.path.join(dirname, 'images', '*.*g'))\\n masks = glob.glob(os.path.join(dirname, 'masks', '*.png'))\\n imgs = list(sorted(imgs))\\n masks = list(sorted(masks))\\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb,symbol='image')\\n mskdata = MaskDataset(masks=masks, object_type=ObjectType.color_mask,symbol='mask')\\n\\n def parse_code(l):\\n if len(l.strip().split(\\\"\\\\t\\\")) == 2:\\n a, b = l.replace('\\\\t\\\\t', '\\\\t').strip().split(\\\"\\\\t\\\")\\n return tuple(int(i) for i in b.split(' ')), a\\n\\n label_codes, label_names = zip(\\n *[parse_code(l) for l in open(os.path.join(dirname, \\\"label_colors.txt\\\")).readlines()])\\n for i in range(len(label_codes)):\\n mskdata.palette[label_names[i]] = label_codes[i]\\n\\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=mskdata))\\n print('get autodrive images :{0}'.format(len(dataset)))\\n return dataset\\n elif dataset_name == 'examples_superresolution':\\n download_file_from_google_drive('1v1uoymrWI_MLSiGvSGW7tWJYSnzzXpEQ', dirname, 'superresolution.tar')\\n tar_file_path = os.path.join(dirname, 'superresolution.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n imgs = glob.glob(os.path.join(dirname, '*.*g'))\\n imgs.extend(glob.glob(os.path.join(dirname, '*.bmp')))\\n imgs = list(sorted(imgs))\\n\\n print('get super resolution images :{0}'.format(len(imgs)))\\n\\n imgdata = ImageDataset(images=imgs * 2, object_type=ObjectType.rgb, symbol='lr')\\n labeldata = ImageDataset(images=imgs * 2, object_type=ObjectType.rgb, symbol='hr')\\n dataset = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=labeldata))\\n return dataset\\n elif dataset_name == 'examples_beauty':\\n download_file_from_google_drive('1aJhxN9IqsxuayhRTm-gmxk6PiLe5wm9X', dirname, 'beauty.tar')\\n tar_file_path = os.path.join(dirname, 'beauty.tar')\\n\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n # 讀取圖片數據\\n images_dict = {}\\n with open(os.path.join(dirname, 'images_dict.pkl'), 'rb') as fp:\\n images_dict = pickle.load(fp)\\n\\n f = open(os.path.join(dirname, 'All_Ratings.txt'), encoding='utf-8-sig').readlines()\\n imgs = []\\n landmarks = []\\n ratings = []\\n for row in f:\\n data = row.strip().split('\\\\t')\\n if 'images\\\\\\\\' + data[0] in images_dict:\\n img = images_dict['images\\\\\\\\' + data[0]][0]\\n img = img.transpose([2, 0, 1])[::-1].transpose([1, 2, 0])\\n imgs.append(img)\\n landmark = images_dict['images\\\\\\\\' + data[0]][1].astype(np.float32)\\n landmarks.append(landmark)\\n rating = (float(data[1])) / 5.00\\n ratings.append(rating)\\n print('{0} faces loaded...'.format(len(imgs)))\\n imgdata = ImageDataset(images=imgs, object_type=ObjectType.rgb, symbol='faces')\\n landmarkdata = LandmarkDataset(landmarks=landmarks, object_type=ObjectType.landmarks, symbol='target_landmarks')\\n labeldata = LabelDataset(data=ratings,object_type=ObjectType.classification_label, symbol='target_beauty')\\n data_provider = DataProvider(dataset_name=dataset_name, traindata=Iterator(data=imgdata, label=Dataset.zip(landmarkdata,labeldata)))\\n return data_provider\\n\\n elif dataset_name == 'examples_facelandmarks':\\n download_file_from_google_drive('1GtswQBAHPa_bXaB4tW2uOOQ8Lxfz2L5B', dirname, 'ibug_300W.tar')\\n tar_file_path = os.path.join(dirname, 'ibug_300W.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n root_dir=os.path.join(dirname, 'ibug_300W_large_face_landmark_dataset')\\n image_paths = {}\\n landmarks = {}\\n crops = {}\\n\\n for mode in ['train','test']:\\n make_dir_if_need(os.path.join(dirname, 'crops',mode))\\n tree = ElementTree.parse(os.path.join(root_dir, 'labels_ibug_300W_{0}.xml'.format(mode)))\\n root = tree.getroot()\\n image_paths[mode]=[]\\n landmarks[mode] = []\\n crops[mode] = []\\n\\n offset=5\\n for j in tqdm(range(len(root[2]))):\\n try:\\n filename=root[2][j]\\n landmark = []\\n for num in range(68):\\n x_coordinate = int(filename[0][num].attrib['x'])\\n y_coordinate = int(filename[0][num].attrib['y'])\\n landmark.append([x_coordinate, y_coordinate])\\n landmark=np.asarray(landmark)\\n\\n crop = filename[0].attrib\\n for k in crop.keys():\\n crop[k] = int(crop[k]) if isinstance(crop[k], str) else crop[k]\\n for k in crop.keys():\\n if k=='top' and int(landmark[:,1].min())<int(crop[k]):\\n crop[k] = int( landmark[:,1].min())\\n crop[ 'height']+=crop[k]-int(landmark[:,1].min())\\n elif k=='left' and int(landmark[:,0].min())<int(crop[k]):\\n crop[k] = int( landmark[:,0].min())\\n crop['width']+= crop[k] - int(landmark[:, 0].min())\\n elif k == 'width' and int(landmark[:, 0].max()-landmark[:, 0].min()) > int(crop[k]):\\n crop[k] = int(landmark[:, 0].max()-landmark[:, 0].min())\\n elif k == 'height' and int(landmark[:, 1].max()-landmark[:, 1].min()) > int(crop[k]):\\n crop[k] = int(landmark[:, 1].max()-landmark[:, 1].min())\\n\\n crop['left']-=offset\\n crop['top'] -= offset\\n crop['width'] += 2*offset\\n crop['height'] += 2*offset\\n\\n\\n landmark[:,0]-=crop['left']\\n landmark[:, 1] -= crop['top']\\n\\n\\n if not os.path.exists(os.path.join(dirname, 'crops', mode, '{0}.png'.format(j))):\\n im=image2array(os.path.join(root_dir, filename.attrib['file']))\\n if im.ndim==2:\\n im=cv2.cvtColor(im,cv2.COLOR_GRAY2RGB)\\n im=im[crop['top']:min(crop['top']+crop['height'],im.shape[0]),crop['left']:min(crop['left']+crop['width'],im.shape[1]),:]\\n\\n if max(im.shape[:2])/max(min(im.shape[:2]),0)<=5:\\n\\n array2image(im).save(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\\n image_paths[mode].append(os.path.join(dirname, 'crops', mode, '{0}.png'.format(j)))\\n crops[mode].append(crop)\\n landmarks[mode].append(landmark)\\n del im\\n else:\\n #im = image2array(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\\n image_paths[mode].append(os.path.join(dirname, 'crops',mode,'{0}.png'.format(j)))\\n crops[mode].append(crop)\\n landmarks[mode].append(landmark)\\n\\n if j%100==0:\\n gc.collect()\\n except Exception as e:\\n pass\\n\\n print('ibug 300w train dataset: images: {0} landmarks:{1} \\\\n'.format(len(image_paths['train']),len(landmarks['train'])))\\n print('ibug 300w test dataset: images: {0} landmarks:{1} \\\\n'.format(len(image_paths['test']), len(landmarks['test'])))\\n imdata=ImageDataset(images=image_paths['train'],symbol='faces',object_type=ObjectType.rgb)\\n landmarkdata = LandmarkDataset(landmarks=landmarks['train'], symbol='landmarks',object_type=ObjectType.landmarks)\\n imtestdata = ImageDataset(images=image_paths['test'], symbol='faces',object_type=ObjectType.rgb)\\n landmarktestdata = LandmarkDataset(landmarks=landmarks['test'], symbol='landmarks',object_type=ObjectType.landmarks)\\n data_provider=DataProvider(traindata=Iterator(data=imdata,label=landmarkdata),testdata=Iterator(data=imtestdata,label=landmarktestdata))\\n return data_provider\\n\\n elif dataset_name == 'examples_antisproofing':\\n download_file_from_google_drive('1e7Zjn2MHNCvA5gXdJUECzY8NjK4KVpa7', dirname, 'antisproofing.tar')\\n tar_file_path = os.path.join(dirname, 'antisproofing.tar')\\n make_dir_if_need(os.path.join(dirname, 'antisproofing'))\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n data_provider = load_folder_images(dataset_name,os.path.join(dirname, 'antisproofing'))\\n return data_provider\\n elif dataset_name == 'examples_anpr':\\n download_file_from_google_drive('1uGBd8tXlP0TZAXNgrR6H0jl5MXj7VPbN', dirname, 'anpr.tar')\\n tar_file_path = os.path.join(dirname, 'anpr.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n imgs = glob.glob(os.path.join(dirname, '*.*g'))\\n imgs = list(sorted(imgs))\\n\\n # CCPD (Chinese City Parking Dataset, ECCV) and PDRC (license Plate Detection and Recognition Challenge)\\n # https://github.com/detectRecog/CCPD\\n provinces = [\\\"皖\\\", \\\"沪\\\", \\\"津\\\", \\\"渝\\\", \\\"冀\\\", \\\"晋\\\", \\\"蒙\\\", \\\"辽\\\", \\\"吉\\\", \\\"黑\\\", \\\"苏\\\", \\\"浙\\\", \\\"京\\\", \\\"闽\\\", \\\"赣\\\", \\\"鲁\\\", \\\"豫\\\", \\\"鄂\\\", \\\"湘\\\", \\\"粤\\\",\\n \\\"桂\\\", \\\"琼\\\", \\\"川\\\", \\\"贵\\\", \\\"云\\\", \\\"藏\\\", \\\"陕\\\", \\\"甘\\\", \\\"青\\\", \\\"宁\\\", \\\"新\\\", \\\"警\\\", \\\"学\\\", \\\"O\\\"]\\n alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',\\n 'W', 'X', 'Y', 'Z', 'O']\\n ads = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W',\\n 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'O']\\n\\n def lp2char(lp):\\n cols = lp.split('_')\\n charstring = ''\\n for i in range(len(cols)):\\n if i == 0:\\n charstring += provinces[int(cols[i])]\\n elif i == 1:\\n charstring += alphabets[int(cols[i])]\\n else:\\n charstring += ads[int(cols[i])]\\n return charstring\\n\\n width = 720\\n height = 1160\\n for im_path in imgs:\\n lbl = im_path.split('/')[-1].rsplit('.', 1)[0].split('-')[-3]\\n charstring = lp2char(lbl)\\n iname = im_path.rsplit('/', 1)[-1].rsplit('.', 1)[0].split('-')\\n [leftUp, rightDown] = [[int(eel) for eel in el.split('&')] for el in iname[2].split('_')]\\n box = [leftUp[0], leftUp[1], rightDown[0], rightDown[1]]\\n ori_w, ori_h = [float(int(el)) for el in [width, height]]\\n new_labels = [(leftUp[0] + rightDown[0]) / (2 * ori_w), (leftUp[1] + rightDown[1]) / (2 * ori_h),\\n (rightDown[0] - leftUp[0]) / ori_w, (rightDown[1] - leftUp[1]) / ori_h]\\n download_file_from_google_drive('1e7Zjn2MHNCvA5gXdJUECzY8NjK4KVpa7', dirname, 'antisproofing.tar')\\n tar_file_path = os.path.join(dirname, 'antisproofing.tar')\\n make_dir_if_need(os.path.join(dirname, 'antisproofing'))\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n data_provider = load_folder_images(dataset_name, os.path.join(dirname, 'antisproofing'))\\n return data_provider\\n\\n\\n\\n elif dataset_name == 'examples_dogs-vs-cats':\\n download_file_from_google_drive('10czW0On7eIXkPP-MuQ-IRxMWdTizWjNC', dirname, 'dogs-vs-cats.tar')\\n tar_file_path = os.path.join(dirname, 'dogs-vs-cats.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n data_provider = load_folder_images(dataset_name, dirname)\\n return data_provider\\n elif dataset_name == 'examples_chinese':\\n to_half=ToHalfWidth()\\n to_sc=ChineseConvert(convert_to='simplified')\\n download_file_from_google_drive('1yzRzXpLuhSUxnixqCgpbdTk16ajnTEWF', dirname, 'chinese.tar')\\n tar_file_path = os.path.join(dirname, 'chinese.tar')\\n extract_archive(tar_file_path, dirname, archive_format='tar')\\n\\n as_train = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'as_training.utf8'), encoding='utf-8-sig').read().strip().replace('\\\\u3000' ,'|'))).splitlines()\\n cityu_train =remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'cityu_training.utf8'), encoding='utf-8-sig').read().strip().replace(' ','|'))).splitlines()\\n\\n as_test = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'as_testing_gold.utf8'), encoding='utf-8-sig').read().strip().replace('\\\\u3000', '|'))).splitlines()\\n cityu_test = remove_nonprintable(to_half(codecs.open(os.path.join(dirname, 'cityu_test_gold.utf8'), encoding='utf-8-sig').read().strip().replace(' ', '|'))).splitlines()\\n\\n\\n data = as_train + cityu_train # 把兩個語料合併\\n test_data=as_test + cityu_test # 把兩個語料合併\\n\\n\\n raw_data_train = [row.strip('\\\\n').strip('\\\\r') for row in data] # 移除分行字元\\n raw_data_test = [row.strip('\\\\n').strip('\\\\r') for row in test_data] # 移除分行字元\\n\\n process_data_train=[]\\n process_seg_label_train = []\\n process_simplifided_label_train = []\\n process_traditional_label_train = []\\n\\n tmp_data_train = []\\n tmp_seg_label_train = []\\n tmp_simplifided_label_train = []\\n tmp_pronunce_label_train = []\\n for k in tqdm(range(len(raw_data_train))):\\n row=raw_data_train[k]\\n if row.startswith('∥'):\\n row=row[1:]\\n words=row.replace('||','|').split('|')\\n for k in range(len(words)):\\n\\n word = words[k]\\n\\n for i in range(len(word)):\\n tmp_data_train.append(word[i])\\n #tmp_simplifided_label_train.append(to_half(to_sc(word[i])))\\n #轉換為BMES\\n\\n if i==0 and len(word)>1: #B 是一個詞的開始\\n tmp_seg_label_train.append('B')\\n elif i==len(word)-1 and len(word)>=2 and tmp_seg_label_train[-1] in ['B','M']: #E 是一個詞的結束\\n tmp_seg_label_train.append('E')\\n elif len(word)==1 and i==0: #S 自己就是一個單詞\\n tmp_seg_label_train.append('S')\\n elif len(word)>=3 and tmp_seg_label_train[-1] in ['B','M']: #M 是一個詞的中間\\n tmp_seg_label_train.append('M')\\n\\n if len(tmp_seg_label_train)>0 and tmp_seg_label_train[-1] in ['E','S']:\\n if len(word) > 1 and (is_alphabet(word) or is_punctuation(word)) and k+1<len(words):\\n if word in [ '。','﹖']:\\n pass\\n\\n elif random.random() < 0.6 or is_alphabet(word):\\n tmp_data_train.append(' ')\\n tmp_seg_label_train.append('S')\\n\\n if (k+1<len(raw_data_train) and not raw_data_train[k+1].startswith( '」')) and words[-1] in [ '。','﹖']:\\n #process_traditional_label_train.append(tmp_data_train)\\n\\n tmp_data_train=to_half(''.join(tmp_data_train))\\n tmp_seg_label_train = ''.join(tmp_seg_label_train)\\n # if len(tmp_data_train)!=len(tmp_seg_label_train):\\n # print('')\\n tmp_simplifided_label_train =to_sc(tmp_data_train)\\n\\n process_data_train.append(tmp_data_train)\\n process_seg_label_train.append(tmp_seg_label_train)\\n process_simplifided_label_train.append(tmp_simplifided_label_train)\\n tmp_data_train = []\\n tmp_seg_label_train = []\\n tmp_simplifided_label_train = []\\n tmp_pronunce_label_train = []\\n # else:\\n # tmp_data_train.append('\\\\n')\\n # tmp_simplifided_label_train.append('\\\\n')\\n # tmp_seg_label_train.append('\\\\n')\\n corpus=process_data_train\\n seg_corpus=process_seg_label_train\\n simplifided_corpus =process_simplifided_label_train\\n\\n process_data_test = []\\n process_seg_label_test = []\\n process_simplifided_label_test = []\\n process_traditional_label_test = []\\n print('generate test labels')\\n tmp_data_test = []\\n tmp_seg_label_test = []\\n tmp_simplifided_label_test = []\\n tmp_pronunce_label_test = []\\n for k in tqdm(range(len(raw_data_test))):\\n row=raw_data_test[k]\\n if row.startswith('∥'):\\n row=row[1:]\\n words = row.replace('||', '|').split('|')\\n for k in range(len(words)):\\n\\n word = words[k]\\n\\n for i in range(len(word)):\\n tmp_data_test.append(word[i])\\n # tmp_simplifided_label_test.append(to_half(to_sc(word[i])))\\n # 轉換為BMES\\n\\n if i == 0 and len(word) > 1: # B 是一個詞的開始\\n tmp_seg_label_test.append('B')\\n elif i == len(word) - 1 and len(word) >= 2 and tmp_seg_label_test[-1] in ['B', 'M']: # E 是一個詞的結束\\n tmp_seg_label_test.append('E')\\n elif len(word) == 1 and i == 0: # S 自己就是一個單詞\\n tmp_seg_label_test.append('S')\\n elif len(word) >= 3 and tmp_seg_label_test[-1] in ['B', 'M']: # M 是一個詞的中間\\n tmp_seg_label_test.append('M')\\n\\n if len(tmp_seg_label_test) > 0 and tmp_seg_label_test[-1] in ['E', 'S'] and k+1<len(words):\\n if len(word) > 1 and (is_alphabet(word) or is_punctuation(word)):\\n if word in ['。', '﹖']:\\n pass\\n elif random.random() < 0.6 or is_alphabet(word):\\n tmp_data_test.append(' ')\\n tmp_seg_label_test.append('S')\\n\\n if (k + 1 < len(raw_data_test) and not raw_data_test[k + 1].startswith('」')) and words[-1] in ['。', '﹖']:\\n # process_traditional_label_test.append(tmp_data_test)\\n\\n tmp_data_test = to_half(''.join(tmp_data_test))\\n tmp_seg_label_test = ''.join(tmp_seg_label_test)\\n # if len(tmp_data_test)!=len(tmp_seg_label_test):\\n # print('')\\n tmp_simplifided_label_test = to_sc(tmp_data_test)\\n\\n process_data_test.append(tmp_data_test)\\n process_seg_label_test.append(tmp_seg_label_test)\\n process_simplifided_label_test.append(tmp_simplifided_label_test)\\n tmp_data_test = []\\n tmp_seg_label_test = []\\n tmp_simplifided_label_test = []\\n tmp_pronunce_label_test = []\\n # else:\\n # tmp_data_test.append('\\\\n')\\n # tmp_simplifided_label_test.append('\\\\n')\\n # tmp_seg_label_test.append('\\\\n')\\n test_corpus = process_data_test\\n test_seg_corpus = process_seg_label_test\\n test_simplifided_corpus = process_simplifided_label_test\\n\\n\\n data=TextSequenceDataset(corpus=corpus,sequence_length=64,sequence_start_at='section_start',object_type=ObjectType.corpus,symbol='input')\\n seg_label = TextSequenceDataset(corpus=seg_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='seg_label')\\n simplifided_label = TextSequenceDataset(corpus=simplifided_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='simplified_label')\\n traditional_label = TextSequenceDataset(corpus= copy.deepcopy(corpus), sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='traditional_label')\\n\\n data_test=TextSequenceDataset(corpus=test_corpus,sequence_length=64,sequence_start_at='section_start',object_type=ObjectType.corpus,symbol='input')\\n seg_test_label = TextSequenceDataset(corpus=test_seg_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='seg_label')\\n simplifided_test_label = TextSequenceDataset(corpus=test_simplifided_corpus,sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='simplified_label')\\n traditional_test_label = TextSequenceDataset(corpus= copy.deepcopy(test_corpus), sequence_length=64, sequence_start_at='section_start', object_type=ObjectType.sequence_label,symbol='traditional_label')\\n\\n\\n chars = list(sorted(set(list( ''.join(corpus) +bpmf_phonetic+'\\\\n\\\\r\\\\t∥'+ ''.join(simplifided_corpus)+''.join(test_data)))))\\n chars.insert(0, '[CLS]')\\n chars.insert(1, '[SEP]')\\n chars.insert(2, '[UNK]')\\n chars.insert(3, '[PAD]')\\n chars.insert(4, '[MASK]')\\n\\n data.vocabs =data_test.vocabs=simplifided_label.vocabs=simplifided_test_label.vocabs = chars\\n data.text2index=data_test.text2index =simplifided_label.text2index=simplifided_test_label.text2index = dict((c, i) for i, c in enumerate(chars))\\n data.index2text =data_test.index2text =simplifided_label.index2text=simplifided_test_label.index2text= dict((i, c) for i, c in enumerate(chars))\\n traditional_label = copy.deepcopy(data)\\n traditional_test_label = copy.deepcopy(data_test)\\n traditional_label.object_type =traditional_test_label.object_type = ObjectType.sequence_label\\n traditional_label.symbol =traditional_test_label.symbol = 'traditional_label'\\n\\n mask_label = copy.deepcopy(data)\\n mask_test_label = copy.deepcopy(data_test)\\n #mask_label.object_type =mask_test_label.object_type= ObjectType.corpus\\n mask_label.symbol = mask_test_label.symbol = 'mask_label'\\n\\n\\n\\n nextword=copy.deepcopy(data)\\n nextword_test = copy.deepcopy(data_test)\\n nextword.object_type=nextword_test.object_type=ObjectType.sequence_label\\n nextword.symbol=nextword_test.symbol='nextword_label'\\n nextword.sequence_offset=nextword_test.sequence_offset=1\\n\\n label=ZipDataset(seg_label,nextword,simplifided_label,traditional_label,mask_label)\\n label_test = ZipDataset(seg_test_label, nextword_test, simplifided_test_label, traditional_test_label, mask_test_label)\\n provider=TextSequenceDataProvider(\\n traindata=Iterator(data=data,label=label),\\n testdata=Iterator(data=data_test,label=label_test))\\n return provider\\n #,sample_filter=lambda x:x[0][-1]==3\\n else:\\n return None\",\n \"def load_data_from_files(self):\\n # separated method to allow mock easier\\n logger.info(\\\"Loading data...\\\")\\n parent = Path(__file__).parent\\n path = parent / \\\"resources\\\" / \\\"scores.txt\\\"\\n self.scorer.load_from_file(path)\\n path = parent / \\\"resources\\\" / \\\"american-english-large.txt\\\"\\n self.trie.load_from_file(path)\\n path = parent / \\\"resources\\\" / \\\"reels.txt\\\"\\n self.reels = Reel.get_from_file(path)\\n logger.info(\\\"Data loaded!\\\")\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n\\t\\traise NotImplementedError()\",\n \"def get_all_profesors(self) -> List[Teacher]:\\n self.cursor.execute(\\n f\\\"SELECT * FROM {self.table_name}\\\")\\n \\n teachers = []\\n for teacher in self.cursor.fetchall():\\n teacher_parsed = list(teacher[0:8]) + [json.loads(t) for t in teacher[8:]]\\n teachers.append(Teacher.parse_tuple(teacher_parsed))\\n \\n return teachers\",\n \"def load_testset_predictions(all, weighted=False):\\n if weighted:\\n return load_data_from_CSV('results/testset_results_weighted_{}.csv'.format(all))\\n else:\\n return load_data_from_CSV('results/testset_results_{}.csv'.format(all))\",\n \"def load_dataset():\\n\\n\\n train_dd_loader = DailyDialogLoader(PATH_TO_TRAIN_DATA, load=False)\\n train_dataloader = DataLoader(train_dd_loader, batch_size=16, shuffle=True, num_workers=0,\\n collate_fn=PadCollate())\\n\\n test_dd_loader = DailyDialogLoader(PATH_TO_TEST_DATA, load=True)\\n test_dataloader = DataLoader(test_dd_loader, batch_size=1, shuffle=False, num_workers=0,\\n collate_fn=PadCollate())\\n\\n assert train_dd_loader.vocabulary.n_words == test_dd_loader.vocabulary.n_words\\n\\n return train_dd_loader, train_dataloader, test_dataloader\",\n \"def loadTrips(analyzer, tripsfile):\\n tripsfile = cf.data_dir + tripsfile\\n input_file = csv.DictReader(open(tripsfile, encoding=\\\"utf-8\\\"),\\n delimiter=\\\",\\\")\\n for trip in input_file:\\n model.addTrip(analyzer, trip)\\n return analyzer\",\n \"def get_teacher(self, **fields):\\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\\n teacher_fields = {}\\n for key, value in fields.items():\\n if key in existing_fields:\\n teacher_fields[key] = value\\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\\n # Expect single value if search by unique fields, list if by non-unique\\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None\",\n \"def load_data(self):\\n with open('data/fordTrain.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n train = [x for i, x in enumerate(data) if i > 0] \\n # Extract features and target variable separately\\n trainx = [x[3:] for x in train]\\n trainy = [x[2] for x in train]\\n\\n with open('data/fordTest.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \\n\\n with open('data/Solution.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testy = [x[2] for i, x in enumerate(data) if i > 0] \\n\\n # Extract features and target variable, convert to numpy array\\n trainx = np.asarray(trainx, dtype=np.float32)\\n trainy = np.asarray(trainy, dtype=np.int8)\\n testx = np.asarray(testx, dtype=np.float32)\\n testy = np.asarray(testy, dtype=np.int8)\\n\\n # Return training and test sets\\n trainSet = Dataset(trainx, trainy)\\n testSet = Dataset(testx, testy)\\n return trainSet, testSet\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def load_train_subjects_names(self):\\n for tf in self.train_db_batches:\\n files = ns.natsorted(os.listdir(os.path.join(self.db_path, tf)))\\n for f in files:\\n if f.startswith('volume'):\\n s_name = str.split(str.split(f, '.')[0], '-')[-1]\\n self.training_subjects.append(s_name)\\n np.random.seed(1)\\n np.random.shuffle(self.training_subjects)\\n self.n_train = len(self.training_subjects)\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n \\n raise NotImplementedError()\",\n \"def get_data_loader(batch_size=10, num_workers=2):\\n \\n data_loader = torch.utils.data.DataLoader(dataset=TempuckeyDataSet(),\\n batch_size=batch_size,\\n shuffle=True,\\n pin_memory=True,\\n num_workers=num_workers,\\n collate_fn=collate)\\n return data_loader\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def preload_all(self):\\n for tp in self.tps:\\n for f in self.featurefiles + self.maskfiles:\\n file = os.path.join(tp, f)\\n print('preloading {}'.format(file))\\n self.load(file, lazy=False)\",\n \"def get_test_loader(test_dataset,\\n batch_size,\\n num_workers=4,\\n pin_memory=False):\\n data_loader = torchutils.DataLoader(\\n test_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)\\n return data_loader\",\n \"def load_pedestrian(self, dataset_dir, subset):\\n # Add classes. We have only one class to add.\\n self.add_class(\\\"pedestrian\\\", 1, \\\"pedestrian\\\")\\n\\n # Train or validation dataset?\\n assert subset in [\\\"train\\\", \\\"val\\\"]\\n dataset_dir = os.path.join(dataset_dir, subset)\\n print(dataset_dir)\\n # Load annotations\\n # VGG Image Annotator (up to version 1.6) saves each image in the form:\\n # { 'filename': '28503151_5b5b7ec140_b.jpg',\\n # 'regions': {\\n # '0': {\\n # 'region_attributes': {},\\n # 'shape_attributes': {\\n # 'all_points_x': [...],\\n # 'all_points_y': [...],\\n # 'name': 'polygon'}},\\n # ... more regions ...\\n # },\\n # 'size': 100202\\n # }\\n # We mostly care about the x and y coordinates of each region\\n # Note: In VIA 2.0, regions was changed from a dict to a list.\\n annotations = json.load(open(os.path.join(dataset_dir, \\\"via_region_data.json\\\")))\\n annotations = list(annotations.values()) # don't need the dict keys\\n\\n # The VIA tool saves images in the JSON even if they don't have any\\n # annotations. Skip unannotated images.\\n annotations = [a for a in annotations if a['regions']]\\n\\n # Add images\\n for a in annotations:\\n # Get the x, y coordinaets of points of the polygons that make up\\n # the outline of each object instance. These are stores in the\\n # shape_attributes (see json format above)\\n # The if condition is needed to support VIA versions 1.x and 2.x.\\n if type(a['regions']) is dict:\\n polygons = [r['shape_attributes'] for r in a['regions'].values()]\\n else:\\n polygons = [r['shape_attributes'] for r in a['regions']] \\n\\n # load_mask() needs the image size to convert polygons to masks.\\n # Unfortunately, VIA doesn't include it in JSON, so we must read\\n # the image. This is only managable since the dataset is tiny.\\n image_path = os.path.join(dataset_dir, a['filename'])\\n image = skimage.io.imread(image_path)\\n height, width = image.shape[:2]\\n\\n self.add_image(\\n \\\"pedestrian\\\",\\n image_id=a['filename'], # use file name as a unique image id\\n path=image_path,\\n width=width, height=height,\\n polygons=polygons)\",\n \"def train(self, train_loader):\\n pass\",\n \"def load_premiership_teams():\\n # list of PremTeams to add\\n team_list = [\\n {'name': 'Arsenal', 'code': 'ARS', 'is_prem': True},\\n {'name': 'Aston Villa', 'code': 'AVL', 'is_prem': True},\\n {'name': 'Brighton and Hove Albion', 'code': 'BTN', 'is_prem': True},\\n {'name': 'Brentford', 'code': 'BRE', 'is_prem': True},\\n {'name': 'Bournemouth', 'code': 'BOU', 'is_prem': False},\\n {'name': 'Burnley', 'code': 'BUR', 'is_prem': True},\\n {'name': 'Cardiff City', 'code': 'CAR', 'is_prem': False},\\n {'name': 'Chelsea', 'code': 'CHE', 'is_prem': True},\\n {'name': 'Crystal Palace', 'code': 'CRY', 'is_prem': True},\\n {'name': 'Everton', 'code': 'EVE', 'is_prem': True},\\n {'name': 'Fulham', 'code': 'FUL', 'is_prem': False},\\n {'name': 'Hull', 'code': 'HUL', 'is_prem': False},\\n {'name': 'Huddersfield Town', 'code': 'HUD', 'is_prem': False},\\n {'name': 'Leeds United', 'code': 'LEE', 'is_prem': True},\\n {'name': 'Leicester City', 'code': 'LEI', 'is_prem': True},\\n {'name': 'Liverpool', 'code': 'LIV', 'is_prem': True},\\n {'name': 'Manchester City', 'code': 'MCY', 'is_prem': True},\\n {'name': 'Manchester United', 'code': 'MUN', 'is_prem': True},\\n {'name': 'Middlesbrough', 'code': 'MID', 'is_prem': False},\\n {'name': 'Newcastle United', 'code': 'NEW', 'is_prem': True},\\n {'name': 'Norwich City', 'code': 'NOR', 'is_prem': True},\\n {'name': 'Queens Park Rangers', 'code': 'QPR', 'is_prem': False},\\n {'name': 'Sheffield United', 'code': 'SHF', 'is_prem': False},\\n {'name': 'Southampton', 'code': 'SOT', 'is_prem': True},\\n {'name': 'Stoke City', 'code': 'STO', 'is_prem': False},\\n {'name': 'Sunderland', 'code': 'SUN', 'is_prem': False},\\n {'name': 'Swansea City', 'code': 'SWA', 'is_prem': False},\\n {'name': 'Tottenham Hotspur', 'code': 'TOT', 'is_prem': True},\\n {'name': 'Watford', 'code': 'WAT', 'is_prem': True},\\n {'name': 'West Bromwich Albion', 'code': 'WBA', 'is_prem': False},\\n {'name': 'West Ham United', 'code': 'WHM', 'is_prem': True},\\n {'name': 'Wolverhampton Wanderers', 'code': 'WLV', 'is_prem': True},\\n ]\\n\\n for team in team_list:\\n print(PremTeam.objects.update_or_create(\\n name=team['name'],\\n code=team['code'],\\n defaults={'is_prem': team['is_prem']}\\n ))\\n # print(pt, created)\",\n \"def load_data(batch_size=batch_size):\\n trainset = LibriSpeechDataset(training_set, int(LIBRISPEECH_SAMPLING_RATE * n_seconds))\\n testset = LibriSpeechDataset(validation_set, int(LIBRISPEECH_SAMPLING_RATE * n_seconds), stochastic=False)\\n\\n train_loader = DataLoader(trainset, batch_size=batch_size, num_workers=1, shuffle=True, drop_last=True)\\n test_loader = DataLoader(testset, batch_size=1, num_workers=1, drop_last=True)\\n\\n return train_loader, test_loader\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_test_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def load_meetings():\\n\\n print \\\"Importing meetings...\\\"\\n\\n # Delete all rows in table, so if we need to run this a second time,\\n # we won't be trying to add duplicate retailers\\n Meeting.query.delete()\\n\\n # Read CSV file\\n with open(\\\"seed_data/meetings.csv\\\") as source_file:\\n example_data = list(csv.reader(source_file))\\n\\n # skip header row for populating db\\n for list_item in example_data[1:]:\\n meeting = Meeting(meeting_title=list_item[1],\\n meeting_time=list_item[2],\\n attendees=list_item[3],\\n length=list_item[4],\\n topic_id=list_item[5])\\n\\n # Add the current retailer to the session\\n db.session.add(meeting)\\n\\n # Commit the db.session changes to the database\\n db.session.commit()\",\n \"def load_trial_data(filelist_path, feature_dir, **kwargs):\\n filelist = readlines_and_split_spaces(filelist_path)\\n\\n\\n if (len(filelist[0]) == 4 and (\\\"spoof\\\" in filelist[0] or \\\"target\\\" in filelist[0])):\\n # Four items per line: speaker_id, utterance, system, target/nontarget/spoof\\n # ASVSpoof2019 logical access trial list\\n return _load_trial_data_asvspoof(filelist, feature_dir, **kwargs)\\n elif len(filelist[0]) == 3 and filelist[0][0] in (\\\"0\\\", \\\"1\\\"):\\n # Three items per line, and first is 0 or 1 (target or nontarget)\\n return _load_trial_data_voxceleb(filelist, feature_dir)\\n else:\\n raise RuntimeError(\\\"Unknown filelist type (file {})\\\".format(filelist_path))\",\n \"def test_practitioner_6(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f002-pv.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_6(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_6(inst2)\",\n \"def PapersFile(filename, researchers=None):\\n try:\\n with open(filename, \\\"r\\\") as file:\\n data = json.load(file)\\n except FileNotFoundError:\\n data = {}\\n return Papers(data, filename=filename, researchers=researchers)\",\n \"def test_practitioner_4(base_settings):\\n filename = (\\n base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-xcda-author.json\\\"\\n )\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_4(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_4(inst2)\",\n \"def test_practitioner_7(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_7(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_7(inst2)\",\n \"def teacher(self):\\n if \\\"teacher\\\" in self._prop_dict:\\n if isinstance(self._prop_dict[\\\"teacher\\\"], OneDriveObjectBase):\\n return self._prop_dict[\\\"teacher\\\"]\\n else :\\n self._prop_dict[\\\"teacher\\\"] = EducationTeacher(self._prop_dict[\\\"teacher\\\"])\\n return self._prop_dict[\\\"teacher\\\"]\\n\\n return None\",\n \"def test_train_on_the_fly(self):\\r\\n input_seqs_file = NamedTemporaryFile(\\r\\n prefix='RdpTaxonAssignerTest_', suffix='.fasta')\\r\\n input_seqs_file.write(test_seq_coll.to_fasta())\\r\\n input_seqs_file.seek(0)\\r\\n\\r\\n exp_assignments = rdp_trained_test1_expected_dict\\r\\n\\r\\n app = RdpTaxonAssigner({\\r\\n 'id_to_taxonomy_fp': self.id_to_taxonomy_file.name,\\r\\n 'reference_sequences_fp': self.reference_seqs_file.name,\\r\\n })\\r\\n obs_assignments = app(self.tmp_seq_filepath)\\r\\n\\r\\n key = 'X67228 some description'\\r\\n self.assertEqual(obs_assignments[key], exp_assignments[key])\",\n \"def load(self):\\n #self.df = read_file(\\\"../data/yelp_academic_dataset_user.json\\\") #Full Data.\\n self.df = read_file(\\\"../data/user300.json\\\") #For local machine.\\n #self.get_friend_list()\\n #self.save_friend_nodes()\",\n \"def load_tas_lookup():\\n logger.info('Loading TAS')\\n load_tas()\",\n \"def setup(self):\\n # TODO check if need both dataset together\\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \\n tokenizer=self.tokenizer, vocab=\\\"bert\\\")\\n self.vocabulary = self.train_dataset.vocabulary\\n\\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\\n tokenizer=self.tokenizer, vocab=self.vocabulary)\\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)\",\n \"def load_data():\\n print(\\\"PARSING TRAIN\\\")\\n ys_train, x_train, ids_train = load_pickle_data(\\\"ys_train\\\"), load_pickle_data(\\\"x_train\\\"), load_pickle_data(\\n \\\"ids_train\\\")\\n if ys_train is None or x_train is None or ids_train is None:\\n ys_train, x_train, ids_train = load_csv_data(\\\"{}/train.csv\\\".format(DATA_DIR))\\n dump_pickle_data(ys_train, \\\"ys_train\\\")\\n dump_pickle_data(x_train, \\\"x_train\\\")\\n dump_pickle_data(ids_train, \\\"ids_train\\\")\\n\\n print(\\\"PARSING TEST\\\")\\n x_test, ids_test = load_pickle_data(\\\"x_test\\\"), load_pickle_data(\\\"ids_test\\\")\\n if x_test is None or ids_test is None:\\n _, x_test, ids_test = load_csv_data(\\\"{}/test.csv\\\".format(DATA_DIR))\\n dump_pickle_data(x_test, \\\"x_test\\\")\\n dump_pickle_data(ids_test, \\\"ids_test\\\")\\n\\n return ys_train, x_train, ids_train, x_test, ids_test\",\n \"def get_data_loaders_4sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"]\\n #history_complete.append(history)\\n if len(history_splitted) > (len(persona)-1):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def fixture_example_data():\\n import_example_data()\",\n \"def get_data_loaders(args, tokenizer):\\n personachat = get_dataset(tokenizer, args.dataset_path, args.dataset_cache, args.train_lang)\\n _ = personachat.pop(\\\"test\\\", None)\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": [], \\\"valid\\\": []}\\n\\n if args.train_lang in [\\\"En\\\", \\\"Fr\\\", \\\"It\\\", \\\"Id\\\", \\\"Jp\\\", \\\"Ko\\\", \\\"Zh\\\"]: #monolingual data\\n for dataset_name, dataset in personachat.items():\\n for dial in dataset[args.train_lang]: #dial: {\\\"persona\\\":[], \\\"history\\\":[], \\\"response\\\":str}\\n instance = build_input_from_segments(dial[\\\"persona\\\"], dial[\\\"history\\\"][-args.max_turns:], dial[\\\"response\\\"], tokenizer, lm_labels = True)\\n datasets[dataset_name].append(instance) \\n else: #multilingual data\\n for dataset_name, dataset in personachat.items():\\n for lang, dials in dataset.items():\\n for dial in dials: #dial: {\\\"persona\\\":[], \\\"history\\\":[], \\\"response\\\":str}\\n instance = build_input_from_segments(dial[\\\"persona\\\"], dial[\\\"history\\\"][-args.max_turns:], dial[\\\"response\\\"], tokenizer, lang_id=\\\"<{}>\\\".format(lang.lower()), lm_labels = True)\\n datasets[dataset_name].append(instance) #all langs together\\n\\n\\n logger.info(\\\"Build train and validation dataloaders\\\")\\n train_dataset = DatasetTrain(datasets[\\\"train\\\"])\\n valid_dataset = DatasetTrain(datasets[\\\"valid\\\"])\\n\\n #logger.info(\\\"Build train and validation dataloaders\\\")\\n #train_dataset, valid_dataset = TensorDataset(*tensor_datasets[\\\"train\\\"]), TensorDataset(*tensor_datasets[\\\"valid\\\"])\\n train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None\\n valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None\\n train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, shuffle=(not args.distributed), collate_fn=collate_fn)\\n valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.valid_batch_size, shuffle=False, collate_fn=collate_fn)\\n\\n # logger.info(\\\"Train dataset (Batch, Candidates, Seq length): {}\\\".format(train_dataset.tensors[0].shape))\\n # #logger.info(\\\"Train dataset (Batch, Candidates, Seq length): {}\\\".format(train_dataset.tensors[1].shape))\\n # logger.info(\\\"Valid dataset (Batch, Candidates, Seq length): {}\\\".format(valid_dataset.tensors[0].shape))\\n logger.info(\\\"Train dataset length: {}\\\".format(len(train_dataset)))\\n logger.info(\\\"Valid dataset length: {}\\\".format(len(valid_dataset)))\\n return train_loader, valid_loader, train_sampler, valid_sampler\",\n \"def load_trec(loc=DATA_DIR):\\n train, test = [], []\\n with open(os.path.join(loc, 'TREC', 'train_5500.label'), 'rb') as f:\\n for line in f:\\n train.append(line.strip())\\n with open(os.path.join(loc, 'TREC', 'TREC_10.label'), 'rb') as f:\\n for line in f:\\n test.append(line.strip())\\n return train, test\",\n \"def load_extractor_engines(reload=False):\\r\\n\\r\\n global youtube_dl, youtube_dlc\\r\\n\\r\\n # youtube-dl ----------------------------------------------------------------------------------------------------\\r\\n start = time.time()\\r\\n\\r\\n if reload and youtube_dl:\\r\\n importlib.reload(youtube_dl)\\r\\n else:\\r\\n import youtube_dl\\r\\n\\r\\n config.youtube_dl_version = youtube_dl.version.__version__\\r\\n\\r\\n # calculate loading time\\r\\n load_time = time.time() - start\\r\\n log(f'youtube_dl version: {config.youtube_dl_version}, load_time= {int(load_time)} seconds')\\r\\n\\r\\n # youtube-dlc ----------------------------------------------------------------------------------------------------\\r\\n start = time.time()\\r\\n\\r\\n if reload and youtube_dlc:\\r\\n importlib.reload(youtube_dlc)\\r\\n else:\\r\\n import youtube_dlc\\r\\n\\r\\n config.youtube_dlc_version = youtube_dlc.version.__version__\\r\\n\\r\\n # calculate loading time\\r\\n load_time = time.time() - start\\r\\n log(f'youtube_dlc version: {config.youtube_dlc_version}, load_time= {int(load_time)} seconds')\\r\\n\\r\\n # set interrupt / kill switch\\r\\n set_interrupt_switch()\\r\\n\\r\\n # set default extractor\\r\\n set_default_extractor()\\r\\n\\r\\n # get a random user agent and update headers\\r\\n if not config.custom_user_agent:\\r\\n config.http_headers['User-Agent'] = youtube_dl.utils.random_user_agent()\",\n \"def test_practitioner_1(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f203-jvg.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_1(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_1(inst2)\",\n \"def _load_training_data(self):\\n self._save_training_data()\",\n \"def load(tweets_file, rtt_file):\\n try:\\n tw_df = pd.read_csv(tweets_file)\\n rtt_df = pd.read_csv(rtt_file)\\n except:\\n print('one or several files were not found')\\n sys.exit()\\n\\n return tw_df, rtt_df\",\n \"def test_practitioner_9(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f204-ce.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_9(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_9(inst2)\",\n \"def load_inst(self):\\n self.sanity_check()\\n\\n fname_pub_auth_all = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_auth_all, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_auth_top = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_auth_top, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_inst_all = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_inst_all, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_inst_top = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_inst_top, '_',\\n self.config.experiment_id, '.pk'])\\n self.pub_auth_all = pickle.load(open(fname_pub_auth_all, 'rb'))\\n self.pub_auth_top = pickle.load(open(fname_pub_auth_top, 'rb'))\\n self.pub_inst_all = pickle.load(open(fname_pub_inst_all, 'rb'))\\n self.pub_inst_top = pickle.load(open(fname_pub_inst_top, 'rb'))\\n\\n fname_pub_history = ''.join([self.config.dir_data, '/history_',\\n self.config.experiment_id, '.pk'])\\n self.history = pickle.load(open(fname_pub_history, 'rb'))\\n\\n fname_pub_staff = ''.join([self.config.dir_data, '/staff_',\\n self.config.experiment_id, '.pk'])\\n self.staff = pickle.load(open(fname_pub_staff, 'rb'))\",\n \"def load_campers():\\n\\n for row in open(\\\"static/campers.csv\\\"):\\n row = row.rstrip()\\n\\n email, password, first_name, last_name, camper_photo, camper_photo_url = row.split(\\\",\\\")\\n\\n camper = Camper(\\n camper_email=email,\\n password=password,\\n first_name=first_name,\\n last_name=last_name,\\n camper_photo=camper_photo,\\n camper_photo_url=camper_photo_url)\\n\\n db.session.add(camper)\\n\\n db.session.commit()\",\n \"def get_teacher(teacher_account_id):\\n query = 'SELECT * FROM teacher JOIN person ON teacher.teacher_account_id=person.account_id WHERE teacher.teacher_account_id=%s;'\\n args = (teacher_account_id,)\\n return database.connection.get_data(query, args)\",\n \"def load_passengers(self, passengers):\\r\\n if len(passengers) > self.available_capacity():\\r\\n raise Exception(\\\"The elevator cannot handle this many passengers \\\"\\r\\n \\\"and will be over capacity\\\")\\r\\n\\r\\n self.passengers += passengers\\r\\n\\r\\n self._update_destinations()\",\n \"def load_data(y_name='Species'):\\n train_path = tf.keras.utils.get_file(args.TRAIN_URL.split('/')[-1], args.TRAIN_URL)\\n test_path = tf.keras.utils.get_file(args.TEST_URL.split('/')[-1], args.TEST_URL)\\n\\n train = pd.read_csv(train_path, names=args.CSV_COLUMN_NAMES, header=0)\\n train_x, train_y = train, train.pop(y_name)\\n\\n test = pd.read_csv(test_path, names=args.CSV_COLUMN_NAMES, header=0)\\n test_x, test_y = test, test.pop(y_name)\\n\\n return (train_x, train_y), (test_x, test_y)\",\n \"def test_pert_file(self):\\n path, case = os.path.split(self.ieee14)\\n\\n # --- with pert file ---\\n ss = andes.run('ieee14.raw', pert='pert.py',\\n input_path=path, no_output=True, default_config=True,\\n )\\n ss.TDS.init()\\n self.assertIsNotNone(ss.TDS.callpert)\\n\\n # --- without pert file ---\\n ss = andes.run('ieee14.raw',\\n input_path=path, no_output=True, default_config=True,\\n )\\n ss.TDS.init()\\n self.assertIsNone(ss.TDS.callpert)\",\n \"def load_all_traj():\\n pdb='/bpti/bpti-prot/bpti-prot.pdb'\\n dcd = lambda x: '/bpti/bpti-prot/bpti-prot-%02d.dcd' % x\\n tr = []\\n for i in range(11):\\n print ('loading ', i)\\n start = dt.datetime.now()\\n tr.append(md.load(DCD_ALL(i), top=PDB_ALL))\\n end = dt.datetime.now()\\n print((end-start).total_seconds())\\n return tr\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7358244","0.5859265","0.5856855","0.5826998","0.5742936","0.552974","0.5405307","0.5320931","0.5187403","0.5179637","0.5173516","0.5170551","0.51504564","0.51137877","0.50919735","0.5080042","0.5077611","0.50590116","0.5048102","0.5040496","0.50286186","0.50239044","0.501151","0.50064164","0.4995266","0.49907285","0.498819","0.498819","0.498819","0.498819","0.498819","0.498819","0.498819","0.49867862","0.4977609","0.49725476","0.4960578","0.49600258","0.49580115","0.49505138","0.49497566","0.49497566","0.49497566","0.49497566","0.49497566","0.4946359","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.49343553","0.4921987","0.49175972","0.49165758","0.49165758","0.49123266","0.49118224","0.48832676","0.4846203","0.48442024","0.48340437","0.48252752","0.48252752","0.4820665","0.4817235","0.48166952","0.48131245","0.480317","0.47975397","0.47958085","0.47892544","0.47885394","0.47799644","0.47752997","0.4770364","0.47659707","0.47515628","0.4747951","0.47440135","0.4740878","0.4723391","0.47195834","0.47044918","0.46988875","0.46988472","0.4698844","0.4696753","0.4691431","0.46901733","0.46894738","0.46887138"],"string":"[\n \"0.7358244\",\n \"0.5859265\",\n \"0.5856855\",\n \"0.5826998\",\n \"0.5742936\",\n \"0.552974\",\n \"0.5405307\",\n \"0.5320931\",\n \"0.5187403\",\n \"0.5179637\",\n \"0.5173516\",\n \"0.5170551\",\n \"0.51504564\",\n \"0.51137877\",\n \"0.50919735\",\n \"0.5080042\",\n \"0.5077611\",\n \"0.50590116\",\n \"0.5048102\",\n \"0.5040496\",\n \"0.50286186\",\n \"0.50239044\",\n \"0.501151\",\n \"0.50064164\",\n \"0.4995266\",\n \"0.49907285\",\n \"0.498819\",\n \"0.498819\",\n \"0.498819\",\n \"0.498819\",\n \"0.498819\",\n \"0.498819\",\n \"0.498819\",\n \"0.49867862\",\n \"0.4977609\",\n \"0.49725476\",\n \"0.4960578\",\n \"0.49600258\",\n \"0.49580115\",\n \"0.49505138\",\n \"0.49497566\",\n \"0.49497566\",\n \"0.49497566\",\n \"0.49497566\",\n \"0.49497566\",\n \"0.4946359\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.49343553\",\n \"0.4921987\",\n \"0.49175972\",\n \"0.49165758\",\n \"0.49165758\",\n \"0.49123266\",\n \"0.49118224\",\n \"0.48832676\",\n \"0.4846203\",\n \"0.48442024\",\n \"0.48340437\",\n \"0.48252752\",\n \"0.48252752\",\n \"0.4820665\",\n \"0.4817235\",\n \"0.48166952\",\n \"0.48131245\",\n \"0.480317\",\n \"0.47975397\",\n \"0.47958085\",\n \"0.47892544\",\n \"0.47885394\",\n \"0.47799644\",\n \"0.47752997\",\n \"0.4770364\",\n \"0.47659707\",\n \"0.47515628\",\n \"0.4747951\",\n \"0.47440135\",\n \"0.4740878\",\n \"0.4723391\",\n \"0.47195834\",\n \"0.47044918\",\n \"0.46988875\",\n \"0.46988472\",\n \"0.4698844\",\n \"0.4696753\",\n \"0.4691431\",\n \"0.46901733\",\n \"0.46894738\",\n \"0.46887138\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7384021"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7180,"cells":{"query":{"kind":"string","value":"Unpack teachers from PDDRTeachers experiment."},"document":{"kind":"string","value":"def unpack_teachers(self, extra: dict):\n self.teacher_policies.extend(extra[\"teacher_policies\"])\n self.teacher_envs.extend(extra[\"teacher_envs\"])\n self.teacher_expl_strats.extend(extra[\"teacher_expl_strats\"])\n self.teacher_critics.extend(extra[\"teacher_critics\"])\n self.teacher_ex_dirs.extend(extra[\"teacher_ex_dirs\"])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_teacher_experiment(self, exp: Experiment):\n _, _, extra = load_experiment(exp)\n self.unpack_teachers(extra)","def prune_teachers(self):\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]","def get_teacher_assign():\n assignment_data = query_db(\n \"SELECT assignments.id, assignments.name, assignments.due_date \"\n \"FROM assignments JOIN topics ON assignments.topic_id=topics.id \"\n \"JOIN classes ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n assignments = []\n for assignment in assignment_data:\n assignment_dict_teach = {}\n assignment_dict_teach[\"id\"] = assignment[0]\n assignment_dict_teach[\"name\"] = assignment[1]\n assignment_dict_teach[\"due_date\"] = assignment[2]\n assignments.append(assignment_dict_teach)\n return assignments","def test_pytd_teacher(self):\n defaults = parser_defaults.copy()\n defaults['datatype'] = 'train:stream'\n defaults['image_mode'] = 'ascii'\n\n with testing_utils.capture_output():\n # Get processed act from agent\n parser = display_setup_args()\n defaults['pytorch_teacher_dataset'] = 'flickr30k'\n del defaults['pytorch_teacher_task']\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n teacher = create_task_agent_from_taskname(opt)[0]\n pytorch_teacher_act = teacher.act()\n\n parser = display_setup_args()\n defaults['task'] = 'flickr30k'\n del defaults['pytorch_teacher_dataset']\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n teacher = create_task_agent_from_taskname(opt)[0]\n regular_teacher_act = teacher.act()\n\n keys = set(pytorch_teacher_act.keys()).intersection(\n set(regular_teacher_act.keys()))\n self.assertTrue(len(keys) != 0)\n for key in keys:\n self.assertTrue(pytorch_teacher_act[key] == regular_teacher_act[key],\n 'PytorchDataTeacher does not have the same value '\n 'as regular teacher for act key: {}'.format(key))","def load_teachers(self):\n # Get the experiment's directory to load from\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\n self.load_teacher_experiment(ex_dir)\n if len(self.teacher_policies) < self.num_teachers:\n print(\n f\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\"\n )\n self.load_teachers()","def load_primers(tsv_filename):\n answer = []\n with open(tsv_filename) as handle:\n for line in handle:\n if line.startswith(\"#\"):\n continue\n parts = line.rstrip(\"\\n\").split(\"\\t\")\n if len(parts) == 2:\n left, right = parts\n name = f\"P{len(answer)}\"\n else:\n name, left, right = parts[:3]\n answer.append((name, left, right))\n return answer","def test_pyt_multitask(self):\n\n def run_display_test(defaults, ep_and_ex_counts):\n with testing_utils.capture_output() as f:\n parser = display_setup_args()\n parser.set_defaults(**defaults)\n opt = parser.parse_args()\n display_data(opt)\n str_output = f.getvalue()\n self.assertTrue(\n '[ loaded {} episodes with a total of {} examples ]'.format(\n ep_and_ex_counts[0], ep_and_ex_counts[1]\n ) in str_output,\n 'PytorchDataTeacher multitasking failed with '\n 'following args: {}'.format(opt)\n )\n\n task1 = 'babi:task1k:1'\n task2 = 'babi:task1k:2'\n dataset1 = 'flickr30k'\n dataset2 = 'vqa_v1'\n\n # Expected example and episode counts\n eps_and_exs_counts = [\n (1800, 1800),\n (1080, 1800),\n (29900, 29900),\n (29180, 29900),\n (277349, 277349)\n ]\n defaults = parser_defaults.copy()\n\n # 1.\n defaults['pytorch_teacher_task'] = '{},{}'.format(task1, task2)\n run_display_test(defaults, eps_and_exs_counts[0])\n\n # 2.\n defaults['pytorch_teacher_task'] = task1\n defaults['task'] = task2\n run_display_test(defaults, eps_and_exs_counts[1])\n\n # 3.\n del defaults['task']\n defaults['pytorch_teacher_dataset'] = dataset1\n run_display_test(defaults, eps_and_exs_counts[2])\n\n # 4.\n del defaults['pytorch_teacher_task']\n defaults['task'] = task1\n run_display_test(defaults, eps_and_exs_counts[3])\n\n # 5.\n del defaults['task']\n defaults['pytorch_teacher_dataset'] = '{},{}'.format(dataset1, dataset2)\n run_display_test(defaults, eps_and_exs_counts[4])","def get_teacher(self, **fields):\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\n teacher_fields = {}\n for key, value in fields.items():\n if key in existing_fields:\n teacher_fields[key] = value\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\n # Expect single value if search by unique fields, list if by non-unique\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None","def get_all_profesors(self) -> List[Teacher]:\n self.cursor.execute(\n f\"SELECT * FROM {self.table_name}\")\n \n teachers = []\n for teacher in self.cursor.fetchall():\n teacher_parsed = list(teacher[0:8]) + [json.loads(t) for t in teacher[8:]]\n teachers.append(Teacher.parse_tuple(teacher_parsed))\n \n return teachers","def ensemble_preds(dataset, nb_teachers, stdnt_data):\n\n # Compute shape of array that will hold probabilities produced by each\n # teacher, for each training point, and each output class\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\n\n # Create array that will hold result\n result = np.zeros(result_shape, dtype=np.float32)\n\n # Get predictions from each teacher\n for teacher_id in xrange(nb_teachers):\n # Compute path of checkpoint file for teacher model with ID teacher_id\n if FLAGS.deeper:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\n else:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\n\n # Get predictions on our training data and store in result array\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\n\n # This can take a while when there are a lot of teachers so output status\n print(\"Computed Teacher \" + str(teacher_id) + \" softmax predictions\")\n\n return result","def multiple_matlab_csv_to_teacher_data(short_runs_dirname):\n subdirname = 'Run-'\n data = None\n data_length = 0\n for i in range(10):\n dirname = os.path.join(short_runs_dirname, subdirname+str(i+1))\n run_data = matlab_csv_to_teacher_data(dirname)\n if i == 0:\n data = run_data\n else:\n for i, phoneme_data in enumerate(run_data):\n data[i] = np.vstack((data[i], phoneme_data))\n\n data_length += run_data[0].shape[0]\n\n for i, phoneme_data in enumerate(data):\n assert phoneme_data.shape[0] == data_length\n\n return data","def get_teachers(self):\n query = Teacher.all().order('teacher')\n return query.fetch()","def load_unpacker_dataset(sentences):\n return TFRecordDataset([path.join(TFRUDIR, sentence+'.tfr')\n for sentence in sentences])\\\n .map(\n lambda record: \\\n tf.parse_single_example(\n record,\n features={\n 's': tf.FixedLenFeature([], tf.string),\n 'l': tf.FixedLenFeature([NL], tf.float32),\n 't': tf.FixedLenFeature([NT], tf.float32)\n }\n )\n )\\\n .map(\n lambda feature: (feature['l'], feature['s'], feature['t'])\n )","def train_teacher (nb_teachers, teacher_id):\n # Load the dataset\n X_train, X_test, y_train, y_test = models.get_dataset()\n\n print(X_train.shape)\n print(y_train.shape)\n print(X_test.shape)\n print(y_test.shape)\n \n # Retrieve subset of data for this teacher\n data, labels = partition.partition_dataset(X_train,\n y_train,\n nb_teachers,\n teacher_id)\n\n print(\"Length of training data: \" + str(len(labels)))\n\n # Define teacher checkpoint filename and full path\n\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\n \n # Perform teacher training need to modify \n \n\n # Create teacher model\n model, opt = models.create_two_layer_mlp(46) # num of cols\n model.compile(loss='binary_crossentropy',\n optimizer=\"Adam\",\n metrics=['accuracy'])\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\n\n #modify\n model_json = model.to_json()\n with open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n# serialize weights to HDF5\n model.save_weights(filename2)\n print(\"Saved model to disk\")\n return True","def extract_tubelets(dname, gpu=-1, redo=False):\n d = GetDataset(dname)\n\n if gpu >= 0:\n caffe.set_mode_gpu()\n caffe.set_device(gpu)\n\n model_dir = os.path.join(os.path.dirname(__file__), '../models/ACT-detector/', dname)\n output_dir = os.path.join(os.path.dirname(__file__), '../results/ACT-detector/', dname)\n \n # load the RGB network\n rgb_proto = os.path.join(model_dir, \"deploy_RGB.prototxt\")\n rgb_model = os.path.join(model_dir, \"../generated_AVA_iter_118662.caffemodel\")\n net_rgb = caffe.Net(rgb_proto, caffe.TEST, weights=rgb_model)\n \n # load the FLOW5 network\n flo_proto = os.path.join(model_dir, \"deploy_FLOW5.prototxt\")\n flo_model = os.path.join(model_dir, \"../generated_AVA_iter_59463.caffemodel\")\n net_flo = caffe.Net(flo_proto, caffe.TEST, weights=flo_model)\n\n vlist = d.test_vlist()\n for iv, v in enumerate(vlist):\n print(\"Processing video {:d}/{:d}: {:s}\".format( iv+1, len(vlist), v))\n h, w = d.resolution(v)\n \n # network output is normalized between 0,1 ; so we will multiply it by the following array\n resolution_array = np.array([w,h,w,h]*K, dtype=np.float32)\n \n # now process each frame\n for i in xrange(1, 1 + d.nframes(v) - K + 1):\n outfile = os.path.join(output_dir, d.frame_format(v,i) + \".pkl\")\n \n # skip if already computed\n if os.path.isfile(outfile) and not redo:\n continue\n \n # read the frames for the forward\n kwargs_rgb = {}\n kwargs_flo = {}\n for j in xrange(K):\n cap = cv2.VideoCapture(d.vidfile(v,0))\n #print(frame)\n #print(int(cap.get(7)))\n cap.set(1,i + j - 1)\n im = cap.read()[1]\n cap.release()\n #im = cv2.imread(d.imfile(v, i + j))\n if im is None:\n print \"Image {:s} does not exist\".format(d.imfile(v, i+j))\n return\n imscale = cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR)\n kwargs_rgb['data_stream' + str(j)] = np.transpose(imscale-MEAN, (2, 0, 1))[None, :, :, :]\n imf = [cv2.imread(d.flowfile(v.split(\".\")[0], min(d.nframes(v), i + j + iflow))) for iflow in xrange(NFLOWS)]\n if np.any(imf) is None:\n print \"Flow image {:s} does not exist\".format(d.flowfile(v, i+j))\n return\n imscalef = [cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR) for im in imf]\n timscale = [np.transpose(im-MEAN, (2, 0, 1))[None, :, :, :] for im in imscalef]\n kwargs_flo['data_stream' + str(j) + 'flow'] = np.concatenate(timscale, axis=1)\n \n # compute rgb and flow scores\n # two forward passes: one for the rgb and one for the flow \n net_rgb.forward(end=\"mbox_conf_flatten\", **kwargs_rgb) # forward of rgb with confidence and regression\n net_flo.forward(end=\"mbox_conf_flatten\", **kwargs_flo) # forward of flow5 with confidence and regression\n \n # compute late fusion of rgb and flow scores (keep regression from rgb)\n # use net_rgb for standard detections, net_flo for having all boxes\n scores = 0.5 * (net_rgb.blobs['mbox_conf_flatten'].data + net_flo.blobs['mbox_conf_flatten'].data)\n net_rgb.blobs['mbox_conf_flatten'].data[...] = scores\n net_flo.blobs['mbox_conf_flatten'].data[...] = scores\n net_flo.blobs['mbox_loc'].data[...] = net_rgb.blobs['mbox_loc'].data\n \n # two forward passes, only for the last layer \n # dets is the detections after per-class NMS and thresholding (stardard)\n # dets_all contains all the scores and regressions for all tubelets \n dets = net_rgb.forward(start='detection_out')['detection_out'][0, 0, :, 1:]\n dets_all = net_flo.forward(start='detection_out_full')['detection_out_full'][0, 0, :, 1:]\n \n # parse detections with per-class NMS\n if dets.shape[0] == 1 and np.all(dets == -1):\n dets = np.empty((0, dets.shape[1]), dtype=np.float32)\n\n dets[:, 2:] *= resolution_array # network output was normalized in [0..1]\n dets[:, 0] -= 1 # label 0 was background, come back to label in [0..nlabels-1]\n dets[:, 2::2] = np.maximum(0, np.minimum(w, dets[:, 2::2]))\n dets[:, 3::2] = np.maximum(0, np.minimum(h, dets[:, 3::2]))\n\n # parse detections with global NMS at 0.7 (top 300)\n # coordinates were normalized in [0..1]\n dets_all[:, 0:4*K] *= resolution_array \n dets_all[:, 0:4*K:2] = np.maximum(0, np.minimum(w, dets_all[:, 0:4*K:2]))\n dets_all[:, 1:4*K:2] = np.maximum(0, np.minimum(h, dets_all[:, 1:4*K:2]))\n idx = nms_tubelets(np.concatenate((dets_all[:, :4*K], np.max(dets_all[:, 4*K+1:], axis=1)[:, None]), axis=1), 0.7, 300)\n dets_all = dets_all[idx, :]\n \n # save file\n if not os.path.isdir(os.path.dirname(outfile)):\n os.system('mkdir -p ' + os.path.dirname(outfile))\n\n with open(outfile, 'wb') as fid:\n pickle.dump((dets, dets_all), fid)","def test_teacher_role():\n response = test_app.post(\"/bkt_service/unwind\", params='''[{\n \"event\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/OutcomeEvent\",\n \"actor\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@id\": \"student-1462300421838-1\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/lis/Person\",\n \"roles\": [\n \"urn:lti:instrole:ims/lis/Teacher\"\n ]\n },\n \"action\": \"http://purl.imsglobal.org/vocab/caliper/v1/action#Graded\",\n \"object\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@id\": \"attempt-1462300421838-4\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/Attempt\",\n \"extensions\": {\n \"assessmentType\": \"Diagnostic Assessment\",\n \"assessmentId\": \"assessment-1462300421838-4\"\n },\n \"count\": 1,\n \"startedAtTime\": \"2016-05-03T21:33:41.844Z\",\n \"endedAtTime\": \"2016-05-03T22:03:41.844Z\"\n },\n \"generated\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@id\": \"result-1462300421838-4\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/Result\",\n \"assignableId\": \"assessment-1462300421838-4\",\n \"normalScore\": 80,\n \"totalScore\": 100,\n \"itemResults\": [\n {\n \"@id\": \"item-result-1462300421838-4-1\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/Result\",\n \"question_type\": \"mcq\",\n \"automarkable\": 1,\n \"score\": 7,\n \"max_score\": 10,\n \"question_reference\": \"c0a3f0c8-eac7-4795-8c7a-adf98e336a7b\",\n \"item_reference\": \"Adaptive_Item2_extract_USMOs\",\n \"sequenceNumber\": 1\n }\n ]\n },\n \"group\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@id\": \"class-01\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/lis/CourseOffering\",\n \"name\": null,\n \"description\": null,\n \"extensions\": {\n \"pageNumber\": null,\n \"courseCode\": \"course-01\",\n \"calmsCourseId\": \"calms-course-01\",\n \"lessonId\": \"lesson-01\",\n \"platform\": \"D2L\",\n \"classroomTypeId\": \"3500.0\",\n \"activityId\": \"10\",\n \"gradeLevel\": \"8\",\n \"CourseOfferingId\": \"1200.0\",\n \"adaptivewrapperId\": \"\",\n \"schoolYear\": \"2015-20116\",\n \"unitId\": \"3201.0\",\n \"moduleId\": \"1110.0\",\n \"courseId\": \"2550.0\",\n \"assessmentId\": \"4520.0\",\n \"originSystemId\": \"sams\",\n \"businessLineId\": \"1300.0\",\n \"contextId\": \"587279312bf9a9afd947ddab\"\n },\n \"dateCreated\": null,\n \"dateModified\": null,\n \"courseNumber\": null,\n \"academicSession\": null,\n \"subOrganizationOf\": {\n \"@context\": \"http://purl.imsglobal.org/ctx/caliper/v1/Context\",\n \"@id\": \"1001.0\",\n \"@type\": \"http://purl.imsglobal.org/caliper/v1/w3c/Organization\",\n \"name\": null,\n \"description\": null,\n \"extensions\": {},\n \"dateCreated\": null,\n \"dateModified\": null,\n \"subOrganizationOf\": null\n }\n },\n \"eventTime\": \"2017-01-09T14:21:00Z\"\n }\n }\n ]''')\n assert response.status == '200 OK'\n assert len(response.json) == 1\n #assert response.json[0][\"error\"][\"code\"] == 21\n #assert \"role\" in response.json[0][\"error\"][\"message\"]","def extract_trpt_data(udp_packet):\n logger.debug('UDP packet sport [%s], dport [%s], len [%s]',\n udp_packet.sport, udp_packet.dport, udp_packet.len)\n\n trpt_pkt = TelemetryReport(_pkt=udp_packet.payload)\n trpt_eth = EthInt(trpt_pkt.payload)\n logger.debug('TRPT ethernet dst - [%s], src - [%s], type - [%s]',\n trpt_eth.dst, trpt_eth.src, trpt_eth.type)\n return extract_int_data(trpt_eth)","def extract(soup):\r\n table = soup.find('div', id='dnn_ctr11396_TimeTableView_PlaceHolder').find('table')\r\n rows = table.findChildren('tr', recursive=False)\r\n return [[col.findAll('div', {'class': 'TTLesson'}) for col in row.findChildren('td', recursive=False)[1:]]\r\n for row in rows[1:]]","def read_triplets(seed_candidates):\n if \"pickle\" in seed_candidates:\n if \"*\" in seed_candidates:\n all_files = glob.glob(seed_candidates)\n new_data = []\n for file_name in all_files:\n with open(file_name, 'rb') as f:\n data = pickle.load(f)\n for dd in data:\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\n else:\n with open(seed_candidates, 'rb') as f:\n data = pickle.load(f)\n new_data = []\n for dd in data:\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\n # idx = int(dd[0][10:])\n # new_data.append((idx, dd[1], dd[2], dd[3]))\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\n else:\n column_names = ['evtid', 'h1', 'h2', 'h3']\n if \"*\" in seed_candidates:\n all_files = glob.glob(seed_candidates)\n new_data = []\n for file_name in all_files:\n df_seed_tmp = pd.read_csv(file_name, header=None, names=column_names,)\n new_data.append(df_seed_tmp)\n df_seed = pd.concat(new_data)\n else:\n df_seed = pd.read_csv(seed_candidates, header=None,\n names=column_names)\n return df_seed","def get_teacher_career_results(self, teacher, career):\n data = []\n\n # Get the active exams of the career.\n exams = EvaluationsExam.objects.filter(\n type__exact=career.type, status=\"ACTIVE\")\n\n # Get the results for each exam.\n for exam in exams:\n\n # Get the signatures of the teacher for the career in the exam.\n signatures_dtl = EvaluationsTeacherSignature.objects.filter(\n fk_teacher__exact=teacher.id, fk_period__exact=exam.fk_period, status=\"ACTIVE\").select_related('fk_signature')\n\n signatures_results = []\n for signature_dtl in signatures_dtl:\n \n # If it raise an exception, it means that the signature isn't evaluated yet or other error.\n try:\n # Get the results of the signature.\n signature_results = EvaluationsSignatureResult.objects.get(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n status=\"ACTIVE\"\n )\n\n # Get the results for each question in the exam for the signature.\n questions_results = EvaluationsSignatureQuestionResult.objects.filter(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n fk_question__optional='NO',\n status=\"ACTIVE\"\n ).values_list('fk_question__description', 'result')\n\n # Get the comments of the signature/group.\n comments_result = EvaluationsSignatureQuestionResult.objects.get(\n group=signature_dtl.group,\n fk_signature=signature_dtl.fk_signature.id,\n fk_exam=exam.id,\n fk_question__optional='YES',\n status=\"ACTIVE\"\n ).result\n\n # Split the comments and add them to a list, only the ones that are not empty.\n comments = list(filter(None, comments_result.split('|')))\n\n # Crate a dictionary with the results of the signature and the questions.\n signatures_results.append({\n 'teacher': teacher.name + ' ' + teacher.last_name + ' ' + teacher.last_name_2,\n 'signature': signature_dtl.fk_signature.description,\n 'group': signature_dtl.group,\n 'average': signature_results.average,\n 'comments': comments,\n 'total_evaluated': signature_results.total_evaluated,\n 'questions': questions_results\n })\n except Exception:\n pass\n\n # Add the results to the exam dictionary.\n exam_results = {\n 'exam': exam.description,\n 'career': career.description,\n 'signatures_results': signatures_results,\n 'period': exam.fk_period.period\n }\n\n # Add the exam results to the list that will be returned at the end.\n data.append(exam_results)\n\n return data","def extractDDE(self, lang, username, screenname, description, tweets):\n if isinstance(tweets, list):\n tweets = ' '.join(tweets)\n form = {\n 'lang': lang,\n 'username': username,\n 'screenname': screenname,\n 'description': description,\n 'tweet': tweets\n }\n return self.POST('extract', {}, form)","def get_quiz_teacher():\n quiz_data = query_db(\n \"SELECT id, name FROM quizzes WHERE creator_id=?;\", [flask.session[\"id\"]]\n )\n quizzes = []\n for quiz in quiz_data:\n quiz_dict = {}\n quiz_dict[\"id\"] = quiz[0]\n quiz_dict[\"name\"] = quiz[1]\n quizzes.append(quiz_dict)\n return quizzes","def decode(self, session, dev_example):\n unzipped_dev_example = list(zip(*dev_example))\n input_feed = self.create_feed_dict(unzipped_dev_example[0:4], dropout = 1)\n output_feed = [self.h_s, self.h_e, self.relevence]\n outputs = session.run(output_feed, input_feed)\n h_s = outputs[0]\n h_e = outputs[1]\n rel = outputs[2]\n return h_s, h_e, rel","def extract_unpack(self, args):\n return self.extract_features(args)","def load_tamper(self, dataset_dir, subset):\n # Add classes. We have one class.\n # Naming the dataset nucleus, and the class nucleus\n self.add_class(\"tampers\", 1, \"tampers\")\n\n # Which subset?\n # \"val\": use hard-coded list above\n # \"train\": use data from stage1_train minus the hard-coded list above\n # else: use the data from the specified sub-directory\n # assert subset in [\"train\", \"val\", \"stage1_train\", \"stage1_test\", \"stage2_test\"]\n # subset_dir = \"stage1_train\" if subset in [\"train\", \"val\"] else subset\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\n if subset == \"val\" or subset == \"test\":\n image_ids = next(os.walk(dataset_dir))[2]\n else:\n # Get image ids from directory names\n image_ids = next(os.walk(dataset_dir))[2]\n \n\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # dirnew_splicing = '/data/tamper'\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\n\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n \n # dircopy_move = '/data/gy/ca2att/train3'\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\n\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\n\n # dirnew_sp = '/data/gy/c2newsp/train'\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\n\n # Add images\n for image_id in image_ids:\n self.add_image(\n \"tampers\",\n image_id=image_id[:-4],\n path=os.path.join(dataset_dir, image_id))\n\n # for image_id in image_ids_copy_move:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dircopy_move, 'images', image_id))\n\n # for image_id in image_ids_new_splicing:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_splicing, 'images', image_id))\n\n # # for image_id in image_ids_txt_sp:\n # # self.add_image(\n # # \"tampers\",\n # # image_id=image_id[:-4],\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\n\n # for image_id in image_ids_new_sp:\n # self.add_image(\n # \"tampers\",\n # image_id=image_id[:-4],\n # path=os.path.join(dirnew_sp, 'images', image_id))","def inference(self):\r\n\t\tfor partition, loader in self.loaders.items():\r\n\t\t\tavg_loss, (y, y_hat), post, attentions, tags = self.eval_loader(\r\n\t\t\t\tloader)\r\n\t\t\tself.preds[partition] = {\r\n\t\t\t\t'tag': tags,\r\n\t\t\t\t'y': y,\r\n\t\t\t\t'y_hat': y_hat,\r\n\t\t\t\t# 'posteriors': post,\r\n\t\t\t\t# 'attentions': attentions\r\n\t\t\t}","def deepscore(teacher):\n if teacher == None:\n print(\"Not a valid teacher\")\n return\n if teacher.get('rmpdata') == None: snc.rateThisProfessor(teacher,instructors)\n print(\"# \" + teacher['name'])\n scoreTeacherlegacy(teacher)\n scoreTeacherues(teacher)\n scoreTeacherrmp(teacher)","def partition_dataset(data, labels, nb_teachers, teacher_id):\n\n # Sanity check\n assert (int(teacher_id) < int(nb_teachers))\n\n # This will floor the possible number of batches\n batch_len = int(len(data) / nb_teachers)\n\n # Compute start, end indices of partition\n start = teacher_id * batch_len\n end = (teacher_id + 1) * batch_len\n\n # Slice partition off\n partition_data = data[start:end]\n if labels is not None:\n partition_labels = labels[start:end]\n else:\n partition_labels = None\n\n return partition_data, partition_labels","def get_train(self, even=None):\n\n #self.images, self.labels, self.traces = trace_data.get_my_teacher()\n _, self.images, self.labels, self.traces, _ = trace_data.get_my_teacher()\n #print(self.labels)\n self.length = len(self.images)\n self.create_teacher()","def legacydatapoints(teacher):\n points = 0\n for surveytype in ['2','1','H']:\n for criteria in range(0,22):\n for degree in range(0,6):\n try:\n points += teacher['data'][surveytype][criteria]['degree' + str(degree)]\n except:\n pass\n for criteria in range(22,27):\n for degree in range(0,7):\n try:\n points += teacher['data'][surveytype][criteria]['degree' + str(degree)]\n except:\n pass\n return points","def get_teacher(teacher_account_id):\n query = 'SELECT * FROM teacher JOIN person ON teacher.teacher_account_id=person.account_id WHERE teacher.teacher_account_id=%s;'\n args = (teacher_account_id,)\n return database.connection.get_data(query, args)","def subjects_parser(root, study, outdir):\n # Welcome\n print(\"Starting subjects parsing...\")\n\n # Parameters\n subjects = defaultdict(\n lambda: defaultdict(dict))\n participants_file = os.path.join(root, \"participants.tsv\")\n\n # Parse the paticipants tsv file\n with open(participants_file) as csvfile:\n reader = csv.DictReader(csvfile, delimiter=\"\\t\")\n for row in reader:\n subject = row[\"participant_id\"].replace(\"sub-\", \"\")\n center = row.get(\"site\", DEFAULT_CENTER)\n subjects[center][subject] = {\n \"identifier\": md5_sum(subject),\n \"code_in_study\": subject,\n \"handedness\": row.get(\"handedness\", \"unknown\"),\n \"gender\": row.get(\"gender\", \"unknown\")}\n\n # Save the results\n print(\"Saving data in '{0}'...\".format(outdir))\n save_parsing(subjects, outdir, study, \"subjects\")\n\n # Goodbye\n print(\"Done.\")\n\n return subjects","def extract_testsuite(self, testsuite, info):\n\n for testcase in testsuite.getchildren():\n\n self.extract_testcase(testcase, info, testsuite.get(\"name\"))","def parse_pitcher(self, response):\n\n params = parse_qs(\n urlparse(response.url).query,\n keep_blank_values=True)\n\n data = {}\n data['year'] = params['year'][0]\n data['month'] = params['month'][0]\n data['day'] = params['day'][0]\n data['pitcher_id'] = params['pitchSel'][0]\n data['game_id'] = params['game'][0]\n\n # Find the raw html table link.\n xpath = '//a[text()=\"Get Expanded Tabled Data\"]'\n\n # Generate the url to open the raw html table page.\n sel = response.xpath(xpath)[0]\n\n link = urlparse(sel.xpath('@href').extract()[0])\n params = parse_qs(link.query, keep_blank_values=True)\n\n for key, value in params.items():\n params[key] = value[0]\n # Override the rows/column size just in case.\n params['h_size'] = 3000\n params['v_size'] = 3000\n\n url = self.base_url + link.path + \"?\" + urlencode(params)\n\n req = Request(url, callback=self.parse_pitcher_stats, meta={'data': data})\n\n yield req","def get_teacher_topic_all():\n topic_data = query_db(\n \"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \"\n \"ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n topics = []\n for topic in topic_data:\n topic_dict_teacher = {}\n topic_dict_teacher[\"id\"] = topic[0]\n topic_dict_teacher[\"name\"] = flask.escape(str(topic[1]))\n topic_dict_teacher[\"class\"] = flask.escape(str(topic[2]))\n topics.append(topic_dict_teacher)\n return topics","def get_data_loaders():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+5):]\n \n #history_complete.append(history)\n if len(persona) == 4:\n if len(history) > (len(persona)+3):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def test_practitioner_6(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f002-pv.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_6(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_6(inst2)","def teacher(self):\n if \"teacher\" in self._prop_dict:\n if isinstance(self._prop_dict[\"teacher\"], OneDriveObjectBase):\n return self._prop_dict[\"teacher\"]\n else :\n self._prop_dict[\"teacher\"] = EducationTeacher(self._prop_dict[\"teacher\"])\n return self._prop_dict[\"teacher\"]\n\n return None","def extract(src_dir,feat_file,ivectors_dir,num_gselect):\n os.system(\"./extract_ivectors.sh --num-gselect \"+str(num_gselect)+ \" \" + src_dir + \" \" + feat_file + \" \" + ivectors_dir)\n keys=[]\n ivectors=np.empty((0,0))\n for key,mat in kaldi_io.read_vec_flt_scp(ivectors_dir+'/ivector.scp'):\n if ivectors.shape[1] != mat.shape[0]:\n ivectors=ivectors.reshape((0,mat.shape[0]))\n ivectors=np.vstack((ivectors,mat))\n keys.append(key)\n\n ivectors=np.asarray(ivectors)\n keys=np.asarray(keys)\n return ivectors,keys","def extract_table_data(path):\n document = zipfile.ZipFile(path)\n xml_content = document.read('word/document.xml')\n document.close()\n tree = XML(xml_content)\n table = tree.find(BODY).find(TABLE)\n rows = table.findall(ROW)\n\n day = \"\"\n dates = \"\"\n teachers = {}\n\n for row in rows[1:]:\n cols = row.findall(COL)\n\n #Code\n code = extract_text(cols[0])\n\n #course\n course = extract_text(cols[1])\n\n #day\n dayTemp = extract_text(cols[2])\n if dayTemp != \"\":\n day = dayTemp\n\n #Calendar/Date\n temp_date = extract_date(cols[3])\n if len(temp_date) != 0:\n dates = temp_date\n\n #hour\n times = extract_time(cols[4])\n\n #Teachers\n teacher = extract_text(cols[5])\n\n if teacher == \"\":\n continue\n\n if teacher not in teachers:\n teachers[teacher] = Teacher(teacher)\n\n segment = Segment(code, course, day, dates, times)\n\n teachers[teacher].add_segment(segment)\n\n return teachers","def decomposeSeed( self, bSeed ):\n\n\t\ttry:\n\t\t\tbSeed = long( bSeed )\n\t\t\t\n\t\t\t# This is a copy of the functions in product-key/ck-common.c\n\t\t\t# They are rewritten here to save on shelling out with each\n\t\t\t# rda-backend that checks in.\n\t\t\tbSerial = self._getSerial( bSeed )\n\t\t\tsVersion = self._getVersion( bSeed )\n\t\t\tbNumcam = self._getNumcam( bSeed )\n\t\t\tsMac = self._getMac( bSeed )\n\n\t\t\treturn ( bSerial, sVersion, bNumcam, sMac )\n\n\t\texcept Exception, e:\n\t\t\terrMsg( 'error while decomposing key' )\n\t\t\traise","def extract_speaker_data(self, X, y):\n\n speaker_names = []\n global_idx = 0\n curr_speaker_num = -1\n old_speaker = ''\n\n # Crawl the base and all sub folders\n for root, directories, filenames in os.walk(self.base_folder):\n # Ignore crp and DOC folder\n if self.valid_speakers and os.path.split(root)[1] not in self.valid_speakers:\n continue\n\n # Check files\n for filename in [filename for filename in filenames if self.file_match_regex.search(filename)]:\n # Extract speaker\n speaker = os.path.split(root)[1]\n if speaker != old_speaker:\n curr_speaker_num += 1\n old_speaker = speaker\n speaker_names.append(speaker)\n print('Extraction progress: %d/%d' % (curr_speaker_num + 1, self.max_speakers))\n\n if curr_speaker_num < self.max_speakers:\n full_path = os.path.join(root, filename)\n global_idx += extract_mel_spectrogram(full_path, X, y, global_idx, curr_speaker_num)\n\n return X[0:global_idx], y[0:global_idx], speaker_names","def get_topics_for_alt(alt_list, pgm_dict):\n for epv in alt_list:\n name = epv.get('package', {}).get('name', [''])[0]\n if name:\n for pgm_pkg_key, pgm_list in pgm_dict.items():\n for pgm_epv in pgm_list:\n if name == pgm_epv.get('package_name', ''):\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\n\n return alt_list","def parse_identifying_data(path, passwd, embedded_file='participants.txt'):\n if path is not None:\n zf = zipfile.ZipFile(path)\n zf.setpassword(passwd)\n\n participants = {}\n for l in zf.read(embedded_file).splitlines():\n if l.startswith('#'):\n continue\n\n bc, name = l.strip().split('\\t')[:2]\n participants[bc] = name.replace(\",\", \"\")\n\n print \"Using identified data!\"\n else:\n participants = None\n\n return participants","def get_teacher():\n\n rows = db.engine.execute(f\"SELECT * FROM teacher_login WHERE loginid = {g.user.loginid}\")\n res = []\n for row in rows:\n res.append(dict(row))\n return jsonify(res)","def extract_pp(self):\n build([srna.ExtractPPW(fastq_dic=self.fastq_dic, num_cpus=self.num_cpus,\n indexfile=self.hisat_index, workdir=self.workdir,\n kingdom=self.kingdom)],\n local_scheduler=self.local_scheduler)","def extract(self, data):","def extract_speaker_data(self, X, y):\r\n\r\n speaker_names = []\r\n global_idx = 0\r\n curr_speaker_num = -1\r\n old_speaker = ''\r\n\r\n # Crawl the base and all sub folders\r\n for root, directories, filenames in os.walk(self.base_folder):\r\n\r\n # Ignore crp and DOC folder\r\n if root[-5:] not in self.valid_speakers:\r\n continue\r\n\r\n # Check files\r\n for filename in filenames:\r\n\r\n # Can't read the other wav files\r\n if '_RIFF.WAV' not in filename:\r\n continue\r\n\r\n # Extract speaker\r\n speaker = root[-5:]\r\n if speaker != old_speaker:\r\n curr_speaker_num += 1\r\n old_speaker = speaker\r\n speaker_names.append(speaker)\r\n print('Extraction progress: %d/%d' % (curr_speaker_num + 1, self.max_speakers))\r\n\r\n if curr_speaker_num < self.max_speakers:\r\n full_path = os.path.join(root, filename)\r\n global_idx += extract_mel_spectrogram(full_path, X, y, global_idx, curr_speaker_num)\r\n\r\n return X[0:global_idx], y[0:global_idx], speaker_names","def extract(self):\n pass","def get_test_loader(id_list = './data/sample_submission.csv', root_dir = './data/test/'):\n data = HumanProteinDataset(id_list, root_dir, transform = transforms.Compose([\n Rescale((256, 256)), \n ToTensor()\n ]))\n\n indices = np.arange(len(data))\n dataloader_test = DataLoader(data, batch_size=10, num_workers=5)\n\n return dataloader_test","def unpack(self) -> Tuple[list, list, list, list, float, int, list]:\n # (nice to have) todo:refactor --> as a namedtuple\n unpacked_super = super().unpack()\n\n observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght = unpacked_super\n\n return observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght, self.V_estimates","def _init_extractors(self):\n @self.extractors_wrapper(\"networkx\")\n def get_nx_extractor(graph):\n \"\"\"\n :param graph: networkx.Graph\n :returns: projx.nx_extractor\n \"\"\"\n return nx_xtrct.nx_extractor(\n self.extractor_json[self.extractor_name], graph\n )\n\n @self.extractors_wrapper(\"neo4j\")\n def get_neo4j_extractor(graph):\n \"\"\"\n :returns: projx.nx_extractor\n \"\"\"\n return neo4j_xtrct.neo4j_extractor(\n self.extractor_json[self.extractor_name], graph\n )\n\n @self.extractors_wrapper(\"edgelist\")\n def get_edgelist_extractor(graph):\n \"\"\"\n :returns: projx.nx_extractor\n \"\"\"\n return edgelist_xtrct.edgelist_extractor(\n self.extractor_json[self.extractor_name], graph\n )","def setup_experiment(testruns, droplist=\"\"):\n ex = Experiment()\n ex.addSoluFile(ALL_SOLU)\n\n regexlist = []\n for x in droplist.split(\",\"):\n # defaultvalue, if empty we don't want to exclude everything\n if x == \"\":\n continue\n try:\n y = re.compile(x)\n regexlist.append(y)\n except:\n pass\n\n excluded_inst = []\n # get data\n for t in testruns:\n # update representation\n additional_data = {\"RubberbandId\": get_rbid_representation(t, \"extended\")}\n\n # collect data and pass to ipet\n ipettestrun = TestRun()\n tr_raw_data = t.get_data(add_data=additional_data)\n\n tr_data = {}\n for i in tr_raw_data.keys():\n for r in regexlist:\n if r.match(i):\n excluded_inst.append(i)\n break\n else:\n tr_data[i] = tr_raw_data[i]\n\n ipettestrun.data = pd.DataFrame(tr_data).T\n\n ex.testruns.append(ipettestrun)\n return ex, excluded_inst","def extract_all_puzzles(self) -> List[Puzzle]:\n pages_urls = self.extract_pages()\n print(f\"\\n{pages_urls}\\n\")\n\n # Extract all puzzles\n puzzles = []\n for path in pages_urls:\n full_path = f\"{DOMAIN}{path}\"\n extracted_puzzle = self.extract_puzzle(full_path)\n puzzles.append(extracted_puzzle)\n # print(extracted_puzzle.title, extracted_puzzle.url, extracted_puzzle.description, extracted_puzzle.clues)\n print(extracted_puzzle.title)\n return puzzles","def unpackRecords(self,unpackTypes):\n for record in self.records:\n if record.name in unpackTypes:\n record.load(unpack=True)","def _forecasters(self):\n return self._get_estimator_tuples(self.forecasters, clone_ests=False)","def generate_teacher_family(p_adults, p_children):\n\tN_adults = np.random.choice(list(p_adults.keys()), p=list(p_adults.values()))\n\tN_children = np.random.choice(list(p_children[N_adults].keys()),\n\t\t\t\t\t\t\t\tp=list(p_children[N_adults].values()))\n\t\n\tages = np.random.randint(0, 18, N_children)\n\treturn ages, N_adults","def getTelemarketers():\n callers = getAllKnownCallers()\n callees = getAllKnownCallees()\n texters = getAllKnownTexters()\n textees = getAllKnownTextees()\n telemarketers = getKnownTelemarketers()\n\n # Remove known telemarketers from list\n t = set(callers).difference(telemarketers)\n # Remove known numbers that have received calls\n t.difference_update(callees)\n # Remove those who have sent a text\n t.difference_update(texters)\n # Remove those who have received a text\n t.difference_update(textees)\n # Join with known telemarketers\n t = set(telemarketers).union(t)\n\n return sorted(t)","def get_data_loaders_2sentences():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+1):]\n #history_complete.append(history)\n if len(history) > 4:\n history_chatbot = history[1::2]\n\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def test_practitioner_4(base_settings):\n filename = (\n base_settings[\"unittest_data_dir\"] / \"practitioner-example-xcda-author.json\"\n )\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_4(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_4(inst2)","def get_data_loaders_4sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"]\n #history_complete.append(history)\n if len(history_splitted) > (len(persona)-1):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def load_premiership_teams():\n # list of PremTeams to add\n team_list = [\n {'name': 'Arsenal', 'code': 'ARS', 'is_prem': True},\n {'name': 'Aston Villa', 'code': 'AVL', 'is_prem': True},\n {'name': 'Brighton and Hove Albion', 'code': 'BTN', 'is_prem': True},\n {'name': 'Brentford', 'code': 'BRE', 'is_prem': True},\n {'name': 'Bournemouth', 'code': 'BOU', 'is_prem': False},\n {'name': 'Burnley', 'code': 'BUR', 'is_prem': True},\n {'name': 'Cardiff City', 'code': 'CAR', 'is_prem': False},\n {'name': 'Chelsea', 'code': 'CHE', 'is_prem': True},\n {'name': 'Crystal Palace', 'code': 'CRY', 'is_prem': True},\n {'name': 'Everton', 'code': 'EVE', 'is_prem': True},\n {'name': 'Fulham', 'code': 'FUL', 'is_prem': False},\n {'name': 'Hull', 'code': 'HUL', 'is_prem': False},\n {'name': 'Huddersfield Town', 'code': 'HUD', 'is_prem': False},\n {'name': 'Leeds United', 'code': 'LEE', 'is_prem': True},\n {'name': 'Leicester City', 'code': 'LEI', 'is_prem': True},\n {'name': 'Liverpool', 'code': 'LIV', 'is_prem': True},\n {'name': 'Manchester City', 'code': 'MCY', 'is_prem': True},\n {'name': 'Manchester United', 'code': 'MUN', 'is_prem': True},\n {'name': 'Middlesbrough', 'code': 'MID', 'is_prem': False},\n {'name': 'Newcastle United', 'code': 'NEW', 'is_prem': True},\n {'name': 'Norwich City', 'code': 'NOR', 'is_prem': True},\n {'name': 'Queens Park Rangers', 'code': 'QPR', 'is_prem': False},\n {'name': 'Sheffield United', 'code': 'SHF', 'is_prem': False},\n {'name': 'Southampton', 'code': 'SOT', 'is_prem': True},\n {'name': 'Stoke City', 'code': 'STO', 'is_prem': False},\n {'name': 'Sunderland', 'code': 'SUN', 'is_prem': False},\n {'name': 'Swansea City', 'code': 'SWA', 'is_prem': False},\n {'name': 'Tottenham Hotspur', 'code': 'TOT', 'is_prem': True},\n {'name': 'Watford', 'code': 'WAT', 'is_prem': True},\n {'name': 'West Bromwich Albion', 'code': 'WBA', 'is_prem': False},\n {'name': 'West Ham United', 'code': 'WHM', 'is_prem': True},\n {'name': 'Wolverhampton Wanderers', 'code': 'WLV', 'is_prem': True},\n ]\n\n for team in team_list:\n print(PremTeam.objects.update_or_create(\n name=team['name'],\n code=team['code'],\n defaults={'is_prem': team['is_prem']}\n ))\n # print(pt, created)","def get_teacher(self) -> str :\n return self.teacher","def posters(self, imdb_id):\n return self.get_art(imdb_id, \"posters\")","def generate_teachers(G, school_type, N_classes, family_member_counter, \n\t\t\t\t\t family_counter, teacher_p_adults, teacher_p_children):\n\tN_teachers = get_N_teachers(school_type, N_classes)\n\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\n\tG.add_nodes_from(teacher_nodes)\n\t\n\tfor t in teacher_nodes:\n\t\tfamily_nodes = [t]\n\t\t# draw a random number of children and adults for the family\n\t\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\n\t\t\n\t\tages = list(ages)\n\t\tfor adult in range(N_adults - 1):\n\t\t\tages.append(20.5) # default age for adults\n\t\t\n\t\t# add the family member nodes and their attributes to the graph\n\t\tfor age in ages:\n\t\t\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\n\t\t\tfamily_nodes.append(family_member_ID)\n\t\t\tG.add_node(family_member_ID)\n\t\t\tfamily_member_counter += 1\n\t\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t\t{family_member_ID:{'type':'family_member',\n\t\t\t\t\t\t\t\t\t\t 'age':age,\n\t\t\t\t\t\t\t\t\t\t 'family':family_counter,\n\t\t\t\t\t\t\t\t\t\t 'unit':'family'}})\n\t\t\t\t\t\n\t\t# finally, also set the teacher's node attributes\n\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t{t:{'type':'teacher', \n\t\t\t\t\t\t# Note: 20.5 is the age at which\n\t\t\t\t\t\t# the symptom and transmission risk\n\t\t\t\t\t\t# is that of an adult\n\t\t\t\t\t\t'age':20.5,\n\t\t\t\t\t\t'unit':'faculty_room',\n\t\t\t\t\t\t'family':family_counter}})\n\t\tfamily_counter += 1","def get_participants_data(self):\n participants = []\n for (email, uid) in self.tokens.items():\n participant = {} \n participant['uid'] = uid\n participant['email'] = email\n response = 0\n questions = 0\n sections = [x for x in self.values() if ISurveySection.providedBy(x)]\n for section in sections:\n response += len(section.responses.get(uid, {}))\n questions += len(section.question_ids)\n if response != 0:\n participant['finished'] = Decimal(response) / Decimal(questions) * 100\n else:\n participant['finished'] = 0 \n participants.append(participant)\n return participants","def unpack(self):\n return []","def prepare_student_data(dataset, nb_teachers, save=False):\n assert input.create_dir_if_needed(FLAGS.train_dir)\n\n # Load the dataset\n if dataset == 'svhn':\n test_data, test_labels = input.ld_svhn(test_only=True)\n elif dataset == 'cifar10':\n test_data, test_labels = input.ld_cifar10(test_only=True)\n elif dataset == 'mnist':\n test_data, test_labels = input.ld_mnist(test_only=True)\n else:\n print(\"Check value of dataset flag\")\n return False\n\n # Make sure there is data leftover to be used as a test set\n assert FLAGS.stdnt_share < len(test_data)\n\n # Prepare [unlabeled] student training data (subset of test set)\n stdnt_data = test_data[:FLAGS.stdnt_share]\n\n # Compute teacher predictions for student training data\n teachers_preds = ensemble_preds(dataset, nb_teachers, stdnt_data)\n\n # Aggregate teacher predictions to get student training labels\n if not save:\n stdnt_labels = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale)\n else:\n # Request clean votes and clean labels as well\n stdnt_labels, clean_votes, labels_for_dump = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale, return_clean_votes=True) #NOLINT(long-line)\n\n # Prepare filepath for numpy dump of clean votes\n filepath = FLAGS.data_dir + \"/\" + str(dataset) + '_' + str(nb_teachers) + '_student_clean_votes_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)\n\n # Prepare filepath for numpy dump of clean labels\n filepath_labels = FLAGS.data_dir + \"/\" + str(dataset) + '_' + str(nb_teachers) + '_teachers_labels_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)\n\n # Dump clean_votes array\n with tf.gfile.Open(filepath, mode='w') as file_obj:\n np.save(file_obj, clean_votes)\n\n # Dump labels_for_dump array\n with tf.gfile.Open(filepath_labels, mode='w') as file_obj:\n np.save(file_obj, labels_for_dump)\n\n # Print accuracy of aggregated labels\n ac_ag_labels = metrics.accuracy(stdnt_labels, test_labels[:FLAGS.stdnt_share])\n print(\"Accuracy of the aggregated labels: \" + str(ac_ag_labels))\n\n # Store unused part of test set for use as a test set after student training\n stdnt_test_data = test_data[FLAGS.stdnt_share:]\n stdnt_test_labels = test_labels[FLAGS.stdnt_share:]\n\n if save:\n # Prepare filepath for numpy dump of labels produced by noisy aggregation\n filepath = FLAGS.data_dir + \"/\" + str(dataset) + '_' + str(nb_teachers) + '_student_labels_lap_' + str(FLAGS.lap_scale) + '.npy' #NOLINT(long-line)\n\n # Dump student noisy labels array\n with tf.gfile.Open(filepath, mode='w') as file_obj:\n np.save(file_obj, stdnt_labels)\n\n return stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels","def matlab_csv_to_teacher_data(dirname):\n samples = np.genfromtxt(os.path.join(dirname, 'samples.csv'), dtype=float,\n delimiter=\",\")\n labels = np.genfromtxt(os.path.join(dirname, 'labels.csv'), dtype=int,\n delimiter=\",\")\n data = [None]*max(labels) # matlab is 1-indexed, so no need to add 1\n for i, z in enumerate(labels):\n if data[z-1] is None:\n data[z-1] = np.copy(samples[i, :])\n else:\n data[z-1] = np.vstack((data[z-1], np.copy(samples[i, :])))\n return data","def ParsePassport(arr):\n\t# initialize new array for values to be copied to\n\tparsed_arr = []\n\tfor p in arr:\n\t\tparsed_arr.append(p[4:])\n\treturn parsed_arr","def applyDemapping(self):\n pass","def extract(self):\n self.build_path_pairs()\n self.extract_field_blocks()\n self.assert_filenames()","def extract_thread(self):\n \"TODO: check if hotel seletecd\"\n hotel_name = SelectableLabel.selected_hotel\n hlink = CityListView.hotels_data[hotel_name]\n self.extract_list, self.hotel_address = scrapper.scrap(\"http://www.booking.com/\"\n + scrapper.make_hotel_review_url(hlink))","def test_gathering_page_pdf_urls(\n lep_dl: LepDL,\n) -> None:\n json_test = \"\"\"\\\n [\n {\n \"episode\": 555,\n \"files\": {\n \"audios\": [],\n \"page_pdf\": [\"https://someurl555.local\"]\n },\n \"index\": 2022011303\n },\n {\n \"episode\": 554,\n \"files\": {\n \"audios\": [],\n \"page_pdf\": [\"https://someurl554.local1\", \"https://someurl554.local2\"]\n },\n \"index\": 2022011302\n },\n {\n \"episode\": 553,\n \"files\": {\n \"audios\": [],\n \"page_pdf\": [\n \"https://someurl553.local1\",\n \"https://someurl553.local2\",\n \"https://someurl553.local3\"\n ]\n },\n \"index\": 2022011302\n }\n ]\n \"\"\" # noqa: E501,B950\n db_episodes = Lep.extract_only_valid_episodes(json_test)\n lep_dl.files = downloader.gather_all_files(db_episodes)\n\n assert len(lep_dl.files) == 3\n\n assert lep_dl.files[0].primary_url == \"https://someurl553.local1\"\n assert lep_dl.files[0].secondary_url == \"https://someurl553.local2\"\n assert lep_dl.files[0].tertiary_url == \"https://someurl553.local3\"\n\n assert lep_dl.files[1].primary_url == \"https://someurl554.local1\"\n assert lep_dl.files[1].secondary_url == \"https://someurl554.local2\"\n\n assert lep_dl.files[2].primary_url == \"https://someurl555.local\"\n assert lep_dl.files[2].secondary_url == \"\"","def decompose_sample(lfads_hps, z):\n ib_dim = lfads_hps['ib_dim']\n ic_dim = lfads_hps['enc_dim']\n ib_k = z[:ib_dim]\n ic_j = z[ib_dim:(ib_dim+ic_dim)]\n ii_ti = z[(ib_dim+ic_dim):]\n ii_txi = np.reshape(ii_ti, (-1, lfads_hps['ii_dim']))\n return ib_k, ic_j, ii_txi","def extract_trials(self):\n self.logdir = os.path.expanduser(os.path.expandvars(self.logdir))\n logdir_content = os.listdir(self.logdir)\n # For the standard hypo_testing analysis, this logdir_content\n # will contain what we need it to and so we can proceed to\n # extract the trials.\n if self.test_type == 'analysis':\n if 'config_summary.json' in logdir_content:\n self.organise_trials(logdir_content=logdir_content)\n else:\n raise ValueError(\n 'config_summary.json cannot be found in the specified '\n 'logdir. It should have been created as part of the '\n 'output of hypo_testing.py and so this postprocessing '\n 'cannot be performed.'\n )\n elif self.test_type == 'injparamscan':\n pickle_there = self.check_pickle_files(logdir_content)\n if pickle_there:\n self.load_from_pickle()\n else:\n toy_names = []\n scan_variables = []\n for folder in logdir_content:\n if '.pckl' not in folder and 'Plots' not in folder:\n bits = folder.split('toy')[1].split('_')\n toy_name = bits[1]\n toy_names.append(\n toy_name\n )\n scan_variable = None\n add_bit = True\n for bit in bits:\n try:\n float(bit)\n add_bit = False\n except:\n if not (bit == '') and not (bit == toy_name):\n if add_bit:\n if scan_variable is None:\n scan_variable = bit\n else:\n scan_variable += '_%s'%bit\n scan_variables.append(scan_variable)\n toy_names = np.array(toy_names)\n scan_variables = np.array(scan_variables)\n # Require all to be the same injected truth model\n if not np.alltrue(toy_names == toy_names[0]):\n raise ValueError(\n 'Not all output is for the same injected truth '\n 'hypothesis. Got %s'%set(toy_names)\n )\n # Require all to be scanning the same variable\n if not np.alltrue(scan_variables == scan_variables[0]):\n raise ValueError(\n 'Not all output is for the same scanned parameter. '\n 'Got %s'%set(scan_variables)\n )\n self.labels = {}\n self.all_params = {}\n self.data_sets = {}\n self.minimiser_info = {}\n for scandir in logdir_content:\n if '.pckl' not in scandir and 'Plots' not in scandir:\n self.scandir = os.path.join(self.logdir, scandir)\n scandir_content = os.listdir(self.scandir)\n if 'config_summary.json' in scandir_content:\n self.extract_scans()\n else:\n raise ValueError(\n 'config_summary.json cannot be found in the '\n 'specified scandir, %s. It should have been '\n 'created as part of the output of '\n 'hypo_testing.py and so this postprocessing '\n 'cannot be performed.'%self.scandir\n )\n # Pickle at the end so all of the scans are in the output\n self.pickle_data()\n self.organise_scans()\n elif self.test_type == 'systtests':\n pickle_there = self.check_pickle_files(logdir_content)\n if pickle_there:\n self.load_from_pickle()\n else:\n self.labels = {}\n self.all_params = {}\n self.data_sets = {}\n for systdir in logdir_content:\n if '.pckl' not in systdir and 'Plots' not in systdir:\n self.systdir = os.path.join(self.logdir, systdir)\n systdir_content = os.listdir(self.systdir)\n if 'config_summary.json' in systdir_content:\n self.extract_systtests()\n else:\n raise ValueError(\n 'config_summary.json cannot be found in the '\n 'specified directory, %s. It should have been '\n 'created as part of the output of '\n 'hypo_testing.py and so this postprocessing '\n 'cannot be performed.'%self.systdir\n )\n # Pickle at the end so all of the truths/systematics\n # are in the output\n self.pickle_data()","def extract_features(extractor, loader, device, feature_transform=None):\r\n for batch_idx, data in enumerate(loader):\r\n with torch.no_grad():\r\n data = data.to(device).float()\r\n features = extractor(data)\r\n if feature_transform:\r\n features = feature_transform(features)\r\n yield features","def test_practitioner_7(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_7(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_7(inst2)","def getPUsers(self):\n model = self.tvPUsers.get_model()\n result = []\n model.foreach(lambda model, path, iter, data:\n result.append(model.get(iter, 0)[0]), None)\n result.sort()\n return result","def _unpack_ies(buf):\n\t\t# each IE starts with an ID and a length\n\t\ties = []\n\t\toff = 0\n\t\tbuflen = len(buf)\n\t\t# logger.debug(\"lazy dissecting: %s\" % buf)\n\n\t\twhile off < buflen:\n\t\t\tie_id = buf[off]\n\t\t\ttry:\n\t\t\t\tparser = IEEE80211.ie_decoder[ie_id]\n\t\t\texcept KeyError:\n\t\t\t\t# some unknown tag, use standard format\n\t\t\t\tparser = IEEE80211.IE\n\n\t\t\tdlen = buf[off + 1]\n\t\t\t# logger.debug(\"IE parser is: %d = %s = %s\" % (ie_id, parser, buf[off: off+2+dlen]))\n\t\t\tie = parser(buf[off: off + 2 + dlen])\n\t\t\ties.append(ie)\n\t\t\toff += 2 + dlen\n\n\t\treturn ies","def test_parse_trflp(self):\r\n\r\n data = \\\r\n \"\"\"\tBin (10bp)\tBin (20bp)\tBin (30bp)\tBin (40 bp)\r\nSamp-le 1\t1000\t2000\t3000\t4000\r\nSample 2\t\t2000\t3000\t4000\r\nSample 3\t\t\t3000\t4000\r\nSample 4\t\t\t\t4000\r\nSample 5\t25\t\t\t\"\"\"\r\n samples, otus, data = parse_trflp(data.split('\\n'))\r\n\r\n samples_exp = [\r\n 'Samp.le.1',\r\n 'Sample.2',\r\n 'Sample.3',\r\n 'Sample.4',\r\n 'Sample.5']\r\n otus_exp = ['Bin__10bp_', 'Bin__20bp_', 'Bin__30bp_', 'Bin__40_bp_']\r\n data_exp = array([[1000, 0, 0, 0, 25],\r\n [2000, 2000, 0, 0, 0],\r\n [3000, 3000, 3000, 0, 0],\r\n [4000, 4000, 4000, 4000, 0]])\r\n\r\n self.assertEqual(samples, samples_exp)\r\n self.assertEqual(otus, otus_exp)\r\n assert_almost_equal(data, data_exp)","def prepare_data(self,d):\n train_loaders, train_iters = {}, {}\n unlabeled_loaders, unlabeled_iters = {}, {}\n for domain in opt.domains:\n #CONVERT TO FLOAT32\n features, target = torch.from_numpy(d[domain].X.todense().astype('float32')), torch.from_numpy(d[domain].y)#.reshape((-1,1))\n train = data_utils.TensorDataset(features,target)\n train_loaders[domain] = DataLoader(train, opt.batch_size, shuffle = True)\n train_iters[domain] = iter(train_loaders[domain])\n for domain in opt.unlabeled_domains:\n features, target = torch.from_numpy(d[domain].X.todense().astype('float32')), torch.from_numpy(d[domain].y)#.reshape(-1,1))\n uset = data_utils.TensorDataset(features,target)\n unlabeled_loaders[domain] = DataLoader(uset,opt.batch_size, shuffle = True)\n unlabeled_iters[domain] = iter(unlabeled_loaders[domain])\n \n return train_loaders, train_iters, unlabeled_loaders, unlabeled_iters","def challenge23():\n seed = random.randint(1, 2 ** 31)\n orig_mt = MersenneTwister(seed)\n copy_mt = MersenneTwister(0)\n for index, p in enumerate(orig_mt.generate(624)):\n copy_mt.y[index] = untempering(p)\n return orig_mt, copy_mt","def populate_targets(prog):\n #proto = []\n #for b in prog.args.inputs:\n # for a in b:\n # if a not in proto:\n # proto += [ a ]\n for p in prog.args.inputs:\n t = Prog.Target(p.cert)\n tlsa = Prog.Tlsa(p.params, None, None, None)\n tlsa.publish = False\n # hack the tlsa.publish member to mean that the Tlsa object\n # corresponds to a specific record, as opposed to one set\n # by the config file, where tlsa.publish is set to 'True'.\n t.tlsa += [ tlsa ]\n prog.target_list += [ t ]\n return Prog.RetVal.ok","def extract(self, data):\n\n # Read authors from the cell with NAME and COLLABORATORS variables\n authors = []\n for cell in data['cells']:\n if cell['cell_type'] != 'code':\n continue\n\n src = cell['source']\n if src and src[0].startswith('NAME'):\n authors = [*self._extract_name(src, 'NAME'),\n *self._extract_name(src, 'COLLABORATORS')]\n break\n\n return [x for x in authors if x.name]","def parse(self, response):\n\n soup = self.get_soup(response.text)\n try:\n results = soup\\\n .find('table', {'id': 'searchResult'})\\\n .find_all('tr')[1:]\n except AttributeError:\n return\n\n for result in results:\n torrent = items.Torrent(spider=self.name)\n torrent['categories'] = [\n self._category_map.get(\n furl.furl(category.attrs['href']).path.segments[-1],\n items.TorrentCategory.Unknown\n ) for category in result.find(\n 'td', {'class': 'vertTh'}\n ).find_all('a')\n ]\n torrent['magnet'] = result.find(\n 'a', {'href': re.compile('^magnet\\:.*')}\n )['href']\n torrent['hash'] = re.match(\n r'.*magnet:\\?xt=urn:(?:btih)+:([a-zA-Z0-9]+).*',\n torrent['magnet']\n ).groups()[0].lower()\n (torrent['seeders'], torrent['leechers'],) = tuple([\n int(column.contents[0])\n for column in result.find_all('td', {'align': 'right'})\n ])\n\n result_links = result.find('a', {'class': 'detLink'})\n if 'href' in result_links.attrs:\n torrent['source'] = furl.furl(response.url).set(\n path=result_links.attrs['href'], args={}\n ).url\n\n torrent['name'] = result_links.contents[0].strip()\n\n result_desc = result.find('font', {'class': 'detDesc'})\n (time_content, size_content,) = \\\n result_desc.contents[0].split(',')[:2]\n torrent['uploaded'] = self.parse_datetime(\n time_content.split(' ')[-1],\n formats=[\n '%m-%d %Y',\n '%m-%d %H:%M',\n '%H:%M',\n 'Y-day %H:%M'\n ]\n )\n torrent['size'] = self.parse_size(\n size_content.split(' ')[-1]\n )\n\n try:\n torrent['uploader'] = result_desc.find(\n 'a', {'href': re.compile('^/user/.*')}\n ).contents[0]\n except AttributeError:\n pass\n\n yield torrent","def ptD(self):\n \n p = dict()\n for d, episode in enumerate(self.episodes):\n users, tempsU = episode[:,0], np.unique(episode[:,1]) # List of users of an episode and distinct\n p[d] = np.ones(self.nbUser) # time\n # A voir ******\n for t in range(1, len(tempsU)): # For each time tU of the episode D\n ptd, hasPred = 1., False\n # Peut être parcourir seulement les users seraient plus interessants\n for u, user in enumerate(users):\n predU = episode[episode[:,1] < tempsU[t]][:,0] # List of predecessors of user u at time tU\n for v in predU: # Proba que ça ne soit aucun des predecesseurs \n if v in self.predecessors[user]: # qui l'infectent\n ptd *= (1 - self.theta[v][user])\n hasPred = True\n if hasPred:\n p[d][u] = 1-ptd # Proba que ça soit l'un deux.\n\n return p","def test_read_participants_data():\n bids_root = _TempDir()\n bids_path = _bids_path.copy().update(root=bids_root, datatype='meg')\n raw = _read_raw_fif(raw_fname, verbose=False)\n\n # if subject info was set, we don't roundtrip birthday\n # due to possible anonymization in mne-bids\n subject_info = {\n 'hand': 1,\n 'sex': 2,\n }\n raw.info['subject_info'] = subject_info\n write_raw_bids(raw, bids_path, overwrite=True, verbose=False)\n raw = read_raw_bids(bids_path=bids_path)\n print(raw.info['subject_info'])\n assert raw.info['subject_info']['hand'] == 1\n assert raw.info['subject_info']['sex'] == 2\n assert raw.info['subject_info'].get('birthday', None) is None\n\n # if modifying participants tsv, then read_raw_bids reflects that\n participants_tsv_fpath = op.join(bids_root, 'participants.tsv')\n participants_tsv = _from_tsv(participants_tsv_fpath)\n participants_tsv['hand'][0] = 'n/a'\n _to_tsv(participants_tsv, participants_tsv_fpath)\n raw = read_raw_bids(bids_path=bids_path)\n assert raw.info['subject_info']['hand'] == 0\n assert raw.info['subject_info']['sex'] == 2\n assert raw.info['subject_info'].get('birthday', None) is None\n\n # make sure things are read even if the entries don't make sense\n participants_tsv = _from_tsv(participants_tsv_fpath)\n participants_tsv['hand'][0] = 'righty'\n participants_tsv['sex'][0] = 'malesy'\n _to_tsv(participants_tsv, participants_tsv_fpath)\n with pytest.warns(RuntimeWarning, match='Unable to map'):\n raw = read_raw_bids(bids_path=bids_path)\n assert raw.info['subject_info']['hand'] is None\n assert raw.info['subject_info']['sex'] is None\n\n # make sure to read in if no participants file\n raw = _read_raw_fif(raw_fname, verbose=False)\n write_raw_bids(raw, bids_path, overwrite=True, verbose=False)\n os.remove(participants_tsv_fpath)\n with pytest.warns(RuntimeWarning, match='Participants file not found'):\n raw = read_raw_bids(bids_path=bids_path)\n assert raw.info['subject_info'] is None","def extractor(self):\n \n if self._extractor is None:\n if self.extractor_type == '29v2':\n self._extractor = self.build_extractor_29layers_v2(name='extract29v2', block=self._res_block, layers=[1, 2, 3, 4])\n elif self.extractor_type == '29':\n self._extractor = self.build_extractor_29layers(name='extract29', block=self._res_block, layers=[1, 2, 3, 4])\n elif self.extractor_type == '9':\n self._extractor = self.build_extractor_9layers(name='extract9')\n \n if self.extractor_weights is not None:\n self._extractor.load_weights(self.extractor_weights)\n \n return self._extractor","def getDetailedSpeakers(self, speakers):\n\n new_speakers = []\n try:\n for speaker in speakers:\n for user in self.users_data:\n if speaker[\"username\"] == user[\"username\"]:\n new_speakers.append(user)\n return new_speakers\n except KeyError as e:\n print(e)\n return \"Invalid\"","def test_NeurosynthDecoder_featuregroup(testdata_laird):\n ids = testdata_laird.ids[:5]\n decoder = discrete.NeurosynthDecoder(feature_group=\"Neurosynth_TFIDF\")\n decoder.fit(testdata_laird)\n decoded_df = decoder.transform(ids=ids)\n assert isinstance(decoded_df, pd.DataFrame)","def test_ipv4v6_secondary_pdn_with_ded_bearer_multi_ue(self):\n num_ues = 4\n ue_ids = []\n default_ip = []\n sec_ip_ipv4 = []\n sec_ip_ipv6 = []\n\n # APN of the secondary PDN\n ims_apn = {\n \"apn_name\": \"ims\", # APN-name\n \"qci\": 5, # qci\n \"priority\": 15, # priority\n \"pre_cap\": 0, # preemption-capability\n \"pre_vul\": 0, # preemption-vulnerability\n \"mbr_ul\": 200000000, # MBR UL\n \"mbr_dl\": 100000000, # MBR DL\n \"pdn_type\": 2, # PDN Type 0-IPv4,1-IPv6,2-IPv4v6\n }\n # UL Flow description #1\n ulFlow1 = {\n \"ipv4_dst\": \"192.168.129.42\", # IPv4 destination address\n \"tcp_dst_port\": 5002, # TCP dest port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.UPLINK, # Direction\n }\n\n # UL Flow description #2\n ulFlow2 = {\n \"ipv4_dst\": \"192.168.129.42\", # IPv4 destination address\n \"tcp_dst_port\": 5001, # TCP dest port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.UPLINK, # Direction\n }\n\n # UL Flow description #3\n ulFlow3 = {\n \"ipv6_dst\": \"5e90:db7b:b18e::1556\", # IPv6 destination address\n \"tcp_dst_port\": 5003, # TCP dest port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.UPLINK, # Direction\n }\n\n # DL Flow description #1\n dlFlow1 = {\n \"ipv4_src\": \"192.168.129.42\", # IPv4 source address\n \"tcp_src_port\": 5001, # TCP source port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.DOWNLINK, # Direction\n }\n\n # DL Flow description #2\n dlFlow2 = {\n \"ipv4_src\": \"192.168.129.64\", # IPv4 source address\n \"tcp_src_port\": 5002, # TCP source port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.DOWNLINK, # Direction\n }\n\n # DL Flow description #3\n dlFlow3 = {\n \"ipv6_src\": \"6e31:1a95:1e7c::df1\", # IPv6 source address\n \"tcp_src_port\": 5003, # TCP source port\n \"ip_proto\": FlowMatch.IPPROTO_TCP, # Protocol Type\n \"direction\": FlowMatch.DOWNLINK, # Direction\n }\n\n # Flow lists to be configured\n flow_list = [\n ulFlow1,\n ulFlow2,\n ulFlow3,\n dlFlow1,\n dlFlow2,\n dlFlow3,\n ]\n\n # QoS\n qos = {\n \"qci\": 1, # qci value [1 to 9]\n \"priority\": 1, # Range [0-255]\n \"max_req_bw_ul\": 10000000, # MAX bw Uplink\n \"max_req_bw_dl\": 15000000, # MAX bw Downlink\n \"gbr_ul\": 1000000, # GBR Uplink\n \"gbr_dl\": 2000000, # GBR Downlink\n \"arp_prio\": 1, # ARP priority\n \"pre_cap\": 1, # pre-emption capability\n \"pre_vul\": 1, # pre-emption vulnerability\n }\n\n policy_id = \"ims\"\n\n self._s1ap_wrapper.configUEDevice(num_ues)\n for i in range(num_ues):\n req = self._s1ap_wrapper.ue_req\n ue_id = req.ue_id\n\n apn_list = [ims_apn]\n self._s1ap_wrapper.configAPN(\n \"IMSI\" + \"\".join([str(i) for i in req.imsi]), apn_list,\n )\n print(\n \"*********************** Running End to End attach for UE id \",\n ue_id,\n )\n\n print(\"***** Sleeping for 5 seconds\")\n time.sleep(5)\n # Attach\n attach = self._s1ap_wrapper.s1_util.attach(\n ue_id,\n s1ap_types.tfwCmd.UE_END_TO_END_ATTACH_REQUEST,\n s1ap_types.tfwCmd.UE_ATTACH_ACCEPT_IND,\n s1ap_types.ueAttachAccept_t,\n )\n addr = attach.esmInfo.pAddr.addrInfo\n default_ip.append(ipaddress.ip_address(bytes(addr[:4])))\n ue_ids.append(ue_id)\n\n # Wait on EMM Information from MME\n self._s1ap_wrapper._s1_util.receive_emm_info()\n\n print(\"***** Sleeping for 5 seconds\")\n time.sleep(5)\n self._s1ap_wrapper._ue_idx = 0\n for i in range(num_ues):\n req = self._s1ap_wrapper.ue_req\n ue_id = req.ue_id\n\n apn = \"ims\"\n # PDN Type 2 = IPv6, 3 = IPv4v6\n pdn_type = 3\n # Send PDN Connectivity Request\n self._s1ap_wrapper.sendPdnConnectivityReq(\n ue_id, apn, pdn_type=pdn_type,\n )\n # Receive PDN CONN RSP/Activate default EPS bearer context request\n response = self._s1ap_wrapper.s1_util.get_response()\n assert response.msg_type == s1ap_types.tfwCmd.UE_PDN_CONN_RSP_IND.value\n act_def_bearer_req = response.cast(s1ap_types.uePdnConRsp_t)\n\n addr = act_def_bearer_req.m.pdnInfo.pAddr.addrInfo\n sec_ip_ipv4.append(ipaddress.ip_address(bytes(addr[8:12])))\n\n print(\n \"********************** Sending Activate default EPS bearer \"\n \"context accept for APN-%s, UE id-%d\" % (apn, ue_id),\n )\n print(\n \"********************** Added default bearer for apn-%s,\"\n \" bearer id-%d, pdn type-%d\"\n % (\n apn,\n act_def_bearer_req.m.pdnInfo.epsBearerId,\n pdn_type,\n ),\n )\n\n # Receive Router Advertisement message\n response = self._s1ap_wrapper.s1_util.get_response()\n assert response.msg_type == s1ap_types.tfwCmd.UE_ROUTER_ADV_IND.value\n routerAdv = response.cast(s1ap_types.ueRouterAdv_t)\n print(\n \"******************* Received Router Advertisement for APN-%s\"\n \" ,bearer id-%d\" % (apn, routerAdv.bearerId),\n )\n\n ipv6_addr = \"\".join([chr(i) for i in routerAdv.ipv6Addr]).rstrip(\n \"\\x00\",\n )\n print(\"******* UE IPv6 address: \", ipv6_addr)\n sec_ip_ipv6.append(ipaddress.ip_address(ipv6_addr))\n\n print(\"***** Sleeping for 5 seconds\")\n time.sleep(5)\n\n # Add dedicated bearer\n print(\"********************** Adding dedicated bearer to ims PDN\")\n print(\n \"********************** Sending RAR for IMSI\",\n \"\".join([str(i) for i in req.imsi]),\n )\n self._sessionManager_util.send_ReAuthRequest(\n \"IMSI\" + \"\".join([str(i) for i in req.imsi]),\n policy_id,\n flow_list,\n qos,\n )\n response = self._s1ap_wrapper.s1_util.get_response()\n assert response.msg_type == s1ap_types.tfwCmd.UE_ACT_DED_BER_REQ.value\n act_ded_ber_req_ims_apn = response.cast(\n s1ap_types.UeActDedBearCtxtReq_t,\n )\n self._s1ap_wrapper.sendActDedicatedBearerAccept(\n req.ue_id, act_ded_ber_req_ims_apn.bearerId,\n )\n print(\n \"************* Added dedicated bearer\",\n act_ded_ber_req_ims_apn.bearerId,\n )\n\n print(\"***** Sleeping for 10 seconds\")\n time.sleep(10)\n # ipv4 default pdn + ipv4v6(ims) pdn +\n # dedicated bearer for ims pdn for 4 UEs\n num_ul_flows = 12\n for i in range(num_ues):\n dl_flow_rules = {\n default_ip[i]: [],\n sec_ip_ipv4[i]: [flow_list],\n sec_ip_ipv6[i]: [flow_list],\n }\n # Verify if flow rules are created\n self._s1ap_wrapper.s1_util.verify_flow_rules(\n num_ul_flows, dl_flow_rules,\n )\n\n print(\"***** Sleeping for 5 seconds\")\n time.sleep(5)\n for ue in ue_ids:\n print(\n \"******************* Running UE detach (switch-off) for \",\n \"UE id \",\n ue,\n )\n # Now detach the UE\n self._s1ap_wrapper.s1_util.detach(\n ue, s1ap_types.ueDetachType_t.UE_SWITCHOFF_DETACH.value, False,\n )","def test_parse_trflp_headerless(self):\r\n\r\n data = \\\r\n \"\"\"Samp-le 1\t1000\t2000\t3000\t4000\r\nSample 2\t\t2000\t3000\t4000\r\nSample 3\t\t\t3000\t4000\r\nSample 4\t\t\t\t4000\r\nSample_5__\t25\t\t\t\"\"\"\r\n samples, otus, data = parse_trflp(data.split('\\n'))\r\n\r\n samples_exp = [\r\n 'Samp.le.1',\r\n 'Sample.2',\r\n 'Sample.3',\r\n 'Sample.4',\r\n 'Sample.5..']\r\n otus_exp = ['Bin__0', 'Bin__1', 'Bin__2', 'Bin__3']\r\n data_exp = array([[1000, 0, 0, 0, 25],\r\n [2000, 2000, 0, 0, 0],\r\n [3000, 3000, 3000, 0, 0],\r\n [4000, 4000, 4000, 4000, 0]])\r\n\r\n self.assertEqual(samples, samples_exp)\r\n self.assertEqual(otus, otus_exp)\r\n assert_almost_equal(data, data_exp)","def get_teacher_class():\n class_data = query_db(\n \"SELECT id, name FROM classes WHERE teacher_id = ?;\", [flask.session[\"id\"]]\n )\n classes = []\n for part in class_data:\n class_dict = {}\n class_dict[\"id\"] = part[0]\n class_dict[\"name\"] = str(part[1])\n classes.append(class_dict)\n return classes","def scrape(self):\n locations = (\n 'Half Moon Bay, California',\n 'Huntington Beach, California',\n 'Providence, Rhode Island',\n 'Wrightsville Beach, North Carolina',\n )\n for location in locations:\n tides = self.extract_tides(location)\n print('\\nLocation:', location)\n for dt, tide_rows in tides.items():\n for tide_row in tide_rows:\n print(dt, tide_row)","def unpack_data(self, data):\n\n datadict = {'filenames': [], 'features': [], 'labels': [] }\n\n for l in data:\n \n datadict['filenames'].append(l[0])\n datadict['features'].append(l[1])\n datadict['labels'].append(l[2])\n \n return datadict","def test_practitioner_5(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f003-mv.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_5(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_5(inst2)","def test_practitioner_9(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"practitioner-example-f204-ce.json\"\n inst = practitioner.Practitioner.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Practitioner\" == inst.resource_type\n\n impl_practitioner_9(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Practitioner\" == data[\"resourceType\"]\n\n inst2 = practitioner.Practitioner(**data)\n impl_practitioner_9(inst2)","def prep_steps(self, *, yearmon: str) -> List[Step]:\n steps = []\n\n year, month = dates.parse_yearmon(yearmon)\n\n monthly_vars = ['2m_temperature', 'total_precipitation']\n hourly_vars = ['total_precipitation']\n\n steps += era5.download(output_filename=era5.filename(self.source, 'month', yearmon),\n duration='month',\n yearmon=yearmon,\n variables=monthly_vars)\n\n if year >= 1979:\n steps += era5.download(output_filename=era5.filename(self.source, 'hour', yearmon),\n duration='hour',\n yearmon=yearmon,\n variables=hourly_vars)\n steps += era5.calc_wetdays(input_filename=era5.filename(self.source, 'hour', yearmon),\n output_filename=self.p_wetdays(yearmon=yearmon).file,\n threshold_mm=0.1)\n\n return steps","def _unpack(self, headerBytes):\n xtraH = struct.unpack(self.PACKAGING_FORMAT, headerBytes)\n\n self.qubit_id = xtraH[0]\n self.remote_app_id = xtraH[1]\n self.remote_node = xtraH[2]\n self.datetime = xtraH[3]\n self.remote_port = xtraH[4]\n self.outcome = xtraH[5]"],"string":"[\n \"def load_teacher_experiment(self, exp: Experiment):\\n _, _, extra = load_experiment(exp)\\n self.unpack_teachers(extra)\",\n \"def prune_teachers(self):\\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]\",\n \"def get_teacher_assign():\\n assignment_data = query_db(\\n \\\"SELECT assignments.id, assignments.name, assignments.due_date \\\"\\n \\\"FROM assignments JOIN topics ON assignments.topic_id=topics.id \\\"\\n \\\"JOIN classes ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n assignments = []\\n for assignment in assignment_data:\\n assignment_dict_teach = {}\\n assignment_dict_teach[\\\"id\\\"] = assignment[0]\\n assignment_dict_teach[\\\"name\\\"] = assignment[1]\\n assignment_dict_teach[\\\"due_date\\\"] = assignment[2]\\n assignments.append(assignment_dict_teach)\\n return assignments\",\n \"def test_pytd_teacher(self):\\n defaults = parser_defaults.copy()\\n defaults['datatype'] = 'train:stream'\\n defaults['image_mode'] = 'ascii'\\n\\n with testing_utils.capture_output():\\n # Get processed act from agent\\n parser = display_setup_args()\\n defaults['pytorch_teacher_dataset'] = 'flickr30k'\\n del defaults['pytorch_teacher_task']\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n teacher = create_task_agent_from_taskname(opt)[0]\\n pytorch_teacher_act = teacher.act()\\n\\n parser = display_setup_args()\\n defaults['task'] = 'flickr30k'\\n del defaults['pytorch_teacher_dataset']\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n teacher = create_task_agent_from_taskname(opt)[0]\\n regular_teacher_act = teacher.act()\\n\\n keys = set(pytorch_teacher_act.keys()).intersection(\\n set(regular_teacher_act.keys()))\\n self.assertTrue(len(keys) != 0)\\n for key in keys:\\n self.assertTrue(pytorch_teacher_act[key] == regular_teacher_act[key],\\n 'PytorchDataTeacher does not have the same value '\\n 'as regular teacher for act key: {}'.format(key))\",\n \"def load_teachers(self):\\n # Get the experiment's directory to load from\\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\\n self.load_teacher_experiment(ex_dir)\\n if len(self.teacher_policies) < self.num_teachers:\\n print(\\n f\\\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\\\"\\n )\\n self.load_teachers()\",\n \"def load_primers(tsv_filename):\\n answer = []\\n with open(tsv_filename) as handle:\\n for line in handle:\\n if line.startswith(\\\"#\\\"):\\n continue\\n parts = line.rstrip(\\\"\\\\n\\\").split(\\\"\\\\t\\\")\\n if len(parts) == 2:\\n left, right = parts\\n name = f\\\"P{len(answer)}\\\"\\n else:\\n name, left, right = parts[:3]\\n answer.append((name, left, right))\\n return answer\",\n \"def test_pyt_multitask(self):\\n\\n def run_display_test(defaults, ep_and_ex_counts):\\n with testing_utils.capture_output() as f:\\n parser = display_setup_args()\\n parser.set_defaults(**defaults)\\n opt = parser.parse_args()\\n display_data(opt)\\n str_output = f.getvalue()\\n self.assertTrue(\\n '[ loaded {} episodes with a total of {} examples ]'.format(\\n ep_and_ex_counts[0], ep_and_ex_counts[1]\\n ) in str_output,\\n 'PytorchDataTeacher multitasking failed with '\\n 'following args: {}'.format(opt)\\n )\\n\\n task1 = 'babi:task1k:1'\\n task2 = 'babi:task1k:2'\\n dataset1 = 'flickr30k'\\n dataset2 = 'vqa_v1'\\n\\n # Expected example and episode counts\\n eps_and_exs_counts = [\\n (1800, 1800),\\n (1080, 1800),\\n (29900, 29900),\\n (29180, 29900),\\n (277349, 277349)\\n ]\\n defaults = parser_defaults.copy()\\n\\n # 1.\\n defaults['pytorch_teacher_task'] = '{},{}'.format(task1, task2)\\n run_display_test(defaults, eps_and_exs_counts[0])\\n\\n # 2.\\n defaults['pytorch_teacher_task'] = task1\\n defaults['task'] = task2\\n run_display_test(defaults, eps_and_exs_counts[1])\\n\\n # 3.\\n del defaults['task']\\n defaults['pytorch_teacher_dataset'] = dataset1\\n run_display_test(defaults, eps_and_exs_counts[2])\\n\\n # 4.\\n del defaults['pytorch_teacher_task']\\n defaults['task'] = task1\\n run_display_test(defaults, eps_and_exs_counts[3])\\n\\n # 5.\\n del defaults['task']\\n defaults['pytorch_teacher_dataset'] = '{},{}'.format(dataset1, dataset2)\\n run_display_test(defaults, eps_and_exs_counts[4])\",\n \"def get_teacher(self, **fields):\\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\\n teacher_fields = {}\\n for key, value in fields.items():\\n if key in existing_fields:\\n teacher_fields[key] = value\\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\\n # Expect single value if search by unique fields, list if by non-unique\\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None\",\n \"def get_all_profesors(self) -> List[Teacher]:\\n self.cursor.execute(\\n f\\\"SELECT * FROM {self.table_name}\\\")\\n \\n teachers = []\\n for teacher in self.cursor.fetchall():\\n teacher_parsed = list(teacher[0:8]) + [json.loads(t) for t in teacher[8:]]\\n teachers.append(Teacher.parse_tuple(teacher_parsed))\\n \\n return teachers\",\n \"def ensemble_preds(dataset, nb_teachers, stdnt_data):\\n\\n # Compute shape of array that will hold probabilities produced by each\\n # teacher, for each training point, and each output class\\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\\n\\n # Create array that will hold result\\n result = np.zeros(result_shape, dtype=np.float32)\\n\\n # Get predictions from each teacher\\n for teacher_id in xrange(nb_teachers):\\n # Compute path of checkpoint file for teacher model with ID teacher_id\\n if FLAGS.deeper:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\\n else:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\\n\\n # Get predictions on our training data and store in result array\\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\\n\\n # This can take a while when there are a lot of teachers so output status\\n print(\\\"Computed Teacher \\\" + str(teacher_id) + \\\" softmax predictions\\\")\\n\\n return result\",\n \"def multiple_matlab_csv_to_teacher_data(short_runs_dirname):\\n subdirname = 'Run-'\\n data = None\\n data_length = 0\\n for i in range(10):\\n dirname = os.path.join(short_runs_dirname, subdirname+str(i+1))\\n run_data = matlab_csv_to_teacher_data(dirname)\\n if i == 0:\\n data = run_data\\n else:\\n for i, phoneme_data in enumerate(run_data):\\n data[i] = np.vstack((data[i], phoneme_data))\\n\\n data_length += run_data[0].shape[0]\\n\\n for i, phoneme_data in enumerate(data):\\n assert phoneme_data.shape[0] == data_length\\n\\n return data\",\n \"def get_teachers(self):\\n query = Teacher.all().order('teacher')\\n return query.fetch()\",\n \"def load_unpacker_dataset(sentences):\\n return TFRecordDataset([path.join(TFRUDIR, sentence+'.tfr')\\n for sentence in sentences])\\\\\\n .map(\\n lambda record: \\\\\\n tf.parse_single_example(\\n record,\\n features={\\n 's': tf.FixedLenFeature([], tf.string),\\n 'l': tf.FixedLenFeature([NL], tf.float32),\\n 't': tf.FixedLenFeature([NT], tf.float32)\\n }\\n )\\n )\\\\\\n .map(\\n lambda feature: (feature['l'], feature['s'], feature['t'])\\n )\",\n \"def train_teacher (nb_teachers, teacher_id):\\n # Load the dataset\\n X_train, X_test, y_train, y_test = models.get_dataset()\\n\\n print(X_train.shape)\\n print(y_train.shape)\\n print(X_test.shape)\\n print(y_test.shape)\\n \\n # Retrieve subset of data for this teacher\\n data, labels = partition.partition_dataset(X_train,\\n y_train,\\n nb_teachers,\\n teacher_id)\\n\\n print(\\\"Length of training data: \\\" + str(len(labels)))\\n\\n # Define teacher checkpoint filename and full path\\n\\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\\n \\n # Perform teacher training need to modify \\n \\n\\n # Create teacher model\\n model, opt = models.create_two_layer_mlp(46) # num of cols\\n model.compile(loss='binary_crossentropy',\\n optimizer=\\\"Adam\\\",\\n metrics=['accuracy'])\\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\\n\\n #modify\\n model_json = model.to_json()\\n with open(\\\"model.json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n# serialize weights to HDF5\\n model.save_weights(filename2)\\n print(\\\"Saved model to disk\\\")\\n return True\",\n \"def extract_tubelets(dname, gpu=-1, redo=False):\\n d = GetDataset(dname)\\n\\n if gpu >= 0:\\n caffe.set_mode_gpu()\\n caffe.set_device(gpu)\\n\\n model_dir = os.path.join(os.path.dirname(__file__), '../models/ACT-detector/', dname)\\n output_dir = os.path.join(os.path.dirname(__file__), '../results/ACT-detector/', dname)\\n \\n # load the RGB network\\n rgb_proto = os.path.join(model_dir, \\\"deploy_RGB.prototxt\\\")\\n rgb_model = os.path.join(model_dir, \\\"../generated_AVA_iter_118662.caffemodel\\\")\\n net_rgb = caffe.Net(rgb_proto, caffe.TEST, weights=rgb_model)\\n \\n # load the FLOW5 network\\n flo_proto = os.path.join(model_dir, \\\"deploy_FLOW5.prototxt\\\")\\n flo_model = os.path.join(model_dir, \\\"../generated_AVA_iter_59463.caffemodel\\\")\\n net_flo = caffe.Net(flo_proto, caffe.TEST, weights=flo_model)\\n\\n vlist = d.test_vlist()\\n for iv, v in enumerate(vlist):\\n print(\\\"Processing video {:d}/{:d}: {:s}\\\".format( iv+1, len(vlist), v))\\n h, w = d.resolution(v)\\n \\n # network output is normalized between 0,1 ; so we will multiply it by the following array\\n resolution_array = np.array([w,h,w,h]*K, dtype=np.float32)\\n \\n # now process each frame\\n for i in xrange(1, 1 + d.nframes(v) - K + 1):\\n outfile = os.path.join(output_dir, d.frame_format(v,i) + \\\".pkl\\\")\\n \\n # skip if already computed\\n if os.path.isfile(outfile) and not redo:\\n continue\\n \\n # read the frames for the forward\\n kwargs_rgb = {}\\n kwargs_flo = {}\\n for j in xrange(K):\\n cap = cv2.VideoCapture(d.vidfile(v,0))\\n #print(frame)\\n #print(int(cap.get(7)))\\n cap.set(1,i + j - 1)\\n im = cap.read()[1]\\n cap.release()\\n #im = cv2.imread(d.imfile(v, i + j))\\n if im is None:\\n print \\\"Image {:s} does not exist\\\".format(d.imfile(v, i+j))\\n return\\n imscale = cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR)\\n kwargs_rgb['data_stream' + str(j)] = np.transpose(imscale-MEAN, (2, 0, 1))[None, :, :, :]\\n imf = [cv2.imread(d.flowfile(v.split(\\\".\\\")[0], min(d.nframes(v), i + j + iflow))) for iflow in xrange(NFLOWS)]\\n if np.any(imf) is None:\\n print \\\"Flow image {:s} does not exist\\\".format(d.flowfile(v, i+j))\\n return\\n imscalef = [cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR) for im in imf]\\n timscale = [np.transpose(im-MEAN, (2, 0, 1))[None, :, :, :] for im in imscalef]\\n kwargs_flo['data_stream' + str(j) + 'flow'] = np.concatenate(timscale, axis=1)\\n \\n # compute rgb and flow scores\\n # two forward passes: one for the rgb and one for the flow \\n net_rgb.forward(end=\\\"mbox_conf_flatten\\\", **kwargs_rgb) # forward of rgb with confidence and regression\\n net_flo.forward(end=\\\"mbox_conf_flatten\\\", **kwargs_flo) # forward of flow5 with confidence and regression\\n \\n # compute late fusion of rgb and flow scores (keep regression from rgb)\\n # use net_rgb for standard detections, net_flo for having all boxes\\n scores = 0.5 * (net_rgb.blobs['mbox_conf_flatten'].data + net_flo.blobs['mbox_conf_flatten'].data)\\n net_rgb.blobs['mbox_conf_flatten'].data[...] = scores\\n net_flo.blobs['mbox_conf_flatten'].data[...] = scores\\n net_flo.blobs['mbox_loc'].data[...] = net_rgb.blobs['mbox_loc'].data\\n \\n # two forward passes, only for the last layer \\n # dets is the detections after per-class NMS and thresholding (stardard)\\n # dets_all contains all the scores and regressions for all tubelets \\n dets = net_rgb.forward(start='detection_out')['detection_out'][0, 0, :, 1:]\\n dets_all = net_flo.forward(start='detection_out_full')['detection_out_full'][0, 0, :, 1:]\\n \\n # parse detections with per-class NMS\\n if dets.shape[0] == 1 and np.all(dets == -1):\\n dets = np.empty((0, dets.shape[1]), dtype=np.float32)\\n\\n dets[:, 2:] *= resolution_array # network output was normalized in [0..1]\\n dets[:, 0] -= 1 # label 0 was background, come back to label in [0..nlabels-1]\\n dets[:, 2::2] = np.maximum(0, np.minimum(w, dets[:, 2::2]))\\n dets[:, 3::2] = np.maximum(0, np.minimum(h, dets[:, 3::2]))\\n\\n # parse detections with global NMS at 0.7 (top 300)\\n # coordinates were normalized in [0..1]\\n dets_all[:, 0:4*K] *= resolution_array \\n dets_all[:, 0:4*K:2] = np.maximum(0, np.minimum(w, dets_all[:, 0:4*K:2]))\\n dets_all[:, 1:4*K:2] = np.maximum(0, np.minimum(h, dets_all[:, 1:4*K:2]))\\n idx = nms_tubelets(np.concatenate((dets_all[:, :4*K], np.max(dets_all[:, 4*K+1:], axis=1)[:, None]), axis=1), 0.7, 300)\\n dets_all = dets_all[idx, :]\\n \\n # save file\\n if not os.path.isdir(os.path.dirname(outfile)):\\n os.system('mkdir -p ' + os.path.dirname(outfile))\\n\\n with open(outfile, 'wb') as fid:\\n pickle.dump((dets, dets_all), fid)\",\n \"def test_teacher_role():\\n response = test_app.post(\\\"/bkt_service/unwind\\\", params='''[{\\n \\\"event\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/OutcomeEvent\\\",\\n \\\"actor\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@id\\\": \\\"student-1462300421838-1\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/lis/Person\\\",\\n \\\"roles\\\": [\\n \\\"urn:lti:instrole:ims/lis/Teacher\\\"\\n ]\\n },\\n \\\"action\\\": \\\"http://purl.imsglobal.org/vocab/caliper/v1/action#Graded\\\",\\n \\\"object\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@id\\\": \\\"attempt-1462300421838-4\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/Attempt\\\",\\n \\\"extensions\\\": {\\n \\\"assessmentType\\\": \\\"Diagnostic Assessment\\\",\\n \\\"assessmentId\\\": \\\"assessment-1462300421838-4\\\"\\n },\\n \\\"count\\\": 1,\\n \\\"startedAtTime\\\": \\\"2016-05-03T21:33:41.844Z\\\",\\n \\\"endedAtTime\\\": \\\"2016-05-03T22:03:41.844Z\\\"\\n },\\n \\\"generated\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@id\\\": \\\"result-1462300421838-4\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/Result\\\",\\n \\\"assignableId\\\": \\\"assessment-1462300421838-4\\\",\\n \\\"normalScore\\\": 80,\\n \\\"totalScore\\\": 100,\\n \\\"itemResults\\\": [\\n {\\n \\\"@id\\\": \\\"item-result-1462300421838-4-1\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/Result\\\",\\n \\\"question_type\\\": \\\"mcq\\\",\\n \\\"automarkable\\\": 1,\\n \\\"score\\\": 7,\\n \\\"max_score\\\": 10,\\n \\\"question_reference\\\": \\\"c0a3f0c8-eac7-4795-8c7a-adf98e336a7b\\\",\\n \\\"item_reference\\\": \\\"Adaptive_Item2_extract_USMOs\\\",\\n \\\"sequenceNumber\\\": 1\\n }\\n ]\\n },\\n \\\"group\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@id\\\": \\\"class-01\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/lis/CourseOffering\\\",\\n \\\"name\\\": null,\\n \\\"description\\\": null,\\n \\\"extensions\\\": {\\n \\\"pageNumber\\\": null,\\n \\\"courseCode\\\": \\\"course-01\\\",\\n \\\"calmsCourseId\\\": \\\"calms-course-01\\\",\\n \\\"lessonId\\\": \\\"lesson-01\\\",\\n \\\"platform\\\": \\\"D2L\\\",\\n \\\"classroomTypeId\\\": \\\"3500.0\\\",\\n \\\"activityId\\\": \\\"10\\\",\\n \\\"gradeLevel\\\": \\\"8\\\",\\n \\\"CourseOfferingId\\\": \\\"1200.0\\\",\\n \\\"adaptivewrapperId\\\": \\\"\\\",\\n \\\"schoolYear\\\": \\\"2015-20116\\\",\\n \\\"unitId\\\": \\\"3201.0\\\",\\n \\\"moduleId\\\": \\\"1110.0\\\",\\n \\\"courseId\\\": \\\"2550.0\\\",\\n \\\"assessmentId\\\": \\\"4520.0\\\",\\n \\\"originSystemId\\\": \\\"sams\\\",\\n \\\"businessLineId\\\": \\\"1300.0\\\",\\n \\\"contextId\\\": \\\"587279312bf9a9afd947ddab\\\"\\n },\\n \\\"dateCreated\\\": null,\\n \\\"dateModified\\\": null,\\n \\\"courseNumber\\\": null,\\n \\\"academicSession\\\": null,\\n \\\"subOrganizationOf\\\": {\\n \\\"@context\\\": \\\"http://purl.imsglobal.org/ctx/caliper/v1/Context\\\",\\n \\\"@id\\\": \\\"1001.0\\\",\\n \\\"@type\\\": \\\"http://purl.imsglobal.org/caliper/v1/w3c/Organization\\\",\\n \\\"name\\\": null,\\n \\\"description\\\": null,\\n \\\"extensions\\\": {},\\n \\\"dateCreated\\\": null,\\n \\\"dateModified\\\": null,\\n \\\"subOrganizationOf\\\": null\\n }\\n },\\n \\\"eventTime\\\": \\\"2017-01-09T14:21:00Z\\\"\\n }\\n }\\n ]''')\\n assert response.status == '200 OK'\\n assert len(response.json) == 1\\n #assert response.json[0][\\\"error\\\"][\\\"code\\\"] == 21\\n #assert \\\"role\\\" in response.json[0][\\\"error\\\"][\\\"message\\\"]\",\n \"def extract_trpt_data(udp_packet):\\n logger.debug('UDP packet sport [%s], dport [%s], len [%s]',\\n udp_packet.sport, udp_packet.dport, udp_packet.len)\\n\\n trpt_pkt = TelemetryReport(_pkt=udp_packet.payload)\\n trpt_eth = EthInt(trpt_pkt.payload)\\n logger.debug('TRPT ethernet dst - [%s], src - [%s], type - [%s]',\\n trpt_eth.dst, trpt_eth.src, trpt_eth.type)\\n return extract_int_data(trpt_eth)\",\n \"def extract(soup):\\r\\n table = soup.find('div', id='dnn_ctr11396_TimeTableView_PlaceHolder').find('table')\\r\\n rows = table.findChildren('tr', recursive=False)\\r\\n return [[col.findAll('div', {'class': 'TTLesson'}) for col in row.findChildren('td', recursive=False)[1:]]\\r\\n for row in rows[1:]]\",\n \"def read_triplets(seed_candidates):\\n if \\\"pickle\\\" in seed_candidates:\\n if \\\"*\\\" in seed_candidates:\\n all_files = glob.glob(seed_candidates)\\n new_data = []\\n for file_name in all_files:\\n with open(file_name, 'rb') as f:\\n data = pickle.load(f)\\n for dd in data:\\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\\n else:\\n with open(seed_candidates, 'rb') as f:\\n data = pickle.load(f)\\n new_data = []\\n for dd in data:\\n new_data.append((dd[0], dd[1], dd[2], dd[3]))\\n # idx = int(dd[0][10:])\\n # new_data.append((idx, dd[1], dd[2], dd[3]))\\n df_seed = pd.DataFrame(new_data, columns=['evtid', 'h1', 'h2', 'h3'], dtype=np.int64)\\n else:\\n column_names = ['evtid', 'h1', 'h2', 'h3']\\n if \\\"*\\\" in seed_candidates:\\n all_files = glob.glob(seed_candidates)\\n new_data = []\\n for file_name in all_files:\\n df_seed_tmp = pd.read_csv(file_name, header=None, names=column_names,)\\n new_data.append(df_seed_tmp)\\n df_seed = pd.concat(new_data)\\n else:\\n df_seed = pd.read_csv(seed_candidates, header=None,\\n names=column_names)\\n return df_seed\",\n \"def get_teacher_career_results(self, teacher, career):\\n data = []\\n\\n # Get the active exams of the career.\\n exams = EvaluationsExam.objects.filter(\\n type__exact=career.type, status=\\\"ACTIVE\\\")\\n\\n # Get the results for each exam.\\n for exam in exams:\\n\\n # Get the signatures of the teacher for the career in the exam.\\n signatures_dtl = EvaluationsTeacherSignature.objects.filter(\\n fk_teacher__exact=teacher.id, fk_period__exact=exam.fk_period, status=\\\"ACTIVE\\\").select_related('fk_signature')\\n\\n signatures_results = []\\n for signature_dtl in signatures_dtl:\\n \\n # If it raise an exception, it means that the signature isn't evaluated yet or other error.\\n try:\\n # Get the results of the signature.\\n signature_results = EvaluationsSignatureResult.objects.get(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n status=\\\"ACTIVE\\\"\\n )\\n\\n # Get the results for each question in the exam for the signature.\\n questions_results = EvaluationsSignatureQuestionResult.objects.filter(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n fk_question__optional='NO',\\n status=\\\"ACTIVE\\\"\\n ).values_list('fk_question__description', 'result')\\n\\n # Get the comments of the signature/group.\\n comments_result = EvaluationsSignatureQuestionResult.objects.get(\\n group=signature_dtl.group,\\n fk_signature=signature_dtl.fk_signature.id,\\n fk_exam=exam.id,\\n fk_question__optional='YES',\\n status=\\\"ACTIVE\\\"\\n ).result\\n\\n # Split the comments and add them to a list, only the ones that are not empty.\\n comments = list(filter(None, comments_result.split('|')))\\n\\n # Crate a dictionary with the results of the signature and the questions.\\n signatures_results.append({\\n 'teacher': teacher.name + ' ' + teacher.last_name + ' ' + teacher.last_name_2,\\n 'signature': signature_dtl.fk_signature.description,\\n 'group': signature_dtl.group,\\n 'average': signature_results.average,\\n 'comments': comments,\\n 'total_evaluated': signature_results.total_evaluated,\\n 'questions': questions_results\\n })\\n except Exception:\\n pass\\n\\n # Add the results to the exam dictionary.\\n exam_results = {\\n 'exam': exam.description,\\n 'career': career.description,\\n 'signatures_results': signatures_results,\\n 'period': exam.fk_period.period\\n }\\n\\n # Add the exam results to the list that will be returned at the end.\\n data.append(exam_results)\\n\\n return data\",\n \"def extractDDE(self, lang, username, screenname, description, tweets):\\n if isinstance(tweets, list):\\n tweets = ' '.join(tweets)\\n form = {\\n 'lang': lang,\\n 'username': username,\\n 'screenname': screenname,\\n 'description': description,\\n 'tweet': tweets\\n }\\n return self.POST('extract', {}, form)\",\n \"def get_quiz_teacher():\\n quiz_data = query_db(\\n \\\"SELECT id, name FROM quizzes WHERE creator_id=?;\\\", [flask.session[\\\"id\\\"]]\\n )\\n quizzes = []\\n for quiz in quiz_data:\\n quiz_dict = {}\\n quiz_dict[\\\"id\\\"] = quiz[0]\\n quiz_dict[\\\"name\\\"] = quiz[1]\\n quizzes.append(quiz_dict)\\n return quizzes\",\n \"def decode(self, session, dev_example):\\n unzipped_dev_example = list(zip(*dev_example))\\n input_feed = self.create_feed_dict(unzipped_dev_example[0:4], dropout = 1)\\n output_feed = [self.h_s, self.h_e, self.relevence]\\n outputs = session.run(output_feed, input_feed)\\n h_s = outputs[0]\\n h_e = outputs[1]\\n rel = outputs[2]\\n return h_s, h_e, rel\",\n \"def extract_unpack(self, args):\\n return self.extract_features(args)\",\n \"def load_tamper(self, dataset_dir, subset):\\n # Add classes. We have one class.\\n # Naming the dataset nucleus, and the class nucleus\\n self.add_class(\\\"tampers\\\", 1, \\\"tampers\\\")\\n\\n # Which subset?\\n # \\\"val\\\": use hard-coded list above\\n # \\\"train\\\": use data from stage1_train minus the hard-coded list above\\n # else: use the data from the specified sub-directory\\n # assert subset in [\\\"train\\\", \\\"val\\\", \\\"stage1_train\\\", \\\"stage1_test\\\", \\\"stage2_test\\\"]\\n # subset_dir = \\\"stage1_train\\\" if subset in [\\\"train\\\", \\\"val\\\"] else subset\\n dataset_dir = os.path.join(dataset_dir, subset, 'images')\\n if subset == \\\"val\\\" or subset == \\\"test\\\":\\n image_ids = next(os.walk(dataset_dir))[2]\\n else:\\n # Get image ids from directory names\\n image_ids = next(os.walk(dataset_dir))[2]\\n \\n\\n # dircopy_move = '/data/twj/copy-move/data_zoo/dataset/images/train'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # dirnew_splicing = '/data/tamper'\\n # image_ids_new_splicing = next(os.walk(os.path.join(dirnew_splicing, 'images')))[2]\\n\\n # dircopy_move = '/home/as/deeplab/wpmrcnn/ca2new/test'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n \\n # dircopy_move = '/data/gy/ca2att/train3'\\n # image_ids_copy_move = next(os.walk(os.path.join(dircopy_move, 'images')))[2]\\n\\n # # # dirtxt_sp = '/data/gy/tamperpre/train'\\n # # # image_ids_txt_sp = next(os.walk(os.path.join(dirtxt_sp, 'images')))[2]\\n\\n # dirnew_sp = '/data/gy/c2newsp/train'\\n # image_ids_new_sp = next(os.walk(os.path.join(dirnew_sp, 'images')))[2]\\n\\n # Add images\\n for image_id in image_ids:\\n self.add_image(\\n \\\"tampers\\\",\\n image_id=image_id[:-4],\\n path=os.path.join(dataset_dir, image_id))\\n\\n # for image_id in image_ids_copy_move:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dircopy_move, 'images', image_id))\\n\\n # for image_id in image_ids_new_splicing:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_splicing, 'images', image_id))\\n\\n # # for image_id in image_ids_txt_sp:\\n # # self.add_image(\\n # # \\\"tampers\\\",\\n # # image_id=image_id[:-4],\\n # # path=os.path.join(dirtxt_sp, 'images', image_id))\\n\\n # for image_id in image_ids_new_sp:\\n # self.add_image(\\n # \\\"tampers\\\",\\n # image_id=image_id[:-4],\\n # path=os.path.join(dirnew_sp, 'images', image_id))\",\n \"def inference(self):\\r\\n\\t\\tfor partition, loader in self.loaders.items():\\r\\n\\t\\t\\tavg_loss, (y, y_hat), post, attentions, tags = self.eval_loader(\\r\\n\\t\\t\\t\\tloader)\\r\\n\\t\\t\\tself.preds[partition] = {\\r\\n\\t\\t\\t\\t'tag': tags,\\r\\n\\t\\t\\t\\t'y': y,\\r\\n\\t\\t\\t\\t'y_hat': y_hat,\\r\\n\\t\\t\\t\\t# 'posteriors': post,\\r\\n\\t\\t\\t\\t# 'attentions': attentions\\r\\n\\t\\t\\t}\",\n \"def deepscore(teacher):\\n if teacher == None:\\n print(\\\"Not a valid teacher\\\")\\n return\\n if teacher.get('rmpdata') == None: snc.rateThisProfessor(teacher,instructors)\\n print(\\\"# \\\" + teacher['name'])\\n scoreTeacherlegacy(teacher)\\n scoreTeacherues(teacher)\\n scoreTeacherrmp(teacher)\",\n \"def partition_dataset(data, labels, nb_teachers, teacher_id):\\n\\n # Sanity check\\n assert (int(teacher_id) < int(nb_teachers))\\n\\n # This will floor the possible number of batches\\n batch_len = int(len(data) / nb_teachers)\\n\\n # Compute start, end indices of partition\\n start = teacher_id * batch_len\\n end = (teacher_id + 1) * batch_len\\n\\n # Slice partition off\\n partition_data = data[start:end]\\n if labels is not None:\\n partition_labels = labels[start:end]\\n else:\\n partition_labels = None\\n\\n return partition_data, partition_labels\",\n \"def get_train(self, even=None):\\n\\n #self.images, self.labels, self.traces = trace_data.get_my_teacher()\\n _, self.images, self.labels, self.traces, _ = trace_data.get_my_teacher()\\n #print(self.labels)\\n self.length = len(self.images)\\n self.create_teacher()\",\n \"def legacydatapoints(teacher):\\n points = 0\\n for surveytype in ['2','1','H']:\\n for criteria in range(0,22):\\n for degree in range(0,6):\\n try:\\n points += teacher['data'][surveytype][criteria]['degree' + str(degree)]\\n except:\\n pass\\n for criteria in range(22,27):\\n for degree in range(0,7):\\n try:\\n points += teacher['data'][surveytype][criteria]['degree' + str(degree)]\\n except:\\n pass\\n return points\",\n \"def get_teacher(teacher_account_id):\\n query = 'SELECT * FROM teacher JOIN person ON teacher.teacher_account_id=person.account_id WHERE teacher.teacher_account_id=%s;'\\n args = (teacher_account_id,)\\n return database.connection.get_data(query, args)\",\n \"def subjects_parser(root, study, outdir):\\n # Welcome\\n print(\\\"Starting subjects parsing...\\\")\\n\\n # Parameters\\n subjects = defaultdict(\\n lambda: defaultdict(dict))\\n participants_file = os.path.join(root, \\\"participants.tsv\\\")\\n\\n # Parse the paticipants tsv file\\n with open(participants_file) as csvfile:\\n reader = csv.DictReader(csvfile, delimiter=\\\"\\\\t\\\")\\n for row in reader:\\n subject = row[\\\"participant_id\\\"].replace(\\\"sub-\\\", \\\"\\\")\\n center = row.get(\\\"site\\\", DEFAULT_CENTER)\\n subjects[center][subject] = {\\n \\\"identifier\\\": md5_sum(subject),\\n \\\"code_in_study\\\": subject,\\n \\\"handedness\\\": row.get(\\\"handedness\\\", \\\"unknown\\\"),\\n \\\"gender\\\": row.get(\\\"gender\\\", \\\"unknown\\\")}\\n\\n # Save the results\\n print(\\\"Saving data in '{0}'...\\\".format(outdir))\\n save_parsing(subjects, outdir, study, \\\"subjects\\\")\\n\\n # Goodbye\\n print(\\\"Done.\\\")\\n\\n return subjects\",\n \"def extract_testsuite(self, testsuite, info):\\n\\n for testcase in testsuite.getchildren():\\n\\n self.extract_testcase(testcase, info, testsuite.get(\\\"name\\\"))\",\n \"def parse_pitcher(self, response):\\n\\n params = parse_qs(\\n urlparse(response.url).query,\\n keep_blank_values=True)\\n\\n data = {}\\n data['year'] = params['year'][0]\\n data['month'] = params['month'][0]\\n data['day'] = params['day'][0]\\n data['pitcher_id'] = params['pitchSel'][0]\\n data['game_id'] = params['game'][0]\\n\\n # Find the raw html table link.\\n xpath = '//a[text()=\\\"Get Expanded Tabled Data\\\"]'\\n\\n # Generate the url to open the raw html table page.\\n sel = response.xpath(xpath)[0]\\n\\n link = urlparse(sel.xpath('@href').extract()[0])\\n params = parse_qs(link.query, keep_blank_values=True)\\n\\n for key, value in params.items():\\n params[key] = value[0]\\n # Override the rows/column size just in case.\\n params['h_size'] = 3000\\n params['v_size'] = 3000\\n\\n url = self.base_url + link.path + \\\"?\\\" + urlencode(params)\\n\\n req = Request(url, callback=self.parse_pitcher_stats, meta={'data': data})\\n\\n yield req\",\n \"def get_teacher_topic_all():\\n topic_data = query_db(\\n \\\"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \\\"\\n \\\"ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n topics = []\\n for topic in topic_data:\\n topic_dict_teacher = {}\\n topic_dict_teacher[\\\"id\\\"] = topic[0]\\n topic_dict_teacher[\\\"name\\\"] = flask.escape(str(topic[1]))\\n topic_dict_teacher[\\\"class\\\"] = flask.escape(str(topic[2]))\\n topics.append(topic_dict_teacher)\\n return topics\",\n \"def get_data_loaders():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+5):]\\n \\n #history_complete.append(history)\\n if len(persona) == 4:\\n if len(history) > (len(persona)+3):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def test_practitioner_6(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f002-pv.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_6(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_6(inst2)\",\n \"def teacher(self):\\n if \\\"teacher\\\" in self._prop_dict:\\n if isinstance(self._prop_dict[\\\"teacher\\\"], OneDriveObjectBase):\\n return self._prop_dict[\\\"teacher\\\"]\\n else :\\n self._prop_dict[\\\"teacher\\\"] = EducationTeacher(self._prop_dict[\\\"teacher\\\"])\\n return self._prop_dict[\\\"teacher\\\"]\\n\\n return None\",\n \"def extract(src_dir,feat_file,ivectors_dir,num_gselect):\\n os.system(\\\"./extract_ivectors.sh --num-gselect \\\"+str(num_gselect)+ \\\" \\\" + src_dir + \\\" \\\" + feat_file + \\\" \\\" + ivectors_dir)\\n keys=[]\\n ivectors=np.empty((0,0))\\n for key,mat in kaldi_io.read_vec_flt_scp(ivectors_dir+'/ivector.scp'):\\n if ivectors.shape[1] != mat.shape[0]:\\n ivectors=ivectors.reshape((0,mat.shape[0]))\\n ivectors=np.vstack((ivectors,mat))\\n keys.append(key)\\n\\n ivectors=np.asarray(ivectors)\\n keys=np.asarray(keys)\\n return ivectors,keys\",\n \"def extract_table_data(path):\\n document = zipfile.ZipFile(path)\\n xml_content = document.read('word/document.xml')\\n document.close()\\n tree = XML(xml_content)\\n table = tree.find(BODY).find(TABLE)\\n rows = table.findall(ROW)\\n\\n day = \\\"\\\"\\n dates = \\\"\\\"\\n teachers = {}\\n\\n for row in rows[1:]:\\n cols = row.findall(COL)\\n\\n #Code\\n code = extract_text(cols[0])\\n\\n #course\\n course = extract_text(cols[1])\\n\\n #day\\n dayTemp = extract_text(cols[2])\\n if dayTemp != \\\"\\\":\\n day = dayTemp\\n\\n #Calendar/Date\\n temp_date = extract_date(cols[3])\\n if len(temp_date) != 0:\\n dates = temp_date\\n\\n #hour\\n times = extract_time(cols[4])\\n\\n #Teachers\\n teacher = extract_text(cols[5])\\n\\n if teacher == \\\"\\\":\\n continue\\n\\n if teacher not in teachers:\\n teachers[teacher] = Teacher(teacher)\\n\\n segment = Segment(code, course, day, dates, times)\\n\\n teachers[teacher].add_segment(segment)\\n\\n return teachers\",\n \"def decomposeSeed( self, bSeed ):\\n\\n\\t\\ttry:\\n\\t\\t\\tbSeed = long( bSeed )\\n\\t\\t\\t\\n\\t\\t\\t# This is a copy of the functions in product-key/ck-common.c\\n\\t\\t\\t# They are rewritten here to save on shelling out with each\\n\\t\\t\\t# rda-backend that checks in.\\n\\t\\t\\tbSerial = self._getSerial( bSeed )\\n\\t\\t\\tsVersion = self._getVersion( bSeed )\\n\\t\\t\\tbNumcam = self._getNumcam( bSeed )\\n\\t\\t\\tsMac = self._getMac( bSeed )\\n\\n\\t\\t\\treturn ( bSerial, sVersion, bNumcam, sMac )\\n\\n\\t\\texcept Exception, e:\\n\\t\\t\\terrMsg( 'error while decomposing key' )\\n\\t\\t\\traise\",\n \"def extract_speaker_data(self, X, y):\\n\\n speaker_names = []\\n global_idx = 0\\n curr_speaker_num = -1\\n old_speaker = ''\\n\\n # Crawl the base and all sub folders\\n for root, directories, filenames in os.walk(self.base_folder):\\n # Ignore crp and DOC folder\\n if self.valid_speakers and os.path.split(root)[1] not in self.valid_speakers:\\n continue\\n\\n # Check files\\n for filename in [filename for filename in filenames if self.file_match_regex.search(filename)]:\\n # Extract speaker\\n speaker = os.path.split(root)[1]\\n if speaker != old_speaker:\\n curr_speaker_num += 1\\n old_speaker = speaker\\n speaker_names.append(speaker)\\n print('Extraction progress: %d/%d' % (curr_speaker_num + 1, self.max_speakers))\\n\\n if curr_speaker_num < self.max_speakers:\\n full_path = os.path.join(root, filename)\\n global_idx += extract_mel_spectrogram(full_path, X, y, global_idx, curr_speaker_num)\\n\\n return X[0:global_idx], y[0:global_idx], speaker_names\",\n \"def get_topics_for_alt(alt_list, pgm_dict):\\n for epv in alt_list:\\n name = epv.get('package', {}).get('name', [''])[0]\\n if name:\\n for pgm_pkg_key, pgm_list in pgm_dict.items():\\n for pgm_epv in pgm_list:\\n if name == pgm_epv.get('package_name', ''):\\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\\n\\n return alt_list\",\n \"def parse_identifying_data(path, passwd, embedded_file='participants.txt'):\\n if path is not None:\\n zf = zipfile.ZipFile(path)\\n zf.setpassword(passwd)\\n\\n participants = {}\\n for l in zf.read(embedded_file).splitlines():\\n if l.startswith('#'):\\n continue\\n\\n bc, name = l.strip().split('\\\\t')[:2]\\n participants[bc] = name.replace(\\\",\\\", \\\"\\\")\\n\\n print \\\"Using identified data!\\\"\\n else:\\n participants = None\\n\\n return participants\",\n \"def get_teacher():\\n\\n rows = db.engine.execute(f\\\"SELECT * FROM teacher_login WHERE loginid = {g.user.loginid}\\\")\\n res = []\\n for row in rows:\\n res.append(dict(row))\\n return jsonify(res)\",\n \"def extract_pp(self):\\n build([srna.ExtractPPW(fastq_dic=self.fastq_dic, num_cpus=self.num_cpus,\\n indexfile=self.hisat_index, workdir=self.workdir,\\n kingdom=self.kingdom)],\\n local_scheduler=self.local_scheduler)\",\n \"def extract(self, data):\",\n \"def extract_speaker_data(self, X, y):\\r\\n\\r\\n speaker_names = []\\r\\n global_idx = 0\\r\\n curr_speaker_num = -1\\r\\n old_speaker = ''\\r\\n\\r\\n # Crawl the base and all sub folders\\r\\n for root, directories, filenames in os.walk(self.base_folder):\\r\\n\\r\\n # Ignore crp and DOC folder\\r\\n if root[-5:] not in self.valid_speakers:\\r\\n continue\\r\\n\\r\\n # Check files\\r\\n for filename in filenames:\\r\\n\\r\\n # Can't read the other wav files\\r\\n if '_RIFF.WAV' not in filename:\\r\\n continue\\r\\n\\r\\n # Extract speaker\\r\\n speaker = root[-5:]\\r\\n if speaker != old_speaker:\\r\\n curr_speaker_num += 1\\r\\n old_speaker = speaker\\r\\n speaker_names.append(speaker)\\r\\n print('Extraction progress: %d/%d' % (curr_speaker_num + 1, self.max_speakers))\\r\\n\\r\\n if curr_speaker_num < self.max_speakers:\\r\\n full_path = os.path.join(root, filename)\\r\\n global_idx += extract_mel_spectrogram(full_path, X, y, global_idx, curr_speaker_num)\\r\\n\\r\\n return X[0:global_idx], y[0:global_idx], speaker_names\",\n \"def extract(self):\\n pass\",\n \"def get_test_loader(id_list = './data/sample_submission.csv', root_dir = './data/test/'):\\n data = HumanProteinDataset(id_list, root_dir, transform = transforms.Compose([\\n Rescale((256, 256)), \\n ToTensor()\\n ]))\\n\\n indices = np.arange(len(data))\\n dataloader_test = DataLoader(data, batch_size=10, num_workers=5)\\n\\n return dataloader_test\",\n \"def unpack(self) -> Tuple[list, list, list, list, float, int, list]:\\n # (nice to have) todo:refactor --> as a namedtuple\\n unpacked_super = super().unpack()\\n\\n observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght = unpacked_super\\n\\n return observations, actions, rewards, Q_values, trajectory_return, _trajectory_lenght, self.V_estimates\",\n \"def _init_extractors(self):\\n @self.extractors_wrapper(\\\"networkx\\\")\\n def get_nx_extractor(graph):\\n \\\"\\\"\\\"\\n :param graph: networkx.Graph\\n :returns: projx.nx_extractor\\n \\\"\\\"\\\"\\n return nx_xtrct.nx_extractor(\\n self.extractor_json[self.extractor_name], graph\\n )\\n\\n @self.extractors_wrapper(\\\"neo4j\\\")\\n def get_neo4j_extractor(graph):\\n \\\"\\\"\\\"\\n :returns: projx.nx_extractor\\n \\\"\\\"\\\"\\n return neo4j_xtrct.neo4j_extractor(\\n self.extractor_json[self.extractor_name], graph\\n )\\n\\n @self.extractors_wrapper(\\\"edgelist\\\")\\n def get_edgelist_extractor(graph):\\n \\\"\\\"\\\"\\n :returns: projx.nx_extractor\\n \\\"\\\"\\\"\\n return edgelist_xtrct.edgelist_extractor(\\n self.extractor_json[self.extractor_name], graph\\n )\",\n \"def setup_experiment(testruns, droplist=\\\"\\\"):\\n ex = Experiment()\\n ex.addSoluFile(ALL_SOLU)\\n\\n regexlist = []\\n for x in droplist.split(\\\",\\\"):\\n # defaultvalue, if empty we don't want to exclude everything\\n if x == \\\"\\\":\\n continue\\n try:\\n y = re.compile(x)\\n regexlist.append(y)\\n except:\\n pass\\n\\n excluded_inst = []\\n # get data\\n for t in testruns:\\n # update representation\\n additional_data = {\\\"RubberbandId\\\": get_rbid_representation(t, \\\"extended\\\")}\\n\\n # collect data and pass to ipet\\n ipettestrun = TestRun()\\n tr_raw_data = t.get_data(add_data=additional_data)\\n\\n tr_data = {}\\n for i in tr_raw_data.keys():\\n for r in regexlist:\\n if r.match(i):\\n excluded_inst.append(i)\\n break\\n else:\\n tr_data[i] = tr_raw_data[i]\\n\\n ipettestrun.data = pd.DataFrame(tr_data).T\\n\\n ex.testruns.append(ipettestrun)\\n return ex, excluded_inst\",\n \"def extract_all_puzzles(self) -> List[Puzzle]:\\n pages_urls = self.extract_pages()\\n print(f\\\"\\\\n{pages_urls}\\\\n\\\")\\n\\n # Extract all puzzles\\n puzzles = []\\n for path in pages_urls:\\n full_path = f\\\"{DOMAIN}{path}\\\"\\n extracted_puzzle = self.extract_puzzle(full_path)\\n puzzles.append(extracted_puzzle)\\n # print(extracted_puzzle.title, extracted_puzzle.url, extracted_puzzle.description, extracted_puzzle.clues)\\n print(extracted_puzzle.title)\\n return puzzles\",\n \"def unpackRecords(self,unpackTypes):\\n for record in self.records:\\n if record.name in unpackTypes:\\n record.load(unpack=True)\",\n \"def _forecasters(self):\\n return self._get_estimator_tuples(self.forecasters, clone_ests=False)\",\n \"def generate_teacher_family(p_adults, p_children):\\n\\tN_adults = np.random.choice(list(p_adults.keys()), p=list(p_adults.values()))\\n\\tN_children = np.random.choice(list(p_children[N_adults].keys()),\\n\\t\\t\\t\\t\\t\\t\\t\\tp=list(p_children[N_adults].values()))\\n\\t\\n\\tages = np.random.randint(0, 18, N_children)\\n\\treturn ages, N_adults\",\n \"def getTelemarketers():\\n callers = getAllKnownCallers()\\n callees = getAllKnownCallees()\\n texters = getAllKnownTexters()\\n textees = getAllKnownTextees()\\n telemarketers = getKnownTelemarketers()\\n\\n # Remove known telemarketers from list\\n t = set(callers).difference(telemarketers)\\n # Remove known numbers that have received calls\\n t.difference_update(callees)\\n # Remove those who have sent a text\\n t.difference_update(texters)\\n # Remove those who have received a text\\n t.difference_update(textees)\\n # Join with known telemarketers\\n t = set(telemarketers).union(t)\\n\\n return sorted(t)\",\n \"def get_data_loaders_2sentences():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+1):]\\n #history_complete.append(history)\\n if len(history) > 4:\\n history_chatbot = history[1::2]\\n\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def test_practitioner_4(base_settings):\\n filename = (\\n base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-xcda-author.json\\\"\\n )\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_4(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_4(inst2)\",\n \"def get_data_loaders_4sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"]\\n #history_complete.append(history)\\n if len(history_splitted) > (len(persona)-1):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def load_premiership_teams():\\n # list of PremTeams to add\\n team_list = [\\n {'name': 'Arsenal', 'code': 'ARS', 'is_prem': True},\\n {'name': 'Aston Villa', 'code': 'AVL', 'is_prem': True},\\n {'name': 'Brighton and Hove Albion', 'code': 'BTN', 'is_prem': True},\\n {'name': 'Brentford', 'code': 'BRE', 'is_prem': True},\\n {'name': 'Bournemouth', 'code': 'BOU', 'is_prem': False},\\n {'name': 'Burnley', 'code': 'BUR', 'is_prem': True},\\n {'name': 'Cardiff City', 'code': 'CAR', 'is_prem': False},\\n {'name': 'Chelsea', 'code': 'CHE', 'is_prem': True},\\n {'name': 'Crystal Palace', 'code': 'CRY', 'is_prem': True},\\n {'name': 'Everton', 'code': 'EVE', 'is_prem': True},\\n {'name': 'Fulham', 'code': 'FUL', 'is_prem': False},\\n {'name': 'Hull', 'code': 'HUL', 'is_prem': False},\\n {'name': 'Huddersfield Town', 'code': 'HUD', 'is_prem': False},\\n {'name': 'Leeds United', 'code': 'LEE', 'is_prem': True},\\n {'name': 'Leicester City', 'code': 'LEI', 'is_prem': True},\\n {'name': 'Liverpool', 'code': 'LIV', 'is_prem': True},\\n {'name': 'Manchester City', 'code': 'MCY', 'is_prem': True},\\n {'name': 'Manchester United', 'code': 'MUN', 'is_prem': True},\\n {'name': 'Middlesbrough', 'code': 'MID', 'is_prem': False},\\n {'name': 'Newcastle United', 'code': 'NEW', 'is_prem': True},\\n {'name': 'Norwich City', 'code': 'NOR', 'is_prem': True},\\n {'name': 'Queens Park Rangers', 'code': 'QPR', 'is_prem': False},\\n {'name': 'Sheffield United', 'code': 'SHF', 'is_prem': False},\\n {'name': 'Southampton', 'code': 'SOT', 'is_prem': True},\\n {'name': 'Stoke City', 'code': 'STO', 'is_prem': False},\\n {'name': 'Sunderland', 'code': 'SUN', 'is_prem': False},\\n {'name': 'Swansea City', 'code': 'SWA', 'is_prem': False},\\n {'name': 'Tottenham Hotspur', 'code': 'TOT', 'is_prem': True},\\n {'name': 'Watford', 'code': 'WAT', 'is_prem': True},\\n {'name': 'West Bromwich Albion', 'code': 'WBA', 'is_prem': False},\\n {'name': 'West Ham United', 'code': 'WHM', 'is_prem': True},\\n {'name': 'Wolverhampton Wanderers', 'code': 'WLV', 'is_prem': True},\\n ]\\n\\n for team in team_list:\\n print(PremTeam.objects.update_or_create(\\n name=team['name'],\\n code=team['code'],\\n defaults={'is_prem': team['is_prem']}\\n ))\\n # print(pt, created)\",\n \"def get_teacher(self) -> str :\\n return self.teacher\",\n \"def posters(self, imdb_id):\\n return self.get_art(imdb_id, \\\"posters\\\")\",\n \"def generate_teachers(G, school_type, N_classes, family_member_counter, \\n\\t\\t\\t\\t\\t family_counter, teacher_p_adults, teacher_p_children):\\n\\tN_teachers = get_N_teachers(school_type, N_classes)\\n\\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\\n\\tG.add_nodes_from(teacher_nodes)\\n\\t\\n\\tfor t in teacher_nodes:\\n\\t\\tfamily_nodes = [t]\\n\\t\\t# draw a random number of children and adults for the family\\n\\t\\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\\n\\t\\t\\n\\t\\tages = list(ages)\\n\\t\\tfor adult in range(N_adults - 1):\\n\\t\\t\\tages.append(20.5) # default age for adults\\n\\t\\t\\n\\t\\t# add the family member nodes and their attributes to the graph\\n\\t\\tfor age in ages:\\n\\t\\t\\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\\n\\t\\t\\tfamily_nodes.append(family_member_ID)\\n\\t\\t\\tG.add_node(family_member_ID)\\n\\t\\t\\tfamily_member_counter += 1\\n\\t\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t\\t{family_member_ID:{'type':'family_member',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'age':age,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'family':family_counter,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'unit':'family'}})\\n\\t\\t\\t\\t\\t\\n\\t\\t# finally, also set the teacher's node attributes\\n\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t{t:{'type':'teacher', \\n\\t\\t\\t\\t\\t\\t# Note: 20.5 is the age at which\\n\\t\\t\\t\\t\\t\\t# the symptom and transmission risk\\n\\t\\t\\t\\t\\t\\t# is that of an adult\\n\\t\\t\\t\\t\\t\\t'age':20.5,\\n\\t\\t\\t\\t\\t\\t'unit':'faculty_room',\\n\\t\\t\\t\\t\\t\\t'family':family_counter}})\\n\\t\\tfamily_counter += 1\",\n \"def get_participants_data(self):\\n participants = []\\n for (email, uid) in self.tokens.items():\\n participant = {} \\n participant['uid'] = uid\\n participant['email'] = email\\n response = 0\\n questions = 0\\n sections = [x for x in self.values() if ISurveySection.providedBy(x)]\\n for section in sections:\\n response += len(section.responses.get(uid, {}))\\n questions += len(section.question_ids)\\n if response != 0:\\n participant['finished'] = Decimal(response) / Decimal(questions) * 100\\n else:\\n participant['finished'] = 0 \\n participants.append(participant)\\n return participants\",\n \"def unpack(self):\\n return []\",\n \"def prepare_student_data(dataset, nb_teachers, save=False):\\n assert input.create_dir_if_needed(FLAGS.train_dir)\\n\\n # Load the dataset\\n if dataset == 'svhn':\\n test_data, test_labels = input.ld_svhn(test_only=True)\\n elif dataset == 'cifar10':\\n test_data, test_labels = input.ld_cifar10(test_only=True)\\n elif dataset == 'mnist':\\n test_data, test_labels = input.ld_mnist(test_only=True)\\n else:\\n print(\\\"Check value of dataset flag\\\")\\n return False\\n\\n # Make sure there is data leftover to be used as a test set\\n assert FLAGS.stdnt_share < len(test_data)\\n\\n # Prepare [unlabeled] student training data (subset of test set)\\n stdnt_data = test_data[:FLAGS.stdnt_share]\\n\\n # Compute teacher predictions for student training data\\n teachers_preds = ensemble_preds(dataset, nb_teachers, stdnt_data)\\n\\n # Aggregate teacher predictions to get student training labels\\n if not save:\\n stdnt_labels = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale)\\n else:\\n # Request clean votes and clean labels as well\\n stdnt_labels, clean_votes, labels_for_dump = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale, return_clean_votes=True) #NOLINT(long-line)\\n\\n # Prepare filepath for numpy dump of clean votes\\n filepath = FLAGS.data_dir + \\\"/\\\" + str(dataset) + '_' + str(nb_teachers) + '_student_clean_votes_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)\\n\\n # Prepare filepath for numpy dump of clean labels\\n filepath_labels = FLAGS.data_dir + \\\"/\\\" + str(dataset) + '_' + str(nb_teachers) + '_teachers_labels_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)\\n\\n # Dump clean_votes array\\n with tf.gfile.Open(filepath, mode='w') as file_obj:\\n np.save(file_obj, clean_votes)\\n\\n # Dump labels_for_dump array\\n with tf.gfile.Open(filepath_labels, mode='w') as file_obj:\\n np.save(file_obj, labels_for_dump)\\n\\n # Print accuracy of aggregated labels\\n ac_ag_labels = metrics.accuracy(stdnt_labels, test_labels[:FLAGS.stdnt_share])\\n print(\\\"Accuracy of the aggregated labels: \\\" + str(ac_ag_labels))\\n\\n # Store unused part of test set for use as a test set after student training\\n stdnt_test_data = test_data[FLAGS.stdnt_share:]\\n stdnt_test_labels = test_labels[FLAGS.stdnt_share:]\\n\\n if save:\\n # Prepare filepath for numpy dump of labels produced by noisy aggregation\\n filepath = FLAGS.data_dir + \\\"/\\\" + str(dataset) + '_' + str(nb_teachers) + '_student_labels_lap_' + str(FLAGS.lap_scale) + '.npy' #NOLINT(long-line)\\n\\n # Dump student noisy labels array\\n with tf.gfile.Open(filepath, mode='w') as file_obj:\\n np.save(file_obj, stdnt_labels)\\n\\n return stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels\",\n \"def matlab_csv_to_teacher_data(dirname):\\n samples = np.genfromtxt(os.path.join(dirname, 'samples.csv'), dtype=float,\\n delimiter=\\\",\\\")\\n labels = np.genfromtxt(os.path.join(dirname, 'labels.csv'), dtype=int,\\n delimiter=\\\",\\\")\\n data = [None]*max(labels) # matlab is 1-indexed, so no need to add 1\\n for i, z in enumerate(labels):\\n if data[z-1] is None:\\n data[z-1] = np.copy(samples[i, :])\\n else:\\n data[z-1] = np.vstack((data[z-1], np.copy(samples[i, :])))\\n return data\",\n \"def ParsePassport(arr):\\n\\t# initialize new array for values to be copied to\\n\\tparsed_arr = []\\n\\tfor p in arr:\\n\\t\\tparsed_arr.append(p[4:])\\n\\treturn parsed_arr\",\n \"def applyDemapping(self):\\n pass\",\n \"def extract(self):\\n self.build_path_pairs()\\n self.extract_field_blocks()\\n self.assert_filenames()\",\n \"def extract_thread(self):\\n \\\"TODO: check if hotel seletecd\\\"\\n hotel_name = SelectableLabel.selected_hotel\\n hlink = CityListView.hotels_data[hotel_name]\\n self.extract_list, self.hotel_address = scrapper.scrap(\\\"http://www.booking.com/\\\"\\n + scrapper.make_hotel_review_url(hlink))\",\n \"def test_gathering_page_pdf_urls(\\n lep_dl: LepDL,\\n) -> None:\\n json_test = \\\"\\\"\\\"\\\\\\n [\\n {\\n \\\"episode\\\": 555,\\n \\\"files\\\": {\\n \\\"audios\\\": [],\\n \\\"page_pdf\\\": [\\\"https://someurl555.local\\\"]\\n },\\n \\\"index\\\": 2022011303\\n },\\n {\\n \\\"episode\\\": 554,\\n \\\"files\\\": {\\n \\\"audios\\\": [],\\n \\\"page_pdf\\\": [\\\"https://someurl554.local1\\\", \\\"https://someurl554.local2\\\"]\\n },\\n \\\"index\\\": 2022011302\\n },\\n {\\n \\\"episode\\\": 553,\\n \\\"files\\\": {\\n \\\"audios\\\": [],\\n \\\"page_pdf\\\": [\\n \\\"https://someurl553.local1\\\",\\n \\\"https://someurl553.local2\\\",\\n \\\"https://someurl553.local3\\\"\\n ]\\n },\\n \\\"index\\\": 2022011302\\n }\\n ]\\n \\\"\\\"\\\" # noqa: E501,B950\\n db_episodes = Lep.extract_only_valid_episodes(json_test)\\n lep_dl.files = downloader.gather_all_files(db_episodes)\\n\\n assert len(lep_dl.files) == 3\\n\\n assert lep_dl.files[0].primary_url == \\\"https://someurl553.local1\\\"\\n assert lep_dl.files[0].secondary_url == \\\"https://someurl553.local2\\\"\\n assert lep_dl.files[0].tertiary_url == \\\"https://someurl553.local3\\\"\\n\\n assert lep_dl.files[1].primary_url == \\\"https://someurl554.local1\\\"\\n assert lep_dl.files[1].secondary_url == \\\"https://someurl554.local2\\\"\\n\\n assert lep_dl.files[2].primary_url == \\\"https://someurl555.local\\\"\\n assert lep_dl.files[2].secondary_url == \\\"\\\"\",\n \"def decompose_sample(lfads_hps, z):\\n ib_dim = lfads_hps['ib_dim']\\n ic_dim = lfads_hps['enc_dim']\\n ib_k = z[:ib_dim]\\n ic_j = z[ib_dim:(ib_dim+ic_dim)]\\n ii_ti = z[(ib_dim+ic_dim):]\\n ii_txi = np.reshape(ii_ti, (-1, lfads_hps['ii_dim']))\\n return ib_k, ic_j, ii_txi\",\n \"def extract_trials(self):\\n self.logdir = os.path.expanduser(os.path.expandvars(self.logdir))\\n logdir_content = os.listdir(self.logdir)\\n # For the standard hypo_testing analysis, this logdir_content\\n # will contain what we need it to and so we can proceed to\\n # extract the trials.\\n if self.test_type == 'analysis':\\n if 'config_summary.json' in logdir_content:\\n self.organise_trials(logdir_content=logdir_content)\\n else:\\n raise ValueError(\\n 'config_summary.json cannot be found in the specified '\\n 'logdir. It should have been created as part of the '\\n 'output of hypo_testing.py and so this postprocessing '\\n 'cannot be performed.'\\n )\\n elif self.test_type == 'injparamscan':\\n pickle_there = self.check_pickle_files(logdir_content)\\n if pickle_there:\\n self.load_from_pickle()\\n else:\\n toy_names = []\\n scan_variables = []\\n for folder in logdir_content:\\n if '.pckl' not in folder and 'Plots' not in folder:\\n bits = folder.split('toy')[1].split('_')\\n toy_name = bits[1]\\n toy_names.append(\\n toy_name\\n )\\n scan_variable = None\\n add_bit = True\\n for bit in bits:\\n try:\\n float(bit)\\n add_bit = False\\n except:\\n if not (bit == '') and not (bit == toy_name):\\n if add_bit:\\n if scan_variable is None:\\n scan_variable = bit\\n else:\\n scan_variable += '_%s'%bit\\n scan_variables.append(scan_variable)\\n toy_names = np.array(toy_names)\\n scan_variables = np.array(scan_variables)\\n # Require all to be the same injected truth model\\n if not np.alltrue(toy_names == toy_names[0]):\\n raise ValueError(\\n 'Not all output is for the same injected truth '\\n 'hypothesis. Got %s'%set(toy_names)\\n )\\n # Require all to be scanning the same variable\\n if not np.alltrue(scan_variables == scan_variables[0]):\\n raise ValueError(\\n 'Not all output is for the same scanned parameter. '\\n 'Got %s'%set(scan_variables)\\n )\\n self.labels = {}\\n self.all_params = {}\\n self.data_sets = {}\\n self.minimiser_info = {}\\n for scandir in logdir_content:\\n if '.pckl' not in scandir and 'Plots' not in scandir:\\n self.scandir = os.path.join(self.logdir, scandir)\\n scandir_content = os.listdir(self.scandir)\\n if 'config_summary.json' in scandir_content:\\n self.extract_scans()\\n else:\\n raise ValueError(\\n 'config_summary.json cannot be found in the '\\n 'specified scandir, %s. It should have been '\\n 'created as part of the output of '\\n 'hypo_testing.py and so this postprocessing '\\n 'cannot be performed.'%self.scandir\\n )\\n # Pickle at the end so all of the scans are in the output\\n self.pickle_data()\\n self.organise_scans()\\n elif self.test_type == 'systtests':\\n pickle_there = self.check_pickle_files(logdir_content)\\n if pickle_there:\\n self.load_from_pickle()\\n else:\\n self.labels = {}\\n self.all_params = {}\\n self.data_sets = {}\\n for systdir in logdir_content:\\n if '.pckl' not in systdir and 'Plots' not in systdir:\\n self.systdir = os.path.join(self.logdir, systdir)\\n systdir_content = os.listdir(self.systdir)\\n if 'config_summary.json' in systdir_content:\\n self.extract_systtests()\\n else:\\n raise ValueError(\\n 'config_summary.json cannot be found in the '\\n 'specified directory, %s. It should have been '\\n 'created as part of the output of '\\n 'hypo_testing.py and so this postprocessing '\\n 'cannot be performed.'%self.systdir\\n )\\n # Pickle at the end so all of the truths/systematics\\n # are in the output\\n self.pickle_data()\",\n \"def extract_features(extractor, loader, device, feature_transform=None):\\r\\n for batch_idx, data in enumerate(loader):\\r\\n with torch.no_grad():\\r\\n data = data.to(device).float()\\r\\n features = extractor(data)\\r\\n if feature_transform:\\r\\n features = feature_transform(features)\\r\\n yield features\",\n \"def test_practitioner_7(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_7(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_7(inst2)\",\n \"def getPUsers(self):\\n model = self.tvPUsers.get_model()\\n result = []\\n model.foreach(lambda model, path, iter, data:\\n result.append(model.get(iter, 0)[0]), None)\\n result.sort()\\n return result\",\n \"def _unpack_ies(buf):\\n\\t\\t# each IE starts with an ID and a length\\n\\t\\ties = []\\n\\t\\toff = 0\\n\\t\\tbuflen = len(buf)\\n\\t\\t# logger.debug(\\\"lazy dissecting: %s\\\" % buf)\\n\\n\\t\\twhile off < buflen:\\n\\t\\t\\tie_id = buf[off]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tparser = IEEE80211.ie_decoder[ie_id]\\n\\t\\t\\texcept KeyError:\\n\\t\\t\\t\\t# some unknown tag, use standard format\\n\\t\\t\\t\\tparser = IEEE80211.IE\\n\\n\\t\\t\\tdlen = buf[off + 1]\\n\\t\\t\\t# logger.debug(\\\"IE parser is: %d = %s = %s\\\" % (ie_id, parser, buf[off: off+2+dlen]))\\n\\t\\t\\tie = parser(buf[off: off + 2 + dlen])\\n\\t\\t\\ties.append(ie)\\n\\t\\t\\toff += 2 + dlen\\n\\n\\t\\treturn ies\",\n \"def test_parse_trflp(self):\\r\\n\\r\\n data = \\\\\\r\\n \\\"\\\"\\\"\\tBin (10bp)\\tBin (20bp)\\tBin (30bp)\\tBin (40 bp)\\r\\nSamp-le 1\\t1000\\t2000\\t3000\\t4000\\r\\nSample 2\\t\\t2000\\t3000\\t4000\\r\\nSample 3\\t\\t\\t3000\\t4000\\r\\nSample 4\\t\\t\\t\\t4000\\r\\nSample 5\\t25\\t\\t\\t\\\"\\\"\\\"\\r\\n samples, otus, data = parse_trflp(data.split('\\\\n'))\\r\\n\\r\\n samples_exp = [\\r\\n 'Samp.le.1',\\r\\n 'Sample.2',\\r\\n 'Sample.3',\\r\\n 'Sample.4',\\r\\n 'Sample.5']\\r\\n otus_exp = ['Bin__10bp_', 'Bin__20bp_', 'Bin__30bp_', 'Bin__40_bp_']\\r\\n data_exp = array([[1000, 0, 0, 0, 25],\\r\\n [2000, 2000, 0, 0, 0],\\r\\n [3000, 3000, 3000, 0, 0],\\r\\n [4000, 4000, 4000, 4000, 0]])\\r\\n\\r\\n self.assertEqual(samples, samples_exp)\\r\\n self.assertEqual(otus, otus_exp)\\r\\n assert_almost_equal(data, data_exp)\",\n \"def prepare_data(self,d):\\n train_loaders, train_iters = {}, {}\\n unlabeled_loaders, unlabeled_iters = {}, {}\\n for domain in opt.domains:\\n #CONVERT TO FLOAT32\\n features, target = torch.from_numpy(d[domain].X.todense().astype('float32')), torch.from_numpy(d[domain].y)#.reshape((-1,1))\\n train = data_utils.TensorDataset(features,target)\\n train_loaders[domain] = DataLoader(train, opt.batch_size, shuffle = True)\\n train_iters[domain] = iter(train_loaders[domain])\\n for domain in opt.unlabeled_domains:\\n features, target = torch.from_numpy(d[domain].X.todense().astype('float32')), torch.from_numpy(d[domain].y)#.reshape(-1,1))\\n uset = data_utils.TensorDataset(features,target)\\n unlabeled_loaders[domain] = DataLoader(uset,opt.batch_size, shuffle = True)\\n unlabeled_iters[domain] = iter(unlabeled_loaders[domain])\\n \\n return train_loaders, train_iters, unlabeled_loaders, unlabeled_iters\",\n \"def challenge23():\\n seed = random.randint(1, 2 ** 31)\\n orig_mt = MersenneTwister(seed)\\n copy_mt = MersenneTwister(0)\\n for index, p in enumerate(orig_mt.generate(624)):\\n copy_mt.y[index] = untempering(p)\\n return orig_mt, copy_mt\",\n \"def populate_targets(prog):\\n #proto = []\\n #for b in prog.args.inputs:\\n # for a in b:\\n # if a not in proto:\\n # proto += [ a ]\\n for p in prog.args.inputs:\\n t = Prog.Target(p.cert)\\n tlsa = Prog.Tlsa(p.params, None, None, None)\\n tlsa.publish = False\\n # hack the tlsa.publish member to mean that the Tlsa object\\n # corresponds to a specific record, as opposed to one set\\n # by the config file, where tlsa.publish is set to 'True'.\\n t.tlsa += [ tlsa ]\\n prog.target_list += [ t ]\\n return Prog.RetVal.ok\",\n \"def extract(self, data):\\n\\n # Read authors from the cell with NAME and COLLABORATORS variables\\n authors = []\\n for cell in data['cells']:\\n if cell['cell_type'] != 'code':\\n continue\\n\\n src = cell['source']\\n if src and src[0].startswith('NAME'):\\n authors = [*self._extract_name(src, 'NAME'),\\n *self._extract_name(src, 'COLLABORATORS')]\\n break\\n\\n return [x for x in authors if x.name]\",\n \"def parse(self, response):\\n\\n soup = self.get_soup(response.text)\\n try:\\n results = soup\\\\\\n .find('table', {'id': 'searchResult'})\\\\\\n .find_all('tr')[1:]\\n except AttributeError:\\n return\\n\\n for result in results:\\n torrent = items.Torrent(spider=self.name)\\n torrent['categories'] = [\\n self._category_map.get(\\n furl.furl(category.attrs['href']).path.segments[-1],\\n items.TorrentCategory.Unknown\\n ) for category in result.find(\\n 'td', {'class': 'vertTh'}\\n ).find_all('a')\\n ]\\n torrent['magnet'] = result.find(\\n 'a', {'href': re.compile('^magnet\\\\:.*')}\\n )['href']\\n torrent['hash'] = re.match(\\n r'.*magnet:\\\\?xt=urn:(?:btih)+:([a-zA-Z0-9]+).*',\\n torrent['magnet']\\n ).groups()[0].lower()\\n (torrent['seeders'], torrent['leechers'],) = tuple([\\n int(column.contents[0])\\n for column in result.find_all('td', {'align': 'right'})\\n ])\\n\\n result_links = result.find('a', {'class': 'detLink'})\\n if 'href' in result_links.attrs:\\n torrent['source'] = furl.furl(response.url).set(\\n path=result_links.attrs['href'], args={}\\n ).url\\n\\n torrent['name'] = result_links.contents[0].strip()\\n\\n result_desc = result.find('font', {'class': 'detDesc'})\\n (time_content, size_content,) = \\\\\\n result_desc.contents[0].split(',')[:2]\\n torrent['uploaded'] = self.parse_datetime(\\n time_content.split(' ')[-1],\\n formats=[\\n '%m-%d %Y',\\n '%m-%d %H:%M',\\n '%H:%M',\\n 'Y-day %H:%M'\\n ]\\n )\\n torrent['size'] = self.parse_size(\\n size_content.split(' ')[-1]\\n )\\n\\n try:\\n torrent['uploader'] = result_desc.find(\\n 'a', {'href': re.compile('^/user/.*')}\\n ).contents[0]\\n except AttributeError:\\n pass\\n\\n yield torrent\",\n \"def ptD(self):\\n \\n p = dict()\\n for d, episode in enumerate(self.episodes):\\n users, tempsU = episode[:,0], np.unique(episode[:,1]) # List of users of an episode and distinct\\n p[d] = np.ones(self.nbUser) # time\\n # A voir ******\\n for t in range(1, len(tempsU)): # For each time tU of the episode D\\n ptd, hasPred = 1., False\\n # Peut être parcourir seulement les users seraient plus interessants\\n for u, user in enumerate(users):\\n predU = episode[episode[:,1] < tempsU[t]][:,0] # List of predecessors of user u at time tU\\n for v in predU: # Proba que ça ne soit aucun des predecesseurs \\n if v in self.predecessors[user]: # qui l'infectent\\n ptd *= (1 - self.theta[v][user])\\n hasPred = True\\n if hasPred:\\n p[d][u] = 1-ptd # Proba que ça soit l'un deux.\\n\\n return p\",\n \"def test_read_participants_data():\\n bids_root = _TempDir()\\n bids_path = _bids_path.copy().update(root=bids_root, datatype='meg')\\n raw = _read_raw_fif(raw_fname, verbose=False)\\n\\n # if subject info was set, we don't roundtrip birthday\\n # due to possible anonymization in mne-bids\\n subject_info = {\\n 'hand': 1,\\n 'sex': 2,\\n }\\n raw.info['subject_info'] = subject_info\\n write_raw_bids(raw, bids_path, overwrite=True, verbose=False)\\n raw = read_raw_bids(bids_path=bids_path)\\n print(raw.info['subject_info'])\\n assert raw.info['subject_info']['hand'] == 1\\n assert raw.info['subject_info']['sex'] == 2\\n assert raw.info['subject_info'].get('birthday', None) is None\\n\\n # if modifying participants tsv, then read_raw_bids reflects that\\n participants_tsv_fpath = op.join(bids_root, 'participants.tsv')\\n participants_tsv = _from_tsv(participants_tsv_fpath)\\n participants_tsv['hand'][0] = 'n/a'\\n _to_tsv(participants_tsv, participants_tsv_fpath)\\n raw = read_raw_bids(bids_path=bids_path)\\n assert raw.info['subject_info']['hand'] == 0\\n assert raw.info['subject_info']['sex'] == 2\\n assert raw.info['subject_info'].get('birthday', None) is None\\n\\n # make sure things are read even if the entries don't make sense\\n participants_tsv = _from_tsv(participants_tsv_fpath)\\n participants_tsv['hand'][0] = 'righty'\\n participants_tsv['sex'][0] = 'malesy'\\n _to_tsv(participants_tsv, participants_tsv_fpath)\\n with pytest.warns(RuntimeWarning, match='Unable to map'):\\n raw = read_raw_bids(bids_path=bids_path)\\n assert raw.info['subject_info']['hand'] is None\\n assert raw.info['subject_info']['sex'] is None\\n\\n # make sure to read in if no participants file\\n raw = _read_raw_fif(raw_fname, verbose=False)\\n write_raw_bids(raw, bids_path, overwrite=True, verbose=False)\\n os.remove(participants_tsv_fpath)\\n with pytest.warns(RuntimeWarning, match='Participants file not found'):\\n raw = read_raw_bids(bids_path=bids_path)\\n assert raw.info['subject_info'] is None\",\n \"def extractor(self):\\n \\n if self._extractor is None:\\n if self.extractor_type == '29v2':\\n self._extractor = self.build_extractor_29layers_v2(name='extract29v2', block=self._res_block, layers=[1, 2, 3, 4])\\n elif self.extractor_type == '29':\\n self._extractor = self.build_extractor_29layers(name='extract29', block=self._res_block, layers=[1, 2, 3, 4])\\n elif self.extractor_type == '9':\\n self._extractor = self.build_extractor_9layers(name='extract9')\\n \\n if self.extractor_weights is not None:\\n self._extractor.load_weights(self.extractor_weights)\\n \\n return self._extractor\",\n \"def getDetailedSpeakers(self, speakers):\\n\\n new_speakers = []\\n try:\\n for speaker in speakers:\\n for user in self.users_data:\\n if speaker[\\\"username\\\"] == user[\\\"username\\\"]:\\n new_speakers.append(user)\\n return new_speakers\\n except KeyError as e:\\n print(e)\\n return \\\"Invalid\\\"\",\n \"def test_NeurosynthDecoder_featuregroup(testdata_laird):\\n ids = testdata_laird.ids[:5]\\n decoder = discrete.NeurosynthDecoder(feature_group=\\\"Neurosynth_TFIDF\\\")\\n decoder.fit(testdata_laird)\\n decoded_df = decoder.transform(ids=ids)\\n assert isinstance(decoded_df, pd.DataFrame)\",\n \"def test_ipv4v6_secondary_pdn_with_ded_bearer_multi_ue(self):\\n num_ues = 4\\n ue_ids = []\\n default_ip = []\\n sec_ip_ipv4 = []\\n sec_ip_ipv6 = []\\n\\n # APN of the secondary PDN\\n ims_apn = {\\n \\\"apn_name\\\": \\\"ims\\\", # APN-name\\n \\\"qci\\\": 5, # qci\\n \\\"priority\\\": 15, # priority\\n \\\"pre_cap\\\": 0, # preemption-capability\\n \\\"pre_vul\\\": 0, # preemption-vulnerability\\n \\\"mbr_ul\\\": 200000000, # MBR UL\\n \\\"mbr_dl\\\": 100000000, # MBR DL\\n \\\"pdn_type\\\": 2, # PDN Type 0-IPv4,1-IPv6,2-IPv4v6\\n }\\n # UL Flow description #1\\n ulFlow1 = {\\n \\\"ipv4_dst\\\": \\\"192.168.129.42\\\", # IPv4 destination address\\n \\\"tcp_dst_port\\\": 5002, # TCP dest port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.UPLINK, # Direction\\n }\\n\\n # UL Flow description #2\\n ulFlow2 = {\\n \\\"ipv4_dst\\\": \\\"192.168.129.42\\\", # IPv4 destination address\\n \\\"tcp_dst_port\\\": 5001, # TCP dest port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.UPLINK, # Direction\\n }\\n\\n # UL Flow description #3\\n ulFlow3 = {\\n \\\"ipv6_dst\\\": \\\"5e90:db7b:b18e::1556\\\", # IPv6 destination address\\n \\\"tcp_dst_port\\\": 5003, # TCP dest port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.UPLINK, # Direction\\n }\\n\\n # DL Flow description #1\\n dlFlow1 = {\\n \\\"ipv4_src\\\": \\\"192.168.129.42\\\", # IPv4 source address\\n \\\"tcp_src_port\\\": 5001, # TCP source port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.DOWNLINK, # Direction\\n }\\n\\n # DL Flow description #2\\n dlFlow2 = {\\n \\\"ipv4_src\\\": \\\"192.168.129.64\\\", # IPv4 source address\\n \\\"tcp_src_port\\\": 5002, # TCP source port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.DOWNLINK, # Direction\\n }\\n\\n # DL Flow description #3\\n dlFlow3 = {\\n \\\"ipv6_src\\\": \\\"6e31:1a95:1e7c::df1\\\", # IPv6 source address\\n \\\"tcp_src_port\\\": 5003, # TCP source port\\n \\\"ip_proto\\\": FlowMatch.IPPROTO_TCP, # Protocol Type\\n \\\"direction\\\": FlowMatch.DOWNLINK, # Direction\\n }\\n\\n # Flow lists to be configured\\n flow_list = [\\n ulFlow1,\\n ulFlow2,\\n ulFlow3,\\n dlFlow1,\\n dlFlow2,\\n dlFlow3,\\n ]\\n\\n # QoS\\n qos = {\\n \\\"qci\\\": 1, # qci value [1 to 9]\\n \\\"priority\\\": 1, # Range [0-255]\\n \\\"max_req_bw_ul\\\": 10000000, # MAX bw Uplink\\n \\\"max_req_bw_dl\\\": 15000000, # MAX bw Downlink\\n \\\"gbr_ul\\\": 1000000, # GBR Uplink\\n \\\"gbr_dl\\\": 2000000, # GBR Downlink\\n \\\"arp_prio\\\": 1, # ARP priority\\n \\\"pre_cap\\\": 1, # pre-emption capability\\n \\\"pre_vul\\\": 1, # pre-emption vulnerability\\n }\\n\\n policy_id = \\\"ims\\\"\\n\\n self._s1ap_wrapper.configUEDevice(num_ues)\\n for i in range(num_ues):\\n req = self._s1ap_wrapper.ue_req\\n ue_id = req.ue_id\\n\\n apn_list = [ims_apn]\\n self._s1ap_wrapper.configAPN(\\n \\\"IMSI\\\" + \\\"\\\".join([str(i) for i in req.imsi]), apn_list,\\n )\\n print(\\n \\\"*********************** Running End to End attach for UE id \\\",\\n ue_id,\\n )\\n\\n print(\\\"***** Sleeping for 5 seconds\\\")\\n time.sleep(5)\\n # Attach\\n attach = self._s1ap_wrapper.s1_util.attach(\\n ue_id,\\n s1ap_types.tfwCmd.UE_END_TO_END_ATTACH_REQUEST,\\n s1ap_types.tfwCmd.UE_ATTACH_ACCEPT_IND,\\n s1ap_types.ueAttachAccept_t,\\n )\\n addr = attach.esmInfo.pAddr.addrInfo\\n default_ip.append(ipaddress.ip_address(bytes(addr[:4])))\\n ue_ids.append(ue_id)\\n\\n # Wait on EMM Information from MME\\n self._s1ap_wrapper._s1_util.receive_emm_info()\\n\\n print(\\\"***** Sleeping for 5 seconds\\\")\\n time.sleep(5)\\n self._s1ap_wrapper._ue_idx = 0\\n for i in range(num_ues):\\n req = self._s1ap_wrapper.ue_req\\n ue_id = req.ue_id\\n\\n apn = \\\"ims\\\"\\n # PDN Type 2 = IPv6, 3 = IPv4v6\\n pdn_type = 3\\n # Send PDN Connectivity Request\\n self._s1ap_wrapper.sendPdnConnectivityReq(\\n ue_id, apn, pdn_type=pdn_type,\\n )\\n # Receive PDN CONN RSP/Activate default EPS bearer context request\\n response = self._s1ap_wrapper.s1_util.get_response()\\n assert response.msg_type == s1ap_types.tfwCmd.UE_PDN_CONN_RSP_IND.value\\n act_def_bearer_req = response.cast(s1ap_types.uePdnConRsp_t)\\n\\n addr = act_def_bearer_req.m.pdnInfo.pAddr.addrInfo\\n sec_ip_ipv4.append(ipaddress.ip_address(bytes(addr[8:12])))\\n\\n print(\\n \\\"********************** Sending Activate default EPS bearer \\\"\\n \\\"context accept for APN-%s, UE id-%d\\\" % (apn, ue_id),\\n )\\n print(\\n \\\"********************** Added default bearer for apn-%s,\\\"\\n \\\" bearer id-%d, pdn type-%d\\\"\\n % (\\n apn,\\n act_def_bearer_req.m.pdnInfo.epsBearerId,\\n pdn_type,\\n ),\\n )\\n\\n # Receive Router Advertisement message\\n response = self._s1ap_wrapper.s1_util.get_response()\\n assert response.msg_type == s1ap_types.tfwCmd.UE_ROUTER_ADV_IND.value\\n routerAdv = response.cast(s1ap_types.ueRouterAdv_t)\\n print(\\n \\\"******************* Received Router Advertisement for APN-%s\\\"\\n \\\" ,bearer id-%d\\\" % (apn, routerAdv.bearerId),\\n )\\n\\n ipv6_addr = \\\"\\\".join([chr(i) for i in routerAdv.ipv6Addr]).rstrip(\\n \\\"\\\\x00\\\",\\n )\\n print(\\\"******* UE IPv6 address: \\\", ipv6_addr)\\n sec_ip_ipv6.append(ipaddress.ip_address(ipv6_addr))\\n\\n print(\\\"***** Sleeping for 5 seconds\\\")\\n time.sleep(5)\\n\\n # Add dedicated bearer\\n print(\\\"********************** Adding dedicated bearer to ims PDN\\\")\\n print(\\n \\\"********************** Sending RAR for IMSI\\\",\\n \\\"\\\".join([str(i) for i in req.imsi]),\\n )\\n self._sessionManager_util.send_ReAuthRequest(\\n \\\"IMSI\\\" + \\\"\\\".join([str(i) for i in req.imsi]),\\n policy_id,\\n flow_list,\\n qos,\\n )\\n response = self._s1ap_wrapper.s1_util.get_response()\\n assert response.msg_type == s1ap_types.tfwCmd.UE_ACT_DED_BER_REQ.value\\n act_ded_ber_req_ims_apn = response.cast(\\n s1ap_types.UeActDedBearCtxtReq_t,\\n )\\n self._s1ap_wrapper.sendActDedicatedBearerAccept(\\n req.ue_id, act_ded_ber_req_ims_apn.bearerId,\\n )\\n print(\\n \\\"************* Added dedicated bearer\\\",\\n act_ded_ber_req_ims_apn.bearerId,\\n )\\n\\n print(\\\"***** Sleeping for 10 seconds\\\")\\n time.sleep(10)\\n # ipv4 default pdn + ipv4v6(ims) pdn +\\n # dedicated bearer for ims pdn for 4 UEs\\n num_ul_flows = 12\\n for i in range(num_ues):\\n dl_flow_rules = {\\n default_ip[i]: [],\\n sec_ip_ipv4[i]: [flow_list],\\n sec_ip_ipv6[i]: [flow_list],\\n }\\n # Verify if flow rules are created\\n self._s1ap_wrapper.s1_util.verify_flow_rules(\\n num_ul_flows, dl_flow_rules,\\n )\\n\\n print(\\\"***** Sleeping for 5 seconds\\\")\\n time.sleep(5)\\n for ue in ue_ids:\\n print(\\n \\\"******************* Running UE detach (switch-off) for \\\",\\n \\\"UE id \\\",\\n ue,\\n )\\n # Now detach the UE\\n self._s1ap_wrapper.s1_util.detach(\\n ue, s1ap_types.ueDetachType_t.UE_SWITCHOFF_DETACH.value, False,\\n )\",\n \"def test_parse_trflp_headerless(self):\\r\\n\\r\\n data = \\\\\\r\\n \\\"\\\"\\\"Samp-le 1\\t1000\\t2000\\t3000\\t4000\\r\\nSample 2\\t\\t2000\\t3000\\t4000\\r\\nSample 3\\t\\t\\t3000\\t4000\\r\\nSample 4\\t\\t\\t\\t4000\\r\\nSample_5__\\t25\\t\\t\\t\\\"\\\"\\\"\\r\\n samples, otus, data = parse_trflp(data.split('\\\\n'))\\r\\n\\r\\n samples_exp = [\\r\\n 'Samp.le.1',\\r\\n 'Sample.2',\\r\\n 'Sample.3',\\r\\n 'Sample.4',\\r\\n 'Sample.5..']\\r\\n otus_exp = ['Bin__0', 'Bin__1', 'Bin__2', 'Bin__3']\\r\\n data_exp = array([[1000, 0, 0, 0, 25],\\r\\n [2000, 2000, 0, 0, 0],\\r\\n [3000, 3000, 3000, 0, 0],\\r\\n [4000, 4000, 4000, 4000, 0]])\\r\\n\\r\\n self.assertEqual(samples, samples_exp)\\r\\n self.assertEqual(otus, otus_exp)\\r\\n assert_almost_equal(data, data_exp)\",\n \"def get_teacher_class():\\n class_data = query_db(\\n \\\"SELECT id, name FROM classes WHERE teacher_id = ?;\\\", [flask.session[\\\"id\\\"]]\\n )\\n classes = []\\n for part in class_data:\\n class_dict = {}\\n class_dict[\\\"id\\\"] = part[0]\\n class_dict[\\\"name\\\"] = str(part[1])\\n classes.append(class_dict)\\n return classes\",\n \"def scrape(self):\\n locations = (\\n 'Half Moon Bay, California',\\n 'Huntington Beach, California',\\n 'Providence, Rhode Island',\\n 'Wrightsville Beach, North Carolina',\\n )\\n for location in locations:\\n tides = self.extract_tides(location)\\n print('\\\\nLocation:', location)\\n for dt, tide_rows in tides.items():\\n for tide_row in tide_rows:\\n print(dt, tide_row)\",\n \"def unpack_data(self, data):\\n\\n datadict = {'filenames': [], 'features': [], 'labels': [] }\\n\\n for l in data:\\n \\n datadict['filenames'].append(l[0])\\n datadict['features'].append(l[1])\\n datadict['labels'].append(l[2])\\n \\n return datadict\",\n \"def test_practitioner_5(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f003-mv.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_5(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_5(inst2)\",\n \"def test_practitioner_9(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"practitioner-example-f204-ce.json\\\"\\n inst = practitioner.Practitioner.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Practitioner\\\" == inst.resource_type\\n\\n impl_practitioner_9(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Practitioner\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = practitioner.Practitioner(**data)\\n impl_practitioner_9(inst2)\",\n \"def prep_steps(self, *, yearmon: str) -> List[Step]:\\n steps = []\\n\\n year, month = dates.parse_yearmon(yearmon)\\n\\n monthly_vars = ['2m_temperature', 'total_precipitation']\\n hourly_vars = ['total_precipitation']\\n\\n steps += era5.download(output_filename=era5.filename(self.source, 'month', yearmon),\\n duration='month',\\n yearmon=yearmon,\\n variables=monthly_vars)\\n\\n if year >= 1979:\\n steps += era5.download(output_filename=era5.filename(self.source, 'hour', yearmon),\\n duration='hour',\\n yearmon=yearmon,\\n variables=hourly_vars)\\n steps += era5.calc_wetdays(input_filename=era5.filename(self.source, 'hour', yearmon),\\n output_filename=self.p_wetdays(yearmon=yearmon).file,\\n threshold_mm=0.1)\\n\\n return steps\",\n \"def _unpack(self, headerBytes):\\n xtraH = struct.unpack(self.PACKAGING_FORMAT, headerBytes)\\n\\n self.qubit_id = xtraH[0]\\n self.remote_app_id = xtraH[1]\\n self.remote_node = xtraH[2]\\n self.datetime = xtraH[3]\\n self.remote_port = xtraH[4]\\n self.outcome = xtraH[5]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6385784","0.5618853","0.54759747","0.54556435","0.5198481","0.5048664","0.49990323","0.4985974","0.49433678","0.4874634","0.47989118","0.47896883","0.4787353","0.47753018","0.4730215","0.46576267","0.46548298","0.46495625","0.4646888","0.46451658","0.46388948","0.454687","0.4523255","0.45193264","0.4513335","0.449714","0.44720286","0.44492343","0.4447388","0.44390026","0.44346026","0.44097203","0.43845743","0.43841347","0.43805403","0.43672597","0.43646434","0.43463144","0.43346798","0.43297213","0.43160826","0.43066314","0.43051037","0.43034545","0.42883697","0.42834306","0.4271848","0.4259581","0.42592984","0.42537972","0.4251826","0.4250439","0.42376214","0.42317843","0.42263317","0.4221276","0.42098","0.42023504","0.4202325","0.420112","0.42002514","0.41996342","0.4196491","0.4195288","0.41941756","0.4189602","0.41756892","0.41734007","0.41723236","0.416866","0.4167351","0.41578582","0.41512817","0.41453055","0.41438392","0.41363174","0.41303328","0.4130061","0.41295213","0.4114103","0.41135308","0.4110586","0.4109239","0.40998915","0.40940896","0.40899107","0.4088083","0.4087046","0.407948","0.40701076","0.4069658","0.40660182","0.40649423","0.40626246","0.40607655","0.40518227","0.40490636","0.40440974","0.40414512","0.40369195"],"string":"[\n \"0.6385784\",\n \"0.5618853\",\n \"0.54759747\",\n \"0.54556435\",\n \"0.5198481\",\n \"0.5048664\",\n \"0.49990323\",\n \"0.4985974\",\n \"0.49433678\",\n \"0.4874634\",\n \"0.47989118\",\n \"0.47896883\",\n \"0.4787353\",\n \"0.47753018\",\n \"0.4730215\",\n \"0.46576267\",\n \"0.46548298\",\n \"0.46495625\",\n \"0.4646888\",\n \"0.46451658\",\n \"0.46388948\",\n \"0.454687\",\n \"0.4523255\",\n \"0.45193264\",\n \"0.4513335\",\n \"0.449714\",\n \"0.44720286\",\n \"0.44492343\",\n \"0.4447388\",\n \"0.44390026\",\n \"0.44346026\",\n \"0.44097203\",\n \"0.43845743\",\n \"0.43841347\",\n \"0.43805403\",\n \"0.43672597\",\n \"0.43646434\",\n \"0.43463144\",\n \"0.43346798\",\n \"0.43297213\",\n \"0.43160826\",\n \"0.43066314\",\n \"0.43051037\",\n \"0.43034545\",\n \"0.42883697\",\n \"0.42834306\",\n \"0.4271848\",\n \"0.4259581\",\n \"0.42592984\",\n \"0.42537972\",\n \"0.4251826\",\n \"0.4250439\",\n \"0.42376214\",\n \"0.42317843\",\n \"0.42263317\",\n \"0.4221276\",\n \"0.42098\",\n \"0.42023504\",\n \"0.4202325\",\n \"0.420112\",\n \"0.42002514\",\n \"0.41996342\",\n \"0.4196491\",\n \"0.4195288\",\n \"0.41941756\",\n \"0.4189602\",\n \"0.41756892\",\n \"0.41734007\",\n \"0.41723236\",\n \"0.416866\",\n \"0.4167351\",\n \"0.41578582\",\n \"0.41512817\",\n \"0.41453055\",\n \"0.41438392\",\n \"0.41363174\",\n \"0.41303328\",\n \"0.4130061\",\n \"0.41295213\",\n \"0.4114103\",\n \"0.41135308\",\n \"0.4110586\",\n \"0.4109239\",\n \"0.40998915\",\n \"0.40940896\",\n \"0.40899107\",\n \"0.4088083\",\n \"0.4087046\",\n \"0.407948\",\n \"0.40701076\",\n \"0.4069658\",\n \"0.40660182\",\n \"0.40649423\",\n \"0.40626246\",\n \"0.40607655\",\n \"0.40518227\",\n \"0.40490636\",\n \"0.40440974\",\n \"0.40414512\",\n \"0.40369195\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67790115"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7181,"cells":{"query":{"kind":"string","value":"Prune teachers to only use the first num_teachers of them."},"document":{"kind":"string","value":"def prune_teachers(self):\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\n self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]\n self.teacher_critics = self.teacher_critics[: self.num_teachers]\n self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cleanOrphanedLearners(self):\n\n # Before deleting Learners, ensure that if any Learners that are about to be\n # deleted point to a Team as their action, then that Team's count of\n # referincing Learners is decremented.\n for learner in self.learner_pop:\n if learner.getNumReferencingTeams() == 0 and not learner.isActionAtomic():\n learner.action.decrementNumReferencingLearners()\n\n # Remove all orphaned Learners from the Learner population\n self.learner_pop = [l for l in self.learner_pop if not l.getNumReferencingTeams() == 0]","def prune(self, min_count):\n if not self.sorted:\n self.sort()\n for k, count in enumerate(self.Nx):\n if count < min_count:\n self.truncate(k)\n break","def _prune_candidates(self, beam_width=None):\n if beam_width is None:\n beam_width = self.beam_width\n if len(self.candidates) <= beam_width:\n return\n neg_scores = np.array([-cand.logp_total() for cand in self.candidates])\n parted_indices = np.argpartition(neg_scores, beam_width - 1)\n self.candidates = np.array(self.candidates)[parted_indices[:beam_width]].tolist()","def prune(self):\n self.sort(key=lambda chunk: chunk.probability)\n del self[:-self.model.num_parses]","def prune_features(self, verbose=False):\n # Collect all features and prune those occurring only once.\n features = defaultdict(int)\n for k in self.utterance_features:\n for f in self.utterance_features[k]:\n features[f] += 1\n\n if verbose:\n print \"Total number of features: \", len(features)\n\n self.remove_features = []\n for k in features:\n if features[k] <= 2:\n self.remove_features.append(k)\n\n if verbose:\n print \"Number of unique features: \", len(self.remove_features)\n\n self.remove_features = set(self.remove_features)\n for k in self.utterance_features:\n self.utterance_features[k].prune(self.remove_features)\n\n features = defaultdict(int)\n for k in self.utterance_features:\n for f in self.utterance_features[k]:\n features[f] += 1\n\n if verbose:\n print \"Total number of features: \", len(features)","def load_teachers(self):\n # Get the experiment's directory to load from\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\n self.load_teacher_experiment(ex_dir)\n if len(self.teacher_policies) < self.num_teachers:\n print(\n f\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\"\n )\n self.load_teachers()","def init_prune_list(self):\n\t\tfor i in range(1, len(self.elements)+1):\n\t\t\tcount = self.getSupport({i})\n\t\t\tif(count >= self.support):\n\t\t\t\tself.prune_list[(i,)] = count","def helper(reviewer: Any, graph: Graph) -> set:\n reviewers_so_far = set()\n\n for movie in graph.get_neighbours(reviewer):\n for user in graph.get_neighbours(movie):\n if graph.get_weight(user, movie) >= 8:\n reviewers_so_far.add(user)\n return reviewers_so_far","def prune_features(self):\r\n for i, features in enumerate(self.curr_features):\r\n # Continue if the number of features in this grid does\r\n # not exceed the upper bound.\r\n if len(features) <= self.config.grid_max_feature_num:\r\n continue\r\n self.curr_features[i] = sorted(features, key=lambda x:x.lifetime, \r\n reverse=True)[:self.config.grid_max_feature_num]","def _remove_experts(self):\n self.experts = [ex for ex in self.experts if np.mean(\n ex.weight) >= self.theta]","def delete_n_volunteers(app_id):\r\n delete_memoized(n_volunteers, app_id)","def prune_(self):\n idx = self.factor_lams() > 0\n self.factors = [f[:, idx] for f in self.factors]\n self.rank = np.sum(idx)","def delSpillOppIRunder(trekk, vinnendeSekvenser):\n runder = []\n i = 0\n runde = []\n while i < len(trekk):\n runde.append(trekk[i])\n personX, personO = delOppRunde(runde)\n if len(runde) >= 5 and (sjekkVinnerRunde(personX, vinnendeSekvenser) or sjekkVinnerRunde(personO, vinnendeSekvenser)):\n runder.append(runde)\n runde = []\n if len(runde) == 9:\n runder.append(runde) \n runde = []\n i += 1\n return runder","def delete_n_anonymous_volunteers(app_id):\r\n delete_memoized(n_anonymous_volunteers, app_id)","def prune_tree(tree, cutoff, posteriors):\n new_tree = []\n for e in tree:\n try:\n if posteriors[e] > cutoff:\n new_tree.append(e)\n except KeyError:\n if posteriors[e[::-1]] > cutoff:\n new_tree.append(e)\n return new_tree","def prune(neuron,\n number_of_nodes):\n n = len(neuron.nodes_list)\n for i in range(n - number_of_nodes):\n index = shortest_tips(neuron)\n neuron = remove_node(neuron, index)\n return neuron","def remove_numbers(self):\n for i in range(len(self.board.board[0])):\n while self.board.board[i].count(0) < 6:\n random_val = random.randint(0, 8)\n self.board.update_board((i, random_val), 0)","def deepscore(teacher):\n if teacher == None:\n print(\"Not a valid teacher\")\n return\n if teacher.get('rmpdata') == None: snc.rateThisProfessor(teacher,instructors)\n print(\"# \" + teacher['name'])\n scoreTeacherlegacy(teacher)\n scoreTeacherues(teacher)\n scoreTeacherrmp(teacher)","def _prune_heads(self, heads_to_prune):\n for layer, heads in heads_to_prune.items():\n group_idx = int(layer / self.config.inner_group_num)\n inner_group_idx = int(layer - group_idx * self.config.inner_group_num)\n self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)","def generate_teachers(G, school_type, N_classes, family_member_counter, \n\t\t\t\t\t family_counter, teacher_p_adults, teacher_p_children):\n\tN_teachers = get_N_teachers(school_type, N_classes)\n\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\n\tG.add_nodes_from(teacher_nodes)\n\t\n\tfor t in teacher_nodes:\n\t\tfamily_nodes = [t]\n\t\t# draw a random number of children and adults for the family\n\t\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\n\t\t\n\t\tages = list(ages)\n\t\tfor adult in range(N_adults - 1):\n\t\t\tages.append(20.5) # default age for adults\n\t\t\n\t\t# add the family member nodes and their attributes to the graph\n\t\tfor age in ages:\n\t\t\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\n\t\t\tfamily_nodes.append(family_member_ID)\n\t\t\tG.add_node(family_member_ID)\n\t\t\tfamily_member_counter += 1\n\t\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t\t{family_member_ID:{'type':'family_member',\n\t\t\t\t\t\t\t\t\t\t 'age':age,\n\t\t\t\t\t\t\t\t\t\t 'family':family_counter,\n\t\t\t\t\t\t\t\t\t\t 'unit':'family'}})\n\t\t\t\t\t\n\t\t# finally, also set the teacher's node attributes\n\t\tnx.set_node_attributes(G, \\\n\t\t\t\t\t{t:{'type':'teacher', \n\t\t\t\t\t\t# Note: 20.5 is the age at which\n\t\t\t\t\t\t# the symptom and transmission risk\n\t\t\t\t\t\t# is that of an adult\n\t\t\t\t\t\t'age':20.5,\n\t\t\t\t\t\t'unit':'faculty_room',\n\t\t\t\t\t\t'family':family_counter}})\n\t\tfamily_counter += 1","def tweet_cleaner(tweets):\n n_tweets = {}\n clean = cleaner()\n for tweet in tweets:\n text = clean.clean_text(tweets[tweet][\"text\"])\n if len(text) > 15:\n n_tweets[tweet] = tweets[tweet]\n return n_tweets","def _prune(self):\n while len(self.data) > self.limit:\n self.data.popleft()","def distribute_reviews(asmt, perStudent):\n\n # Get all submissions for this assignment.\n subs = AssignmentSubmission.objects.filter(submission_for=asmt)\n #print subs\n course = asmt.course_code\n users = ReviewUser.objects.filter(courses=course)\n numUsers = len(users)\n latestSubmissions = get_latest(course, asmt, subs, users)\n # print 'filtered subs are ', latestSubmissions\n numSubs = len(latestSubmissions)\n print 'number of submissions: ', numSubs\n asmt.reviews_per_student = perStudent\n \n for user in users:\n print user\n # Don't want to make staff review stuff.\n if(user.djangoUser.is_staff or user.djangoUser.is_superuser):\n continue\n\n review = AssignmentReview.objects.get_or_create(by=user, assignment=asmt)[0]\n reviewsAssigned = len(review.submissions.all())\n # In case lecturer assigns reviews multiple times, or number of reviews changes. \n print (\"reviewsAssigned\", reviewsAssigned)\n if(reviewsAssigned < perStudent):\n for i in range(perStudent-reviewsAssigned):\n index = random.randint(0, numSubs-1)\n submission = latestSubmissions[index]\n \n # Make sure user isn't assigned to review their own submission\n # NB in the amazing edge case where this user is the only person who \n # submitted the assignment, we get an infinite loopevi\n # Also don't want student to be assigned same submission twice.\n while((submission.by == user) or (submission in review.submissions.all())):\n print submission.by == user\n #print review.submissions.all()\n #print \"in the while loop\"\n index = random.randint(0, numSubs-1)\n print index\n submission = latestSubmissions[index]\n \n review.submissions.add(submission)\n # Lecturer has reduced number of reviews per user \n elif(reviewsAssigned > perStudent):\n # Number of submissions we want to de-assign\n deassign = reviewsAssigned - perStudent \n # Get all the submissions the student has not yet completed.\n incomplete = []\n for sub in review.submissions.all():\n annotationsDone = AssignmentReview.numAnnotations(review, sub)\n if(annotationsDone < asmt.min_annotations):\n incomplete.append((sub, annotationsDone))\n\n # Choose the least-complete deassign submission reviews to remove.\n # Ascending list \n sortedList = sorted(incomplete, key=itemgetter(1))\n removeFromIncomplete = min(deassign, len(sortedList))\n for i in range(removeFromIncomplete):\n review.submissions.remove(sortedList[i][0]) \n \n if deassign > len(sortedList):\n for i in range(deassign):\n review.submissions.remove(reviews.submissions.all()[i])\n else: # nothing to assign or remove\n return \n \n return","def prune(self, age_hours):\r\n pass","def remove_promotional_tweets(tweets):\n clean = cleaner()\n n_tweets = {}\n for tweet in tweets:\n if not clean.linkChecker(tweets[tweet][\"text\"]):\n n_tweets[tweet] = tweets[tweet]\n return n_tweets","def _prunelowestweight(self):\r\n # note: must be called with acquired self._lock!\r\n numentries = len(self._dict)\r\n if numentries >= self.maxentries:\r\n # evict according to entry's weight\r\n items = [(entry.weight, key) for key, entry in self._dict.iteritems()]\r\n items.sort()\r\n index = numentries - self.prunenum\r\n if index > 0:\r\n for weight, key in items[:index]:\r\n del self._dict[key]","def suggest_movies(self, reviewer: Any, other: Any) -> list[Any]:\n potential_recs = self.get_neighbours(other)\n suggestions_so_far = []\n neighbours = self.get_neighbours(reviewer)\n\n for p_rec in potential_recs:\n if p_rec not in neighbours and self.get_weight(other, p_rec) >= 9:\n suggestions_so_far.append(p_rec)\n\n return suggestions_so_far","def prune(self, upper, lower):\n # max_count = sorted([self.counts[key] for key in self.counts.keys()])[::-1][upper]\n max_count = upper\n\n print('Removed all words that occur less than {} times and more than {} times'.format(lower, upper))\n for i, doc in enumerate(self.docs):\n new_doc = []\n for word in doc:\n if self.counts[word] <= max_count and self.counts[word] > lower:\n new_doc.append(word)\n self.docs[i] = new_doc","def ensemble_preds(dataset, nb_teachers, stdnt_data):\n\n # Compute shape of array that will hold probabilities produced by each\n # teacher, for each training point, and each output class\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\n\n # Create array that will hold result\n result = np.zeros(result_shape, dtype=np.float32)\n\n # Get predictions from each teacher\n for teacher_id in xrange(nb_teachers):\n # Compute path of checkpoint file for teacher model with ID teacher_id\n if FLAGS.deeper:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\n else:\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\n\n # Get predictions on our training data and store in result array\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\n\n # This can take a while when there are a lot of teachers so output status\n print(\"Computed Teacher \" + str(teacher_id) + \" softmax predictions\")\n\n return result","def remove_n_nos(self, num_nos):\n for i in range(num_nos):\n elem = random.randint(1, 11 ** 4)\n self.remove(elem)","def delete_n_registered_volunteers(app_id):\r\n delete_memoized(n_registered_volunteers, app_id)","def get_teacher_num(self):\n return self.teacher_set.all().count()","def _next_to_prune(tree, children=None):\n\n if children is None:\n children = tree.children\n\n t_nodes = _get_terminal_nodes(children)\n g_i = tree.init_error[t_nodes] - tree.best_error[t_nodes]\n\n return t_nodes[np.argmin(g_i)]","def _reweight_and_discard_irrelevant(self, weighted_sample_pool, t):\n tmp = []\n ret = []\n wc = self.classifiers[t]\n theta_a = wc.theta_a\n theta_b = wc.theta_b\n\n norm_factor = 0\n discarded = 0\n for patch, w in weighted_sample_pool:\n response = self.h_t(patch, t)\n # if t > 3:\n # if response < theta_a or response > theta_b: # throw it away\n # discarded += 1\n # continue\n r = self.classify(patch)\n label = patch.label\n new_weight = w * np.exp(-label * r)\n\n tmp.append([patch, new_weight])\n norm_factor += new_weight\n for patch, w in tmp: # normalize weights\n normalized_weight = w / norm_factor\n ret.append([patch, normalized_weight])\n print \"Discarded %d training samples\" % discarded\n return ret","def get_teachers(self):\n query = Teacher.all().order('teacher')\n return query.fetch()","def _prune_heads(self, heads_to_prune):\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)","def _prune_heads(self, heads_to_prune):\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)","def _prune_heads(self, heads_to_prune):\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)","def prune_trie(trie, threshold):\n\tnode = trie.root\n\tpq = []\n\tfor i in node.children.keys():\n\t\tpq.append((node.children[i],node.children[i].char))\n\twhile len(pq) > 0:\n\t\tcur_node, char = pq.pop()\n\t\tif cur_node.isEnd == False:\n\t\t\tfor i in cur_node.children.keys():\n\t\t\t\tpq.append((cur_node.children[i],char + cur_node.children[i].char))\n\t\telse:\n\t\t\tif cur_node.weight < threshold:\n\t\t\t\tdelete(trie, char)\n\t\t\telse:\n\t\t\t\tcontinue\n\treturn trie","def _prior_teach(self):\n pass","def _prune_heads(self, heads_to_prune):\n\n for layer, heads in heads_to_prune.items():\n\n self.encoder.layer[layer].attention.prune_heads(heads)","def test18_remove_first_student_with_teacher(self):\n first_student = self.students_page.students_table()[0]\n students_list_without_first_student = self.students_page. \\\n click_edit_students_list_button(). \\\n click_delete_first_student_button(). \\\n click_exit_students_list_editor_button(). \\\n students_table()\n self.assertEqual(self.main_page.get_current_url(),\n data['expected_url'])\n self.assertNotIn(first_student,\n students_list_without_first_student)","def delete_all_teachers(connection):\r\n with connection:\r\n return connection.execute(DELETE_ALL_TEACHERS)","def remove_prob():\n\n #var for tuple\n weekly_probs = display_problems()\n\n cc_name1 = weekly_probs[0]\n url_link1 = weekly_probs[1]\n cc_name2 = weekly_probs[2]\n url_link2 = weekly_probs[3]\n cc_name3 = weekly_probs[4]\n url_link3 = weekly_probs[5]\n\n names_to_remove = [cc_name1, cc_name2, cc_name3]\n urls_to_remove = [url_link1, url_link2, url_link3]\n\n for name in names_to_remove:\n pass\n\n for url in urls_to_remove:\n pass\n\n return names_to_remove, urls_to_remove","def wipe_all_topics(self):\n # doc_count = self.posts_read.find({'subreddit':self.subreddit, 'postwise.topic_assignment':{'$exists':True}}).count()\n doc_count = self.posts_write.update({'subreddit':self.subreddit, 'postwise.topic_assignment':{'$exists':True}},\n {'$unset':{'postwise.topic_distro':True,'postwise.topic_assignment':True}}, multi=True)\n\n print 'wiped topics from %i documents' % doc_count['nModified']","def review(self):\n for player in self.team.players:\n player.career.potentially_retire()","def filterByReview(minReviewNum, stories):\n\tsortedstories = filter(lambda s: s[\"reviews\"] >= minReviewNum, stories)\n\treturn [x for x in sortedstories]","def prune(self, alignment_infos):\n alignments = []\n best_score = 0\n\n for alignment_info in alignment_infos:\n score = IBMModel4.model4_prob_t_a_given_s(alignment_info, self)\n best_score = max(score, best_score)\n alignments.append((alignment_info, score))\n\n threshold = IBMModel5.MIN_SCORE_FACTOR * best_score\n alignments = [a[0] for a in alignments if a[1] > threshold]\n return set(alignments)","def test_with_data(data):\r\n i = 0\r\n tuning_set = []\r\n training_set = []\r\n num_reps = len(data)\r\n for i in range(0, num_reps-1):\r\n if (i % 4 == 0):\r\n tuning_set.append(data[i])\r\n else:\r\n training_set.append(data[i])\r\n\r\n unpruned = induce_node_tree(training_set, original_issues, \"D\", -1)\r\n pruned = prune_tree(unpruned, tuning_set)\r\n\r\n return pruned","def pickten(divforround):\n seeds = [seed for seed in \"abcdefghij\"]\n players = []\n for seed in seeds:\n k = list(filter(lambda stu: stu.seed == seed, divforround))\n players.append(sample(k, 1)[0])\n for player in players:\n divforround.remove(player)\n return players","def prune(self):\n target_user_ids = self.get_queryset().values_list('id', flat=True)\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\n drip=self.drip_model,\n user__id__in=target_user_ids)\\\n .values_list('user_id', flat=True)\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)","def trim_texts_by_count(self, min_count=100):\n\n for tid, text in self.graph.nodes(data=True):\n if text['count'] < min_count:\n self.graph.remove_node(tid)","def _reducedProtToPeps(protToPeps, proteins):\n return {k: v for k, v in viewitems(protToPeps) if k not in proteins}","def _prune_vocab(self,\n counter,\n n_total_files,\n n_seen_files,\n min_count=None,\n max_count=None,\n trim_rule=None):\n ## Initialize Exclusion List\n if not hasattr(self, \"_exclusion_list\"):\n self._exclusion_list = set()\n ## Compute Thresholds (With Margin)\n if min_count is None:\n min_count = None if self._min_token_freq == 0 else math.ceil(self._min_token_freq * 0.8 * n_seen_files / n_total_files)\n if max_count is None:\n max_count = None if self._max_token_freq is None else math.ceil(self._max_token_freq * 1.2 * n_seen_files / n_total_files)\n ## Get Acceptable Terms\n accept = set()\n for ngram, count in counter.items():\n keep = self._keep_vocab_item(ngram,\n count,\n min_count=min_count,\n max_count=max_count,\n trim_rule=trim_rule)\n if keep:\n accept.add(ngram)\n ## Update and Return Count\n counter_pruned = Counter(dict((n, c) for n, c in counter.items() if n in accept))\n return counter_pruned","def prune_ratios(ratios, bad_words):\n for word in bad_words:\n ratios.pop(word, None)","def trim_recommendation_pool(self, n):\n # {k:v for k, v in list(d.items())[:2]}\n self.recommendation_pool = {\n k: v for k, v in list(self.recommendation_pool.items())[:n]}","def towers(n: int, from_pile=1, to_pile=2):\n if n == 1:\n print(\"Remove 1 disk from {} pile to {} pile\"\n .format(from_pile, to_pile))\n else:\n tmp = 6 - from_pile - to_pile\n towers(n-1, from_pile, tmp)\n print(\"Remove {} disk from {} pile to {} pile\"\n .format(n, from_pile, to_pile))\n towers(n-1, tmp, to_pile)","def prune(i):\n\n return {'return':1, 'error':'pruning is not yet supported in this scenario'}","def _remove_additional_elements(self):\n # Produces a list of keys in sample sorted by seed\n sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])\n\n # Removes the keys with largest seed values (beyond the\n # first k keys)\n for i in range(self.k, len(sorted_elements)):\n del self.elements[sorted_elements[i][0]]","def refilter_previous_rts():\n rtlist = get_list_of_rts()\n if rtlist == None or len(rtlist) == 0:\n return\n for tweet in rtlist:\n if not filter_tweet(tweet['retweeted_status']):\n print(\"Previous tweet does not pass the filter anymore !\")\n print(\"@%s (%s) : %s\" % (\n tweet['retweeted_status']['user']['screen_name'],\n tweet['retweeted_status']['source'],\n tweet['retweeted_status']['text']))\n answer = raw_input(\"Delete ? [y/N] \")\n if answer == 'y':\n delete_tweet(tweet['id_str'])","def test_partition_may_skip_train():\n ratings = lktu.ml_test.ratings\n # make a data set where some users only have 1 rating\n ratings = ratings.sample(frac=0.1)\n users = ratings.groupby('user')['rating'].count()\n assert users.min() == 1.0 # we should have some small users!\n users.name = 'ur_count'\n\n splits = xf.partition_users(ratings, 5, xf.SampleN(1))\n splits = list(splits)\n assert len(splits) == 5\n\n # now we go make sure we're missing some users! And don't have any NaN ratings\n for train, test in splits:\n # no null ratings\n assert all(train['rating'].notna())\n # see if test users with 1 rating are missing from train\n test = test.join(users, on='user')\n assert all(~(test.loc[test['ur_count'] == 1, 'user'].isin(train['user'].unique())))\n # and users with more than one rating are in train\n assert all(test.loc[test['ur_count'] > 1, 'user'].isin(train['user'].unique()))","def getTelemarketers():\n callers = getAllKnownCallers()\n callees = getAllKnownCallees()\n texters = getAllKnownTexters()\n textees = getAllKnownTextees()\n telemarketers = getKnownTelemarketers()\n\n # Remove known telemarketers from list\n t = set(callers).difference(telemarketers)\n # Remove known numbers that have received calls\n t.difference_update(callees)\n # Remove those who have sent a text\n t.difference_update(texters)\n # Remove those who have received a text\n t.difference_update(textees)\n # Join with known telemarketers\n t = set(telemarketers).union(t)\n\n return sorted(t)","def prune(self, n_leaves):\n self.tree_ = prune(self.tree_, n_leaves)\n return self","def _remove_additional_elements(self):\n # Produces a list of keys in sample sorted by seed\n sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])\n\n # Removes the keys with largest seed values (beyond the first k keys)\n for i in range(self.k, len(sorted_elements)):\n del self.elements[sorted_elements[i][0]]","def top_controversial(self, n):\n return top_movies","def pruning_routine(self):\n pass","def k_most_talkative(self):\n word_counts = self.get_usercounts() # {u1: 3, u2: 4, }\n word_counts_heap = [(-count, username) for username, count in word_counts.items()] # [(-4, username), (-3, username)]\n heapify(word_counts_heap) # [(-4, u2), (-3, u1)]\n counter = 0\n while word_counts_heap or counter < k:\n _, username = heappop(word_counts_heap)\n counter += 1 # 1, 2\n yield username # u2, u1","def prune_losers(self):\n self.log.debug(\"PRUNE LOSERS\")\n # check to see if people i followed follow me back\n cutoff_time = (datetime.now()\n - timedelta(hours=self.reciprocation_window))\n ingrates = Target.objects.filter(\n hunter=self.user, status=Target.PURGATORY,\n modified__lt=cutoff_time) # They didn't follow back in time\n\n for ingrate in ingrates:\n ingrate.status = Target.INGRATE\n ingrate.save()\n self.log.debug(\" => Unfollowed %s\" % ingrate.hunted.screen_name)\n try:\n self.api.destroy_friendship(ingrate.hunted)\n except Exception, e:\n print e\n return\n finally:\n pass\n #self.contact(ingrate)","def select_people(torrent, select_seeders=True, num=50):\n selector = torrent.seeders if select_seeders else torrent.leechers\n resp = random.sample(selector, min(num, len(selector)))\n return resp","def _compactness_pruning(self):\n feature_phrases = [phrase for phrase in self.frequent_features if self._is_compact(phrase)]\n self.features_phrases = feature_phrases","def remove_premature_departures(trips):\n\t# sort ascending by arrival \n\t# then iteratively remove trips not also sorted by departure\n\tstarting_length = len(trips) # for logging\n\t#\n\ttrips.sort(key = lambda x: x.arrive_ts) # arrival, first to last\n\ti = 1\n\twhile i < len(trips):\n\t\t# if departure is before that of earlier-arriving trip\n\t\tif trips[i].depart_ts <= trips[i-1].depart_ts: \n\t\t\ttrips.pop(i)\n\t\t\tcontinue\n\t\ti+=1\n\t# there should be no simultaneous departures\n\tassert len(set([t.depart_ts for t in trips])) == len(trips)","def test_remove_learner_specific_for_coach_pt1(self):\n self.assertTrue(self.coach2.has_perm(self.AUTH_REMOVE_LEARNER, self.learner_groups[1]))","def first_phase(players):\n\n free_players = players[:]\n while free_players:\n player = free_players.pop()\n favourite = player.get_favourite()\n\n current = favourite.matching\n if current is not None:\n favourite._unmatch()\n free_players.append(current)\n\n favourite._match(player)\n\n for successor in favourite.get_successors():\n _delete_pair(successor, favourite)\n if not successor.prefs and successor in free_players:\n free_players.remove(successor)\n\n return players","def get_to_review(self) -> VerbsList:\n to_review = [verb for verb, stats in self.practice_list.items() if pick(stats)]\n\n if len(to_review) < self.min_to_review:\n for _ in range(self.min_to_review - len(to_review)):\n to_review.append(self.to_learn_list.pop())\n\n return to_review","def selection(self):\n\n # Sort root Teams from best to worst\n ranked_agents = sorted(self.agent_pop, key=lambda rt : rt.team.fitness, reverse=True)\n\n # Save trainer and top agent so far\n self.save()\n if self.AGENT_NAME == \"\":\n ranked_agents[0].save(Trainer.ENV_NAME)\n else:\n ranked_agents[0].save(Trainer.AGENT_NAME)\n\n # Sanity check: There should always be R_SIZE root Teams at this point\n if len(self.agent_pop) != Trainer.R_SIZE:\n print(\"WARNING - Trainer::selection - len(self.agent_pop) != Trainer.R_SIZE\")\n print(\" len(self.agent_pop) = \", len(self.agent_pop))\n\n # Calculate the number of root Teams to retain\n num_keep = int(Trainer.PERCENT_KEEP * Trainer.R_SIZE)\n\n # Grab slice of sorted root Team references to delete\n agents_to_delete = ranked_agents[num_keep:]\n\n # Clean all root Teams in the Teams-to-delete list\n # Note: Still need to clean the population of Learners which\n # may now contain orphans and update the root Team population.\n for agent in agents_to_delete:\n\n team = agent.team\n\n # Safety and sanity check: These should ALL be root Teams\n if team.getNumReferencingLearners() != 0:\n print(\"WARNING - Trainer::selection - A non-root Team is being deleted!\")\n\n team.removeLearners()\n self.team_pop.remove(team)\n\n # Clean up orphanced Learners after Team removal\n self.cleanOrphanedLearners()","def _remove_points(self, points_to_remove, teams_population):\n for team in teams_population:\n for point in points_to_remove:\n if point.point_id_ in team.results_per_points_:\n team.results_per_points_.pop(point.point_id_)","def remove_pruned_supersets(supersets, max_non_deps):\n for n in supersets[:]:\n if max_non_deps.contains_subset(n.attrs):\n supersets.remove(n)","def prune_heads(self, heads_to_prune):\n base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed\n base_model._prune_heads(heads_to_prune)","def prune_conformers(self, param={'M':'cml1', 'rp':1.0,'thresh':0.25,'wz':F,'sort':T}):\n if param['M'] in ['rmsd']:\n ds = self.get_rmsd()\n elif param['M'] in ['cm','cml1']:\n ds = self.get_dcm(param)\n else:\n raise '#ERROR: unknow rep'\n #print ' ++ ds = ', ds\n #print ' |__ es = ', np.array(self.es)\n seq = np.argsort(self.es) # sort by increasing energy\n ccids = []\n for i in seq:\n # always keep lowest-energy conformer\n if len(ccids) == 0:\n ccids.append(i)\n continue\n\n # discard conformers within the RMSD threshold\n if np.all(ds[i][ccids] >= thresh):\n ccids.append(i)\n self.nconf = len(ccids)\n # creat a new mol object with unique conformers\n new = Chem.Mol(self.mol)\n new.RemoveAllConformers()\n for i in ccids:\n ci = self.mol.GetConformer(i)\n new.AddConformer(ci, assignId=True)\n self.mol = new","def trim(self, ratio=10000):\n trimmed, total = 0, 0\n for sources in self.sources():\n for s in (self.tp_by_source_and_text[sources],\n self.fp_by_source_and_text[sources],\n self.fn_by_source_and_text[sources],\n self.overlap_by_source_and_text[sources]):\n try:\n max_count = s.most_common(1)[0][1]\n except IndexError:\n continue\n for k, v in list(s.items()):\n if v * ratio < max_count:\n trimmed += 1\n del s[k]\n total += 1\n print(f'trimmed {trimmed}/{total} ({trimmed/total:.1%})',\n file=sys.stderr, flush=True)","def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context[\"teachers\"] = Teacher.objects.filter(grade__isnull=True)\n return context","def getMembersForReproduction(self, numMembers, pickProb):\n\t\tselectedMembers = []\n\t\tmembersWithErrorsModifiable = list(self.membersWithErrors)\n\t\twhile len(selectedMembers) < numMembers:\n\t\t\tindexSelected = 0\n\t\t\twhile rnd.randint(0, 100) > int(pickProb * 100) and indexSelected != len(membersWithErrorsModifiable) - 1:\n\t\t\t\tindexSelected += 1\n\t\t\tmemberWithErrorSelected = membersWithErrorsModifiable[indexSelected]\n\t\t\tif memberWithErrorSelected[0] not in selectedMembers:\n\t\t\t\tselectedMembers.append(memberWithErrorSelected[0])\n\t\t\t\tmembersWithErrorsModifiable.remove(memberWithErrorSelected)\n\t\treturn selectedMembers","def reduce_class_size(ratio, class_size, N_classes, G, student_schedule, \n\t\t\t\t\t copy=False):\n\tif copy:\n\t\tG = G.copy()\n\t\tstudent_schedule = student_schedule.copy()\n\t\n\tN_remove = round(ratio * class_size)\n\n\t# link types that are affected by students not being present at school\n\taffected_links = ['student_student_intra_class', \n\t\t\t\t\t 'student_student_table_neighbour',\n\t\t\t\t\t 'student_student_daycare',\n\t\t\t\t\t 'teaching_teacher_student',\n\t\t\t\t\t 'daycare_supervision_teacher_student']\n\n\tfor wd in range(1, 6):\n\t\tfor c in range(1, N_classes + 1):\n\t\t\tstudent_nodes = student_schedule[student_schedule['hour_1'] == c]\\\n\t\t\t\t\t.loc[wd].index\n\t\t\t# pick a number of students from every class and remove them\n\t\t\tstudents_to_remove = np.random.choice(student_nodes, N_remove, \\\n\t\t\t\treplace=False)\n\n\t\t\t## remove edges from the graph\n\t\t\t# find all edges on the given weekday in which at least one student\n\t\t\t# from the list of students to remove is involved. Only edges with a\n\t\t\t# link type that is affected by the absence from school are selected \n\t\t\t# (i.e. no family or friendship contacts)\n\t\t\tedges_to_remove = [(u, v, k) for u, v, k, data in \\\n\t\t\tG.edges(keys=True, data=True) if data['link_type'] in \\\n\t\t\taffected_links and data['weekday'] == wd and \\\n\t\t\t(u in students_to_remove or v in students_to_remove)]\n\t\t\t# remove affected edges from the graph\n\t\t\tfor e in edges_to_remove:\n\t\t\t\tG.remove_edge(e[0], e[1], key=e[2])\n\t\n\t\t\t## remove entries in the student schedule at the corresponding days\n\t\n\t\t\t# set all entries for students on the given weekday to nan in the \n\t\t\t# student schedule\n\t\t\tfor s in students_to_remove:\n\t\t\t\tfor hour in range(1, 10):\n\t\t\t\t\tstudent_schedule.loc[wd, s]['hour_{}'.format(hour)] = pd.NA\n\t\t\t\t\t\t\t\t\t\n\tif copy:\n\t\treturn G, student_schedule","def reject_fairness(experiment):\n num_heads = len([flip for flip in experiment if flip])\n return num_heads < 469 or num_heads > 531","def reject_fairness(experiment):\n num_heads = len([flip for flip in experiment if flip])\n return num_heads < 469 or num_heads > 531","def reject_fairness(experiment):\n num_heads = len([flip for flip in experiment if flip])\n return num_heads < 469 or num_heads > 531","def reject_fairness(experiment):\n num_heads = len([flip for flip in experiment if flip])\n return num_heads < 468 or num_heads > 531","def delete_small_trajectories(trajectories, best_parameters):\n print('Filtering small trajectories...', end = ' ')\n size = best_parameters['min_size']\n pop_ind =[]\n for k, trajectory in enumerate(trajectories):\n traj = vis.get_points(trajectory)\n if len(np.unique(traj, axis = 0))<=size:\n pop_ind.append(k)\n for index in sorted(pop_ind, reverse = True):\n del trajectories[index]\n print('Done.')","def reset(self):\n self.epochs = 0\n self.num_classes = 2 # Minimum of 2 classes\n self._random_state = check_random_state(self.random_state)\n if self.base_estimators:\n self.experts = [\n self.WeightedExpert(\n cp.deepcopy(be), 1, self.labels)\n for be in self.base_estimators\n ]\n else:\n self.experts = [\n self._construct_new_expert()\n ]","def trim_vocab(n: int, vocab: Dict[str, int]) -> None:\r\n pair_counts = sorted(vocab.items(), key=lambda p: -p[1])\r\n pairs_to_trim = [pair for pair, count in pair_counts[n:]]\r\n for pair in pairs_to_trim:\r\n del vocab[pair]","def get_teacher(self, **fields):\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\n teacher_fields = {}\n for key, value in fields.items():\n if key in existing_fields:\n teacher_fields[key] = value\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\n # Expect single value if search by unique fields, list if by non-unique\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None","def prune(tree, testSet, res, technique):\n assert technique in [\"reduced_error\"]\n if technique == \"reduced_error\":\n tbSet = testSet[testSet[tree.col] >= tree.value] #find which test observations belong to this tree's true branch\n fbSet = testSet[testSet[tree.col] < tree.value] #find which test observations belong to this tree's false branch\n \n if tree.tb.results is None: #Check if the true branch of this sub-tree is a leaf\n ptb = prune(tree.tb, tbSet, res, technique) #If not, recursively travel down the true branch and prune it.\n else:\n ptb = tree.tb #If the true branch is a leaf, then the true branch has--in essence--already been pruned.\n if tree.fb.results is None: #Check if the false branch of this sub-tree is a leaf\n pfb = prune(tree.fb, fbSet, res, technique) #If not, recursively travel down the false branch and prune it.\n else:\n pfb = tree.fb #If the false branch is a leaf, then the false branch has--in essence--already been pruned.\n \n #Sum the number of misclassifications of the test data at each of the leaves of this node\n wrong_in_leaves = __deep_count_errors(ptb, tbSet, res) + __deep_count_errors(pfb, fbSet, res)\n \n #Count the number of misclassificationsof the test data that would occur if this node were treated as a leaf\n wrong_at_node = __count_errors(tree, testSet, res)\n \n #Assess whether or not treating the node as a leaf improves the accuracy on the test set\n if wrong_at_node <= wrong_in_leaves: \n #NOTE:The following line of code seems slightly redundant since count_errors(tree, testSet, res) had to call \n #__get_results(tree). I should set up some way to save the output of that function call instead of calling it twice.\n return decisionNode(results = __get_results(tree)) #If so, return a decisionNode where the node is a leaf\n else:\n #If not, return a decisionNode where the node splits on the same column and value as before, but the \n #true and false branches are the pruned-versions of the original true and false branches. See above for\n #definition of ptb and pfb\n return decisionNode(col = tree.col, value = tree.value, tb = ptb, fb = pfb)","def test_remove_learner_specific_for_coach_pt2(self):\n self.assertFalse(self.coach1.has_perm(self.AUTH_REMOVE_LEARNER, self.learner_groups[1]))","def cleanUp(self):\n\n tapeList = sorted(glob.glob('TAPE?'))\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\n for tape in tapeList:\n if os.path.isfile(tape): os.remove(tape)\n # end TAPE loop","def agents_cleanup(agents, n) -> set:\n return set(agent for agent in agents if agent[0] < n and agent[1] < n)","def _normalise(self):\n if len(self.user_skip) == 0:\n return\n m = min(self.user_skip.values())\n for x in [k for k in self.user_skip]:\n self.user_skip[x] -= m\n if self.user_skip[x] == 0 and not self.user_queue[x]:\n self._purge_user(x)\n if not self.user_order:\n self.nonempty.clear()","def pop_krakens(cls):\n pass","def get_N_teachers(school_type, N_classes):\n\tteachers = {\n\t\t'primary':N_classes + int(N_classes / 2),\n\t\t'primary_dc':N_classes * 2,\n\t\t'lower_secondary':int(N_classes * 2.5),\n\t\t'lower_secondary_dc':N_classes * 3,\n\t\t'upper_secondary':int(N_classes * 2.85),\n\t\t'secondary':int(N_classes * 2.5),\n\t\t'secondary_dc':int(N_classes * 2.5)\n\t}\n\treturn teachers[school_type]","def straight_prune_subsample(neuron, number_of_nodes):\n if(neuron.n_node > 200):\n neuron, distance = straight_subsample_with_fixed_number(neuron, 200)\n sp_neuron, state = prune(neuron=neuron,\n threshold=2*distance,\n lowest_number=number_of_nodes)\n while(~state):\n distance += 1\n sp_neuron = straigh_subsample(neuron, distance)\n sp_neuron, state = prune(neuron=sp_neuron,\n threshold=2*distance,\n lowest_number=number_of_nodes)\n return sp_neuron","def prune(self, rel=None):\n deleted = self._registry._get_not_reachable(self.root, rel=rel)\n for d in deleted:\n self._delete_cuds_triples(d)"],"string":"[\n \"def cleanOrphanedLearners(self):\\n\\n # Before deleting Learners, ensure that if any Learners that are about to be\\n # deleted point to a Team as their action, then that Team's count of\\n # referincing Learners is decremented.\\n for learner in self.learner_pop:\\n if learner.getNumReferencingTeams() == 0 and not learner.isActionAtomic():\\n learner.action.decrementNumReferencingLearners()\\n\\n # Remove all orphaned Learners from the Learner population\\n self.learner_pop = [l for l in self.learner_pop if not l.getNumReferencingTeams() == 0]\",\n \"def prune(self, min_count):\\n if not self.sorted:\\n self.sort()\\n for k, count in enumerate(self.Nx):\\n if count < min_count:\\n self.truncate(k)\\n break\",\n \"def _prune_candidates(self, beam_width=None):\\n if beam_width is None:\\n beam_width = self.beam_width\\n if len(self.candidates) <= beam_width:\\n return\\n neg_scores = np.array([-cand.logp_total() for cand in self.candidates])\\n parted_indices = np.argpartition(neg_scores, beam_width - 1)\\n self.candidates = np.array(self.candidates)[parted_indices[:beam_width]].tolist()\",\n \"def prune(self):\\n self.sort(key=lambda chunk: chunk.probability)\\n del self[:-self.model.num_parses]\",\n \"def prune_features(self, verbose=False):\\n # Collect all features and prune those occurring only once.\\n features = defaultdict(int)\\n for k in self.utterance_features:\\n for f in self.utterance_features[k]:\\n features[f] += 1\\n\\n if verbose:\\n print \\\"Total number of features: \\\", len(features)\\n\\n self.remove_features = []\\n for k in features:\\n if features[k] <= 2:\\n self.remove_features.append(k)\\n\\n if verbose:\\n print \\\"Number of unique features: \\\", len(self.remove_features)\\n\\n self.remove_features = set(self.remove_features)\\n for k in self.utterance_features:\\n self.utterance_features[k].prune(self.remove_features)\\n\\n features = defaultdict(int)\\n for k in self.utterance_features:\\n for f in self.utterance_features[k]:\\n features[f] += 1\\n\\n if verbose:\\n print \\\"Total number of features: \\\", len(features)\",\n \"def load_teachers(self):\\n # Get the experiment's directory to load from\\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\\n self.load_teacher_experiment(ex_dir)\\n if len(self.teacher_policies) < self.num_teachers:\\n print(\\n f\\\"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!\\\"\\n )\\n self.load_teachers()\",\n \"def init_prune_list(self):\\n\\t\\tfor i in range(1, len(self.elements)+1):\\n\\t\\t\\tcount = self.getSupport({i})\\n\\t\\t\\tif(count >= self.support):\\n\\t\\t\\t\\tself.prune_list[(i,)] = count\",\n \"def helper(reviewer: Any, graph: Graph) -> set:\\n reviewers_so_far = set()\\n\\n for movie in graph.get_neighbours(reviewer):\\n for user in graph.get_neighbours(movie):\\n if graph.get_weight(user, movie) >= 8:\\n reviewers_so_far.add(user)\\n return reviewers_so_far\",\n \"def prune_features(self):\\r\\n for i, features in enumerate(self.curr_features):\\r\\n # Continue if the number of features in this grid does\\r\\n # not exceed the upper bound.\\r\\n if len(features) <= self.config.grid_max_feature_num:\\r\\n continue\\r\\n self.curr_features[i] = sorted(features, key=lambda x:x.lifetime, \\r\\n reverse=True)[:self.config.grid_max_feature_num]\",\n \"def _remove_experts(self):\\n self.experts = [ex for ex in self.experts if np.mean(\\n ex.weight) >= self.theta]\",\n \"def delete_n_volunteers(app_id):\\r\\n delete_memoized(n_volunteers, app_id)\",\n \"def prune_(self):\\n idx = self.factor_lams() > 0\\n self.factors = [f[:, idx] for f in self.factors]\\n self.rank = np.sum(idx)\",\n \"def delSpillOppIRunder(trekk, vinnendeSekvenser):\\n runder = []\\n i = 0\\n runde = []\\n while i < len(trekk):\\n runde.append(trekk[i])\\n personX, personO = delOppRunde(runde)\\n if len(runde) >= 5 and (sjekkVinnerRunde(personX, vinnendeSekvenser) or sjekkVinnerRunde(personO, vinnendeSekvenser)):\\n runder.append(runde)\\n runde = []\\n if len(runde) == 9:\\n runder.append(runde) \\n runde = []\\n i += 1\\n return runder\",\n \"def delete_n_anonymous_volunteers(app_id):\\r\\n delete_memoized(n_anonymous_volunteers, app_id)\",\n \"def prune_tree(tree, cutoff, posteriors):\\n new_tree = []\\n for e in tree:\\n try:\\n if posteriors[e] > cutoff:\\n new_tree.append(e)\\n except KeyError:\\n if posteriors[e[::-1]] > cutoff:\\n new_tree.append(e)\\n return new_tree\",\n \"def prune(neuron,\\n number_of_nodes):\\n n = len(neuron.nodes_list)\\n for i in range(n - number_of_nodes):\\n index = shortest_tips(neuron)\\n neuron = remove_node(neuron, index)\\n return neuron\",\n \"def remove_numbers(self):\\n for i in range(len(self.board.board[0])):\\n while self.board.board[i].count(0) < 6:\\n random_val = random.randint(0, 8)\\n self.board.update_board((i, random_val), 0)\",\n \"def deepscore(teacher):\\n if teacher == None:\\n print(\\\"Not a valid teacher\\\")\\n return\\n if teacher.get('rmpdata') == None: snc.rateThisProfessor(teacher,instructors)\\n print(\\\"# \\\" + teacher['name'])\\n scoreTeacherlegacy(teacher)\\n scoreTeacherues(teacher)\\n scoreTeacherrmp(teacher)\",\n \"def _prune_heads(self, heads_to_prune):\\n for layer, heads in heads_to_prune.items():\\n group_idx = int(layer / self.config.inner_group_num)\\n inner_group_idx = int(layer - group_idx * self.config.inner_group_num)\\n self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)\",\n \"def generate_teachers(G, school_type, N_classes, family_member_counter, \\n\\t\\t\\t\\t\\t family_counter, teacher_p_adults, teacher_p_children):\\n\\tN_teachers = get_N_teachers(school_type, N_classes)\\n\\tteacher_nodes = ['t{:04d}'.format(i) for i in range(1, N_teachers + 1)]\\n\\tG.add_nodes_from(teacher_nodes)\\n\\t\\n\\tfor t in teacher_nodes:\\n\\t\\tfamily_nodes = [t]\\n\\t\\t# draw a random number of children and adults for the family\\n\\t\\tages, N_adults = generate_teacher_family(teacher_p_adults, teacher_p_children)\\n\\t\\t\\n\\t\\tages = list(ages)\\n\\t\\tfor adult in range(N_adults - 1):\\n\\t\\t\\tages.append(20.5) # default age for adults\\n\\t\\t\\n\\t\\t# add the family member nodes and their attributes to the graph\\n\\t\\tfor age in ages:\\n\\t\\t\\tfamily_member_ID = 'f{:04d}'.format(family_member_counter)\\n\\t\\t\\tfamily_nodes.append(family_member_ID)\\n\\t\\t\\tG.add_node(family_member_ID)\\n\\t\\t\\tfamily_member_counter += 1\\n\\t\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t\\t{family_member_ID:{'type':'family_member',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'age':age,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'family':family_counter,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'unit':'family'}})\\n\\t\\t\\t\\t\\t\\n\\t\\t# finally, also set the teacher's node attributes\\n\\t\\tnx.set_node_attributes(G, \\\\\\n\\t\\t\\t\\t\\t{t:{'type':'teacher', \\n\\t\\t\\t\\t\\t\\t# Note: 20.5 is the age at which\\n\\t\\t\\t\\t\\t\\t# the symptom and transmission risk\\n\\t\\t\\t\\t\\t\\t# is that of an adult\\n\\t\\t\\t\\t\\t\\t'age':20.5,\\n\\t\\t\\t\\t\\t\\t'unit':'faculty_room',\\n\\t\\t\\t\\t\\t\\t'family':family_counter}})\\n\\t\\tfamily_counter += 1\",\n \"def tweet_cleaner(tweets):\\n n_tweets = {}\\n clean = cleaner()\\n for tweet in tweets:\\n text = clean.clean_text(tweets[tweet][\\\"text\\\"])\\n if len(text) > 15:\\n n_tweets[tweet] = tweets[tweet]\\n return n_tweets\",\n \"def _prune(self):\\n while len(self.data) > self.limit:\\n self.data.popleft()\",\n \"def distribute_reviews(asmt, perStudent):\\n\\n # Get all submissions for this assignment.\\n subs = AssignmentSubmission.objects.filter(submission_for=asmt)\\n #print subs\\n course = asmt.course_code\\n users = ReviewUser.objects.filter(courses=course)\\n numUsers = len(users)\\n latestSubmissions = get_latest(course, asmt, subs, users)\\n # print 'filtered subs are ', latestSubmissions\\n numSubs = len(latestSubmissions)\\n print 'number of submissions: ', numSubs\\n asmt.reviews_per_student = perStudent\\n \\n for user in users:\\n print user\\n # Don't want to make staff review stuff.\\n if(user.djangoUser.is_staff or user.djangoUser.is_superuser):\\n continue\\n\\n review = AssignmentReview.objects.get_or_create(by=user, assignment=asmt)[0]\\n reviewsAssigned = len(review.submissions.all())\\n # In case lecturer assigns reviews multiple times, or number of reviews changes. \\n print (\\\"reviewsAssigned\\\", reviewsAssigned)\\n if(reviewsAssigned < perStudent):\\n for i in range(perStudent-reviewsAssigned):\\n index = random.randint(0, numSubs-1)\\n submission = latestSubmissions[index]\\n \\n # Make sure user isn't assigned to review their own submission\\n # NB in the amazing edge case where this user is the only person who \\n # submitted the assignment, we get an infinite loopevi\\n # Also don't want student to be assigned same submission twice.\\n while((submission.by == user) or (submission in review.submissions.all())):\\n print submission.by == user\\n #print review.submissions.all()\\n #print \\\"in the while loop\\\"\\n index = random.randint(0, numSubs-1)\\n print index\\n submission = latestSubmissions[index]\\n \\n review.submissions.add(submission)\\n # Lecturer has reduced number of reviews per user \\n elif(reviewsAssigned > perStudent):\\n # Number of submissions we want to de-assign\\n deassign = reviewsAssigned - perStudent \\n # Get all the submissions the student has not yet completed.\\n incomplete = []\\n for sub in review.submissions.all():\\n annotationsDone = AssignmentReview.numAnnotations(review, sub)\\n if(annotationsDone < asmt.min_annotations):\\n incomplete.append((sub, annotationsDone))\\n\\n # Choose the least-complete deassign submission reviews to remove.\\n # Ascending list \\n sortedList = sorted(incomplete, key=itemgetter(1))\\n removeFromIncomplete = min(deassign, len(sortedList))\\n for i in range(removeFromIncomplete):\\n review.submissions.remove(sortedList[i][0]) \\n \\n if deassign > len(sortedList):\\n for i in range(deassign):\\n review.submissions.remove(reviews.submissions.all()[i])\\n else: # nothing to assign or remove\\n return \\n \\n return\",\n \"def prune(self, age_hours):\\r\\n pass\",\n \"def remove_promotional_tweets(tweets):\\n clean = cleaner()\\n n_tweets = {}\\n for tweet in tweets:\\n if not clean.linkChecker(tweets[tweet][\\\"text\\\"]):\\n n_tweets[tweet] = tweets[tweet]\\n return n_tweets\",\n \"def _prunelowestweight(self):\\r\\n # note: must be called with acquired self._lock!\\r\\n numentries = len(self._dict)\\r\\n if numentries >= self.maxentries:\\r\\n # evict according to entry's weight\\r\\n items = [(entry.weight, key) for key, entry in self._dict.iteritems()]\\r\\n items.sort()\\r\\n index = numentries - self.prunenum\\r\\n if index > 0:\\r\\n for weight, key in items[:index]:\\r\\n del self._dict[key]\",\n \"def suggest_movies(self, reviewer: Any, other: Any) -> list[Any]:\\n potential_recs = self.get_neighbours(other)\\n suggestions_so_far = []\\n neighbours = self.get_neighbours(reviewer)\\n\\n for p_rec in potential_recs:\\n if p_rec not in neighbours and self.get_weight(other, p_rec) >= 9:\\n suggestions_so_far.append(p_rec)\\n\\n return suggestions_so_far\",\n \"def prune(self, upper, lower):\\n # max_count = sorted([self.counts[key] for key in self.counts.keys()])[::-1][upper]\\n max_count = upper\\n\\n print('Removed all words that occur less than {} times and more than {} times'.format(lower, upper))\\n for i, doc in enumerate(self.docs):\\n new_doc = []\\n for word in doc:\\n if self.counts[word] <= max_count and self.counts[word] > lower:\\n new_doc.append(word)\\n self.docs[i] = new_doc\",\n \"def ensemble_preds(dataset, nb_teachers, stdnt_data):\\n\\n # Compute shape of array that will hold probabilities produced by each\\n # teacher, for each training point, and each output class\\n result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)\\n\\n # Create array that will hold result\\n result = np.zeros(result_shape, dtype=np.float32)\\n\\n # Get predictions from each teacher\\n for teacher_id in xrange(nb_teachers):\\n # Compute path of checkpoint file for teacher model with ID teacher_id\\n if FLAGS.deeper:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)\\n else:\\n ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)\\n\\n # Get predictions on our training data and store in result array\\n result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)\\n\\n # This can take a while when there are a lot of teachers so output status\\n print(\\\"Computed Teacher \\\" + str(teacher_id) + \\\" softmax predictions\\\")\\n\\n return result\",\n \"def remove_n_nos(self, num_nos):\\n for i in range(num_nos):\\n elem = random.randint(1, 11 ** 4)\\n self.remove(elem)\",\n \"def delete_n_registered_volunteers(app_id):\\r\\n delete_memoized(n_registered_volunteers, app_id)\",\n \"def get_teacher_num(self):\\n return self.teacher_set.all().count()\",\n \"def _next_to_prune(tree, children=None):\\n\\n if children is None:\\n children = tree.children\\n\\n t_nodes = _get_terminal_nodes(children)\\n g_i = tree.init_error[t_nodes] - tree.best_error[t_nodes]\\n\\n return t_nodes[np.argmin(g_i)]\",\n \"def _reweight_and_discard_irrelevant(self, weighted_sample_pool, t):\\n tmp = []\\n ret = []\\n wc = self.classifiers[t]\\n theta_a = wc.theta_a\\n theta_b = wc.theta_b\\n\\n norm_factor = 0\\n discarded = 0\\n for patch, w in weighted_sample_pool:\\n response = self.h_t(patch, t)\\n # if t > 3:\\n # if response < theta_a or response > theta_b: # throw it away\\n # discarded += 1\\n # continue\\n r = self.classify(patch)\\n label = patch.label\\n new_weight = w * np.exp(-label * r)\\n\\n tmp.append([patch, new_weight])\\n norm_factor += new_weight\\n for patch, w in tmp: # normalize weights\\n normalized_weight = w / norm_factor\\n ret.append([patch, normalized_weight])\\n print \\\"Discarded %d training samples\\\" % discarded\\n return ret\",\n \"def get_teachers(self):\\n query = Teacher.all().order('teacher')\\n return query.fetch()\",\n \"def _prune_heads(self, heads_to_prune):\\n for layer, heads in heads_to_prune.items():\\n self.encoder.layer[layer].attention.prune_heads(heads)\",\n \"def _prune_heads(self, heads_to_prune):\\n for layer, heads in heads_to_prune.items():\\n self.encoder.layer[layer].attention.prune_heads(heads)\",\n \"def _prune_heads(self, heads_to_prune):\\n for layer, heads in heads_to_prune.items():\\n self.encoder.layer[layer].attention.prune_heads(heads)\",\n \"def prune_trie(trie, threshold):\\n\\tnode = trie.root\\n\\tpq = []\\n\\tfor i in node.children.keys():\\n\\t\\tpq.append((node.children[i],node.children[i].char))\\n\\twhile len(pq) > 0:\\n\\t\\tcur_node, char = pq.pop()\\n\\t\\tif cur_node.isEnd == False:\\n\\t\\t\\tfor i in cur_node.children.keys():\\n\\t\\t\\t\\tpq.append((cur_node.children[i],char + cur_node.children[i].char))\\n\\t\\telse:\\n\\t\\t\\tif cur_node.weight < threshold:\\n\\t\\t\\t\\tdelete(trie, char)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcontinue\\n\\treturn trie\",\n \"def _prior_teach(self):\\n pass\",\n \"def _prune_heads(self, heads_to_prune):\\n\\n for layer, heads in heads_to_prune.items():\\n\\n self.encoder.layer[layer].attention.prune_heads(heads)\",\n \"def test18_remove_first_student_with_teacher(self):\\n first_student = self.students_page.students_table()[0]\\n students_list_without_first_student = self.students_page. \\\\\\n click_edit_students_list_button(). \\\\\\n click_delete_first_student_button(). \\\\\\n click_exit_students_list_editor_button(). \\\\\\n students_table()\\n self.assertEqual(self.main_page.get_current_url(),\\n data['expected_url'])\\n self.assertNotIn(first_student,\\n students_list_without_first_student)\",\n \"def delete_all_teachers(connection):\\r\\n with connection:\\r\\n return connection.execute(DELETE_ALL_TEACHERS)\",\n \"def remove_prob():\\n\\n #var for tuple\\n weekly_probs = display_problems()\\n\\n cc_name1 = weekly_probs[0]\\n url_link1 = weekly_probs[1]\\n cc_name2 = weekly_probs[2]\\n url_link2 = weekly_probs[3]\\n cc_name3 = weekly_probs[4]\\n url_link3 = weekly_probs[5]\\n\\n names_to_remove = [cc_name1, cc_name2, cc_name3]\\n urls_to_remove = [url_link1, url_link2, url_link3]\\n\\n for name in names_to_remove:\\n pass\\n\\n for url in urls_to_remove:\\n pass\\n\\n return names_to_remove, urls_to_remove\",\n \"def wipe_all_topics(self):\\n # doc_count = self.posts_read.find({'subreddit':self.subreddit, 'postwise.topic_assignment':{'$exists':True}}).count()\\n doc_count = self.posts_write.update({'subreddit':self.subreddit, 'postwise.topic_assignment':{'$exists':True}},\\n {'$unset':{'postwise.topic_distro':True,'postwise.topic_assignment':True}}, multi=True)\\n\\n print 'wiped topics from %i documents' % doc_count['nModified']\",\n \"def review(self):\\n for player in self.team.players:\\n player.career.potentially_retire()\",\n \"def filterByReview(minReviewNum, stories):\\n\\tsortedstories = filter(lambda s: s[\\\"reviews\\\"] >= minReviewNum, stories)\\n\\treturn [x for x in sortedstories]\",\n \"def prune(self, alignment_infos):\\n alignments = []\\n best_score = 0\\n\\n for alignment_info in alignment_infos:\\n score = IBMModel4.model4_prob_t_a_given_s(alignment_info, self)\\n best_score = max(score, best_score)\\n alignments.append((alignment_info, score))\\n\\n threshold = IBMModel5.MIN_SCORE_FACTOR * best_score\\n alignments = [a[0] for a in alignments if a[1] > threshold]\\n return set(alignments)\",\n \"def test_with_data(data):\\r\\n i = 0\\r\\n tuning_set = []\\r\\n training_set = []\\r\\n num_reps = len(data)\\r\\n for i in range(0, num_reps-1):\\r\\n if (i % 4 == 0):\\r\\n tuning_set.append(data[i])\\r\\n else:\\r\\n training_set.append(data[i])\\r\\n\\r\\n unpruned = induce_node_tree(training_set, original_issues, \\\"D\\\", -1)\\r\\n pruned = prune_tree(unpruned, tuning_set)\\r\\n\\r\\n return pruned\",\n \"def pickten(divforround):\\n seeds = [seed for seed in \\\"abcdefghij\\\"]\\n players = []\\n for seed in seeds:\\n k = list(filter(lambda stu: stu.seed == seed, divforround))\\n players.append(sample(k, 1)[0])\\n for player in players:\\n divforround.remove(player)\\n return players\",\n \"def prune(self):\\n target_user_ids = self.get_queryset().values_list('id', flat=True)\\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\\n drip=self.drip_model,\\n user__id__in=target_user_ids)\\\\\\n .values_list('user_id', flat=True)\\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)\",\n \"def trim_texts_by_count(self, min_count=100):\\n\\n for tid, text in self.graph.nodes(data=True):\\n if text['count'] < min_count:\\n self.graph.remove_node(tid)\",\n \"def _reducedProtToPeps(protToPeps, proteins):\\n return {k: v for k, v in viewitems(protToPeps) if k not in proteins}\",\n \"def _prune_vocab(self,\\n counter,\\n n_total_files,\\n n_seen_files,\\n min_count=None,\\n max_count=None,\\n trim_rule=None):\\n ## Initialize Exclusion List\\n if not hasattr(self, \\\"_exclusion_list\\\"):\\n self._exclusion_list = set()\\n ## Compute Thresholds (With Margin)\\n if min_count is None:\\n min_count = None if self._min_token_freq == 0 else math.ceil(self._min_token_freq * 0.8 * n_seen_files / n_total_files)\\n if max_count is None:\\n max_count = None if self._max_token_freq is None else math.ceil(self._max_token_freq * 1.2 * n_seen_files / n_total_files)\\n ## Get Acceptable Terms\\n accept = set()\\n for ngram, count in counter.items():\\n keep = self._keep_vocab_item(ngram,\\n count,\\n min_count=min_count,\\n max_count=max_count,\\n trim_rule=trim_rule)\\n if keep:\\n accept.add(ngram)\\n ## Update and Return Count\\n counter_pruned = Counter(dict((n, c) for n, c in counter.items() if n in accept))\\n return counter_pruned\",\n \"def prune_ratios(ratios, bad_words):\\n for word in bad_words:\\n ratios.pop(word, None)\",\n \"def trim_recommendation_pool(self, n):\\n # {k:v for k, v in list(d.items())[:2]}\\n self.recommendation_pool = {\\n k: v for k, v in list(self.recommendation_pool.items())[:n]}\",\n \"def towers(n: int, from_pile=1, to_pile=2):\\n if n == 1:\\n print(\\\"Remove 1 disk from {} pile to {} pile\\\"\\n .format(from_pile, to_pile))\\n else:\\n tmp = 6 - from_pile - to_pile\\n towers(n-1, from_pile, tmp)\\n print(\\\"Remove {} disk from {} pile to {} pile\\\"\\n .format(n, from_pile, to_pile))\\n towers(n-1, tmp, to_pile)\",\n \"def prune(i):\\n\\n return {'return':1, 'error':'pruning is not yet supported in this scenario'}\",\n \"def _remove_additional_elements(self):\\n # Produces a list of keys in sample sorted by seed\\n sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])\\n\\n # Removes the keys with largest seed values (beyond the\\n # first k keys)\\n for i in range(self.k, len(sorted_elements)):\\n del self.elements[sorted_elements[i][0]]\",\n \"def refilter_previous_rts():\\n rtlist = get_list_of_rts()\\n if rtlist == None or len(rtlist) == 0:\\n return\\n for tweet in rtlist:\\n if not filter_tweet(tweet['retweeted_status']):\\n print(\\\"Previous tweet does not pass the filter anymore !\\\")\\n print(\\\"@%s (%s) : %s\\\" % (\\n tweet['retweeted_status']['user']['screen_name'],\\n tweet['retweeted_status']['source'],\\n tweet['retweeted_status']['text']))\\n answer = raw_input(\\\"Delete ? [y/N] \\\")\\n if answer == 'y':\\n delete_tweet(tweet['id_str'])\",\n \"def test_partition_may_skip_train():\\n ratings = lktu.ml_test.ratings\\n # make a data set where some users only have 1 rating\\n ratings = ratings.sample(frac=0.1)\\n users = ratings.groupby('user')['rating'].count()\\n assert users.min() == 1.0 # we should have some small users!\\n users.name = 'ur_count'\\n\\n splits = xf.partition_users(ratings, 5, xf.SampleN(1))\\n splits = list(splits)\\n assert len(splits) == 5\\n\\n # now we go make sure we're missing some users! And don't have any NaN ratings\\n for train, test in splits:\\n # no null ratings\\n assert all(train['rating'].notna())\\n # see if test users with 1 rating are missing from train\\n test = test.join(users, on='user')\\n assert all(~(test.loc[test['ur_count'] == 1, 'user'].isin(train['user'].unique())))\\n # and users with more than one rating are in train\\n assert all(test.loc[test['ur_count'] > 1, 'user'].isin(train['user'].unique()))\",\n \"def getTelemarketers():\\n callers = getAllKnownCallers()\\n callees = getAllKnownCallees()\\n texters = getAllKnownTexters()\\n textees = getAllKnownTextees()\\n telemarketers = getKnownTelemarketers()\\n\\n # Remove known telemarketers from list\\n t = set(callers).difference(telemarketers)\\n # Remove known numbers that have received calls\\n t.difference_update(callees)\\n # Remove those who have sent a text\\n t.difference_update(texters)\\n # Remove those who have received a text\\n t.difference_update(textees)\\n # Join with known telemarketers\\n t = set(telemarketers).union(t)\\n\\n return sorted(t)\",\n \"def prune(self, n_leaves):\\n self.tree_ = prune(self.tree_, n_leaves)\\n return self\",\n \"def _remove_additional_elements(self):\\n # Produces a list of keys in sample sorted by seed\\n sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])\\n\\n # Removes the keys with largest seed values (beyond the first k keys)\\n for i in range(self.k, len(sorted_elements)):\\n del self.elements[sorted_elements[i][0]]\",\n \"def top_controversial(self, n):\\n return top_movies\",\n \"def pruning_routine(self):\\n pass\",\n \"def k_most_talkative(self):\\n word_counts = self.get_usercounts() # {u1: 3, u2: 4, }\\n word_counts_heap = [(-count, username) for username, count in word_counts.items()] # [(-4, username), (-3, username)]\\n heapify(word_counts_heap) # [(-4, u2), (-3, u1)]\\n counter = 0\\n while word_counts_heap or counter < k:\\n _, username = heappop(word_counts_heap)\\n counter += 1 # 1, 2\\n yield username # u2, u1\",\n \"def prune_losers(self):\\n self.log.debug(\\\"PRUNE LOSERS\\\")\\n # check to see if people i followed follow me back\\n cutoff_time = (datetime.now()\\n - timedelta(hours=self.reciprocation_window))\\n ingrates = Target.objects.filter(\\n hunter=self.user, status=Target.PURGATORY,\\n modified__lt=cutoff_time) # They didn't follow back in time\\n\\n for ingrate in ingrates:\\n ingrate.status = Target.INGRATE\\n ingrate.save()\\n self.log.debug(\\\" => Unfollowed %s\\\" % ingrate.hunted.screen_name)\\n try:\\n self.api.destroy_friendship(ingrate.hunted)\\n except Exception, e:\\n print e\\n return\\n finally:\\n pass\\n #self.contact(ingrate)\",\n \"def select_people(torrent, select_seeders=True, num=50):\\n selector = torrent.seeders if select_seeders else torrent.leechers\\n resp = random.sample(selector, min(num, len(selector)))\\n return resp\",\n \"def _compactness_pruning(self):\\n feature_phrases = [phrase for phrase in self.frequent_features if self._is_compact(phrase)]\\n self.features_phrases = feature_phrases\",\n \"def remove_premature_departures(trips):\\n\\t# sort ascending by arrival \\n\\t# then iteratively remove trips not also sorted by departure\\n\\tstarting_length = len(trips) # for logging\\n\\t#\\n\\ttrips.sort(key = lambda x: x.arrive_ts) # arrival, first to last\\n\\ti = 1\\n\\twhile i < len(trips):\\n\\t\\t# if departure is before that of earlier-arriving trip\\n\\t\\tif trips[i].depart_ts <= trips[i-1].depart_ts: \\n\\t\\t\\ttrips.pop(i)\\n\\t\\t\\tcontinue\\n\\t\\ti+=1\\n\\t# there should be no simultaneous departures\\n\\tassert len(set([t.depart_ts for t in trips])) == len(trips)\",\n \"def test_remove_learner_specific_for_coach_pt1(self):\\n self.assertTrue(self.coach2.has_perm(self.AUTH_REMOVE_LEARNER, self.learner_groups[1]))\",\n \"def first_phase(players):\\n\\n free_players = players[:]\\n while free_players:\\n player = free_players.pop()\\n favourite = player.get_favourite()\\n\\n current = favourite.matching\\n if current is not None:\\n favourite._unmatch()\\n free_players.append(current)\\n\\n favourite._match(player)\\n\\n for successor in favourite.get_successors():\\n _delete_pair(successor, favourite)\\n if not successor.prefs and successor in free_players:\\n free_players.remove(successor)\\n\\n return players\",\n \"def get_to_review(self) -> VerbsList:\\n to_review = [verb for verb, stats in self.practice_list.items() if pick(stats)]\\n\\n if len(to_review) < self.min_to_review:\\n for _ in range(self.min_to_review - len(to_review)):\\n to_review.append(self.to_learn_list.pop())\\n\\n return to_review\",\n \"def selection(self):\\n\\n # Sort root Teams from best to worst\\n ranked_agents = sorted(self.agent_pop, key=lambda rt : rt.team.fitness, reverse=True)\\n\\n # Save trainer and top agent so far\\n self.save()\\n if self.AGENT_NAME == \\\"\\\":\\n ranked_agents[0].save(Trainer.ENV_NAME)\\n else:\\n ranked_agents[0].save(Trainer.AGENT_NAME)\\n\\n # Sanity check: There should always be R_SIZE root Teams at this point\\n if len(self.agent_pop) != Trainer.R_SIZE:\\n print(\\\"WARNING - Trainer::selection - len(self.agent_pop) != Trainer.R_SIZE\\\")\\n print(\\\" len(self.agent_pop) = \\\", len(self.agent_pop))\\n\\n # Calculate the number of root Teams to retain\\n num_keep = int(Trainer.PERCENT_KEEP * Trainer.R_SIZE)\\n\\n # Grab slice of sorted root Team references to delete\\n agents_to_delete = ranked_agents[num_keep:]\\n\\n # Clean all root Teams in the Teams-to-delete list\\n # Note: Still need to clean the population of Learners which\\n # may now contain orphans and update the root Team population.\\n for agent in agents_to_delete:\\n\\n team = agent.team\\n\\n # Safety and sanity check: These should ALL be root Teams\\n if team.getNumReferencingLearners() != 0:\\n print(\\\"WARNING - Trainer::selection - A non-root Team is being deleted!\\\")\\n\\n team.removeLearners()\\n self.team_pop.remove(team)\\n\\n # Clean up orphanced Learners after Team removal\\n self.cleanOrphanedLearners()\",\n \"def _remove_points(self, points_to_remove, teams_population):\\n for team in teams_population:\\n for point in points_to_remove:\\n if point.point_id_ in team.results_per_points_:\\n team.results_per_points_.pop(point.point_id_)\",\n \"def remove_pruned_supersets(supersets, max_non_deps):\\n for n in supersets[:]:\\n if max_non_deps.contains_subset(n.attrs):\\n supersets.remove(n)\",\n \"def prune_heads(self, heads_to_prune):\\n base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed\\n base_model._prune_heads(heads_to_prune)\",\n \"def prune_conformers(self, param={'M':'cml1', 'rp':1.0,'thresh':0.25,'wz':F,'sort':T}):\\n if param['M'] in ['rmsd']:\\n ds = self.get_rmsd()\\n elif param['M'] in ['cm','cml1']:\\n ds = self.get_dcm(param)\\n else:\\n raise '#ERROR: unknow rep'\\n #print ' ++ ds = ', ds\\n #print ' |__ es = ', np.array(self.es)\\n seq = np.argsort(self.es) # sort by increasing energy\\n ccids = []\\n for i in seq:\\n # always keep lowest-energy conformer\\n if len(ccids) == 0:\\n ccids.append(i)\\n continue\\n\\n # discard conformers within the RMSD threshold\\n if np.all(ds[i][ccids] >= thresh):\\n ccids.append(i)\\n self.nconf = len(ccids)\\n # creat a new mol object with unique conformers\\n new = Chem.Mol(self.mol)\\n new.RemoveAllConformers()\\n for i in ccids:\\n ci = self.mol.GetConformer(i)\\n new.AddConformer(ci, assignId=True)\\n self.mol = new\",\n \"def trim(self, ratio=10000):\\n trimmed, total = 0, 0\\n for sources in self.sources():\\n for s in (self.tp_by_source_and_text[sources],\\n self.fp_by_source_and_text[sources],\\n self.fn_by_source_and_text[sources],\\n self.overlap_by_source_and_text[sources]):\\n try:\\n max_count = s.most_common(1)[0][1]\\n except IndexError:\\n continue\\n for k, v in list(s.items()):\\n if v * ratio < max_count:\\n trimmed += 1\\n del s[k]\\n total += 1\\n print(f'trimmed {trimmed}/{total} ({trimmed/total:.1%})',\\n file=sys.stderr, flush=True)\",\n \"def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n context[\\\"teachers\\\"] = Teacher.objects.filter(grade__isnull=True)\\n return context\",\n \"def getMembersForReproduction(self, numMembers, pickProb):\\n\\t\\tselectedMembers = []\\n\\t\\tmembersWithErrorsModifiable = list(self.membersWithErrors)\\n\\t\\twhile len(selectedMembers) < numMembers:\\n\\t\\t\\tindexSelected = 0\\n\\t\\t\\twhile rnd.randint(0, 100) > int(pickProb * 100) and indexSelected != len(membersWithErrorsModifiable) - 1:\\n\\t\\t\\t\\tindexSelected += 1\\n\\t\\t\\tmemberWithErrorSelected = membersWithErrorsModifiable[indexSelected]\\n\\t\\t\\tif memberWithErrorSelected[0] not in selectedMembers:\\n\\t\\t\\t\\tselectedMembers.append(memberWithErrorSelected[0])\\n\\t\\t\\t\\tmembersWithErrorsModifiable.remove(memberWithErrorSelected)\\n\\t\\treturn selectedMembers\",\n \"def reduce_class_size(ratio, class_size, N_classes, G, student_schedule, \\n\\t\\t\\t\\t\\t copy=False):\\n\\tif copy:\\n\\t\\tG = G.copy()\\n\\t\\tstudent_schedule = student_schedule.copy()\\n\\t\\n\\tN_remove = round(ratio * class_size)\\n\\n\\t# link types that are affected by students not being present at school\\n\\taffected_links = ['student_student_intra_class', \\n\\t\\t\\t\\t\\t 'student_student_table_neighbour',\\n\\t\\t\\t\\t\\t 'student_student_daycare',\\n\\t\\t\\t\\t\\t 'teaching_teacher_student',\\n\\t\\t\\t\\t\\t 'daycare_supervision_teacher_student']\\n\\n\\tfor wd in range(1, 6):\\n\\t\\tfor c in range(1, N_classes + 1):\\n\\t\\t\\tstudent_nodes = student_schedule[student_schedule['hour_1'] == c]\\\\\\n\\t\\t\\t\\t\\t.loc[wd].index\\n\\t\\t\\t# pick a number of students from every class and remove them\\n\\t\\t\\tstudents_to_remove = np.random.choice(student_nodes, N_remove, \\\\\\n\\t\\t\\t\\treplace=False)\\n\\n\\t\\t\\t## remove edges from the graph\\n\\t\\t\\t# find all edges on the given weekday in which at least one student\\n\\t\\t\\t# from the list of students to remove is involved. Only edges with a\\n\\t\\t\\t# link type that is affected by the absence from school are selected \\n\\t\\t\\t# (i.e. no family or friendship contacts)\\n\\t\\t\\tedges_to_remove = [(u, v, k) for u, v, k, data in \\\\\\n\\t\\t\\tG.edges(keys=True, data=True) if data['link_type'] in \\\\\\n\\t\\t\\taffected_links and data['weekday'] == wd and \\\\\\n\\t\\t\\t(u in students_to_remove or v in students_to_remove)]\\n\\t\\t\\t# remove affected edges from the graph\\n\\t\\t\\tfor e in edges_to_remove:\\n\\t\\t\\t\\tG.remove_edge(e[0], e[1], key=e[2])\\n\\t\\n\\t\\t\\t## remove entries in the student schedule at the corresponding days\\n\\t\\n\\t\\t\\t# set all entries for students on the given weekday to nan in the \\n\\t\\t\\t# student schedule\\n\\t\\t\\tfor s in students_to_remove:\\n\\t\\t\\t\\tfor hour in range(1, 10):\\n\\t\\t\\t\\t\\tstudent_schedule.loc[wd, s]['hour_{}'.format(hour)] = pd.NA\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\tif copy:\\n\\t\\treturn G, student_schedule\",\n \"def reject_fairness(experiment):\\n num_heads = len([flip for flip in experiment if flip])\\n return num_heads < 469 or num_heads > 531\",\n \"def reject_fairness(experiment):\\n num_heads = len([flip for flip in experiment if flip])\\n return num_heads < 469 or num_heads > 531\",\n \"def reject_fairness(experiment):\\n num_heads = len([flip for flip in experiment if flip])\\n return num_heads < 469 or num_heads > 531\",\n \"def reject_fairness(experiment):\\n num_heads = len([flip for flip in experiment if flip])\\n return num_heads < 468 or num_heads > 531\",\n \"def delete_small_trajectories(trajectories, best_parameters):\\n print('Filtering small trajectories...', end = ' ')\\n size = best_parameters['min_size']\\n pop_ind =[]\\n for k, trajectory in enumerate(trajectories):\\n traj = vis.get_points(trajectory)\\n if len(np.unique(traj, axis = 0))<=size:\\n pop_ind.append(k)\\n for index in sorted(pop_ind, reverse = True):\\n del trajectories[index]\\n print('Done.')\",\n \"def reset(self):\\n self.epochs = 0\\n self.num_classes = 2 # Minimum of 2 classes\\n self._random_state = check_random_state(self.random_state)\\n if self.base_estimators:\\n self.experts = [\\n self.WeightedExpert(\\n cp.deepcopy(be), 1, self.labels)\\n for be in self.base_estimators\\n ]\\n else:\\n self.experts = [\\n self._construct_new_expert()\\n ]\",\n \"def trim_vocab(n: int, vocab: Dict[str, int]) -> None:\\r\\n pair_counts = sorted(vocab.items(), key=lambda p: -p[1])\\r\\n pairs_to_trim = [pair for pair, count in pair_counts[n:]]\\r\\n for pair in pairs_to_trim:\\r\\n del vocab[pair]\",\n \"def get_teacher(self, **fields):\\n existing_fields = [i.name for i in self._db.get_columns('teachers')]\\n teacher_fields = {}\\n for key, value in fields.items():\\n if key in existing_fields:\\n teacher_fields[key] = value\\n teachers = [i for i in Teachers.select().filter(**teacher_fields)]\\n # Expect single value if search by unique fields, list if by non-unique\\n return teachers if len(teachers) > 1 else teachers[0] if len(teachers) == 1 else None\",\n \"def prune(tree, testSet, res, technique):\\n assert technique in [\\\"reduced_error\\\"]\\n if technique == \\\"reduced_error\\\":\\n tbSet = testSet[testSet[tree.col] >= tree.value] #find which test observations belong to this tree's true branch\\n fbSet = testSet[testSet[tree.col] < tree.value] #find which test observations belong to this tree's false branch\\n \\n if tree.tb.results is None: #Check if the true branch of this sub-tree is a leaf\\n ptb = prune(tree.tb, tbSet, res, technique) #If not, recursively travel down the true branch and prune it.\\n else:\\n ptb = tree.tb #If the true branch is a leaf, then the true branch has--in essence--already been pruned.\\n if tree.fb.results is None: #Check if the false branch of this sub-tree is a leaf\\n pfb = prune(tree.fb, fbSet, res, technique) #If not, recursively travel down the false branch and prune it.\\n else:\\n pfb = tree.fb #If the false branch is a leaf, then the false branch has--in essence--already been pruned.\\n \\n #Sum the number of misclassifications of the test data at each of the leaves of this node\\n wrong_in_leaves = __deep_count_errors(ptb, tbSet, res) + __deep_count_errors(pfb, fbSet, res)\\n \\n #Count the number of misclassificationsof the test data that would occur if this node were treated as a leaf\\n wrong_at_node = __count_errors(tree, testSet, res)\\n \\n #Assess whether or not treating the node as a leaf improves the accuracy on the test set\\n if wrong_at_node <= wrong_in_leaves: \\n #NOTE:The following line of code seems slightly redundant since count_errors(tree, testSet, res) had to call \\n #__get_results(tree). I should set up some way to save the output of that function call instead of calling it twice.\\n return decisionNode(results = __get_results(tree)) #If so, return a decisionNode where the node is a leaf\\n else:\\n #If not, return a decisionNode where the node splits on the same column and value as before, but the \\n #true and false branches are the pruned-versions of the original true and false branches. See above for\\n #definition of ptb and pfb\\n return decisionNode(col = tree.col, value = tree.value, tb = ptb, fb = pfb)\",\n \"def test_remove_learner_specific_for_coach_pt2(self):\\n self.assertFalse(self.coach1.has_perm(self.AUTH_REMOVE_LEARNER, self.learner_groups[1]))\",\n \"def cleanUp(self):\\n\\n tapeList = sorted(glob.glob('TAPE?'))\\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\\n for tape in tapeList:\\n if os.path.isfile(tape): os.remove(tape)\\n # end TAPE loop\",\n \"def agents_cleanup(agents, n) -> set:\\n return set(agent for agent in agents if agent[0] < n and agent[1] < n)\",\n \"def _normalise(self):\\n if len(self.user_skip) == 0:\\n return\\n m = min(self.user_skip.values())\\n for x in [k for k in self.user_skip]:\\n self.user_skip[x] -= m\\n if self.user_skip[x] == 0 and not self.user_queue[x]:\\n self._purge_user(x)\\n if not self.user_order:\\n self.nonempty.clear()\",\n \"def pop_krakens(cls):\\n pass\",\n \"def get_N_teachers(school_type, N_classes):\\n\\tteachers = {\\n\\t\\t'primary':N_classes + int(N_classes / 2),\\n\\t\\t'primary_dc':N_classes * 2,\\n\\t\\t'lower_secondary':int(N_classes * 2.5),\\n\\t\\t'lower_secondary_dc':N_classes * 3,\\n\\t\\t'upper_secondary':int(N_classes * 2.85),\\n\\t\\t'secondary':int(N_classes * 2.5),\\n\\t\\t'secondary_dc':int(N_classes * 2.5)\\n\\t}\\n\\treturn teachers[school_type]\",\n \"def straight_prune_subsample(neuron, number_of_nodes):\\n if(neuron.n_node > 200):\\n neuron, distance = straight_subsample_with_fixed_number(neuron, 200)\\n sp_neuron, state = prune(neuron=neuron,\\n threshold=2*distance,\\n lowest_number=number_of_nodes)\\n while(~state):\\n distance += 1\\n sp_neuron = straigh_subsample(neuron, distance)\\n sp_neuron, state = prune(neuron=sp_neuron,\\n threshold=2*distance,\\n lowest_number=number_of_nodes)\\n return sp_neuron\",\n \"def prune(self, rel=None):\\n deleted = self._registry._get_not_reachable(self.root, rel=rel)\\n for d in deleted:\\n self._delete_cuds_triples(d)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5696694","0.545549","0.54449904","0.5432693","0.52488464","0.5203805","0.5048298","0.50102764","0.493123","0.49195728","0.48889333","0.48790887","0.4860135","0.48572624","0.48336893","0.48332903","0.4776946","0.47586012","0.47427273","0.47353852","0.47345516","0.47345436","0.4726536","0.46756825","0.46719763","0.46627593","0.46603566","0.4659124","0.46532997","0.4649696","0.46484143","0.46233067","0.46178305","0.46160367","0.45931882","0.45848283","0.45848283","0.45848283","0.45828706","0.45763105","0.45720527","0.456591","0.45650417","0.45636752","0.45499948","0.45364475","0.4534927","0.4527715","0.4520064","0.451617","0.45153177","0.45110956","0.45040894","0.4496145","0.44913995","0.44843227","0.44806832","0.44722754","0.44650915","0.4465083","0.44646874","0.44614813","0.44579953","0.44541898","0.44501442","0.44464856","0.44399077","0.4436096","0.44324774","0.44224966","0.44174382","0.44155967","0.44127753","0.44062304","0.439987","0.43987185","0.43986547","0.4391068","0.43898523","0.4383399","0.43755078","0.4372566","0.43508196","0.43417758","0.43417758","0.43417758","0.43346128","0.4333685","0.4328661","0.43270615","0.4326436","0.43243933","0.43135744","0.4312008","0.43106455","0.42990547","0.42919475","0.42898074","0.42873552","0.4286367"],"string":"[\n \"0.5696694\",\n \"0.545549\",\n \"0.54449904\",\n \"0.5432693\",\n \"0.52488464\",\n \"0.5203805\",\n \"0.5048298\",\n \"0.50102764\",\n \"0.493123\",\n \"0.49195728\",\n \"0.48889333\",\n \"0.48790887\",\n \"0.4860135\",\n \"0.48572624\",\n \"0.48336893\",\n \"0.48332903\",\n \"0.4776946\",\n \"0.47586012\",\n \"0.47427273\",\n \"0.47353852\",\n \"0.47345516\",\n \"0.47345436\",\n \"0.4726536\",\n \"0.46756825\",\n \"0.46719763\",\n \"0.46627593\",\n \"0.46603566\",\n \"0.4659124\",\n \"0.46532997\",\n \"0.4649696\",\n \"0.46484143\",\n \"0.46233067\",\n \"0.46178305\",\n \"0.46160367\",\n \"0.45931882\",\n \"0.45848283\",\n \"0.45848283\",\n \"0.45848283\",\n \"0.45828706\",\n \"0.45763105\",\n \"0.45720527\",\n \"0.456591\",\n \"0.45650417\",\n \"0.45636752\",\n \"0.45499948\",\n \"0.45364475\",\n \"0.4534927\",\n \"0.4527715\",\n \"0.4520064\",\n \"0.451617\",\n \"0.45153177\",\n \"0.45110956\",\n \"0.45040894\",\n \"0.4496145\",\n \"0.44913995\",\n \"0.44843227\",\n \"0.44806832\",\n \"0.44722754\",\n \"0.44650915\",\n \"0.4465083\",\n \"0.44646874\",\n \"0.44614813\",\n \"0.44579953\",\n \"0.44541898\",\n \"0.44501442\",\n \"0.44464856\",\n \"0.44399077\",\n \"0.4436096\",\n \"0.44324774\",\n \"0.44224966\",\n \"0.44174382\",\n \"0.44155967\",\n \"0.44127753\",\n \"0.44062304\",\n \"0.439987\",\n \"0.43987185\",\n \"0.43986547\",\n \"0.4391068\",\n \"0.43898523\",\n \"0.4383399\",\n \"0.43755078\",\n \"0.4372566\",\n \"0.43508196\",\n \"0.43417758\",\n \"0.43417758\",\n \"0.43417758\",\n \"0.43346128\",\n \"0.4333685\",\n \"0.4328661\",\n \"0.43270615\",\n \"0.4326436\",\n \"0.43243933\",\n \"0.43135744\",\n \"0.4312008\",\n \"0.43106455\",\n \"0.42990547\",\n \"0.42919475\",\n \"0.42898074\",\n \"0.42873552\",\n \"0.4286367\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7730154"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7182,"cells":{"query":{"kind":"string","value":"Mirror a point over the diagonal of the map"},"document":{"kind":"string","value":"def __mirror(self, x, y):\n return (self.width - x - 1, self.height - y - 1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def mirror_point_point(point, mirror):\n return add_vectors(mirror, subtract_vectors(mirror, point))","def mirror(self, p1=(0, 1), p2=(0, 0)):\n self.position = _reflect_points(self.position, p1, p2)\n return self","def op_mirror():\n mir = np.array([[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, -1, 0],\n [0, 0, 0, -1]])\n return mir","def horizontalMirror(self, mirror):\n mirror_x = self.x\n distToMir = self.y - mirror\n mirror_y = self.y - (2 * distToMir)\n return Point(mirror_x, mirror_y)","def horizontalMirror(self, mirror):\n mirror_x = self.x\n distToMir = self.y - mirror\n mirror_y = self.y - (2 * distToMir)\n return Point(mirror_x, mirror_y)","def mirror(self, about_x=False, about_y=False, about_axis=False):\n if about_axis:\n c = (0, 0)\n elif about_axis is False:\n c = self.center()\n\n if about_y:\n x_new = []\n for p in self.x:\n x_new.append(c[0] + (c[0] - p))\n else:\n x_new = self.x\n\n if about_x:\n y_new = []\n for p in self.y:\n y_new.append(c[1] + (c[1] - p))\n else:\n y_new = self.y\n\n return Route(x_new, y_new, z=self.z)","def mirrored( self ):\n return self._modifier(\n self,\n lambda x: mirror_bits( x, self.nr_of_pins )\n )","def mirror(self, p1=(0, 1), p2=(0, 0)):\n if hasattr(p1, \"center\"):\n p1 = p1.center\n if hasattr(p2, \"center\"):\n p2 = p2.center\n p1 = np.array(p1)\n p2 = np.array(p2)\n # Translate so reflection axis passes through origin\n self.origin = self.origin - p1\n\n # Rotate so reflection axis aligns with x-axis\n angle = np.arctan2((p2[1] - p1[1]), (p2[0] - p1[0])) * 180 / pi\n self.origin = _rotate_points(self.origin, angle=-angle, center=[0, 0])\n self.rotation -= angle\n\n # Reflect across x-axis\n self.x_reflection = not self.x_reflection\n self.origin[1] = -self.origin[1]\n self.rotation = -self.rotation\n\n # Un-rotate and un-translate\n self.origin = _rotate_points(self.origin, angle=angle, center=[0, 0])\n self.rotation += angle\n self.origin = self.origin + p1\n\n if self.owner is not None:\n self.owner._bb_valid = False\n return self","def mirror_feature(self, x=None, y=None):\n\n #mirrored = Feature()\n mirrored = copy.deepcopy(self)\n if not x == None:\n for i in range(mirrored.feature_len()):\n mirrored.coord[i] = np.array([[2*x-c[0],c[1]] for c in mirrored.coord[i]])\n if not y == None:\n for i in range(mirrored.feature_len()):\n mirrored.coord[i] = np.array([[c[0],2*y-c[1]] for c in mirrored.coord[i]]) \n return mirrored","def new_mirror(self,alongx,alongy):\n Knew = K.clone()\n if alongx:\n Knew[0,2] = size[0]-Knew[0,2]\n if alongy:\n Knew[1,2] = size[1]-Knew[1,2]\n return CameraInfo(self.size,Knew,self.dist)","def verticalMirror(self, mirror):\n mirror_y = self.y\n distToMir = self.x - mirror\n mirror_x = self.x - (2 * distToMir)\n return Point(mirror_x, mirror_y)","def verticalMirror(self, mirror):\n mirror_y = self.y\n distToMir = self.x - mirror\n mirror_x = self.x - (2 * distToMir)\n return Point(mirror_x, mirror_y)","def mirror(img):\n return img[:, ::-1]","def mirror_points_point(points, mirror):\n return [mirror_point_point(point, mirror) for point in points]","def mirror(self, p1=(0, 1), p2=(0, 0)):\n for n, points in enumerate(self.polygons):\n self.polygons[n] = _reflect_points(points, p1, p2)\n if self.parent is not None:\n self.parent._bb_valid = False\n return self","def reverse(self, lon, lat):","def mirror_point_to_plane(point, plane):\n assert isinstance(plane, cg3d_plane.CGPlane)\n pn, norm = plane.get_point_and_normal()\n norm.normalize()\n return point - 2.0 * ((point - pn) * norm) * norm","def mirror(f, j=0):\n return f.per(dmp_mirror_in(f.rep, j, f.lev, f.dom))","def draw_mirrored_cell(self, pos, color):\n self.hexes.set_cells(helpfunc.mirror_hexes(pos), color)","def mirror(self):\n newRot = [x for x in self.rot]\n newRot[1] = -newRot[1]\n newLRot = [x for x in self.lrot]\n newLRot[1] = -newLRot[1]\n newLRot[2] = -newLRot[2]\n return Wall(self.beat, self.dur, -self.r, -self.l, self.d, self.u, newRot, newLRot)","def panTo(self, p=None):\n if p == None:\n p = self.focus\n MV = self.MV\n vr = self.getViewRight()\n vu = self.getViewUp()\n p = -p\n x = np.dot(p, vr) # dot product\n y = np.dot(p, vu)\n MV[3, :2] = x, y # set first two entries of 4th row to x, y\n self.MV = MV","def panTo(self, p=None):\n if p == None:\n p = self.focus\n MV = self.MV\n vr = self.getViewRight()\n vu = self.getViewUp()\n p = -p\n x = np.dot(p, vr) # dot product\n y = np.dot(p, vu)\n MV[3, :2] = x, y # set first two entries of 4th row to x, y\n self.MV = MV","def transform_matrix_offset_center(matrix, x, y):\n o_x = float(x) / 2 + 0.5\n o_y = float(y) / 2 + 0.5\n offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])\n reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])\n transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)\n return transform_matrix","def transform_matrix_offset_center(matrix, y, x):\n o_x = (x - 1) / 2.0\n o_y = (y - 1) / 2.0\n offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])\n reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])\n transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)\n return transform_matrix","def distort_point(point):\n fx, fy = _camera_tuned_matrix[0][0], _camera_tuned_matrix[1][1]\n cx, cy = _camera_tuned_matrix[0][2], _camera_tuned_matrix[1][2]\n x, y = (point.x - cx) / fx, (point.y - cy) / fy\n\n k1, k2, p1, p2, k3 = _camera_distortion[0]\n r2 = x ** 2 + y ** 2\n r4 = r2 * r2\n r6 = r2 * r4\n x = x * (1 + k1 * r2 + k2 * r4 + k3 * r6) + 2 * p1 * x * y + p2 * (r2 + 2 * x * x)\n y = y * (1 + k1 * r2 + k2 * r4 + k3 * r6) + p1 * (r2 + 2 * y * y) + 2 * p2 * x * y\n\n fx2, fy2 = _camera_matrix[0][0], _camera_matrix[1][1]\n cx2, cy2 = _camera_matrix[0][2], _camera_matrix[1][2]\n x2 = x * fx2 + cx2\n y2 = y * fy2 + cy2\n return ge.Point(x2, y2)","def mirror(self):\n self.__mirror = not self.__mirror","def un_distort_point(point):\n points = np.array([[(point.x, point.y)]], np.float32)\n temp = cv2.undistortPoints(points, _camera_matrix, _camera_distortion)\n fx, fy = _camera_tuned_matrix[0][0], _camera_tuned_matrix[1][1]\n cx, cy = _camera_tuned_matrix[0][2], _camera_tuned_matrix[1][2]\n x = temp[0][0][0] * fx + cx\n y = temp[0][0][1] * fy + cy\n return ge.Point(x, y)","def mirrorTour(tour):\n M0 = reflection_matrix((0,0,0), (1,0,0))\n mvps = []\n for vp in tour:\n p = vp.camera.position\n q = vp.camera.orientation\n print(p,q)\n mp = np.array((p.x,p.y,p.z,1.0))*np.matrix(M0)\n mp = np.squeeze(np.asarray(mp))\n # mq = quaternion_matrix((q.x,q.y,q.z,q.w))*np.matrix(M0)\n # mq = quaternion_from_matrix(mq)\n mq = (q.x,-q.y,-q.z,q.w)\n # print(mp,mq)\n mvp = ViewPoint(vp.id+10000,mp[0],mp[1],mp[2],mq[0],mq[1],mq[2],mq[3])\n mvps.append(mvp)\n return mvps","def shift_point_wline(mutated_genome,index):\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\n point = mutated_genome[index][2][point_index]\n newpoint = (point[0]+Xval,point[1]+Yval)\n mutated_genome[index][2][point_index] = newpoint","def setAsMirror(self, mirrorNp):\r\n mirrorNp.setTexture( self.mirrorBuffer.getTexture(), 1 )","def recenter(self, point=(0, 0)):\n self.center = Point(*point)","def reflect_ghost(self, p0):\n # Instead of self.p1, one could take any point on the line p1--p2.\n dist = self.p1 - p0\n alpha = numpy.einsum(\"ij, ij->i\", dist, self.mirror_edge)\n # q is sits at the perpendicular intersection of the reflection\n q = dist - (alpha / self.beta)[:, None] * self.mirror_edge\n return p0 + 2 * q","def mirror(self):\n \n screen_width = self.game.screen.get_width()\n screen_height = self.game.screen.get_height()\n\n if self.rect.centerx < 0 and self.vx < 0:\n self.rect.centerx = screen_width\n if self.rect.centerx > screen_width:\n self.rect.centerx = 0\n if self.rect.centery < 0 and self.vy < 0:\n self.rect.centery = 0\n if self.rect.centery > screen_height and self.vy > 0:\n self.rect.centery = screen_height","def rotate(self):\n\n self.pins = self.pins[1:] + list(self.pins[0])\n self.mapping = self.mapping[1:] + list(self.mapping[0])","def RearangePoinAnnotation(point,PaddZAxis=0,PaddYAxis=0,PaddXAxis=0):\n point[:,0] = point[:,0] ^ point[:,2]\n point[:,2] = point[:,2] ^ point[:,0]\n point[:,0] = point[:,0] ^ point[:,2]\n if PaddZAxis!=0:\n point[:,0]+=PaddZAxis\n if PaddYAxis!=0:\n point[:,1]+=PaddYAxis\n if PaddXAxis!=0:\n point[:,2]+=PaddXAxis\n return(point)","def swap_xy(tensor: tf.Tensor) -> tf.Tensor:\n return moveaxis(tensor, -2, -1)","def zoom(self, dr):\n d = self.getDistance()\n vn = self.getViewNormal()\n vn *= dr*d\n GL.glTranslate(vn[0], vn[1], vn[2])","def diagonalAtPoint(self, point=..., idx:int=...):\n point, idx = self.resolvePoint(point=point, idx=idx)\n print(idx, type(idx))\n return point.lineToPoint(self.vertices[idx-2])","def inv_projmap(self, img, nside=None):\n pass","def rotate(self, matrix):\n n = len(matrix)\n #转置\n for i in range(n):\n for j in range(i+1,n):\n matrix[i][j],matrix[j][i] = matrix[j][i],matrix[i][j]\n #镜像\n mid = n//2\n for i in range(n):\n for j in range(mid):\n matrix[i][j],matrix[i][n-j-1] = matrix[i][n-j-1],matrix[i][j]","def mirror_matrix(theta):\n c = np.cos(theta)\n s = np.sin(theta)\n return np.array([[c**2-s**2, 2*c*s],\n [2*c*s, s**2-c**2]])","def reproject(point):\n wgs84 = pyproj.Proj('+init=epsg:4326')\n native = pyproj.Proj(DEM_PROJECTION)\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\n return geom.Point(x, y)","def zoom(self, dr):\n d = self.getDistance()\n vn = self.getViewNormal()\n vn *= dr*d\n GL.glTranslatef(vn[0], vn[1], vn[2])","def mirror_gun (glider_gun):\n return glider_gun[2] (-1, 0, swap_xy)","def diagonal_across(self, current_state):\n # Find the index of the '0' tile\n index = current_state.index('0')\n tile_that_was_swapped = '0'\n\n # If the current state can't perform the 'diagonal' action, then exit the function\n if not self.can_move_diagonally(index):\n return 0, tile_that_was_swapped\n\n # Else, we can move the '0' tile to the other side of the grid\n else:\n # We need to find the diagonal-across position of the '0' tile\n index_to_swap = index\n\n # If the '0' tile is in the top-left corner, then we need to switch it with the bottom-right corner\n if index == 0:\n index_to_swap = self.puzzle_length - 1\n\n # If the '0' tile is in the top-right corner, then we need to switch it with the bottom-left corner\n elif index == self.puzzle_width - 1:\n index_to_swap = self.puzzle_length - self.puzzle_width\n\n # If the '0' tile is in the bottom-left corner, then we need to switch it with the top-right corner\n elif index == self.puzzle_length - self.puzzle_width:\n index_to_swap = self.puzzle_width - 1\n\n # If the '0' tile is in the bottom-right corner, then we need to switch it with the top-left corner\n elif index == self.puzzle_length - 1:\n index_to_swap = 0\n\n # Swap the '0' tile with the other index\n tile_that_was_swapped = current_state[index_to_swap]\n current_state[index] = current_state[index_to_swap]\n current_state[index_to_swap] = '0'\n # end: if-else\n\n return self.cost_of_diagonal_across, tile_that_was_swapped","def mirror(self):\n for l in range(self.numRows):\n mirrorGrid = []\n for l in range(self.numCols):\n row = self.grid[l][::-1]\n mirrorGrid.append(row)\n return mirrorGrid","def move_to_origin(x):\n # Correct x so it is centered at (0,0)\n tx = np.mean(x[:no_points, :])\n ty = np.mean(x[no_points:, :])\n x[:no_points, :] = (x[:no_points, :] - tx)\n x[no_points:, :] = (x[no_points:, :] - ty)\n return x, tx, ty","def rotate(self, m):\n n = len(m)\n for i in range(n//2):\n for j in range(i,n-i-1):\n m[j][~i],m[~i][~j],m[~j][i],m[i][j] = \\\n m[i][j],m[j][~i],m[~i][~j],m[~j][i]","def mirror_horiz(pixels):\n copy = blank_image(len(pixels), len(pixels[0])) \n for r in range(len(pixels)):\n for c in range(len(pixels[0])):\n copy[r][c] = pixels[r][c]\n a = len(pixels[0])//2\n for r in range(len(pixels)):\n for c in range(a, len(pixels[0])):\n copy[r][c] = copy[r][-c-1]\n return copy","def mirror_augmentation():\n return lambda image: ImageOps.mirror(image)","def lift(point):\n return gs.copy(point)","def inverse_coord_shift(kalman_coord):\n x_c, y_c, aspect_ratio, height = kalman_coord[:4]\n width = height / aspect_ratio\n x_min = x_c - width / 2\n x_max = x_c + width / 2\n y_min = y_c - height / 2\n y_max = y_c + height / 2\n std_coord = np.array([x_min, y_min, x_max, y_max])\n return np.int_(np.round(std_coord))","def move_diagonal(self):\r\n if self.movement == \"diagonal\" and self.flag_move:\r\n self.move_ball()\r\n self.canvas.after(50, self.move_diagonal)","def rotate(self, matrix: List[List[int]]) -> None:\r\n # Vertical Mirror\r\n for i in range(len(matrix)):\r\n for j in range(floor(len(matrix)/2)):\r\n t = matrix[i][j]\r\n matrix[i][j] = matrix[i][len(matrix)-1-j]\r\n matrix[i][len(matrix)-1-j] = t\r\n \r\n # Top right to bottom left diagonal mirror\r\n for i in range(len(matrix)):\r\n for j in range(len(matrix)):\r\n if i < len(matrix)-1-j:\r\n t = matrix[i][j]\r\n matrix[i][j] = matrix[len(matrix)-j-1][len(matrix)-1-i]\r\n matrix[len(matrix)-j-1][len(matrix)-1-i] = t","def mirrorImage(self):\n\n im = Image.open(self.ActivePhoto)\n out = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n out.save(self.ActivePhoto)\n self.photo.setPixmap(QtGui.QPixmap(self.ActivePhoto))\n print (\"Flipped image\")","def mirror(to='IN'):\n to = to.strip().upper()\n if (to==\"OUT\"):\n opticalcoupler.MoveMirror(0)\n logger.info('Mirror: '+to)\n camera.status.mirror = 'OUT'\n elif (to==\"IN\"):\n opticalcoupler.MoveMirror(opticalcoupler.MIRROR_IN)\n logger.info('Mirror: '+to)\n camera.status.mirror = 'IN'\n else:\n logger.error(\"Usage: 'mirror IN' or 'mirror OUT'\")","def putDiagonal(self, vector):\n if type(vector) in [type(1), type(1.)]:\n ids = numerix.arange(self._shape[0])\n tmp = numerix.zeros((self._shape[0],), 'd')\n tmp[:] = vector\n self.put(tmp, ids, ids)\n else:\n ids = numerix.arange(len(vector))\n self.put(vector, ids, ids)","def mirror_y(board):\n new_board = board[:]\n new_board.reverse()\n return new_board","def diag_line((lat0, lon0, alt0), (lat, lon, alt), k=5):\n\tlats = np.linspace(lat0, lat, k)\n\tlons = np.linspace(lon0, lon, k)\n\talts = np.linspace(alt0, alt, k)\n\tp = zip(lats, lons, alts)\n\treturn p","def pinhole_projection_image_to_world(uv, z, K):\n\n u_v_1 = np.array([uv[0], uv[1], 1])\n pos = z * np.matmul(inv(K),u_v_1)\n return pos","def reflect(self):\n self.vertices[-1, :] = self.reflected","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def diagonal(t, x, y):\n from math import atan2, sqrt, pi\n angle = atan2(y, x) * 180 / pi\n dist = sqrt(x**2 + y**2)\n lt(t, angle)\n fdbk(t, dist)\n rt(t, angle)","def pad(self, nxp, nyp):\n assert (nxp > self.nx)\n assert (nyp > self.ny)\n assert (np.mod(nxp - self.nx, 2) == 0)\n assert (np.mod(nyp - self.ny, 2) == 0)\n\n ret = rmap(nx=nxp, dx=self.dx, ny=nyp, dy=self.dy)\n ret.map[(nyp - self.ny) / 2:(nyp + self.ny) / 2, (nxp - self.nx) / 2:(\n nxp + self.nx) / 2] = self.map\n return ret","def diagonal_adjacent(self, current_state):\n # Find the index of the '0' tile\n index = current_state.index('0')\n tile_that_was_swapped = '0'\n\n # If the current state can't perform the 'diagonal' action, then exit the function\n if not self.can_move_diagonally(index):\n return 0, tile_that_was_swapped\n\n # Else, we can move the '0' tile to the other side of the grid\n else:\n # We need to find the adjacent-diagonal position of the '0' tile\n index_to_swap = index\n\n # If the '0' tile is in the top-left corner, then we need to switch it with the tile +1 row and +1 column\n if index == 0:\n index_to_swap = index + self.puzzle_width + 1\n\n # If the '0' tile is in the top-right corner, then we need to switch it with the tile +1 row and -1 column\n elif index == self.puzzle_width - 1:\n index_to_swap = index + self.puzzle_width - 1\n\n # If the tile is in the bottom-left corner, then we need to switch with the tile -1 row and +1 column\n elif index == self.puzzle_length - self.puzzle_width:\n index_to_swap = index - self.puzzle_width + 1\n\n # If the tile is in the bottom-right corner, then we need to switch with the tile -1 row and -1 column\n elif index == self.puzzle_length - 1:\n index_to_swap = index - self.puzzle_width - 1\n\n # Swap the '0' tile with the other index\n tile_that_was_swapped = current_state[index_to_swap]\n current_state[index] = current_state[index_to_swap]\n current_state[index_to_swap] = '0'\n # end: if-else\n\n return self.cost_of_diagonal_adjacent, tile_that_was_swapped","def change_map_down(self):\n self.change_map(self.current_map_idx + 1)","def rotate(self, matrix):\n newCoord = np.zeros(self.coord.shape)\n newCoord[:,0]=matrix[0,0]+matrix[0,1]*self.coord[:,0]+matrix[0,2]*self.coord[:,1]+matrix[0,3]*self.coord[:,2]\n newCoord[:,1]=matrix[1,0]+matrix[1,1]*self.coord[:,0]+matrix[1,2]*self.coord[:,1]+matrix[1,3]*self.coord[:,2]\n newCoord[:,2]=matrix[2,0]+matrix[2,1]*self.coord[:,0]+matrix[2,2]*self.coord[:,1]+matrix[2,3]*self.coord[:,2]\n self.coord = deepcopy(newCoord)","def mirror_x(board):\n new_board = board[:]\n for (ix, item) in enumerate(board):\n new_board[ix] = (len(board) - 1) - item\n\n return new_board","def clearCross(self, coord, lmap):\n for n in range(-3, 3):\n color = self.colorMap(lmap.getCell((coord[0] + n, coord[1] + n)))\n self._drawPoint(color, (coord[0] + n, coord[1] + n))\n color = self.colorMap(lmap.getCell((coord[0] + n, coord[1] - n)))\n self._drawPoint(color, (coord[0] + n, coord[1] - n))\n self.zoomMap(self.scale)","def shift_point(mutated_genome,index):\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\n point_index = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n point = mutated_genome[index][2][point_index]\n newpoint = (point[0]+Xval,point[1]+Yval)\n mutated_genome[index][2][point_index] = newpoint","def add_on_land_position(self) -> Point2:\n return self.position.offset(Point2((-2.5, 0.5)))","def rotate(self, matrix: List[List[int]]) -> None:\n n = len(matrix) # 行\n\n # 以x=y为轴翻转\n # [[1,2,3],\n # [4,5,6],\n # [7,8,9]]\n # 变为\n # [1 4 7]\n # [2 5 8]\n # [3 6 9]\n for i in range(n):\n for j in range(i, n):\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n\n # 以中点为轴翻转\n for i in range(n):\n for j in range(n // 2):\n matrix[i][j], matrix[i][n - j - 1] = matrix[i][n - j - 1], \\\n matrix[i][j]\n\n # 非原地修改写法，先上下翻转，再以x=y为轴复制对应数字\n # n = len(matrix)\n # r = list(zip(*matrix[::-1]))\n # for i in range(n):\n # for j in range(n):\n # matrix[i][j] = r[i][j]","def _maping(x,y,l,a):\n newx = (x**2 *(l* ((x**2 + y**2)**(a/2) - 1) + 2) - l * y**2 *((x**2 + y**2)**(a/2) - 1))/(x**2 + y**2) \n newy = (2 * x* y *(l* ((x**2 + y**2)**(a/2) - 1) + 1))/(x**2 + y**2)\n return newx, newy","def mirror_positions(positions, domain, mirror):\n for d in range(3):\n if mirror[d]:\n mirrored_xs_grid = positions.copy()\n mirrored_xs_grid[:, :, :, d] = (domain[2 * d + 1] - domain[2 * d]) + positions[:, :, :, d]\n positions = np.concatenate((positions, mirrored_xs_grid), axis=d)\n return positions","def mirror_densities(densities, mirror):\n nx, ny, nz = densities.shape\n if mirror[0]:\n densities = np.concatenate(\n (densities, densities[range(nx - 1, -1, -1), :, :]), axis=0)\n if mirror[1]:\n densities = np.concatenate(\n (densities, densities[:, range(ny - 1, -1, -1), :]), axis=1)\n if mirror[2]:\n densities = np.concatenate(\n (densities, densities[:, :, range(nz - 1, -1, -1)]), axis=2)\n return densities","def rotate(self, matrix: List[List[int]]) -> None:\n n = len(matrix)\n for l in range(n // 2):\n r = n - 1 - l\n for p in range(l, r):\n q = n - 1 - p\n cache = matrix[l][p]\n matrix[l][p] = matrix[q][l]\n matrix[q][l] = matrix[r][q]\n matrix[r][q] = matrix[p][r]\n matrix[p][r] = cache","def in_place_offset(self, offset):\n self.p += offset * self.cross_z.normalized()","def __invert__(self) -> PointType:\n return Point(~self.x, ~self.y)","def cw_rotate(self):\n self.grid = [list(x) for x in zip(*self.grid[::-1])]\n self.find_edges()","def implicit_matrix_1d(xsize, centervalue, neighborvalue):\n matrix1d = np.zeros([xsize, xsize])\n np.fill_diagonal(matrix1d, centervalue)\n matrix1d = np.roll(matrix1d, 1, 0)\n np.fill_diagonal(matrix1d, neighborvalue)\n matrix1d = np.roll(matrix1d, -2, 0)\n np.fill_diagonal(matrix1d, neighborvalue)\n matrix1d = np.roll(matrix1d, 1, 0)\n return matrix1d","def rotater(self, direction):\n if self.center:\n mapDel(self, theFallen)\n rotate(self, direction)\n mapAdd(self, theFallen)","def mrotate(self):\n result_matrix = [[0 for col in range(len(self.matrix[0]))] for row in range(len(self.matrix))]\n for i in range(len(self.matrix)):\n for j in range(len(self.matrix[0])):\n result_matrix[i][j] = self.matrix[i][len(self.matrix[0]) - 1 - j]\n # left turn -> result_matrix[i][j] = self.matrix[len(self.matrix) - 1 - i][j]\n self.matrix = result_matrix\n pass","def reverse(self):\n x = self._x * -1\n y = self._y * -1\n return Point(x,y)","def change_map_up(self):\n if self.current_map_idx > 0:\n self.change_map(self.current_map_idx + 1)","def mirror_path(path):\n points, env = path\n \n points = [mirror(p) for p in points]\n if env == 'c':\n env = 'l'\n elif env == 'l':\n env = 'c'\n else:\n raise Exception('unknown env in table')\n\n return (points, env)","def warp_pos(pos, warp_matrix):\n import torch\n p1 = torch.Tensor([pos[0], pos[1], 1]).view(3, 1)\n p2 = torch.Tensor([pos[2], pos[3], 1]).view(3, 1)\n p1_n = torch.mm(warp_matrix, p1).view(1, 2)\n p2_n = torch.mm(warp_matrix, p2).view(1, 2)\n return torch.cat((p1_n, p2_n), 1).view(1, -1)","def flip_around(self, row: int, col: int) -> None:\n # we treat the 8 adjacent cases :\n neighbours = np.array([[-1, -1], [-1, 0], [-1, 1],\n [0, -1], [0, 1],\n [1, -1], [1, 0], [1, 1]], dtype=int)\n for dr, dc in neighbours:\n row_neighbour = row + dr\n col_neighbour = col + dc\n if not self.is_in_the_grid(row_neighbour, col_neighbour):\n continue\n self.flip(row_neighbour, col_neighbour)","def rec_lat(matrix):\n rec_lat = np.zeros([3,3])\n V = np.linalg.det(matrix)\n rec_lat[0] = np.cross(matrix[1], matrix[2])/V\n rec_lat[1] = np.cross(matrix[2], matrix[0])/V\n rec_lat[2] = np.cross(matrix[0], matrix[1])/V\n return rec_lat #* 2 * pi","def translate(self, displacement):\n\n self.center = (self.center[0] + displacement[0],\n self.center[1] + displacement[1])","def rotate_to_local(self,vxyz):\n return sp.mxv(self.mtxtofov,vxyz)","def translate_point(point, y_offset=0, x_offset=0):\n\n out_point = point.copy()\n\n out_point[:, 0] += y_offset\n out_point[:, 1] += x_offset\n\n return out_point","def rotate(self, matrix):\n # matrix[:] = zip(*matrix[::-1])\n n = len(matrix)\n # 水平翻转\n for i in range(n // 2):\n for j in range(n):\n matrix[i][j], matrix[n - i - 1][j] = matrix[n - i - 1][j], matrix[i][j]\n # 主对角线翻转\n for i in range(n):\n for j in range(i):\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]","def rotate(self, matrix: List[List[int]]) -> None:\n n = len(matrix)\n for dig in range(n):\n row = dig\n for col in range(dig+1, n):\n matrix[row][col] , matrix[col][row] = matrix[col][row], matrix[row][col]\n print(matrix)\n left = 0\n right = n-1\n while left < right:\n for row in range(n):\n matrix[row][left], matrix[row][right] = matrix[row][right], matrix[row][left]\n left+=1\n right-=1","def move_point_wline(mutated_genome,index):\n Xval = random.randint(-int(imagewidth/5.),int(imagewidth*6./5.))\n Yval = random.randint(-int(imageheight/5.),int(imageheight*6./5.))\n point = (Xval,Yval)\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\n mutated_genome[index][2][point_index] = point","def test_pan():\n _c = _a.copy()\n _c = _c.piv.pan(1.0, -1.0) # note the use of .piv.\n assert np.allclose(_c.coords[\"x\"][0], 1.312480)\n assert np.allclose(_c.coords[\"y\"][0], -1.31248)","def mirror_world(some_list):\n l = some_list[::-1]\n print(f\"Our original list: {some_list}\")\n print(f\"Our original list location: {id(some_list)}\")\n print(f\"Our new list: {l}\")\n print(f\"Our new list location: {id(l)}\")\n return \"done!\"","def set_diag(M,d,idx=0):\n n, m = shape_mat(M)\n if idx >= 0:\n for i, di in enumerate( d ):\n M[i][i+idx] = di\n else:\n for i, di in enumerate( d ):\n M[i-idx][i] = di","def apply(self, point):\n m = numpy.dot(self.matrix, numpy.array([point[0], point[1], 1.0]).transpose())\n return pygame.Vector2(m[0], m[1])","def rotateMatrixAttempt(matrix):\n for idxring in range(len(matrix) / 2):\n swap = []\n ringgen = ringCoords(len(matrix), idxring)\n for x, y in ringgen:\n swap.append(matrix[y][x])\n ringgen = ringCoords(len(matrix), idxring)\n start_idx = len(matrix) - 1 - idxring\n for swapidx in range(-1 * start_idx, len(swap) - start_idx):\n x, y = ringgen.next()\n matrix[y][x] = swap[swapidx]","def d4out():\n\td4x.moveTo(d4x_out)\n\td4y.moveTo(d4y_out)"],"string":"[\n \"def mirror_point_point(point, mirror):\\n return add_vectors(mirror, subtract_vectors(mirror, point))\",\n \"def mirror(self, p1=(0, 1), p2=(0, 0)):\\n self.position = _reflect_points(self.position, p1, p2)\\n return self\",\n \"def op_mirror():\\n mir = np.array([[1, 0, 0, 0],\\n [0, 1, 0, 0],\\n [0, 0, -1, 0],\\n [0, 0, 0, -1]])\\n return mir\",\n \"def horizontalMirror(self, mirror):\\n mirror_x = self.x\\n distToMir = self.y - mirror\\n mirror_y = self.y - (2 * distToMir)\\n return Point(mirror_x, mirror_y)\",\n \"def horizontalMirror(self, mirror):\\n mirror_x = self.x\\n distToMir = self.y - mirror\\n mirror_y = self.y - (2 * distToMir)\\n return Point(mirror_x, mirror_y)\",\n \"def mirror(self, about_x=False, about_y=False, about_axis=False):\\n if about_axis:\\n c = (0, 0)\\n elif about_axis is False:\\n c = self.center()\\n\\n if about_y:\\n x_new = []\\n for p in self.x:\\n x_new.append(c[0] + (c[0] - p))\\n else:\\n x_new = self.x\\n\\n if about_x:\\n y_new = []\\n for p in self.y:\\n y_new.append(c[1] + (c[1] - p))\\n else:\\n y_new = self.y\\n\\n return Route(x_new, y_new, z=self.z)\",\n \"def mirrored( self ):\\n return self._modifier(\\n self,\\n lambda x: mirror_bits( x, self.nr_of_pins )\\n )\",\n \"def mirror(self, p1=(0, 1), p2=(0, 0)):\\n if hasattr(p1, \\\"center\\\"):\\n p1 = p1.center\\n if hasattr(p2, \\\"center\\\"):\\n p2 = p2.center\\n p1 = np.array(p1)\\n p2 = np.array(p2)\\n # Translate so reflection axis passes through origin\\n self.origin = self.origin - p1\\n\\n # Rotate so reflection axis aligns with x-axis\\n angle = np.arctan2((p2[1] - p1[1]), (p2[0] - p1[0])) * 180 / pi\\n self.origin = _rotate_points(self.origin, angle=-angle, center=[0, 0])\\n self.rotation -= angle\\n\\n # Reflect across x-axis\\n self.x_reflection = not self.x_reflection\\n self.origin[1] = -self.origin[1]\\n self.rotation = -self.rotation\\n\\n # Un-rotate and un-translate\\n self.origin = _rotate_points(self.origin, angle=angle, center=[0, 0])\\n self.rotation += angle\\n self.origin = self.origin + p1\\n\\n if self.owner is not None:\\n self.owner._bb_valid = False\\n return self\",\n \"def mirror_feature(self, x=None, y=None):\\n\\n #mirrored = Feature()\\n mirrored = copy.deepcopy(self)\\n if not x == None:\\n for i in range(mirrored.feature_len()):\\n mirrored.coord[i] = np.array([[2*x-c[0],c[1]] for c in mirrored.coord[i]])\\n if not y == None:\\n for i in range(mirrored.feature_len()):\\n mirrored.coord[i] = np.array([[c[0],2*y-c[1]] for c in mirrored.coord[i]]) \\n return mirrored\",\n \"def new_mirror(self,alongx,alongy):\\n Knew = K.clone()\\n if alongx:\\n Knew[0,2] = size[0]-Knew[0,2]\\n if alongy:\\n Knew[1,2] = size[1]-Knew[1,2]\\n return CameraInfo(self.size,Knew,self.dist)\",\n \"def verticalMirror(self, mirror):\\n mirror_y = self.y\\n distToMir = self.x - mirror\\n mirror_x = self.x - (2 * distToMir)\\n return Point(mirror_x, mirror_y)\",\n \"def verticalMirror(self, mirror):\\n mirror_y = self.y\\n distToMir = self.x - mirror\\n mirror_x = self.x - (2 * distToMir)\\n return Point(mirror_x, mirror_y)\",\n \"def mirror(img):\\n return img[:, ::-1]\",\n \"def mirror_points_point(points, mirror):\\n return [mirror_point_point(point, mirror) for point in points]\",\n \"def mirror(self, p1=(0, 1), p2=(0, 0)):\\n for n, points in enumerate(self.polygons):\\n self.polygons[n] = _reflect_points(points, p1, p2)\\n if self.parent is not None:\\n self.parent._bb_valid = False\\n return self\",\n \"def reverse(self, lon, lat):\",\n \"def mirror_point_to_plane(point, plane):\\n assert isinstance(plane, cg3d_plane.CGPlane)\\n pn, norm = plane.get_point_and_normal()\\n norm.normalize()\\n return point - 2.0 * ((point - pn) * norm) * norm\",\n \"def mirror(f, j=0):\\n return f.per(dmp_mirror_in(f.rep, j, f.lev, f.dom))\",\n \"def draw_mirrored_cell(self, pos, color):\\n self.hexes.set_cells(helpfunc.mirror_hexes(pos), color)\",\n \"def mirror(self):\\n newRot = [x for x in self.rot]\\n newRot[1] = -newRot[1]\\n newLRot = [x for x in self.lrot]\\n newLRot[1] = -newLRot[1]\\n newLRot[2] = -newLRot[2]\\n return Wall(self.beat, self.dur, -self.r, -self.l, self.d, self.u, newRot, newLRot)\",\n \"def panTo(self, p=None):\\n if p == None:\\n p = self.focus\\n MV = self.MV\\n vr = self.getViewRight()\\n vu = self.getViewUp()\\n p = -p\\n x = np.dot(p, vr) # dot product\\n y = np.dot(p, vu)\\n MV[3, :2] = x, y # set first two entries of 4th row to x, y\\n self.MV = MV\",\n \"def panTo(self, p=None):\\n if p == None:\\n p = self.focus\\n MV = self.MV\\n vr = self.getViewRight()\\n vu = self.getViewUp()\\n p = -p\\n x = np.dot(p, vr) # dot product\\n y = np.dot(p, vu)\\n MV[3, :2] = x, y # set first two entries of 4th row to x, y\\n self.MV = MV\",\n \"def transform_matrix_offset_center(matrix, x, y):\\n o_x = float(x) / 2 + 0.5\\n o_y = float(y) / 2 + 0.5\\n offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])\\n reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])\\n transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)\\n return transform_matrix\",\n \"def transform_matrix_offset_center(matrix, y, x):\\n o_x = (x - 1) / 2.0\\n o_y = (y - 1) / 2.0\\n offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])\\n reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])\\n transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)\\n return transform_matrix\",\n \"def distort_point(point):\\n fx, fy = _camera_tuned_matrix[0][0], _camera_tuned_matrix[1][1]\\n cx, cy = _camera_tuned_matrix[0][2], _camera_tuned_matrix[1][2]\\n x, y = (point.x - cx) / fx, (point.y - cy) / fy\\n\\n k1, k2, p1, p2, k3 = _camera_distortion[0]\\n r2 = x ** 2 + y ** 2\\n r4 = r2 * r2\\n r6 = r2 * r4\\n x = x * (1 + k1 * r2 + k2 * r4 + k3 * r6) + 2 * p1 * x * y + p2 * (r2 + 2 * x * x)\\n y = y * (1 + k1 * r2 + k2 * r4 + k3 * r6) + p1 * (r2 + 2 * y * y) + 2 * p2 * x * y\\n\\n fx2, fy2 = _camera_matrix[0][0], _camera_matrix[1][1]\\n cx2, cy2 = _camera_matrix[0][2], _camera_matrix[1][2]\\n x2 = x * fx2 + cx2\\n y2 = y * fy2 + cy2\\n return ge.Point(x2, y2)\",\n \"def mirror(self):\\n self.__mirror = not self.__mirror\",\n \"def un_distort_point(point):\\n points = np.array([[(point.x, point.y)]], np.float32)\\n temp = cv2.undistortPoints(points, _camera_matrix, _camera_distortion)\\n fx, fy = _camera_tuned_matrix[0][0], _camera_tuned_matrix[1][1]\\n cx, cy = _camera_tuned_matrix[0][2], _camera_tuned_matrix[1][2]\\n x = temp[0][0][0] * fx + cx\\n y = temp[0][0][1] * fy + cy\\n return ge.Point(x, y)\",\n \"def mirrorTour(tour):\\n M0 = reflection_matrix((0,0,0), (1,0,0))\\n mvps = []\\n for vp in tour:\\n p = vp.camera.position\\n q = vp.camera.orientation\\n print(p,q)\\n mp = np.array((p.x,p.y,p.z,1.0))*np.matrix(M0)\\n mp = np.squeeze(np.asarray(mp))\\n # mq = quaternion_matrix((q.x,q.y,q.z,q.w))*np.matrix(M0)\\n # mq = quaternion_from_matrix(mq)\\n mq = (q.x,-q.y,-q.z,q.w)\\n # print(mp,mq)\\n mvp = ViewPoint(vp.id+10000,mp[0],mp[1],mp[2],mq[0],mq[1],mq[2],mq[3])\\n mvps.append(mvp)\\n return mvps\",\n \"def shift_point_wline(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\\n point = mutated_genome[index][2][point_index]\\n newpoint = (point[0]+Xval,point[1]+Yval)\\n mutated_genome[index][2][point_index] = newpoint\",\n \"def setAsMirror(self, mirrorNp):\\r\\n mirrorNp.setTexture( self.mirrorBuffer.getTexture(), 1 )\",\n \"def recenter(self, point=(0, 0)):\\n self.center = Point(*point)\",\n \"def reflect_ghost(self, p0):\\n # Instead of self.p1, one could take any point on the line p1--p2.\\n dist = self.p1 - p0\\n alpha = numpy.einsum(\\\"ij, ij->i\\\", dist, self.mirror_edge)\\n # q is sits at the perpendicular intersection of the reflection\\n q = dist - (alpha / self.beta)[:, None] * self.mirror_edge\\n return p0 + 2 * q\",\n \"def mirror(self):\\n \\n screen_width = self.game.screen.get_width()\\n screen_height = self.game.screen.get_height()\\n\\n if self.rect.centerx < 0 and self.vx < 0:\\n self.rect.centerx = screen_width\\n if self.rect.centerx > screen_width:\\n self.rect.centerx = 0\\n if self.rect.centery < 0 and self.vy < 0:\\n self.rect.centery = 0\\n if self.rect.centery > screen_height and self.vy > 0:\\n self.rect.centery = screen_height\",\n \"def rotate(self):\\n\\n self.pins = self.pins[1:] + list(self.pins[0])\\n self.mapping = self.mapping[1:] + list(self.mapping[0])\",\n \"def RearangePoinAnnotation(point,PaddZAxis=0,PaddYAxis=0,PaddXAxis=0):\\n point[:,0] = point[:,0] ^ point[:,2]\\n point[:,2] = point[:,2] ^ point[:,0]\\n point[:,0] = point[:,0] ^ point[:,2]\\n if PaddZAxis!=0:\\n point[:,0]+=PaddZAxis\\n if PaddYAxis!=0:\\n point[:,1]+=PaddYAxis\\n if PaddXAxis!=0:\\n point[:,2]+=PaddXAxis\\n return(point)\",\n \"def swap_xy(tensor: tf.Tensor) -> tf.Tensor:\\n return moveaxis(tensor, -2, -1)\",\n \"def zoom(self, dr):\\n d = self.getDistance()\\n vn = self.getViewNormal()\\n vn *= dr*d\\n GL.glTranslate(vn[0], vn[1], vn[2])\",\n \"def diagonalAtPoint(self, point=..., idx:int=...):\\n point, idx = self.resolvePoint(point=point, idx=idx)\\n print(idx, type(idx))\\n return point.lineToPoint(self.vertices[idx-2])\",\n \"def inv_projmap(self, img, nside=None):\\n pass\",\n \"def rotate(self, matrix):\\n n = len(matrix)\\n #转置\\n for i in range(n):\\n for j in range(i+1,n):\\n matrix[i][j],matrix[j][i] = matrix[j][i],matrix[i][j]\\n #镜像\\n mid = n//2\\n for i in range(n):\\n for j in range(mid):\\n matrix[i][j],matrix[i][n-j-1] = matrix[i][n-j-1],matrix[i][j]\",\n \"def mirror_matrix(theta):\\n c = np.cos(theta)\\n s = np.sin(theta)\\n return np.array([[c**2-s**2, 2*c*s],\\n [2*c*s, s**2-c**2]])\",\n \"def reproject(point):\\n wgs84 = pyproj.Proj('+init=epsg:4326')\\n native = pyproj.Proj(DEM_PROJECTION)\\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\\n return geom.Point(x, y)\",\n \"def zoom(self, dr):\\n d = self.getDistance()\\n vn = self.getViewNormal()\\n vn *= dr*d\\n GL.glTranslatef(vn[0], vn[1], vn[2])\",\n \"def mirror_gun (glider_gun):\\n return glider_gun[2] (-1, 0, swap_xy)\",\n \"def diagonal_across(self, current_state):\\n # Find the index of the '0' tile\\n index = current_state.index('0')\\n tile_that_was_swapped = '0'\\n\\n # If the current state can't perform the 'diagonal' action, then exit the function\\n if not self.can_move_diagonally(index):\\n return 0, tile_that_was_swapped\\n\\n # Else, we can move the '0' tile to the other side of the grid\\n else:\\n # We need to find the diagonal-across position of the '0' tile\\n index_to_swap = index\\n\\n # If the '0' tile is in the top-left corner, then we need to switch it with the bottom-right corner\\n if index == 0:\\n index_to_swap = self.puzzle_length - 1\\n\\n # If the '0' tile is in the top-right corner, then we need to switch it with the bottom-left corner\\n elif index == self.puzzle_width - 1:\\n index_to_swap = self.puzzle_length - self.puzzle_width\\n\\n # If the '0' tile is in the bottom-left corner, then we need to switch it with the top-right corner\\n elif index == self.puzzle_length - self.puzzle_width:\\n index_to_swap = self.puzzle_width - 1\\n\\n # If the '0' tile is in the bottom-right corner, then we need to switch it with the top-left corner\\n elif index == self.puzzle_length - 1:\\n index_to_swap = 0\\n\\n # Swap the '0' tile with the other index\\n tile_that_was_swapped = current_state[index_to_swap]\\n current_state[index] = current_state[index_to_swap]\\n current_state[index_to_swap] = '0'\\n # end: if-else\\n\\n return self.cost_of_diagonal_across, tile_that_was_swapped\",\n \"def mirror(self):\\n for l in range(self.numRows):\\n mirrorGrid = []\\n for l in range(self.numCols):\\n row = self.grid[l][::-1]\\n mirrorGrid.append(row)\\n return mirrorGrid\",\n \"def move_to_origin(x):\\n # Correct x so it is centered at (0,0)\\n tx = np.mean(x[:no_points, :])\\n ty = np.mean(x[no_points:, :])\\n x[:no_points, :] = (x[:no_points, :] - tx)\\n x[no_points:, :] = (x[no_points:, :] - ty)\\n return x, tx, ty\",\n \"def rotate(self, m):\\n n = len(m)\\n for i in range(n//2):\\n for j in range(i,n-i-1):\\n m[j][~i],m[~i][~j],m[~j][i],m[i][j] = \\\\\\n m[i][j],m[j][~i],m[~i][~j],m[~j][i]\",\n \"def mirror_horiz(pixels):\\n copy = blank_image(len(pixels), len(pixels[0])) \\n for r in range(len(pixels)):\\n for c in range(len(pixels[0])):\\n copy[r][c] = pixels[r][c]\\n a = len(pixels[0])//2\\n for r in range(len(pixels)):\\n for c in range(a, len(pixels[0])):\\n copy[r][c] = copy[r][-c-1]\\n return copy\",\n \"def mirror_augmentation():\\n return lambda image: ImageOps.mirror(image)\",\n \"def lift(point):\\n return gs.copy(point)\",\n \"def inverse_coord_shift(kalman_coord):\\n x_c, y_c, aspect_ratio, height = kalman_coord[:4]\\n width = height / aspect_ratio\\n x_min = x_c - width / 2\\n x_max = x_c + width / 2\\n y_min = y_c - height / 2\\n y_max = y_c + height / 2\\n std_coord = np.array([x_min, y_min, x_max, y_max])\\n return np.int_(np.round(std_coord))\",\n \"def move_diagonal(self):\\r\\n if self.movement == \\\"diagonal\\\" and self.flag_move:\\r\\n self.move_ball()\\r\\n self.canvas.after(50, self.move_diagonal)\",\n \"def rotate(self, matrix: List[List[int]]) -> None:\\r\\n # Vertical Mirror\\r\\n for i in range(len(matrix)):\\r\\n for j in range(floor(len(matrix)/2)):\\r\\n t = matrix[i][j]\\r\\n matrix[i][j] = matrix[i][len(matrix)-1-j]\\r\\n matrix[i][len(matrix)-1-j] = t\\r\\n \\r\\n # Top right to bottom left diagonal mirror\\r\\n for i in range(len(matrix)):\\r\\n for j in range(len(matrix)):\\r\\n if i < len(matrix)-1-j:\\r\\n t = matrix[i][j]\\r\\n matrix[i][j] = matrix[len(matrix)-j-1][len(matrix)-1-i]\\r\\n matrix[len(matrix)-j-1][len(matrix)-1-i] = t\",\n \"def mirrorImage(self):\\n\\n im = Image.open(self.ActivePhoto)\\n out = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\\n out.save(self.ActivePhoto)\\n self.photo.setPixmap(QtGui.QPixmap(self.ActivePhoto))\\n print (\\\"Flipped image\\\")\",\n \"def mirror(to='IN'):\\n to = to.strip().upper()\\n if (to==\\\"OUT\\\"):\\n opticalcoupler.MoveMirror(0)\\n logger.info('Mirror: '+to)\\n camera.status.mirror = 'OUT'\\n elif (to==\\\"IN\\\"):\\n opticalcoupler.MoveMirror(opticalcoupler.MIRROR_IN)\\n logger.info('Mirror: '+to)\\n camera.status.mirror = 'IN'\\n else:\\n logger.error(\\\"Usage: 'mirror IN' or 'mirror OUT'\\\")\",\n \"def putDiagonal(self, vector):\\n if type(vector) in [type(1), type(1.)]:\\n ids = numerix.arange(self._shape[0])\\n tmp = numerix.zeros((self._shape[0],), 'd')\\n tmp[:] = vector\\n self.put(tmp, ids, ids)\\n else:\\n ids = numerix.arange(len(vector))\\n self.put(vector, ids, ids)\",\n \"def mirror_y(board):\\n new_board = board[:]\\n new_board.reverse()\\n return new_board\",\n \"def diag_line((lat0, lon0, alt0), (lat, lon, alt), k=5):\\n\\tlats = np.linspace(lat0, lat, k)\\n\\tlons = np.linspace(lon0, lon, k)\\n\\talts = np.linspace(alt0, alt, k)\\n\\tp = zip(lats, lons, alts)\\n\\treturn p\",\n \"def pinhole_projection_image_to_world(uv, z, K):\\n\\n u_v_1 = np.array([uv[0], uv[1], 1])\\n pos = z * np.matmul(inv(K),u_v_1)\\n return pos\",\n \"def reflect(self):\\n self.vertices[-1, :] = self.reflected\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def diagonal(t, x, y):\\n from math import atan2, sqrt, pi\\n angle = atan2(y, x) * 180 / pi\\n dist = sqrt(x**2 + y**2)\\n lt(t, angle)\\n fdbk(t, dist)\\n rt(t, angle)\",\n \"def pad(self, nxp, nyp):\\n assert (nxp > self.nx)\\n assert (nyp > self.ny)\\n assert (np.mod(nxp - self.nx, 2) == 0)\\n assert (np.mod(nyp - self.ny, 2) == 0)\\n\\n ret = rmap(nx=nxp, dx=self.dx, ny=nyp, dy=self.dy)\\n ret.map[(nyp - self.ny) / 2:(nyp + self.ny) / 2, (nxp - self.nx) / 2:(\\n nxp + self.nx) / 2] = self.map\\n return ret\",\n \"def diagonal_adjacent(self, current_state):\\n # Find the index of the '0' tile\\n index = current_state.index('0')\\n tile_that_was_swapped = '0'\\n\\n # If the current state can't perform the 'diagonal' action, then exit the function\\n if not self.can_move_diagonally(index):\\n return 0, tile_that_was_swapped\\n\\n # Else, we can move the '0' tile to the other side of the grid\\n else:\\n # We need to find the adjacent-diagonal position of the '0' tile\\n index_to_swap = index\\n\\n # If the '0' tile is in the top-left corner, then we need to switch it with the tile +1 row and +1 column\\n if index == 0:\\n index_to_swap = index + self.puzzle_width + 1\\n\\n # If the '0' tile is in the top-right corner, then we need to switch it with the tile +1 row and -1 column\\n elif index == self.puzzle_width - 1:\\n index_to_swap = index + self.puzzle_width - 1\\n\\n # If the tile is in the bottom-left corner, then we need to switch with the tile -1 row and +1 column\\n elif index == self.puzzle_length - self.puzzle_width:\\n index_to_swap = index - self.puzzle_width + 1\\n\\n # If the tile is in the bottom-right corner, then we need to switch with the tile -1 row and -1 column\\n elif index == self.puzzle_length - 1:\\n index_to_swap = index - self.puzzle_width - 1\\n\\n # Swap the '0' tile with the other index\\n tile_that_was_swapped = current_state[index_to_swap]\\n current_state[index] = current_state[index_to_swap]\\n current_state[index_to_swap] = '0'\\n # end: if-else\\n\\n return self.cost_of_diagonal_adjacent, tile_that_was_swapped\",\n \"def change_map_down(self):\\n self.change_map(self.current_map_idx + 1)\",\n \"def rotate(self, matrix):\\n newCoord = np.zeros(self.coord.shape)\\n newCoord[:,0]=matrix[0,0]+matrix[0,1]*self.coord[:,0]+matrix[0,2]*self.coord[:,1]+matrix[0,3]*self.coord[:,2]\\n newCoord[:,1]=matrix[1,0]+matrix[1,1]*self.coord[:,0]+matrix[1,2]*self.coord[:,1]+matrix[1,3]*self.coord[:,2]\\n newCoord[:,2]=matrix[2,0]+matrix[2,1]*self.coord[:,0]+matrix[2,2]*self.coord[:,1]+matrix[2,3]*self.coord[:,2]\\n self.coord = deepcopy(newCoord)\",\n \"def mirror_x(board):\\n new_board = board[:]\\n for (ix, item) in enumerate(board):\\n new_board[ix] = (len(board) - 1) - item\\n\\n return new_board\",\n \"def clearCross(self, coord, lmap):\\n for n in range(-3, 3):\\n color = self.colorMap(lmap.getCell((coord[0] + n, coord[1] + n)))\\n self._drawPoint(color, (coord[0] + n, coord[1] + n))\\n color = self.colorMap(lmap.getCell((coord[0] + n, coord[1] - n)))\\n self._drawPoint(color, (coord[0] + n, coord[1] - n))\\n self.zoomMap(self.scale)\",\n \"def shift_point(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth*0.1),int(imagewidth*0.1))\\n Yval = random.randint(-int(imageheight*0.1),int(imageheight*0.1))\\n point_index = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n point = mutated_genome[index][2][point_index]\\n newpoint = (point[0]+Xval,point[1]+Yval)\\n mutated_genome[index][2][point_index] = newpoint\",\n \"def add_on_land_position(self) -> Point2:\\n return self.position.offset(Point2((-2.5, 0.5)))\",\n \"def rotate(self, matrix: List[List[int]]) -> None:\\n n = len(matrix) # 行\\n\\n # 以x=y为轴翻转\\n # [[1,2,3],\\n # [4,5,6],\\n # [7,8,9]]\\n # 变为\\n # [1 4 7]\\n # [2 5 8]\\n # [3 6 9]\\n for i in range(n):\\n for j in range(i, n):\\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\\n\\n # 以中点为轴翻转\\n for i in range(n):\\n for j in range(n // 2):\\n matrix[i][j], matrix[i][n - j - 1] = matrix[i][n - j - 1], \\\\\\n matrix[i][j]\\n\\n # 非原地修改写法，先上下翻转，再以x=y为轴复制对应数字\\n # n = len(matrix)\\n # r = list(zip(*matrix[::-1]))\\n # for i in range(n):\\n # for j in range(n):\\n # matrix[i][j] = r[i][j]\",\n \"def _maping(x,y,l,a):\\n newx = (x**2 *(l* ((x**2 + y**2)**(a/2) - 1) + 2) - l * y**2 *((x**2 + y**2)**(a/2) - 1))/(x**2 + y**2) \\n newy = (2 * x* y *(l* ((x**2 + y**2)**(a/2) - 1) + 1))/(x**2 + y**2)\\n return newx, newy\",\n \"def mirror_positions(positions, domain, mirror):\\n for d in range(3):\\n if mirror[d]:\\n mirrored_xs_grid = positions.copy()\\n mirrored_xs_grid[:, :, :, d] = (domain[2 * d + 1] - domain[2 * d]) + positions[:, :, :, d]\\n positions = np.concatenate((positions, mirrored_xs_grid), axis=d)\\n return positions\",\n \"def mirror_densities(densities, mirror):\\n nx, ny, nz = densities.shape\\n if mirror[0]:\\n densities = np.concatenate(\\n (densities, densities[range(nx - 1, -1, -1), :, :]), axis=0)\\n if mirror[1]:\\n densities = np.concatenate(\\n (densities, densities[:, range(ny - 1, -1, -1), :]), axis=1)\\n if mirror[2]:\\n densities = np.concatenate(\\n (densities, densities[:, :, range(nz - 1, -1, -1)]), axis=2)\\n return densities\",\n \"def rotate(self, matrix: List[List[int]]) -> None:\\n n = len(matrix)\\n for l in range(n // 2):\\n r = n - 1 - l\\n for p in range(l, r):\\n q = n - 1 - p\\n cache = matrix[l][p]\\n matrix[l][p] = matrix[q][l]\\n matrix[q][l] = matrix[r][q]\\n matrix[r][q] = matrix[p][r]\\n matrix[p][r] = cache\",\n \"def in_place_offset(self, offset):\\n self.p += offset * self.cross_z.normalized()\",\n \"def __invert__(self) -> PointType:\\n return Point(~self.x, ~self.y)\",\n \"def cw_rotate(self):\\n self.grid = [list(x) for x in zip(*self.grid[::-1])]\\n self.find_edges()\",\n \"def implicit_matrix_1d(xsize, centervalue, neighborvalue):\\n matrix1d = np.zeros([xsize, xsize])\\n np.fill_diagonal(matrix1d, centervalue)\\n matrix1d = np.roll(matrix1d, 1, 0)\\n np.fill_diagonal(matrix1d, neighborvalue)\\n matrix1d = np.roll(matrix1d, -2, 0)\\n np.fill_diagonal(matrix1d, neighborvalue)\\n matrix1d = np.roll(matrix1d, 1, 0)\\n return matrix1d\",\n \"def rotater(self, direction):\\n if self.center:\\n mapDel(self, theFallen)\\n rotate(self, direction)\\n mapAdd(self, theFallen)\",\n \"def mrotate(self):\\n result_matrix = [[0 for col in range(len(self.matrix[0]))] for row in range(len(self.matrix))]\\n for i in range(len(self.matrix)):\\n for j in range(len(self.matrix[0])):\\n result_matrix[i][j] = self.matrix[i][len(self.matrix[0]) - 1 - j]\\n # left turn -> result_matrix[i][j] = self.matrix[len(self.matrix) - 1 - i][j]\\n self.matrix = result_matrix\\n pass\",\n \"def reverse(self):\\n x = self._x * -1\\n y = self._y * -1\\n return Point(x,y)\",\n \"def change_map_up(self):\\n if self.current_map_idx > 0:\\n self.change_map(self.current_map_idx + 1)\",\n \"def mirror_path(path):\\n points, env = path\\n \\n points = [mirror(p) for p in points]\\n if env == 'c':\\n env = 'l'\\n elif env == 'l':\\n env = 'c'\\n else:\\n raise Exception('unknown env in table')\\n\\n return (points, env)\",\n \"def warp_pos(pos, warp_matrix):\\n import torch\\n p1 = torch.Tensor([pos[0], pos[1], 1]).view(3, 1)\\n p2 = torch.Tensor([pos[2], pos[3], 1]).view(3, 1)\\n p1_n = torch.mm(warp_matrix, p1).view(1, 2)\\n p2_n = torch.mm(warp_matrix, p2).view(1, 2)\\n return torch.cat((p1_n, p2_n), 1).view(1, -1)\",\n \"def flip_around(self, row: int, col: int) -> None:\\n # we treat the 8 adjacent cases :\\n neighbours = np.array([[-1, -1], [-1, 0], [-1, 1],\\n [0, -1], [0, 1],\\n [1, -1], [1, 0], [1, 1]], dtype=int)\\n for dr, dc in neighbours:\\n row_neighbour = row + dr\\n col_neighbour = col + dc\\n if not self.is_in_the_grid(row_neighbour, col_neighbour):\\n continue\\n self.flip(row_neighbour, col_neighbour)\",\n \"def rec_lat(matrix):\\n rec_lat = np.zeros([3,3])\\n V = np.linalg.det(matrix)\\n rec_lat[0] = np.cross(matrix[1], matrix[2])/V\\n rec_lat[1] = np.cross(matrix[2], matrix[0])/V\\n rec_lat[2] = np.cross(matrix[0], matrix[1])/V\\n return rec_lat #* 2 * pi\",\n \"def translate(self, displacement):\\n\\n self.center = (self.center[0] + displacement[0],\\n self.center[1] + displacement[1])\",\n \"def rotate_to_local(self,vxyz):\\n return sp.mxv(self.mtxtofov,vxyz)\",\n \"def translate_point(point, y_offset=0, x_offset=0):\\n\\n out_point = point.copy()\\n\\n out_point[:, 0] += y_offset\\n out_point[:, 1] += x_offset\\n\\n return out_point\",\n \"def rotate(self, matrix):\\n # matrix[:] = zip(*matrix[::-1])\\n n = len(matrix)\\n # 水平翻转\\n for i in range(n // 2):\\n for j in range(n):\\n matrix[i][j], matrix[n - i - 1][j] = matrix[n - i - 1][j], matrix[i][j]\\n # 主对角线翻转\\n for i in range(n):\\n for j in range(i):\\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\",\n \"def rotate(self, matrix: List[List[int]]) -> None:\\n n = len(matrix)\\n for dig in range(n):\\n row = dig\\n for col in range(dig+1, n):\\n matrix[row][col] , matrix[col][row] = matrix[col][row], matrix[row][col]\\n print(matrix)\\n left = 0\\n right = n-1\\n while left < right:\\n for row in range(n):\\n matrix[row][left], matrix[row][right] = matrix[row][right], matrix[row][left]\\n left+=1\\n right-=1\",\n \"def move_point_wline(mutated_genome,index):\\n Xval = random.randint(-int(imagewidth/5.),int(imagewidth*6./5.))\\n Yval = random.randint(-int(imageheight/5.),int(imageheight*6./5.))\\n point = (Xval,Yval)\\n point_index = random.randint(1,max(1,len(mutated_genome[index][2])-1))\\n mutated_genome[index][2][point_index] = point\",\n \"def test_pan():\\n _c = _a.copy()\\n _c = _c.piv.pan(1.0, -1.0) # note the use of .piv.\\n assert np.allclose(_c.coords[\\\"x\\\"][0], 1.312480)\\n assert np.allclose(_c.coords[\\\"y\\\"][0], -1.31248)\",\n \"def mirror_world(some_list):\\n l = some_list[::-1]\\n print(f\\\"Our original list: {some_list}\\\")\\n print(f\\\"Our original list location: {id(some_list)}\\\")\\n print(f\\\"Our new list: {l}\\\")\\n print(f\\\"Our new list location: {id(l)}\\\")\\n return \\\"done!\\\"\",\n \"def set_diag(M,d,idx=0):\\n n, m = shape_mat(M)\\n if idx >= 0:\\n for i, di in enumerate( d ):\\n M[i][i+idx] = di\\n else:\\n for i, di in enumerate( d ):\\n M[i-idx][i] = di\",\n \"def apply(self, point):\\n m = numpy.dot(self.matrix, numpy.array([point[0], point[1], 1.0]).transpose())\\n return pygame.Vector2(m[0], m[1])\",\n \"def rotateMatrixAttempt(matrix):\\n for idxring in range(len(matrix) / 2):\\n swap = []\\n ringgen = ringCoords(len(matrix), idxring)\\n for x, y in ringgen:\\n swap.append(matrix[y][x])\\n ringgen = ringCoords(len(matrix), idxring)\\n start_idx = len(matrix) - 1 - idxring\\n for swapidx in range(-1 * start_idx, len(swap) - start_idx):\\n x, y = ringgen.next()\\n matrix[y][x] = swap[swapidx]\",\n \"def d4out():\\n\\td4x.moveTo(d4x_out)\\n\\td4y.moveTo(d4y_out)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71411884","0.640484","0.6257936","0.59739065","0.59739065","0.58550274","0.5843202","0.5798668","0.5773249","0.57277185","0.57091093","0.57091093","0.5707049","0.56990564","0.5662024","0.5583394","0.55673605","0.55462587","0.5533044","0.54987687","0.54721487","0.54721487","0.54370487","0.54353464","0.5419352","0.5380543","0.53656626","0.53381765","0.53187495","0.53162867","0.53144664","0.53015316","0.5299219","0.5290637","0.52742565","0.52707374","0.52675045","0.5259585","0.5250074","0.52480274","0.52479047","0.52451587","0.5244753","0.5240814","0.52229154","0.5221927","0.5221772","0.5213725","0.52020156","0.5177779","0.5166243","0.5157699","0.51389366","0.5135721","0.513556","0.5122954","0.50986314","0.5085434","0.50755465","0.50648725","0.50639504","0.50637275","0.5062372","0.50613266","0.5039482","0.5038953","0.5029106","0.5026238","0.5017619","0.5015508","0.5011479","0.50075585","0.50068176","0.5001002","0.49915054","0.49866605","0.49739286","0.4960575","0.49586886","0.49566677","0.49491137","0.49425083","0.49419555","0.4941934","0.49393725","0.4938464","0.49346706","0.4930358","0.4926582","0.49228695","0.49223375","0.49221238","0.4915631","0.49095634","0.49008864","0.48969388","0.48951173","0.48943552","0.48915234","0.48898923"],"string":"[\n \"0.71411884\",\n \"0.640484\",\n \"0.6257936\",\n \"0.59739065\",\n \"0.59739065\",\n \"0.58550274\",\n \"0.5843202\",\n \"0.5798668\",\n \"0.5773249\",\n \"0.57277185\",\n \"0.57091093\",\n \"0.57091093\",\n \"0.5707049\",\n \"0.56990564\",\n \"0.5662024\",\n \"0.5583394\",\n \"0.55673605\",\n \"0.55462587\",\n \"0.5533044\",\n \"0.54987687\",\n \"0.54721487\",\n \"0.54721487\",\n \"0.54370487\",\n \"0.54353464\",\n \"0.5419352\",\n \"0.5380543\",\n \"0.53656626\",\n \"0.53381765\",\n \"0.53187495\",\n \"0.53162867\",\n \"0.53144664\",\n \"0.53015316\",\n \"0.5299219\",\n \"0.5290637\",\n \"0.52742565\",\n \"0.52707374\",\n \"0.52675045\",\n \"0.5259585\",\n \"0.5250074\",\n \"0.52480274\",\n \"0.52479047\",\n \"0.52451587\",\n \"0.5244753\",\n \"0.5240814\",\n \"0.52229154\",\n \"0.5221927\",\n \"0.5221772\",\n \"0.5213725\",\n \"0.52020156\",\n \"0.5177779\",\n \"0.5166243\",\n \"0.5157699\",\n \"0.51389366\",\n \"0.5135721\",\n \"0.513556\",\n \"0.5122954\",\n \"0.50986314\",\n \"0.5085434\",\n \"0.50755465\",\n \"0.50648725\",\n \"0.50639504\",\n \"0.50637275\",\n \"0.5062372\",\n \"0.50613266\",\n \"0.5039482\",\n \"0.5038953\",\n \"0.5029106\",\n \"0.5026238\",\n \"0.5017619\",\n \"0.5015508\",\n \"0.5011479\",\n \"0.50075585\",\n \"0.50068176\",\n \"0.5001002\",\n \"0.49915054\",\n \"0.49866605\",\n \"0.49739286\",\n \"0.4960575\",\n \"0.49586886\",\n \"0.49566677\",\n \"0.49491137\",\n \"0.49425083\",\n \"0.49419555\",\n \"0.4941934\",\n \"0.49393725\",\n \"0.4938464\",\n \"0.49346706\",\n \"0.4930358\",\n \"0.4926582\",\n \"0.49228695\",\n \"0.49223375\",\n \"0.49221238\",\n \"0.4915631\",\n \"0.49095634\",\n \"0.49008864\",\n \"0.48969388\",\n \"0.48951173\",\n \"0.48943552\",\n \"0.48915234\",\n \"0.48898923\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6138612"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":7183,"cells":{"query":{"kind":"string","value":"Compute several gaussians and build the payout map"},"document":{"kind":"string","value":"def __generate_payouts(self, num_hills, hill_size):\n hills = []\n for i in range(num_hills):\n cx = random.randint(0, self.width - 1)\n cy = random.randint(0, self.height - 1)\n sx = random.random() * hill_size + 1\n sy = random.random() * hill_size + 1\n theta = random.random() * math.pi\n hills.append(Gaussian2D((cx, cy), (sx, sy), theta))\n # Add a mirror image one too to make the map fair\n hills.append(Gaussian2D(self.__mirror(cx, cy), (sx, sy), theta + math.pi))\n\n # Sum all the hills\n money_payout_rates = [[0.0] * self.height for x in range(self.width)]\n for y in range(self.height):\n for x in range(self.width):\n money_payout_rates[x][y] = sum([h.value((x,y)) for h in hills])\n\n # Normalize the rates from 0->1\n max_payout = max([max(row) for row in money_payout_rates])\n min_payout = min([min(row) for row in money_payout_rates])\n for y in range(self.height):\n for x in range(self.width):\n offset = money_payout_rates[x][y] - min_payout\n money_payout_rates[x][y] = offset / (max_payout - min_payout)\n money_payout_rates[x][y] = int(1000 * money_payout_rates[x][y]) / 1000.0\n\n return money_payout_rates"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\r\n template = np.zeros((outputSize,outputSize))\r\n ppd=float(outputSize)/float(angularSize) # pixels per deg\r\n \r\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\r\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\r\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\r\n \r\n for i in range(10000,200001,20000):\r\n if i == 130000:\r\n continue\r\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\r\n BG = np.zeros((outputSize,outputSize)) \r\n for j in events:\r\n xIDX = int(j[1]*ppd+float(outputSize/2))\r\n yIDX = int(j[2]*ppd+float(outputSize/2))\r\n BG[yIDX][xIDX] += 1.0\r\n \r\n psfDeg = .2+float(200)/float(i)\r\n psfOut = psfDeg*ppd\r\n #print i/1e3, psfDeg, psfOut\r\n \r\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\r\n \r\n template = template/np.max(template)\r\n \r\n # Write to file \r\n outFile = open(fileOut, \"wb\" )\r\n pickle.dump(template, outFile)\r\n print 'Rate Map saved to ', fileOut\r\n \r\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\r\n\r\n plt.xlabel(r'$l [^\\circ]$')\r\n plt.ylabel(r'$b [^\\circ]$')\r\n plt.xlim(5,-5)\r\n plt.ylim(-5,5)\r\n plt.colorbar()\r\n\r\n x,y = Find_Centroid(template)\r\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\r\n print x,y\r\n plt.scatter(x,y, s=10, c='r', marker = '+')\r\n \r\n X,Y = FormatEvents(events110)\r\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\r\n \r\n X,Y = FormatEvents(events130)\r\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\r\n \r\n X,Y = FormatEvents(events150)\r\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\r\n \r\n from matplotlib.font_manager import FontProperties\r\n fontP = FontProperties()\r\n fontP.set_size('small')\r\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\r\n \r\n from matplotlib.backends.backend_pdf import PdfPages\r\n if fileOut != '':\r\n pp = PdfPages(fileOut + '_sideband.pdf')\r\n plt.savefig(pp, format='pdf')\r\n print \"Figures saved to \", str(fileOut)+ '_sideband.pdf\\n',\r\n pp.close()\r\n \r\n plt.show()\r\n return template","def sw_gaussians(mu1, mu2, Sigma1, Sigma2, n_proj=100):\n d = mu1.shape[0]\n # Project data\n thetas = np.random.randn(n_proj, d)\n thetas = thetas / (np.sqrt((thetas ** 2).sum(axis=1)))[:, None] # Normalize\n proj_mu1 = thetas @ mu1\n proj_mu2 = thetas @ mu2\n sw2 = 0\n for l in range(n_proj):\n th = thetas[l]\n proj_sigma1 = (th @ Sigma1) @ th\n proj_sigma2 = (th @ Sigma2) @ th\n sw2 += wass_gaussians(np.array([proj_mu1[l]]), np.array([proj_mu2[l]]),\n np.array([proj_sigma1]), np.array([proj_sigma2]))**2\n sw2 /= n_proj\n return np.sqrt(sw2)","def plot(self):\n \n \n x_ibs=[] \n x_gss=[]\n y_ibs=[] \n y_gss=[]\n x_pso=[]\n x_bgd=[]\n y_bgd=[]\n y_pso=[]\n x_gd=[]\n y_gd=[]\n \n i=0.0000001\n \n # for k in range(1,51):\n # i= random.uniform(0.00000001, 1)\n # t_avg_ibs=[]\n # t_avg_gss=[]\n # for j in range(1,51):\n #L=random.randint(-100, 0)\n #U=random.randint(0, 100)\n max_iter=self.Max_iter \n L=self.Lower_bound\n U=self.Upper_bound\n \n minima=self.gss(L,U,i,1000)\n #print(\"minima at X = \",minima[1])\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\n x_gss.append(self.gss(L,U,i,max_iter)[0])\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\n #print(x_pso)\n for i in x_ibs[0]:\n #print(self.Func(i)) \n y_ibs.append(self.Func(i))\n for i in x_gss[0]:\n y_gss.append(self.Func(i)) \n for i in x_pso[0]:\n y_pso.append(self.Func(i)) \n for i in x_gd[0]:\n y_gd.append(self.Func(i)) \n for i in x_bgd[0]:\n y_bgd.append(self.Func(i)) \n #print(y_gss)\n\n plt.plot(x_ibs[0], y_ibs, 'r.')\n plt.plot(x_gss[0], y_gss, '.')\n plt.plot(x_pso[0], y_pso, 'y.')\n #plt.plot(x_gd[0], y_gd, 'y.')\n #plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y')\n \n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\n #plt.axis([0, 100, 0.00000001, 1]) \n plt.show()\n plt.plot(x_gd[0], y_gd, 'r.')\n plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y') \n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\n \n plt.show()\n \n start_time = timeit.default_timer()\n ibs=self.I_bisection(L,U,minima[1],max_iter)\n print(\" Execution time for Interval bisection Method is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gss=self.gss(L,U,i,max_iter)\n print(\" Execution time for Golden Section Search is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\n print(\" Execution time for Particle swarm optimization is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\n print(\" Execution time for Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\n print(\" Execution time for Batch Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n plt.plot(ibs[1], ibs[2], 'r.')\n plt.text(ibs[1], ibs[2],\"IB\")\n plt.plot(gss[1], gss[2], '.')\n plt.text(gss[1], gss[2],\" GSS\")\n plt.plot(pso[1], pso[2], 'y.')\n plt.text(pso[1], pso[2],\" PSO\")\n plt.plot(gd[1], gd[2], 'g.')\n plt.text(gd[1], gd[2],\" GD \")\n plt.plot(bgd[1],bgd[2], 'k.')\n plt.text(bgd[1], bgd[2],\" Batch_GD\")\n \n plt.xlabel('Value of X')\n plt.ylabel('NUmber of iteration') \n plt.suptitle('Number of iterations vs minimum value of x')\n \n plt.show()","def construct_mog(self, outputs):\n # check for correct number of input dimensions.\n assert outputs.shape[-1] == (self.n_components)*3 + 1\n out_idx = 0 # keep track of which output we are working with.\n self.shift = outputs[0, :, out_idx:out_idx+1]; out_idx += 1\n with tf.name_scope(\"Mixture_of_Gaussians\"):\n with tf.variable_scope('network'):\n # get mixing proportions\n theta_raw = outputs[:, :,out_idx:out_idx+self.n_components];\n out_idx += self.n_components\n self.theta = tf.nn.softmax(theta_raw)\n\n log_sigmas = outputs[:, :, out_idx:out_idx+self.n_components];\n out_idx += self.n_components\n self.sigmas = tf.exp(log_sigmas,name=\"sigmas\")\n\n self.mus = outputs[:, :, out_idx:out_idx+self.n_components];\n out_idx += self.n_components\n for k in range(self.n_components):\n tf.summary.histogram(\"Gaussian_%d_sigma\"%k,self.sigmas[:,k])\n tf.summary.histogram(\"Gaussian_%d_proportion\"%k,self.theta[:,k])\n tf.summary.histogram(\"Gaussian_%d_mus\"%k,self.mus[:,k])\n\n ## Check that every output has been used\n assert out_idx == outputs.shape[-1]","def main():\n\n\n ## Groups showing similar noise profile\n #grp1 = [ 1, 4, 5, 8, 9 ]\n #grp2 = [ 18, 19, 22, 23, 30, 31 ]\n grp1 = [ 0, 1, 6, 7, 4, 5 ]\n grp2 = [ 12, 13, 16, 17, 18, 19 ]\n #grp3 = [ 18, 19, 22, 23, 26, 27 ]\n with tb.open_file(sys.argv[1], 'r') as dataF:\n\n npm = len(dataF.root.Sensors.DataPMT)#len(dataF.root.RD.pmtrwf[0])\n nevt = len(dataF.root.RD.pmtrwf)\n\n ## Filter definition\n fSample = 40E6\n freqLPF = 100E3\n freqLPFd = 2*freqLPF / fSample\n b, a = signal.butter(1, freqLPFd, 'low', analog=False)\n ##\n fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(20,6))\n #fig.tight_layout()\n fig.show()\n wf_len = len(dataF.root.RD.pmtrwf[0][0])\n if len(sys.argv) > 3:\n wf_len = wf_len/2+1 \n elif len(sys.argv) == 3:\n g1_first = np.zeros(wf_len, np.float64)\n g2_first = np.zeros(wf_len, np.float64)\n g3_first = np.zeros(wf_len, np.float64)\n mean_first = np.zeros(wf_len, np.float64)\n ##\n for ievt in range(nevt):\n ## clear the axies\n for ax in axes.flatten():\n ax.cla()\n plt_frq = np.zeros(wf_len, np.float64)\n fwf_mean = np.zeros(wf_len, np.float64)\n wf_mean = np.zeros(wf_len, np.float64) # No filter\n g1_mean = np.zeros(wf_len, np.float64)\n g2_mean = np.zeros(wf_len, np.float64)\n g3_mean = np.zeros(wf_len, np.float64)\n for ipm in range(npm):\n\n sg = getWF(dataF, ipm, ievt)\n sg = sg - np.mean(sg)\n\n sgf = signal.lfilter(b, a, sg)\n ## remove mean again just in case\n sgf = sgf - np.mean(sgf)\n #sgf = sg\n\n pmID = getPMid(dataF, ipm)\n\n if len(sys.argv) == 3:\n axes[0][0].plot(sgf, label='pmt '+str(pmID))\n fwf_mean += sgf/npm\n wf_mean += sg/npm\n if pmID in grp1:\n g1_mean += sgf/len(grp1)\n elif pmID in grp2:\n g2_mean += sgf/len(grp2)\n elif pmID in grp3:\n g3_mean += sgf/len(grp3)\n else:\n ft = np.fft.rfft(sgf)\n freq = np.fft.rfftfreq(len(sgf), d=25E-9)\n if ipm == 0:\n plt_frq = freq\n if sys.argv[2] == 'mag':\n ft_mag = np.absolute(ft)\n axes[0][0].plot(freq, ft_mag, label='pmt '+str(pmID))\n fwf_mean += ft_mag/npm\n if pmID in grp1:\n g1_mean += ft_mag/len(grp1)\n elif pmID in grp2:\n g2_mean += ft_mag/len(grp2)\n elif pmID in grp3:\n g3_mean += ft_mag/len(grp3)\n elif sys.argv[2] == 'phase':\n ft_pha = np.angle(ft)\n axes[0][0].plot(freq, ft_pha, label='pmt '+str(pmID))\n fwf_mean += ft_pha/npm\n if pmID in grp1:\n g1_mean += ft_pha/len(grp1)\n elif pmID in grp2:\n g2_mean += ft_pha/len(grp2)\n elif pmID in grp3:\n g3_mean += ft_pha/len(grp3)\n \n \n ## The axes not set\n if len(sys.argv) == 3:\n axes[0][1].plot(g1_mean)\n axes[0][1].set_title('Group 1 mean waveform')\n axes[1][0].plot(g2_mean)\n axes[1][0].set_title('Group 2 mean waveform')\n axes[1][1].plot(g3_mean)\n axes[1][1].set_title('Group 3 mean waveform')\n axes[2][0].plot(fwf_mean)\n axes[2][0].set_title('Mean waveform')\n if ievt == 0:\n g1_first = g1_mean\n g2_first = g2_mean\n g3_first = g3_mean\n mean_first = fwf_mean\n else:\n axes[0][1].plot(g1_first)\n axes[1][0].plot(g2_first)\n axes[1][1].plot(g3_first)\n axes[2][0].plot(mean_first)\n axes[2][1].plot(wf_mean)\n axes[2][1].set_title('Mean waveform and corrected')\n axes[2][1].plot(wf_mean-fwf_mean)\n axes[2][1].set_xlim(0, 1000)\n else:\n axes[0][0].set_xlim(0,50000)\n axes[0][1].plot(plt_frq, g1_mean)\n axes[0][1].set_title('Group 1 mean '+sys.argv[2])\n axes[0][1].set_xlim(0,50000)\n axes[1][0].plot(plt_frq, g2_mean)\n axes[1][0].set_title('Group 2 mean '+sys.argv[2])\n axes[1][0].set_xlim(0,50000)\n axes[1][1].plot(plt_frq, g3_mean)\n axes[1][1].set_title('Group 3 mean '+sys.argv[2])\n axes[1][1].set_xlim(0,50000)\n axes[2][0].plot(plt_frq, fwf_mean)\n axes[2][0].set_title('Mean '+sys.argv[2])\n axes[2][0].set_xlim(0,50000)\n plt.draw()\n #fig.legend(loc=0)\n catcher = input(\"next plot?\")\n if catcher == 'q':\n exit()\n plt.cla()","def main(ngrains=100,sigma=15.,c2a=1.6235,mu=0.,\n prc='cst',isc=False,tilt_1=0.,\n tilts_about_ax1=0.,tilts_about_ax2=0.):\n if isc:\n h = mmm()\n else:\n h=np.array([np.identity(3)])\n gr = []\n for i in range(ngrains):\n dth = random.uniform(-180., 180.)\n if prc=='cst': g = gen_gr_fiber(th=dth,sigma=sigma,mu=mu,tilt=tilt_1,iopt=0) # Basal//ND\n elif prc=='ext': g = gen_gr_fiber(th=dth,sigma=sigma,mu=mu,tilt=tilt_1,iopt=1) # Basal//ED\n else:\n raise IOError('Unexpected option')\n for j in range(len(h)):\n temp = np.dot(g,h[j].T)\n\n ## tilts_about_ax1\n if abs(tilts_about_ax1)>0:\n g_tilt = rd_rot(tilts_about_ax1)\n temp = np.dot(temp,g_tilt.T)\n ## tilts_about_ax2?\n elif abs(tilts_about_ax2)>0:\n g_tilt = td_rot(tilts_about_ax2)\n temp = np.dot(temp,g_tilt.T)\n elif abs(tilts_about_ax2)>0 and abs(tilts_about_ax2)>0:\n raise IOError('One tilt at a time is allowed.')\n\n phi1,phi,phi2 = euler(a=temp, echo=False)\n gr.append([phi1,phi,phi2,1./ngrains])\n\n mypf=upf.polefigure(grains=gr,csym='hexag',cdim=[1,1,c2a])\n mypf.pf_new(poles=[[0,0,0,2],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\n return np.array(gr)","def map_all_sig_p(limitregion=False, region=\"allsky\"):\n \n # Get ids of all pixels that contain RHT data\n rht_cursor, tablename = get_rht_cursor(region = region)\n all_ids = get_all_rht_ids(rht_cursor, tablename)\n \n planck_tqu_db = sqlite3.connect(\"planck_TQU_gal_2048_db.sqlite\")\n planck_tqu_cursor = planck_tqu_db.cursor()\n planck_cov_db = sqlite3.connect(\"planck_cov_gal_2048_db.sqlite\")\n planck_cov_cursor = planck_cov_db.cursor()\n \n if limitregion is True:\n print(\"Loading all allsky data points that are in the SC_241 region\")\n # Get all ids that are in both allsky data and SC_241\n all_ids_SC = pickle.load(open(\"SC_241_healpix_ids.p\", \"rb\"))\n all_ids = list(set(all_ids).intersection(all_ids_SC))\n \n all_sigpGsq = np.zeros(len(all_ids))\n\n update_progress(0.0)\n for i, hp_index in enumerate(all_ids):\n #likelihood = Likelihood(_id[0], planck_tqu_cursor, planck_cov_cursor, p0_all, psi0_all, adaptivep0 = adaptivep0)\n (hp_index, T, Q, U) = planck_tqu_cursor.execute(\"SELECT * FROM Planck_Nside_2048_TQU_Galactic WHERE id = ?\", hp_index).fetchone()\n (hp_index, TT, TQ, TU, TQa, QQ, QU, TUa, QUa, UU) = planck_cov_cursor.execute(\"SELECT * FROM Planck_Nside_2048_cov_Galactic WHERE id = ?\", (hp_index,)).fetchone()\n \n # sigma_p as defined in arxiv:1407.0178v1 Eqn 3.\n sigma_p = np.zeros((2, 2), np.float_) # [sig_Q^2, sig_QU // sig_QU, UU]\n sigma_p[0, 0] = (1.0/T**2)*QQ #QQ\n sigma_p[0, 1] = (1.0/T**2)*QU #QU\n sigma_p[1, 0] = (1.0/T**2)*QU #QU\n sigma_p[1, 1] = (1.0/T**2)*UU #UU\n \n # det(sigma_p) = sigma_p,G^4\n det_sigma_p = np.linalg.det(sigma_p)\n sigpGsq = np.sqrt(det_sigma_p)\n \n all_sigpGsq[i] = sigpGsq\n \n update_progress((i+1.0)/len(all_ids), message='Calculating: ', final_message='Finished Calculating: ')\n \n # Place into healpix map\n hp_sigpGsq = make_hp_map(all_sigpGsq, all_ids, Nside = 2048, nest = True)\n \n out_root = \"/disks/jansky/a/users/goldston/susan/Wide_maps/\"\n if limitregion:\n hp.fitsfunc.write_map(out_root + \"planck_sigpGsq_SC_241.fits\", hp_sigpGsq, coord = \"G\", nest = True) \n else:\n hp.fitsfunc.write_map(out_root + \"planck_sigpGsq_DR2sky.fits\", hp_sigpGsq, coord = \"G\", nest = True)","def synthetic_gen(self):\r\n logging.debug('generating synthetic map...')\r\n data = self.realData\r\n unit = Params.unitGrid\r\n x_min = np.floor(Params.LOW[0] / unit) * unit\r\n x_max = np.ceil(Params.HIGH[0] / unit) * unit\r\n y_min = np.floor(Params.LOW[1] / unit) * unit\r\n y_max = np.ceil(Params.HIGH[1] / unit) * unit\r\n\r\n x_CELL = int(np.rint((x_max - x_min) / unit))\r\n y_CELL = int(np.rint((y_max - y_min) / unit))\r\n\r\n self.root.n_box = np.array([[x_min, y_min], [x_max, y_max]])\r\n\r\n self.mapp = np.zeros((x_CELL, y_CELL)) - 1 # ## initialize every cell with -1\r\n for i in range(Params.NDATA): # ## populate the map\r\n point = data[:, i]\r\n cell_x = int(np.floor((point[0] - x_min) / unit))\r\n cell_y = int(np.floor((point[1] - y_min) / unit))\r\n if self.mapp[cell_x, cell_y] != -1:\r\n self.mapp[cell_x, cell_y] += 1\r\n else:\r\n self.mapp[cell_x, cell_y] = 1\r\n\r\n for i in range(x_CELL): # ## perturb the counts\r\n for j in range(y_CELL):\r\n if self.mapp[i, j] != -1:\r\n self.mapp[i, j] += np.rint(self.differ.getNoise(1, 0.5 * self.param.Eps))\r\n else:\r\n self.mapp[i, j] = np.rint(self.differ.getNoise(1, 0.5 * self.param.Eps))\r\n # if noisy count is negative, ignore the noise and generate no points\r\n if self.mapp[i, j] < 0:\r\n self.mapp[i, j] = 0","def _initialize_gaussians(self, n_gaussians):\n if self.gaussian_init == 'manual':\n gaussians = torch.Tensor([\n list(product([0.25, 0.5, 0.75], repeat=2)) +\n [(0.5, 0.25), (0.5, 0.5), (0.5, 0.75)] +\n [(0.25, 0.5), (0.5, 0.5), (0.75, 0.5)] +\n [(0.5, 0.5)],\n [(-1.5, -1.5)] * 9 + [(0, -1.5)] * 3 + [(-1.5, 0)] * 3 +\n [(0, 0)],\n ]).permute(1, 2, 0)\n\n elif self.gaussian_init == 'random':\n with torch.no_grad():\n gaussians = torch.stack([\n torch.randn(\n n_gaussians, 2, dtype=torch.float) * .1 + 0.5,\n torch.randn(\n n_gaussians, 2, dtype=torch.float) * .2 - 1,],\n dim=2)\n\n else:\n raise NotImplementedError\n\n gaussians = nn.Parameter(gaussians, requires_grad=True)\n return gaussians","def plot_gheat_g(seed=1):\n fig, ax = plt.subplots(figsize=[2.5*plotdl.latex_width_inch, 3*plotdl.latex_height_inch])\n \n r = Factory_psi1_psiN( \"aapta_of_s_N{number_of_points[0]}.npz\", N=400)\n ckg = r.create_if_missing(dict(model_name= [\"Anderson\",], \n number_of_points=[400,], bandwidth=[1,],\n dis_param=np.linspace(0,1,100),c=[1,], k=[1.57,], seed=np.arange(1,6))) \n color_seq = itertools.cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])\n for (seed,c) in zip(np.arange(1,6),color_seq):\n ck = ckg[ckg['seed']==seed]\n g, psi_1, psi_N = ck['g'], ck['psi_N'], ck['psi_1']\n\n psi_heat = 2*(abs(psi_1)**2)*(abs(psi_N)**2) / ((abs(psi_1)**2) + (abs(psi_N)**2))\n \n phs = np.nansum(psi_heat,axis=1)\n \n psi1psiN = np.nansum(abs(psi_1*psi_N), axis=1)\n #print(ckg['dis_param'], phs)\n ax.plot(ck['dis_param'], phs,'.', color=c)\n ax.plot(ck['dis_param'], abs(g),'+', color=c)\n ax.plot(ck['dis_param'], psi1psiN,'d', color=c)\n ax.set_xlabel('dis_param')\n mkdir_and_savefig(fig, 'pta_comparison_of_s_N400.png')\n plt.close(fig)\n ## use last ck\n fig1, axes1 = plt.subplots(3,2,figsize=[2*plotdl.latex_width_inch, 3*plotdl.latex_height_inch],\n sharex=True, sharey=True)\n axes1.flat[0].xaxis.set_major_locator(MaxNLocator(4))\n axes1.flat[0].yaxis.set_major_locator(MaxNLocator(4))\n for n, ax1 in zip(range(1,20,3), axes1.flat):\n ax1.plot(abs(ck['psi_1'][n]), abs(ck['psi_N'][n]), '.') \n ax1.set_title(\"W = {:0.2}\".format(ck['dis_param'][n]))\n fig1.savefig('pta_psi_1_psi_2_N400.png')\n \n ax.cla()\n ax.plot(ck['dis_param'], np.real(g), label='real')\n ax.plot(ck['dis_param'], np.imag(g), label='imag')\n ax.plot(ck['dis_param'], np.abs(g), label='abs')\n ax.legend(loc = 'upper right')\n ax.set_xlabel('dis_param')\n ax.set_ylabel('g')\n mkdir_and_savefig(fig, 'pta_real_imag_g_s_N400')","def FormG():\n for i in range(2):\n for j in range(2):\n G[i, j] = 0.0\n for k in range(2):\n for l in range(2):\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])","def calculate_gains(self,data, map_fits, avg_map_fits):\n nFeeds,nBands,nChans,nParams = map_fits['Values'].shape\n frequencies = data[f'{self.level2}/averaged_frequency'][...]\n kb = 1.38064852e-23\n c = 2.99792458e8\n scale = 2 * kb * (1e9/ c)**2 * 1e26\n\n source = self.getSource(data)\n self.flux = np.zeros((len(self.feeds),nBands, nChans))\n self.gain = np.zeros((len(self.feeds),nBands, nChans))\n\n for i,(ifeed,feed) in enumerate(zip(self.feedlist,self.feeds)):\n for iband in range(nBands):\n nu = frequencies[iband]\n sigx = avg_map_fits[iband]['Values'][2] \n sigy = avg_map_fits[iband]['Values'][2]*avg_map_fits[iband]['Values'][4]\n amps = map_fits['Values'][i,iband,:,0]\n self.flux[i,iband,:] = 2*np.pi*amps*sigx*sigy*(np.pi/180.)**2 * scale*nu**2\n mdl_flux = self.models[source](nu,map_fits['MJD'],return_jansky=True,allpos=True)\n self.gain[i,iband,:] = self.flux[i,iband,:]/mdl_flux\n\n return self.flux, self.gain","def g_func(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoef, N):\r\n\tL, g = ilike(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoef, N) \t\r\n\tp = np.zeros(N)\r\n\tfor i in range(N):\r\n\t if np.isfinite(vrad_obs[i]):\r\n\t p[i] = chi2.sf(g[i],3)\r\n\t else:\r\n\t p[i] = chi2.sf(g[i],2)\r\n\t \r\n\treturn p","def sgd(self):\n for i, j, r in self.samples:\n # Computer prediction and error\n if (self.type=='bias'):\n prediction = self.get_rating_bias(i, j)\n elif(self.type=='nonbias') :\n prediction = self.get_rating(i, j)\n # print(i, j, r,prediction)\n e = (r - prediction)\n\n # Update biases\n self.b_u[i] =self.b_u[i]+ self.alpha * (e - self.beta * self.b_u[i])\n self.b_i[j] = self.b_i[j] + self.alpha * (e - self.beta * self.b_i[j])\n\n # Create copy of row of P since we need to update it but use older values for update on Q\n P_i = self.P[i, :][:]\n\n # Update user and item latent feature matrices\n # print(self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :]))\n # print(self.P[i, :])\n self.P[i, :] =self.P[i, :] + self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :])\n # print(self.P[i, :],\"&&&&&&\")\n self.Q[j, :] = self.Q[j, :] + self.alpha * (e * P_i - self.beta * self.Q[j, :])\n # print(self.Q[j, :])","def dataModel():\n srcmap001 = fits.open('dataFiles/6gev_srcmap_001.fits')\n srcmap03 = fits.open('dataFiles/6gev_srcmap_03.fits')\n\n image_data = fits.getdata('6gev_image.fits')\n filename = get_pkg_data_filename('6gev_image.fits')\n hdu = fits.open(filename)[0]\n wcs = WCS(hdu.header)\n\n #Given the results of the fit, calculate the model\n modelData001 = np.zeros(srcmap001[0].shape)\n modelData03 = np.zeros(srcmap03[0].shape)\n\n file = open('plotsData/fitResults001.pk1','rb')\n fit001 = pickle.load(file)\n file.close()\n\n file = open('plotsData/fitResults03.pk1','rb')\n fit03 = pickle.load(file)\n file.close()\n\n\n for source in fit001:\n the_index = srcmap001.index_of(source)\n\n modelData001 += fit001[source][:, None, None]*srcmap001[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap001[the_index].data, axis=2), axis=1)[:-1, None, None]\n for source in fit03:\n the_index = srcmap03.index_of(source)\n modelData03 += fit03[source][:, None, None]*srcmap03[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap03[the_index].data, axis=2), axis=1)[:-1, None, None]\n\n fig = plt.figure(figsize=[12, 4.5])\n\n vmin = 0\n vmax = 70.0\n cbStep = 10.0\n ax = fig.add_subplot(121, projection=wcs)\n ax=plt.gca()\n ax.tick_params(direction='in')\n c = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax.get_transform('galactic'))\n ax.add_patch(c)\n mappable=plt.imshow((image_data),cmap='inferno',origin='lower',norm=colors.PowerNorm(gamma=0.6),vmin=vmin, vmax=vmax, interpolation='gaussian')#\n plt.xlabel('Galactic Longitude')\n plt.ylabel('Galactic Latitude')\n plt.title('Data ($>6$ GeV)')\n cb = plt.colorbar(mappable, label='Counts per pixel', pad=0.01,ticks=np.arange(vmin, vmax+cbStep, cbStep))\n cb.ax.tick_params(width=0)\n\n\n ax2=fig.add_subplot(122, projection=wcs)\n ax2 = plt.gca()\n\n sources = []\n sources.append({\n 'Name':'3FGL J1745.3-2903c',\n 'RA':266.3434922,\n 'DEC':-29.06274323,\n 'color':'xkcd:bright light blue'})\n\n sources.append({\n 'Name':'1FIG J1748.2-2816',\n 'RA':267.1000722,\n 'DEC':-28.27707114,\n 'color':'xkcd:fire engine red'\n })\n\n sources.append({\n 'Name':'1FIG J1746.4-2843',\n 'RA':266.5942898,\n 'DEC':-28.86244442,\n 'color':'xkcd:fluorescent green'\n })\n\n sources.append({\n 'Name':'Galactic Center',\n 'RA':266.417,\n 'DEC':-29.0079,\n 'color':'black'\n })\n\n #Add source names:\n for source in sources:\n l, b = ra_dec_to_l_b(source['RA'], source['DEC'])\n ax2.scatter(l, b, color=source['color'],marker='x',s=45.0, transform=ax2.get_transform('galactic'), label=source['Name'])\n\n c2 = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax2.get_transform('galactic'))\n ax2.add_patch(c2)\n mappable2 = plt.imshow((np.sum(modelData03,axis=0)), cmap='inferno',norm=colors.PowerNorm(gamma=0.6),origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')\n plt.xlabel('Galactic Longitude')\n plt.ylabel('Galactic Latitude')\n plt.title('Model ($>6$ GeV)')\n cb2 = plt.colorbar(mappable2, label='Counts per pixel', pad=0.01, ticks=np.arange(vmin, vmax+cbStep, cbStep))\n cb2.ax.tick_params(width=0)\n leg = plt.legend(loc=1,frameon=True)\n leg.get_frame().set_alpha(0.5)\n leg.get_frame().set_edgecolor('white')\n text1 = leg.get_texts()\n for text in text1:\n text.set_color('black')\n\n fig.tight_layout()\n plt.subplots_adjust(wspace = 0.13, left=0.04, bottom=0.13, top=0.92)\n plt.show()\n #plt.savefig('plots/dataModelComparison.pdf',bbox_inches='tight')","def make_100gaussians_image(noise=True):\n n_sources = 100\n flux_range = [500, 1000]\n xmean_range = [0, 500]\n ymean_range = [0, 300]\n xstddev_range = [1, 5]\n ystddev_range = [1, 5]\n params = {'flux': flux_range,\n 'x_mean': xmean_range,\n 'y_mean': ymean_range,\n 'x_stddev': xstddev_range,\n 'y_stddev': ystddev_range,\n 'theta': [0, 2 * np.pi]}\n\n rng = np.random.RandomState(12345)\n sources = QTable()\n for param_name, (lower, upper) in params.items():\n # Generate a column for every item in param_ranges, even if it\n # is not in the model (e.g., flux). However, such columns will\n # be ignored when rendering the image.\n sources[param_name] = rng.uniform(lower, upper, n_sources)\n xstd = sources['x_stddev']\n ystd = sources['y_stddev']\n sources['amplitude'] = sources['flux'] / (2.0 * np.pi * xstd * ystd)\n\n shape = (300, 500)\n data = make_gaussian_sources_image(shape, sources) + 5.0\n\n if noise:\n rng = np.random.RandomState(12345)\n data += rng.normal(loc=0.0, scale=2.0, size=shape)\n\n return data","def graphe_gains(res_alea, res_glou, res_glou_e, res_ucb):\n T = np.arange(res_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Gain\")\n \n ax.plot(T, res_alea, label = 'aléatoire') \n ax.plot(T, res_glou, label = 'glouton')\n ax.plot(T, res_glou_e, label = 'e-glouton')\n ax.plot(T, res_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Gains des 4 algorithmes par rapport à T\")","def test_GA():\n\tgenerationSize = 150\n\tmutationProb = 0.01\n\tgenerations = 500\n\tX = []\n\tT = []\n\tY = [] \n\tfitnesses = [0]*generationSize\n\tfor i in range(DATA_POINTS_NUM):\n\t\tX.append((i - DATA_POINTS_NUM/2)*0.1)\n\t\tT.append(polynomi_3N(REFERENCE, X[-1]))\n\t\tY.append(0)\n\t\n\tga = GA.GA(generationSize, 4, mutationProb)\n\tgenomes = ga.seedGenomes()\n\t#plot initial genomes\n\tplt.figure(1)\n\tplt.title('Initial genomes')\n\tfor i in range(len(genomes)):\n\t\tGenome = prescale(genomes[i])\n\t\tprint Genome\n\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\tY[j] = (polynomi_3N(Genome, X[j]))\n\t\tfitnesses[i] = calculate_fitness(T, Y)\n\t\tplt.plot(X,Y, 'b-')\n\tplt.plot(X,T, 'r-')\n\t\t\n\t\n\t#live and learn\n\tfor k in range(generations):\n\t\tprint \".\",\n\t\tfor i in range(len(genomes)):\n\t\t\tGenome = prescale(genomes[i])\n\t\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\t\tY[j] = (polynomi_3N(Genome,X[j]))\n\t\t\tfitnesses[i] = calculate_fitness(T, Y)\n\t\tga.fitnessUpdate(fitnesses)\n\t\tgenomes = ga.createNextGeneration()\n\t\t\n\t#plot final genomes\n\tplt.figure(2)\n\tplt.title('Final genomes')\n\tprint \"\\nfinal Genomes\"\n\tfor i in range(len(genomes)):\n\t\tGenome = prescale(genomes[i])\n\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\tY[j] = (polynomi_3N(Genome,X[j]))\n\t\tprint \"fit:%5.1f [%7.4f, %7.4f, %7.4f, %7.4f]\"%\\\n\t\t (calculate_fitness(T, Y), Genome[0],\n\t\t Genome[1], Genome[2], Genome[3])\n\t\tplt.plot(X,Y, 'b-')\n\tplt.plot(X,T, 'r-')\n\t\t\n\t#plot progress\n\tP = []\n\thistory = ga.generations[:]\n\tfor f in history:\n\t\t#f[1].sort()\n\t\tP.append(max(f[1]))\n\tplt.figure(3)\n\tplt.title('progress')\n\tplt.plot(P)\n\tplt.show()\n\t\n\t#print the result:\t\n\tbestGene = fitnesses.index(max(fitnesses))\n\tG = prescale(genomes[bestGene])\n print \"\"\n\tprint \"And the result is:\"\n\tprint \"%.4f => %.4f (%.4f)\"%(A, G[0], abs(A - G[0]))\n\tprint \"%.4f => %.4f (%.4f)\"%(B, G[1], abs(B - G[1]))\n\tprint \"%.4f => %.4f (%.4f)\"%(C, G[2], abs(C - G[2]))\n\tprint \"%.4f => %.4f (%.4f)\"%(D, G[3], abs(D - G[3]))","def prediction_aggregation(self, xt_s,mu_s,var_s, method='PoE', weighting='uniform', power=26):\n\n nt = xt_s.shape[0]\n mu = np.zeros([nt, self.C],dtype='float64')\n var = np.zeros([nt, self.C],dtype='float64')\n\n prior_var = self.experts[0].kernel(xt_s[0], xt_s[0])\n\n \n #Process each latent gp individually \n for j in range(self.C):\n \n mu_s_c = mu_s[:, :, j]\n var_s_c = var_s[:, :, j]\n \n weight_matrix = compute_weights(mu_s_c, var_s_c, power, weighting, prior_var)\n \n prec_s= 1/var_s_c\n\n if method == 'PoE':\n \n prec = tf.reduce_sum(prec_s, axis=0)\n \n\n if method == 'gPoE':\n \n weight_matrix = normalize_weights(weight_matrix)\n\n prec = tf.reduce_sum(weight_matrix * prec_s , axis=0)\n \n\n if method == 'BCM':\n \n prec = tf.reduce_sum(prec_s, axis=0) + (1 - self.M) / prior_var \n\n if method == 'rBCM':\n \n \n prec = tf.reduce_sum(weight_matrix * prec_s, axis=0) \\\n + (1 - tf.reduce_sum(weight_matrix, axis=0)) / prior_var\n \n \n \n if method != 'bar':\n \n var[:, j] = 1 / prec\n\n mu[:, j] = var[:, j] * tf.reduce_sum(weight_matrix * prec_s * mu_s_c, axis=0)\n \n else:\n \n weight_matrix = normalize_weights(weight_matrix)\n\n mu[:, j] = tf.reduce_sum(weight_matrix * mu_s_c, axis=0)\n var[:, j] = tf.reduce_sum(weight_matrix * var_s_c, axis=0)\n \n \n return self.lik_aggregation(mu, var)","def learnGauss(metricArray): \n fit = gaussReg() \n n= 100 #You should probably change this...\n overlap = 0.3 \n imageSize = [100,400]\n densMap(fit, metricArray, n, overlap, imageSize )\n overlayMap('SmallTile.jpg', 'ContourPlot.jpg') \n # Final map is saved as OverlayMap.jpg","def output_grib2(forecast,proj_dict,grid_dict,start_hour,end_hour,\n stride,size,run_date,target_dataset,smoothing,config):\n for d,date in enumerate(run_date):\n date_outpath = config.forecast_out_path+'20{0}/grib/'.format(\n date.strftime(config.run_date_format))\n \n if not os.path.exists(date_outpath):\n os.makedirs(date_outpath)\n\n lscale = 1e6\n grib_id_start = [7, 0, 14, 14, 2]\n \n filtered_forecast = gaussian_filter(forecast[d],smoothing,mode='constant')\n \n gdsinfo = np.array([0, np.product(filtered_forecast.shape[-2:]), 0, 0, 30], dtype=np.int32)\n \n lon_0 = proj_dict[\"lon_0\"]\n sw_lon = grid_dict[\"sw_lon\"]\n \n if lon_0 < 0:\n lon_0 += 360\n if sw_lon < 0:\n sw_lon += 360\n\n gdtmp1 = [1, 0, proj_dict['a'], 0, float(proj_dict['a']), 0, float(proj_dict['b']),\n filtered_forecast.shape[-1], filtered_forecast.shape[-2], grid_dict[\"sw_lat\"] * lscale,\n sw_lon * lscale, 0, proj_dict[\"lat_0\"] * lscale,\n lon_0 * lscale,\n grid_dict[\"dx\"] * 1e3 * stride, grid_dict[\"dy\"] * 1e3 * stride, 0b00000000, 0b01000000,\n proj_dict[\"lat_1\"] * lscale,\n proj_dict[\"lat_2\"] * lscale, -90 * lscale, 0]\n pdtmp1 = np.array([1, 31, 4, 0, 31, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1], dtype=np.int32)\n drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)\n time_list = list(date.utctimetuple()[0:6])\n grib_objects = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))\n grib_objects.addgrid(gdsinfo, gdtmp1)\n pdtmp1[8] = end_hour\n pdtmp1[-2] = 0\n grib_objects.addfield(1, pdtmp1, 0, drtmp1, filtered_forecast)\n grib_objects.end()\n filename = date_outpath + \"{0}_{6}_Hail_{1}_Cali_NMEP_{2}mm_{3}_Hours_{4}-{5}.grib2\".format(\n config.ensemble_name,\n target_dataset,\n size,\n date.strftime(config.run_date_format),\n start_hour,end_hour,config.forecast_model_names)\n print(\"Writing to \" + filename )\n \n grib_file = open(filename, \"wb\")\n grib_file.write(grib_objects.msg)\n grib_file.close()\n\n return","def createDefaultFilterbank(window):\n # Gaussians:: G1 = N(0, 1), G2 = N(0, 2), G3 = N(0, 4)\n # Laplacian of Gaussians:: LoG1 = Lap(N(0, 1)), LoG2=Lap(N(0, 2)), LoG3=Lap(N(0, 4)), LoG4=Lap(N(0, 8))\n # Derivative of Gaussian (x):: Div1xG1 = d/dx N(0,2), Div1xG2=d/dx N(0,4)\n # Derivative of Gaussian (y): Div1yG1 = d/dy N(0,2), Div1yG2=d/dy N(0,4)\n \n G1 = gaussian_kernel(window, window, 1)\n G2 = gaussian_kernel(window, window, 2)\n G3 = gaussian_kernel(window, window, 4)\n \n # see http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm\n LoG1 = laplacianOfGaussian_kernel(window, window, 1)\n LoG2 = laplacianOfGaussian_kernel(window, window, 2)\n LoG3 = laplacianOfGaussian_kernel(window, window, 4)\n LoG4 = laplacianOfGaussian_kernel(window, window, 8)\n \n dx_G1 = gaussian_1xDerivative_kernel(window, window, 2)\n dx_G2 = gaussian_1xDerivative_kernel(window, window, 4)\n \n dy_G1 = gaussian_1yDerivative_kernel(window, window, 2)\n dy_G2 = gaussian_1yDerivative_kernel(window, window, 4)\n \n return np.array([G1, G2, G3, LoG1, LoG2, LoG3, LoG4, dx_G1, dx_G2, dy_G1, dy_G2])","def pure_gabor():\n \n dots = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dot-images-coh1-2000ms-s02.p\", \"rb\" ), encoding='latin1') \n x = np.arange(-40, 41, 1)\n gaborx, gabory = make_gabors(x)\n centres = np.array([[200,200]])\n \n nf = dots.shape[2]\n nrf = centres.shape[0] # number of receptive fields\n ng = gaborx.shape[1] # number of gabors per receptive field\n \n # offsets (from RF centres) of subimages to multiply with kernels\n vw = int(np.floor(gabory.size/2))\n v_offsets = np.arange(-vw, vw+1)\n hw = int(np.floor(gaborx.shape[0]/2))\n h_offsets = np.arange(-hw, hw+1)\n \n result = np.zeros((nrf, ng, nf))\n for i in range(dots.shape[2]): \n for j in range(nrf): \n v_indices = v_offsets + centres[j,0]\n h_indices = h_offsets + centres[j,1]\n region = dots[v_indices[:,np.newaxis],h_indices,i]\n for k in range(ng): \n gabor = np.outer(gabory, gaborx[:,k])\n result[j,k,i] = np.sum(gabor * region)\n return result","def run_grav(self):\n\n # Solucao direta\n self.prod_w = []\n self.prod_o = []\n t0 = time.time()\n # self.set_volumes_in_primal()\n self.set_sat_in()\n self.set_lamb_2()\n self.set_global_problem_vf_3_gr1_bif()\n self.Pf = self.solve_linear_problem(self.trans_fine, self.b, len(self.all_fine_vols_ic))\n self.organize_Pf()\n del self.Pf\n self.mb.tag_set_data(self.pf_tag, self.all_fine_vols, np.asarray(self.Pf_all))\n del self.Pf_all\n self.test_conservation_fine()\n # self.store_flux_pf_gr_bif = self.create_flux_vector_pf_gr_bif_1()\n\n \"\"\"\n ################################################################\n # Solucao Multiescala\n self.calculate_restriction_op_2()\n self.calculate_prolongation_op_het()\n self.organize_op()\n self.Tc = self.modificar_matriz(self.pymultimat(self.pymultimat(self.trilOR, self.trans_fine, self.nf_ic), self.trilOP, self.nf_ic), self.nc, self.nc)\n self.Qc = self.modificar_vetor(self.multimat_vector(self.trilOR, self.nf_ic, self.b), self.nc)\n self.Pc = self.solve_linear_problem(self.Tc, self.Qc, self.nc)\n self.set_Pc()\n self.Pms = self.multimat_vector(self.trilOP, self.nf_ic, self.Pc)\n\n del self.trilOP\n del self.trilOR\n del self.Tc\n del self.Qc\n del self.Pc\n\n self.organize_Pms()\n del self.Pms\n self.mb.tag_set_data(self.pms_tag, self.all_fine_vols, np.asarray(self.Pms_all))\n del self.Pms_all\n self.erro()\n\n self.test_conservation_coarse_gr()\n # self.Neuman_problem_6_gr()\n # self.store_flux_pms_gr = self.create_flux_vector_pms_gr()\n ####################################################################\n \"\"\"\n\n\n\n\n\n\n\n print('acaboooou')\n self.mb.write_file('new_out_bif_gr.vtk')\n\n\n shutil.copytree(self.caminho1, self.pasta)","def _compute_gp_all_passbands_2D(obj, dataset, number_gp, t_min, t_max,\n output_root=None, **kwargs):\n obj_data = dataset.data[obj] # object's lightcurve\n gp_times = np.linspace(t_min, t_max, number_gp)\n filter_set = np.asarray(dataset.filter_set)\n\n kernel, gp_params, gp_predict = fit_2d_gp(obj_data, return_kernel=True,\n return_gp_params=True)\n gp_wavelengths = np.vectorize(pb_wavelengths.get)(filter_set)\n obj_gps = predict_2d_gp(gp_predict, gp_times, gp_wavelengths)\n\n if output_root is not None:\n obj_gps.write(os.path.join(output_root, f'gp_{obj}'), format='fits',\n overwrite=True)\n path_save_gps = os.path.join(output_root, f'used_gp_{obj}.pckl')\n path_save_kernels = os.path.join(output_root, f'used_kernels_{obj}'\n '.pckl')\n path_save_params = os.path.join(output_root, f'used_params_{obj}.pckl')\n # Save the GP already conditioned on a specific set of observations\n with open(path_save_gps, 'wb') as f:\n pickle.dump(gp_predict, f, pickle.HIGHEST_PROTOCOL)\n with open(path_save_kernels, 'wb') as f:\n pickle.dump(kernel, f, pickle.HIGHEST_PROTOCOL)\n with open(path_save_params, 'wb') as f:\n pickle.dump(gp_params, f, pickle.HIGHEST_PROTOCOL)\n\n return obj_gps","def extract_features(img, sigmas, n_features): \n dims = img.shape # dimensions of the image\n \n features = np.zeros((dims[0], dims[1], n_features)) # each feature map has the same size as the input image\n \n # the first feature we use is the pixel intensity in the green channel itself\n img_g = img[:,:,1] #I just assume it follows the RGB convention and not GBR or BGR...\n features[:,:,0] = img_g\n features[:,:,1] = np.sum(img,axis=2) \n \n gabors = get_gabors() \n \n # >>> YOUR CODE STARTS HERE <<<\n i = 2\n# for s in sigmas:\n# gfilters = gauss_filter(s)\n# for gf in gfilters:\n# features[:,:,i] = scipy.signal.fftconvolve(img_g, gf, mode='same') ;i+=1\n for s in sigmas:\n gauss = gauss_filter(s)\n for g in gauss:\n features[:,:,i] = scipy.signal.fftconvolve(img_g, g, mode='same') ;i+=1\n \n for gabor in gabors:\n features[:,:,i] = scipy.signal.fftconvolve(img_g, gabor, mode='same') ;i+=1\n \n \n features[:,:,i] = sobel(img_g, axis=0) ;i+=1\n features[:,:,i] = sobel(img_g, axis=1) ;i+=1\n features[:,:,i] = sobel(img_g, axis=0)+sobel(img_g, axis=1) ;i+=1\n features[:,:,i] = feature.canny(img_g, sigma=0.0) ;i+=1\n features[:,:,i] = feature.canny(img_g, sigma=0, low_threshold=13, high_threshold=50);i+=1\n features[:,:,i] = feature.canny(img_g, sigma=1)\n # >>> YOUR CODE ENDS HERE <<< \n \n return features","def IR():\n s = np.array(\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\n )\n\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\n\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\n\n comp = [comp1, comp2, comp3, comp4, comp5]\n MSE = []\n colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\"]\n x_new = np.linspace(0, 3000, 10000)\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\n plt.scatter(TI, i, c=j)\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\n # M_z0, T1, M0 = popt\n M0, T1 = popt\n y_new = MZ(x_new, *popt)\n plt.plot(x_new, y_new, \"--\", c=j, label=f\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\")\n MSE.append(mean_squared_error(i,y_new[TI]))\n print(MSE)\n print(np.mean(MSE))\n plt.grid()\n plt.legend(loc=\"best\")\n plt.xlabel(\"TI\")\n plt.ylabel(r\"Singal Intensity $M_z$\")\n plt.show()","def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\n panel_properties, latitude):\n\n\n n0 = panel_properties['n0']\n c1 = panel_properties['c1']\n c2 = panel_properties['c2']\n mB0_r = panel_properties['mB0_r']\n mB_max_r = panel_properties['mB_max_r']\n mB_min_r = panel_properties['mB_min_r']\n C_eff = panel_properties['C_eff']\n t_max = panel_properties['t_max']\n IAM_d = panel_properties['IAM_d']\n Aratio = panel_properties['aperture_area_ratio']\n Apanel = panel_properties['module_area']\n dP1 = panel_properties['dP1']\n dP2 = panel_properties['dP2']\n dP3 = panel_properties['dP3']\n dP4 = panel_properties['dP4']\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\n\n # create lists to store results\n list_results = [None] * number_groups\n list_areas_groups = [None] * number_groups\n Sum_mcp_kWperC = np.zeros(8760)\n Sum_qout_kWh = np.zeros(8760)\n Sum_Eaux_kWh = np.zeros(8760)\n Sum_qloss = np.zeros(8760)\n Sum_radiation_kWh = np.zeros(8760)\n\n Tin_array_C = np.zeros(8760) + Tin_C\n aperature_area_per_module = Aratio * Apanel\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\n\n # calculate equivalent length of pipes\n lv = panel_properties['module_length'] # module length\n number_modules = round(total_area_module/Apanel) # this is an estimation\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\n\n if panel_properties['type'] == 'ET': # for evacuated tubes\n Nseg = 100 # default number of subsdivisions for the calculation\n else:\n Nseg = 10 # default number of subsdivisions for the calculation\n\n for group in range(number_groups):\n # load panel angles from group\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\n area_per_group = prop_observers.loc[group, 'total_area_module']\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\n\n # create dataframe with irradiation from group\n\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\n\n # calculate incidence angle modifier for beam radiation\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\n\n # calculate heat production from a solar collector of each group\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\n aperature_area_per_module, dP1, dP2, dP3, dP4,\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\n l_int_mperm2, Nseg)\n\n\n # multiplying the results with the number of panels in each group and write to list\n number_modules_per_group = area_per_group / Apanel\n list_areas_groups[group] = area_per_group\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\n\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\n\n Final = pd.DataFrame(\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\n\n return list_results, Final","def global_analysis(tomo, b_th, c=18):\n\n ## Thesholding and Volume analysis\n if c == 6:\n con_mat = [ [[0, 0, 0], [0, 1, 0], [0, 0, 0]],\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]],\n [[0, 0, 0], [0, 1, 0], [0, 0, 0]] ]\n elif c == 18:\n con_mat = [[[0, 1, 0], [1, 1, 1], [0, 1, 0]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]]]\n elif c == 26:\n con_mat = [[[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]\n else:\n raise ValueError\n tomo_lbl, num_lbls = sp.ndimage.label(tomo >= b_th, structure=np.ones(shape=[3, 3, 3]))\n tomo_out = np.zeros(shape=tomo.shape, dtype=int)\n lut = np.zeros(shape=num_lbls+1, dtype=int)\n\n ## COUNTING REGIONS METHODS\n # import time\n # hold_t = time.time()\n # for lbl in range(1, num_lbls + 1):\n # ids = tomo == lbl\n # feat_sz = len(ids)\n # tomo_out[ids] = feat_sz\n # # print('[1]:', lbl, 'of', num_lbls)\n # print time.time() - hold_t\n\n ## COUNTING PIXELS METHOD\n ## Count loop\n # cont, total = 0, np.prod(tomo.shape)\n # import time\n # hold_t = time.time()\n for x in range(tomo.shape[0]):\n for y in range(tomo.shape[1]):\n for z in range(tomo.shape[2]):\n id = tomo_lbl[x, y, z]\n lut[id] += 1\n # cont += 1\n # print('[1]:', cont, 'of', total)\n #\n ## Write loop\n # cont, total = 0, np.prod(tomo.shape)\n\n for x in range(tomo.shape[0]):\n for y in range(tomo.shape[1]):\n for z in range(tomo.shape[2]):\n id = tomo_lbl[x, y, z]\n if id > 0:\n tomo_out[x, y, z] = lut[id]\n # cont += 1\n # print('[1]:', cont, 'of', total)\n # print time.time() - hold_t\n\n return tomo_out","def _make_gaussian_maps(x, gaussians, size=None, scaling=6.):\n if size is None:\n size = x.shape[-2:]\n bs = x.shape[0]\n else:\n size = [size] * 2\n bs = 1\n dtype = x.dtype\n device = x.device\n\n gaussian_maps = []\n map_template = torch.ones(*size, dtype=dtype, device=device)\n meshgrids = torch.meshgrid(\n [torch.linspace(0, 1, size[0], dtype=dtype, device=device),\n torch.linspace(0, 1, size[1], dtype=dtype, device=device),])\n\n for gaussian_idx, yx_mu_logstd in enumerate(torch.unbind(gaussians)):\n map = map_template.clone()\n for mu_logstd, mgrid in zip(yx_mu_logstd, meshgrids):\n mu = mu_logstd[0]\n std = torch.exp(mu_logstd[1])\n map *= torch.exp(-((mgrid - mu) / std) ** 2 / 2)\n\n map *= scaling\n gaussian_maps.append(map)\n\n gaussian_maps = torch.stack(gaussian_maps)\n gaussian_maps = gaussian_maps.unsqueeze(0).expand(bs, -1, -1, -1)\n return gaussian_maps","def residmapComparison():\n srcmap001 = fits.open('dataFiles/6gev_srcmap_001.fits')\n srcmap03 = fits.open('dataFiles/6gev_srcmap_03.fits')\n\n image_data = fits.getdata('dataFiles/6gev_image.fits')\n filename = get_pkg_data_filename('dataFiles/6gev_image.fits')\n hdu = fits.open(filename)[0]\n wcs = WCS(hdu.header)\n\n #Given the results of the fit, calculate the model\n modelData001 = np.zeros(srcmap001[0].shape)\n modelData03 = np.zeros(srcmap03[0].shape)\n\n file = open('plotsData/fitResults001.pk1','rb')\n fit001 = pickle.load(file)\n file.close()\n\n file = open('plotsData/fitResults03.pk1','rb')\n fit03 = pickle.load(file)\n file.close()\n\n\n for source in fit001:\n the_index = srcmap001.index_of(source)\n\n modelData001 += fit001[source][:, None, None]*srcmap001[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap001[the_index].data, axis=2), axis=1)[:-1, None, None]\n for source in fit03:\n the_index = srcmap03.index_of(source)\n modelData03 += fit03[source][:, None, None]*srcmap03[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap03[the_index].data, axis=2), axis=1)[:-1, None, None]\n\n fig = plt.figure(figsize=[12, 4.5])\n\n vmin = -25.0\n vmax = 25.0\n cbStep = 5.0\n ax = fig.add_subplot(121, projection=wcs)\n ax=plt.gca()\n ax.tick_params(direction='in')\n c = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax.get_transform('galactic'))\n ax.add_patch(c)\n mappable=plt.imshow((image_data-np.sum(modelData001,axis=0)),cmap='seismic',origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')#\n plt.xlabel('Galactic Longitude')\n plt.ylabel('Galactic Latitude')\n plt.title('GC Point Source ($>6$ GeV)')\n cb = plt.colorbar(mappable, label='Residual counts per pixel', pad=0.01,ticks=np.arange(vmin, vmax+cbStep, cbStep))\n cb.ax.tick_params(width=0)\n\n\n ax2=fig.add_subplot(122, projection=wcs)\n ax2 = plt.gca()\n c2 = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax2.get_transform('galactic'))\n ax2.add_patch(c2)\n mappable2 = plt.imshow((image_data-np.sum(modelData03,axis=0)), cmap='seismic',origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')\n plt.xlabel('Galactic Longitude')\n plt.ylabel('Galactic Latitude')\n plt.title('GC Extended Source ($>6$ GeV)')\n cb2 = plt.colorbar(mappable2, label='Residual counts per pixel', pad=0.01, ticks=np.arange(vmin, vmax+cbStep, cbStep))\n cb2.ax.tick_params(width=0)\n fig.tight_layout()\n plt.subplots_adjust(wspace = 0.13, left=0.04, bottom=0.13, top=0.92)\n #plt.savefig('plots/residComparison.pdf',bbox_inches='tight')\n plt.show()","def womgau(hop):\n import numpy as np\n import logging\n import matplotlib.pyplot as plt\n from scipy.optimize import curve_fit\n from tmath.wombat.womwaverange import womwaverange\n from tmath.wombat.womget_element import womget_element\n from tmath.wombat.inputter import inputter\n from tmath.wombat.inputter_single import inputter_single\n from tmath.wombat.gauss import gauss\n from tmath.wombat.gauss_cont import gauss_cont\n from tmath.wombat.yesno import yesno\n print(' ')\n logging.info('Object is {}'.format(hop[0].obname))\n print(' ')\n print('Spectrum runs from {} to {}'.format(hop[0].wave[0],hop[0].wave[-1]))\n print(' ')\n print('This routine expects the spectrum to be in flambda units.')\n print('It also expects a linear wavelength scale.')\n print(' ')\n print('Choose general region of spectrum\\n')\n nwave,nflux,mode=womwaverange(hop[0].wave,hop[0].flux,'none')\n print('\\nNow pick the exact range for the fit')\n waveint,fluxint,mode=womwaverange(nwave,nflux,mode)\n indexblue=womget_element(nwave, waveint[0])\n indexred=womget_element(nwave,waveint[-1])\n if (mode == 'w'):\n done = False\n while (not done):\n print(' ')\n wavecenter=inputter('Enter approximate center of Gaussian : ','float',False)\n indexcenter=womget_element(waveint,wavecenter)\n if (indexcenter <= 0) or (wavecenter > waveint[-1]):\n print('Bad central wavelength, try again')\n else:\n done = True\n else:\n done=False\n while (not done):\n print('Mark the approximate center of the Gaussian')\n pickcent=plt.ginput(1,timeout=-1)\n indexcenter=womget_element(waveint,pickcent[0][0])\n print('\\nApproximate center at {}'.format(waveint[indexcenter]))\n print('\\nIs this OK?')\n answer=yesno('y')\n if (answer == 'y'):\n done=True\n weights=np.sqrt(hop[0].var[indexblue:indexred+1])\n print(' ')\n continuum=inputter_single('Do you want to fit gaussian with (c)ontinuum, or (n)o continuum? ','cn')\n if (continuum == 'c'):\n p=[fluxint[indexcenter], waveint[indexcenter],3.0,1.0,waveint[0]]\n result=curve_fit(gauss_cont,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\n else:\n p=[fluxint[indexcenter], waveint[indexcenter],3.0]\n result=curve_fit(gauss,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\n coefferr=np.sqrt(np.diag(result[1]))\n coeff=result[0]\n # make 'finer-grained' version of fit, 0.2A/pix for calculations\n wavecalc=np.arange(2*5*50*abs(coeff[2]))*0.2+coeff[1]-0.2*5*50*abs(coeff[2])\n calccenter=womget_element(wavecalc,coeff[1])\n if (continuum == 'c'):\n fluxcalc=gauss_cont(wavecalc,*coeff)\n fluxcont=wavecalc*coeff[3]+coeff[4]\n fluxgaussian=fluxcalc-fluxcont\n linecont=fluxcont[calccenter]\n else:\n fluxcalc=gauss(wavecalc,*coeff)\n \n \n deltafit=wavecalc[1]-wavecalc[0]\n calcindexblue=womget_element(wavecalc,waveint[0])\n calcindexred=womget_element(wavecalc,waveint[-1])\n sumfluxcalc=np.sum(fluxcalc[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxcalc=np.sum(fluxcalc*deltafit)\n chi=(result[2]['fvec']**2).sum()\n redchi=chi/(len(waveint)-len(coeff))\n if (continuum == 'c'):\n sumfluxgaussian=np.sum(fluxgaussian[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxgaussian=np.sum(fluxgaussian*deltafit)\n sumfluxcont=np.sum(fluxcont[calcindexblue:calcindexred+1]*deltafit)\n sumallfluxcont=np.sum(fluxcont*deltafit)\n sumallfluxcont_test=np.sum(fluxcont)\n # propagate uncertainty (from old version) not sure this is correct\n height_pct=coefferr[0]/coeff[0]\n sigma_pct=coefferr[2]/coeff[2]\n flux_pct=np.sqrt(height_pct**2+sigma_pct**2)\n sumfluxgaussiansig=sumfluxgaussian*flux_pct\n sumallfluxgaussiansig=sumallfluxgaussian*flux_pct\n plt.cla()\n plt.plot(nwave,nflux,drawstyle='steps-mid',color='k')\n plt.ylabel('Flux')\n plt.xlabel('Wavelength')\n xmin,xmax=plt.xlim()\n ymin,ymax=plt.ylim()\n plt.plot(wavecalc,fluxcalc,drawstyle='steps-mid',color='b')\n if (continuum == 'c'):\n plt.plot(wavecalc,fluxgaussian,drawstyle='steps-mid',color='r')\n plt.plot(wavecalc,fluxcont,drawstyle='steps-mid',color='g')\n plt.plot([waveint[0],waveint[0]],[ymin,ymax],color='k',linestyle='--')\n plt.plot([waveint[-1],waveint[-1]],[ymin,ymax],color='k',linestyle='--')\n plt.xlim([xmin,xmax])\n plt.ylim([ymin,ymax])\n logging.info('For object {} Gaussian fit'.format(hop[0].obname))\n if (continuum == 'c'):\n print('\\nData = Black, Fit = Blue, Continuum = Green, Fit-Continuum = Red\\n')\n else:\n print('\\nData = Black, Fit = Blue\\n')\n logging.info('Height {:16.8f}+/-{:16.8f}'.format(coeff[0],coefferr[0]))\n logging.info('Center {:16.8f}+/-{:16.8f}'.format(coeff[1],coefferr[1]))\n logging.info('Sigma {:16.8f}+/-{:16.8f}'.format(coeff[2],coefferr[2]))\n if (continuum == 'c'):\n FWHM = 2.35482*np.abs(coeff[2])\n rest_wave = input('Rest wavelength [N/A]: ') or None\n redshift = input('Redshift [N/A]: ') or None\n if rest_wave:\n rest_wave = float(rest_wave)\n w1 = (rest_wave - FWHM/2.)/(1.+float(redshift)) \n w2 = (rest_wave + FWHM/2.)/(1.+float(redshift)) \n c = 299792.458\n v1 = -1.*c*((rest_wave/w1)**2. - 1)/(1+((rest_wave/w1)**2.))\n v2 = -1.*c*((rest_wave/w2)**2. - 1)/(1+((rest_wave/w2)**2.))\n logging.info('Slope {:16.8f}+/-{:16.8f}'.format(coeff[3],coefferr[3]))\n logging.info('Y-intercept {:16.8f}+/-{:16.8f}'.format(coeff[4],coefferr[4]))\n logging.info('FWHM {:16.8f}+/-{:16.8f}'.format(2.35482*np.abs(coeff[2]),2.35482*coefferr[2]))\n logging.info('FWHM (velocity) {:16.8f} km/s'.format(v2-v1))\n logging.info('Flux between dotted lines (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumfluxgaussian, sumfluxgaussiansig))\n logging.info('EW between dotted lines (Gaussian): {:16.8f}'.format(sumfluxgaussian/linecont))\n logging.info('Flux for full (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumallfluxgaussian, sumallfluxgaussiansig))\n logging.info('EW for full (Gaussian): {:16.8f}'.format(sumallfluxgaussian/linecont))\n logging.info('Continuum flux at line center: {:16.8f}'.format(linecont))\n\n \n logging.info('Chi^2: {}'.format(chi))\n logging.info('Reduced chi^2: {}'.format(redchi))\n logging.info('All fluxes might need to be scaled by 1e-15')\n print(' ')\n return hop","def check_gabor_approx(): \n x = np.arange(-40, 41, 1)\n DOG2 = make_DOG(3, x)\n DOGs = get_DOGs(3, x, (x.size, x.size))\n gaborx, gabory = make_gabors(x)\n n_gabors = gaborx.shape[1]\n\n w_kernel = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-new.p\", \"rb\" ))\n\n plt.figure()\n for i in range(n_gabors):\n gabor = np.outer(gabory, gaborx[:,i])\n approx = np.dot(DOGs, w_kernel[:,:,i].flatten())\n plt.subplot(3,n_gabors,i+1)\n plt.imshow(gabor)\n plt.subplot(3,n_gabors,i+n_gabors+1)\n plt.imshow(np.reshape(approx, (x.size, x.size)))\n plt.subplot(3,n_gabors,i+2*n_gabors+1) \n plt.imshow(nd.filters.convolve(w_kernel[:,:,i].T, DOG2))\n plt.show()","def two_gaussians(x):\n log_like1 = (\n -0.5 * n * pt.log(2 * np.pi)\n - 0.5 * pt.log(dsigma)\n - 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)\n )\n log_like2 = (\n -0.5 * n * pt.log(2 * np.pi)\n - 0.5 * pt.log(dsigma)\n - 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)\n )\n return pt.log(w1 * pt.exp(log_like1) + w2 * pt.exp(log_like2))","def _plot_pr2(gc_val_lst: list, at_val_lst: list, g3_val_lst: list, a3_val_lst: list, organism_name: str | None = None,\n save_image: bool = False, folder_path: str = 'Report', gene_analysis: bool = True):\n N = len(gc_val_lst)\n gc_arr = np.array(gc_val_lst)\n g3_arr = np.array(g3_val_lst)\n at_arr = np.array(at_val_lst)\n a3_arr = np.array(a3_val_lst)\n x = g3_arr / gc_arr\n y = a3_arr / at_arr\n # [[x_val], [y_val]]\n ag_lst = [[], []]\n ag_count = 0\n tg_lst = [[], []]\n tg_count = 0\n tc_lst = [[], []]\n tc_count = 0\n ac_lst = [[], []]\n ac_count = 0\n no_bias = [[], []]\n no_bias_count = 0\n for x_val, y_val in zip(x, y):\n if x_val > 0.5 and y_val > 0.5:\n ag_lst[0].append(x_val)\n ag_lst[1].append(y_val)\n ag_count += 1\n elif x_val > 0.5 > y_val:\n tg_lst[0].append(x_val)\n tg_lst[1].append(y_val)\n tg_count += 1\n elif x_val < 0.5 and y_val < 0.5:\n tc_lst[0].append(x_val)\n tc_lst[1].append(y_val)\n tc_count += 1\n elif x_val < 0.5 < y_val:\n ac_lst[0].append(x_val)\n ac_lst[1].append(y_val)\n ac_count += 1\n else:\n no_bias[0].append(x_val)\n no_bias[1].append(y_val)\n no_bias_count += 1\n fig, ax = plt.subplots(figsize=(9, 5.25))\n ax.scatter(ag_lst[0], ag_lst[1], s=5, label='AG', alpha=0.5, zorder=2)\n ax.scatter(tg_lst[0], tg_lst[1], s=5, label='TG', alpha=0.5, zorder=2)\n ax.scatter(tc_lst[0], tc_lst[1], s=5, label='TC', alpha=0.5, zorder=2)\n ax.scatter(ac_lst[0], ac_lst[1], s=5, label='AC', alpha=0.5, zorder=2)\n ax.scatter(no_bias[0], no_bias[1], s=5, label=f'No Bias', alpha=0.5, zorder=3)\n ax.grid(True, linestyle=':')\n ax.legend()\n ax.text(0.6, 0.95, 'AG')\n ax.text(0.4, 0.95, 'AC')\n ax.text(0.6, 0.05, 'TG')\n ax.text(0.4, 0.05, 'TC')\n ax.text(0, 0, f'AG: {ag_count}\\nTG: {tg_count}\\nTC: {tc_count}\\nAC: {ac_count}\\nNo Bias: {no_bias_count}',\n transform=ax.transAxes)\n plt.xlim([0, 1])\n plt.ylim([0, 1])\n plt.axvline(0.5, color='grey', zorder=1)\n plt.axhline(0.5, color='grey', zorder=1)\n plt.xlabel(r\"$G_3/GC_3$ Values\")\n plt.ylabel(r\"$A_3/AT_3$ Values\")\n suptitle = r'Parity Rule 2 plot' if organism_name is None else f\"Parity Rule 2 plot for {organism_name}\"\n plt.suptitle(suptitle, fontsize=16)\n title = f'Total genes: {N}' if gene_analysis else f'Total genome: {N}'\n plt.title(title, fontsize=12)\n if save_image:\n make_dir(folder_path)\n name = 'PR2_plot.png' if organism_name is None else f\"PR2_plot_{organism_name}.png\"\n file_name = join(folder_path, name)\n if is_file_writeable(file_name):\n plt.savefig(file_name, dpi=500)\n print(f'Saved file can be found as {abspath(file_name)}')\n plt.show()\n plt.close()","def generate_stat(sample_size, sparsity = 0, amplitude = 0, sigma = 1):\n var = generate_variable(sample_size, sparsity, amplitude, sigma)\n y_obs = var[0]\n \n \"\"\" \n f is equal to -X(t,theta) and we will minimize f (max. X)\n \"\"\"\n def f(x):\n \"\"\" \n f(x)=-X(t,theta) where x[0]=t and x[1]=theta\n \"\"\"\n res = np.real(np.exp(-1j*x[1])*\\\n sum(y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\n for k in range(-sample_size,sample_size+1))) \n res = -res/np.sqrt(2*sample_size+1) \n return res\n \n def grad_f(x):\n \"\"\" \n gradient of f\n \"\"\"\n res1 = np.real(np.exp(-1j*x[1])*\\\n sum(1j*k*y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\n for k in range(-sample_size,sample_size+1)))\n res1 = -res1/np.sqrt(2*sample_size+1)\n \n res2 = np.real(np.exp(-1j*x[1])*\\\n sum(-1j*y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\n for k in range(-sample_size,sample_size+1)))\n res2 = -res2/np.sqrt(2*sample_size+1)\n return np.array([res1, res2])\n \n #% Minimizing f\n \n \"\"\" \n we minimize on [0, 2pi]^2\n \"\"\"\n bnds = ((0, 2*np.pi), (0, 2*np.pi))\n \n \"\"\" \n We begin by a greedy search of the initialization point over a grid of size 126^2\n the initialization point is init\n \"\"\"\n x = y = np.arange(0, 2*np.pi, 0.05)\n steps = 126\n X, Y = np.meshgrid(x, y)\n val = np.array([f([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])\n init = np.argmin(val)\n x1 = init%steps\n x2 = (init-x1)/steps\n init = [x1*0.05, x2*0.05]\n \n \"\"\" \n we minimize f...\n \"\"\"\n result = sco.minimize(f, init, method=\"L-BFGS-B\",\\\n jac=grad_f, bounds=bnds, tol=1e-15)\n \n \"\"\" \n (t1,theta1) is the argmax of X(t, theta) and l1=$\\lambda_1$\n \"\"\"\n t1 = result.x[0]\n theta1 = result.x[1]\n l1 = -f([t1,theta1])\n \n \n \"\"\" \n Function g(x) is equal to (X(t1,theta1)-X(x))/(1-rho((t1,theta1)-x))\n \"\"\"\n def g(x):\n a0 = x[0]-t1\n a1 = x[1]-theta1\n N = 2*sample_size+1\n \n vec = np.array([a0,a1])\n r = np.linalg.norm(vec)\n \"\"\" \n the value for r=0 is set to l1 (note that r=0 corresponds to x=(t1,theta1))\n \"\"\" \n res = l1 \n \n if (0<r) & (r<0.00001):\n \"\"\" \n we look a values near (t1,theta1) for which an indetermination occurs\n \"\"\" \n alpha= np.arccos(np.clip(a0/np.sqrt(a0**2+a1**2), -1.0, 1.0))\n u0 = np.cos(alpha)\n u1 = np.sin(alpha)\n \"\"\" \n u0,u1 defines the direction (unit vector)\n \"\"\"\n denom = sum((k*np.cos(alpha)-np.sin(alpha))**2*\\\n (np.sinc((r*(k*np.cos(alpha)-np.sin(alpha)))/(2*np.pi)))**2\\\n for k in range(-sample_size,sample_size+1))/N\n \"\"\" \n denom computes the denominator\n \"\"\"\n \n# \"\"\" \n# We use simpson rule for the numerator\n# \"\"\"\n# h = np.linspace(0,1,500)\n# \n# b0 = t1 + h*a0\n# b1 = theta1 + h*a1\n# \n# value = (1-h)*(u0**2*\\\n# np.real(np.exp(-1j*b1)*sum(-k**2*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\n# for k in range(-sample_size,sample_size+1)))\\\n# +2*u0*u1*\\\n# np.real(np.exp(-1j*b1)*sum(k*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\n# for k in range(-sample_size,sample_size+1)))\\\n# +u1**2*\\\n# np.real(np.exp(-1j*b1)*sum((-1)*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\n# for k in range(-sample_size,sample_size+1)))) \n# value = value/np.sqrt(N)\n# \n# num = sci.simps(value, h)\n \n \"\"\" \n we use a quadrature for the numerator\n \"\"\" \n fun_int = lambda w: (1-w)*(u0**2*\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\n sum(-k**2*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\n for k in range(-sample_size,sample_size+1)))\\\n +2*u0*u1*\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\n sum(k*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\n for k in range(-sample_size,sample_size+1)))\\\n +u1**2*\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\n sum((-1)*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\n for k in range(-sample_size,sample_size+1)))) \n \n num = np.mean(sci.quad(fun_int, 0, 1, epsabs=1e-15, epsrel=1e-15, limit=1000))\n \n res = -num/denom\n \n if (r>=0.00001):\n \"\"\" \n we look a values far (t1,theta1) for which there is no indetermination\n \"\"\" \n res = (l1+f(x))/(1-(np.cos(a1)*dirichlet(a0,N)/N))\n \n return res \n \"\"\" \n we minimize g on [0, 2pi]^2 an dwe llok for the initialization point\n \"\"\"\n val2 = np.array([g([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])\n init2 = np.argmin(val2)\n x1 = init2%steps\n x2 = (init2-x1)/steps\n init2 = [x1*0.05, x2*0.05] \n result2 = sco.minimize(g, init2, method=\"L-BFGS-B\", bounds=bnds, tol=1e-15) \n \"\"\" \n argmin of g\n \"\"\"\n t2 = result2.x[0]\n theta2 = result2.x[1] \n \"\"\" \n value of lambda_2\n \"\"\"\n l21 = l1-result2.fun \n a0 = t2-t1\n a1 = theta2-theta1\n N = 2*sample_size+1\n l22 = l1-(l1+f([t2,theta2]))/(1-(np.cos(a1)*dirichlet(a0,N)/N))\n l2 = max(l21,l22)\n \"\"\" \n we compute the statistic\n \"\"\"\n alpha1 = (1/3)*sample_size*(sample_size+1)\n alpha2 = (1/np.sqrt(N))*\\\n sum((k**2-alpha1)*\\\n np.real(y_obs[k+sample_size]*np.exp(1j*(k*t1-theta1))) \\\n for k in range(-sample_size,sample_size+1))\n alpha3 = (1/np.sqrt(N))*sum(k*np.real(y_obs[k+sample_size]*\\\n np.exp(1j*(k*t1-theta1))) for k in range(-sample_size,sample_size+1)) \n stat = (sigma*(alpha1*l1+alpha2)*scs.norm.pdf(l1/sigma)+\\\n (alpha1*sigma**2-alpha3**2)*(1-scs.norm.cdf(l1/sigma)))/\\\n (sigma*(alpha1*l2+alpha2)*scs.norm.pdf(l2/sigma)+\\\n (alpha1*sigma**2-alpha3**2)*(1-scs.norm.cdf(l2/sigma))) \n \n return stat","def compute_GS(GMtcs):\n\n GS = np.mean(GMtcs,axis=0) #average over voxels\n\n return GS","def forward(self, y):\n # landmarks are the weighted average y\n # activations across each channel\n y_projected = torch.zeros_like(y)\n y = self.softmax(y)\n\n # self.save_image(y[0, :, :, :])\n\n # print(y.size())\n # y_0 = y\n y = y.view(y.size(0), y.size(1), -1)\n # print(y[0, :].min(), y[0, :].max())\n mu_y = y * self.yv.reshape(1, 1, -1)\n mu_y = mu_y.sum(dim=-1) #/ y.sum(dim=-1)\n mu_x = y * self.xv.reshape(1, 1, -1)\n mu_x = mu_x.sum(dim=-1) #/ y.sum(dim=-1)\n means = torch.cat((mu_y.unsqueeze(-1), mu_x.unsqueeze(-1)), dim=-1)\n\n # project landmarsk to guassain fuction with fixed standard deviation\n # yv, xv = torch.meshgrid([torch.arange(0, y.size(-2)), torch.arange(0, y.size(-1))])\n # h_act = torch.zeros(y_projected.size(0), y_projected.size(1), device=y.device)\n for batch_id in range(y_projected.size(0)):\n for heatmap_id in range(y_projected.size(1)):\n gdist = MultivariateNormal(means[batch_id, heatmap_id, :], covariance_matrix=self.cov)\n logprobs = gdist.log_prob(torch.cat((self.yv.unsqueeze(0), self.xv.unsqueeze(0)), dim=0).t())\n y_projected[batch_id, heatmap_id, :, :] = torch.exp(logprobs).reshape(y_projected.size(-2), y_projected.size(-1))\n # y_projected[batch_id, heatmap_id, :, :] -= y_projected[batch_id, heatmap_id, :, :].min()\n # y_projected[batch_id, heatmap_id, :, :] /= y_projected[batch_id, heatmap_id, :, :].max()\n # coords = means[batch_id, heatmap_id, :].round().long()\n # h_act[batch_id, heatmap_id] = y_0[batch_id, heatmap_id, coords[0], coords[1]]\n # h_act[batch_id, heatmap_id]\n # max_coords = y_0[batch_id, heatmap_id, :, :].clone()\n # max_coords[max_coords < max_coords.max()] = 0\n # # print(max_coords.size())\n # if batch_id == 0 and heatmap_id == 0:\n # print(means[batch_id, heatmap_id, :], max_coords.nonzero().float().mean(0))\n # a = 'fail' if y.pow(2).mean() > h_act.mean() else 'pass'\n # print(h_act.mean(), y.mean())\n # print(y_projected.max(), y_projected.min())\n # y_out = y_projected.sum(dim=1)\n # self.save_image(y_out.unsqueeze(1), normalize=True)\n # self.save_image(y_out.unsqueeze(1), normalize=True, output='landmarks')\n return y_projected, self.prior_loss(means, y)","def map_sim_property(**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n counter = 0\n fignum = 1\n if p.gal_index == 'all':\n\n for gal_index in GR.N_gal - np.arange(GR.N_gal) - 1:\n\n if counter == 0:\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\n counter = 9\n\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='simgas')\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \n\n # Plot\n Rmax = max_scale/2\n ax1 = axs[9 - counter]\n if p.log:\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\n map2D = np.log10(map2D)\n if not p.log:\n map2D[map2D < p.vmin] = p.vmin/2\n map2D[map2D > p.vmax] = p.vmax\n im = ax1.imshow(map2D,\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\n # Limit axes limits a bit to avoid area with no particles...\n ax1.set_xlim([-0.99*Rmax,0.99*Rmax])\n ax1.set_ylim([-0.99*Rmax,0.99*Rmax])\n if (p.prop == 'm') & (p.text == True):\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\odot}$' % np.sum(simgas.m),\\\n fontsize=14,transform=ax1.transAxes,color='white')\n ax1.text(0.05,0.75,'SFR=%.2eM$_{\\odot}$/yr' % GR.SFR[gal_index],\\\n fontsize=14,transform=ax1.transAxes,color='white')\n\n counter -= 1\n\n #if counter == 0:\n # ax1 = plt.subplots(1, 1)\n #cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\n # fig.colorbar(im,shrink=0.8,label=lab)\n\n if counter == 0 or gal_index == GR.N_gal-1:\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format))\n # plt.tight_layout()\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/')\n plt.savefig(p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format), format=p.format, dpi=250, facecolor='w')\n fignum += 1\n\n else:\n if p.add:\n fig, ax1 = plt.gcf(), p.ax\n if not p.add:\n fig = plt.figure(figsize=(8,6))\n ax1 = fig.add_axes([0.1, 0.01, 0.8, 0.8]) \n ax1.axis('equal')\n\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type=p.sim_type)\n if p.R_max:\n # Cut out square\n simgas = simgas[(np.abs(simgas.x) < p.R_max) & (np.abs(simgas.y) < p.R_max)]\n # Add bottom left corner\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\n extra_row['x'],extra_row['y'] = -p.R_max,-p.R_max\n extra_row[p.prop] = 0\n simgas = simgas.append(extra_row).reset_index(drop=True) \n # Add top right corner\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\n extra_row['x'],extra_row['y'] = p.R_max,p.R_max\n extra_row[p.prop] = 0\n simgas = simgas.append(extra_row).reset_index(drop=True) \n else:\n pass\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \n print('Min and max of map: ',map2D.min(),map2D.max())\n #map2D[map2D < 1e4] = 1e6\n # Plot map\n if not p.R_max:\n p.R_max = max_scale/2\n if p.log: \n if not p.vmax: p.vmax = np.log10(map2D).max()\n if not p.vmin: p.vmin = np.log10(map2D).max() - 4\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\n map2D = np.log10(map2D)\n else:\n if not p.vmax: p.vmax = np.max(map2D)\n if not p.vmin: p.vmin = np.min(map2D) / 1e3\n map2D[map2D < p.vmin] = p.vmin #np.min(map2D[map2D > 0])\n map2D = np.flipud(map2D)\n\n im = ax1.imshow(map2D,\\\n extent=[-max_scale/2,max_scale/2,-max_scale/2,max_scale/2],vmin=p.vmin,vmax=p.vmax,cmap=p.cmap)\n # Limit axes limits a bit to avoid area with no particles...\n zoom = 1#/1.5\n ax1.set_xlim([-1/zoom * p.R_max,1/zoom * p.R_max])\n ax1.set_ylim([-1/zoom * p.R_max,1/zoom * p.R_max])\n if p.colorbar: \n divider = make_axes_locatable(ax1)\n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(im,cax=cax1,label=lab)\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\n if (p.prop == 'm') & (p.text == True):\n simstar = aux.load_temp_file(gal_ob=gal_ob,data_type='simstar')\n ax1.text(0.05,0.92,'M$_{star}$=%.1e M$_{\\odot}$' % np.sum(simstar.m),\\\n fontsize=14,transform=ax1.transAxes,color='white')\n ax1.text(0.05,0.86,'M$_{gas}$=%.1e M$_{\\odot}$' % np.sum(simgas.m),\\\n fontsize=14,transform=ax1.transAxes,color='white')\n ax1.text(0.05,0.80,'SFR=%.2f M$_{\\odot}$/yr' % GR.SFR[p.gal_index],\\\n fontsize=14,transform=ax1.transAxes,color='white')\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/') \n plt.savefig(p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index), format=p.format, dpi=250, facecolor='w')\n\n if not p.colorbar: return(im)","def make_4gaussians_image(noise=True):\n table = QTable()\n table['amplitude'] = [50, 70, 150, 210]\n table['x_mean'] = [160, 25, 150, 90]\n table['y_mean'] = [70, 40, 25, 60]\n table['x_stddev'] = [15.2, 5.1, 3.0, 8.1]\n table['y_stddev'] = [2.6, 2.5, 3.0, 4.7]\n table['theta'] = np.radians(np.array([145.0, 20.0, 0.0, 60.0]))\n\n shape = (100, 200)\n data = make_gaussian_sources_image(shape, table) + 5.0\n\n if noise:\n rng = np.random.RandomState(12345)\n data += rng.normal(loc=0.0, scale=5.0, size=shape)\n\n return data","def sample_values(self, positions, gibbs):\n \"\"\"Sample important values\"\"\"\n\n if gibbs:\n self.local_energy = self.h.local_energy_gibbs(positions)\n self.accumulate_energy += self.h.local_energy_gibbs(positions)\n self.accumulate_energy_sq += self.local_energy*self.local_energy\n gradient_wf_a = 0.5*self.w.gradient_wavefunction_a(positions)\n gradient_wf_b = 0.5*self.w.gradient_wavefunction_b(positions)\n gradient_wf_W = 0.5*self.w.gradient_wavefunction_W(positions)\n else:\n self.local_energy = self.h.local_energy(positions)\n self.accumulate_energy += self.h.local_energy(positions)\n self.accumulate_energy_sq += self.local_energy*self.local_energy\n gradient_wf_a = self.w.gradient_wavefunction_a(positions)\n gradient_wf_b = self.w.gradient_wavefunction_b(positions)\n gradient_wf_W = self.w.gradient_wavefunction_W(positions)\n # self.local_energy = self.h.local_energy_numerical(positions)\n # self.accumulate_energy += self.h.local_energy_numerical(positions)\n # gradient_wf_a = np.zeros(self.w.M)\n # gradient_wf_b = np.zeros(self.w.N)\n # gradient_wf_W = np.zeros((self.w.M, self.w.N))\n\n self.accumulate_psi_term_a += gradient_wf_a\n self.accumulate_psi_term_b += gradient_wf_b\n self.accumulate_psi_term_W += gradient_wf_W\n self.accumulate_both_a += gradient_wf_a*self.local_energy\n self.accumulate_both_b += gradient_wf_b*self.local_energy\n self.accumulate_both_W += gradient_wf_W*self.local_energy","def gaussp(y1, y2, n):\n\n # First check for trivial or stupid requests\n if n <= 0:\n raise ValueError(\"Zero (or less) grid points is stupid. Stop it.\")\n if n == 1:\n r = np.array([0.5*(y2 + y1)])\n wt = np.array([y2 - y1])\n return r, wt\n N_pi = 3.14159265358979323844 # Fortran uses stupid pi because of course it does\n EPS = 1e-14 # Desired accuracy\n n_sav = -1\n\n if n != n_sav:\n n_sav = n\n m = n\n m, r, wt = GridGenerator.gausspp(m)\n m = 0\n\n if m != n:\n m = int((n+1)/2) # Care, integer division\n x = np.zeros((2*m)) # Working r, not returned\n w = np.zeros((2*m)) # Working wt, not returned\n r = np.zeros((2*m))\n wt = np.zeros((2*m))\n for i in range(m):\n r[i] = N_pi*(i+0.75)/(n+0.5)\n r = np.cos(r)\n\n for i in range(m):\n z = r[i]\n z1 = 1e20 # Arbitrary large number to ensure at least 1 loop\n while abs(z-z1) > EPS:\n p1 = 1.0\n p2 = 0.0\n for j in range(n):\n p3 = p2\n p2 = p1\n p1 = ((2*(j + 1) - 1)*z*p2 - j*p3)/(j + 1)\n pp = n*(z*p1 - p2)/(z*z - 1.0)\n z1 = z\n z = z1 - p1/pp\n x[i] = -z\n x[n - (i + 1)] = z\n w[i] = 2.0/((1.0 - z*z)*pp*pp)\n w[n - (i + 1)] = w[i]\n\n for i in range(n):\n fact = 0.5*(y2-y1)\n r[i] = y1 + fact*(x[i] + 1.0)\n wt[i] = fact*w[i]\n\n return n, r, wt","def zonal_avg2(data,Log=False):\n print 'setting up the destination grid'\n # get lat and lon for new regular grid\n# fpin = Nio.open_file('/home/ivan/Python/data/lat_t.nc','r')\n fpin = Nio.open_file('/home/emunoz/Python/mapping/model_grid/lat_t.nc','r')\n lat_t = fpin.variables['lat_t'][:]\n lat_t_edges = fpin.variables['lat_t_edges'][:]\n fpin.close()\n# fpin = Nio.open_file('/home/ivan/Python/data/gx3v5.nc','r')\n fpin = Nio.open_file('/home/emunoz/Python/mapping/model_grid/gx3v5.nc','r')\n lon_t = N.sort(fpin.variables['TLONG'][0,:])\n ulon = N.sort(fpin.variables['ULONG'][0,:])\n lon_t_edges = N.concatenate((ulon,ulon[0,N.newaxis]+360.),0)\n # get gx3v5 lat and lon\n tlon = fpin.variables['TLONG'][:]\n tlat = fpin.variables['TLAT'][:]\n fpin.close()\n\n # compute area of cells in new regular grid\n area = grid_area(lon_t_edges,lat_t_edges)\n\n nlat = lat_t.shape[0]\n nlon = lon_t.shape[0]\n\n print 'computing weights for grid cell'\n ilist = []\n jlist = []\n wghts2D = []\n wghts3D = []\n for i in range(nlat):\n for j in range(nlon):\n i_inds, j_inds = find_stn_idx(lon_t[j], lat_t[i], tlon, tlat)\n ilist.append(i_inds)\n jlist.append(j_inds)\n dist = gc_dist(lon_t[i], lat_t[i], tlon, tlat)\n # make weights=0 on land\n work2D = 1./MA.array(dist,mask=data[0,...].mask)\n wghts2D.append(MA.filled(N.take(N.take(work2D,i_inds,0),j_inds,1)\n ,0))\n\n work3D = 1./MA.array(N.resize(dist,data.shape),mask=data.mask)\n wghts3D.append(MA.filled(N.take(N.take(work3D,i_inds,-2),j_inds,-1)\n ,0))\n\n #print 'computing zonal average'\n return lon_t, lat_t, ilist, jlist, wghts2D, wghts3D","def _compute_gp_all_passbands_1D(obj, dataset, number_gp, t_min, t_max,\n output_root=None, **kwargs):\n try:\n kernel_param = kwargs[\"kernel_param\"]\n except KeyError:\n kernel_param = [500., 20.]\n\n obj_data = dataset.data[obj] # object's light curve\n obj_data = cs.rename_passband_column(obj_data.to_pandas())\n unique_pbs = np.unique(obj_data.passband)\n gp_times = np.linspace(t_min, t_max, number_gp)\n\n # Store the output in another astropy table\n obj_gps = []\n used_gp_dict = {}\n used_kernels_dict = {}\n filter_set = np.asarray(dataset.filter_set)\n for pb in filter_set:\n used_kernels_dict[pb] = None # inilialize None kernel to each passband\n if pb in unique_pbs:\n is_pb = obj_data.passband == pb # observations in this passband\n obj_data_pb = obj_data.loc[is_pb]\n\n gp_obs, gp, chosen_kernel = fit_best_gp(kernel_param,\n obj_data_pb, gp_times)\n\n mu, std = gp_obs.flux.values, gp_obs.flux_error.values\n # stack the GP results in a array momentarily\n obj_gp_pb_array = np.column_stack((gp_times, mu, std))\n used_kernels_dict[pb] = chosen_kernel\n # Save the GP already conditioned on a specific set of observations\n gp_predict = partial(gp.predict, obj_data_pb.flux)\n used_gp_dict[pb] = gp_predict\n else:\n obj_gp_pb_array = np.zeros([number_gp, 3])\n obj_gp_pb = Table([obj_gp_pb_array[:, 0], obj_gp_pb_array[:, 1],\n obj_gp_pb_array[:, 2], [pb] * number_gp],\n names=['mjd', 'flux', 'flux_error', 'filter'])\n if len(obj_gps) == 0: # initialize the table for 1st passband\n obj_gps = obj_gp_pb\n else:\n obj_gps = vstack((obj_gps, obj_gp_pb))\n\n if output_root is not None:\n obj_gps.write(os.path.join(output_root, 'gp_' + obj), format='fits',\n overwrite=True)\n path_save_gps = os.path.join(output_root, 'used_gp_dict_{}.pckl'\n ''.format(obj))\n path_save_kernels = os.path.join(output_root, 'used_kernels_dict_{}.'\n 'pckl'.format(obj))\n with open(path_save_gps, 'wb') as f:\n pickle.dump(used_gp_dict, f, pickle.HIGHEST_PROTOCOL)\n with open(path_save_kernels, 'wb') as f:\n pickle.dump(used_kernels_dict, f, pickle.HIGHEST_PROTOCOL)\n\n return obj_gps","def get_gaussian_maps(mu, sigmax, sigmay, covs, shape_hw, mode='rot'):\n with tf.name_scope(None, 'gauss_map', [mu]):\n # mu_y, mu_x = mu[:, :, 0:1], mu[:, :, 1:2]\n\n y = tf.to_float(tf.linspace(-1.0, 1.0, shape_hw[0]))\n\n x = tf.to_float(tf.linspace(-1.0, 1.0, shape_hw[1]))\n [x,y] = tf.meshgrid(x,y)\n xy = tf.stack([x, y], axis=-1)\n xy = tf.stack([xy] * nb_landmarks, axis=0)\n xy = xy[None, : ,:, :, :]\n if mode in ['rot', 'flat']:\n mu = mu[:,:,None, None,:]\n\n invsigma = tf.stack([sigmay**2, -covs, -covs, sigmax**2], axis=-1)\n invsigma = tf.reshape(invsigma, [-1, nb_landmarks, 2,2])\n denominator = (sigmax*sigmay)**2 - covs**2\n denominator = tf.expand_dims(tf.expand_dims(denominator, -1), -1)\n invsigma = invsigma/(denominator+1e-7)\n invsigma = tf.cast(invsigma, tf.float32)\n pp = tf.tile(invsigma[:, :, None, :, :], [1, 1, shape_hw[1], 1, 1])\n X = xy-mu\n dist = tf.matmul(X,pp)\n dist = tf.reduce_sum((dist*X), axis=-1)\n\n\n if mode == 'rot':\n g_yx = tf.exp(-dist)\n else:\n g_yx = tf.exp(-tf.pow(dist + 1e-5, 0.25))\n\n else:\n raise ValueError('Unknown mode: ' + str(mode))\n\n g_yx = tf.transpose(g_yx, perm=[0, 2, 3, 1])\n return g_yx","def task2_extra():\n N = 0\n lam = 0\n Ls = numpy.array([2*L for L in range(1,23)])\n h = 0.01\n tau = 0.000099\n\n iterss = []\n\n for L in Ls:\n a = L // 2\n print(L)\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\n # eps = int(0.1 * len(x))\n\n Vm = V1D(lam, x)\n state = phi(N, x-a)\n\n iters = 0\n while True:\n prob = numpy.abs(state)**2\n mid = int(2*L/h) // 2\n # if max(prob) in prob[mid-eps:mid+eps]:\n if numpy.argmax(prob) <= mid:\n print(iters)\n iterss.append(iters)\n break\n\n state[0] = 0\n state[-1] = 0\n state = implicit_scheme_step(state, tau, h, Vm)\n iters += 1\n\n fig = plt.figure()\n plt.title(\"Iterations of Gaussian travel to center\")\n plt.xlabel(\"$L$\")\n plt.ylabel(\"Time\")\n plt.plot(Ls, tau*numpy.array(iterss))\n plt.show()\n fig.savefig(\"naloga2_iters_of_gaussian_travel.pdf\", bbox_inches=\"tight\")","def cmd_gaus():\n cmds = []\n cmds.append(\"r_m[0.0,-1,1]\")\n cmds.append(\"r_s[2.5,0,10]\")\n cmds.append('Gaussian::res(x,r_m,r_s)')\n return cmds","def three_mass_FUV_maps(gal_indices,**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n fig = plt.figure(figsize=(15,14.5),constrained_layout=False)\n gs1 = fig.add_gridspec(nrows=3, ncols=3, wspace=0.0, hspace=0.0)\n\n rotate = False\n for row_i,gal_index in enumerate(gal_indices):\n\n R_max = p.R_max[row_i]\n\n ax1 = fig.add_subplot(gs1[row_i, 0])\n m = map_sim_property(add=True,ax=ax1,gal_index=gal_index, \\\n prop='m',R_max=R_max,vmin=0,vmax=7,\\\n pix_size_kpc=0.5,sim_type='simgas',cmap='viridis',log=True,colorbar=False,rotate=rotate,text=p.text)\n frame = plt.gca()\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\n if row_i != 2: ax1.set_xlabel('')\n if row_i == 0:\n cbaxes = fig.add_axes([0.05, 0.93, 0.25, 0.01]) \n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes)\n cbaxes.xaxis.set_ticks_position('top')\n cb.ax.set_title(\"log $\\Sigma_{\\mathrm{gas}}$ [M$_{\\odot}$ kpc$^{-2}$]\")\n # Make a size indicator\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='white')\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='white',fontsize=12)\n # Remove axes ticks\n ax1.tick_params(axis='x',which='both',labelbottom=False)\n ax1.tick_params(axis='y',which='both',labelleft=False)\n\n ax1 = fig.add_subplot(gs1[row_i, 1])\n m = star_map(add=True,ax=ax1,R_max=R_max,vmin=6,vmax=9,\\\n gal_index=gal_index,colorbar=False,rotate=rotate)\n frame = plt.gca()\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\n if row_i != 2: ax1.set_xlabel('')\n frame.axes.get_yaxis().set_visible(False)\n if row_i == 0:\n cbaxes = fig.add_axes([0.375, 0.93, 0.25, 0.01]) \n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes) \n cbaxes.xaxis.set_ticks_position('top')\n cb.ax.set_title(\"log stellar age [yr]\")\n # Make a size indicator\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='k')\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='k',fontsize=12)\n # Remove axes ticks\n ax1.tick_params(axis='x',which='both',labelbottom=False)\n ax1.tick_params(axis='y',which='both',labelleft=False)\n\n ax1 = fig.add_subplot(gs1[row_i, 2])\n m = FUV_map(add=True,ax=ax1,gal_index=gal_index,R_max=R_max,vmin=-10,vmax=3,select=p.select,cmap='twilight',colorbar=False,rotate=rotate)\n frame = plt.gca()\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\n if row_i != 2: ax1.set_xlabel('')\n frame.axes.get_yaxis().set_visible(False)\n if row_i == 0:\n cbaxes = fig.add_axes([0.69, 0.93, 0.25, 0.01]) \n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes) \n cbaxes.xaxis.set_ticks_position('top')\n cb.ax.set_title('FUV flux [W/m$^2$/arcsec$^2$]')\n # Make a size indicator\n # if row_i == 2:\n # print('Adding size indicator')\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='w',fontsize=12)\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='w')\n # else:\n ax1.text(R_max-16,-R_max+7,'10 kpc',color='k',fontsize=12)\n ax1.plot([R_max-15,R_max-5],[-R_max+5,-R_max+5],lw=4,color='k')\n # Remove axes ticks\n ax1.tick_params(axis='x',which='both',labelbottom=False)\n ax1.tick_params(axis='y',which='both',labelleft=False)\n\n # s = segs\n gs1.update(top=0.92,bottom=0.02,left=0.02,right=0.98)\n\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'pretty/'): os.mkdir(p.d_plot + 'pretty/')\n plt.savefig('plots/pretty/mass_FUV_maps_%s%s.png' % (p.sim_name,p.sim_run),format='png',dpi=200)","def f4():\n n = 4\n v = np.arange(n)**0.75 * 0.2\n e = (np.arange(n)+1)**0.7 * 1e-1\n\n n = 12\n v = np.arange(n)\n e = np.array([0.1]*n) * 10e-0\n\n print(Sumb(v,e))\n\n f = plt.figure()\n a = f.add_subplot(111)\n\n dx = 0.0001\n x = np.arange(-1,v[-1]+1,dx)\n y = x.copy()\n y[:] = 0.\n for i in range(n):\n yx = lg(x,v[i],e[i])\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\n y += yx\n y = np.exp((y - np.max(y))/n**2)\n y /= np.sum(y) * dx \n a.plot(x,y,label='sum')\n s = np.argsort(y)[::-1]\n ys = np.cumsum(y[s]) * dx\n yi = np.argwhere(ys > 0.682689492137)[0][0]\n print('mean = {:2f}'.format(x[s[0]]))\n print('sigma = {:2f}'.format(yi*dx/2))\n xy = np.ndarray((yi+2,2))\n i0,i1 = min(s[:yi]), max(s[:yi])\n xy[:yi,0] = x[i0:i1+1]\n xy[:yi,1] = y[i0:i1+1]\n xy[yi:,1] = 0\n xy[yi:,0] = x[[i1,i0]]\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\n \n leg = plt.legend()\n plt.draw()","def make_interferometry(data,algorithm=None):\n # Obteniendo el mapa de coherencia\n Im = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_10.npy\") # Se carga la imagen 10\n f1 = np.zeros((len(Im),len(Im.T)),dtype=complex)\n f2 = np.zeros((len(Im),len(Im.T)),dtype=complex)\n coh = np.zeros((len(Im),len(Im.T)),dtype=complex)\n\n for i in range(n_im-1):\n i += i_o # Valor inicial\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i+1)+\".npy\")\n f1 += Im1*Im2.conjugate()\n f2 += np.sqrt((abs(Im1)**2)*(abs(Im2)**2))\n\n coh = abs(f1/f2)\n\n # Graficando la coherencia\n if show:\n plt.close('all')\n cmap =\"plasma\"\n if algorithm == \"BP\":\n title_name ='Mapa de coherencia(BP)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='Coherencia_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n\n elif algorithm == \"RMA\":\n title_name ='Mapa de coherencia(RMA)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='Coherencia_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n\n fig, ax = plt.subplots()\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Coherence_maps/\"+direction, orientation='landscape')\n\n # Sacando una mascara de [0.7,1] para la coherencia\n mask = coh<=0.7\n coh[mask] = np.nan\n\n # Graficando con la mascara\n if show:\n cmap =\"plasma\"\n if algorithm == \"BP\":\n title_name ='Mapa de coherencia recortada(BP)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='CoherenciaCut_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n\n elif algorithm == \"RMA\":\n title_name ='Mapa de coherencia recortada(RMA)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='CoherenciaCut_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Coherence_maps/\"+direction, orientation='landscape')\n\n # Obteniendo el mapa de desplazamientos, aka, Interferograma\n par = sp.get_parameters()\n global c,fc\n c,fc = par['c'],par['fc']\n\n if show:\n for i in range(n_im-1):\n # Hallando el interferograma 'i'-esimo\n i1 = i+10\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1+1)+\".npy\")\n disp = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc) # Distancias en mm\n disp[mask] = np.nan\n # Graficando y guardando\n if algorithm == \"BP\":\n title_name ='Interferograma (BP) \\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\n direction ='Itf_BP_dset'+str(i)+'.png'\n\n elif algorithm == \"RMA\":\n title_name ='Interferograma (RMA) \\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\n direction ='Itf_RMA_dset'+str(i)+'.png'\n cmap = plt.cm.plasma #brg\n cmap.set_bad('black',1.)\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(disp,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Interferograms_complete/Images/\"+direction,orientation='landscape')\n plt.close()\n\n # Hallando la curva de distancias vs tiempo\n #-------Definiendo las zonas--------------\n zone0 = np.array([0,650,0,400])\n zone1 = np.array([0,100,100,200])\n zone2 = np.array([0,100,200,300])\n zone3 = np.array([100,200,100,200])\n zone4 = np.array([100,200,200,300])\n zone5 = np.array([200,300,100,200])\n zone6 = np.array([200,300,200,300])\n zone_indexes = {0:zone0,1:zone1,2:zone2,3:zone3,4:zone4,5:zone5,6:zone6}\n\n desp = np.zeros((len(zone_indexes),n_im-1)) # Variable y: desplazamiento\n\n for z in range(len(zone_indexes)): # Hallando los desplazamientos promedios por zona\n idc = zone_indexes[z]\n for i in range(n_im-1):\n i1 = i+10 # 10 es el valor inicial de las imagenes\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1+1)+\".npy\")\n d_i = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc)\n # ----- Grafica ------\n d_i2 = d_i.copy()\n d_i2[mask] = np.nan\n d_i2 = d_i2[idc[0]:idc[1],idc[2]:idc[3]]\n # ----- end --------\n d_i[mask] = 0\n d_i = d_i[idc[0]:idc[1],idc[2]:idc[3]]\n if i==0: desp[z,i] = d_i.mean()\n else: desp[z,i] = d_i.mean()+desp[z,i-1]\n\n # Graficando la zona z-esima\n if show and i == 0: #\n if algorithm == \"BP\":\n title_name ='Interferograma recortado(BP) \\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\n direction ='Itf_BP_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+str(i)+'.png'\n\n elif algorithm == \"RMA\":\n title_name ='Interferograma recortado(RMA) \\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\n direction ='Itf_RMA_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+str(i)+'.png'\n cmap = plt.cm.plasma #brg\n cmap.set_bad('black',1.)\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(d_i2,cmap,origin='lower',aspect='equal', extent=[idc[2]-200,idc[3]-200,idc[0]+300,idc[1]+300]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Interferograms_complete/Images/\"+direction,orientation='landscape')\n\n #t = np.arange(len(Ims)-1)+10 # Variable x, tiempo\n time_dset = np.load(\"Dates_BP.npy\")\n time_dset = time_dset[:-1]\n time_dset2 = np.array([datetime.datetime.strptime(idx,\"%d-%m-%y %H:%M:%S\") for idx in time_dset])\n\n # Grafica de desplazamiento vs tiempo por cada algoritmo\n \"\"\"if show:\n if algorithm == \"BP\":\n title_name ='Desplazamiento promedio(BP)'\n direction ='desplazamientoPromedio_BP_dset10-100.png'\n\n fig, ax= plt.subplots()\n ax.plot(t,desp,'b',marker='o',markerfacecolor='b',markeredgecolor='b')\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\n #ax.set_xlim([R.min(),R.max()])\n ax.set_ylim([-c*100/(4*fc),c*100/(4*fc)])\n ax.grid()\n plt.show()\n fig.savefig(os.getcwd()+\"/Results/Desplazamientos/\"+direction,orientation='landscape')\n\n elif algorithm == \"RMA\":\n title_name ='Desplazamiento promedio(RMA)'\n direction ='desplazamientoPromedio_RMA_dset10-100.png'\n\n fig, ax= plt.subplots()\n ax.plot(t,desp,'r',marker='o',markerfacecolor='r',markeredgecolor='r')\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\n #ax.set_xlim([R.min(),R.max()])\n ax.set_ylim([-c*50/(4*fc),c*50/(4*fc)])\n ax.grid()\n plt.show()\n fig.savefig(os.getcwd()+\"/Results/Desplazamientos/\"+direction,orientation='landscape')\n \"\"\"\n return {'d_t':desp, 't':time_dset2}","def compute_g(self, i, j):\n #Compute variance and mean denominator (same denominator for both)\n g_next = 0\n if (i+1 < self.nb_days-1):\n g_next = self.g_heat[i+1,j]\n\n denominator = 2 * self.sigma2\n numerator_mean = self.sigma2 * (self.g_heat[i-1,j] + g_next)\n if (self.u_heat > self.temperatures[i]):\n denominator = denominator + self.sigma_g_star_2[0, j] * ((self.temperatures[i] - self.u_heat)**2)\n numerator_mean = numerator_mean + \\\n self.sigma_g_star_2[0, j] * (self.temperatures[i] - self.u_heat) * (self.consumptions[i] - self.s[i,j] * self.kappa[self.daytypes[i]])\n\n #Mean\n mean = numerator_mean / denominator\n\n #Compute variance numerator\n variance_numerator = (self.sigma2 * self.sigma_g_star_2[0, j])\n #Variance\n variance = variance_numerator / denominator\n\n self.g_heat[i,j] = self.truncated_norm(-inf, 0, mean, variance)","def _calc(self):\r\n u = self._fadefunc(self.xf)\r\n v = self._fadefunc(self.yf)\r\n w = self._fadefunc(self.zf)\r\n\r\n # populate the hashes dict\r\n self._hash()\r\n \r\n # once the hash dict is populated, start calculating the dot product between \r\n # the gradient vector and the distance vectors, which is done in the _grad method.\r\n # finally linearly interpolate the values to get the avg value\r\n # first interpolate in the x-dir, then in y-dir\r\n x1: float = self._lerp(self._grad(self.hashes[\"aaa\"], self.xf, self.yf, self.zf),\r\n self._grad(self.hashes[\"baa\"], self.xf - 1, self.yf, self.zf), u)\r\n\r\n x2: float = self._lerp(self._grad(self.hashes[\"aba\"], self.xf, self.yf - 1, self.zf),\r\n self._grad(self.hashes[\"bba\"], self.xf - 1, self.yf - 1, self.zf), u)\r\n\r\n # the first y-dir lerp\r\n y1: float = self._lerp(x1, x2, v)\r\n\r\n x1: float = self._lerp(self._grad(self.hashes[\"aab\"], self.xf, self.yf, self.zf - 1),\r\n self._grad(self.hashes[\"bab\"], self.xf - 1, self.yf, self.zf - 1), u)\r\n\r\n x2: float = self._lerp(self._grad(self.hashes[\"abb\"], self.xf, self.yf - 1, self.zf - 1),\r\n self._grad(self.hashes[\"bbb\"], self.xf-1, self.yf-1, self.zf-1), u)\r\n\r\n # the second y-dir lerp\r\n y2: float = self._lerp(x1, x2, v)\r\n\r\n # the final noise value, which will be in the range [0, 1]\r\n self.value = (self._lerp(y1, y2, w) + 1)/2\r\n return self.value","def past_weight_grad_calculator2(xs, es, kp_x, kd_x, kp_e, kd_e, shapes):\n kp_x, kd_x, kp_e, kd_e = [as_floatx(k) for k in (kp_x, kd_x, kp_e, kd_e)]\n n_samples, n_in, n_out = shapes\n rx = kd_x/(kp_x+kd_x)\n re = kd_e/(kp_e+kd_e)\n\n xr = create_shared_variable(np.zeros((n_samples, n_in)))\n er = create_shared_variable(np.zeros((n_samples, n_out)))\n\n\n\n\n # xr_new = xr*rx + xs/(kp_x+kd_x)\n # er_new = er*re + es/(kp_e+kd_e)\n\n arr = rx*re/(1-rx*re)\n\n xr_new = xr*arr + xs/(kp_x+kd_x)\n er_new = er*arr + es/(kp_e+kd_e)\n\n xsum = create_shared_variable(np.zeros((n_samples, n_in)))\n esum = create_shared_variable(np.zeros((n_samples, n_out)))\n\n xsum_new = xsum+xr_new\n esum_new = esum+er_new\n\n x_nospikes = tt.eq(xs, 0)\n e_nospikes = tt.eq(es, 0)\n\n dw = xs.T.dot(esum_new) + xsum_new.T.dot(es)\n\n add_update(xr, xr_new)\n add_update(er, er_new)\n add_update(xsum, xsum_new*x_nospikes)\n add_update(esum, esum_new*e_nospikes)\n\n return xs.T.dot(er) + xr.T.dot(es)\n # return xr.T.dot(er)\n # return dw","def modelOnBetaGrid(sample,bins,N,l,u):\r\n\r\n betaGrid=np.linspace(l,u,N)\r\n traces=[]\r\n WAIC=dict()\r\n index=0\r\n\r\n for beta in betaGrid:\r\n trace=intensityLogGauss(sample,bins,beta)\r\n traces.append(trace['intensity'])\r\n WAIC[index]=trace\r\n index+=1\r\n\r\n df=pm.compare(WAIC,ic='WAIC')\r\n\r\n return betaGrid,df,traces","def app(ngr=100,c2a=1.6235):\n import matplotlib.pyplot as plt\n\n ## small donuts\n # plt.gcf().clf()\n grs = main(mu=0,ngrains=ngr,tilt_1=30.,sigma=15)\n plt.gcf().savefig('small_doughnut.pdf',bbox_inches='tight')\n plt.gcf().clf()\n f = gen_file(lab='sm_doughnut',ngr=ngr)\n write_gr(f,grs)\n\n ## Big donuts\n grs = main(mu=0,ngrains=ngr,tilt_1=50.,sigma=15)\n plt.gcf().savefig('big_doughnut.pdf',bbox_inches='tight')\n plt.gcf().clf()\n f = gen_file(lab='big_doughnut',ngr=ngr)\n write_gr(f,grs)\n\n ## twin tilts (30).\n gr1=main(mu=0,ngrains=ngr/2,tilts_about_ax1=30.,sigma=45)\n plt.gcf().clf()\n gr2=main(mu=0,ngrains=ngr/2,tilts_about_ax1=-30.,sigma=45)\n plt.gcf().clf()\n grs =[]\n for i in range(len(gr1)):\n grs.append(gr1[i])\n grs.append(gr2[i])\n grs=np.array(grs)\n mypf=upf.polefigure(grains=grs,csym='hexag',cdim=[1,1,c2a])\n mypf.pf_new(poles=[[0,0,0,1],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\n plt.gcf().savefig('t30.pdf',bbox_inches='tight')\n f = gen_file(lab='dbl_lets_30',ngr=ngr)\n write_gr(f,grs)\n\n ## twin tilts (50).\n gr1=main(mu=0,ngrains=ngr/2,tilts_about_ax1=50.,sigma=45)\n plt.gcf().clf()\n gr2=main(mu=0,ngrains=ngr/2,tilts_about_ax1=-50.,sigma=45)\n plt.gcf().clf()\n gr =[]\n for i in range(len(gr1)):\n gr.append(gr1[i])\n gr.append(gr2[i])\n gr=np.array(gr)\n mypf=upf.polefigure(grains=gr,csym='hexag',cdim=[1,1,c2a])\n mypf.pf_new(poles=[[0,0,0,1],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\n plt.gcf().savefig('t50.pdf',bbox_inches='tight')\n plt.gcf().clf()\n f = gen_file(lab='dbl_lets_50',ngr=ngr)\n write_gr(f,gr)","def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, plot=True):\r\n\r\n # Contour over a range of length scales and signal/noise ratios.\r\n length_scales = np.linspace(0.1, 60., resolution)\r\n log_SNRs = np.linspace(-3., 4., resolution)\r\n\r\n data = GPy.util.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)\r\n # data['Y'] = data['Y'][0::2, :]\r\n # data['X'] = data['X'][0::2, :]\r\n\r\n data['Y'] = data['Y'] - np.mean(data['Y'])\r\n\r\n lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)\r\n if plot:\r\n pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)\r\n ax = pb.gca()\r\n pb.xlabel('length scale')\r\n pb.ylabel('log_10 SNR')\r\n\r\n xlim = ax.get_xlim()\r\n ylim = ax.get_ylim()\r\n\r\n # Now run a few optimizations\r\n models = []\r\n optim_point_x = np.empty(2)\r\n optim_point_y = np.empty(2)\r\n np.random.seed(seed=seed)\r\n for i in range(0, model_restarts):\r\n # kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))\r\n kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))\r\n\r\n m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)\r\n m['noise_variance'] = np.random.uniform(1e-3, 1)\r\n optim_point_x[0] = m['rbf_lengthscale']\r\n optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\r\n\r\n # optimize\r\n if optimize:\r\n m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters)\r\n\r\n optim_point_x[1] = m['rbf_lengthscale']\r\n optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\r\n\r\n if plot:\r\n pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')\r\n models.append(m)\r\n\r\n if plot:\r\n ax.set_xlim(xlim)\r\n ax.set_ylim(ylim)\r\n return m # (models, lls)\r","def awGrid(vis,HA,uvw,image_params,obs_params,Mterms,Mterms_ij):\t\n\tStokes = image_params['Stokes']\n\t\n\n\n\tprint '--------------Gridding X pol--------------------'\n\txgrid_wt, xgrid_uv = gridOnePolAWproj(vis[0],HA,uvw,image_params,obs_params,Mterms[0],Mterms_ij[0])\n\tprint '--------------Gridding Y pol--------------------'\n\tygrid_wt, ygrid_uv = gridOnePolAWproj(vis[1],HA,uvw,image_params,obs_params,Mterms[1],Mterms_ij[1])\n\n\tN = np.shape(xgrid_wt)[0]\n\tgrid_uv = np.zeros([N, N], dtype=complex)\n\tgrid_wt = np.zeros([N, N], dtype=complex)\n\t\n\tif Stokes == 'I':\n\t\t#combine X and Y gridded vis to create the I pol gridded vis\n\t\t# I = (XX+YY)/2\n\t\tgrid_uv.real = (ygrid_uv.real + xgrid_uv.real)/2\n\t\tgrid_uv.imag = (ygrid_uv.imag + xgrid_uv.imag)/2\n\n\t\t#combine X and Y gridded wt to create the I pol gridded wt\n\t\tgrid_wt.real = (ygrid_wt.real + xgrid_wt.real)/2\n\t\tgrid_wt.imag = (ygrid_wt.imag + xgrid_wt.imag)/2\n\n\telif Stokes == 'Q':\n\t\t#combine X and Y gridded vis to create the I pol gridded vis\n\t\t# Q = (XX-YY)/2\n\t\tgrid_uv.real = (ygrid_uv.real - xgrid_uv.real)/2\n\t\tgrid_uv.imag = (ygrid_uv.imag - xgrid_uv.imag)/2\n\n\t\t#combine X and Y gridded wt to create the I pol gridded wt\n\t\tgrid_wt.real = (ygrid_wt.real - xgrid_wt.real)/2\n\t\tgrid_wt.imag = (ygrid_wt.imag - xgrid_wt.imag)/2\n\n\n\tdty_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_uv)))\n\tpsf_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_wt)))\n\n\treturn dty_image, psf_image","def task2_extra2():\n N = 0\n lam = 0\n L = 10\n h = 0.001\n tau = 0.000099\n aa = numpy.array([0.25*a for a in range((L-1)*4)])\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\n Vm = V1D(lam, x)\n # eps=int(0.1*len(x))\n\n iterss = []\n for a in aa:\n print(a)\n state = phi(N, x-a)\n\n iters = 0\n while True:\n prob = numpy.abs(state)**2\n mid = int(2*L/h) // 2\n # if max(prob) in prob[mid-eps:mid+eps]:\n if numpy.argmax(prob) <= mid:\n print(iters)\n iterss.append(iters)\n break\n\n state[0] = 0\n state[-1] = 0\n state = implicit_scheme_step(state, tau, h, Vm)\n iters += 1\n\n fig = plt.figure()\n plt.title(\"Iterations of Gaussian travel to center ($L={}$)\".format(L))\n plt.xlabel(\"$a$\")\n plt.ylabel(\"Time\")\n plt.plot(aa, tau*numpy.array(iterss))\n plt.show()\n fig.savefig(\"naloga2_iters_of_gaussian_travel_fixedL={}.pdf\".format(L), bbox_inches=\"tight\")","def refugia_adj_1_growthall(params, ns):\n #14 parameters \n nu10, nu1, nuA0, nuA, nu20, nu2, nu30, nu3, m3_1, m3_2, m3_3, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\n ## Population function and migration matrix for T1\n nuA_func = lambda t: nuA0 * (nuA/nuA0)**(t/T1) \n ## Population function for T1\n nu_T1_func = lambda t: [nu1, nuA_func(t)]\n fs.integrate(nu_T1_func, T1)\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T2\n nu_T2 = [nu1, nu2, nu3]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3\n nu1_func = lambda t: nu10 * (nu1/nu10)**(t/T3) \n nu2_func = lambda t: nu20 * (nu2/nu20)**(t/T3) \n nu3_func = lambda t: nu30 * (nu3/nu30)**(t/T3) \n nu_T3_func = lambda t: [nu1_func(t), nu2_func(t), nu3_func(t)] \n mig3 = numpy.array([[0, m3_1, m3_3],[m3_1, 0, m3_2], [m3_3, m3_2, 0]]) \n fs.integrate(nu_T3_func, T3, m=mig3) \n return fs","def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\n\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\n\n S12 = 0.0\n T11 = 0.0\n T12 = 0.0\n T22 = 0.0\n V11A = 0.0\n V12A = 0.0\n V22A = 0.0\n V11B = 0.0\n V12B = 0.0\n V22B = 0.0\n V1111 = 0.0\n V2111 = 0.0\n V2121 = 0.0\n V2211 = 0.0\n V2221 = 0.0\n V2222 = 0.0\n\n R2 = R * R\n\n # The coefficients for the contracted Gaussian functions are below\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\n [0.678914, 0.430129, 0.000000],\n [0.444635, 0.535328, 0.154329]])\n\n Expon = np.array([[0.270950, 0.000000, 0.000000],\n [0.151623, 0.851819, 0.000000],\n [0.109818, 0.405771, 2.227660]])\n D1 = np.zeros([3])\n A1 = np.zeros([3])\n D2 = np.zeros([3])\n A2 = np.zeros([3])\n\n # This loop constructs the contracted Gaussian functions\n for i in range(N):\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\n\n # Calculate one electron integrals\n # Centre A is first atom centre B is second atom\n # Origin is on second atom\n # V12A - off diagonal nuclear attraction to centre A etc.\n for i in range(N):\n for j in range(N):\n # Rap2 - squared distance between centre A and centre P\n Rap = A2[j] * R / (A1[i] + A2[j])\n Rap2 = Rap ** 2\n Rbp2 = (R - Rap) ** 2\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\n\n # Calculate two electron integrals\n\n for i in range(N):\n for j in range(N):\n for k in range(N):\n for l in range(N):\n Rap = A2[i] * R / (A2[i] + A1[j])\n Rbp = R - Rap\n Raq = A2[k] * R / (A2[k] + A1[l])\n Rbq = R - Raq\n Rpq = Rap - Raq\n Rap2 = Rap * Rap\n Rbp2 = Rbp * Rbp\n Raq2 = Raq * Raq\n Rbq2 = Rbq * Rbq\n Rpq2 = Rpq * Rpq\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\n return","def source_adj_gsdf(gmdata_sim,gmdata_obs,IsolationFilter,num_pts,dt): \n t = np.arange(num_pts)*dt\n ts=np.flip(-t[1:], axis=0)\n lTime = np.concatenate((ts,t), axis=0)#Lag time \n \n #convolve the waveforms for the cross- and auto-correlagrams \n cross = np.correlate(IsolationFilter,gmdata_obs,'full')\n auto = np.correlate(IsolationFilter,gmdata_sim,'full') \n \n #GSDF Parameters \n w0=2*np.pi/(lTime[-1]) \n# wN=2*np.pi/(2*dt)\n# w(:,1)=-wN:w0:wN\n wf=w0*np.linspace(-int(num_pts/2),int(num_pts/2),num_pts) \n fi = [0.05, 0.075, 0.1]\n# fi = [0.02, 0.03, 0.04, 0.05]\n# fi = [0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2]\n Tw = 2/np.mean(fi) # Effective window\n# sw = 2*np.pi*0.72/Tw; # Sigma w ~ 0.2827433388230814\n sw=0.1 \n \n# #% A local maximum will be selected closest to 0-lag\n# I_O=np.argmax(cross)\n# I_S=np.argmax(auto) \n\n I_O, peaks_O = find_peaks(np.abs(hilbert(cross))/np.max(np.abs(hilbert(cross))), height=0.25)\n I_S, peaks_S = find_peaks(np.abs(hilbert(auto))/np.max(np.abs(hilbert(auto))), height=0.25)\n\n PkO = peaks_O.get(\"peak_heights\", \"\")\n PkS = peaks_S.get(\"peak_heights\", \"\")\n\n if (I_O==[] or I_S==[]):\n I_O=np.argmax(cross)\n I_S=np.argmax(auto)\n else:\n I_O_min = np.argmin(np.multiply((1+np.abs(lTime[I_O]))**2,np.abs(1-PkO)))\n I_O = I_O[I_O_min]\n\n I_S_min = np.argmin(np.multiply((1+np.abs(lTime[I_S]))**2,np.abs(1-PkS)))\n I_S = I_S[I_S_min]\n \n ##Windowing\n win1=np.exp(-(0.5*sw**2)*(lTime-lTime[I_O])**2)\n win2=np.exp(-(0.5*sw**2)*(lTime-lTime[I_S])**2) \n \n #\n WO = np.multiply(win1,cross)\n WS = np.multiply(win2,auto)\n WS = WS*np.max(WO)/np.max(WS) #Normalized window by amplitude\n #% Parameters for \"bootstraping\"\n InOR=np.argmax(WO)\n InSR=np.argmax(WS) \n \n #% Isolation filter FFT for perturbation kernel\n tff=np.conj(fftshift(fft(IsolationFilter)))*1/num_pts \n \n adj_sim_decompose = np.zeros((len(fi),num_pts))\n adj_sim_sum = np.zeros(num_pts)\n TauP_arr = np.zeros(len(fi)) \n \n ne = int(np.min([2/np.min(fi)/dt,num_pts/2])) #% Effective bandwidth for inversion\n \n for i in range(0,len(fi)): \n si = 0.1*fi[i]\n #Crosscorrelagram and Autocorrelagram filtering\n dO=computebandfftfilter_gauss(WO,dt,fi[i],si,lTime);\n dS=computebandfftfilter_gauss(WS,dt,fi[i],si,lTime); \n \n # % Check bootstraping\n InO=np.argmax(np.real(dO))\n InS=np.argmax(np.real(dS)) \n \n BS = 1; Cn = 0;\n while BS == 1 or Cn < 10:\n InO=int(InO)\n if (lTime[InO] < lTime[InOR]+0.51/fi[i]) and (lTime[InO] >= lTime[InOR]-0.51/fi[i]):\n BS = 0\n elif (lTime[InO] >= (lTime[InOR]+0.45/fi[i])):\n InO=InO-np.round(1/fi[i]/dt)\n elif (lTime[InO] < lTime[InOR]-0.45/fi[i]):\n InO=InO+np.round(1/fi[i]/dt)\n Cn = Cn+1\n \n BS = 1; Cn = 0;\n while BS == 1 or Cn < 10:\n InS=int(InS) \n if (lTime[InS] < lTime[InSR]+0.51/fi[i]) and (lTime[InS] >= lTime[InSR]-0.51/fi[i]):\n BS = 0\n elif (lTime[InS] >= (lTime[InSR]+0.45/fi[i])):\n InS=InS-np.round(1/fi[i]/dt)\n elif (lTime[InS] < lTime[InSR]-0.45/fi[i]):\n InS=InS+np.round(1/fi[i]/dt)\n Cn = Cn+1 \n\n # Five parameter Gaussian wavelet fitting \n Ao = np.max(envelope(np.real(dO))); Io = np.argmax(envelope(np.real(dO)));\n As = np.max(envelope(np.real(dS))); Is = np.argmax(envelope(np.real(dS))); \n ##Constrain the initial values \n # Parameters for curve_fit\n wi=2*np.pi*fi[i] \n \n try:\n GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]))\n GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne])) \n except:\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \n\n# GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]),bounds=(0,[Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]))\n# GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne]),bounds=(0,[As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]])) \n \n# % Check fitting\n if ((GaO[0]/GaS[0]) > 10**5) or np.abs(GaO[4]-GaS[4]) > lTime[-1]/2:\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \n \n wP=((si**2)*wf+(sw**2)*wi)/(sw**2+si**2)\n wPP=((si**2)*wf-(sw**2)*wi)/(sw**2+si**2)\n siP=((si**2)*(sw**2)/(sw**2+si**2))**0.5 \n #Estimate waveform perturbation kernel (WPK)\n IW=(siP/(sw*GaS[0]))*np.multiply(np.exp(-0.5*(wf-2*np.pi*fi[i])**2/(sw**2+si**2)),np.divide(tff,wP))+\\\n (siP/(sw*GaS[0]))*np.exp(-0.5*(wf+2*np.pi*fi[i])**2/(sw**2+si**2))*tff/wPP\n \n IW[0:int(len(IW)/2)]=0*IW[0:int(len(IW)/2)]\n \n itff = ifft(fftshift(num_pts*IW)) \n \n #Save the GSDF measurements\n TauP_arr[i] = GaO[4]-GaS[4]; #% delta_P\n \n# Jp = np.real(itff)\n# Jp = np.imag(itff)\n Jp = -np.imag(itff) \n adj_sim_decompose[i,:] = np.flip(Jp,axis=0)*TauP_arr[i] \n \n #if i>0:\n adj_sim_sum = adj_sim_sum + adj_sim_decompose[i,:] \n \n return adj_sim_sum, TauP_arr","def set_sobol_g_func():\n a = np.zeros(21)\n x = np.zeros(21) \n\n # a[0:2] = 0\n # a[2:9] = [0.005, 0.020, 0.040, 0.060, 0.08, 0.090, 1]\n # a[9:16] = 2\n # a[16:24] = [2.10, 2.25, 2.75, 3, 3.10, 3.15, 3.25, 3.50]\n # a[24:30] = 9\n # a[30:44] = [8, 8.5, 9, 10, 10.5, 11, 12, 12.5, 13, 13.5, 14, 14.5, 15, 16]\n # a[44:] = [70, 75, 80, 85, 90, 99]\n\n a[0:2] = 0\n a[2:4] = [0.005, 0.090]\n a[4:7] = 2\n a[7:11] = [2.10, 2.75, 3, 3.15]\n a[11:15] = [8, 13, 13.5, 16]\n a[15:] = [70, 75, 80, 85, 90, 99]\n\n x_names = ['x' + str(i+1) for i in range(21)]\n len_params = len(x_names)\n x_bounds = np.zeros((21, 2))\n x_bounds[:, 0] = 0\n x_bounds[:, 1] = 1\n\n problem = {\n 'num_vars': len(x),\n 'names': x_names,\n 'bounds': x_bounds\n }\n return a, x, x_bounds, x_names, len_params, problem","def grd_posterior_gaussian(self, ) -> Tuple[np.ndarray, np.ndarray]:\n xmin, xmax = self.x_range\n ymin, ymax = self.y_range\n\n mu = np.array([0, 0])\n sigma = np.zeros((2, 2))\n\n _sample = self._sample\n _prior = self.prior\n\n def mean_x(x: float, y: float):\n return x * _sample(x, y) * _prior.eval(x, y)\n\n def mean_y(x: float, y: float):\n return y * _sample(x, y) * _prior.eval(x, y)\n\n def var_x(x: float, y: float):\n return x * mean_x(x, y)\n\n def var_y(x: float, y: float):\n return y * mean_y(x, y)\n\n # def var_xy(x: float, y: float):\n # return x * mean_y(x, y)\n\n # First moment\n (mu[0], mu[1]) = (integrate.dblquad(mean_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\n integrate.dblquad(mean_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0])\n (sigma[0, 0], sigma[1, 1]) = \\\n (integrate.dblquad(var_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\n integrate.dblquad(var_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\n # integrate.dblquad(var_xy, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\n return mu, sigma","def map_sim_positions(**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n fig, ax1 = plt.subplots(figsize=(10,10))\n # p.gal_index = np.where(GR.file_name == 'z0.00_G7169_cG29270')[0][0]\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\n # print('TEST!',gal_ob.file_name,p.gal_index)\n simdata = aux.load_temp_file(gal_ob=gal_ob,data_type=p.sim_type)\n\n # Plot\n print(simdata.head())\n ax1.plot(simdata.x,simdata.y,'o',ms=2,mew=2)\n\n print(gal_ob.radius)\n # Limit axes limits a bit to avoid area with no particles...\n # ax1.set_xlim([-2/3*gal_ob.radius,2/3*gal_ob.radius])make_projec\n # ax1.set_ylim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')","def plt_npr_gaussian_all(tb, npr, sigma, soil, snow, onset, figname='all_plot_test0420', size=(12, 8), xlims=[0, 365],\n shade=False, title=False, site_no='947', pp=False, subs=5, s_symbol='k.',\n day_tout=-1, end_ax1=[0, 0], end_ax2=[0, 0], end_ax3=[0, 0], tair=[], snow_plot=False):\n # ylim for each station\n site_lim = {'947': [-17, -7], '949': [-13, -7], '950': [-13, -7], '960': [-14, -8], '962': [-15, -8], '967': [-12, -8], '968': [-17, -7],\n '1089': [-13, -7], '1090': [-14, -7], '1175': [-15, -8], '1177': [-19, -10],\n '1233': [-17, -6], '2065': [-14, -8], '2081': [-15, -7], '2210': [-16, -8], '2211': [-16, -8], '2212': [-16, -8],\n '2213': [-17, -10]}\n axs = []\n fig = plt.figure(figsize=size)\n gs0 = gridspec.GridSpec(5, 1)\n gs00 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs0[0])\n ax0 = plt.Subplot(fig, gs00[-1, :])\n # ax1, ax2, ax3, ax4 = plt.subplot(fig, gs0[1]), plt.subplot(fig, gs0[2]), \\\n # plt.subplot(fig, gs0[3]), plt.subplot(fig, gs0[4])\n fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=size, sharex=True) # sharex\n params = {'mathtext.default': 'regular'}\n plt.rcParams.update(params)\n\n # # add a time window bar 2018/05 updated\n # nr, st = 20, 9\n # sub_no = 4*nr+st\n # ax0, ax1, ax2, ax3, ax4 = plt.subplot2grid((sub_no, 1), (0, 0), rowspan=st), \\\n # plt.subplot2grid((sub_no, 1), (st, 0), rowspan=nr), \\\n # plt.subplot2grid((sub_no, 1), (st+nr, 0), rowspan=nr), \\\n # plt.subplot2grid((sub_no, 1), (st+2*nr, 0), rowspan=nr), \\\n # plt.subplot2grid((sub_no, 1), (st+3*nr, 0), rowspan=nr)\n\n # params = {'mathtext.default': 'regular'}\n # plt.rcParams.update(params)\n # row 1 tb\n # ax1 = fig.add_subplot(511) # tb\n # ax0 setting, for boundary of seasons\n # timings = [60, 150, 260, 350, 366]\n # timing_color = ['aqua', 'red', 'orange', 'blue', 'aqua']\n # timing_color_rgba = plot_funcs.check_rgba(timing_color)\n # timing_color_rgba[3] = [0., .3, 1., 1.]\n # print timing_color_rgba\n # timing_name = [\"Frozen\", \"Thawing\", \"Thawed\", \"Freezing\", \" \"]\n # fill_y1 = 1\n # ax0.plot(soil[0][1], soil[0][2]*0)\n # plot_funcs.make_ticklabels_invisible(ax0) # hide the y_tick\n # ax0.tick_params(axis='x', which='both', bottom='off', top='off')\n # ax0.tick_params(axis='y', which='both', left='off', right='off')\n # text_x0 = 0\n #\n # for i in range(0, len(timings)):\n # ax0.fill_between(np.arange(text_x0, timings[i]), fill_y1, color=timing_color_rgba[i])\n # text_x = 0.5*(timings[i]+text_x0)\n # print text_x\n # ax0.text(text_x, 0.5, timing_name[i], va=\"center\", ha=\"center\") # 1.3 up\n # text_x0 = timings[i]+1\n # if i < len(timings)-1:\n\n # # add vertical line and label\n # ax0.axvline(timings[i])\n # ax0.text(timings[i], 1.3, timings[i], va=\"center\", ha=\"center\")\n\n\n print np.nanmax(soil[0][1]), np.nanmin(soil[0][1])\n ax0.set_xlim(xlims)\n axs.append(ax1)\n # l1, = ax1.plot(tb[0][0], tb[0][1], 'bo', markersize=2)\n _, ax1_2, l1 = plot_funcs.pltyy(tb[0][0], tb[0][1], 'test_comp2', 'T$_b$ (K)',\n t2=tb[2][0], s2=tb[2][1], label_y2= '$E_{Tbv}$\\n(K/day)',\n symbol=['k.', 'g-'],\n handle=[fig, ax1], nbins2=6) # plot tbv\n l1_le = plot_funcs.plt_more(ax1, tb[1][0], tb[1][1], line_list=[l1])\n # ax1.locator_params(axis='y', nbins=4)\n # ax1_2.axhline(y=0)\n ax1.set_ylabel('T$_b$ (K)')\n # ax1.legend([l1_le[0][0], l1_le[1]], ['T$_{BV}$', 'T$_{BH}$'], loc=3, prop={'size': 6})\n if title is not False:\n plt.title(title)\n #ax4.legend([l4], [fe[0]], loc=2, prop={'size': 10})\n # ax4.text(0.85, 0.85, '(a)')\n # fig.text(0.85, 0.21, '(e)')\n # fig.text(0.85, 0.37, '(d)')\n # fig.text(0.85, 0.53, '(c)')\n # fig.text(0.85, 0.705, '(b)')\n # fig.text(0.85, 0.87, '(a)')\n #ax1.annotate(str(vline[0]), xy=(vline[0], 260))\n #ax1.annotate(str(vline[1]), xy=(vline[1], 260))\n # row2 npr\n # ax2 = fig.add_subplot(512) # npr\n axs.append(ax2)\n _, ax2_2, l2 = plot_funcs.pltyy(npr[0][0], npr[0][1], 'test_comp2', 'NPR ($10^{-2}$)',\n t2=npr[1][0], s2=npr[1][1], label_y2='$E_{NPR}$\\n($10^{-2}$/day)',\n symbol=[s_symbol, 'g-'], handle=[fig, ax2], nbins2=6)\n # ax2.locator_params(axis='y', nbins=5)\n #ax0.set_ylim([0, 0.06])\n #ax0.set_ylim([0, 0.06])\n\n # sigma\n # ax3 = fig.add_subplot(513) # sigma\n axs.append(ax3)\n _, ax3_2, l2 = plot_funcs.pltyy(sigma[0][0], sigma[0][1], 'test_comp2', '$\\sigma^0_{45} (dB)$',\n t2=sigma[1][0], s2=sigma[1][1], label_y2='$E_{\\sigma^0_{45}}$\\n(dB/day)',\n symbol=[s_symbol, 'g-'], handle=[fig, ax3], nbins2=6)\n # ax3.set_ylim(site_lim[site_no])\n # ax3.locator_params(axis='y', nbins=4)\n\n # moisture and temperature\n # ax4 = fig.add_subplot(514) # T soil and temperature\n axs.append(ax4)\n if snow_plot is False:\n _, ax4_2, l2 = plot_funcs.pltyy(soil[0][1], soil[0][2], 'test_comp2', 'VWC (%)',\n t2=soil[1][1], s2=soil[1][2], label_y2='T$_{soil}$ ($^\\circ$C)',\n symbol=['k-', 'b-'], handle=[fig, ax4], nbins2=6)\n for ax_2 in [ax4_2]:\n ax_2.axhline(ls='--', lw=1.5)\n else:\n ax4.plot(snow[1], snow[2], 'k', linewidth=2.0)\n ax4_2 = ax4.twinx()\n if len(tair) > 0:\n tair[1][tair[1] < -60] = np.nan\n ax4_2.plot(tair[0], tair[1], 'k:')\n ax4_2.set_ylim([-30, 30])\n ax4_2.axhline(ls='--', lw=1.5)\n ax4_2.yaxis.set_major_locator(MaxNLocator(5))\n ax4_2.set_ylabel('T$_{air}$ ($^o$C)')\n\n\n ax2s = [ax1_2, ax2_2, ax3_2, ax4_2]\n ax_ins = [ax4]\n # swe\n # ax5 = fig.add_subplot(515) # swe\n # axs.append(ax5)\n # ax_ins.append(ax5)\n # ax5.plot(snow[1], snow[2], 'k', linewidth=2.0)\n # add air temperature\n # if len(tair) > 0:\n # ax5_2 = ax5.twinx()\n # ax2s.append(ax5_2)\n # tair[1][tair[1] < -60] = np.nan\n # ax5_2.plot(tair[0], tair[1], 'k:')\n # ax5_2.set_ylim([-30, 30])\n # ax5_2.axhline(ls='--', lw=1.5)\n # ax5_2.yaxis.set_major_locator(MaxNLocator(5))\n # ax5_2.set_ylabel('T$_{air}$ ($^o$C)')\n # if not pp:\n # if site_no in ['947', '949', '950', '967', '1089']:\n # ax5.set_ylabel('SWE (mm)')\n # ax5.set_ylim([0, 200])\n # else:\n # ax5.set_ylabel('SD (cm)')\n # ax5.set_ylim([0, 100])\n # if site_no in ['950', '1089']:\n # ax5.set_ylim([0, 500])\n # else:\n # ax5.set_ylabel('precipitation (mm)')\n # ax4.set_xlabel('Day of year 2016')\n\n # add vertical line\n lz = ['--', '--', '--']\n labelz = ['$\\sigma^0$', 'TB', 'NPR']\n if onset.size> 4: # freeze and thaw\n i2 = -1\n # for ax in [ax4, ax5]:\n # # for i in [0, 1, 2]:\n # for i in [0, 1, 2]:\n # ax.axvline(x=onset[i*2], color='k', ls=lz[i], label=labelz[i])\n # ax.axvline(x=onset[i*2+1], color='k', ls=lz[i])\n for ax in [ax3, ax1, ax2]:\n # ax.axvline(x=onset[-2], color='r', ls='-', label='in situ')\n # ax.axvline(x=onset[-1], color='r', ls='-')\n i2 += 1\n ax.axvline(x=onset[i2*2], color='k', ls=lz[i2], label=labelz[i2])\n ax.axvline(x=onset[i2*2+1], color='k', ls=lz[i2])\n elif onset.size <=4:\n for ax in ax_ins:\n for i in [0]:\n ax.axvline(x=onset[i], color='k', ls=lz[i], label=labelz[i])\n ax.axvline(x=onset[i+1], color='k', ls=lz[i+1])\n ax.axvline(x=onset[i+2], color='k', ls=lz[i+2])\n\n l2d_sm = ax4.axvline(x=onset[6], color='r', ls='--')\n ax4.axvline(x=onset[7], color='r', ls='--')\n\n # special vline\n if day_tout > 0:\n l2d, = ax4.axvline(x=day_tout, color='r', ls=':')\n ax1.axvline(x=end_ax1[0], color='b', ls='--')\n ax1.axvline(x=end_ax1[1], color='b', ls='--')\n ax2.axvline(x=end_ax2[0], color='b', ls='-')\n ax2.axvline(x=end_ax2[1], color='b', ls='-')\n ax3.axvline(x=end_ax3[0], color='b', ls=':')\n ax3.axvline(x=end_ax3[1], color='b', ls=':')\n\n # plot settings\n # ticks setting\n for ax in ax_ins:\n ax.yaxis.set_major_locator(MaxNLocator(4))\n for ax in axs:\n yticks = ax.yaxis.get_major_ticks()\n yticks[0].label1.set_visible(False)\n yticks[-1].label1.set_visible(False)\n for ax in ax2s:\n yticks = ax.yaxis.get_major_ticks()\n yticks[0].label2.set_visible(False)\n yticks[-1].label2.set_visible(False)\n # label location\n text4 = ['a', 'b', 'c', 'd', 'e']\n i4 = -1\n for i, ax in enumerate(axs):\n ax.yaxis.set_major_locator(MaxNLocator(4))\n i4 += 1\n ax.get_yaxis().set_label_coords(-0.09, 0.5)\n ax.text(0.02, 0.95, text4[i], transform=ax.transAxes, va='top', fontsize=16)\n # ax.annotate(text4[i4], xy=get_axis_limits(ax), fontweight='bold')\n for ax in ax2s:\n ax.yaxis.set_major_locator(MaxNLocator(4))\n ax.get_yaxis().set_label_coords(1.10, 0.5) # position of labels\n\n # ylims\n ax3_2.set_ylim([-3, 2])\n ax1.set_ylim([210, 280])\n if site_no == '1233':\n ax1.set_ylim([180, 275])\n ax3_2.set_ylim([-3, 3])\n # ax1_2.set_ylim([-9, 9])\n if site_no == '1177':\n st = 0\n else:\n ax2.set_ylim([0, 6])\n ax2_2.set_ylim([-2, 2])\n\n # x_label\n for i3 in range(0, 4):\n axs[i3].set_xlabel('')\n\n if shade is False:\n shade_window = 'no shade'\n else:\n for ax in axs:\n for shade0 in shade:\n ax.axvspan(shade0[0], shade0[1], color=(0.8, 0.8, 0.8), alpha=0.5, lw=0)\n if xlims:\n for ax in axs:\n ax.set_xlim(xlims)\n\n # legend setting\n leg1 = ax1.legend([l1_le[0][0], l1_le[1]], ['T$_{bv}$', 'T$_{bh}$'],\n loc=3, ncol=1, prop={'size': 12}, numpoints=1)\n # for leg in [leg1]:\n # leg.get_frame().set_linewidth(0.0)\n # layout setting\n ax4.set_xlabel('Day of year 2016')\n plt.tight_layout()\n\n # if site_no == '1233':\n # ax1.set_visible(False)\n # ax1_2.set_visible(False)\n # ax3.set_visible(False)\n # ax3_2.set_visible(False)\n\n fig.subplots_adjust(hspace=0.05)\n\n # other setting like the title\n\n\n # ax_name = ['tb', 'npr', 'sig', 'VWC', 'SWE', 'tbG', 'nprG', 'sigG', 'tsoil']\n # ax_i = 0\n # yticks = ax2.yaxis.get_major_ticks()\n # yticks[0].label1.set_visible(False)\n # yticks[-2].label1.set_visible(False)\n # for ax in [ax1, ax2, ax3, ax4, ax5]:\n # yticks = ax.yaxis.get_major_ticks()\n # yticks[0].label1.set_visible(False)\n # yticks[-1].label1.set_visible(False)\n # for ax in [ax1_2, ax2_2, ax3_2, ax4_2]:\n # yticks = ax.yaxis.get_major_ticks()\n # yticks[0].label2.set_visible(False)\n # yticks[-1].label2.set_visible(False)\n plt.rcParams.update({'font.size': 16})\n print figname\n plt.savefig(figname, dpi=300)\n plt.close()\n\n return 0\n if vline is not False:\n ax_count = 0\n for ax in axs:\n ax.set_xlim([0, 350])\n ax_count += 1\n if type(vline) is list:\n ax.axvline(x=vline[0], color='k', ls='--')\n ax.axvline(x=vline[1], color='k', ls='-.')\n continue\n ax.axvline(x=vline[0, 1], color='k', label=repr(vline[0, 1]), ls='--')\n ax.axvline(x=vline[1, 1], color='k', label=repr(vline[1, 1]), ls='-.')\n ax.axvline(x=vline[2, 1], color='k', label=repr(vline[1, 1]), ls='--')\n ax.axvline(x=vline[3, 1], color='k', label=repr(vline[1, 1]), ls='-.')\n # ax.xaxis.set_minor_locator(months)\n # ax.xaxis.set_minor_formatter(monthsFmt)\n # ax.xaxis.set_major_locator(years)\n # ax.xaxis.set_major_formatter(yearsFmt)\n # ax.locator_params(axis='x', nbins=16)\n tick_num = np.array([50, 100, 150, 200, 250, 300, 350, 365, 415, 465, 515, 565, 615, 665, 715], dtype=int)\n ax.xaxis.set_ticks(tick_num)\n labels = [item.get_text() for item in ax.get_xticklabels()]\n n = 0\n for label in labels:\n if tick_num[n] == 50:\n if ax_count == 5:\n labels[n] = \"50\\nYear'15 \"\n else:\n labels[n] = \"50\"\n elif tick_num[n] == 350:\n labels[n] = ' '\n elif tick_num[n] == 365:\n labels[n] = \"365\\n Year'16\"\n elif tick_num[n] == 415:\n labels[n] = repr(tick_num[n]-365)\n elif tick_num[n] > 415:\n labels[n] = repr(tick_num[n]-365)\n else:\n labels[n] = repr(tick_num[n])\n n += 1\n # labels[0] = 'Year\\n2015'\n # labels[1] = '100'\n # labels[2] = '150'\n # labels[3] = '200'\n # labels[4] = '250'\n # labels[5] = '300'\n # labels[6] = ''\n # labels[7] = 'Year\\n2016'\n # labels[8] = '50'\n # labels[9] = '100'\n # labels[10] = '150'\n # labels[11] = '200'\n\n ax.set_xticklabels(labels)\n plt.savefig(figname, dpi=300)\n plt.close()","def _compute_GP_variables(self):\r\n Wi = 1.0/self.W\r\n self.Sigma_tilde = np.diagflat(Wi)\r\n\r\n Y_tilde = Wi*self.Ki_f + self.f_hat\r\n\r\n self.Wi_K_i = self.W12BiW12\r\n ln_det_Wi_K = pddet(self.Sigma_tilde + self.K)\r\n lik = self.noise_model.logpdf(self.f_hat, self.data, extra_data=self.extra_data)\r\n y_Wi_K_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)\r\n\r\n Z_tilde = (+ lik\r\n - 0.5*self.ln_B_det\r\n + 0.5*ln_det_Wi_K\r\n - 0.5*self.f_Ki_f\r\n + 0.5*y_Wi_K_i_y\r\n + self.NORMAL_CONST\r\n )\r\n\r\n #Convert to float as its (1, 1) and Z must be a scalar\r\n self.Z = np.float64(Z_tilde)\r\n self.Y = Y_tilde\r\n self.YYT = np.dot(self.Y, self.Y.T)\r\n self.covariance_matrix = self.Sigma_tilde\r\n self.precision = 1.0 / np.diag(self.covariance_matrix)[:, None]\r\n\r\n #Compute dZ_dK which is how the approximated distributions gradients differ from the dL_dK computed for other likelihoods\r\n self.dZ_dK = self._Kgradients()\r\n #+ 0.5*self.Wi_K_i - 0.5*np.dot(self.Ki_f, self.Ki_f.T) #since we are not adding the K gradients explicit part theres no need to compute this again\r","def addGaussian(ax, ismulti):\n shape = (96, 288) #ax.shape[:2]\n intensity_noise = np.random.uniform(low=0, high=0.05)\n if ismulti:\n ax[:,:,0] = ax[:,:,0]*(1+ intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1]))\n else:\n ax[:,:,0] = ax[:,:,0] + intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1])\n return ax","def gaus(x,A,B,C):\n return A * np.exp( -(x-B)**2 / (2*C**2))","def make_egge(w,minZ,maxZ,ires=1,m=mz0):\n cmds = []\n # coefficients for the amplitudes\n cmds.append(\"A[1,0,1000000]\")\n cmds.append(\"B[1,0,1000000]\")\n cmds.append(\"C[10000.0,0,1000000]\")\n # amplitudes\n cmds.append('m[%s,%s,%s]'%(m,minZ,maxZ))\n cmds.append('g[8,0,100]')\n denom = '((x^2-m^2)^2+g^2*m^2)'\n cmds.append(\"expr::z_rbw('x^2/%s',x,m,g)\"%denom)\n cmds.append(\"expr::z_int('(x^2-m^2)/%s',x,m,g)\"%denom)\n cmds.append(\"expr::z_rad('1/(x^2+1)',x)\")\n # resolution model\n cmds += resolutions[ires]()\n [w.factory(cmd) for cmd in cmds]\n # sum-of-amplitudes pdf\n lshape = RooRealSumPdf('lshape','lshape',RooArgList(w.function('z_rad'),w.function('z_int'),w.function('z_rbw')),RooArgList(w.var('A'),w.var('B'),w.var('C')))\n getattr(w,'import')(lshape)\n # convolution\n pdf = w.pdf('lshape')\n if w.pdf('res'):\n w.var('x').setBins(10000,'cache')\n cmd = 'FCONV::sum(x,lshape,res)'\n w.factory(cmd)\n pdf = w.pdf('sum')\n return pdf, kFALSE","def perform_bivariate_3_gaussians_exp(N, pp, mu_1, mu_2, mu_3,\n sigma_1, sigma_2, sigma_3, \n truncation_bounds, censoring_bounds,\n max_iteration=50, seed=100):\n # Fix the random state\n random.seed(seed)\n np.random.seed(seed)\n\n # Stack and reorder the means and covariance matrices into unified matrices\n mu = np.stack([mu_1, mu_2, mu_3], axis=0)\n sigma = np.stack([sigma_1, sigma_2, sigma_3], axis=2)\n pp, mu, sigma = reorder_gmm_compoments(pp, mu, sigma)\n K = mu.shape[0]\n\n # Generate GMM data\n print(\"Step #1: Generating a Gaussian-Mixture-Model dataset\")\n print(\"True parameters:\")\n print(\"pp: \\n{}\\n\".format(pp))\n print(\"mu: \\n{}\\n\".format(mu))\n print(\"sigma: \\n{}\\n\".format(sigma.T))\n y = generate_gmm_data(pp, mu, sigma, N)\n\n # Plot the GMM data\n plt.figure()\n ax = plot_gmm_data(y, mu, sigma, point_color='black')\n plt.title(\"The Original Data Generated by Three Gaussian Components\")\n print(\"\\n\" + \"*\"*80)\n\n # Perform censoring and truncation on the original data\n print(\"Step #2: Censoring and truncating the data\")\n x = censor_and_truncate_data(y)\n\n # Plot the censored and truncated data\n plt.figure()\n ax = plot_gmm_data(y, mu, sigma, point_color='red')\n plt.title(\"Truncated and Censored Data\")\n print(\"\\n\" + \"*\"*80)\n\n # Init parameters using K-means\n print(\"Step #3: Initializing parameters using K-means\")\n par = init_kmeans(x, K)\n print(\"\\n\" + \"*\"*80)\n\n # Estimating parameters using truncated and censored EM\n print(\"Step #4: Estimating parameters using truncated and censored EM\")\n tc_em_results = perform_truncated_em(x, K, \n truncation_bounds, censoring_bounds, \n par['pp'], par['mu'], par['sigma'], \n max_iteration)\n\n print(\"Estimated parameters by standard EM:\")\n print(\"pp: \\n{}\\n\".format(tc_em_results['pp']))\n print(\"mu: \\n{}\\n\".format(tc_em_results['mu']))\n print(\"sigma: \\n{}\\n\".format(tc_em_results['sigma'].T))\n \n plt.figure()\n plt.plot(range(len(tc_em_results['ll_hist'])), tc_em_results['ll_hist'])\n plt.title(\"Learning Curve of the Truncated and Censored EM\")\n plt.xlabel(\"Iteration\")\n plt.ylabel(\"Log-likelihood\")\n\n plt.figure()\n ax = plot_gmm_data(x, tc_em_results['mu'], tc_em_results['sigma'])\n plt.title(\"Truncated and Censored EM\")\n print(\"\\n\" + \"*\"*80)\n\n print(\"Step #5: Estimating parameters using standard EM\")\n std_em_results = perform_standard_em(x, K, seed)\n\n print(\"Estimated parameters by standard EM:\")\n print(\"pp: \\n{}\\n\".format(std_em_results['pp']))\n print(\"mu: \\n{}\\n\".format(std_em_results['mu']))\n print(\"sigma: \\n{}\\n\".format(std_em_results['sigma'].T))\n\n plt.figure()\n ax = plot_gmm_data(x, std_em_results['mu'], std_em_results['sigma'])\n plt.title(\"Standard EM\")\n print(\"\\n\" + \"*\"*80)\n\n # Evaluate the KL-Divergence between true distribution and estimated \n # distributions\n print(\"Step #6: Evaluating the estimated parameters\")\n pp, mu, sigma = reorder_gmm_compoments(pp, mu, sigma)\n tc_em_results['pp'], tc_em_results['mu'], tc_em_results['sigma'] =\\\n reorder_gmm_compoments(tc_em_results['pp'], tc_em_results['mu'],\n tc_em_results['sigma'])\n std_em_results['pp'], std_em_results['mu'], std_em_results['sigma'] =\\\n reorder_gmm_compoments(std_em_results['pp'], std_em_results['mu'],\n std_em_results['sigma'])\n \n true_gmm = build_GMM_model(pp, mu, sigma, seed)\n tc_gmm = build_GMM_model(tc_em_results['pp'], tc_em_results['mu'],\n tc_em_results['sigma'], seed)\n std_gmm = build_GMM_model(std_em_results['pp'], std_em_results['mu'],\n std_em_results['sigma'], seed)\n \n tc_kl = estimate_kl_divergence_gmm(true_gmm, tc_gmm)\n std_kl = estimate_kl_divergence_gmm(true_gmm, std_gmm) \n\n print(\"\\t* KL-Divergence corresponding to truncated and censored EM: {}\".\\\n format(tc_kl))\n print(\"\\t* KL-Divergence corresponding to standard EM: {}\".\\\n format(std_kl))\n print(\"\\n\" + \"*\"*80)\n\n # Show the plots\n print(\"Step #7: Showing the plots\")\n plt.show()","def _get_gaussian_maps(self, x, source_str, prefix='coarse_', **kwargs):\n suffix = source_str if self.ds_gaussians else ''\n gaussians = self.__getattr__(prefix + \"gaussians\" + suffix)\n gaussian_maps = self._make_gaussian_maps(x, gaussians, **kwargs)\n return gaussian_maps","def cell_params(x,y,**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n cloudy_library = clo.library()\n lookup_table = cloudy_library._restore_lookup_table()\n lookup_table['logG0s'] = lookup_table['logFUVs']\n if x == 'NH': \n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\n if y == 'NH': \n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\n\n if not p.ylim:\n p.ylim = [1e-3,30]\n if not p.xlim:\n p.xlim = [1e-7,1e3]\n \n # SELECT GALAXIES\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\n if p.gal_index: \n rand_gal_index = [p.gal_index]\n print(rand_gal_index)\n xs = np.array([])\n ys = np.array([])\n m_tot,m_encomp,m_y0 = 0,0,0\n for gal_index in rand_gal_index:\n print(gal_index)\n gal_ob = gal.galaxy(gal_index)\n df = gal_ob.cell_data.get_dataframe()\n df['nSFR'] = df.nSFR.values#/(0.2**3)\n #df['nSFR'] = df['SFR_density']\n #df['NH'] = 10.**df['NH']\n x1 = df[x].values\n y1 = df[y].values\n x1[x1 <= p.xlim[0]] = p.xlim[0]\n y1[y1 <= p.ylim[0]] = p.ylim[0]\n m_tot += np.sum(df.m.values)\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\n m_y0 += np.sum(df.m[(y1 == 0)].values)\n #print(x,x1.min(),x1.max())\n #print(y,y1.min(),y1.max())\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n print('Min max of %s:' % x)\n print(xs.min(),xs.max())\n print('Min max of %s:' % y)\n print(ys.min(),ys.max())\n fig,ax = plt.subplots(figsize=(10,8))\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\n cb = fig.colorbar(hb, ax=ax)\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\n ax.set_xlabel(getlabel(x))\n ax.set_ylabel(getlabel(y))\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\n ax.set_xlim(p.xlim)\n ax.set_ylim(p.ylim)\n # Overplot Cloudy grid params\n print(x,x_cloudy)\n print(y,y_cloudy)\n for x1 in x_cloudy:\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\n for y1 in y_cloudy:\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\n\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')","def skybass_sampling_rates(data):\n for i in range(4):\n fig = plt.figure()\n TODO: finish","def WENOSG_interpolation(sp0,sp1,sp2,sp3,sp4):\n\n epsilon = 10**-12\n\n p0 = (1./3.)*sp0 - (7./6.)*sp1 + (11./6.)*sp2\n p1 = -(1./6.)*sp1 + (5./6.)*sp2 + (1./3.)*sp3\n p2 = (1./3.)*sp2 + (5./6.)*sp3 - (1./6.)*sp4\n\n gamma = [.1, .6, .3]\n\n beta0 = (13./12.)*(sp0 - 2*sp1 + sp2)**2 + .25*(sp0 - 4*sp1 + 3*sp2)**2\n beta1 = (13./12.)*(sp1 - 2*sp2 + sp3)**2 + .25*(sp1 - sp3)**2\n beta2 = (13./12.)*(sp2 - 2*sp3 + sp4)**2 + .25*(3*sp2 - 4*sp3 + sp4)**2\n\n c00 = .25\n c10 = .75\n c01 = .5\n c11 = .5\n\n tau0 = abs(beta1 - beta0)\n tau1 = abs(beta2 - beta1)\n\n alpha00 = c00*(1 + (tau0 / (beta0 + epsilon))**2)\n alpha10 = c10*(1 + (tau0 / (beta1 + epsilon))**2)\n alpha01 = c01*(1 + (tau1 / (beta1 + epsilon))**2)\n alpha11 = c11*(1 + (tau1 / (beta2 + epsilon))**2)\n\n alphakl_sum = alpha00 + alpha10 + alpha01 + alpha11\n\n psi00 = alpha00 / alphakl_sum\n psi10 = alpha11 / alphakl_sum\n psi01 = alpha01 / alphakl_sum\n psi11 = alpha11 / alphakl_sum\n\n def g(k, w):\n return (w * (gamma[k] + gamma[k]**2 - 3*gamma[k]*w + w**2))/(gamma[k]**2 + w*(1-2*gamma[k]))\n\n alphastar00 = g(0, psi00)\n alphastar10 = g(1, psi10)\n alphastar01 = g(0, psi01)\n alphastar11 = g(1, psi11)\n\n alphastar_sum = alphastar00 + alphastar10 + alphastar01 + alphastar11\n\n w00 = alphastar00 / alphastar_sum\n w10 = alphastar10 / alphastar_sum\n w01 = alphastar01 / alphastar_sum\n w11 = alphastar11 / alphastar_sum\n\n h0 = w00*p0 + w10*p1\n h1 = w01*p1 + w11*p2\n\n tau = abs(beta2 - beta0)\n\n c0 = .4\n c1 = .6\n\n alpha0 = c0 * (1 + (tau / (beta0 + epsilon))**2)\n alpha1 = c1 * (1 + (tau / (beta2 + epsilon))**2)\n\n alpha_sum = alpha0 + alpha1\n\n psi0 = alpha0 / alpha_sum\n psi1 = alpha1 / alpha_sum\n\n psi_sum = g(0, psi0) + g(1, psi1)\n\n w0SG = g(0, psi0) / psi_sum\n w1SG = g(1, psi1) / psi_sum\n\n u_half = w0SG * h0 + w1SG * h1\n\n return u_half","def evolute(self, ngen, x0=None, verbose=True):\r\n self.history = {'local_fitness':[], 'global_fitness':[], 'a': [], 'A': []}\r\n self.best_fitness=float(\"inf\") \r\n self.verbose=verbose\r\n self.Positions = np.zeros((self.nwhales, self.dim))\r\n if x0:\r\n assert len(x0) == self.nwhales, '--error: the length of x0 ({}) MUST equal the number of whales in the group ({})'.format(len(x0), self.nwhales)\r\n for i in range(self.nwhales):\r\n self.Positions[i,:] = x0[i]\r\n else:\r\n #self.Positions=self.init_sample(self.bounds) #TODO, update later for mixed-integer optimisation\r\n # Initialize the positions of whales\r\n \r\n for i in range(self.dim):\r\n self.Positions[:, i] = (np.random.uniform(0, 1, self.nwhales) * (self.ub[i] - self.lb[i]) + self.lb[i])\r\n \r\n fitness0=self.eval_whales()\r\n \r\n self.best_position, self.best_fitness = self.select(self.Positions, fitness0)\r\n \r\n for k in range(0, ngen):\r\n \r\n # a is annealed from 2 to 0\r\n self.a = self.a0 - k * ((self.a0) / (ngen))\r\n # fac is annealed from -1 to -2 to estimate l\r\n self.fac = -1 + k * ((-1) / ngen)\r\n #-----------------------------\r\n # Update Whale Positions\r\n #-----------------------------\r\n self.UpdateWhales()\r\n \r\n #----------------------\r\n # Evaluate New Whales\r\n #----------------------\r\n fitness=self.eval_whales()\r\n \r\n for i, fits in enumerate(fitness):\r\n #save the best of the best!!!\r\n if fits < self.best_fitness:\r\n self.best_fitness=fits\r\n self.best_position=self.Positions[i, :].copy()\r\n \r\n #--mir\r\n if self.mode=='max':\r\n self.fitness_best_correct=-self.best_fitness\r\n self.local_fitness=-np.min(fitness)\r\n else:\r\n self.fitness_best_correct=self.best_fitness\r\n self.local_fitness=np.min(fitness)\r\n\r\n self.history['local_fitness'].append(self.local_fitness)\r\n self.history['global_fitness'].append(self.fitness_best_correct)\r\n self.history['a'].append(self.a)\r\n self.history['A'].append(self.A)\r\n \r\n # Print statistics\r\n if self.verbose and i % self.nwhales:\r\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\r\n print('WOA step {}/{}, nwhales={}, Ncores={}'.format((k+1)*self.nwhales, ngen*self.nwhales, self.nwhales, self.ncores))\r\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\r\n print('Best Whale Fitness:', np.round(self.fitness_best_correct,6))\r\n print('Best Whale Position:', np.round(self.best_position,6))\r\n print('a:', np.round(self.a,3))\r\n print('A:', np.round(self.A,3))\r\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\r\n\r\n if self.verbose:\r\n print('------------------------ WOA Summary --------------------------')\r\n print('Best fitness (y) found:', self.fitness_best_correct)\r\n print('Best individual (x) found:', self.best_position)\r\n print('--------------------------------------------------------------') \r\n \r\n return self.best_position, self.fitness_best_correct, self.history","def Gen_Annihilation_Map(angularSize, size, profile,fileOut):\r\n print 'Generating Annihilation Rate Map'\r\n \r\n # Behavioral Parameters \r\n solarDist = 8.3 # Solar Distance in kpc\r\n stopRadius = 60.0 # Radius from sun in kpc to stop LOS integration\r\n zSteps = 100 # Steps for LOS integration\r\n kpc2cm = 3.08568025e21\r\n \r\n # Constants\r\n deg2rad = math.pi/180.0 \r\n map = np.zeros((size, size)) # Initialize map\r\n \r\n # Code\r\n zStepSize = stopRadius/float(zSteps) # Size of one step in kpc\r\n aPP = float(angularSize)/float(size) # Angle per pixel\r\n solidAngle = (aPP*deg2rad)**2.0 # Multiply this by radial portion to get wedge volume.\r\n # Based on the input DM_model, we integrate rho**2 along the LOS\r\n max = 0\r\n for x in range(0,size): \r\n for y in range(0,size):\r\n rate = 0.0\r\n gamma = math.sqrt((float(x)-size/2.0)**2 + (float(y)-size/2.0)**2)*aPP # Inclusive angle for law of cosines\r\n if profile[0] != 'PULSAR': \r\n for z in range(0,zSteps):\r\n # Compute wedge volume. Currently this assumes a relatively small angular region around galactic center.\r\n volume = ((z+1)**3.0-z**3.0)*solidAngle/3.#\r\n # Compute radius from galactic center using law of cosines\r\n a = (z*zStepSize)\r\n #r = math.sqrt(a**2 + b**2 - 2*a*b*math.cos(gamma)) \r\n l = (float(x)-size/2.0)*aPP # longitude\r\n b = (float(y)-size/2.0)*aPP # latitude \r\n r = math.sqrt(a**2 + solarDist**2 - 2*a*solarDist*math.cos(l*deg2rad)*math.cos(b*deg2rad))\r\n #if gamma>max:\r\n # max = gamma\r\n # Get square DM density to obtain rate\r\n if (profile[0] == 'NFW'):\r\n rate += (volume*rho_DM_NFW(r,profile[1],profile[2]))**2\r\n elif (profile[0] == 'EIN'):\r\n rate += (0.0780763*rho_DM_EIN(r,profile[1],profile[2]))**2*zStepSize*kpc2cm*solidAngle\r\n \r\n elif (profile[0] == 'FLAT'):\r\n rate = 1 # just keep everything flat. += will give integration limit dependent results\r\n elif (profile[0] == 'NFWDECAY'): # NFW not squared.\r\n rate += volume*rho_DM_NFW(r,profile[1],profile[2])\r\n map[x,y] = rate\r\n else:\r\n l = (float(x)-size/2.0)*aPP # longitude\r\n b = (float(y)-size/2.0)*aPP # latitude\r\n r = math.sqrt(l**2.0+b**2.0)\r\n if (r<=0.05):\r\n map[x,y] = 0.05**-1.6\r\n else:\r\n map[x,y] = r**-1.2 \r\n # Write to file \r\n outFile = open(fileOut, \"wb\" )\r\n pickle.dump(map/np.max(map), outFile)\r\n print 'Rate Map saved to ', fileOut\r\n #print np.max(map)\r\n print 'J-Factor (GeV^2/cm^5): ' , np.sum(map)\r\n return map/np.max(map)","def workflow(save_dir):\n year = 2016\n month_series = range(1, 13)\n total_potential_biomass_multiplier = 48.8\n total_standing_biomass_multiplier = 45.25\n biomass_jitter = 3.\n diet_sufficiency_multiplier = 0.28\n diet_sufficiency_jitter = 0.01\n avg_animal_density = 0.0175\n animal_density_jitter = 0.005\n\n # twelve months of precipitation rasters covering the study area\n precip_basename_list = [\n 'chirps-v2.0.{}.{:02d}.tif'.format(year, month) for month in\n month_series]\n\n # reclassify 0 to NoData in CHIRPS rasters\n output_precip_dir = os.path.join(save_dir, 'precip')\n if not os.path.exists(output_precip_dir):\n os.makedirs(output_precip_dir)\n for bn in precip_basename_list:\n base_raster = os.path.join(PRECIP_DIR, bn)\n target_raster = os.path.join(output_precip_dir, bn)\n pygeoprocessing.raster_calculator(\n [(base_raster, 1)], zero_to_nodata, target_raster,\n gdal.GDT_Float32, _IC_NODATA)\n\n # generate outputs\n for month in month_series:\n precip_raster = os.path.join(\n output_precip_dir, 'chirps-v2.0.{}.{:02d}.tif'.format(year, month))\n\n total_potential_biomass_path = os.path.join(\n save_dir, 'potential_biomass_{}_{:02d}.tif'.format(year, month))\n pygeoprocessing.raster_calculator(\n [(precip_raster, 1)] + [(path, 'raw') for path in [\n total_potential_biomass_multiplier,\n biomass_jitter]],\n precip_to_correlated_output, total_potential_biomass_path,\n gdal.GDT_Float32, _IC_NODATA)\n\n total_standing_biomass_path = os.path.join(\n save_dir, 'standing_biomass_{}_{:02d}.tif'.format(year, month))\n pygeoprocessing.raster_calculator(\n [(precip_raster, 1)] + [(path, 'raw') for path in [\n total_standing_biomass_multiplier,\n biomass_jitter]],\n precip_to_correlated_output, total_standing_biomass_path,\n gdal.GDT_Float32, _IC_NODATA)\n\n diet_sufficiency_path = os.path.join(\n save_dir, 'diet_sufficiency_{}_{:02d}.tif'.format(year, month))\n pygeoprocessing.raster_calculator(\n [(precip_raster, 1)] + [(path, 'raw') for path in [\n diet_sufficiency_multiplier,\n diet_sufficiency_jitter]],\n precip_to_correlated_output, diet_sufficiency_path,\n gdal.GDT_Float32, _IC_NODATA)\n\n animal_density_path = os.path.join(\n save_dir, 'animal_density_{}_{:02d}.tif'.format(year, month))\n pygeoprocessing.raster_calculator(\n [(precip_raster, 1)] + [(path, 'raw') for path in [\n avg_animal_density,\n animal_density_jitter]],\n precip_to_animal_density, animal_density_path,\n gdal.GDT_Float32, _IC_NODATA)","def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\n T = np.arange(rgt_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Regret\")\n \n ax.plot(T, rgt_alea, label = 'aléatoire') \n ax.plot(T, rgt_glou, label = 'glouton')\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\n ax.plot(T, rgt_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Regrets des 4 algorithmes par rapport à T\")","def analyse_plots(plot_dict, data_dict) :\n for component in [ '_x', '_y' ] :\n z_pos = array.array( 'd' )\n trans_pos = array.array( 'd' )\n errors = array.array( 'd' )\n zeros = array.array( 'd' )\n\n plot = plot_dict['beam_positions'+component]\n\n for i in range( plot.GetXaxis().GetNbins()+2 ) :\n projection = plot.ProjectionY( \\\n 'profile'+component+'_pro_'+str(i), i, i )\n if projection.GetEntries() == 0 :\n continue\n\n pro_mean, pro_mean_err, pro_std, pro_std_err = \\\n scifi.fit_gaussian( projection )\n\n errors.append( pro_mean_err )\n trans_pos.append( pro_mean )\n z_pos.append( data_dict['station_positions'][ i-6 ] )\n zeros.append(0.0)\n\n position_graph = ROOT.TGraphErrors( len(zeros), z_pos, trans_pos, \\\n zeros, errors )\n position_graph.SetName('beam_profile'+component)\n plot_dict['beam_profile'+component] = position_graph\n\n profile_x = plot_dict['beam_profile_x']\n profile_y = plot_dict['beam_profile_y']\n\n up_x_func = ROOT.TF1( \"up_fit_x\", \"pol1\", -5000.0, 0.0 )\n up_y_func = ROOT.TF1( \"up_fit_y\", \"pol1\", -5000.0, 0.0 )\n down_x_func = ROOT.TF1( \"down_fit_x\", \"pol1\", 0.0, 5000.0 )\n down_y_func = ROOT.TF1( \"down_fit_y\", \"pol1\", 0.0, 5000.0 )\n\n up_fit_x = profile_x.Fit( 'up_fit_x', \"QSR\" )\n up_fit_y = profile_y.Fit( 'up_fit_y', \"QSR\" )\n down_fit_x = profile_x.Fit( 'down_fit_x', \"QSR\" )\n down_fit_y = profile_y.Fit( 'down_fit_y', \"QSR\" )\n\n plot_dict['beam_profile_x_up_fit'] = up_x_func\n plot_dict['beam_profile_y_up_fit'] = up_y_func\n plot_dict['beam_profile_x_down_fit'] = down_x_func\n plot_dict['beam_profile_y_down_fit'] = down_y_func\n\n\n up_beam_gra_x = up_x_func.GetParameter(1)\n up_beam_gra_x_err = up_x_func.GetParError(1)\n up_beam_gra_y = up_y_func.GetParameter(1)\n up_beam_gra_y_err = up_y_func.GetParError(1)\n\n up_beam_pos_x = data_dict['station_positions'][-1]*up_beam_gra_x + up_x_func.GetParameter(0)\n up_beam_pos_x_err = up_x_func.GetParError(0)\n up_beam_pos_y = data_dict['station_positions'][-1]*up_beam_gra_y + up_y_func.GetParameter(0)\n up_beam_pos_y_err = up_y_func.GetParError(0)\n\n up_beam_rot_x = math.atan( up_beam_gra_x )\n up_beam_rot_x_err = up_beam_gra_x_err # Approx linear\n up_beam_rot_y = math.atan( up_beam_gra_y )\n up_beam_rot_y_err = up_beam_gra_y_err # Approx linear\n\n\n\n down_beam_gra_x = down_x_func.GetParameter(1)\n down_beam_gra_x_err = down_x_func.GetParError(1)\n down_beam_gra_y = down_y_func.GetParameter(1)\n down_beam_gra_y_err = down_y_func.GetParError(1)\n\n down_beam_pos_x = data_dict['station_positions'][1]*down_beam_gra_x + down_x_func.GetParameter(0)\n down_beam_pos_x_err = down_x_func.GetParError(0)\n down_beam_pos_y = data_dict['station_positions'][1]*down_beam_gra_y + down_y_func.GetParameter(0)\n down_beam_pos_y_err = down_y_func.GetParError(0)\n\n down_beam_rot_x = math.atan( down_beam_gra_x )\n down_beam_rot_x_err = down_beam_gra_x_err # Approx linear\n down_beam_rot_y = math.atan( down_beam_gra_y )\n down_beam_rot_y_err = down_beam_gra_y_err # Approx linear\n\n\n# down_pos_x = down_beam_pos_x - data_dict['station_positions'][1]*up_beam_gra_x + up_x_func.GetParameter(0)\n# down_pos_x_err = math.sqrt( up_x_func.GetParError(0)**2 + down_beam_pos_x_err**2 )\n# down_pos_y = down_beam_pos_y - data_dict['station_positions'][1]*up_beam_gra_y + up_y_func.GetParameter(0)\n# down_pos_y_err = math.sqrt( up_y_func.GetParError(0)**2 + down_beam_pos_y_err**2 )\n\n length = TRACKER_SEPARATION\n down_pos_x = down_beam_pos_x - ( up_beam_pos_x + length*up_beam_gra_x )\n down_pos_x_err = math.sqrt( up_beam_pos_x_err**2 + down_beam_pos_x_err**2 + (length*up_beam_gra_x_err)**2 )\n down_pos_y = down_beam_pos_y - ( up_beam_pos_y + length*up_beam_gra_y )\n down_pos_y_err = math.sqrt( up_beam_pos_y_err**2 + down_beam_pos_y_err**2 + (length*up_beam_gra_y_err)**2 )\n\n down_rot_x = down_beam_rot_x - up_beam_rot_x\n down_rot_x_err = math.sqrt( down_beam_rot_x_err**2 + up_beam_rot_x_err**2 )\n down_rot_y = down_beam_rot_y - up_beam_rot_y\n down_rot_y_err = math.sqrt( down_beam_rot_y_err**2 + up_beam_rot_y_err**2 )\n\n\n print\n print \"Incoming Beam Misalignments:\"\n print\n print \"Displacement and rotation of beam with respect to upstream tracker:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( up_beam_pos_x, up_beam_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( up_beam_pos_y, up_beam_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( up_beam_rot_x*1000.0, up_beam_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( up_beam_rot_y*1000.0, up_beam_rot_y_err*1000.0 )\n print\n\n print\n print \"Downstream Tracker Beam Misalignments:\"\n print\n print \"Displacement and rotation of beam with respect to downstream tracker:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_beam_pos_x, down_beam_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_beam_pos_y, down_beam_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_beam_rot_x*1000.0, down_beam_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_beam_rot_y*1000.0, down_beam_rot_y_err*1000.0 )\n print\n\n print\n print \"Downstream Tracker Alignment:\"\n print\n print \"Displacement and rotation of between the two trackers:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_pos_x, down_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_pos_y, down_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_rot_x*1000.0, down_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_rot_y*1000.0, down_rot_y_err*1000.0 )\n print","def eg_sk():\n\n rxs = []\n a = []\n b = []\n c = []\n d = []\n e = []\n f = []\n g = []\n h = []\n i = []\n j = []\n\n for _ in range(1000):\n a.append(utils.gaussian(10, 1))\n\n for _ in range(1000):\n b.append(utils.gaussian(10.1, 1))\n\n for _ in range(1000):\n c.append(utils.gaussian(20, 1))\n\n for _ in range(1000):\n d.append(utils.gaussian(30, 1))\n\n for _ in range(1000):\n e.append(utils.gaussian(30.1, 1))\n\n for _ in range(1000):\n f.append(utils.gaussian(10, 1))\n\n for _ in range(1000):\n g.append(utils.gaussian(10, 1))\n\n for _ in range(1000):\n h.append(utils.gaussian(40, 1))\n\n for _ in range(1000):\n i.append(utils.gaussian(40, 3))\n\n for _ in range(1000):\n j.append(utils.gaussian(10, 1))\n\n for k, v in enumerate([a, b, c, d, e, f, g, h, i, j]):\n rxs.append(creation.RX(v, \"rx{}\".format(k)))\n\n for rx in stats.tiles(stats.scottKnot(rxs)):\n print(\"\", rx[\"rank\"], rx[\"name\"], rx[\"show\"], sep=\"\\t\")","def setup(self):\n igd = self.options['input_grid_data']\n ogd = self.options['output_grid_data']\n output_subset = self.options['output_subset']\n\n if ogd is None:\n ogd = igd\n\n # Build the interpolation matrix which maps from the input grid to the output grid.\n # Rather than a single phase-wide interpolating polynomial, map each segment.\n # To do this, find the nodes in the output grid which fall in each segment of the input\n # grid. Then build a Lagrange interpolating polynomial for that segment\n L_blocks = []\n output_nodes_ptau = list(ogd.node_ptau[ogd.subset_node_indices[output_subset]])\n\n for iseg in range(igd.num_segments):\n i1, i2 = igd.segment_indices[iseg]\n iptau_segi = np.take(igd.node_ptau, (i1, i2-1))\n istau_segi = np.take(igd.node_stau, (i1, i2-1))\n\n # The indices of the output grid that fall within this segment of the input grid\n if ogd is igd:\n optau_segi = iptau_segi\n else:\n ptau_hi = igd.segment_ends[iseg+1]\n if iseg < igd.num_segments - 1:\n idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]\n else:\n idxs_in_iseg = np.arange(len(output_nodes_ptau))\n optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]\n # Remove the captured nodes so we don't accidentally include them again\n output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]\n\n # Now get the output nodes which fall in iseg in iseg's segment tau space.\n ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (iptau_segi[-1] - iptau_segi[0]) - 1\n\n # Create the interpolation matrix and add it to the blocks\n L, _ = lagrange_matrices(istau_segi, ostau_segi)\n L_blocks.append(L)\n\n self.interpolation_matrix = block_diag(*L_blocks)\n r, c = np.nonzero(self.interpolation_matrix)\n\n output_num_nodes, input_num_nodes = self.interpolation_matrix.shape\n\n for (name, kwargs) in self._timeseries_outputs:\n\n input_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\n input_name = 'input_values:{0}'.format(name)\n self.add_input(input_name,\n shape=(input_num_nodes,) + kwargs['shape'],\n **input_kwargs)\n\n output_name = name\n output_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\n output_kwargs['shape'] = (output_num_nodes,) + kwargs['shape']\n self.add_output(output_name, **output_kwargs)\n\n self._vars.append((input_name, output_name, kwargs['shape']))\n\n size = np.prod(kwargs['shape'])\n val_jac = np.zeros((output_num_nodes, size, input_num_nodes, size))\n\n for i in range(size):\n val_jac[:, i, :, i] = self.interpolation_matrix\n\n val_jac = val_jac.reshape((output_num_nodes * size, input_num_nodes * size),\n order='C')\n\n val_jac_rows, val_jac_cols = np.where(val_jac != 0)\n\n rs, cs = val_jac_rows, val_jac_cols\n self.declare_partials(of=output_name,\n wrt=input_name,\n rows=rs, cols=cs, val=val_jac[rs, cs])","def CII_vs_CO(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\n fig,ax1 = plt.subplots()\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\n # print('indices with L_CO < 1e0:')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\n\n # Observations\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\n K16_LCII = K16['[CII]158_Lsun']\n K16_LCO = K16['CO(1-0)_Lsun']\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\n label='Mixed type galaxies [Kamenetzky+16]')\n\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\n C15_LCII = C15['L_[CII]158']\n C15_LCO = C15['L_CO(1-0)']\n C15_Z = C15['Z']\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\n # if len(L_ul) > 0:\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\n\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \n\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\n CII_obs = CII_obs[index]\n CO_obs = CO_obs[index]\n\n x = np.linspace(0, 7, 100)\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\n CII_obs.reshape(-1, 1))\n L_fit = fit.predict(x.reshape(-1, 1))\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\n\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\n plt.colorbar(sc,label=r'log $\\langle$Z$\\rangle_{\\rm SFR}$ [Z$_{\\rm \\odot}$]')\n\n handles, labels = ax1.get_legend_handles_labels()\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\n\n ax1.set_xlim([-3,6.2])\n ax1.set_ylim([4,10])\n\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)","def CalculateG(g, s):\n for i in range(len(y_train)):\n g[i] = 1 - y_train[i]*s[i]\n return g","def plot():\n xvals = np.arange(-50, 250, step=0.1)\n\n fig = plt.figure()\n plt.suptitle(\"Gaussian with smooth transition to power law\")\n\n A0vals = [10, 11]\n avals = [5*10**-3, 10**-3, 5*10**-4]\n ttvals = [10., 50., 100.]\n cvals = [-0.1, -0.9, -5./3., -4.]\n offset = [-30, 0.0, 30]\n\n paramvals = [A0vals, avals, ttvals,cvals, offset]\n titles, labels = return_parameter_names()\n\n nplots = len(paramvals)\n\n for i in range(nplots):\n plt.subplot(nplots, 1, i+1)\n vals = paramvals[i]\n for j in range(len(vals)):\n pset = list(default())\n pset[i] = vals[j]\n yvals=[]\n ypower=[]\n ypeak=[]\n for x in xvals:\n yvals.append(fitfunc(x, pset))\n ypeak.append(logpeak(x,pset))\n if x > 0:\n ypower.append(logpowerlaw(x,pset))\n label = labels[i] + \"=\"+str(vals[j])\n plt.plot(xvals, yvals, label = label)\n\n plt.title(titles[i])\n plt.legend()\n\n fig.set_size_inches(15, 30)\n plt.savefig(\"graphs/misc/lightcurve_models.pdf\")\n plt.close()","def _gaussian_weighted_map_const_multi(\n y_grid, x_grid, heatmap, points_y, points_x, boxes,\n gaussian_denom_ratio):\n num_instances, _ = _get_shape(boxes, 2)\n height, width, num_keypoints = _get_shape(heatmap, 3)\n\n # [height, width, num_instances, num_keypoints].\n # Note that we intentionally avoid using tf.newaxis as TfLite converter\n # doesn't like it.\n y_diff = (\n tf.reshape(y_grid, [height, width, 1, 1]) -\n tf.reshape(points_y, [1, 1, num_instances, num_keypoints]))\n x_diff = (\n tf.reshape(x_grid, [height, width, 1, 1]) -\n tf.reshape(points_x, [1, 1, num_instances, num_keypoints]))\n distance_square = y_diff * y_diff + x_diff * x_diff\n\n y_min, x_min, y_max, x_max = tf.split(boxes, 4, axis=1)\n\n # Make the mask with all 1.0 in the box regions.\n # Shape: [height, width, num_instances]\n in_boxes = tf.math.logical_and(\n tf.math.logical_and(\n tf.reshape(y_grid, [height, width, 1]) >= tf.reshape(\n y_min, [1, 1, num_instances]),\n tf.reshape(y_grid, [height, width, 1]) < tf.reshape(\n y_max, [1, 1, num_instances])),\n tf.math.logical_and(\n tf.reshape(x_grid, [height, width, 1]) >= tf.reshape(\n x_min, [1, 1, num_instances]),\n tf.reshape(x_grid, [height, width, 1]) < tf.reshape(\n x_max, [1, 1, num_instances])))\n in_boxes = tf.cast(in_boxes, dtype=tf.float32)\n\n gaussian_denom = tf.cast(\n tf.minimum(height, width), dtype=tf.float32) * gaussian_denom_ratio\n # shape: [height, width, num_instances, num_keypoints]\n gaussian_map = tf.exp((-1 * distance_square) / gaussian_denom)\n return tf.expand_dims(heatmap, axis=2) * gaussian_map * tf.reshape(\n in_boxes, [height, width, num_instances, 1])","def gredo(x,y,guessx,guessy,guesspar):\n\n global BTRACK, GSTRUC, NPIX\n \n flag = True # do it unless proven wrong \n \n # FROM **ANY** PREVIOUS POSITION \n prev, = np.where((BTRACK['x']==x) & (BTRACK['y']==y))\n nprev = len(prev)\n\n if guesspar is None:\n return False\n tguesspar = guesspar\n nguesspar = len(tguesspar) \n ngg = nguesspar//3 \n \n # FROM **ANY** PREVIOUS POSITION \n # We have used a guess from this position before \n # but have the parameters changed sufficiently\n nogaussians = True # no gaussians found by default\n if (nprev > 0): \n \n # Looping through the previous ones \n for i in range(nprev):\n guesspar2 = BTRACK['data'][prev[i]]['guesspar']\n \n # Some gaussians found \n if (guesspar2 is not None):\n nogaussians = False\n tpar = guesspar2\n ntpar = len(tpar) \n ntg = ntpar//3 # number of gaussians in this guess \n \n # Same number of gaussians \n if (ntpar == nguesspar): \n # Sorting, largest first \n tpar2 = utils.gsort(tpar) \n tguesspar2 = utils.gsort(tguesspar) \n \n # Fixing possible zeros that could ruin the ratio \n dum = np.copy(tpar2)\n bd, = np.where(dum == 0.) \n if len(bd) > 0:\n dum[bd] = 1e-5 \n diff = np.abs(tpar2 - tguesspar2) \n ratio = diff/np.abs(dum) \n \n # These differences are too small, NO redo \n if (np.max(ratio) < 0.01): \n return False\n \n # Some previous visits, but no Gaussians detected, redo=False\n if nogaussians:\n return False\n \n return flag","def transition_function(grid, neighbourstates, neighbourcounts, decay_grid,\n water_decay_grid):\n\n global water_counter\n global ignition_grid\n neighbourstates = np.array(neighbourstates)\n init_grid = initial_grid.astype(int)\n ig_grid = np.array(ignition_grid)\n windspeed_ignition_modifiers = wind_speed_rvalue(\"NE\", 10)\n new_ig_grid = []\n for i, row in enumerate(grid):\n new_ig_grid.append([\n ignite(cell, neighbourstates[:, i, j],\n windspeed_ignition_modifiers) for j, cell in enumerate(row)\n ])\n new_ig_grid = np.array(new_ig_grid)\n started_to_burn = []\n for i, row in enumerate(grid):\n started_to_burn.append([\n started_burning(cell, ig_grid[i, j], new_ig_grid[i, j])\n for j, cell in enumerate(row)\n ])\n grid[started_to_burn] = START_BURN\n ig_grid = np.add(new_ig_grid, ig_grid)\n full_burn = []\n for i, row in enumerate(grid):\n full_burn.append([\n fully_burning(cell, ig_grid[i, j], decay_grid[i, j])\n for j, cell in enumerate(row)\n ])\n grid[full_burn] = BURNING\n end_burning = []\n for i, row in enumerate(grid):\n end_burning.append([\n ending_burn(cell, decay_grid[i, j], decay_values[int(\n initial_grid[i, j])]) for j, cell in enumerate(row)\n ])\n grid[end_burning] = END_BURN\n decay_grid[(grid == BURNING) | (grid == END_BURN)] -= 1\n burnt_out = (decay_grid == 0) # find those which have decayed to 0\n grid[(decay_grid == 0\n )] = BURNT #set all that have decayed to zero to BURNT(7)\n water_counter += 1\n\n if (water_counter == 100):\n grid[120:160, 80:120] = initial_grid[120:160, 80:120]\n water_decay_grid[(grid != LAKE)] -= 1 # take one off their decay value\n grid[(water_decay_grid == 0)] = BURNT # switch their state to 5\n ignition_grid = ig_grid\n return grid","def traces(mndata,Params,srate,imagepath):\n\t#plot high gamma traces\n\t#data should be bandpassed (todo)\n\t#resample to srate\n\tst = resample(Params[\"st\"],srate)\n\ten = resample(Params[\"en\"],srate)\n\tbl_en = resample(Params[\"bl_en\"],srate)\n\tbl_st = resample(Params[\"bl_st\"],srate)\n\tplot_tp = resample(Params[\"plot\"],srate)\n\tcue = resample(500,srate)\n\t\n\tcolors = ['red','orange','green','blue']\n\tx = np.array(range(st,en+1))\n\tf, (ax,ax2) = plt.subplots(1,2, sharex = False)\n\tax.axhline(y = 0,color = 'k',linewidth=2)\n\tax.axvline(x = 0,color='k',linewidth=2)\n\tax.axvline(x = cue,color = 'gray',linewidth = 2)\n\tax.axvline(x = cue+cue,color = 'gray',linewidth = 2)\n\tax.axvspan(cue, cue+cue, facecolor='0.5', alpha=0.25,label = 'cue')\n\n\tfor j in range(len(Params[\"conditions\"])):\n\t\tcondition = Params['conditions'][j]\n\t\ty = mndata[condition]['data']\n\t\tax.plot(x,y, label = condition,linewidth = 2,color = colors[j])\n\t\n\tax.set_ylim((-30,85))\n\tax.set_xlim(st,en)\n\tax.legend()\n\tax.xaxis.set_ticklabels(['', '0', '','500', '', '1000', '', '1500', '', '2000','','2500','', '3000'],minor=False)\n\tax.xaxis.set_ticks(range(st,en,plot_tp))\n\n\tax.set_xlabel(\"time (ms)\")\n\tax.set_ylabel(\"% change baseline\")\n\tax.set_title('Analytic Amplitude - High Gamma (70-150Hz)', fontsize = 18)\n\n\t#plot brain with elec location\n\t#brain = plt.imread(imagepath)\n\t#aa = pylab.mean(brain,2)\n\t#ax2.imshow(aa)\n\t#a2.gray()\n\n\t#brain = Image.open(imagepath)\n\t#ax2.set_axis_off()\n\t#im = plt.imshow(brain, origin = 'lower')\n\n\t#brain = _png.read_png(imagepath)\n\t#imagebox = OffsetImage(brain,zoom =5)\n\t#ab = AnnotationBbox(imagebox,)\n\n\tim = Image.open(imagepath)\n\tax2.imshow(im,aspect = 'auto',origin = 'lower')\n\tax2.set_xlim((0,750))\n\tax2.set_title('Electrode Location',fontsize = 18)\n\n\n\n\treturn f, (ax, ax2)","def update_grhs():\n init_gradient()\n costs_per_batch = []\n for i in range(n_train_batches):\n c = update_gradient_batch(i,*args)\n costs_per_batch.append(c)\n return numpy.mean(costs_per_batch,axis=0)","def flatNoiseCGH():\n #Get data\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/NoiseStudy/FlatMeasurements/'\n d1,dx1 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas1.fits')\n d2,dx2 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas2.fits')\n p1,px1 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas1.fits')\n p2,px2 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas2.fits')\n p3,px3 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas1.fits')\n p4,px4 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas2.fits')\n\n #Construct baseline power spectra\n f1,pow1 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1)\n f2,pow2 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1,axis=1)\n \n #Construct parroted power spectra\n f3,pow3 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx1)\n f4,pow4 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx2,axis=1)\n f5,pow5 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx1)\n f6,pow6 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx2,axis=1)\n\n #Plot\n plt.loglog(f1,pow1/f1[0],label='Axial Baseline')\n plt.loglog(f2,pow2/f2[0],label='Azimuthal Baseline')\n plt.loglog(f3,pow3/f3[0],label='Pitch Axial')\n plt.loglog(f4,pow4/f4[0],label='Pitch Azimuthal')\n plt.loglog(f5,pow5/f5[0],label='Roll Axial')\n plt.loglog(f6,pow6/f6[0],label='Roll Azimuthal')\n plt.title('Residual Fringe Repeatability Impact')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n plt.legend(loc='lower left')\n\n return f1,pow1","def compute_g_0(self, j):\n #Compute variance and mean denominator (same denominator for both)\n denominator = self.sigma2 * self.sigma_g_star_2[0, j] + (10**8) * self.sigma2\n\n numerator_mean = (10**8) * self.sigma2 * self.g_heat[1,j]\n if (self.u_heat > self.temperatures[0]):\n denominator = denominator + (10**8) * self.sigma_g_star_2[0, j] * ((self.temperatures[0] - self.u_heat )**2)\n numerator_mean = numerator_mean + \\\n (10**8) * self.sigma_g_star_2[0, j] * (self.temperatures[0] - self.u_heat) * (self.consumptions[0] - self.s[0,j] * self.kappa[self.daytypes[0]])\n\n #Mean\n mean = numerator_mean / denominator\n\n #Compute variance numerator\n variance_numerator = ((10**8) * self.sigma2 * self.sigma_g_star_2[0, j])\n #Variance\n variance = variance_numerator / denominator\n\n self.g_heat[0,j] = self.truncated_norm(-inf, 0, mean, variance)","def swe(lon, lat, month):\n\n im = month - 1\n\n h0 = np.array( [ 8.37, 9.43, 10.74, 11.67, 11.80, 12.48,\n 4.01, 1.08, 3.84, 6.24, 7.54, 8.00 ] )\n a = np.array( [ -0.0270, 0.0058, 0.1618, 0.0841, -0.0043, 0.2084,\n 0.0970, 0.0712, 0.0393, 0.1158, 0.0567, -0.0540 ] )\n b = np.array( [ -0.3400, -0.1309, 0.0276, -0.1328, -0.4284, -0.5739,\n -0.4930, -0.1450, -0.2107, -0.2803, -0.3201, -0.3650 ] )\n c = np.array( [ -0.0319, 0.0017, 0.0213, 0.0081, -0.0380, -0.0468,\n -0.0333, -0.0155, -0.0182, -0.0215, -0.0284, -0.0362 ] )\n d = np.array( [ -0.0056, -0.0021, 0.0076, -0.0003, -0.0071, -0.0023,\n -0.0026, 0.0014, -0.0053, 0.0015, -0.0032, -0.0112 ] )\n e = np.array( [ -0.0005, -0.0072, -0.0125, -0.0301, -0.0063, -0.0253,\n -0.0343, -0.0000, -0.0190, -0.0176, -0.0129, -0.0035 ] )\n\n x = (90. - lat) * np.cos( np.radians(lon) )\n y = (90. - lat) * np.sin( np.radians(lon) )\n\n h = ( h0[im] + ( a[im] * x ) + ( b[im] * y ) + ( c[im] * x * y ) +\n ( d[im] * x * x ) + ( e[im] * y * y ) )\n\n# h = np.where( h < 0., 0., h)\n \n return h","def _get_sum_gaussian_image(s_gen, xs, ys, sdev, n=50):\n m1, m2 = xs.min(), xs.max()\n xx = np.linspace(m1, m2, n)\n XX, YY = np.meshgrid(xx, xx)\n XX, YY = [u.ravel()[np.newaxis, :] for u in [XX, YY]]\n xs, ys, S_gen = [u[:, np.newaxis] for u in [xs, ys, s_gen]]\n res = np.sum(\n S_gen * np.exp(((xs - XX) ** 2 + (ys - YY) ** 2) /\n (-2 * sdev ** 2)), axis=0)\n return res.reshape(n, n)","def garch(ω, α, β, n_out=1000):\r\n p = len(α)\r\n q = len(β)\r\n\r\n\r\n # Größe Einbrennphase\r\n n_pre = 1000\r\n n = n_pre + n_out\r\n\r\n\r\n # Sample noise\r\n ɛ = np.random.standard_normal(size=n)#st.t.rvs(df=5,size=n))\r\n #st.t.var(df=5))#np.random.normal(size=n)\r\n\r\n\r\n y = np.zeros(n)\r\n σ = np.zeros(n)\r\n\r\n #Start-Values\r\n for k in range(np.max([p, q])):\r\n σ[k] = (np.random.standard_normal())\r\n\r\n #Create GARCH data\r\n\r\n for k in range(np.max([p, q]), n):\r\n α_term = sum([α[i] * y[k-i-1]**2 for i in range(p)])\r\n β_term = sum([β[i] * ɛ[k-i-1]**2 for i in range(q)])\r\n σ[k] = np.sqrt(ω + α_term + β_term)\r\n y[k] = σ[k] * ɛ[k]\r\n\r\n\r\n return y[n_pre:]","def CalcGreenFunctions(x, y, z, x_src_l, y_src_l, alpha, dire, Lambda_y, \\\n gamma_l, c, omega, G_sen, dir_meas, dir_meas_deg, airloss_alpha, f, n):\n \n G = greens_fct(repmat(x_src_l, np.shape(x)[0],1), repmat(y_src_l, np.shape(y)[0],1), omega, c, \\\n np.transpose(repmat(x, np.shape(x_src_l)[0], 1)), np.transpose(repmat(y, np.shape(y_src_l)[0], 1)), z)\n\n G = G_sen * G\n \n beta = np.arcsin((np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1)) \\\n * np.sqrt((np.transpose(repmat(x, np.shape(x_src_l)[0], 1)) - \\\n repmat(x_src_l, np.shape(x)[0], 1))**2 + \\\n (np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1))**2)**(-1)) \\\n + repmat(gamma_l, np.shape(x)[0], 1)\n \n # air attenuation\n src_rec_dist = np.sqrt((np.transpose(repmat(x, np.shape(x_src_l)[0], 1)) - repmat(x_src_l, np.shape(x)[0], 1))**2 \\\n + (np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1))**2)\n \n air_att = airloss_alpha * src_rec_dist\n air_att = 10**(-air_att / 20)\n G = G * air_att\n \n H_post = calc_directivity(dire, alpha, Lambda_y, beta, omega, c, f, dir_meas, dir_meas_deg, n)\n\n G = G * H_post\n \n return G","def visualSignMap(phasemap1, phasemap2):\r\n\r\n if phasemap1.shape != phasemap2.shape:\r\n raise LookupError(\"'phasemap1' and 'phasemap2' should have same size.\")\r\n\r\n gradmap1 = np.gradient(phasemap1)\r\n gradmap2 = np.gradient(phasemap2)\r\n\r\n # gradmap1 = ni.filters.median_filter(gradmap1,100.)\r\n # gradmap2 = ni.filters.median_filter(gradmap2,100.)\r\n\r\n graddir1 = np.zeros(np.shape(gradmap1[0]))\r\n # gradmag1 = np.zeros(np.shape(gradmap1[0]))\r\n\r\n graddir2 = np.zeros(np.shape(gradmap2[0]))\r\n # gradmag2 = np.zeros(np.shape(gradmap2[0]))\r\n\r\n for i in range(phasemap1.shape[0]):\r\n for j in range(phasemap2.shape[1]):\r\n graddir1[i, j] = math.atan2(gradmap1[1][i, j], gradmap1[0][i, j])\r\n graddir2[i, j] = math.atan2(gradmap2[1][i, j], gradmap2[0][i, j])\r\n\r\n # gradmag1[i,j] = np.sqrt((gradmap1[1][i,j]**2)+(gradmap1[0][i,j]**2))\r\n # gradmag2[i,j] = np.sqrt((gradmap2[1][i,j]**2)+(gradmap2[0][i,j]**2))\r\n\r\n vdiff = np.multiply(np.exp(1j * graddir1), np.exp(-1j * graddir2))\r\n\r\n areamap = np.sin(np.angle(vdiff))\r\n\r\n return areamap","def optimize_at_with_gd(y_true, output, thresholds, average):\n opt = Adam()\n opt.alpha = 1e-2\n metric = calculate_f1(y_true, output, thresholds, average)\n metric_asfo_epoch = [metric]\n for i in range(100):\n grads = calculate_at_gradient(y_true, output, thresholds, average)\n # if i==0: print(\"grads:\", grads)\n grads = [-e for e in grads]\n thresholds = opt.GetNewParams(thresholds, grads)\n metric = calculate_f1(y_true, output, thresholds, average)\n if i % 50 == 0:\n print('Iteration: {}, Score: {:.3f}, thresholds: {}'.format(\n i, metric, np.array(thresholds)))\n metric_asfo_epoch.append(metric)\n\n return metric, thresholds, metric_asfo_epoch","def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\n \n random.seed(rand)\n NGEN = ngen\n MU = mu\n LAMBDA = lamb\n CXPB = cxpb\n MUTPB = mutpb\n \n # Used for printing the results. It is the parameter that is changed one run from another\n if param==\"rand\" or param==\"optimal\":\n list_results=[rand]\n elif param==\"mu\":\n list_results=[mu]\n elif param==\"lamb\":\n list_results=[lamb]\n elif param==\"cross\":\n list_results=[cxpb]\n elif param==\"mutate\":\n list_results=[mutpb]\n elif param==\"ngen\":\n list_results=[ngen]\n elif param==\"original\":\n list_results=[0]\n \n # Initialization of the objects for the GA\n pop = toolbox.population(n=MU)\n hof = tools.ParetoFront()\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean, axis=0)\n stats.register(\"std\", np.std, axis=0)\n stats.register(\"min\", np.min, axis=0)\n stats.register(\"max\", np.max, axis=0)\n\n # Run of the GA\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n halloffame=hof,verbose=0)\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\n w = csv.DictWriter(f, logbook[0].keys())\n w.writeheader()\n for el in logbook:\n w.writerow(el)\n w.writerow({})\n \n # Takes the max fitness of the population from all of the runs\n max_fit=0\n max_gen=0\n for elt in logbook:\n if elt['max'][0]>max_fit:\n max_fit=elt['max'][0]\n max_gen=elt['gen']\n list_results.append(max_fit)\n list_results.append(max_gen)\n \n #TODO\n# for ind in hof:\n# dist = numpy.linalg.norm(a-b)\n\n print (\"{0} {1} {2} {3}\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\n \n return pop, stats, hof","def _run_rbf_interpolation(out_dir, layer, bounds, function, smooth):\n # if running scipy methods prepend root dir to out path\n out_dir = OPJ(path_root, out_dir)\n if not os.path.isdir(out_dir):\n print(\n os.path.abspath(out_dir),\n ' does not exist, creating directory.\\n'\n )\n Path(out_dir).mkdir(parents=True, exist_ok=True)\n\n out_file = OPJ(\n out_dir, \n '{time_agg}.tiff'.format(time_agg=layer)\n )\n print(\n '\\nInterpolating {g} point bias ratios for: {t}\\n'.\\\n format(g=grid_var, t=layer),\n 'Using the \"{}\" method\\n'.format(function),\n 'Resolution (pixel size) of output raster: {} degrees'.format(res)\n )\n print( \n 'GeoTIFF raster will be saved to: \\n',\n os.path.abspath(out_file)\n )\n\n\n # get grid extent based on station locations in CSV\n if not bounds:\n bounds = get_subgrid_bounds(in_path, buffer=buffer, grid_res=CS) \n lon_min, lon_max, lat_min, lat_max = bounds\n # fix any minor adjustments to make raster fit gridMET fishnet extent\n # if scale_factor=1 make sure raster pixels align exactly w/gridcells\n # raster extent may exceed fishnet grid to fill gaps for zonal stats\n if scale_factor:\n nxcells = abs(lon_min-lon_max) / (CS*scale_factor)\n nycells = abs(lat_min-lat_max) / (CS*scale_factor)\n remainder_x = int(nxcells) - nxcells\n remainder_y = int(nycells) - nycells\n if abs(remainder_x) > CS:\n remainder_x -= CS * (remainder_x / CS) \n if abs(remainder_y) > CS:\n remainder_y -= CS * (remainder_y / CS)\n lon_min -= remainder_x\n lon_max += CS\n lat_min -= remainder_y\n lat_min -= CS\n \n # check if layer is in summary CSV \n existing_layers = pd.read_csv(in_path).columns\n if not layer in existing_layers:\n print('Column {} does not exist in input CSV:\\n {}'.format(\n layer, in_path),\n '\\nSkipping interpolation.'\n )\n return\n \n # get point station data from summary CSV\n in_df = pd.read_csv(in_path, na_values=[-999])\n lon_pts, lat_pts = in_df.STATION_LON.values, in_df.STATION_LAT.values\n values = in_df[layer].values\n \n # mask out stations with missing data\n if in_df[layer].isnull().sum() > 0:\n mask = in_df[layer].notnull()\n n_missing = in_df[layer].isna().sum()\n # if one point or less data points exists exit\n if len(mask) == n_missing or len(values) - n_missing == 1:\n print('Missing sufficient point data for variable: {} {}'.\\\n format(grid_var, layer),\n '\\nNeed at least two stations with data, skipping.')\n return\n print('Warning:\\n',\n 'Data missing for {} of {} stations for variable: {} {}'.\\\n format(n_missing, len(values), grid_var, layer),\n '\\nproceeding with interpolation.')\n # get locations where ratio is not nan\n values = values[mask]\n lon_pts = lon_pts[mask]\n lat_pts = lat_pts[mask]\n\n nx_cells = int(np.round(np.abs((lon_min - lon_max) / CS)))\n ny_cells = int(np.round(np.abs((lat_min - lat_max) / CS)))\n # rbf requires uniform grid (n X n) so \n # extend short dimension and clip later \n nx_cells_out = copy.copy(nx_cells)\n ny_cells_out = copy.copy(ny_cells)\n # gdal requires \"upper left\" corner coordinates\n lat_max_out = copy.copy(lat_max)\n lon_max_out = copy.copy(lon_max)\n # extend short dimension to make square grid\n if not nx_cells == ny_cells:\n diff = np.abs(nx_cells - ny_cells)\n if nx_cells > ny_cells:\n lat_max += diff * CS\n ny_cells += diff\n else:\n lon_max += diff * CS\n nx_cells += diff\n\n if scale_factor == 1:\n # make finer/coarse grid by scale factor\n lons = np.linspace(lon_min, lon_max, \n int(np.round(nx_cells/scale_factor))-1)\n lats = np.linspace(lat_min, lat_max, \n int(np.round(ny_cells/scale_factor))-1)\n # extent for original created by spatial.build_subgrid\n # add one to make sure raster covers full extent\n lons_out = np.linspace(lon_min, lon_max_out, \n int(np.round(nx_cells_out/scale_factor))-1)\n lats_out = np.linspace(lat_min, lat_max_out, \n int(np.round(ny_cells_out/scale_factor))-1)\n\n else:\n # add one extra cell to cover grid buffer extent for upscaling\n # raster extent always >= grid buffer\n lons = np.linspace(lon_min, lon_max, \n int(np.round(nx_cells/scale_factor)))\n lats = np.linspace(lat_min, lat_max, \n int(np.round(ny_cells/scale_factor)))\n lons_out = np.linspace(lon_min, lon_max_out, \n int(np.round(nx_cells_out/scale_factor)))\n lats_out = np.linspace(lat_min, lat_max_out, \n int(np.round(ny_cells_out/scale_factor)))\n\n # if function was 'linear_rbf' \n function = function.replace('_rbf', '')\n # make sampling square grid\n XI, YI = np.meshgrid(lons, lats)\n # apply rbf interpolation\n rbf = Rbf(lon_pts, lat_pts, values, function=function, smooth=smooth)\n ZI = rbf(XI, YI)\n # clip to original extent, rbf array flips axes, and row order... \n ZI_out = ZI[0:len(lats_out),0:len(lons_out)]\n ZI_out = np.flip(ZI_out,axis=0)\n\n #### save scipy interpolated data as raster \n pixel_size = CS * scale_factor\n # number of pixels in each direction\n x_size = len(lons_out)\n y_size = len(lats_out)\n # set geotransform info\n gt = [\n lon_min,\n pixel_size,\n 0,\n lat_max_out,\n 0,\n -pixel_size\n ]\n # make geotiff raster\n driver = gdal.GetDriverByName('GTiff')\n ds = driver.Create(\n out_file,\n x_size, \n y_size, \n 1, \n gdal.GDT_Float32, \n )\n # set projection geographic lat/lon WGS 84\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n ds.SetProjection(srs.ExportToWkt())\n # assign spatial dimensions \n ds.SetGeoTransform(gt)\n outband = ds.GetRasterBand(1)\n # save rbf interpolated array as geotiff raster close\n outband.WriteArray(ZI_out)\n ds = None\n\n # calc residuals add to shapefile and in_path CSV, move shape to out_dir\n # only residuals for bias ratios, i.e. not for std dev, etc\n if layer in InterpGdal.default_layers:\n calc_pt_error(in_path, out_dir, layer, grid_var, \n grid_id_name=grid_id_name\n )\n # calculate zonal statistics save means for each gridMET cell\n if z_stats:\n zonal_stats(in_path, out_file, grid_id_name=grid_id_name)\n \n # plot layer's interpolated residuals as bar plot witheach Wx station \n # only produce residual plots for bias ratios, i.e. not for std dev, etc\n if res_plot and layer in InterpGdal.default_layers:\n layer = InterpGdal.var_residual_names.get(\n layer, \n layer.replace('mean','res')\n )\n y_label = 'residual (interpolated minus station value)'\n title = 'layer: {} algorithm: {} (RBF) resolution: {} deg.'.format(\n layer, function ,res)\n res_plot_dir = Path(out_dir)/'residual_plots'\n subtitle='parameters: smooth={}'.format(smooth)\n source_file = Path(out_dir)/Path(in_path).name\n\n station_bar_plot(source_file, layer, out_dir=res_plot_dir, \n y_label=y_label, title=title, subtitle=subtitle)","def interpolating_model_DA(temp,grav,m_type='da2014'):\n # PARAMETERS # \n dir_models = basedir + '/WDModels_Koester.'+m_type+'_npy/'\n if m_type==\"pier\":\n teff=np.array([1500.,1750.,2000.,2250.,2500.,2750.,3000.,3250.,3500.,\n 3750.,4000.,4250.,4500.,4750.,5000.,5250.,5500.,6000.,\n 6500.,7000.,7500.,8000.,8500.,9000.,9500.,10000.,10500.,\n 11000.,11500.,12000.,12500.,13000.,13500.,14000.,14500.,\n 15000.,15500.,16000.,16500.,17000.,20000.,25000.,30000.,\n 35000.,40000.,45000.,50000.,55000.,60000.,65000.,70000.,\n 75000.,80000.,85000.,90000.])\n logg=np.array([6.50,7.00,7.50,7.75,8.00,8.25,8.50,9.00,9.50])\n elif m_type==\"da2014\":\n teff=np.array([6000.,6250.,6500.,6750.,7000.,7250.,7500.,7750.,8000.,\n 8250.,8500.,8750.,9000.,9250.,9500.,9750.,10000.,10100.,\n 10200.,10250.,10300.,10400.,10500.,10600.,10700.,10750.,\n 10800.,10900.,11000.,11100.,11200.,11250.,11300.,11400.,\n 11500.,11600.,11700.,11750.,11800.,11900.,12000.,12100.,\n 12200.,12250.,12300.,12400.,12500.,12600.,12700.,12750.,\n 12800.,12900.,13000.,13500.,14000.,14250.,14500.,14750.,\n 15000.,15250.,15500.,15750.,16000.,16250.,16500.,16750.,\n 17000.,17250.,17500.,17750.,18000.,18250.,18500.,18750.,\n 19000.,19250.,19500.,19750.,20000.,21000.,22000.,23000.,\n 24000.,25000.,26000.,27000.,28000.,29000.,30000.,35000.,\n 40000.,45000.,50000.,55000.,60000.,65000.,70000.,75000.,\n 80000.,90000.,100000.])\n logg=np.array([4.00,4.25,4.50,4.75,5.00,5.25,5.50,5.75,6.00,6.25,6.50,\n 6.75,7.00,7.25,7.50,7.75,8.00,8.25,8.50,8.75,9.00,9.25,\n 9.50])\n if (m_type=='pier') & (temp<1500. or temp>90000. or grav<6.5 or grav>9.5): \n return [],[]\n elif (m_type=='da2014') & (temp<6000. or temp>100000. or grav<4.0 or grav>9.5): \n return [],[]\n # INTERPOLATION #\n g1,g2 = np.max(logg[logg<=grav]),np.min(logg[logg>=grav])\n if g1!=g2: g = (grav-g1)/(g2-g1)\n else: g=0\n t1,t2 = np.max(teff[teff<=temp]),np.min(teff[teff>=temp])\n if t1!=t2: t = (temp-t1)/(t2-t1) \n else: t=0\t\n if m_type =='da2014': models = ['da%06d_%d_2.7.npy'%(i, j*100) for i in [t1,t2] \n for j in [g1,g2]]\n else: models = ['WD_%.2f_%d.0.npy'%(j, i) for i in [t1,t2] for j in [g1,g2]]\n try:\n m11, m12 = np.load(dir_models+models[0]), np.load(dir_models+models[1])\t\n m21, m22 = np.load(dir_models+models[2]), np.load(dir_models+models[3])\t\n flux_i = (1-t)*(1-g)*m11[:,1]+t*(1-g)*m21[:,1]+t*g*m22[:,1]+(1-t)*g*m12[:,1]\n return np.dstack((m11[:,0], flux_i))[0]\n except: return [],[]","def stamps(d_data='',**kwargs):\n GR = glo.global_results()\n if p.gal_index == 'all':\n gal_indices = np.arange(GR.N_gal)\n else:\n gal_indices = p.gal_index\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n for gal_index in gal_indices:\n\n fig, ax = plt.subplots(figsize=(8,8))\n\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\n simgas = gal_ob.particle_data.get_dataframe('simgas',d_data=d_data)\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop,pix_size_kpc=p.pix_size_kpc,scale=1.5)\n\n # Plot\n ax.set_facecolor(\"black\")\n Rmax = max_scale/2\n if p.log:\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\n map2D = np.log10(map2D)\n if not p.log:\n map2D[map2D < p.vmin] = p.vmin/2\n map2D[map2D > p.vmax] = p.vmax\n im = ax.imshow(map2D,\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,vmax=p.vmax,cmap=p.cmap)\n Rmax = p.R_max\n ax.set_xlim([-Rmax,Rmax])\n ax.set_ylim([-Rmax,Rmax])\n ax.text(0.05,0.05,'G%i' % gal_index,\\\n fontsize=55,transform=ax.transAxes,color='white')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.set_aspect('equal')\n\n #plt.gca().set_axis_off()\n #plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \n # hspace = 0, wspace = 0)\n #plt.margins(0,0)\n #plt.gca().xaxis.set_major_locator(plt.NullLocator())\n #plt.gca().yaxis.set_major_locator(plt.NullLocator())\n if not os.path.isdir(p.d_plot + 'sim_data/stamps/'): os.mkdir(p.d_plot + 'sim_data/stamps/') \n plt.savefig(p.d_plot + 'sim_data/stamps/%s%s_G%i.png' % (p.sim_name,p.sim_run,gal_index),\\\n bbox_inches = 'tight', pad_inches = 0)"],"string":"[\n \"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\\r\\n template = np.zeros((outputSize,outputSize))\\r\\n ppd=float(outputSize)/float(angularSize) # pixels per deg\\r\\n \\r\\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\\r\\n \\r\\n for i in range(10000,200001,20000):\\r\\n if i == 130000:\\r\\n continue\\r\\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\\r\\n BG = np.zeros((outputSize,outputSize)) \\r\\n for j in events:\\r\\n xIDX = int(j[1]*ppd+float(outputSize/2))\\r\\n yIDX = int(j[2]*ppd+float(outputSize/2))\\r\\n BG[yIDX][xIDX] += 1.0\\r\\n \\r\\n psfDeg = .2+float(200)/float(i)\\r\\n psfOut = psfDeg*ppd\\r\\n #print i/1e3, psfDeg, psfOut\\r\\n \\r\\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\\r\\n \\r\\n template = template/np.max(template)\\r\\n \\r\\n # Write to file \\r\\n outFile = open(fileOut, \\\"wb\\\" )\\r\\n pickle.dump(template, outFile)\\r\\n print 'Rate Map saved to ', fileOut\\r\\n \\r\\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\\r\\n\\r\\n plt.xlabel(r'$l [^\\\\circ]$')\\r\\n plt.ylabel(r'$b [^\\\\circ]$')\\r\\n plt.xlim(5,-5)\\r\\n plt.ylim(-5,5)\\r\\n plt.colorbar()\\r\\n\\r\\n x,y = Find_Centroid(template)\\r\\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\\r\\n print x,y\\r\\n plt.scatter(x,y, s=10, c='r', marker = '+')\\r\\n \\r\\n X,Y = FormatEvents(events110)\\r\\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\\r\\n \\r\\n X,Y = FormatEvents(events130)\\r\\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\\r\\n \\r\\n X,Y = FormatEvents(events150)\\r\\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\\r\\n \\r\\n from matplotlib.font_manager import FontProperties\\r\\n fontP = FontProperties()\\r\\n fontP.set_size('small')\\r\\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\\r\\n \\r\\n from matplotlib.backends.backend_pdf import PdfPages\\r\\n if fileOut != '':\\r\\n pp = PdfPages(fileOut + '_sideband.pdf')\\r\\n plt.savefig(pp, format='pdf')\\r\\n print \\\"Figures saved to \\\", str(fileOut)+ '_sideband.pdf\\\\n',\\r\\n pp.close()\\r\\n \\r\\n plt.show()\\r\\n return template\",\n \"def sw_gaussians(mu1, mu2, Sigma1, Sigma2, n_proj=100):\\n d = mu1.shape[0]\\n # Project data\\n thetas = np.random.randn(n_proj, d)\\n thetas = thetas / (np.sqrt((thetas ** 2).sum(axis=1)))[:, None] # Normalize\\n proj_mu1 = thetas @ mu1\\n proj_mu2 = thetas @ mu2\\n sw2 = 0\\n for l in range(n_proj):\\n th = thetas[l]\\n proj_sigma1 = (th @ Sigma1) @ th\\n proj_sigma2 = (th @ Sigma2) @ th\\n sw2 += wass_gaussians(np.array([proj_mu1[l]]), np.array([proj_mu2[l]]),\\n np.array([proj_sigma1]), np.array([proj_sigma2]))**2\\n sw2 /= n_proj\\n return np.sqrt(sw2)\",\n \"def plot(self):\\n \\n \\n x_ibs=[] \\n x_gss=[]\\n y_ibs=[] \\n y_gss=[]\\n x_pso=[]\\n x_bgd=[]\\n y_bgd=[]\\n y_pso=[]\\n x_gd=[]\\n y_gd=[]\\n \\n i=0.0000001\\n \\n # for k in range(1,51):\\n # i= random.uniform(0.00000001, 1)\\n # t_avg_ibs=[]\\n # t_avg_gss=[]\\n # for j in range(1,51):\\n #L=random.randint(-100, 0)\\n #U=random.randint(0, 100)\\n max_iter=self.Max_iter \\n L=self.Lower_bound\\n U=self.Upper_bound\\n \\n minima=self.gss(L,U,i,1000)\\n #print(\\\"minima at X = \\\",minima[1])\\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\\n x_gss.append(self.gss(L,U,i,max_iter)[0])\\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\\n #print(x_pso)\\n for i in x_ibs[0]:\\n #print(self.Func(i)) \\n y_ibs.append(self.Func(i))\\n for i in x_gss[0]:\\n y_gss.append(self.Func(i)) \\n for i in x_pso[0]:\\n y_pso.append(self.Func(i)) \\n for i in x_gd[0]:\\n y_gd.append(self.Func(i)) \\n for i in x_bgd[0]:\\n y_bgd.append(self.Func(i)) \\n #print(y_gss)\\n\\n plt.plot(x_ibs[0], y_ibs, 'r.')\\n plt.plot(x_gss[0], y_gss, '.')\\n plt.plot(x_pso[0], y_pso, 'y.')\\n #plt.plot(x_gd[0], y_gd, 'y.')\\n #plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y')\\n \\n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\\n #plt.axis([0, 100, 0.00000001, 1]) \\n plt.show()\\n plt.plot(x_gd[0], y_gd, 'r.')\\n plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y') \\n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\\n \\n plt.show()\\n \\n start_time = timeit.default_timer()\\n ibs=self.I_bisection(L,U,minima[1],max_iter)\\n print(\\\" Execution time for Interval bisection Method is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gss=self.gss(L,U,i,max_iter)\\n print(\\\" Execution time for Golden Section Search is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\\n print(\\\" Execution time for Particle swarm optimization is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\\n print(\\\" Execution time for Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\\n print(\\\" Execution time for Batch Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n plt.plot(ibs[1], ibs[2], 'r.')\\n plt.text(ibs[1], ibs[2],\\\"IB\\\")\\n plt.plot(gss[1], gss[2], '.')\\n plt.text(gss[1], gss[2],\\\" GSS\\\")\\n plt.plot(pso[1], pso[2], 'y.')\\n plt.text(pso[1], pso[2],\\\" PSO\\\")\\n plt.plot(gd[1], gd[2], 'g.')\\n plt.text(gd[1], gd[2],\\\" GD \\\")\\n plt.plot(bgd[1],bgd[2], 'k.')\\n plt.text(bgd[1], bgd[2],\\\" Batch_GD\\\")\\n \\n plt.xlabel('Value of X')\\n plt.ylabel('NUmber of iteration') \\n plt.suptitle('Number of iterations vs minimum value of x')\\n \\n plt.show()\",\n \"def construct_mog(self, outputs):\\n # check for correct number of input dimensions.\\n assert outputs.shape[-1] == (self.n_components)*3 + 1\\n out_idx = 0 # keep track of which output we are working with.\\n self.shift = outputs[0, :, out_idx:out_idx+1]; out_idx += 1\\n with tf.name_scope(\\\"Mixture_of_Gaussians\\\"):\\n with tf.variable_scope('network'):\\n # get mixing proportions\\n theta_raw = outputs[:, :,out_idx:out_idx+self.n_components];\\n out_idx += self.n_components\\n self.theta = tf.nn.softmax(theta_raw)\\n\\n log_sigmas = outputs[:, :, out_idx:out_idx+self.n_components];\\n out_idx += self.n_components\\n self.sigmas = tf.exp(log_sigmas,name=\\\"sigmas\\\")\\n\\n self.mus = outputs[:, :, out_idx:out_idx+self.n_components];\\n out_idx += self.n_components\\n for k in range(self.n_components):\\n tf.summary.histogram(\\\"Gaussian_%d_sigma\\\"%k,self.sigmas[:,k])\\n tf.summary.histogram(\\\"Gaussian_%d_proportion\\\"%k,self.theta[:,k])\\n tf.summary.histogram(\\\"Gaussian_%d_mus\\\"%k,self.mus[:,k])\\n\\n ## Check that every output has been used\\n assert out_idx == outputs.shape[-1]\",\n \"def main():\\n\\n\\n ## Groups showing similar noise profile\\n #grp1 = [ 1, 4, 5, 8, 9 ]\\n #grp2 = [ 18, 19, 22, 23, 30, 31 ]\\n grp1 = [ 0, 1, 6, 7, 4, 5 ]\\n grp2 = [ 12, 13, 16, 17, 18, 19 ]\\n #grp3 = [ 18, 19, 22, 23, 26, 27 ]\\n with tb.open_file(sys.argv[1], 'r') as dataF:\\n\\n npm = len(dataF.root.Sensors.DataPMT)#len(dataF.root.RD.pmtrwf[0])\\n nevt = len(dataF.root.RD.pmtrwf)\\n\\n ## Filter definition\\n fSample = 40E6\\n freqLPF = 100E3\\n freqLPFd = 2*freqLPF / fSample\\n b, a = signal.butter(1, freqLPFd, 'low', analog=False)\\n ##\\n fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(20,6))\\n #fig.tight_layout()\\n fig.show()\\n wf_len = len(dataF.root.RD.pmtrwf[0][0])\\n if len(sys.argv) > 3:\\n wf_len = wf_len/2+1 \\n elif len(sys.argv) == 3:\\n g1_first = np.zeros(wf_len, np.float64)\\n g2_first = np.zeros(wf_len, np.float64)\\n g3_first = np.zeros(wf_len, np.float64)\\n mean_first = np.zeros(wf_len, np.float64)\\n ##\\n for ievt in range(nevt):\\n ## clear the axies\\n for ax in axes.flatten():\\n ax.cla()\\n plt_frq = np.zeros(wf_len, np.float64)\\n fwf_mean = np.zeros(wf_len, np.float64)\\n wf_mean = np.zeros(wf_len, np.float64) # No filter\\n g1_mean = np.zeros(wf_len, np.float64)\\n g2_mean = np.zeros(wf_len, np.float64)\\n g3_mean = np.zeros(wf_len, np.float64)\\n for ipm in range(npm):\\n\\n sg = getWF(dataF, ipm, ievt)\\n sg = sg - np.mean(sg)\\n\\n sgf = signal.lfilter(b, a, sg)\\n ## remove mean again just in case\\n sgf = sgf - np.mean(sgf)\\n #sgf = sg\\n\\n pmID = getPMid(dataF, ipm)\\n\\n if len(sys.argv) == 3:\\n axes[0][0].plot(sgf, label='pmt '+str(pmID))\\n fwf_mean += sgf/npm\\n wf_mean += sg/npm\\n if pmID in grp1:\\n g1_mean += sgf/len(grp1)\\n elif pmID in grp2:\\n g2_mean += sgf/len(grp2)\\n elif pmID in grp3:\\n g3_mean += sgf/len(grp3)\\n else:\\n ft = np.fft.rfft(sgf)\\n freq = np.fft.rfftfreq(len(sgf), d=25E-9)\\n if ipm == 0:\\n plt_frq = freq\\n if sys.argv[2] == 'mag':\\n ft_mag = np.absolute(ft)\\n axes[0][0].plot(freq, ft_mag, label='pmt '+str(pmID))\\n fwf_mean += ft_mag/npm\\n if pmID in grp1:\\n g1_mean += ft_mag/len(grp1)\\n elif pmID in grp2:\\n g2_mean += ft_mag/len(grp2)\\n elif pmID in grp3:\\n g3_mean += ft_mag/len(grp3)\\n elif sys.argv[2] == 'phase':\\n ft_pha = np.angle(ft)\\n axes[0][0].plot(freq, ft_pha, label='pmt '+str(pmID))\\n fwf_mean += ft_pha/npm\\n if pmID in grp1:\\n g1_mean += ft_pha/len(grp1)\\n elif pmID in grp2:\\n g2_mean += ft_pha/len(grp2)\\n elif pmID in grp3:\\n g3_mean += ft_pha/len(grp3)\\n \\n \\n ## The axes not set\\n if len(sys.argv) == 3:\\n axes[0][1].plot(g1_mean)\\n axes[0][1].set_title('Group 1 mean waveform')\\n axes[1][0].plot(g2_mean)\\n axes[1][0].set_title('Group 2 mean waveform')\\n axes[1][1].plot(g3_mean)\\n axes[1][1].set_title('Group 3 mean waveform')\\n axes[2][0].plot(fwf_mean)\\n axes[2][0].set_title('Mean waveform')\\n if ievt == 0:\\n g1_first = g1_mean\\n g2_first = g2_mean\\n g3_first = g3_mean\\n mean_first = fwf_mean\\n else:\\n axes[0][1].plot(g1_first)\\n axes[1][0].plot(g2_first)\\n axes[1][1].plot(g3_first)\\n axes[2][0].plot(mean_first)\\n axes[2][1].plot(wf_mean)\\n axes[2][1].set_title('Mean waveform and corrected')\\n axes[2][1].plot(wf_mean-fwf_mean)\\n axes[2][1].set_xlim(0, 1000)\\n else:\\n axes[0][0].set_xlim(0,50000)\\n axes[0][1].plot(plt_frq, g1_mean)\\n axes[0][1].set_title('Group 1 mean '+sys.argv[2])\\n axes[0][1].set_xlim(0,50000)\\n axes[1][0].plot(plt_frq, g2_mean)\\n axes[1][0].set_title('Group 2 mean '+sys.argv[2])\\n axes[1][0].set_xlim(0,50000)\\n axes[1][1].plot(plt_frq, g3_mean)\\n axes[1][1].set_title('Group 3 mean '+sys.argv[2])\\n axes[1][1].set_xlim(0,50000)\\n axes[2][0].plot(plt_frq, fwf_mean)\\n axes[2][0].set_title('Mean '+sys.argv[2])\\n axes[2][0].set_xlim(0,50000)\\n plt.draw()\\n #fig.legend(loc=0)\\n catcher = input(\\\"next plot?\\\")\\n if catcher == 'q':\\n exit()\\n plt.cla()\",\n \"def main(ngrains=100,sigma=15.,c2a=1.6235,mu=0.,\\n prc='cst',isc=False,tilt_1=0.,\\n tilts_about_ax1=0.,tilts_about_ax2=0.):\\n if isc:\\n h = mmm()\\n else:\\n h=np.array([np.identity(3)])\\n gr = []\\n for i in range(ngrains):\\n dth = random.uniform(-180., 180.)\\n if prc=='cst': g = gen_gr_fiber(th=dth,sigma=sigma,mu=mu,tilt=tilt_1,iopt=0) # Basal//ND\\n elif prc=='ext': g = gen_gr_fiber(th=dth,sigma=sigma,mu=mu,tilt=tilt_1,iopt=1) # Basal//ED\\n else:\\n raise IOError('Unexpected option')\\n for j in range(len(h)):\\n temp = np.dot(g,h[j].T)\\n\\n ## tilts_about_ax1\\n if abs(tilts_about_ax1)>0:\\n g_tilt = rd_rot(tilts_about_ax1)\\n temp = np.dot(temp,g_tilt.T)\\n ## tilts_about_ax2?\\n elif abs(tilts_about_ax2)>0:\\n g_tilt = td_rot(tilts_about_ax2)\\n temp = np.dot(temp,g_tilt.T)\\n elif abs(tilts_about_ax2)>0 and abs(tilts_about_ax2)>0:\\n raise IOError('One tilt at a time is allowed.')\\n\\n phi1,phi,phi2 = euler(a=temp, echo=False)\\n gr.append([phi1,phi,phi2,1./ngrains])\\n\\n mypf=upf.polefigure(grains=gr,csym='hexag',cdim=[1,1,c2a])\\n mypf.pf_new(poles=[[0,0,0,2],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\\n return np.array(gr)\",\n \"def map_all_sig_p(limitregion=False, region=\\\"allsky\\\"):\\n \\n # Get ids of all pixels that contain RHT data\\n rht_cursor, tablename = get_rht_cursor(region = region)\\n all_ids = get_all_rht_ids(rht_cursor, tablename)\\n \\n planck_tqu_db = sqlite3.connect(\\\"planck_TQU_gal_2048_db.sqlite\\\")\\n planck_tqu_cursor = planck_tqu_db.cursor()\\n planck_cov_db = sqlite3.connect(\\\"planck_cov_gal_2048_db.sqlite\\\")\\n planck_cov_cursor = planck_cov_db.cursor()\\n \\n if limitregion is True:\\n print(\\\"Loading all allsky data points that are in the SC_241 region\\\")\\n # Get all ids that are in both allsky data and SC_241\\n all_ids_SC = pickle.load(open(\\\"SC_241_healpix_ids.p\\\", \\\"rb\\\"))\\n all_ids = list(set(all_ids).intersection(all_ids_SC))\\n \\n all_sigpGsq = np.zeros(len(all_ids))\\n\\n update_progress(0.0)\\n for i, hp_index in enumerate(all_ids):\\n #likelihood = Likelihood(_id[0], planck_tqu_cursor, planck_cov_cursor, p0_all, psi0_all, adaptivep0 = adaptivep0)\\n (hp_index, T, Q, U) = planck_tqu_cursor.execute(\\\"SELECT * FROM Planck_Nside_2048_TQU_Galactic WHERE id = ?\\\", hp_index).fetchone()\\n (hp_index, TT, TQ, TU, TQa, QQ, QU, TUa, QUa, UU) = planck_cov_cursor.execute(\\\"SELECT * FROM Planck_Nside_2048_cov_Galactic WHERE id = ?\\\", (hp_index,)).fetchone()\\n \\n # sigma_p as defined in arxiv:1407.0178v1 Eqn 3.\\n sigma_p = np.zeros((2, 2), np.float_) # [sig_Q^2, sig_QU // sig_QU, UU]\\n sigma_p[0, 0] = (1.0/T**2)*QQ #QQ\\n sigma_p[0, 1] = (1.0/T**2)*QU #QU\\n sigma_p[1, 0] = (1.0/T**2)*QU #QU\\n sigma_p[1, 1] = (1.0/T**2)*UU #UU\\n \\n # det(sigma_p) = sigma_p,G^4\\n det_sigma_p = np.linalg.det(sigma_p)\\n sigpGsq = np.sqrt(det_sigma_p)\\n \\n all_sigpGsq[i] = sigpGsq\\n \\n update_progress((i+1.0)/len(all_ids), message='Calculating: ', final_message='Finished Calculating: ')\\n \\n # Place into healpix map\\n hp_sigpGsq = make_hp_map(all_sigpGsq, all_ids, Nside = 2048, nest = True)\\n \\n out_root = \\\"/disks/jansky/a/users/goldston/susan/Wide_maps/\\\"\\n if limitregion:\\n hp.fitsfunc.write_map(out_root + \\\"planck_sigpGsq_SC_241.fits\\\", hp_sigpGsq, coord = \\\"G\\\", nest = True) \\n else:\\n hp.fitsfunc.write_map(out_root + \\\"planck_sigpGsq_DR2sky.fits\\\", hp_sigpGsq, coord = \\\"G\\\", nest = True)\",\n \"def synthetic_gen(self):\\r\\n logging.debug('generating synthetic map...')\\r\\n data = self.realData\\r\\n unit = Params.unitGrid\\r\\n x_min = np.floor(Params.LOW[0] / unit) * unit\\r\\n x_max = np.ceil(Params.HIGH[0] / unit) * unit\\r\\n y_min = np.floor(Params.LOW[1] / unit) * unit\\r\\n y_max = np.ceil(Params.HIGH[1] / unit) * unit\\r\\n\\r\\n x_CELL = int(np.rint((x_max - x_min) / unit))\\r\\n y_CELL = int(np.rint((y_max - y_min) / unit))\\r\\n\\r\\n self.root.n_box = np.array([[x_min, y_min], [x_max, y_max]])\\r\\n\\r\\n self.mapp = np.zeros((x_CELL, y_CELL)) - 1 # ## initialize every cell with -1\\r\\n for i in range(Params.NDATA): # ## populate the map\\r\\n point = data[:, i]\\r\\n cell_x = int(np.floor((point[0] - x_min) / unit))\\r\\n cell_y = int(np.floor((point[1] - y_min) / unit))\\r\\n if self.mapp[cell_x, cell_y] != -1:\\r\\n self.mapp[cell_x, cell_y] += 1\\r\\n else:\\r\\n self.mapp[cell_x, cell_y] = 1\\r\\n\\r\\n for i in range(x_CELL): # ## perturb the counts\\r\\n for j in range(y_CELL):\\r\\n if self.mapp[i, j] != -1:\\r\\n self.mapp[i, j] += np.rint(self.differ.getNoise(1, 0.5 * self.param.Eps))\\r\\n else:\\r\\n self.mapp[i, j] = np.rint(self.differ.getNoise(1, 0.5 * self.param.Eps))\\r\\n # if noisy count is negative, ignore the noise and generate no points\\r\\n if self.mapp[i, j] < 0:\\r\\n self.mapp[i, j] = 0\",\n \"def _initialize_gaussians(self, n_gaussians):\\n if self.gaussian_init == 'manual':\\n gaussians = torch.Tensor([\\n list(product([0.25, 0.5, 0.75], repeat=2)) +\\n [(0.5, 0.25), (0.5, 0.5), (0.5, 0.75)] +\\n [(0.25, 0.5), (0.5, 0.5), (0.75, 0.5)] +\\n [(0.5, 0.5)],\\n [(-1.5, -1.5)] * 9 + [(0, -1.5)] * 3 + [(-1.5, 0)] * 3 +\\n [(0, 0)],\\n ]).permute(1, 2, 0)\\n\\n elif self.gaussian_init == 'random':\\n with torch.no_grad():\\n gaussians = torch.stack([\\n torch.randn(\\n n_gaussians, 2, dtype=torch.float) * .1 + 0.5,\\n torch.randn(\\n n_gaussians, 2, dtype=torch.float) * .2 - 1,],\\n dim=2)\\n\\n else:\\n raise NotImplementedError\\n\\n gaussians = nn.Parameter(gaussians, requires_grad=True)\\n return gaussians\",\n \"def plot_gheat_g(seed=1):\\n fig, ax = plt.subplots(figsize=[2.5*plotdl.latex_width_inch, 3*plotdl.latex_height_inch])\\n \\n r = Factory_psi1_psiN( \\\"aapta_of_s_N{number_of_points[0]}.npz\\\", N=400)\\n ckg = r.create_if_missing(dict(model_name= [\\\"Anderson\\\",], \\n number_of_points=[400,], bandwidth=[1,],\\n dis_param=np.linspace(0,1,100),c=[1,], k=[1.57,], seed=np.arange(1,6))) \\n color_seq = itertools.cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])\\n for (seed,c) in zip(np.arange(1,6),color_seq):\\n ck = ckg[ckg['seed']==seed]\\n g, psi_1, psi_N = ck['g'], ck['psi_N'], ck['psi_1']\\n\\n psi_heat = 2*(abs(psi_1)**2)*(abs(psi_N)**2) / ((abs(psi_1)**2) + (abs(psi_N)**2))\\n \\n phs = np.nansum(psi_heat,axis=1)\\n \\n psi1psiN = np.nansum(abs(psi_1*psi_N), axis=1)\\n #print(ckg['dis_param'], phs)\\n ax.plot(ck['dis_param'], phs,'.', color=c)\\n ax.plot(ck['dis_param'], abs(g),'+', color=c)\\n ax.plot(ck['dis_param'], psi1psiN,'d', color=c)\\n ax.set_xlabel('dis_param')\\n mkdir_and_savefig(fig, 'pta_comparison_of_s_N400.png')\\n plt.close(fig)\\n ## use last ck\\n fig1, axes1 = plt.subplots(3,2,figsize=[2*plotdl.latex_width_inch, 3*plotdl.latex_height_inch],\\n sharex=True, sharey=True)\\n axes1.flat[0].xaxis.set_major_locator(MaxNLocator(4))\\n axes1.flat[0].yaxis.set_major_locator(MaxNLocator(4))\\n for n, ax1 in zip(range(1,20,3), axes1.flat):\\n ax1.plot(abs(ck['psi_1'][n]), abs(ck['psi_N'][n]), '.') \\n ax1.set_title(\\\"W = {:0.2}\\\".format(ck['dis_param'][n]))\\n fig1.savefig('pta_psi_1_psi_2_N400.png')\\n \\n ax.cla()\\n ax.plot(ck['dis_param'], np.real(g), label='real')\\n ax.plot(ck['dis_param'], np.imag(g), label='imag')\\n ax.plot(ck['dis_param'], np.abs(g), label='abs')\\n ax.legend(loc = 'upper right')\\n ax.set_xlabel('dis_param')\\n ax.set_ylabel('g')\\n mkdir_and_savefig(fig, 'pta_real_imag_g_s_N400')\",\n \"def FormG():\\n for i in range(2):\\n for j in range(2):\\n G[i, j] = 0.0\\n for k in range(2):\\n for l in range(2):\\n G[i, j] = G[i, j] + P[k, l] * (TT[i, j, k, l] - 0.5 * TT[i, j, k, l])\",\n \"def calculate_gains(self,data, map_fits, avg_map_fits):\\n nFeeds,nBands,nChans,nParams = map_fits['Values'].shape\\n frequencies = data[f'{self.level2}/averaged_frequency'][...]\\n kb = 1.38064852e-23\\n c = 2.99792458e8\\n scale = 2 * kb * (1e9/ c)**2 * 1e26\\n\\n source = self.getSource(data)\\n self.flux = np.zeros((len(self.feeds),nBands, nChans))\\n self.gain = np.zeros((len(self.feeds),nBands, nChans))\\n\\n for i,(ifeed,feed) in enumerate(zip(self.feedlist,self.feeds)):\\n for iband in range(nBands):\\n nu = frequencies[iband]\\n sigx = avg_map_fits[iband]['Values'][2] \\n sigy = avg_map_fits[iband]['Values'][2]*avg_map_fits[iband]['Values'][4]\\n amps = map_fits['Values'][i,iband,:,0]\\n self.flux[i,iband,:] = 2*np.pi*amps*sigx*sigy*(np.pi/180.)**2 * scale*nu**2\\n mdl_flux = self.models[source](nu,map_fits['MJD'],return_jansky=True,allpos=True)\\n self.gain[i,iband,:] = self.flux[i,iband,:]/mdl_flux\\n\\n return self.flux, self.gain\",\n \"def g_func(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoef, N):\\r\\n\\tL, g = ilike(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoef, N) \\t\\r\\n\\tp = np.zeros(N)\\r\\n\\tfor i in range(N):\\r\\n\\t if np.isfinite(vrad_obs[i]):\\r\\n\\t p[i] = chi2.sf(g[i],3)\\r\\n\\t else:\\r\\n\\t p[i] = chi2.sf(g[i],2)\\r\\n\\t \\r\\n\\treturn p\",\n \"def sgd(self):\\n for i, j, r in self.samples:\\n # Computer prediction and error\\n if (self.type=='bias'):\\n prediction = self.get_rating_bias(i, j)\\n elif(self.type=='nonbias') :\\n prediction = self.get_rating(i, j)\\n # print(i, j, r,prediction)\\n e = (r - prediction)\\n\\n # Update biases\\n self.b_u[i] =self.b_u[i]+ self.alpha * (e - self.beta * self.b_u[i])\\n self.b_i[j] = self.b_i[j] + self.alpha * (e - self.beta * self.b_i[j])\\n\\n # Create copy of row of P since we need to update it but use older values for update on Q\\n P_i = self.P[i, :][:]\\n\\n # Update user and item latent feature matrices\\n # print(self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :]))\\n # print(self.P[i, :])\\n self.P[i, :] =self.P[i, :] + self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :])\\n # print(self.P[i, :],\\\"&&&&&&\\\")\\n self.Q[j, :] = self.Q[j, :] + self.alpha * (e * P_i - self.beta * self.Q[j, :])\\n # print(self.Q[j, :])\",\n \"def dataModel():\\n srcmap001 = fits.open('dataFiles/6gev_srcmap_001.fits')\\n srcmap03 = fits.open('dataFiles/6gev_srcmap_03.fits')\\n\\n image_data = fits.getdata('6gev_image.fits')\\n filename = get_pkg_data_filename('6gev_image.fits')\\n hdu = fits.open(filename)[0]\\n wcs = WCS(hdu.header)\\n\\n #Given the results of the fit, calculate the model\\n modelData001 = np.zeros(srcmap001[0].shape)\\n modelData03 = np.zeros(srcmap03[0].shape)\\n\\n file = open('plotsData/fitResults001.pk1','rb')\\n fit001 = pickle.load(file)\\n file.close()\\n\\n file = open('plotsData/fitResults03.pk1','rb')\\n fit03 = pickle.load(file)\\n file.close()\\n\\n\\n for source in fit001:\\n the_index = srcmap001.index_of(source)\\n\\n modelData001 += fit001[source][:, None, None]*srcmap001[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap001[the_index].data, axis=2), axis=1)[:-1, None, None]\\n for source in fit03:\\n the_index = srcmap03.index_of(source)\\n modelData03 += fit03[source][:, None, None]*srcmap03[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap03[the_index].data, axis=2), axis=1)[:-1, None, None]\\n\\n fig = plt.figure(figsize=[12, 4.5])\\n\\n vmin = 0\\n vmax = 70.0\\n cbStep = 10.0\\n ax = fig.add_subplot(121, projection=wcs)\\n ax=plt.gca()\\n ax.tick_params(direction='in')\\n c = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax.get_transform('galactic'))\\n ax.add_patch(c)\\n mappable=plt.imshow((image_data),cmap='inferno',origin='lower',norm=colors.PowerNorm(gamma=0.6),vmin=vmin, vmax=vmax, interpolation='gaussian')#\\n plt.xlabel('Galactic Longitude')\\n plt.ylabel('Galactic Latitude')\\n plt.title('Data ($>6$ GeV)')\\n cb = plt.colorbar(mappable, label='Counts per pixel', pad=0.01,ticks=np.arange(vmin, vmax+cbStep, cbStep))\\n cb.ax.tick_params(width=0)\\n\\n\\n ax2=fig.add_subplot(122, projection=wcs)\\n ax2 = plt.gca()\\n\\n sources = []\\n sources.append({\\n 'Name':'3FGL J1745.3-2903c',\\n 'RA':266.3434922,\\n 'DEC':-29.06274323,\\n 'color':'xkcd:bright light blue'})\\n\\n sources.append({\\n 'Name':'1FIG J1748.2-2816',\\n 'RA':267.1000722,\\n 'DEC':-28.27707114,\\n 'color':'xkcd:fire engine red'\\n })\\n\\n sources.append({\\n 'Name':'1FIG J1746.4-2843',\\n 'RA':266.5942898,\\n 'DEC':-28.86244442,\\n 'color':'xkcd:fluorescent green'\\n })\\n\\n sources.append({\\n 'Name':'Galactic Center',\\n 'RA':266.417,\\n 'DEC':-29.0079,\\n 'color':'black'\\n })\\n\\n #Add source names:\\n for source in sources:\\n l, b = ra_dec_to_l_b(source['RA'], source['DEC'])\\n ax2.scatter(l, b, color=source['color'],marker='x',s=45.0, transform=ax2.get_transform('galactic'), label=source['Name'])\\n\\n c2 = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax2.get_transform('galactic'))\\n ax2.add_patch(c2)\\n mappable2 = plt.imshow((np.sum(modelData03,axis=0)), cmap='inferno',norm=colors.PowerNorm(gamma=0.6),origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')\\n plt.xlabel('Galactic Longitude')\\n plt.ylabel('Galactic Latitude')\\n plt.title('Model ($>6$ GeV)')\\n cb2 = plt.colorbar(mappable2, label='Counts per pixel', pad=0.01, ticks=np.arange(vmin, vmax+cbStep, cbStep))\\n cb2.ax.tick_params(width=0)\\n leg = plt.legend(loc=1,frameon=True)\\n leg.get_frame().set_alpha(0.5)\\n leg.get_frame().set_edgecolor('white')\\n text1 = leg.get_texts()\\n for text in text1:\\n text.set_color('black')\\n\\n fig.tight_layout()\\n plt.subplots_adjust(wspace = 0.13, left=0.04, bottom=0.13, top=0.92)\\n plt.show()\\n #plt.savefig('plots/dataModelComparison.pdf',bbox_inches='tight')\",\n \"def make_100gaussians_image(noise=True):\\n n_sources = 100\\n flux_range = [500, 1000]\\n xmean_range = [0, 500]\\n ymean_range = [0, 300]\\n xstddev_range = [1, 5]\\n ystddev_range = [1, 5]\\n params = {'flux': flux_range,\\n 'x_mean': xmean_range,\\n 'y_mean': ymean_range,\\n 'x_stddev': xstddev_range,\\n 'y_stddev': ystddev_range,\\n 'theta': [0, 2 * np.pi]}\\n\\n rng = np.random.RandomState(12345)\\n sources = QTable()\\n for param_name, (lower, upper) in params.items():\\n # Generate a column for every item in param_ranges, even if it\\n # is not in the model (e.g., flux). However, such columns will\\n # be ignored when rendering the image.\\n sources[param_name] = rng.uniform(lower, upper, n_sources)\\n xstd = sources['x_stddev']\\n ystd = sources['y_stddev']\\n sources['amplitude'] = sources['flux'] / (2.0 * np.pi * xstd * ystd)\\n\\n shape = (300, 500)\\n data = make_gaussian_sources_image(shape, sources) + 5.0\\n\\n if noise:\\n rng = np.random.RandomState(12345)\\n data += rng.normal(loc=0.0, scale=2.0, size=shape)\\n\\n return data\",\n \"def graphe_gains(res_alea, res_glou, res_glou_e, res_ucb):\\n T = np.arange(res_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Gain\\\")\\n \\n ax.plot(T, res_alea, label = 'aléatoire') \\n ax.plot(T, res_glou, label = 'glouton')\\n ax.plot(T, res_glou_e, label = 'e-glouton')\\n ax.plot(T, res_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Gains des 4 algorithmes par rapport à T\\\")\",\n \"def test_GA():\\n\\tgenerationSize = 150\\n\\tmutationProb = 0.01\\n\\tgenerations = 500\\n\\tX = []\\n\\tT = []\\n\\tY = [] \\n\\tfitnesses = [0]*generationSize\\n\\tfor i in range(DATA_POINTS_NUM):\\n\\t\\tX.append((i - DATA_POINTS_NUM/2)*0.1)\\n\\t\\tT.append(polynomi_3N(REFERENCE, X[-1]))\\n\\t\\tY.append(0)\\n\\t\\n\\tga = GA.GA(generationSize, 4, mutationProb)\\n\\tgenomes = ga.seedGenomes()\\n\\t#plot initial genomes\\n\\tplt.figure(1)\\n\\tplt.title('Initial genomes')\\n\\tfor i in range(len(genomes)):\\n\\t\\tGenome = prescale(genomes[i])\\n\\t\\tprint Genome\\n\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\tY[j] = (polynomi_3N(Genome, X[j]))\\n\\t\\tfitnesses[i] = calculate_fitness(T, Y)\\n\\t\\tplt.plot(X,Y, 'b-')\\n\\tplt.plot(X,T, 'r-')\\n\\t\\t\\n\\t\\n\\t#live and learn\\n\\tfor k in range(generations):\\n\\t\\tprint \\\".\\\",\\n\\t\\tfor i in range(len(genomes)):\\n\\t\\t\\tGenome = prescale(genomes[i])\\n\\t\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\t\\tY[j] = (polynomi_3N(Genome,X[j]))\\n\\t\\t\\tfitnesses[i] = calculate_fitness(T, Y)\\n\\t\\tga.fitnessUpdate(fitnesses)\\n\\t\\tgenomes = ga.createNextGeneration()\\n\\t\\t\\n\\t#plot final genomes\\n\\tplt.figure(2)\\n\\tplt.title('Final genomes')\\n\\tprint \\\"\\\\nfinal Genomes\\\"\\n\\tfor i in range(len(genomes)):\\n\\t\\tGenome = prescale(genomes[i])\\n\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\tY[j] = (polynomi_3N(Genome,X[j]))\\n\\t\\tprint \\\"fit:%5.1f [%7.4f, %7.4f, %7.4f, %7.4f]\\\"%\\\\\\n\\t\\t (calculate_fitness(T, Y), Genome[0],\\n\\t\\t Genome[1], Genome[2], Genome[3])\\n\\t\\tplt.plot(X,Y, 'b-')\\n\\tplt.plot(X,T, 'r-')\\n\\t\\t\\n\\t#plot progress\\n\\tP = []\\n\\thistory = ga.generations[:]\\n\\tfor f in history:\\n\\t\\t#f[1].sort()\\n\\t\\tP.append(max(f[1]))\\n\\tplt.figure(3)\\n\\tplt.title('progress')\\n\\tplt.plot(P)\\n\\tplt.show()\\n\\t\\n\\t#print the result:\\t\\n\\tbestGene = fitnesses.index(max(fitnesses))\\n\\tG = prescale(genomes[bestGene])\\n print \\\"\\\"\\n\\tprint \\\"And the result is:\\\"\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(A, G[0], abs(A - G[0]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(B, G[1], abs(B - G[1]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(C, G[2], abs(C - G[2]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(D, G[3], abs(D - G[3]))\",\n \"def prediction_aggregation(self, xt_s,mu_s,var_s, method='PoE', weighting='uniform', power=26):\\n\\n nt = xt_s.shape[0]\\n mu = np.zeros([nt, self.C],dtype='float64')\\n var = np.zeros([nt, self.C],dtype='float64')\\n\\n prior_var = self.experts[0].kernel(xt_s[0], xt_s[0])\\n\\n \\n #Process each latent gp individually \\n for j in range(self.C):\\n \\n mu_s_c = mu_s[:, :, j]\\n var_s_c = var_s[:, :, j]\\n \\n weight_matrix = compute_weights(mu_s_c, var_s_c, power, weighting, prior_var)\\n \\n prec_s= 1/var_s_c\\n\\n if method == 'PoE':\\n \\n prec = tf.reduce_sum(prec_s, axis=0)\\n \\n\\n if method == 'gPoE':\\n \\n weight_matrix = normalize_weights(weight_matrix)\\n\\n prec = tf.reduce_sum(weight_matrix * prec_s , axis=0)\\n \\n\\n if method == 'BCM':\\n \\n prec = tf.reduce_sum(prec_s, axis=0) + (1 - self.M) / prior_var \\n\\n if method == 'rBCM':\\n \\n \\n prec = tf.reduce_sum(weight_matrix * prec_s, axis=0) \\\\\\n + (1 - tf.reduce_sum(weight_matrix, axis=0)) / prior_var\\n \\n \\n \\n if method != 'bar':\\n \\n var[:, j] = 1 / prec\\n\\n mu[:, j] = var[:, j] * tf.reduce_sum(weight_matrix * prec_s * mu_s_c, axis=0)\\n \\n else:\\n \\n weight_matrix = normalize_weights(weight_matrix)\\n\\n mu[:, j] = tf.reduce_sum(weight_matrix * mu_s_c, axis=0)\\n var[:, j] = tf.reduce_sum(weight_matrix * var_s_c, axis=0)\\n \\n \\n return self.lik_aggregation(mu, var)\",\n \"def learnGauss(metricArray): \\n fit = gaussReg() \\n n= 100 #You should probably change this...\\n overlap = 0.3 \\n imageSize = [100,400]\\n densMap(fit, metricArray, n, overlap, imageSize )\\n overlayMap('SmallTile.jpg', 'ContourPlot.jpg') \\n # Final map is saved as OverlayMap.jpg\",\n \"def output_grib2(forecast,proj_dict,grid_dict,start_hour,end_hour,\\n stride,size,run_date,target_dataset,smoothing,config):\\n for d,date in enumerate(run_date):\\n date_outpath = config.forecast_out_path+'20{0}/grib/'.format(\\n date.strftime(config.run_date_format))\\n \\n if not os.path.exists(date_outpath):\\n os.makedirs(date_outpath)\\n\\n lscale = 1e6\\n grib_id_start = [7, 0, 14, 14, 2]\\n \\n filtered_forecast = gaussian_filter(forecast[d],smoothing,mode='constant')\\n \\n gdsinfo = np.array([0, np.product(filtered_forecast.shape[-2:]), 0, 0, 30], dtype=np.int32)\\n \\n lon_0 = proj_dict[\\\"lon_0\\\"]\\n sw_lon = grid_dict[\\\"sw_lon\\\"]\\n \\n if lon_0 < 0:\\n lon_0 += 360\\n if sw_lon < 0:\\n sw_lon += 360\\n\\n gdtmp1 = [1, 0, proj_dict['a'], 0, float(proj_dict['a']), 0, float(proj_dict['b']),\\n filtered_forecast.shape[-1], filtered_forecast.shape[-2], grid_dict[\\\"sw_lat\\\"] * lscale,\\n sw_lon * lscale, 0, proj_dict[\\\"lat_0\\\"] * lscale,\\n lon_0 * lscale,\\n grid_dict[\\\"dx\\\"] * 1e3 * stride, grid_dict[\\\"dy\\\"] * 1e3 * stride, 0b00000000, 0b01000000,\\n proj_dict[\\\"lat_1\\\"] * lscale,\\n proj_dict[\\\"lat_2\\\"] * lscale, -90 * lscale, 0]\\n pdtmp1 = np.array([1, 31, 4, 0, 31, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1], dtype=np.int32)\\n drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)\\n time_list = list(date.utctimetuple()[0:6])\\n grib_objects = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))\\n grib_objects.addgrid(gdsinfo, gdtmp1)\\n pdtmp1[8] = end_hour\\n pdtmp1[-2] = 0\\n grib_objects.addfield(1, pdtmp1, 0, drtmp1, filtered_forecast)\\n grib_objects.end()\\n filename = date_outpath + \\\"{0}_{6}_Hail_{1}_Cali_NMEP_{2}mm_{3}_Hours_{4}-{5}.grib2\\\".format(\\n config.ensemble_name,\\n target_dataset,\\n size,\\n date.strftime(config.run_date_format),\\n start_hour,end_hour,config.forecast_model_names)\\n print(\\\"Writing to \\\" + filename )\\n \\n grib_file = open(filename, \\\"wb\\\")\\n grib_file.write(grib_objects.msg)\\n grib_file.close()\\n\\n return\",\n \"def createDefaultFilterbank(window):\\n # Gaussians:: G1 = N(0, 1), G2 = N(0, 2), G3 = N(0, 4)\\n # Laplacian of Gaussians:: LoG1 = Lap(N(0, 1)), LoG2=Lap(N(0, 2)), LoG3=Lap(N(0, 4)), LoG4=Lap(N(0, 8))\\n # Derivative of Gaussian (x):: Div1xG1 = d/dx N(0,2), Div1xG2=d/dx N(0,4)\\n # Derivative of Gaussian (y): Div1yG1 = d/dy N(0,2), Div1yG2=d/dy N(0,4)\\n \\n G1 = gaussian_kernel(window, window, 1)\\n G2 = gaussian_kernel(window, window, 2)\\n G3 = gaussian_kernel(window, window, 4)\\n \\n # see http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm\\n LoG1 = laplacianOfGaussian_kernel(window, window, 1)\\n LoG2 = laplacianOfGaussian_kernel(window, window, 2)\\n LoG3 = laplacianOfGaussian_kernel(window, window, 4)\\n LoG4 = laplacianOfGaussian_kernel(window, window, 8)\\n \\n dx_G1 = gaussian_1xDerivative_kernel(window, window, 2)\\n dx_G2 = gaussian_1xDerivative_kernel(window, window, 4)\\n \\n dy_G1 = gaussian_1yDerivative_kernel(window, window, 2)\\n dy_G2 = gaussian_1yDerivative_kernel(window, window, 4)\\n \\n return np.array([G1, G2, G3, LoG1, LoG2, LoG3, LoG4, dx_G1, dx_G2, dy_G1, dy_G2])\",\n \"def pure_gabor():\\n \\n dots = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dot-images-coh1-2000ms-s02.p\\\", \\\"rb\\\" ), encoding='latin1') \\n x = np.arange(-40, 41, 1)\\n gaborx, gabory = make_gabors(x)\\n centres = np.array([[200,200]])\\n \\n nf = dots.shape[2]\\n nrf = centres.shape[0] # number of receptive fields\\n ng = gaborx.shape[1] # number of gabors per receptive field\\n \\n # offsets (from RF centres) of subimages to multiply with kernels\\n vw = int(np.floor(gabory.size/2))\\n v_offsets = np.arange(-vw, vw+1)\\n hw = int(np.floor(gaborx.shape[0]/2))\\n h_offsets = np.arange(-hw, hw+1)\\n \\n result = np.zeros((nrf, ng, nf))\\n for i in range(dots.shape[2]): \\n for j in range(nrf): \\n v_indices = v_offsets + centres[j,0]\\n h_indices = h_offsets + centres[j,1]\\n region = dots[v_indices[:,np.newaxis],h_indices,i]\\n for k in range(ng): \\n gabor = np.outer(gabory, gaborx[:,k])\\n result[j,k,i] = np.sum(gabor * region)\\n return result\",\n \"def run_grav(self):\\n\\n # Solucao direta\\n self.prod_w = []\\n self.prod_o = []\\n t0 = time.time()\\n # self.set_volumes_in_primal()\\n self.set_sat_in()\\n self.set_lamb_2()\\n self.set_global_problem_vf_3_gr1_bif()\\n self.Pf = self.solve_linear_problem(self.trans_fine, self.b, len(self.all_fine_vols_ic))\\n self.organize_Pf()\\n del self.Pf\\n self.mb.tag_set_data(self.pf_tag, self.all_fine_vols, np.asarray(self.Pf_all))\\n del self.Pf_all\\n self.test_conservation_fine()\\n # self.store_flux_pf_gr_bif = self.create_flux_vector_pf_gr_bif_1()\\n\\n \\\"\\\"\\\"\\n ################################################################\\n # Solucao Multiescala\\n self.calculate_restriction_op_2()\\n self.calculate_prolongation_op_het()\\n self.organize_op()\\n self.Tc = self.modificar_matriz(self.pymultimat(self.pymultimat(self.trilOR, self.trans_fine, self.nf_ic), self.trilOP, self.nf_ic), self.nc, self.nc)\\n self.Qc = self.modificar_vetor(self.multimat_vector(self.trilOR, self.nf_ic, self.b), self.nc)\\n self.Pc = self.solve_linear_problem(self.Tc, self.Qc, self.nc)\\n self.set_Pc()\\n self.Pms = self.multimat_vector(self.trilOP, self.nf_ic, self.Pc)\\n\\n del self.trilOP\\n del self.trilOR\\n del self.Tc\\n del self.Qc\\n del self.Pc\\n\\n self.organize_Pms()\\n del self.Pms\\n self.mb.tag_set_data(self.pms_tag, self.all_fine_vols, np.asarray(self.Pms_all))\\n del self.Pms_all\\n self.erro()\\n\\n self.test_conservation_coarse_gr()\\n # self.Neuman_problem_6_gr()\\n # self.store_flux_pms_gr = self.create_flux_vector_pms_gr()\\n ####################################################################\\n \\\"\\\"\\\"\\n\\n\\n\\n\\n\\n\\n\\n print('acaboooou')\\n self.mb.write_file('new_out_bif_gr.vtk')\\n\\n\\n shutil.copytree(self.caminho1, self.pasta)\",\n \"def _compute_gp_all_passbands_2D(obj, dataset, number_gp, t_min, t_max,\\n output_root=None, **kwargs):\\n obj_data = dataset.data[obj] # object's lightcurve\\n gp_times = np.linspace(t_min, t_max, number_gp)\\n filter_set = np.asarray(dataset.filter_set)\\n\\n kernel, gp_params, gp_predict = fit_2d_gp(obj_data, return_kernel=True,\\n return_gp_params=True)\\n gp_wavelengths = np.vectorize(pb_wavelengths.get)(filter_set)\\n obj_gps = predict_2d_gp(gp_predict, gp_times, gp_wavelengths)\\n\\n if output_root is not None:\\n obj_gps.write(os.path.join(output_root, f'gp_{obj}'), format='fits',\\n overwrite=True)\\n path_save_gps = os.path.join(output_root, f'used_gp_{obj}.pckl')\\n path_save_kernels = os.path.join(output_root, f'used_kernels_{obj}'\\n '.pckl')\\n path_save_params = os.path.join(output_root, f'used_params_{obj}.pckl')\\n # Save the GP already conditioned on a specific set of observations\\n with open(path_save_gps, 'wb') as f:\\n pickle.dump(gp_predict, f, pickle.HIGHEST_PROTOCOL)\\n with open(path_save_kernels, 'wb') as f:\\n pickle.dump(kernel, f, pickle.HIGHEST_PROTOCOL)\\n with open(path_save_params, 'wb') as f:\\n pickle.dump(gp_params, f, pickle.HIGHEST_PROTOCOL)\\n\\n return obj_gps\",\n \"def extract_features(img, sigmas, n_features): \\n dims = img.shape # dimensions of the image\\n \\n features = np.zeros((dims[0], dims[1], n_features)) # each feature map has the same size as the input image\\n \\n # the first feature we use is the pixel intensity in the green channel itself\\n img_g = img[:,:,1] #I just assume it follows the RGB convention and not GBR or BGR...\\n features[:,:,0] = img_g\\n features[:,:,1] = np.sum(img,axis=2) \\n \\n gabors = get_gabors() \\n \\n # >>> YOUR CODE STARTS HERE <<<\\n i = 2\\n# for s in sigmas:\\n# gfilters = gauss_filter(s)\\n# for gf in gfilters:\\n# features[:,:,i] = scipy.signal.fftconvolve(img_g, gf, mode='same') ;i+=1\\n for s in sigmas:\\n gauss = gauss_filter(s)\\n for g in gauss:\\n features[:,:,i] = scipy.signal.fftconvolve(img_g, g, mode='same') ;i+=1\\n \\n for gabor in gabors:\\n features[:,:,i] = scipy.signal.fftconvolve(img_g, gabor, mode='same') ;i+=1\\n \\n \\n features[:,:,i] = sobel(img_g, axis=0) ;i+=1\\n features[:,:,i] = sobel(img_g, axis=1) ;i+=1\\n features[:,:,i] = sobel(img_g, axis=0)+sobel(img_g, axis=1) ;i+=1\\n features[:,:,i] = feature.canny(img_g, sigma=0.0) ;i+=1\\n features[:,:,i] = feature.canny(img_g, sigma=0, low_threshold=13, high_threshold=50);i+=1\\n features[:,:,i] = feature.canny(img_g, sigma=1)\\n # >>> YOUR CODE ENDS HERE <<< \\n \\n return features\",\n \"def IR():\\n s = np.array(\\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\\n )\\n\\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\\n\\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\\n\\n comp = [comp1, comp2, comp3, comp4, comp5]\\n MSE = []\\n colors = [\\\"#1f77b4\\\", \\\"#ff7f0e\\\", \\\"#2ca02c\\\", \\\"#d62728\\\", \\\"#9467bd\\\"]\\n x_new = np.linspace(0, 3000, 10000)\\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\\n plt.scatter(TI, i, c=j)\\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\\n # M_z0, T1, M0 = popt\\n M0, T1 = popt\\n y_new = MZ(x_new, *popt)\\n plt.plot(x_new, y_new, \\\"--\\\", c=j, label=f\\\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\\\")\\n MSE.append(mean_squared_error(i,y_new[TI]))\\n print(MSE)\\n print(np.mean(MSE))\\n plt.grid()\\n plt.legend(loc=\\\"best\\\")\\n plt.xlabel(\\\"TI\\\")\\n plt.ylabel(r\\\"Singal Intensity $M_z$\\\")\\n plt.show()\",\n \"def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\\n panel_properties, latitude):\\n\\n\\n n0 = panel_properties['n0']\\n c1 = panel_properties['c1']\\n c2 = panel_properties['c2']\\n mB0_r = panel_properties['mB0_r']\\n mB_max_r = panel_properties['mB_max_r']\\n mB_min_r = panel_properties['mB_min_r']\\n C_eff = panel_properties['C_eff']\\n t_max = panel_properties['t_max']\\n IAM_d = panel_properties['IAM_d']\\n Aratio = panel_properties['aperture_area_ratio']\\n Apanel = panel_properties['module_area']\\n dP1 = panel_properties['dP1']\\n dP2 = panel_properties['dP2']\\n dP3 = panel_properties['dP3']\\n dP4 = panel_properties['dP4']\\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\\n\\n # create lists to store results\\n list_results = [None] * number_groups\\n list_areas_groups = [None] * number_groups\\n Sum_mcp_kWperC = np.zeros(8760)\\n Sum_qout_kWh = np.zeros(8760)\\n Sum_Eaux_kWh = np.zeros(8760)\\n Sum_qloss = np.zeros(8760)\\n Sum_radiation_kWh = np.zeros(8760)\\n\\n Tin_array_C = np.zeros(8760) + Tin_C\\n aperature_area_per_module = Aratio * Apanel\\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\\n\\n # calculate equivalent length of pipes\\n lv = panel_properties['module_length'] # module length\\n number_modules = round(total_area_module/Apanel) # this is an estimation\\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\\n\\n if panel_properties['type'] == 'ET': # for evacuated tubes\\n Nseg = 100 # default number of subsdivisions for the calculation\\n else:\\n Nseg = 10 # default number of subsdivisions for the calculation\\n\\n for group in range(number_groups):\\n # load panel angles from group\\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\\n area_per_group = prop_observers.loc[group, 'total_area_module']\\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\\n\\n # create dataframe with irradiation from group\\n\\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\\n\\n # calculate incidence angle modifier for beam radiation\\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\\n\\n # calculate heat production from a solar collector of each group\\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\\n aperature_area_per_module, dP1, dP2, dP3, dP4,\\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\\n l_int_mperm2, Nseg)\\n\\n\\n # multiplying the results with the number of panels in each group and write to list\\n number_modules_per_group = area_per_group / Apanel\\n list_areas_groups[group] = area_per_group\\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\\n\\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\\n\\n Final = pd.DataFrame(\\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\\n\\n return list_results, Final\",\n \"def global_analysis(tomo, b_th, c=18):\\n\\n ## Thesholding and Volume analysis\\n if c == 6:\\n con_mat = [ [[0, 0, 0], [0, 1, 0], [0, 0, 0]],\\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]],\\n [[0, 0, 0], [0, 1, 0], [0, 0, 0]] ]\\n elif c == 18:\\n con_mat = [[[0, 1, 0], [1, 1, 1], [0, 1, 0]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]]]\\n elif c == 26:\\n con_mat = [[[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]\\n else:\\n raise ValueError\\n tomo_lbl, num_lbls = sp.ndimage.label(tomo >= b_th, structure=np.ones(shape=[3, 3, 3]))\\n tomo_out = np.zeros(shape=tomo.shape, dtype=int)\\n lut = np.zeros(shape=num_lbls+1, dtype=int)\\n\\n ## COUNTING REGIONS METHODS\\n # import time\\n # hold_t = time.time()\\n # for lbl in range(1, num_lbls + 1):\\n # ids = tomo == lbl\\n # feat_sz = len(ids)\\n # tomo_out[ids] = feat_sz\\n # # print('[1]:', lbl, 'of', num_lbls)\\n # print time.time() - hold_t\\n\\n ## COUNTING PIXELS METHOD\\n ## Count loop\\n # cont, total = 0, np.prod(tomo.shape)\\n # import time\\n # hold_t = time.time()\\n for x in range(tomo.shape[0]):\\n for y in range(tomo.shape[1]):\\n for z in range(tomo.shape[2]):\\n id = tomo_lbl[x, y, z]\\n lut[id] += 1\\n # cont += 1\\n # print('[1]:', cont, 'of', total)\\n #\\n ## Write loop\\n # cont, total = 0, np.prod(tomo.shape)\\n\\n for x in range(tomo.shape[0]):\\n for y in range(tomo.shape[1]):\\n for z in range(tomo.shape[2]):\\n id = tomo_lbl[x, y, z]\\n if id > 0:\\n tomo_out[x, y, z] = lut[id]\\n # cont += 1\\n # print('[1]:', cont, 'of', total)\\n # print time.time() - hold_t\\n\\n return tomo_out\",\n \"def _make_gaussian_maps(x, gaussians, size=None, scaling=6.):\\n if size is None:\\n size = x.shape[-2:]\\n bs = x.shape[0]\\n else:\\n size = [size] * 2\\n bs = 1\\n dtype = x.dtype\\n device = x.device\\n\\n gaussian_maps = []\\n map_template = torch.ones(*size, dtype=dtype, device=device)\\n meshgrids = torch.meshgrid(\\n [torch.linspace(0, 1, size[0], dtype=dtype, device=device),\\n torch.linspace(0, 1, size[1], dtype=dtype, device=device),])\\n\\n for gaussian_idx, yx_mu_logstd in enumerate(torch.unbind(gaussians)):\\n map = map_template.clone()\\n for mu_logstd, mgrid in zip(yx_mu_logstd, meshgrids):\\n mu = mu_logstd[0]\\n std = torch.exp(mu_logstd[1])\\n map *= torch.exp(-((mgrid - mu) / std) ** 2 / 2)\\n\\n map *= scaling\\n gaussian_maps.append(map)\\n\\n gaussian_maps = torch.stack(gaussian_maps)\\n gaussian_maps = gaussian_maps.unsqueeze(0).expand(bs, -1, -1, -1)\\n return gaussian_maps\",\n \"def residmapComparison():\\n srcmap001 = fits.open('dataFiles/6gev_srcmap_001.fits')\\n srcmap03 = fits.open('dataFiles/6gev_srcmap_03.fits')\\n\\n image_data = fits.getdata('dataFiles/6gev_image.fits')\\n filename = get_pkg_data_filename('dataFiles/6gev_image.fits')\\n hdu = fits.open(filename)[0]\\n wcs = WCS(hdu.header)\\n\\n #Given the results of the fit, calculate the model\\n modelData001 = np.zeros(srcmap001[0].shape)\\n modelData03 = np.zeros(srcmap03[0].shape)\\n\\n file = open('plotsData/fitResults001.pk1','rb')\\n fit001 = pickle.load(file)\\n file.close()\\n\\n file = open('plotsData/fitResults03.pk1','rb')\\n fit03 = pickle.load(file)\\n file.close()\\n\\n\\n for source in fit001:\\n the_index = srcmap001.index_of(source)\\n\\n modelData001 += fit001[source][:, None, None]*srcmap001[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap001[the_index].data, axis=2), axis=1)[:-1, None, None]\\n for source in fit03:\\n the_index = srcmap03.index_of(source)\\n modelData03 += fit03[source][:, None, None]*srcmap03[the_index].data[:-1, :, :]/np.sum(np.sum(srcmap03[the_index].data, axis=2), axis=1)[:-1, None, None]\\n\\n fig = plt.figure(figsize=[12, 4.5])\\n\\n vmin = -25.0\\n vmax = 25.0\\n cbStep = 5.0\\n ax = fig.add_subplot(121, projection=wcs)\\n ax=plt.gca()\\n ax.tick_params(direction='in')\\n c = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax.get_transform('galactic'))\\n ax.add_patch(c)\\n mappable=plt.imshow((image_data-np.sum(modelData001,axis=0)),cmap='seismic',origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')#\\n plt.xlabel('Galactic Longitude')\\n plt.ylabel('Galactic Latitude')\\n plt.title('GC Point Source ($>6$ GeV)')\\n cb = plt.colorbar(mappable, label='Residual counts per pixel', pad=0.01,ticks=np.arange(vmin, vmax+cbStep, cbStep))\\n cb.ax.tick_params(width=0)\\n\\n\\n ax2=fig.add_subplot(122, projection=wcs)\\n ax2 = plt.gca()\\n c2 = Wedge((gc_l, gc_b), 1.0, theta1=0.0, theta2=360.0, width=14.0, edgecolor='black', facecolor='#474747', transform=ax2.get_transform('galactic'))\\n ax2.add_patch(c2)\\n mappable2 = plt.imshow((image_data-np.sum(modelData03,axis=0)), cmap='seismic',origin='lower',vmin=vmin, vmax=vmax, interpolation='gaussian')\\n plt.xlabel('Galactic Longitude')\\n plt.ylabel('Galactic Latitude')\\n plt.title('GC Extended Source ($>6$ GeV)')\\n cb2 = plt.colorbar(mappable2, label='Residual counts per pixel', pad=0.01, ticks=np.arange(vmin, vmax+cbStep, cbStep))\\n cb2.ax.tick_params(width=0)\\n fig.tight_layout()\\n plt.subplots_adjust(wspace = 0.13, left=0.04, bottom=0.13, top=0.92)\\n #plt.savefig('plots/residComparison.pdf',bbox_inches='tight')\\n plt.show()\",\n \"def womgau(hop):\\n import numpy as np\\n import logging\\n import matplotlib.pyplot as plt\\n from scipy.optimize import curve_fit\\n from tmath.wombat.womwaverange import womwaverange\\n from tmath.wombat.womget_element import womget_element\\n from tmath.wombat.inputter import inputter\\n from tmath.wombat.inputter_single import inputter_single\\n from tmath.wombat.gauss import gauss\\n from tmath.wombat.gauss_cont import gauss_cont\\n from tmath.wombat.yesno import yesno\\n print(' ')\\n logging.info('Object is {}'.format(hop[0].obname))\\n print(' ')\\n print('Spectrum runs from {} to {}'.format(hop[0].wave[0],hop[0].wave[-1]))\\n print(' ')\\n print('This routine expects the spectrum to be in flambda units.')\\n print('It also expects a linear wavelength scale.')\\n print(' ')\\n print('Choose general region of spectrum\\\\n')\\n nwave,nflux,mode=womwaverange(hop[0].wave,hop[0].flux,'none')\\n print('\\\\nNow pick the exact range for the fit')\\n waveint,fluxint,mode=womwaverange(nwave,nflux,mode)\\n indexblue=womget_element(nwave, waveint[0])\\n indexred=womget_element(nwave,waveint[-1])\\n if (mode == 'w'):\\n done = False\\n while (not done):\\n print(' ')\\n wavecenter=inputter('Enter approximate center of Gaussian : ','float',False)\\n indexcenter=womget_element(waveint,wavecenter)\\n if (indexcenter <= 0) or (wavecenter > waveint[-1]):\\n print('Bad central wavelength, try again')\\n else:\\n done = True\\n else:\\n done=False\\n while (not done):\\n print('Mark the approximate center of the Gaussian')\\n pickcent=plt.ginput(1,timeout=-1)\\n indexcenter=womget_element(waveint,pickcent[0][0])\\n print('\\\\nApproximate center at {}'.format(waveint[indexcenter]))\\n print('\\\\nIs this OK?')\\n answer=yesno('y')\\n if (answer == 'y'):\\n done=True\\n weights=np.sqrt(hop[0].var[indexblue:indexred+1])\\n print(' ')\\n continuum=inputter_single('Do you want to fit gaussian with (c)ontinuum, or (n)o continuum? ','cn')\\n if (continuum == 'c'):\\n p=[fluxint[indexcenter], waveint[indexcenter],3.0,1.0,waveint[0]]\\n result=curve_fit(gauss_cont,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\\n else:\\n p=[fluxint[indexcenter], waveint[indexcenter],3.0]\\n result=curve_fit(gauss,waveint,fluxint,sigma=weights,p0=p,absolute_sigma=True,full_output=True)\\n coefferr=np.sqrt(np.diag(result[1]))\\n coeff=result[0]\\n # make 'finer-grained' version of fit, 0.2A/pix for calculations\\n wavecalc=np.arange(2*5*50*abs(coeff[2]))*0.2+coeff[1]-0.2*5*50*abs(coeff[2])\\n calccenter=womget_element(wavecalc,coeff[1])\\n if (continuum == 'c'):\\n fluxcalc=gauss_cont(wavecalc,*coeff)\\n fluxcont=wavecalc*coeff[3]+coeff[4]\\n fluxgaussian=fluxcalc-fluxcont\\n linecont=fluxcont[calccenter]\\n else:\\n fluxcalc=gauss(wavecalc,*coeff)\\n \\n \\n deltafit=wavecalc[1]-wavecalc[0]\\n calcindexblue=womget_element(wavecalc,waveint[0])\\n calcindexred=womget_element(wavecalc,waveint[-1])\\n sumfluxcalc=np.sum(fluxcalc[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxcalc=np.sum(fluxcalc*deltafit)\\n chi=(result[2]['fvec']**2).sum()\\n redchi=chi/(len(waveint)-len(coeff))\\n if (continuum == 'c'):\\n sumfluxgaussian=np.sum(fluxgaussian[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxgaussian=np.sum(fluxgaussian*deltafit)\\n sumfluxcont=np.sum(fluxcont[calcindexblue:calcindexred+1]*deltafit)\\n sumallfluxcont=np.sum(fluxcont*deltafit)\\n sumallfluxcont_test=np.sum(fluxcont)\\n # propagate uncertainty (from old version) not sure this is correct\\n height_pct=coefferr[0]/coeff[0]\\n sigma_pct=coefferr[2]/coeff[2]\\n flux_pct=np.sqrt(height_pct**2+sigma_pct**2)\\n sumfluxgaussiansig=sumfluxgaussian*flux_pct\\n sumallfluxgaussiansig=sumallfluxgaussian*flux_pct\\n plt.cla()\\n plt.plot(nwave,nflux,drawstyle='steps-mid',color='k')\\n plt.ylabel('Flux')\\n plt.xlabel('Wavelength')\\n xmin,xmax=plt.xlim()\\n ymin,ymax=plt.ylim()\\n plt.plot(wavecalc,fluxcalc,drawstyle='steps-mid',color='b')\\n if (continuum == 'c'):\\n plt.plot(wavecalc,fluxgaussian,drawstyle='steps-mid',color='r')\\n plt.plot(wavecalc,fluxcont,drawstyle='steps-mid',color='g')\\n plt.plot([waveint[0],waveint[0]],[ymin,ymax],color='k',linestyle='--')\\n plt.plot([waveint[-1],waveint[-1]],[ymin,ymax],color='k',linestyle='--')\\n plt.xlim([xmin,xmax])\\n plt.ylim([ymin,ymax])\\n logging.info('For object {} Gaussian fit'.format(hop[0].obname))\\n if (continuum == 'c'):\\n print('\\\\nData = Black, Fit = Blue, Continuum = Green, Fit-Continuum = Red\\\\n')\\n else:\\n print('\\\\nData = Black, Fit = Blue\\\\n')\\n logging.info('Height {:16.8f}+/-{:16.8f}'.format(coeff[0],coefferr[0]))\\n logging.info('Center {:16.8f}+/-{:16.8f}'.format(coeff[1],coefferr[1]))\\n logging.info('Sigma {:16.8f}+/-{:16.8f}'.format(coeff[2],coefferr[2]))\\n if (continuum == 'c'):\\n FWHM = 2.35482*np.abs(coeff[2])\\n rest_wave = input('Rest wavelength [N/A]: ') or None\\n redshift = input('Redshift [N/A]: ') or None\\n if rest_wave:\\n rest_wave = float(rest_wave)\\n w1 = (rest_wave - FWHM/2.)/(1.+float(redshift)) \\n w2 = (rest_wave + FWHM/2.)/(1.+float(redshift)) \\n c = 299792.458\\n v1 = -1.*c*((rest_wave/w1)**2. - 1)/(1+((rest_wave/w1)**2.))\\n v2 = -1.*c*((rest_wave/w2)**2. - 1)/(1+((rest_wave/w2)**2.))\\n logging.info('Slope {:16.8f}+/-{:16.8f}'.format(coeff[3],coefferr[3]))\\n logging.info('Y-intercept {:16.8f}+/-{:16.8f}'.format(coeff[4],coefferr[4]))\\n logging.info('FWHM {:16.8f}+/-{:16.8f}'.format(2.35482*np.abs(coeff[2]),2.35482*coefferr[2]))\\n logging.info('FWHM (velocity) {:16.8f} km/s'.format(v2-v1))\\n logging.info('Flux between dotted lines (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumfluxgaussian, sumfluxgaussiansig))\\n logging.info('EW between dotted lines (Gaussian): {:16.8f}'.format(sumfluxgaussian/linecont))\\n logging.info('Flux for full (Gaussian): {:16.8f}+/-{:16.8f}'.format(sumallfluxgaussian, sumallfluxgaussiansig))\\n logging.info('EW for full (Gaussian): {:16.8f}'.format(sumallfluxgaussian/linecont))\\n logging.info('Continuum flux at line center: {:16.8f}'.format(linecont))\\n\\n \\n logging.info('Chi^2: {}'.format(chi))\\n logging.info('Reduced chi^2: {}'.format(redchi))\\n logging.info('All fluxes might need to be scaled by 1e-15')\\n print(' ')\\n return hop\",\n \"def check_gabor_approx(): \\n x = np.arange(-40, 41, 1)\\n DOG2 = make_DOG(3, x)\\n DOGs = get_DOGs(3, x, (x.size, x.size))\\n gaborx, gabory = make_gabors(x)\\n n_gabors = gaborx.shape[1]\\n\\n w_kernel = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-new.p\\\", \\\"rb\\\" ))\\n\\n plt.figure()\\n for i in range(n_gabors):\\n gabor = np.outer(gabory, gaborx[:,i])\\n approx = np.dot(DOGs, w_kernel[:,:,i].flatten())\\n plt.subplot(3,n_gabors,i+1)\\n plt.imshow(gabor)\\n plt.subplot(3,n_gabors,i+n_gabors+1)\\n plt.imshow(np.reshape(approx, (x.size, x.size)))\\n plt.subplot(3,n_gabors,i+2*n_gabors+1) \\n plt.imshow(nd.filters.convolve(w_kernel[:,:,i].T, DOG2))\\n plt.show()\",\n \"def two_gaussians(x):\\n log_like1 = (\\n -0.5 * n * pt.log(2 * np.pi)\\n - 0.5 * pt.log(dsigma)\\n - 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)\\n )\\n log_like2 = (\\n -0.5 * n * pt.log(2 * np.pi)\\n - 0.5 * pt.log(dsigma)\\n - 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)\\n )\\n return pt.log(w1 * pt.exp(log_like1) + w2 * pt.exp(log_like2))\",\n \"def _plot_pr2(gc_val_lst: list, at_val_lst: list, g3_val_lst: list, a3_val_lst: list, organism_name: str | None = None,\\n save_image: bool = False, folder_path: str = 'Report', gene_analysis: bool = True):\\n N = len(gc_val_lst)\\n gc_arr = np.array(gc_val_lst)\\n g3_arr = np.array(g3_val_lst)\\n at_arr = np.array(at_val_lst)\\n a3_arr = np.array(a3_val_lst)\\n x = g3_arr / gc_arr\\n y = a3_arr / at_arr\\n # [[x_val], [y_val]]\\n ag_lst = [[], []]\\n ag_count = 0\\n tg_lst = [[], []]\\n tg_count = 0\\n tc_lst = [[], []]\\n tc_count = 0\\n ac_lst = [[], []]\\n ac_count = 0\\n no_bias = [[], []]\\n no_bias_count = 0\\n for x_val, y_val in zip(x, y):\\n if x_val > 0.5 and y_val > 0.5:\\n ag_lst[0].append(x_val)\\n ag_lst[1].append(y_val)\\n ag_count += 1\\n elif x_val > 0.5 > y_val:\\n tg_lst[0].append(x_val)\\n tg_lst[1].append(y_val)\\n tg_count += 1\\n elif x_val < 0.5 and y_val < 0.5:\\n tc_lst[0].append(x_val)\\n tc_lst[1].append(y_val)\\n tc_count += 1\\n elif x_val < 0.5 < y_val:\\n ac_lst[0].append(x_val)\\n ac_lst[1].append(y_val)\\n ac_count += 1\\n else:\\n no_bias[0].append(x_val)\\n no_bias[1].append(y_val)\\n no_bias_count += 1\\n fig, ax = plt.subplots(figsize=(9, 5.25))\\n ax.scatter(ag_lst[0], ag_lst[1], s=5, label='AG', alpha=0.5, zorder=2)\\n ax.scatter(tg_lst[0], tg_lst[1], s=5, label='TG', alpha=0.5, zorder=2)\\n ax.scatter(tc_lst[0], tc_lst[1], s=5, label='TC', alpha=0.5, zorder=2)\\n ax.scatter(ac_lst[0], ac_lst[1], s=5, label='AC', alpha=0.5, zorder=2)\\n ax.scatter(no_bias[0], no_bias[1], s=5, label=f'No Bias', alpha=0.5, zorder=3)\\n ax.grid(True, linestyle=':')\\n ax.legend()\\n ax.text(0.6, 0.95, 'AG')\\n ax.text(0.4, 0.95, 'AC')\\n ax.text(0.6, 0.05, 'TG')\\n ax.text(0.4, 0.05, 'TC')\\n ax.text(0, 0, f'AG: {ag_count}\\\\nTG: {tg_count}\\\\nTC: {tc_count}\\\\nAC: {ac_count}\\\\nNo Bias: {no_bias_count}',\\n transform=ax.transAxes)\\n plt.xlim([0, 1])\\n plt.ylim([0, 1])\\n plt.axvline(0.5, color='grey', zorder=1)\\n plt.axhline(0.5, color='grey', zorder=1)\\n plt.xlabel(r\\\"$G_3/GC_3$ Values\\\")\\n plt.ylabel(r\\\"$A_3/AT_3$ Values\\\")\\n suptitle = r'Parity Rule 2 plot' if organism_name is None else f\\\"Parity Rule 2 plot for {organism_name}\\\"\\n plt.suptitle(suptitle, fontsize=16)\\n title = f'Total genes: {N}' if gene_analysis else f'Total genome: {N}'\\n plt.title(title, fontsize=12)\\n if save_image:\\n make_dir(folder_path)\\n name = 'PR2_plot.png' if organism_name is None else f\\\"PR2_plot_{organism_name}.png\\\"\\n file_name = join(folder_path, name)\\n if is_file_writeable(file_name):\\n plt.savefig(file_name, dpi=500)\\n print(f'Saved file can be found as {abspath(file_name)}')\\n plt.show()\\n plt.close()\",\n \"def generate_stat(sample_size, sparsity = 0, amplitude = 0, sigma = 1):\\n var = generate_variable(sample_size, sparsity, amplitude, sigma)\\n y_obs = var[0]\\n \\n \\\"\\\"\\\" \\n f is equal to -X(t,theta) and we will minimize f (max. X)\\n \\\"\\\"\\\"\\n def f(x):\\n \\\"\\\"\\\" \\n f(x)=-X(t,theta) where x[0]=t and x[1]=theta\\n \\\"\\\"\\\"\\n res = np.real(np.exp(-1j*x[1])*\\\\\\n sum(y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\\\\n for k in range(-sample_size,sample_size+1))) \\n res = -res/np.sqrt(2*sample_size+1) \\n return res\\n \\n def grad_f(x):\\n \\\"\\\"\\\" \\n gradient of f\\n \\\"\\\"\\\"\\n res1 = np.real(np.exp(-1j*x[1])*\\\\\\n sum(1j*k*y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\\\\n for k in range(-sample_size,sample_size+1)))\\n res1 = -res1/np.sqrt(2*sample_size+1)\\n \\n res2 = np.real(np.exp(-1j*x[1])*\\\\\\n sum(-1j*y_obs[k+sample_size]*np.exp(1j*k*x[0]) \\\\\\n for k in range(-sample_size,sample_size+1)))\\n res2 = -res2/np.sqrt(2*sample_size+1)\\n return np.array([res1, res2])\\n \\n #% Minimizing f\\n \\n \\\"\\\"\\\" \\n we minimize on [0, 2pi]^2\\n \\\"\\\"\\\"\\n bnds = ((0, 2*np.pi), (0, 2*np.pi))\\n \\n \\\"\\\"\\\" \\n We begin by a greedy search of the initialization point over a grid of size 126^2\\n the initialization point is init\\n \\\"\\\"\\\"\\n x = y = np.arange(0, 2*np.pi, 0.05)\\n steps = 126\\n X, Y = np.meshgrid(x, y)\\n val = np.array([f([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])\\n init = np.argmin(val)\\n x1 = init%steps\\n x2 = (init-x1)/steps\\n init = [x1*0.05, x2*0.05]\\n \\n \\\"\\\"\\\" \\n we minimize f...\\n \\\"\\\"\\\"\\n result = sco.minimize(f, init, method=\\\"L-BFGS-B\\\",\\\\\\n jac=grad_f, bounds=bnds, tol=1e-15)\\n \\n \\\"\\\"\\\" \\n (t1,theta1) is the argmax of X(t, theta) and l1=$\\\\lambda_1$\\n \\\"\\\"\\\"\\n t1 = result.x[0]\\n theta1 = result.x[1]\\n l1 = -f([t1,theta1])\\n \\n \\n \\\"\\\"\\\" \\n Function g(x) is equal to (X(t1,theta1)-X(x))/(1-rho((t1,theta1)-x))\\n \\\"\\\"\\\"\\n def g(x):\\n a0 = x[0]-t1\\n a1 = x[1]-theta1\\n N = 2*sample_size+1\\n \\n vec = np.array([a0,a1])\\n r = np.linalg.norm(vec)\\n \\\"\\\"\\\" \\n the value for r=0 is set to l1 (note that r=0 corresponds to x=(t1,theta1))\\n \\\"\\\"\\\" \\n res = l1 \\n \\n if (0<r) & (r<0.00001):\\n \\\"\\\"\\\" \\n we look a values near (t1,theta1) for which an indetermination occurs\\n \\\"\\\"\\\" \\n alpha= np.arccos(np.clip(a0/np.sqrt(a0**2+a1**2), -1.0, 1.0))\\n u0 = np.cos(alpha)\\n u1 = np.sin(alpha)\\n \\\"\\\"\\\" \\n u0,u1 defines the direction (unit vector)\\n \\\"\\\"\\\"\\n denom = sum((k*np.cos(alpha)-np.sin(alpha))**2*\\\\\\n (np.sinc((r*(k*np.cos(alpha)-np.sin(alpha)))/(2*np.pi)))**2\\\\\\n for k in range(-sample_size,sample_size+1))/N\\n \\\"\\\"\\\" \\n denom computes the denominator\\n \\\"\\\"\\\"\\n \\n# \\\"\\\"\\\" \\n# We use simpson rule for the numerator\\n# \\\"\\\"\\\"\\n# h = np.linspace(0,1,500)\\n# \\n# b0 = t1 + h*a0\\n# b1 = theta1 + h*a1\\n# \\n# value = (1-h)*(u0**2*\\\\\\n# np.real(np.exp(-1j*b1)*sum(-k**2*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\\\\n# for k in range(-sample_size,sample_size+1)))\\\\\\n# +2*u0*u1*\\\\\\n# np.real(np.exp(-1j*b1)*sum(k*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\\\\n# for k in range(-sample_size,sample_size+1)))\\\\\\n# +u1**2*\\\\\\n# np.real(np.exp(-1j*b1)*sum((-1)*y_obs[k+sample_size]*np.exp(1j*k*b0) \\\\\\n# for k in range(-sample_size,sample_size+1)))) \\n# value = value/np.sqrt(N)\\n# \\n# num = sci.simps(value, h)\\n \\n \\\"\\\"\\\" \\n we use a quadrature for the numerator\\n \\\"\\\"\\\" \\n fun_int = lambda w: (1-w)*(u0**2*\\\\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\\\\n sum(-k**2*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\\\\n for k in range(-sample_size,sample_size+1)))\\\\\\n +2*u0*u1*\\\\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\\\\n sum(k*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\\\\n for k in range(-sample_size,sample_size+1)))\\\\\\n +u1**2*\\\\\\n np.real(np.exp(-1j*(theta1+w*a1))*\\\\\\n sum((-1)*y_obs[k+sample_size]*np.exp(1j*k*(t1+w*a0)) \\\\\\n for k in range(-sample_size,sample_size+1)))) \\n \\n num = np.mean(sci.quad(fun_int, 0, 1, epsabs=1e-15, epsrel=1e-15, limit=1000))\\n \\n res = -num/denom\\n \\n if (r>=0.00001):\\n \\\"\\\"\\\" \\n we look a values far (t1,theta1) for which there is no indetermination\\n \\\"\\\"\\\" \\n res = (l1+f(x))/(1-(np.cos(a1)*dirichlet(a0,N)/N))\\n \\n return res \\n \\\"\\\"\\\" \\n we minimize g on [0, 2pi]^2 an dwe llok for the initialization point\\n \\\"\\\"\\\"\\n val2 = np.array([g([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])\\n init2 = np.argmin(val2)\\n x1 = init2%steps\\n x2 = (init2-x1)/steps\\n init2 = [x1*0.05, x2*0.05] \\n result2 = sco.minimize(g, init2, method=\\\"L-BFGS-B\\\", bounds=bnds, tol=1e-15) \\n \\\"\\\"\\\" \\n argmin of g\\n \\\"\\\"\\\"\\n t2 = result2.x[0]\\n theta2 = result2.x[1] \\n \\\"\\\"\\\" \\n value of lambda_2\\n \\\"\\\"\\\"\\n l21 = l1-result2.fun \\n a0 = t2-t1\\n a1 = theta2-theta1\\n N = 2*sample_size+1\\n l22 = l1-(l1+f([t2,theta2]))/(1-(np.cos(a1)*dirichlet(a0,N)/N))\\n l2 = max(l21,l22)\\n \\\"\\\"\\\" \\n we compute the statistic\\n \\\"\\\"\\\"\\n alpha1 = (1/3)*sample_size*(sample_size+1)\\n alpha2 = (1/np.sqrt(N))*\\\\\\n sum((k**2-alpha1)*\\\\\\n np.real(y_obs[k+sample_size]*np.exp(1j*(k*t1-theta1))) \\\\\\n for k in range(-sample_size,sample_size+1))\\n alpha3 = (1/np.sqrt(N))*sum(k*np.real(y_obs[k+sample_size]*\\\\\\n np.exp(1j*(k*t1-theta1))) for k in range(-sample_size,sample_size+1)) \\n stat = (sigma*(alpha1*l1+alpha2)*scs.norm.pdf(l1/sigma)+\\\\\\n (alpha1*sigma**2-alpha3**2)*(1-scs.norm.cdf(l1/sigma)))/\\\\\\n (sigma*(alpha1*l2+alpha2)*scs.norm.pdf(l2/sigma)+\\\\\\n (alpha1*sigma**2-alpha3**2)*(1-scs.norm.cdf(l2/sigma))) \\n \\n return stat\",\n \"def compute_GS(GMtcs):\\n\\n GS = np.mean(GMtcs,axis=0) #average over voxels\\n\\n return GS\",\n \"def forward(self, y):\\n # landmarks are the weighted average y\\n # activations across each channel\\n y_projected = torch.zeros_like(y)\\n y = self.softmax(y)\\n\\n # self.save_image(y[0, :, :, :])\\n\\n # print(y.size())\\n # y_0 = y\\n y = y.view(y.size(0), y.size(1), -1)\\n # print(y[0, :].min(), y[0, :].max())\\n mu_y = y * self.yv.reshape(1, 1, -1)\\n mu_y = mu_y.sum(dim=-1) #/ y.sum(dim=-1)\\n mu_x = y * self.xv.reshape(1, 1, -1)\\n mu_x = mu_x.sum(dim=-1) #/ y.sum(dim=-1)\\n means = torch.cat((mu_y.unsqueeze(-1), mu_x.unsqueeze(-1)), dim=-1)\\n\\n # project landmarsk to guassain fuction with fixed standard deviation\\n # yv, xv = torch.meshgrid([torch.arange(0, y.size(-2)), torch.arange(0, y.size(-1))])\\n # h_act = torch.zeros(y_projected.size(0), y_projected.size(1), device=y.device)\\n for batch_id in range(y_projected.size(0)):\\n for heatmap_id in range(y_projected.size(1)):\\n gdist = MultivariateNormal(means[batch_id, heatmap_id, :], covariance_matrix=self.cov)\\n logprobs = gdist.log_prob(torch.cat((self.yv.unsqueeze(0), self.xv.unsqueeze(0)), dim=0).t())\\n y_projected[batch_id, heatmap_id, :, :] = torch.exp(logprobs).reshape(y_projected.size(-2), y_projected.size(-1))\\n # y_projected[batch_id, heatmap_id, :, :] -= y_projected[batch_id, heatmap_id, :, :].min()\\n # y_projected[batch_id, heatmap_id, :, :] /= y_projected[batch_id, heatmap_id, :, :].max()\\n # coords = means[batch_id, heatmap_id, :].round().long()\\n # h_act[batch_id, heatmap_id] = y_0[batch_id, heatmap_id, coords[0], coords[1]]\\n # h_act[batch_id, heatmap_id]\\n # max_coords = y_0[batch_id, heatmap_id, :, :].clone()\\n # max_coords[max_coords < max_coords.max()] = 0\\n # # print(max_coords.size())\\n # if batch_id == 0 and heatmap_id == 0:\\n # print(means[batch_id, heatmap_id, :], max_coords.nonzero().float().mean(0))\\n # a = 'fail' if y.pow(2).mean() > h_act.mean() else 'pass'\\n # print(h_act.mean(), y.mean())\\n # print(y_projected.max(), y_projected.min())\\n # y_out = y_projected.sum(dim=1)\\n # self.save_image(y_out.unsqueeze(1), normalize=True)\\n # self.save_image(y_out.unsqueeze(1), normalize=True, output='landmarks')\\n return y_projected, self.prior_loss(means, y)\",\n \"def map_sim_property(**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n counter = 0\\n fignum = 1\\n if p.gal_index == 'all':\\n\\n for gal_index in GR.N_gal - np.arange(GR.N_gal) - 1:\\n\\n if counter == 0:\\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\\n counter = 9\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='simgas')\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \\n\\n # Plot\\n Rmax = max_scale/2\\n ax1 = axs[9 - counter]\\n if p.log:\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\\n map2D = np.log10(map2D)\\n if not p.log:\\n map2D[map2D < p.vmin] = p.vmin/2\\n map2D[map2D > p.vmax] = p.vmax\\n im = ax1.imshow(map2D,\\\\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-0.99*Rmax,0.99*Rmax])\\n ax1.set_ylim([-0.99*Rmax,0.99*Rmax])\\n if (p.prop == 'm') & (p.text == True):\\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.75,'SFR=%.2eM$_{\\\\odot}$/yr' % GR.SFR[gal_index],\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n\\n counter -= 1\\n\\n #if counter == 0:\\n # ax1 = plt.subplots(1, 1)\\n #cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\\n # fig.colorbar(im,shrink=0.8,label=lab)\\n\\n if counter == 0 or gal_index == GR.N_gal-1:\\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format))\\n # plt.tight_layout()\\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/')\\n plt.savefig(p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format), format=p.format, dpi=250, facecolor='w')\\n fignum += 1\\n\\n else:\\n if p.add:\\n fig, ax1 = plt.gcf(), p.ax\\n if not p.add:\\n fig = plt.figure(figsize=(8,6))\\n ax1 = fig.add_axes([0.1, 0.01, 0.8, 0.8]) \\n ax1.axis('equal')\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type=p.sim_type)\\n if p.R_max:\\n # Cut out square\\n simgas = simgas[(np.abs(simgas.x) < p.R_max) & (np.abs(simgas.y) < p.R_max)]\\n # Add bottom left corner\\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\\n extra_row['x'],extra_row['y'] = -p.R_max,-p.R_max\\n extra_row[p.prop] = 0\\n simgas = simgas.append(extra_row).reset_index(drop=True) \\n # Add top right corner\\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\\n extra_row['x'],extra_row['y'] = p.R_max,p.R_max\\n extra_row[p.prop] = 0\\n simgas = simgas.append(extra_row).reset_index(drop=True) \\n else:\\n pass\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \\n print('Min and max of map: ',map2D.min(),map2D.max())\\n #map2D[map2D < 1e4] = 1e6\\n # Plot map\\n if not p.R_max:\\n p.R_max = max_scale/2\\n if p.log: \\n if not p.vmax: p.vmax = np.log10(map2D).max()\\n if not p.vmin: p.vmin = np.log10(map2D).max() - 4\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\\n map2D = np.log10(map2D)\\n else:\\n if not p.vmax: p.vmax = np.max(map2D)\\n if not p.vmin: p.vmin = np.min(map2D) / 1e3\\n map2D[map2D < p.vmin] = p.vmin #np.min(map2D[map2D > 0])\\n map2D = np.flipud(map2D)\\n\\n im = ax1.imshow(map2D,\\\\\\n extent=[-max_scale/2,max_scale/2,-max_scale/2,max_scale/2],vmin=p.vmin,vmax=p.vmax,cmap=p.cmap)\\n # Limit axes limits a bit to avoid area with no particles...\\n zoom = 1#/1.5\\n ax1.set_xlim([-1/zoom * p.R_max,1/zoom * p.R_max])\\n ax1.set_ylim([-1/zoom * p.R_max,1/zoom * p.R_max])\\n if p.colorbar: \\n divider = make_axes_locatable(ax1)\\n cax1 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n fig.colorbar(im,cax=cax1,label=lab)\\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n if (p.prop == 'm') & (p.text == True):\\n simstar = aux.load_temp_file(gal_ob=gal_ob,data_type='simstar')\\n ax1.text(0.05,0.92,'M$_{star}$=%.1e M$_{\\\\odot}$' % np.sum(simstar.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.86,'M$_{gas}$=%.1e M$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.80,'SFR=%.2f M$_{\\\\odot}$/yr' % GR.SFR[p.gal_index],\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/') \\n plt.savefig(p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index), format=p.format, dpi=250, facecolor='w')\\n\\n if not p.colorbar: return(im)\",\n \"def make_4gaussians_image(noise=True):\\n table = QTable()\\n table['amplitude'] = [50, 70, 150, 210]\\n table['x_mean'] = [160, 25, 150, 90]\\n table['y_mean'] = [70, 40, 25, 60]\\n table['x_stddev'] = [15.2, 5.1, 3.0, 8.1]\\n table['y_stddev'] = [2.6, 2.5, 3.0, 4.7]\\n table['theta'] = np.radians(np.array([145.0, 20.0, 0.0, 60.0]))\\n\\n shape = (100, 200)\\n data = make_gaussian_sources_image(shape, table) + 5.0\\n\\n if noise:\\n rng = np.random.RandomState(12345)\\n data += rng.normal(loc=0.0, scale=5.0, size=shape)\\n\\n return data\",\n \"def sample_values(self, positions, gibbs):\\n \\\"\\\"\\\"Sample important values\\\"\\\"\\\"\\n\\n if gibbs:\\n self.local_energy = self.h.local_energy_gibbs(positions)\\n self.accumulate_energy += self.h.local_energy_gibbs(positions)\\n self.accumulate_energy_sq += self.local_energy*self.local_energy\\n gradient_wf_a = 0.5*self.w.gradient_wavefunction_a(positions)\\n gradient_wf_b = 0.5*self.w.gradient_wavefunction_b(positions)\\n gradient_wf_W = 0.5*self.w.gradient_wavefunction_W(positions)\\n else:\\n self.local_energy = self.h.local_energy(positions)\\n self.accumulate_energy += self.h.local_energy(positions)\\n self.accumulate_energy_sq += self.local_energy*self.local_energy\\n gradient_wf_a = self.w.gradient_wavefunction_a(positions)\\n gradient_wf_b = self.w.gradient_wavefunction_b(positions)\\n gradient_wf_W = self.w.gradient_wavefunction_W(positions)\\n # self.local_energy = self.h.local_energy_numerical(positions)\\n # self.accumulate_energy += self.h.local_energy_numerical(positions)\\n # gradient_wf_a = np.zeros(self.w.M)\\n # gradient_wf_b = np.zeros(self.w.N)\\n # gradient_wf_W = np.zeros((self.w.M, self.w.N))\\n\\n self.accumulate_psi_term_a += gradient_wf_a\\n self.accumulate_psi_term_b += gradient_wf_b\\n self.accumulate_psi_term_W += gradient_wf_W\\n self.accumulate_both_a += gradient_wf_a*self.local_energy\\n self.accumulate_both_b += gradient_wf_b*self.local_energy\\n self.accumulate_both_W += gradient_wf_W*self.local_energy\",\n \"def gaussp(y1, y2, n):\\n\\n # First check for trivial or stupid requests\\n if n <= 0:\\n raise ValueError(\\\"Zero (or less) grid points is stupid. Stop it.\\\")\\n if n == 1:\\n r = np.array([0.5*(y2 + y1)])\\n wt = np.array([y2 - y1])\\n return r, wt\\n N_pi = 3.14159265358979323844 # Fortran uses stupid pi because of course it does\\n EPS = 1e-14 # Desired accuracy\\n n_sav = -1\\n\\n if n != n_sav:\\n n_sav = n\\n m = n\\n m, r, wt = GridGenerator.gausspp(m)\\n m = 0\\n\\n if m != n:\\n m = int((n+1)/2) # Care, integer division\\n x = np.zeros((2*m)) # Working r, not returned\\n w = np.zeros((2*m)) # Working wt, not returned\\n r = np.zeros((2*m))\\n wt = np.zeros((2*m))\\n for i in range(m):\\n r[i] = N_pi*(i+0.75)/(n+0.5)\\n r = np.cos(r)\\n\\n for i in range(m):\\n z = r[i]\\n z1 = 1e20 # Arbitrary large number to ensure at least 1 loop\\n while abs(z-z1) > EPS:\\n p1 = 1.0\\n p2 = 0.0\\n for j in range(n):\\n p3 = p2\\n p2 = p1\\n p1 = ((2*(j + 1) - 1)*z*p2 - j*p3)/(j + 1)\\n pp = n*(z*p1 - p2)/(z*z - 1.0)\\n z1 = z\\n z = z1 - p1/pp\\n x[i] = -z\\n x[n - (i + 1)] = z\\n w[i] = 2.0/((1.0 - z*z)*pp*pp)\\n w[n - (i + 1)] = w[i]\\n\\n for i in range(n):\\n fact = 0.5*(y2-y1)\\n r[i] = y1 + fact*(x[i] + 1.0)\\n wt[i] = fact*w[i]\\n\\n return n, r, wt\",\n \"def zonal_avg2(data,Log=False):\\n print 'setting up the destination grid'\\n # get lat and lon for new regular grid\\n# fpin = Nio.open_file('/home/ivan/Python/data/lat_t.nc','r')\\n fpin = Nio.open_file('/home/emunoz/Python/mapping/model_grid/lat_t.nc','r')\\n lat_t = fpin.variables['lat_t'][:]\\n lat_t_edges = fpin.variables['lat_t_edges'][:]\\n fpin.close()\\n# fpin = Nio.open_file('/home/ivan/Python/data/gx3v5.nc','r')\\n fpin = Nio.open_file('/home/emunoz/Python/mapping/model_grid/gx3v5.nc','r')\\n lon_t = N.sort(fpin.variables['TLONG'][0,:])\\n ulon = N.sort(fpin.variables['ULONG'][0,:])\\n lon_t_edges = N.concatenate((ulon,ulon[0,N.newaxis]+360.),0)\\n # get gx3v5 lat and lon\\n tlon = fpin.variables['TLONG'][:]\\n tlat = fpin.variables['TLAT'][:]\\n fpin.close()\\n\\n # compute area of cells in new regular grid\\n area = grid_area(lon_t_edges,lat_t_edges)\\n\\n nlat = lat_t.shape[0]\\n nlon = lon_t.shape[0]\\n\\n print 'computing weights for grid cell'\\n ilist = []\\n jlist = []\\n wghts2D = []\\n wghts3D = []\\n for i in range(nlat):\\n for j in range(nlon):\\n i_inds, j_inds = find_stn_idx(lon_t[j], lat_t[i], tlon, tlat)\\n ilist.append(i_inds)\\n jlist.append(j_inds)\\n dist = gc_dist(lon_t[i], lat_t[i], tlon, tlat)\\n # make weights=0 on land\\n work2D = 1./MA.array(dist,mask=data[0,...].mask)\\n wghts2D.append(MA.filled(N.take(N.take(work2D,i_inds,0),j_inds,1)\\n ,0))\\n\\n work3D = 1./MA.array(N.resize(dist,data.shape),mask=data.mask)\\n wghts3D.append(MA.filled(N.take(N.take(work3D,i_inds,-2),j_inds,-1)\\n ,0))\\n\\n #print 'computing zonal average'\\n return lon_t, lat_t, ilist, jlist, wghts2D, wghts3D\",\n \"def _compute_gp_all_passbands_1D(obj, dataset, number_gp, t_min, t_max,\\n output_root=None, **kwargs):\\n try:\\n kernel_param = kwargs[\\\"kernel_param\\\"]\\n except KeyError:\\n kernel_param = [500., 20.]\\n\\n obj_data = dataset.data[obj] # object's light curve\\n obj_data = cs.rename_passband_column(obj_data.to_pandas())\\n unique_pbs = np.unique(obj_data.passband)\\n gp_times = np.linspace(t_min, t_max, number_gp)\\n\\n # Store the output in another astropy table\\n obj_gps = []\\n used_gp_dict = {}\\n used_kernels_dict = {}\\n filter_set = np.asarray(dataset.filter_set)\\n for pb in filter_set:\\n used_kernels_dict[pb] = None # inilialize None kernel to each passband\\n if pb in unique_pbs:\\n is_pb = obj_data.passband == pb # observations in this passband\\n obj_data_pb = obj_data.loc[is_pb]\\n\\n gp_obs, gp, chosen_kernel = fit_best_gp(kernel_param,\\n obj_data_pb, gp_times)\\n\\n mu, std = gp_obs.flux.values, gp_obs.flux_error.values\\n # stack the GP results in a array momentarily\\n obj_gp_pb_array = np.column_stack((gp_times, mu, std))\\n used_kernels_dict[pb] = chosen_kernel\\n # Save the GP already conditioned on a specific set of observations\\n gp_predict = partial(gp.predict, obj_data_pb.flux)\\n used_gp_dict[pb] = gp_predict\\n else:\\n obj_gp_pb_array = np.zeros([number_gp, 3])\\n obj_gp_pb = Table([obj_gp_pb_array[:, 0], obj_gp_pb_array[:, 1],\\n obj_gp_pb_array[:, 2], [pb] * number_gp],\\n names=['mjd', 'flux', 'flux_error', 'filter'])\\n if len(obj_gps) == 0: # initialize the table for 1st passband\\n obj_gps = obj_gp_pb\\n else:\\n obj_gps = vstack((obj_gps, obj_gp_pb))\\n\\n if output_root is not None:\\n obj_gps.write(os.path.join(output_root, 'gp_' + obj), format='fits',\\n overwrite=True)\\n path_save_gps = os.path.join(output_root, 'used_gp_dict_{}.pckl'\\n ''.format(obj))\\n path_save_kernels = os.path.join(output_root, 'used_kernels_dict_{}.'\\n 'pckl'.format(obj))\\n with open(path_save_gps, 'wb') as f:\\n pickle.dump(used_gp_dict, f, pickle.HIGHEST_PROTOCOL)\\n with open(path_save_kernels, 'wb') as f:\\n pickle.dump(used_kernels_dict, f, pickle.HIGHEST_PROTOCOL)\\n\\n return obj_gps\",\n \"def get_gaussian_maps(mu, sigmax, sigmay, covs, shape_hw, mode='rot'):\\n with tf.name_scope(None, 'gauss_map', [mu]):\\n # mu_y, mu_x = mu[:, :, 0:1], mu[:, :, 1:2]\\n\\n y = tf.to_float(tf.linspace(-1.0, 1.0, shape_hw[0]))\\n\\n x = tf.to_float(tf.linspace(-1.0, 1.0, shape_hw[1]))\\n [x,y] = tf.meshgrid(x,y)\\n xy = tf.stack([x, y], axis=-1)\\n xy = tf.stack([xy] * nb_landmarks, axis=0)\\n xy = xy[None, : ,:, :, :]\\n if mode in ['rot', 'flat']:\\n mu = mu[:,:,None, None,:]\\n\\n invsigma = tf.stack([sigmay**2, -covs, -covs, sigmax**2], axis=-1)\\n invsigma = tf.reshape(invsigma, [-1, nb_landmarks, 2,2])\\n denominator = (sigmax*sigmay)**2 - covs**2\\n denominator = tf.expand_dims(tf.expand_dims(denominator, -1), -1)\\n invsigma = invsigma/(denominator+1e-7)\\n invsigma = tf.cast(invsigma, tf.float32)\\n pp = tf.tile(invsigma[:, :, None, :, :], [1, 1, shape_hw[1], 1, 1])\\n X = xy-mu\\n dist = tf.matmul(X,pp)\\n dist = tf.reduce_sum((dist*X), axis=-1)\\n\\n\\n if mode == 'rot':\\n g_yx = tf.exp(-dist)\\n else:\\n g_yx = tf.exp(-tf.pow(dist + 1e-5, 0.25))\\n\\n else:\\n raise ValueError('Unknown mode: ' + str(mode))\\n\\n g_yx = tf.transpose(g_yx, perm=[0, 2, 3, 1])\\n return g_yx\",\n \"def task2_extra():\\n N = 0\\n lam = 0\\n Ls = numpy.array([2*L for L in range(1,23)])\\n h = 0.01\\n tau = 0.000099\\n\\n iterss = []\\n\\n for L in Ls:\\n a = L // 2\\n print(L)\\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\\n # eps = int(0.1 * len(x))\\n\\n Vm = V1D(lam, x)\\n state = phi(N, x-a)\\n\\n iters = 0\\n while True:\\n prob = numpy.abs(state)**2\\n mid = int(2*L/h) // 2\\n # if max(prob) in prob[mid-eps:mid+eps]:\\n if numpy.argmax(prob) <= mid:\\n print(iters)\\n iterss.append(iters)\\n break\\n\\n state[0] = 0\\n state[-1] = 0\\n state = implicit_scheme_step(state, tau, h, Vm)\\n iters += 1\\n\\n fig = plt.figure()\\n plt.title(\\\"Iterations of Gaussian travel to center\\\")\\n plt.xlabel(\\\"$L$\\\")\\n plt.ylabel(\\\"Time\\\")\\n plt.plot(Ls, tau*numpy.array(iterss))\\n plt.show()\\n fig.savefig(\\\"naloga2_iters_of_gaussian_travel.pdf\\\", bbox_inches=\\\"tight\\\")\",\n \"def cmd_gaus():\\n cmds = []\\n cmds.append(\\\"r_m[0.0,-1,1]\\\")\\n cmds.append(\\\"r_s[2.5,0,10]\\\")\\n cmds.append('Gaussian::res(x,r_m,r_s)')\\n return cmds\",\n \"def three_mass_FUV_maps(gal_indices,**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n fig = plt.figure(figsize=(15,14.5),constrained_layout=False)\\n gs1 = fig.add_gridspec(nrows=3, ncols=3, wspace=0.0, hspace=0.0)\\n\\n rotate = False\\n for row_i,gal_index in enumerate(gal_indices):\\n\\n R_max = p.R_max[row_i]\\n\\n ax1 = fig.add_subplot(gs1[row_i, 0])\\n m = map_sim_property(add=True,ax=ax1,gal_index=gal_index, \\\\\\n prop='m',R_max=R_max,vmin=0,vmax=7,\\\\\\n pix_size_kpc=0.5,sim_type='simgas',cmap='viridis',log=True,colorbar=False,rotate=rotate,text=p.text)\\n frame = plt.gca()\\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\\n if row_i != 2: ax1.set_xlabel('')\\n if row_i == 0:\\n cbaxes = fig.add_axes([0.05, 0.93, 0.25, 0.01]) \\n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes)\\n cbaxes.xaxis.set_ticks_position('top')\\n cb.ax.set_title(\\\"log $\\\\Sigma_{\\\\mathrm{gas}}$ [M$_{\\\\odot}$ kpc$^{-2}$]\\\")\\n # Make a size indicator\\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='white')\\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='white',fontsize=12)\\n # Remove axes ticks\\n ax1.tick_params(axis='x',which='both',labelbottom=False)\\n ax1.tick_params(axis='y',which='both',labelleft=False)\\n\\n ax1 = fig.add_subplot(gs1[row_i, 1])\\n m = star_map(add=True,ax=ax1,R_max=R_max,vmin=6,vmax=9,\\\\\\n gal_index=gal_index,colorbar=False,rotate=rotate)\\n frame = plt.gca()\\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\\n if row_i != 2: ax1.set_xlabel('')\\n frame.axes.get_yaxis().set_visible(False)\\n if row_i == 0:\\n cbaxes = fig.add_axes([0.375, 0.93, 0.25, 0.01]) \\n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes) \\n cbaxes.xaxis.set_ticks_position('top')\\n cb.ax.set_title(\\\"log stellar age [yr]\\\")\\n # Make a size indicator\\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='k')\\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='k',fontsize=12)\\n # Remove axes ticks\\n ax1.tick_params(axis='x',which='both',labelbottom=False)\\n ax1.tick_params(axis='y',which='both',labelleft=False)\\n\\n ax1 = fig.add_subplot(gs1[row_i, 2])\\n m = FUV_map(add=True,ax=ax1,gal_index=gal_index,R_max=R_max,vmin=-10,vmax=3,select=p.select,cmap='twilight',colorbar=False,rotate=rotate)\\n frame = plt.gca()\\n #if row_i != 2: frame.axes.get_xaxis().set_visible(False)\\n if row_i != 2: ax1.set_xlabel('')\\n frame.axes.get_yaxis().set_visible(False)\\n if row_i == 0:\\n cbaxes = fig.add_axes([0.69, 0.93, 0.25, 0.01]) \\n cb = plt.colorbar(m, orientation='horizontal', cax = cbaxes) \\n cbaxes.xaxis.set_ticks_position('top')\\n cb.ax.set_title('FUV flux [W/m$^2$/arcsec$^2$]')\\n # Make a size indicator\\n # if row_i == 2:\\n # print('Adding size indicator')\\n #ax1.text(p.R_max-16,-p.R_max+7,'10 kpc',color='w',fontsize=12)\\n #ax1.plot([p.R_max-15,p.R_max-5],[-p.R_max+5,-p.R_max+5],lw=4,color='w')\\n # else:\\n ax1.text(R_max-16,-R_max+7,'10 kpc',color='k',fontsize=12)\\n ax1.plot([R_max-15,R_max-5],[-R_max+5,-R_max+5],lw=4,color='k')\\n # Remove axes ticks\\n ax1.tick_params(axis='x',which='both',labelbottom=False)\\n ax1.tick_params(axis='y',which='both',labelleft=False)\\n\\n # s = segs\\n gs1.update(top=0.92,bottom=0.02,left=0.02,right=0.98)\\n\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'pretty/'): os.mkdir(p.d_plot + 'pretty/')\\n plt.savefig('plots/pretty/mass_FUV_maps_%s%s.png' % (p.sim_name,p.sim_run),format='png',dpi=200)\",\n \"def f4():\\n n = 4\\n v = np.arange(n)**0.75 * 0.2\\n e = (np.arange(n)+1)**0.7 * 1e-1\\n\\n n = 12\\n v = np.arange(n)\\n e = np.array([0.1]*n) * 10e-0\\n\\n print(Sumb(v,e))\\n\\n f = plt.figure()\\n a = f.add_subplot(111)\\n\\n dx = 0.0001\\n x = np.arange(-1,v[-1]+1,dx)\\n y = x.copy()\\n y[:] = 0.\\n for i in range(n):\\n yx = lg(x,v[i],e[i])\\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\\n y += yx\\n y = np.exp((y - np.max(y))/n**2)\\n y /= np.sum(y) * dx \\n a.plot(x,y,label='sum')\\n s = np.argsort(y)[::-1]\\n ys = np.cumsum(y[s]) * dx\\n yi = np.argwhere(ys > 0.682689492137)[0][0]\\n print('mean = {:2f}'.format(x[s[0]]))\\n print('sigma = {:2f}'.format(yi*dx/2))\\n xy = np.ndarray((yi+2,2))\\n i0,i1 = min(s[:yi]), max(s[:yi])\\n xy[:yi,0] = x[i0:i1+1]\\n xy[:yi,1] = y[i0:i1+1]\\n xy[yi:,1] = 0\\n xy[yi:,0] = x[[i1,i0]]\\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\\n \\n leg = plt.legend()\\n plt.draw()\",\n \"def make_interferometry(data,algorithm=None):\\n # Obteniendo el mapa de coherencia\\n Im = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_10.npy\\\") # Se carga la imagen 10\\n f1 = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n f2 = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n coh = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n\\n for i in range(n_im-1):\\n i += i_o # Valor inicial\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i+1)+\\\".npy\\\")\\n f1 += Im1*Im2.conjugate()\\n f2 += np.sqrt((abs(Im1)**2)*(abs(Im2)**2))\\n\\n coh = abs(f1/f2)\\n\\n # Graficando la coherencia\\n if show:\\n plt.close('all')\\n cmap =\\\"plasma\\\"\\n if algorithm == \\\"BP\\\":\\n title_name ='Mapa de coherencia(BP)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='Coherencia_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Mapa de coherencia(RMA)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='Coherencia_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n\\n fig, ax = plt.subplots()\\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Coherence_maps/\\\"+direction, orientation='landscape')\\n\\n # Sacando una mascara de [0.7,1] para la coherencia\\n mask = coh<=0.7\\n coh[mask] = np.nan\\n\\n # Graficando con la mascara\\n if show:\\n cmap =\\\"plasma\\\"\\n if algorithm == \\\"BP\\\":\\n title_name ='Mapa de coherencia recortada(BP)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='CoherenciaCut_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Mapa de coherencia recortada(RMA)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='CoherenciaCut_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Coherence_maps/\\\"+direction, orientation='landscape')\\n\\n # Obteniendo el mapa de desplazamientos, aka, Interferograma\\n par = sp.get_parameters()\\n global c,fc\\n c,fc = par['c'],par['fc']\\n\\n if show:\\n for i in range(n_im-1):\\n # Hallando el interferograma 'i'-esimo\\n i1 = i+10\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1+1)+\\\".npy\\\")\\n disp = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc) # Distancias en mm\\n disp[mask] = np.nan\\n # Graficando y guardando\\n if algorithm == \\\"BP\\\":\\n title_name ='Interferograma (BP) \\\\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\\n direction ='Itf_BP_dset'+str(i)+'.png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Interferograma (RMA) \\\\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\\n direction ='Itf_RMA_dset'+str(i)+'.png'\\n cmap = plt.cm.plasma #brg\\n cmap.set_bad('black',1.)\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(disp,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Interferograms_complete/Images/\\\"+direction,orientation='landscape')\\n plt.close()\\n\\n # Hallando la curva de distancias vs tiempo\\n #-------Definiendo las zonas--------------\\n zone0 = np.array([0,650,0,400])\\n zone1 = np.array([0,100,100,200])\\n zone2 = np.array([0,100,200,300])\\n zone3 = np.array([100,200,100,200])\\n zone4 = np.array([100,200,200,300])\\n zone5 = np.array([200,300,100,200])\\n zone6 = np.array([200,300,200,300])\\n zone_indexes = {0:zone0,1:zone1,2:zone2,3:zone3,4:zone4,5:zone5,6:zone6}\\n\\n desp = np.zeros((len(zone_indexes),n_im-1)) # Variable y: desplazamiento\\n\\n for z in range(len(zone_indexes)): # Hallando los desplazamientos promedios por zona\\n idc = zone_indexes[z]\\n for i in range(n_im-1):\\n i1 = i+10 # 10 es el valor inicial de las imagenes\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1+1)+\\\".npy\\\")\\n d_i = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc)\\n # ----- Grafica ------\\n d_i2 = d_i.copy()\\n d_i2[mask] = np.nan\\n d_i2 = d_i2[idc[0]:idc[1],idc[2]:idc[3]]\\n # ----- end --------\\n d_i[mask] = 0\\n d_i = d_i[idc[0]:idc[1],idc[2]:idc[3]]\\n if i==0: desp[z,i] = d_i.mean()\\n else: desp[z,i] = d_i.mean()+desp[z,i-1]\\n\\n # Graficando la zona z-esima\\n if show and i == 0: #\\n if algorithm == \\\"BP\\\":\\n title_name ='Interferograma recortado(BP) \\\\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\\n direction ='Itf_BP_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+str(i)+'.png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Interferograma recortado(RMA) \\\\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\\n direction ='Itf_RMA_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+str(i)+'.png'\\n cmap = plt.cm.plasma #brg\\n cmap.set_bad('black',1.)\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(d_i2,cmap,origin='lower',aspect='equal', extent=[idc[2]-200,idc[3]-200,idc[0]+300,idc[1]+300]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Interferograms_complete/Images/\\\"+direction,orientation='landscape')\\n\\n #t = np.arange(len(Ims)-1)+10 # Variable x, tiempo\\n time_dset = np.load(\\\"Dates_BP.npy\\\")\\n time_dset = time_dset[:-1]\\n time_dset2 = np.array([datetime.datetime.strptime(idx,\\\"%d-%m-%y %H:%M:%S\\\") for idx in time_dset])\\n\\n # Grafica de desplazamiento vs tiempo por cada algoritmo\\n \\\"\\\"\\\"if show:\\n if algorithm == \\\"BP\\\":\\n title_name ='Desplazamiento promedio(BP)'\\n direction ='desplazamientoPromedio_BP_dset10-100.png'\\n\\n fig, ax= plt.subplots()\\n ax.plot(t,desp,'b',marker='o',markerfacecolor='b',markeredgecolor='b')\\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\\n #ax.set_xlim([R.min(),R.max()])\\n ax.set_ylim([-c*100/(4*fc),c*100/(4*fc)])\\n ax.grid()\\n plt.show()\\n fig.savefig(os.getcwd()+\\\"/Results/Desplazamientos/\\\"+direction,orientation='landscape')\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Desplazamiento promedio(RMA)'\\n direction ='desplazamientoPromedio_RMA_dset10-100.png'\\n\\n fig, ax= plt.subplots()\\n ax.plot(t,desp,'r',marker='o',markerfacecolor='r',markeredgecolor='r')\\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\\n #ax.set_xlim([R.min(),R.max()])\\n ax.set_ylim([-c*50/(4*fc),c*50/(4*fc)])\\n ax.grid()\\n plt.show()\\n fig.savefig(os.getcwd()+\\\"/Results/Desplazamientos/\\\"+direction,orientation='landscape')\\n \\\"\\\"\\\"\\n return {'d_t':desp, 't':time_dset2}\",\n \"def compute_g(self, i, j):\\n #Compute variance and mean denominator (same denominator for both)\\n g_next = 0\\n if (i+1 < self.nb_days-1):\\n g_next = self.g_heat[i+1,j]\\n\\n denominator = 2 * self.sigma2\\n numerator_mean = self.sigma2 * (self.g_heat[i-1,j] + g_next)\\n if (self.u_heat > self.temperatures[i]):\\n denominator = denominator + self.sigma_g_star_2[0, j] * ((self.temperatures[i] - self.u_heat)**2)\\n numerator_mean = numerator_mean + \\\\\\n self.sigma_g_star_2[0, j] * (self.temperatures[i] - self.u_heat) * (self.consumptions[i] - self.s[i,j] * self.kappa[self.daytypes[i]])\\n\\n #Mean\\n mean = numerator_mean / denominator\\n\\n #Compute variance numerator\\n variance_numerator = (self.sigma2 * self.sigma_g_star_2[0, j])\\n #Variance\\n variance = variance_numerator / denominator\\n\\n self.g_heat[i,j] = self.truncated_norm(-inf, 0, mean, variance)\",\n \"def _calc(self):\\r\\n u = self._fadefunc(self.xf)\\r\\n v = self._fadefunc(self.yf)\\r\\n w = self._fadefunc(self.zf)\\r\\n\\r\\n # populate the hashes dict\\r\\n self._hash()\\r\\n \\r\\n # once the hash dict is populated, start calculating the dot product between \\r\\n # the gradient vector and the distance vectors, which is done in the _grad method.\\r\\n # finally linearly interpolate the values to get the avg value\\r\\n # first interpolate in the x-dir, then in y-dir\\r\\n x1: float = self._lerp(self._grad(self.hashes[\\\"aaa\\\"], self.xf, self.yf, self.zf),\\r\\n self._grad(self.hashes[\\\"baa\\\"], self.xf - 1, self.yf, self.zf), u)\\r\\n\\r\\n x2: float = self._lerp(self._grad(self.hashes[\\\"aba\\\"], self.xf, self.yf - 1, self.zf),\\r\\n self._grad(self.hashes[\\\"bba\\\"], self.xf - 1, self.yf - 1, self.zf), u)\\r\\n\\r\\n # the first y-dir lerp\\r\\n y1: float = self._lerp(x1, x2, v)\\r\\n\\r\\n x1: float = self._lerp(self._grad(self.hashes[\\\"aab\\\"], self.xf, self.yf, self.zf - 1),\\r\\n self._grad(self.hashes[\\\"bab\\\"], self.xf - 1, self.yf, self.zf - 1), u)\\r\\n\\r\\n x2: float = self._lerp(self._grad(self.hashes[\\\"abb\\\"], self.xf, self.yf - 1, self.zf - 1),\\r\\n self._grad(self.hashes[\\\"bbb\\\"], self.xf-1, self.yf-1, self.zf-1), u)\\r\\n\\r\\n # the second y-dir lerp\\r\\n y2: float = self._lerp(x1, x2, v)\\r\\n\\r\\n # the final noise value, which will be in the range [0, 1]\\r\\n self.value = (self._lerp(y1, y2, w) + 1)/2\\r\\n return self.value\",\n \"def past_weight_grad_calculator2(xs, es, kp_x, kd_x, kp_e, kd_e, shapes):\\n kp_x, kd_x, kp_e, kd_e = [as_floatx(k) for k in (kp_x, kd_x, kp_e, kd_e)]\\n n_samples, n_in, n_out = shapes\\n rx = kd_x/(kp_x+kd_x)\\n re = kd_e/(kp_e+kd_e)\\n\\n xr = create_shared_variable(np.zeros((n_samples, n_in)))\\n er = create_shared_variable(np.zeros((n_samples, n_out)))\\n\\n\\n\\n\\n # xr_new = xr*rx + xs/(kp_x+kd_x)\\n # er_new = er*re + es/(kp_e+kd_e)\\n\\n arr = rx*re/(1-rx*re)\\n\\n xr_new = xr*arr + xs/(kp_x+kd_x)\\n er_new = er*arr + es/(kp_e+kd_e)\\n\\n xsum = create_shared_variable(np.zeros((n_samples, n_in)))\\n esum = create_shared_variable(np.zeros((n_samples, n_out)))\\n\\n xsum_new = xsum+xr_new\\n esum_new = esum+er_new\\n\\n x_nospikes = tt.eq(xs, 0)\\n e_nospikes = tt.eq(es, 0)\\n\\n dw = xs.T.dot(esum_new) + xsum_new.T.dot(es)\\n\\n add_update(xr, xr_new)\\n add_update(er, er_new)\\n add_update(xsum, xsum_new*x_nospikes)\\n add_update(esum, esum_new*e_nospikes)\\n\\n return xs.T.dot(er) + xr.T.dot(es)\\n # return xr.T.dot(er)\\n # return dw\",\n \"def modelOnBetaGrid(sample,bins,N,l,u):\\r\\n\\r\\n betaGrid=np.linspace(l,u,N)\\r\\n traces=[]\\r\\n WAIC=dict()\\r\\n index=0\\r\\n\\r\\n for beta in betaGrid:\\r\\n trace=intensityLogGauss(sample,bins,beta)\\r\\n traces.append(trace['intensity'])\\r\\n WAIC[index]=trace\\r\\n index+=1\\r\\n\\r\\n df=pm.compare(WAIC,ic='WAIC')\\r\\n\\r\\n return betaGrid,df,traces\",\n \"def app(ngr=100,c2a=1.6235):\\n import matplotlib.pyplot as plt\\n\\n ## small donuts\\n # plt.gcf().clf()\\n grs = main(mu=0,ngrains=ngr,tilt_1=30.,sigma=15)\\n plt.gcf().savefig('small_doughnut.pdf',bbox_inches='tight')\\n plt.gcf().clf()\\n f = gen_file(lab='sm_doughnut',ngr=ngr)\\n write_gr(f,grs)\\n\\n ## Big donuts\\n grs = main(mu=0,ngrains=ngr,tilt_1=50.,sigma=15)\\n plt.gcf().savefig('big_doughnut.pdf',bbox_inches='tight')\\n plt.gcf().clf()\\n f = gen_file(lab='big_doughnut',ngr=ngr)\\n write_gr(f,grs)\\n\\n ## twin tilts (30).\\n gr1=main(mu=0,ngrains=ngr/2,tilts_about_ax1=30.,sigma=45)\\n plt.gcf().clf()\\n gr2=main(mu=0,ngrains=ngr/2,tilts_about_ax1=-30.,sigma=45)\\n plt.gcf().clf()\\n grs =[]\\n for i in range(len(gr1)):\\n grs.append(gr1[i])\\n grs.append(gr2[i])\\n grs=np.array(grs)\\n mypf=upf.polefigure(grains=grs,csym='hexag',cdim=[1,1,c2a])\\n mypf.pf_new(poles=[[0,0,0,1],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\\n plt.gcf().savefig('t30.pdf',bbox_inches='tight')\\n f = gen_file(lab='dbl_lets_30',ngr=ngr)\\n write_gr(f,grs)\\n\\n ## twin tilts (50).\\n gr1=main(mu=0,ngrains=ngr/2,tilts_about_ax1=50.,sigma=45)\\n plt.gcf().clf()\\n gr2=main(mu=0,ngrains=ngr/2,tilts_about_ax1=-50.,sigma=45)\\n plt.gcf().clf()\\n gr =[]\\n for i in range(len(gr1)):\\n gr.append(gr1[i])\\n gr.append(gr2[i])\\n gr=np.array(gr)\\n mypf=upf.polefigure(grains=gr,csym='hexag',cdim=[1,1,c2a])\\n mypf.pf_new(poles=[[0,0,0,1],[1,0,-1,0]],cmap='jet',ix='TD',iy='RD')\\n plt.gcf().savefig('t50.pdf',bbox_inches='tight')\\n plt.gcf().clf()\\n f = gen_file(lab='dbl_lets_50',ngr=ngr)\\n write_gr(f,gr)\",\n \"def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, plot=True):\\r\\n\\r\\n # Contour over a range of length scales and signal/noise ratios.\\r\\n length_scales = np.linspace(0.1, 60., resolution)\\r\\n log_SNRs = np.linspace(-3., 4., resolution)\\r\\n\\r\\n data = GPy.util.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)\\r\\n # data['Y'] = data['Y'][0::2, :]\\r\\n # data['X'] = data['X'][0::2, :]\\r\\n\\r\\n data['Y'] = data['Y'] - np.mean(data['Y'])\\r\\n\\r\\n lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)\\r\\n if plot:\\r\\n pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)\\r\\n ax = pb.gca()\\r\\n pb.xlabel('length scale')\\r\\n pb.ylabel('log_10 SNR')\\r\\n\\r\\n xlim = ax.get_xlim()\\r\\n ylim = ax.get_ylim()\\r\\n\\r\\n # Now run a few optimizations\\r\\n models = []\\r\\n optim_point_x = np.empty(2)\\r\\n optim_point_y = np.empty(2)\\r\\n np.random.seed(seed=seed)\\r\\n for i in range(0, model_restarts):\\r\\n # kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))\\r\\n kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))\\r\\n\\r\\n m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)\\r\\n m['noise_variance'] = np.random.uniform(1e-3, 1)\\r\\n optim_point_x[0] = m['rbf_lengthscale']\\r\\n optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\\r\\n\\r\\n # optimize\\r\\n if optimize:\\r\\n m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters)\\r\\n\\r\\n optim_point_x[1] = m['rbf_lengthscale']\\r\\n optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\\r\\n\\r\\n if plot:\\r\\n pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')\\r\\n models.append(m)\\r\\n\\r\\n if plot:\\r\\n ax.set_xlim(xlim)\\r\\n ax.set_ylim(ylim)\\r\\n return m # (models, lls)\\r\",\n \"def awGrid(vis,HA,uvw,image_params,obs_params,Mterms,Mterms_ij):\\t\\n\\tStokes = image_params['Stokes']\\n\\t\\n\\n\\n\\tprint '--------------Gridding X pol--------------------'\\n\\txgrid_wt, xgrid_uv = gridOnePolAWproj(vis[0],HA,uvw,image_params,obs_params,Mterms[0],Mterms_ij[0])\\n\\tprint '--------------Gridding Y pol--------------------'\\n\\tygrid_wt, ygrid_uv = gridOnePolAWproj(vis[1],HA,uvw,image_params,obs_params,Mterms[1],Mterms_ij[1])\\n\\n\\tN = np.shape(xgrid_wt)[0]\\n\\tgrid_uv = np.zeros([N, N], dtype=complex)\\n\\tgrid_wt = np.zeros([N, N], dtype=complex)\\n\\t\\n\\tif Stokes == 'I':\\n\\t\\t#combine X and Y gridded vis to create the I pol gridded vis\\n\\t\\t# I = (XX+YY)/2\\n\\t\\tgrid_uv.real = (ygrid_uv.real + xgrid_uv.real)/2\\n\\t\\tgrid_uv.imag = (ygrid_uv.imag + xgrid_uv.imag)/2\\n\\n\\t\\t#combine X and Y gridded wt to create the I pol gridded wt\\n\\t\\tgrid_wt.real = (ygrid_wt.real + xgrid_wt.real)/2\\n\\t\\tgrid_wt.imag = (ygrid_wt.imag + xgrid_wt.imag)/2\\n\\n\\telif Stokes == 'Q':\\n\\t\\t#combine X and Y gridded vis to create the I pol gridded vis\\n\\t\\t# Q = (XX-YY)/2\\n\\t\\tgrid_uv.real = (ygrid_uv.real - xgrid_uv.real)/2\\n\\t\\tgrid_uv.imag = (ygrid_uv.imag - xgrid_uv.imag)/2\\n\\n\\t\\t#combine X and Y gridded wt to create the I pol gridded wt\\n\\t\\tgrid_wt.real = (ygrid_wt.real - xgrid_wt.real)/2\\n\\t\\tgrid_wt.imag = (ygrid_wt.imag - xgrid_wt.imag)/2\\n\\n\\n\\tdty_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_uv)))\\n\\tpsf_image=np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(grid_wt)))\\n\\n\\treturn dty_image, psf_image\",\n \"def task2_extra2():\\n N = 0\\n lam = 0\\n L = 10\\n h = 0.001\\n tau = 0.000099\\n aa = numpy.array([0.25*a for a in range((L-1)*4)])\\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\\n Vm = V1D(lam, x)\\n # eps=int(0.1*len(x))\\n\\n iterss = []\\n for a in aa:\\n print(a)\\n state = phi(N, x-a)\\n\\n iters = 0\\n while True:\\n prob = numpy.abs(state)**2\\n mid = int(2*L/h) // 2\\n # if max(prob) in prob[mid-eps:mid+eps]:\\n if numpy.argmax(prob) <= mid:\\n print(iters)\\n iterss.append(iters)\\n break\\n\\n state[0] = 0\\n state[-1] = 0\\n state = implicit_scheme_step(state, tau, h, Vm)\\n iters += 1\\n\\n fig = plt.figure()\\n plt.title(\\\"Iterations of Gaussian travel to center ($L={}$)\\\".format(L))\\n plt.xlabel(\\\"$a$\\\")\\n plt.ylabel(\\\"Time\\\")\\n plt.plot(aa, tau*numpy.array(iterss))\\n plt.show()\\n fig.savefig(\\\"naloga2_iters_of_gaussian_travel_fixedL={}.pdf\\\".format(L), bbox_inches=\\\"tight\\\")\",\n \"def refugia_adj_1_growthall(params, ns):\\n #14 parameters \\n nu10, nu1, nuA0, nuA, nu20, nu2, nu30, nu3, m3_1, m3_2, m3_3, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\\n ## Population function and migration matrix for T1\\n nuA_func = lambda t: nuA0 * (nuA/nuA0)**(t/T1) \\n ## Population function for T1\\n nu_T1_func = lambda t: [nu1, nuA_func(t)]\\n fs.integrate(nu_T1_func, T1)\\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1, nu2, nu3]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3\\n nu1_func = lambda t: nu10 * (nu1/nu10)**(t/T3) \\n nu2_func = lambda t: nu20 * (nu2/nu20)**(t/T3) \\n nu3_func = lambda t: nu30 * (nu3/nu30)**(t/T3) \\n nu_T3_func = lambda t: [nu1_func(t), nu2_func(t), nu3_func(t)] \\n mig3 = numpy.array([[0, m3_1, m3_3],[m3_1, 0, m3_2], [m3_3, m3_2, 0]]) \\n fs.integrate(nu_T3_func, T3, m=mig3) \\n return fs\",\n \"def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\\n\\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\\n\\n S12 = 0.0\\n T11 = 0.0\\n T12 = 0.0\\n T22 = 0.0\\n V11A = 0.0\\n V12A = 0.0\\n V22A = 0.0\\n V11B = 0.0\\n V12B = 0.0\\n V22B = 0.0\\n V1111 = 0.0\\n V2111 = 0.0\\n V2121 = 0.0\\n V2211 = 0.0\\n V2221 = 0.0\\n V2222 = 0.0\\n\\n R2 = R * R\\n\\n # The coefficients for the contracted Gaussian functions are below\\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\\n [0.678914, 0.430129, 0.000000],\\n [0.444635, 0.535328, 0.154329]])\\n\\n Expon = np.array([[0.270950, 0.000000, 0.000000],\\n [0.151623, 0.851819, 0.000000],\\n [0.109818, 0.405771, 2.227660]])\\n D1 = np.zeros([3])\\n A1 = np.zeros([3])\\n D2 = np.zeros([3])\\n A2 = np.zeros([3])\\n\\n # This loop constructs the contracted Gaussian functions\\n for i in range(N):\\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\\n\\n # Calculate one electron integrals\\n # Centre A is first atom centre B is second atom\\n # Origin is on second atom\\n # V12A - off diagonal nuclear attraction to centre A etc.\\n for i in range(N):\\n for j in range(N):\\n # Rap2 - squared distance between centre A and centre P\\n Rap = A2[j] * R / (A1[i] + A2[j])\\n Rap2 = Rap ** 2\\n Rbp2 = (R - Rap) ** 2\\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\\n\\n # Calculate two electron integrals\\n\\n for i in range(N):\\n for j in range(N):\\n for k in range(N):\\n for l in range(N):\\n Rap = A2[i] * R / (A2[i] + A1[j])\\n Rbp = R - Rap\\n Raq = A2[k] * R / (A2[k] + A1[l])\\n Rbq = R - Raq\\n Rpq = Rap - Raq\\n Rap2 = Rap * Rap\\n Rbp2 = Rbp * Rbp\\n Raq2 = Raq * Raq\\n Rbq2 = Rbq * Rbq\\n Rpq2 = Rpq * Rpq\\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\\n return\",\n \"def source_adj_gsdf(gmdata_sim,gmdata_obs,IsolationFilter,num_pts,dt): \\n t = np.arange(num_pts)*dt\\n ts=np.flip(-t[1:], axis=0)\\n lTime = np.concatenate((ts,t), axis=0)#Lag time \\n \\n #convolve the waveforms for the cross- and auto-correlagrams \\n cross = np.correlate(IsolationFilter,gmdata_obs,'full')\\n auto = np.correlate(IsolationFilter,gmdata_sim,'full') \\n \\n #GSDF Parameters \\n w0=2*np.pi/(lTime[-1]) \\n# wN=2*np.pi/(2*dt)\\n# w(:,1)=-wN:w0:wN\\n wf=w0*np.linspace(-int(num_pts/2),int(num_pts/2),num_pts) \\n fi = [0.05, 0.075, 0.1]\\n# fi = [0.02, 0.03, 0.04, 0.05]\\n# fi = [0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2]\\n Tw = 2/np.mean(fi) # Effective window\\n# sw = 2*np.pi*0.72/Tw; # Sigma w ~ 0.2827433388230814\\n sw=0.1 \\n \\n# #% A local maximum will be selected closest to 0-lag\\n# I_O=np.argmax(cross)\\n# I_S=np.argmax(auto) \\n\\n I_O, peaks_O = find_peaks(np.abs(hilbert(cross))/np.max(np.abs(hilbert(cross))), height=0.25)\\n I_S, peaks_S = find_peaks(np.abs(hilbert(auto))/np.max(np.abs(hilbert(auto))), height=0.25)\\n\\n PkO = peaks_O.get(\\\"peak_heights\\\", \\\"\\\")\\n PkS = peaks_S.get(\\\"peak_heights\\\", \\\"\\\")\\n\\n if (I_O==[] or I_S==[]):\\n I_O=np.argmax(cross)\\n I_S=np.argmax(auto)\\n else:\\n I_O_min = np.argmin(np.multiply((1+np.abs(lTime[I_O]))**2,np.abs(1-PkO)))\\n I_O = I_O[I_O_min]\\n\\n I_S_min = np.argmin(np.multiply((1+np.abs(lTime[I_S]))**2,np.abs(1-PkS)))\\n I_S = I_S[I_S_min]\\n \\n ##Windowing\\n win1=np.exp(-(0.5*sw**2)*(lTime-lTime[I_O])**2)\\n win2=np.exp(-(0.5*sw**2)*(lTime-lTime[I_S])**2) \\n \\n #\\n WO = np.multiply(win1,cross)\\n WS = np.multiply(win2,auto)\\n WS = WS*np.max(WO)/np.max(WS) #Normalized window by amplitude\\n #% Parameters for \\\"bootstraping\\\"\\n InOR=np.argmax(WO)\\n InSR=np.argmax(WS) \\n \\n #% Isolation filter FFT for perturbation kernel\\n tff=np.conj(fftshift(fft(IsolationFilter)))*1/num_pts \\n \\n adj_sim_decompose = np.zeros((len(fi),num_pts))\\n adj_sim_sum = np.zeros(num_pts)\\n TauP_arr = np.zeros(len(fi)) \\n \\n ne = int(np.min([2/np.min(fi)/dt,num_pts/2])) #% Effective bandwidth for inversion\\n \\n for i in range(0,len(fi)): \\n si = 0.1*fi[i]\\n #Crosscorrelagram and Autocorrelagram filtering\\n dO=computebandfftfilter_gauss(WO,dt,fi[i],si,lTime);\\n dS=computebandfftfilter_gauss(WS,dt,fi[i],si,lTime); \\n \\n # % Check bootstraping\\n InO=np.argmax(np.real(dO))\\n InS=np.argmax(np.real(dS)) \\n \\n BS = 1; Cn = 0;\\n while BS == 1 or Cn < 10:\\n InO=int(InO)\\n if (lTime[InO] < lTime[InOR]+0.51/fi[i]) and (lTime[InO] >= lTime[InOR]-0.51/fi[i]):\\n BS = 0\\n elif (lTime[InO] >= (lTime[InOR]+0.45/fi[i])):\\n InO=InO-np.round(1/fi[i]/dt)\\n elif (lTime[InO] < lTime[InOR]-0.45/fi[i]):\\n InO=InO+np.round(1/fi[i]/dt)\\n Cn = Cn+1\\n \\n BS = 1; Cn = 0;\\n while BS == 1 or Cn < 10:\\n InS=int(InS) \\n if (lTime[InS] < lTime[InSR]+0.51/fi[i]) and (lTime[InS] >= lTime[InSR]-0.51/fi[i]):\\n BS = 0\\n elif (lTime[InS] >= (lTime[InSR]+0.45/fi[i])):\\n InS=InS-np.round(1/fi[i]/dt)\\n elif (lTime[InS] < lTime[InSR]-0.45/fi[i]):\\n InS=InS+np.round(1/fi[i]/dt)\\n Cn = Cn+1 \\n\\n # Five parameter Gaussian wavelet fitting \\n Ao = np.max(envelope(np.real(dO))); Io = np.argmax(envelope(np.real(dO)));\\n As = np.max(envelope(np.real(dS))); Is = np.argmax(envelope(np.real(dS))); \\n ##Constrain the initial values \\n # Parameters for curve_fit\\n wi=2*np.pi*fi[i] \\n \\n try:\\n GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]))\\n GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne])) \\n except:\\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \\n\\n# GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]),bounds=(0,[Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]))\\n# GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne]),bounds=(0,[As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]])) \\n \\n# % Check fitting\\n if ((GaO[0]/GaS[0]) > 10**5) or np.abs(GaO[4]-GaS[4]) > lTime[-1]/2:\\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \\n \\n wP=((si**2)*wf+(sw**2)*wi)/(sw**2+si**2)\\n wPP=((si**2)*wf-(sw**2)*wi)/(sw**2+si**2)\\n siP=((si**2)*(sw**2)/(sw**2+si**2))**0.5 \\n #Estimate waveform perturbation kernel (WPK)\\n IW=(siP/(sw*GaS[0]))*np.multiply(np.exp(-0.5*(wf-2*np.pi*fi[i])**2/(sw**2+si**2)),np.divide(tff,wP))+\\\\\\n (siP/(sw*GaS[0]))*np.exp(-0.5*(wf+2*np.pi*fi[i])**2/(sw**2+si**2))*tff/wPP\\n \\n IW[0:int(len(IW)/2)]=0*IW[0:int(len(IW)/2)]\\n \\n itff = ifft(fftshift(num_pts*IW)) \\n \\n #Save the GSDF measurements\\n TauP_arr[i] = GaO[4]-GaS[4]; #% delta_P\\n \\n# Jp = np.real(itff)\\n# Jp = np.imag(itff)\\n Jp = -np.imag(itff) \\n adj_sim_decompose[i,:] = np.flip(Jp,axis=0)*TauP_arr[i] \\n \\n #if i>0:\\n adj_sim_sum = adj_sim_sum + adj_sim_decompose[i,:] \\n \\n return adj_sim_sum, TauP_arr\",\n \"def set_sobol_g_func():\\n a = np.zeros(21)\\n x = np.zeros(21) \\n\\n # a[0:2] = 0\\n # a[2:9] = [0.005, 0.020, 0.040, 0.060, 0.08, 0.090, 1]\\n # a[9:16] = 2\\n # a[16:24] = [2.10, 2.25, 2.75, 3, 3.10, 3.15, 3.25, 3.50]\\n # a[24:30] = 9\\n # a[30:44] = [8, 8.5, 9, 10, 10.5, 11, 12, 12.5, 13, 13.5, 14, 14.5, 15, 16]\\n # a[44:] = [70, 75, 80, 85, 90, 99]\\n\\n a[0:2] = 0\\n a[2:4] = [0.005, 0.090]\\n a[4:7] = 2\\n a[7:11] = [2.10, 2.75, 3, 3.15]\\n a[11:15] = [8, 13, 13.5, 16]\\n a[15:] = [70, 75, 80, 85, 90, 99]\\n\\n x_names = ['x' + str(i+1) for i in range(21)]\\n len_params = len(x_names)\\n x_bounds = np.zeros((21, 2))\\n x_bounds[:, 0] = 0\\n x_bounds[:, 1] = 1\\n\\n problem = {\\n 'num_vars': len(x),\\n 'names': x_names,\\n 'bounds': x_bounds\\n }\\n return a, x, x_bounds, x_names, len_params, problem\",\n \"def grd_posterior_gaussian(self, ) -> Tuple[np.ndarray, np.ndarray]:\\n xmin, xmax = self.x_range\\n ymin, ymax = self.y_range\\n\\n mu = np.array([0, 0])\\n sigma = np.zeros((2, 2))\\n\\n _sample = self._sample\\n _prior = self.prior\\n\\n def mean_x(x: float, y: float):\\n return x * _sample(x, y) * _prior.eval(x, y)\\n\\n def mean_y(x: float, y: float):\\n return y * _sample(x, y) * _prior.eval(x, y)\\n\\n def var_x(x: float, y: float):\\n return x * mean_x(x, y)\\n\\n def var_y(x: float, y: float):\\n return y * mean_y(x, y)\\n\\n # def var_xy(x: float, y: float):\\n # return x * mean_y(x, y)\\n\\n # First moment\\n (mu[0], mu[1]) = (integrate.dblquad(mean_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\\n integrate.dblquad(mean_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0])\\n (sigma[0, 0], sigma[1, 1]) = \\\\\\n (integrate.dblquad(var_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\\n integrate.dblquad(var_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\\n # integrate.dblquad(var_xy, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\\n return mu, sigma\",\n \"def map_sim_positions(**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n fig, ax1 = plt.subplots(figsize=(10,10))\\n # p.gal_index = np.where(GR.file_name == 'z0.00_G7169_cG29270')[0][0]\\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\\n # print('TEST!',gal_ob.file_name,p.gal_index)\\n simdata = aux.load_temp_file(gal_ob=gal_ob,data_type=p.sim_type)\\n\\n # Plot\\n print(simdata.head())\\n ax1.plot(simdata.x,simdata.y,'o',ms=2,mew=2)\\n\\n print(gal_ob.radius)\\n # Limit axes limits a bit to avoid area with no particles...\\n # ax1.set_xlim([-2/3*gal_ob.radius,2/3*gal_ob.radius])make_projec\\n # ax1.set_ylim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\",\n \"def plt_npr_gaussian_all(tb, npr, sigma, soil, snow, onset, figname='all_plot_test0420', size=(12, 8), xlims=[0, 365],\\n shade=False, title=False, site_no='947', pp=False, subs=5, s_symbol='k.',\\n day_tout=-1, end_ax1=[0, 0], end_ax2=[0, 0], end_ax3=[0, 0], tair=[], snow_plot=False):\\n # ylim for each station\\n site_lim = {'947': [-17, -7], '949': [-13, -7], '950': [-13, -7], '960': [-14, -8], '962': [-15, -8], '967': [-12, -8], '968': [-17, -7],\\n '1089': [-13, -7], '1090': [-14, -7], '1175': [-15, -8], '1177': [-19, -10],\\n '1233': [-17, -6], '2065': [-14, -8], '2081': [-15, -7], '2210': [-16, -8], '2211': [-16, -8], '2212': [-16, -8],\\n '2213': [-17, -10]}\\n axs = []\\n fig = plt.figure(figsize=size)\\n gs0 = gridspec.GridSpec(5, 1)\\n gs00 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs0[0])\\n ax0 = plt.Subplot(fig, gs00[-1, :])\\n # ax1, ax2, ax3, ax4 = plt.subplot(fig, gs0[1]), plt.subplot(fig, gs0[2]), \\\\\\n # plt.subplot(fig, gs0[3]), plt.subplot(fig, gs0[4])\\n fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=size, sharex=True) # sharex\\n params = {'mathtext.default': 'regular'}\\n plt.rcParams.update(params)\\n\\n # # add a time window bar 2018/05 updated\\n # nr, st = 20, 9\\n # sub_no = 4*nr+st\\n # ax0, ax1, ax2, ax3, ax4 = plt.subplot2grid((sub_no, 1), (0, 0), rowspan=st), \\\\\\n # plt.subplot2grid((sub_no, 1), (st, 0), rowspan=nr), \\\\\\n # plt.subplot2grid((sub_no, 1), (st+nr, 0), rowspan=nr), \\\\\\n # plt.subplot2grid((sub_no, 1), (st+2*nr, 0), rowspan=nr), \\\\\\n # plt.subplot2grid((sub_no, 1), (st+3*nr, 0), rowspan=nr)\\n\\n # params = {'mathtext.default': 'regular'}\\n # plt.rcParams.update(params)\\n # row 1 tb\\n # ax1 = fig.add_subplot(511) # tb\\n # ax0 setting, for boundary of seasons\\n # timings = [60, 150, 260, 350, 366]\\n # timing_color = ['aqua', 'red', 'orange', 'blue', 'aqua']\\n # timing_color_rgba = plot_funcs.check_rgba(timing_color)\\n # timing_color_rgba[3] = [0., .3, 1., 1.]\\n # print timing_color_rgba\\n # timing_name = [\\\"Frozen\\\", \\\"Thawing\\\", \\\"Thawed\\\", \\\"Freezing\\\", \\\" \\\"]\\n # fill_y1 = 1\\n # ax0.plot(soil[0][1], soil[0][2]*0)\\n # plot_funcs.make_ticklabels_invisible(ax0) # hide the y_tick\\n # ax0.tick_params(axis='x', which='both', bottom='off', top='off')\\n # ax0.tick_params(axis='y', which='both', left='off', right='off')\\n # text_x0 = 0\\n #\\n # for i in range(0, len(timings)):\\n # ax0.fill_between(np.arange(text_x0, timings[i]), fill_y1, color=timing_color_rgba[i])\\n # text_x = 0.5*(timings[i]+text_x0)\\n # print text_x\\n # ax0.text(text_x, 0.5, timing_name[i], va=\\\"center\\\", ha=\\\"center\\\") # 1.3 up\\n # text_x0 = timings[i]+1\\n # if i < len(timings)-1:\\n\\n # # add vertical line and label\\n # ax0.axvline(timings[i])\\n # ax0.text(timings[i], 1.3, timings[i], va=\\\"center\\\", ha=\\\"center\\\")\\n\\n\\n print np.nanmax(soil[0][1]), np.nanmin(soil[0][1])\\n ax0.set_xlim(xlims)\\n axs.append(ax1)\\n # l1, = ax1.plot(tb[0][0], tb[0][1], 'bo', markersize=2)\\n _, ax1_2, l1 = plot_funcs.pltyy(tb[0][0], tb[0][1], 'test_comp2', 'T$_b$ (K)',\\n t2=tb[2][0], s2=tb[2][1], label_y2= '$E_{Tbv}$\\\\n(K/day)',\\n symbol=['k.', 'g-'],\\n handle=[fig, ax1], nbins2=6) # plot tbv\\n l1_le = plot_funcs.plt_more(ax1, tb[1][0], tb[1][1], line_list=[l1])\\n # ax1.locator_params(axis='y', nbins=4)\\n # ax1_2.axhline(y=0)\\n ax1.set_ylabel('T$_b$ (K)')\\n # ax1.legend([l1_le[0][0], l1_le[1]], ['T$_{BV}$', 'T$_{BH}$'], loc=3, prop={'size': 6})\\n if title is not False:\\n plt.title(title)\\n #ax4.legend([l4], [fe[0]], loc=2, prop={'size': 10})\\n # ax4.text(0.85, 0.85, '(a)')\\n # fig.text(0.85, 0.21, '(e)')\\n # fig.text(0.85, 0.37, '(d)')\\n # fig.text(0.85, 0.53, '(c)')\\n # fig.text(0.85, 0.705, '(b)')\\n # fig.text(0.85, 0.87, '(a)')\\n #ax1.annotate(str(vline[0]), xy=(vline[0], 260))\\n #ax1.annotate(str(vline[1]), xy=(vline[1], 260))\\n # row2 npr\\n # ax2 = fig.add_subplot(512) # npr\\n axs.append(ax2)\\n _, ax2_2, l2 = plot_funcs.pltyy(npr[0][0], npr[0][1], 'test_comp2', 'NPR ($10^{-2}$)',\\n t2=npr[1][0], s2=npr[1][1], label_y2='$E_{NPR}$\\\\n($10^{-2}$/day)',\\n symbol=[s_symbol, 'g-'], handle=[fig, ax2], nbins2=6)\\n # ax2.locator_params(axis='y', nbins=5)\\n #ax0.set_ylim([0, 0.06])\\n #ax0.set_ylim([0, 0.06])\\n\\n # sigma\\n # ax3 = fig.add_subplot(513) # sigma\\n axs.append(ax3)\\n _, ax3_2, l2 = plot_funcs.pltyy(sigma[0][0], sigma[0][1], 'test_comp2', '$\\\\sigma^0_{45} (dB)$',\\n t2=sigma[1][0], s2=sigma[1][1], label_y2='$E_{\\\\sigma^0_{45}}$\\\\n(dB/day)',\\n symbol=[s_symbol, 'g-'], handle=[fig, ax3], nbins2=6)\\n # ax3.set_ylim(site_lim[site_no])\\n # ax3.locator_params(axis='y', nbins=4)\\n\\n # moisture and temperature\\n # ax4 = fig.add_subplot(514) # T soil and temperature\\n axs.append(ax4)\\n if snow_plot is False:\\n _, ax4_2, l2 = plot_funcs.pltyy(soil[0][1], soil[0][2], 'test_comp2', 'VWC (%)',\\n t2=soil[1][1], s2=soil[1][2], label_y2='T$_{soil}$ ($^\\\\circ$C)',\\n symbol=['k-', 'b-'], handle=[fig, ax4], nbins2=6)\\n for ax_2 in [ax4_2]:\\n ax_2.axhline(ls='--', lw=1.5)\\n else:\\n ax4.plot(snow[1], snow[2], 'k', linewidth=2.0)\\n ax4_2 = ax4.twinx()\\n if len(tair) > 0:\\n tair[1][tair[1] < -60] = np.nan\\n ax4_2.plot(tair[0], tair[1], 'k:')\\n ax4_2.set_ylim([-30, 30])\\n ax4_2.axhline(ls='--', lw=1.5)\\n ax4_2.yaxis.set_major_locator(MaxNLocator(5))\\n ax4_2.set_ylabel('T$_{air}$ ($^o$C)')\\n\\n\\n ax2s = [ax1_2, ax2_2, ax3_2, ax4_2]\\n ax_ins = [ax4]\\n # swe\\n # ax5 = fig.add_subplot(515) # swe\\n # axs.append(ax5)\\n # ax_ins.append(ax5)\\n # ax5.plot(snow[1], snow[2], 'k', linewidth=2.0)\\n # add air temperature\\n # if len(tair) > 0:\\n # ax5_2 = ax5.twinx()\\n # ax2s.append(ax5_2)\\n # tair[1][tair[1] < -60] = np.nan\\n # ax5_2.plot(tair[0], tair[1], 'k:')\\n # ax5_2.set_ylim([-30, 30])\\n # ax5_2.axhline(ls='--', lw=1.5)\\n # ax5_2.yaxis.set_major_locator(MaxNLocator(5))\\n # ax5_2.set_ylabel('T$_{air}$ ($^o$C)')\\n # if not pp:\\n # if site_no in ['947', '949', '950', '967', '1089']:\\n # ax5.set_ylabel('SWE (mm)')\\n # ax5.set_ylim([0, 200])\\n # else:\\n # ax5.set_ylabel('SD (cm)')\\n # ax5.set_ylim([0, 100])\\n # if site_no in ['950', '1089']:\\n # ax5.set_ylim([0, 500])\\n # else:\\n # ax5.set_ylabel('precipitation (mm)')\\n # ax4.set_xlabel('Day of year 2016')\\n\\n # add vertical line\\n lz = ['--', '--', '--']\\n labelz = ['$\\\\sigma^0$', 'TB', 'NPR']\\n if onset.size> 4: # freeze and thaw\\n i2 = -1\\n # for ax in [ax4, ax5]:\\n # # for i in [0, 1, 2]:\\n # for i in [0, 1, 2]:\\n # ax.axvline(x=onset[i*2], color='k', ls=lz[i], label=labelz[i])\\n # ax.axvline(x=onset[i*2+1], color='k', ls=lz[i])\\n for ax in [ax3, ax1, ax2]:\\n # ax.axvline(x=onset[-2], color='r', ls='-', label='in situ')\\n # ax.axvline(x=onset[-1], color='r', ls='-')\\n i2 += 1\\n ax.axvline(x=onset[i2*2], color='k', ls=lz[i2], label=labelz[i2])\\n ax.axvline(x=onset[i2*2+1], color='k', ls=lz[i2])\\n elif onset.size <=4:\\n for ax in ax_ins:\\n for i in [0]:\\n ax.axvline(x=onset[i], color='k', ls=lz[i], label=labelz[i])\\n ax.axvline(x=onset[i+1], color='k', ls=lz[i+1])\\n ax.axvline(x=onset[i+2], color='k', ls=lz[i+2])\\n\\n l2d_sm = ax4.axvline(x=onset[6], color='r', ls='--')\\n ax4.axvline(x=onset[7], color='r', ls='--')\\n\\n # special vline\\n if day_tout > 0:\\n l2d, = ax4.axvline(x=day_tout, color='r', ls=':')\\n ax1.axvline(x=end_ax1[0], color='b', ls='--')\\n ax1.axvline(x=end_ax1[1], color='b', ls='--')\\n ax2.axvline(x=end_ax2[0], color='b', ls='-')\\n ax2.axvline(x=end_ax2[1], color='b', ls='-')\\n ax3.axvline(x=end_ax3[0], color='b', ls=':')\\n ax3.axvline(x=end_ax3[1], color='b', ls=':')\\n\\n # plot settings\\n # ticks setting\\n for ax in ax_ins:\\n ax.yaxis.set_major_locator(MaxNLocator(4))\\n for ax in axs:\\n yticks = ax.yaxis.get_major_ticks()\\n yticks[0].label1.set_visible(False)\\n yticks[-1].label1.set_visible(False)\\n for ax in ax2s:\\n yticks = ax.yaxis.get_major_ticks()\\n yticks[0].label2.set_visible(False)\\n yticks[-1].label2.set_visible(False)\\n # label location\\n text4 = ['a', 'b', 'c', 'd', 'e']\\n i4 = -1\\n for i, ax in enumerate(axs):\\n ax.yaxis.set_major_locator(MaxNLocator(4))\\n i4 += 1\\n ax.get_yaxis().set_label_coords(-0.09, 0.5)\\n ax.text(0.02, 0.95, text4[i], transform=ax.transAxes, va='top', fontsize=16)\\n # ax.annotate(text4[i4], xy=get_axis_limits(ax), fontweight='bold')\\n for ax in ax2s:\\n ax.yaxis.set_major_locator(MaxNLocator(4))\\n ax.get_yaxis().set_label_coords(1.10, 0.5) # position of labels\\n\\n # ylims\\n ax3_2.set_ylim([-3, 2])\\n ax1.set_ylim([210, 280])\\n if site_no == '1233':\\n ax1.set_ylim([180, 275])\\n ax3_2.set_ylim([-3, 3])\\n # ax1_2.set_ylim([-9, 9])\\n if site_no == '1177':\\n st = 0\\n else:\\n ax2.set_ylim([0, 6])\\n ax2_2.set_ylim([-2, 2])\\n\\n # x_label\\n for i3 in range(0, 4):\\n axs[i3].set_xlabel('')\\n\\n if shade is False:\\n shade_window = 'no shade'\\n else:\\n for ax in axs:\\n for shade0 in shade:\\n ax.axvspan(shade0[0], shade0[1], color=(0.8, 0.8, 0.8), alpha=0.5, lw=0)\\n if xlims:\\n for ax in axs:\\n ax.set_xlim(xlims)\\n\\n # legend setting\\n leg1 = ax1.legend([l1_le[0][0], l1_le[1]], ['T$_{bv}$', 'T$_{bh}$'],\\n loc=3, ncol=1, prop={'size': 12}, numpoints=1)\\n # for leg in [leg1]:\\n # leg.get_frame().set_linewidth(0.0)\\n # layout setting\\n ax4.set_xlabel('Day of year 2016')\\n plt.tight_layout()\\n\\n # if site_no == '1233':\\n # ax1.set_visible(False)\\n # ax1_2.set_visible(False)\\n # ax3.set_visible(False)\\n # ax3_2.set_visible(False)\\n\\n fig.subplots_adjust(hspace=0.05)\\n\\n # other setting like the title\\n\\n\\n # ax_name = ['tb', 'npr', 'sig', 'VWC', 'SWE', 'tbG', 'nprG', 'sigG', 'tsoil']\\n # ax_i = 0\\n # yticks = ax2.yaxis.get_major_ticks()\\n # yticks[0].label1.set_visible(False)\\n # yticks[-2].label1.set_visible(False)\\n # for ax in [ax1, ax2, ax3, ax4, ax5]:\\n # yticks = ax.yaxis.get_major_ticks()\\n # yticks[0].label1.set_visible(False)\\n # yticks[-1].label1.set_visible(False)\\n # for ax in [ax1_2, ax2_2, ax3_2, ax4_2]:\\n # yticks = ax.yaxis.get_major_ticks()\\n # yticks[0].label2.set_visible(False)\\n # yticks[-1].label2.set_visible(False)\\n plt.rcParams.update({'font.size': 16})\\n print figname\\n plt.savefig(figname, dpi=300)\\n plt.close()\\n\\n return 0\\n if vline is not False:\\n ax_count = 0\\n for ax in axs:\\n ax.set_xlim([0, 350])\\n ax_count += 1\\n if type(vline) is list:\\n ax.axvline(x=vline[0], color='k', ls='--')\\n ax.axvline(x=vline[1], color='k', ls='-.')\\n continue\\n ax.axvline(x=vline[0, 1], color='k', label=repr(vline[0, 1]), ls='--')\\n ax.axvline(x=vline[1, 1], color='k', label=repr(vline[1, 1]), ls='-.')\\n ax.axvline(x=vline[2, 1], color='k', label=repr(vline[1, 1]), ls='--')\\n ax.axvline(x=vline[3, 1], color='k', label=repr(vline[1, 1]), ls='-.')\\n # ax.xaxis.set_minor_locator(months)\\n # ax.xaxis.set_minor_formatter(monthsFmt)\\n # ax.xaxis.set_major_locator(years)\\n # ax.xaxis.set_major_formatter(yearsFmt)\\n # ax.locator_params(axis='x', nbins=16)\\n tick_num = np.array([50, 100, 150, 200, 250, 300, 350, 365, 415, 465, 515, 565, 615, 665, 715], dtype=int)\\n ax.xaxis.set_ticks(tick_num)\\n labels = [item.get_text() for item in ax.get_xticklabels()]\\n n = 0\\n for label in labels:\\n if tick_num[n] == 50:\\n if ax_count == 5:\\n labels[n] = \\\"50\\\\nYear'15 \\\"\\n else:\\n labels[n] = \\\"50\\\"\\n elif tick_num[n] == 350:\\n labels[n] = ' '\\n elif tick_num[n] == 365:\\n labels[n] = \\\"365\\\\n Year'16\\\"\\n elif tick_num[n] == 415:\\n labels[n] = repr(tick_num[n]-365)\\n elif tick_num[n] > 415:\\n labels[n] = repr(tick_num[n]-365)\\n else:\\n labels[n] = repr(tick_num[n])\\n n += 1\\n # labels[0] = 'Year\\\\n2015'\\n # labels[1] = '100'\\n # labels[2] = '150'\\n # labels[3] = '200'\\n # labels[4] = '250'\\n # labels[5] = '300'\\n # labels[6] = ''\\n # labels[7] = 'Year\\\\n2016'\\n # labels[8] = '50'\\n # labels[9] = '100'\\n # labels[10] = '150'\\n # labels[11] = '200'\\n\\n ax.set_xticklabels(labels)\\n plt.savefig(figname, dpi=300)\\n plt.close()\",\n \"def _compute_GP_variables(self):\\r\\n Wi = 1.0/self.W\\r\\n self.Sigma_tilde = np.diagflat(Wi)\\r\\n\\r\\n Y_tilde = Wi*self.Ki_f + self.f_hat\\r\\n\\r\\n self.Wi_K_i = self.W12BiW12\\r\\n ln_det_Wi_K = pddet(self.Sigma_tilde + self.K)\\r\\n lik = self.noise_model.logpdf(self.f_hat, self.data, extra_data=self.extra_data)\\r\\n y_Wi_K_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)\\r\\n\\r\\n Z_tilde = (+ lik\\r\\n - 0.5*self.ln_B_det\\r\\n + 0.5*ln_det_Wi_K\\r\\n - 0.5*self.f_Ki_f\\r\\n + 0.5*y_Wi_K_i_y\\r\\n + self.NORMAL_CONST\\r\\n )\\r\\n\\r\\n #Convert to float as its (1, 1) and Z must be a scalar\\r\\n self.Z = np.float64(Z_tilde)\\r\\n self.Y = Y_tilde\\r\\n self.YYT = np.dot(self.Y, self.Y.T)\\r\\n self.covariance_matrix = self.Sigma_tilde\\r\\n self.precision = 1.0 / np.diag(self.covariance_matrix)[:, None]\\r\\n\\r\\n #Compute dZ_dK which is how the approximated distributions gradients differ from the dL_dK computed for other likelihoods\\r\\n self.dZ_dK = self._Kgradients()\\r\\n #+ 0.5*self.Wi_K_i - 0.5*np.dot(self.Ki_f, self.Ki_f.T) #since we are not adding the K gradients explicit part theres no need to compute this again\\r\",\n \"def addGaussian(ax, ismulti):\\n shape = (96, 288) #ax.shape[:2]\\n intensity_noise = np.random.uniform(low=0, high=0.05)\\n if ismulti:\\n ax[:,:,0] = ax[:,:,0]*(1+ intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1]))\\n else:\\n ax[:,:,0] = ax[:,:,0] + intensity_noise*np.random.normal(loc=0, scale=1, size=shape[0]*shape[1]).reshape(shape[0],shape[1])\\n return ax\",\n \"def gaus(x,A,B,C):\\n return A * np.exp( -(x-B)**2 / (2*C**2))\",\n \"def make_egge(w,minZ,maxZ,ires=1,m=mz0):\\n cmds = []\\n # coefficients for the amplitudes\\n cmds.append(\\\"A[1,0,1000000]\\\")\\n cmds.append(\\\"B[1,0,1000000]\\\")\\n cmds.append(\\\"C[10000.0,0,1000000]\\\")\\n # amplitudes\\n cmds.append('m[%s,%s,%s]'%(m,minZ,maxZ))\\n cmds.append('g[8,0,100]')\\n denom = '((x^2-m^2)^2+g^2*m^2)'\\n cmds.append(\\\"expr::z_rbw('x^2/%s',x,m,g)\\\"%denom)\\n cmds.append(\\\"expr::z_int('(x^2-m^2)/%s',x,m,g)\\\"%denom)\\n cmds.append(\\\"expr::z_rad('1/(x^2+1)',x)\\\")\\n # resolution model\\n cmds += resolutions[ires]()\\n [w.factory(cmd) for cmd in cmds]\\n # sum-of-amplitudes pdf\\n lshape = RooRealSumPdf('lshape','lshape',RooArgList(w.function('z_rad'),w.function('z_int'),w.function('z_rbw')),RooArgList(w.var('A'),w.var('B'),w.var('C')))\\n getattr(w,'import')(lshape)\\n # convolution\\n pdf = w.pdf('lshape')\\n if w.pdf('res'):\\n w.var('x').setBins(10000,'cache')\\n cmd = 'FCONV::sum(x,lshape,res)'\\n w.factory(cmd)\\n pdf = w.pdf('sum')\\n return pdf, kFALSE\",\n \"def perform_bivariate_3_gaussians_exp(N, pp, mu_1, mu_2, mu_3,\\n sigma_1, sigma_2, sigma_3, \\n truncation_bounds, censoring_bounds,\\n max_iteration=50, seed=100):\\n # Fix the random state\\n random.seed(seed)\\n np.random.seed(seed)\\n\\n # Stack and reorder the means and covariance matrices into unified matrices\\n mu = np.stack([mu_1, mu_2, mu_3], axis=0)\\n sigma = np.stack([sigma_1, sigma_2, sigma_3], axis=2)\\n pp, mu, sigma = reorder_gmm_compoments(pp, mu, sigma)\\n K = mu.shape[0]\\n\\n # Generate GMM data\\n print(\\\"Step #1: Generating a Gaussian-Mixture-Model dataset\\\")\\n print(\\\"True parameters:\\\")\\n print(\\\"pp: \\\\n{}\\\\n\\\".format(pp))\\n print(\\\"mu: \\\\n{}\\\\n\\\".format(mu))\\n print(\\\"sigma: \\\\n{}\\\\n\\\".format(sigma.T))\\n y = generate_gmm_data(pp, mu, sigma, N)\\n\\n # Plot the GMM data\\n plt.figure()\\n ax = plot_gmm_data(y, mu, sigma, point_color='black')\\n plt.title(\\\"The Original Data Generated by Three Gaussian Components\\\")\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n # Perform censoring and truncation on the original data\\n print(\\\"Step #2: Censoring and truncating the data\\\")\\n x = censor_and_truncate_data(y)\\n\\n # Plot the censored and truncated data\\n plt.figure()\\n ax = plot_gmm_data(y, mu, sigma, point_color='red')\\n plt.title(\\\"Truncated and Censored Data\\\")\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n # Init parameters using K-means\\n print(\\\"Step #3: Initializing parameters using K-means\\\")\\n par = init_kmeans(x, K)\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n # Estimating parameters using truncated and censored EM\\n print(\\\"Step #4: Estimating parameters using truncated and censored EM\\\")\\n tc_em_results = perform_truncated_em(x, K, \\n truncation_bounds, censoring_bounds, \\n par['pp'], par['mu'], par['sigma'], \\n max_iteration)\\n\\n print(\\\"Estimated parameters by standard EM:\\\")\\n print(\\\"pp: \\\\n{}\\\\n\\\".format(tc_em_results['pp']))\\n print(\\\"mu: \\\\n{}\\\\n\\\".format(tc_em_results['mu']))\\n print(\\\"sigma: \\\\n{}\\\\n\\\".format(tc_em_results['sigma'].T))\\n \\n plt.figure()\\n plt.plot(range(len(tc_em_results['ll_hist'])), tc_em_results['ll_hist'])\\n plt.title(\\\"Learning Curve of the Truncated and Censored EM\\\")\\n plt.xlabel(\\\"Iteration\\\")\\n plt.ylabel(\\\"Log-likelihood\\\")\\n\\n plt.figure()\\n ax = plot_gmm_data(x, tc_em_results['mu'], tc_em_results['sigma'])\\n plt.title(\\\"Truncated and Censored EM\\\")\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n print(\\\"Step #5: Estimating parameters using standard EM\\\")\\n std_em_results = perform_standard_em(x, K, seed)\\n\\n print(\\\"Estimated parameters by standard EM:\\\")\\n print(\\\"pp: \\\\n{}\\\\n\\\".format(std_em_results['pp']))\\n print(\\\"mu: \\\\n{}\\\\n\\\".format(std_em_results['mu']))\\n print(\\\"sigma: \\\\n{}\\\\n\\\".format(std_em_results['sigma'].T))\\n\\n plt.figure()\\n ax = plot_gmm_data(x, std_em_results['mu'], std_em_results['sigma'])\\n plt.title(\\\"Standard EM\\\")\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n # Evaluate the KL-Divergence between true distribution and estimated \\n # distributions\\n print(\\\"Step #6: Evaluating the estimated parameters\\\")\\n pp, mu, sigma = reorder_gmm_compoments(pp, mu, sigma)\\n tc_em_results['pp'], tc_em_results['mu'], tc_em_results['sigma'] =\\\\\\n reorder_gmm_compoments(tc_em_results['pp'], tc_em_results['mu'],\\n tc_em_results['sigma'])\\n std_em_results['pp'], std_em_results['mu'], std_em_results['sigma'] =\\\\\\n reorder_gmm_compoments(std_em_results['pp'], std_em_results['mu'],\\n std_em_results['sigma'])\\n \\n true_gmm = build_GMM_model(pp, mu, sigma, seed)\\n tc_gmm = build_GMM_model(tc_em_results['pp'], tc_em_results['mu'],\\n tc_em_results['sigma'], seed)\\n std_gmm = build_GMM_model(std_em_results['pp'], std_em_results['mu'],\\n std_em_results['sigma'], seed)\\n \\n tc_kl = estimate_kl_divergence_gmm(true_gmm, tc_gmm)\\n std_kl = estimate_kl_divergence_gmm(true_gmm, std_gmm) \\n\\n print(\\\"\\\\t* KL-Divergence corresponding to truncated and censored EM: {}\\\".\\\\\\n format(tc_kl))\\n print(\\\"\\\\t* KL-Divergence corresponding to standard EM: {}\\\".\\\\\\n format(std_kl))\\n print(\\\"\\\\n\\\" + \\\"*\\\"*80)\\n\\n # Show the plots\\n print(\\\"Step #7: Showing the plots\\\")\\n plt.show()\",\n \"def _get_gaussian_maps(self, x, source_str, prefix='coarse_', **kwargs):\\n suffix = source_str if self.ds_gaussians else ''\\n gaussians = self.__getattr__(prefix + \\\"gaussians\\\" + suffix)\\n gaussian_maps = self._make_gaussian_maps(x, gaussians, **kwargs)\\n return gaussian_maps\",\n \"def cell_params(x,y,**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n cloudy_library = clo.library()\\n lookup_table = cloudy_library._restore_lookup_table()\\n lookup_table['logG0s'] = lookup_table['logFUVs']\\n if x == 'NH': \\n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\\n if y == 'NH': \\n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\\n\\n if not p.ylim:\\n p.ylim = [1e-3,30]\\n if not p.xlim:\\n p.xlim = [1e-7,1e3]\\n \\n # SELECT GALAXIES\\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\\n if p.gal_index: \\n rand_gal_index = [p.gal_index]\\n print(rand_gal_index)\\n xs = np.array([])\\n ys = np.array([])\\n m_tot,m_encomp,m_y0 = 0,0,0\\n for gal_index in rand_gal_index:\\n print(gal_index)\\n gal_ob = gal.galaxy(gal_index)\\n df = gal_ob.cell_data.get_dataframe()\\n df['nSFR'] = df.nSFR.values#/(0.2**3)\\n #df['nSFR'] = df['SFR_density']\\n #df['NH'] = 10.**df['NH']\\n x1 = df[x].values\\n y1 = df[y].values\\n x1[x1 <= p.xlim[0]] = p.xlim[0]\\n y1[y1 <= p.ylim[0]] = p.ylim[0]\\n m_tot += np.sum(df.m.values)\\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\\n m_y0 += np.sum(df.m[(y1 == 0)].values)\\n #print(x,x1.min(),x1.max())\\n #print(y,y1.min(),y1.max())\\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n print('Min max of %s:' % x)\\n print(xs.min(),xs.max())\\n print('Min max of %s:' % y)\\n print(ys.min(),ys.max())\\n fig,ax = plt.subplots(figsize=(10,8))\\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\\n cb = fig.colorbar(hb, ax=ax)\\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\\n ax.set_xlabel(getlabel(x))\\n ax.set_ylabel(getlabel(y))\\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\\n ax.set_xlim(p.xlim)\\n ax.set_ylim(p.ylim)\\n # Overplot Cloudy grid params\\n print(x,x_cloudy)\\n print(y,y_cloudy)\\n for x1 in x_cloudy:\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\\n for y1 in y_cloudy:\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\\n\\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \\n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')\",\n \"def skybass_sampling_rates(data):\\n for i in range(4):\\n fig = plt.figure()\\n TODO: finish\",\n \"def WENOSG_interpolation(sp0,sp1,sp2,sp3,sp4):\\n\\n epsilon = 10**-12\\n\\n p0 = (1./3.)*sp0 - (7./6.)*sp1 + (11./6.)*sp2\\n p1 = -(1./6.)*sp1 + (5./6.)*sp2 + (1./3.)*sp3\\n p2 = (1./3.)*sp2 + (5./6.)*sp3 - (1./6.)*sp4\\n\\n gamma = [.1, .6, .3]\\n\\n beta0 = (13./12.)*(sp0 - 2*sp1 + sp2)**2 + .25*(sp0 - 4*sp1 + 3*sp2)**2\\n beta1 = (13./12.)*(sp1 - 2*sp2 + sp3)**2 + .25*(sp1 - sp3)**2\\n beta2 = (13./12.)*(sp2 - 2*sp3 + sp4)**2 + .25*(3*sp2 - 4*sp3 + sp4)**2\\n\\n c00 = .25\\n c10 = .75\\n c01 = .5\\n c11 = .5\\n\\n tau0 = abs(beta1 - beta0)\\n tau1 = abs(beta2 - beta1)\\n\\n alpha00 = c00*(1 + (tau0 / (beta0 + epsilon))**2)\\n alpha10 = c10*(1 + (tau0 / (beta1 + epsilon))**2)\\n alpha01 = c01*(1 + (tau1 / (beta1 + epsilon))**2)\\n alpha11 = c11*(1 + (tau1 / (beta2 + epsilon))**2)\\n\\n alphakl_sum = alpha00 + alpha10 + alpha01 + alpha11\\n\\n psi00 = alpha00 / alphakl_sum\\n psi10 = alpha11 / alphakl_sum\\n psi01 = alpha01 / alphakl_sum\\n psi11 = alpha11 / alphakl_sum\\n\\n def g(k, w):\\n return (w * (gamma[k] + gamma[k]**2 - 3*gamma[k]*w + w**2))/(gamma[k]**2 + w*(1-2*gamma[k]))\\n\\n alphastar00 = g(0, psi00)\\n alphastar10 = g(1, psi10)\\n alphastar01 = g(0, psi01)\\n alphastar11 = g(1, psi11)\\n\\n alphastar_sum = alphastar00 + alphastar10 + alphastar01 + alphastar11\\n\\n w00 = alphastar00 / alphastar_sum\\n w10 = alphastar10 / alphastar_sum\\n w01 = alphastar01 / alphastar_sum\\n w11 = alphastar11 / alphastar_sum\\n\\n h0 = w00*p0 + w10*p1\\n h1 = w01*p1 + w11*p2\\n\\n tau = abs(beta2 - beta0)\\n\\n c0 = .4\\n c1 = .6\\n\\n alpha0 = c0 * (1 + (tau / (beta0 + epsilon))**2)\\n alpha1 = c1 * (1 + (tau / (beta2 + epsilon))**2)\\n\\n alpha_sum = alpha0 + alpha1\\n\\n psi0 = alpha0 / alpha_sum\\n psi1 = alpha1 / alpha_sum\\n\\n psi_sum = g(0, psi0) + g(1, psi1)\\n\\n w0SG = g(0, psi0) / psi_sum\\n w1SG = g(1, psi1) / psi_sum\\n\\n u_half = w0SG * h0 + w1SG * h1\\n\\n return u_half\",\n \"def evolute(self, ngen, x0=None, verbose=True):\\r\\n self.history = {'local_fitness':[], 'global_fitness':[], 'a': [], 'A': []}\\r\\n self.best_fitness=float(\\\"inf\\\") \\r\\n self.verbose=verbose\\r\\n self.Positions = np.zeros((self.nwhales, self.dim))\\r\\n if x0:\\r\\n assert len(x0) == self.nwhales, '--error: the length of x0 ({}) MUST equal the number of whales in the group ({})'.format(len(x0), self.nwhales)\\r\\n for i in range(self.nwhales):\\r\\n self.Positions[i,:] = x0[i]\\r\\n else:\\r\\n #self.Positions=self.init_sample(self.bounds) #TODO, update later for mixed-integer optimisation\\r\\n # Initialize the positions of whales\\r\\n \\r\\n for i in range(self.dim):\\r\\n self.Positions[:, i] = (np.random.uniform(0, 1, self.nwhales) * (self.ub[i] - self.lb[i]) + self.lb[i])\\r\\n \\r\\n fitness0=self.eval_whales()\\r\\n \\r\\n self.best_position, self.best_fitness = self.select(self.Positions, fitness0)\\r\\n \\r\\n for k in range(0, ngen):\\r\\n \\r\\n # a is annealed from 2 to 0\\r\\n self.a = self.a0 - k * ((self.a0) / (ngen))\\r\\n # fac is annealed from -1 to -2 to estimate l\\r\\n self.fac = -1 + k * ((-1) / ngen)\\r\\n #-----------------------------\\r\\n # Update Whale Positions\\r\\n #-----------------------------\\r\\n self.UpdateWhales()\\r\\n \\r\\n #----------------------\\r\\n # Evaluate New Whales\\r\\n #----------------------\\r\\n fitness=self.eval_whales()\\r\\n \\r\\n for i, fits in enumerate(fitness):\\r\\n #save the best of the best!!!\\r\\n if fits < self.best_fitness:\\r\\n self.best_fitness=fits\\r\\n self.best_position=self.Positions[i, :].copy()\\r\\n \\r\\n #--mir\\r\\n if self.mode=='max':\\r\\n self.fitness_best_correct=-self.best_fitness\\r\\n self.local_fitness=-np.min(fitness)\\r\\n else:\\r\\n self.fitness_best_correct=self.best_fitness\\r\\n self.local_fitness=np.min(fitness)\\r\\n\\r\\n self.history['local_fitness'].append(self.local_fitness)\\r\\n self.history['global_fitness'].append(self.fitness_best_correct)\\r\\n self.history['a'].append(self.a)\\r\\n self.history['A'].append(self.A)\\r\\n \\r\\n # Print statistics\\r\\n if self.verbose and i % self.nwhales:\\r\\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\\r\\n print('WOA step {}/{}, nwhales={}, Ncores={}'.format((k+1)*self.nwhales, ngen*self.nwhales, self.nwhales, self.ncores))\\r\\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\\r\\n print('Best Whale Fitness:', np.round(self.fitness_best_correct,6))\\r\\n print('Best Whale Position:', np.round(self.best_position,6))\\r\\n print('a:', np.round(self.a,3))\\r\\n print('A:', np.round(self.A,3))\\r\\n print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\\r\\n\\r\\n if self.verbose:\\r\\n print('------------------------ WOA Summary --------------------------')\\r\\n print('Best fitness (y) found:', self.fitness_best_correct)\\r\\n print('Best individual (x) found:', self.best_position)\\r\\n print('--------------------------------------------------------------') \\r\\n \\r\\n return self.best_position, self.fitness_best_correct, self.history\",\n \"def Gen_Annihilation_Map(angularSize, size, profile,fileOut):\\r\\n print 'Generating Annihilation Rate Map'\\r\\n \\r\\n # Behavioral Parameters \\r\\n solarDist = 8.3 # Solar Distance in kpc\\r\\n stopRadius = 60.0 # Radius from sun in kpc to stop LOS integration\\r\\n zSteps = 100 # Steps for LOS integration\\r\\n kpc2cm = 3.08568025e21\\r\\n \\r\\n # Constants\\r\\n deg2rad = math.pi/180.0 \\r\\n map = np.zeros((size, size)) # Initialize map\\r\\n \\r\\n # Code\\r\\n zStepSize = stopRadius/float(zSteps) # Size of one step in kpc\\r\\n aPP = float(angularSize)/float(size) # Angle per pixel\\r\\n solidAngle = (aPP*deg2rad)**2.0 # Multiply this by radial portion to get wedge volume.\\r\\n # Based on the input DM_model, we integrate rho**2 along the LOS\\r\\n max = 0\\r\\n for x in range(0,size): \\r\\n for y in range(0,size):\\r\\n rate = 0.0\\r\\n gamma = math.sqrt((float(x)-size/2.0)**2 + (float(y)-size/2.0)**2)*aPP # Inclusive angle for law of cosines\\r\\n if profile[0] != 'PULSAR': \\r\\n for z in range(0,zSteps):\\r\\n # Compute wedge volume. Currently this assumes a relatively small angular region around galactic center.\\r\\n volume = ((z+1)**3.0-z**3.0)*solidAngle/3.#\\r\\n # Compute radius from galactic center using law of cosines\\r\\n a = (z*zStepSize)\\r\\n #r = math.sqrt(a**2 + b**2 - 2*a*b*math.cos(gamma)) \\r\\n l = (float(x)-size/2.0)*aPP # longitude\\r\\n b = (float(y)-size/2.0)*aPP # latitude \\r\\n r = math.sqrt(a**2 + solarDist**2 - 2*a*solarDist*math.cos(l*deg2rad)*math.cos(b*deg2rad))\\r\\n #if gamma>max:\\r\\n # max = gamma\\r\\n # Get square DM density to obtain rate\\r\\n if (profile[0] == 'NFW'):\\r\\n rate += (volume*rho_DM_NFW(r,profile[1],profile[2]))**2\\r\\n elif (profile[0] == 'EIN'):\\r\\n rate += (0.0780763*rho_DM_EIN(r,profile[1],profile[2]))**2*zStepSize*kpc2cm*solidAngle\\r\\n \\r\\n elif (profile[0] == 'FLAT'):\\r\\n rate = 1 # just keep everything flat. += will give integration limit dependent results\\r\\n elif (profile[0] == 'NFWDECAY'): # NFW not squared.\\r\\n rate += volume*rho_DM_NFW(r,profile[1],profile[2])\\r\\n map[x,y] = rate\\r\\n else:\\r\\n l = (float(x)-size/2.0)*aPP # longitude\\r\\n b = (float(y)-size/2.0)*aPP # latitude\\r\\n r = math.sqrt(l**2.0+b**2.0)\\r\\n if (r<=0.05):\\r\\n map[x,y] = 0.05**-1.6\\r\\n else:\\r\\n map[x,y] = r**-1.2 \\r\\n # Write to file \\r\\n outFile = open(fileOut, \\\"wb\\\" )\\r\\n pickle.dump(map/np.max(map), outFile)\\r\\n print 'Rate Map saved to ', fileOut\\r\\n #print np.max(map)\\r\\n print 'J-Factor (GeV^2/cm^5): ' , np.sum(map)\\r\\n return map/np.max(map)\",\n \"def workflow(save_dir):\\n year = 2016\\n month_series = range(1, 13)\\n total_potential_biomass_multiplier = 48.8\\n total_standing_biomass_multiplier = 45.25\\n biomass_jitter = 3.\\n diet_sufficiency_multiplier = 0.28\\n diet_sufficiency_jitter = 0.01\\n avg_animal_density = 0.0175\\n animal_density_jitter = 0.005\\n\\n # twelve months of precipitation rasters covering the study area\\n precip_basename_list = [\\n 'chirps-v2.0.{}.{:02d}.tif'.format(year, month) for month in\\n month_series]\\n\\n # reclassify 0 to NoData in CHIRPS rasters\\n output_precip_dir = os.path.join(save_dir, 'precip')\\n if not os.path.exists(output_precip_dir):\\n os.makedirs(output_precip_dir)\\n for bn in precip_basename_list:\\n base_raster = os.path.join(PRECIP_DIR, bn)\\n target_raster = os.path.join(output_precip_dir, bn)\\n pygeoprocessing.raster_calculator(\\n [(base_raster, 1)], zero_to_nodata, target_raster,\\n gdal.GDT_Float32, _IC_NODATA)\\n\\n # generate outputs\\n for month in month_series:\\n precip_raster = os.path.join(\\n output_precip_dir, 'chirps-v2.0.{}.{:02d}.tif'.format(year, month))\\n\\n total_potential_biomass_path = os.path.join(\\n save_dir, 'potential_biomass_{}_{:02d}.tif'.format(year, month))\\n pygeoprocessing.raster_calculator(\\n [(precip_raster, 1)] + [(path, 'raw') for path in [\\n total_potential_biomass_multiplier,\\n biomass_jitter]],\\n precip_to_correlated_output, total_potential_biomass_path,\\n gdal.GDT_Float32, _IC_NODATA)\\n\\n total_standing_biomass_path = os.path.join(\\n save_dir, 'standing_biomass_{}_{:02d}.tif'.format(year, month))\\n pygeoprocessing.raster_calculator(\\n [(precip_raster, 1)] + [(path, 'raw') for path in [\\n total_standing_biomass_multiplier,\\n biomass_jitter]],\\n precip_to_correlated_output, total_standing_biomass_path,\\n gdal.GDT_Float32, _IC_NODATA)\\n\\n diet_sufficiency_path = os.path.join(\\n save_dir, 'diet_sufficiency_{}_{:02d}.tif'.format(year, month))\\n pygeoprocessing.raster_calculator(\\n [(precip_raster, 1)] + [(path, 'raw') for path in [\\n diet_sufficiency_multiplier,\\n diet_sufficiency_jitter]],\\n precip_to_correlated_output, diet_sufficiency_path,\\n gdal.GDT_Float32, _IC_NODATA)\\n\\n animal_density_path = os.path.join(\\n save_dir, 'animal_density_{}_{:02d}.tif'.format(year, month))\\n pygeoprocessing.raster_calculator(\\n [(precip_raster, 1)] + [(path, 'raw') for path in [\\n avg_animal_density,\\n animal_density_jitter]],\\n precip_to_animal_density, animal_density_path,\\n gdal.GDT_Float32, _IC_NODATA)\",\n \"def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\\n T = np.arange(rgt_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Regret\\\")\\n \\n ax.plot(T, rgt_alea, label = 'aléatoire') \\n ax.plot(T, rgt_glou, label = 'glouton')\\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\\n ax.plot(T, rgt_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Regrets des 4 algorithmes par rapport à T\\\")\",\n \"def analyse_plots(plot_dict, data_dict) :\\n for component in [ '_x', '_y' ] :\\n z_pos = array.array( 'd' )\\n trans_pos = array.array( 'd' )\\n errors = array.array( 'd' )\\n zeros = array.array( 'd' )\\n\\n plot = plot_dict['beam_positions'+component]\\n\\n for i in range( plot.GetXaxis().GetNbins()+2 ) :\\n projection = plot.ProjectionY( \\\\\\n 'profile'+component+'_pro_'+str(i), i, i )\\n if projection.GetEntries() == 0 :\\n continue\\n\\n pro_mean, pro_mean_err, pro_std, pro_std_err = \\\\\\n scifi.fit_gaussian( projection )\\n\\n errors.append( pro_mean_err )\\n trans_pos.append( pro_mean )\\n z_pos.append( data_dict['station_positions'][ i-6 ] )\\n zeros.append(0.0)\\n\\n position_graph = ROOT.TGraphErrors( len(zeros), z_pos, trans_pos, \\\\\\n zeros, errors )\\n position_graph.SetName('beam_profile'+component)\\n plot_dict['beam_profile'+component] = position_graph\\n\\n profile_x = plot_dict['beam_profile_x']\\n profile_y = plot_dict['beam_profile_y']\\n\\n up_x_func = ROOT.TF1( \\\"up_fit_x\\\", \\\"pol1\\\", -5000.0, 0.0 )\\n up_y_func = ROOT.TF1( \\\"up_fit_y\\\", \\\"pol1\\\", -5000.0, 0.0 )\\n down_x_func = ROOT.TF1( \\\"down_fit_x\\\", \\\"pol1\\\", 0.0, 5000.0 )\\n down_y_func = ROOT.TF1( \\\"down_fit_y\\\", \\\"pol1\\\", 0.0, 5000.0 )\\n\\n up_fit_x = profile_x.Fit( 'up_fit_x', \\\"QSR\\\" )\\n up_fit_y = profile_y.Fit( 'up_fit_y', \\\"QSR\\\" )\\n down_fit_x = profile_x.Fit( 'down_fit_x', \\\"QSR\\\" )\\n down_fit_y = profile_y.Fit( 'down_fit_y', \\\"QSR\\\" )\\n\\n plot_dict['beam_profile_x_up_fit'] = up_x_func\\n plot_dict['beam_profile_y_up_fit'] = up_y_func\\n plot_dict['beam_profile_x_down_fit'] = down_x_func\\n plot_dict['beam_profile_y_down_fit'] = down_y_func\\n\\n\\n up_beam_gra_x = up_x_func.GetParameter(1)\\n up_beam_gra_x_err = up_x_func.GetParError(1)\\n up_beam_gra_y = up_y_func.GetParameter(1)\\n up_beam_gra_y_err = up_y_func.GetParError(1)\\n\\n up_beam_pos_x = data_dict['station_positions'][-1]*up_beam_gra_x + up_x_func.GetParameter(0)\\n up_beam_pos_x_err = up_x_func.GetParError(0)\\n up_beam_pos_y = data_dict['station_positions'][-1]*up_beam_gra_y + up_y_func.GetParameter(0)\\n up_beam_pos_y_err = up_y_func.GetParError(0)\\n\\n up_beam_rot_x = math.atan( up_beam_gra_x )\\n up_beam_rot_x_err = up_beam_gra_x_err # Approx linear\\n up_beam_rot_y = math.atan( up_beam_gra_y )\\n up_beam_rot_y_err = up_beam_gra_y_err # Approx linear\\n\\n\\n\\n down_beam_gra_x = down_x_func.GetParameter(1)\\n down_beam_gra_x_err = down_x_func.GetParError(1)\\n down_beam_gra_y = down_y_func.GetParameter(1)\\n down_beam_gra_y_err = down_y_func.GetParError(1)\\n\\n down_beam_pos_x = data_dict['station_positions'][1]*down_beam_gra_x + down_x_func.GetParameter(0)\\n down_beam_pos_x_err = down_x_func.GetParError(0)\\n down_beam_pos_y = data_dict['station_positions'][1]*down_beam_gra_y + down_y_func.GetParameter(0)\\n down_beam_pos_y_err = down_y_func.GetParError(0)\\n\\n down_beam_rot_x = math.atan( down_beam_gra_x )\\n down_beam_rot_x_err = down_beam_gra_x_err # Approx linear\\n down_beam_rot_y = math.atan( down_beam_gra_y )\\n down_beam_rot_y_err = down_beam_gra_y_err # Approx linear\\n\\n\\n# down_pos_x = down_beam_pos_x - data_dict['station_positions'][1]*up_beam_gra_x + up_x_func.GetParameter(0)\\n# down_pos_x_err = math.sqrt( up_x_func.GetParError(0)**2 + down_beam_pos_x_err**2 )\\n# down_pos_y = down_beam_pos_y - data_dict['station_positions'][1]*up_beam_gra_y + up_y_func.GetParameter(0)\\n# down_pos_y_err = math.sqrt( up_y_func.GetParError(0)**2 + down_beam_pos_y_err**2 )\\n\\n length = TRACKER_SEPARATION\\n down_pos_x = down_beam_pos_x - ( up_beam_pos_x + length*up_beam_gra_x )\\n down_pos_x_err = math.sqrt( up_beam_pos_x_err**2 + down_beam_pos_x_err**2 + (length*up_beam_gra_x_err)**2 )\\n down_pos_y = down_beam_pos_y - ( up_beam_pos_y + length*up_beam_gra_y )\\n down_pos_y_err = math.sqrt( up_beam_pos_y_err**2 + down_beam_pos_y_err**2 + (length*up_beam_gra_y_err)**2 )\\n\\n down_rot_x = down_beam_rot_x - up_beam_rot_x\\n down_rot_x_err = math.sqrt( down_beam_rot_x_err**2 + up_beam_rot_x_err**2 )\\n down_rot_y = down_beam_rot_y - up_beam_rot_y\\n down_rot_y_err = math.sqrt( down_beam_rot_y_err**2 + up_beam_rot_y_err**2 )\\n\\n\\n print\\n print \\\"Incoming Beam Misalignments:\\\"\\n print\\n print \\\"Displacement and rotation of beam with respect to upstream tracker:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( up_beam_pos_x, up_beam_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( up_beam_pos_y, up_beam_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( up_beam_rot_x*1000.0, up_beam_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( up_beam_rot_y*1000.0, up_beam_rot_y_err*1000.0 )\\n print\\n\\n print\\n print \\\"Downstream Tracker Beam Misalignments:\\\"\\n print\\n print \\\"Displacement and rotation of beam with respect to downstream tracker:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_beam_pos_x, down_beam_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_beam_pos_y, down_beam_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_beam_rot_x*1000.0, down_beam_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_beam_rot_y*1000.0, down_beam_rot_y_err*1000.0 )\\n print\\n\\n print\\n print \\\"Downstream Tracker Alignment:\\\"\\n print\\n print \\\"Displacement and rotation of between the two trackers:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_pos_x, down_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_pos_y, down_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_rot_x*1000.0, down_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_rot_y*1000.0, down_rot_y_err*1000.0 )\\n print\",\n \"def eg_sk():\\n\\n rxs = []\\n a = []\\n b = []\\n c = []\\n d = []\\n e = []\\n f = []\\n g = []\\n h = []\\n i = []\\n j = []\\n\\n for _ in range(1000):\\n a.append(utils.gaussian(10, 1))\\n\\n for _ in range(1000):\\n b.append(utils.gaussian(10.1, 1))\\n\\n for _ in range(1000):\\n c.append(utils.gaussian(20, 1))\\n\\n for _ in range(1000):\\n d.append(utils.gaussian(30, 1))\\n\\n for _ in range(1000):\\n e.append(utils.gaussian(30.1, 1))\\n\\n for _ in range(1000):\\n f.append(utils.gaussian(10, 1))\\n\\n for _ in range(1000):\\n g.append(utils.gaussian(10, 1))\\n\\n for _ in range(1000):\\n h.append(utils.gaussian(40, 1))\\n\\n for _ in range(1000):\\n i.append(utils.gaussian(40, 3))\\n\\n for _ in range(1000):\\n j.append(utils.gaussian(10, 1))\\n\\n for k, v in enumerate([a, b, c, d, e, f, g, h, i, j]):\\n rxs.append(creation.RX(v, \\\"rx{}\\\".format(k)))\\n\\n for rx in stats.tiles(stats.scottKnot(rxs)):\\n print(\\\"\\\", rx[\\\"rank\\\"], rx[\\\"name\\\"], rx[\\\"show\\\"], sep=\\\"\\\\t\\\")\",\n \"def setup(self):\\n igd = self.options['input_grid_data']\\n ogd = self.options['output_grid_data']\\n output_subset = self.options['output_subset']\\n\\n if ogd is None:\\n ogd = igd\\n\\n # Build the interpolation matrix which maps from the input grid to the output grid.\\n # Rather than a single phase-wide interpolating polynomial, map each segment.\\n # To do this, find the nodes in the output grid which fall in each segment of the input\\n # grid. Then build a Lagrange interpolating polynomial for that segment\\n L_blocks = []\\n output_nodes_ptau = list(ogd.node_ptau[ogd.subset_node_indices[output_subset]])\\n\\n for iseg in range(igd.num_segments):\\n i1, i2 = igd.segment_indices[iseg]\\n iptau_segi = np.take(igd.node_ptau, (i1, i2-1))\\n istau_segi = np.take(igd.node_stau, (i1, i2-1))\\n\\n # The indices of the output grid that fall within this segment of the input grid\\n if ogd is igd:\\n optau_segi = iptau_segi\\n else:\\n ptau_hi = igd.segment_ends[iseg+1]\\n if iseg < igd.num_segments - 1:\\n idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]\\n else:\\n idxs_in_iseg = np.arange(len(output_nodes_ptau))\\n optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]\\n # Remove the captured nodes so we don't accidentally include them again\\n output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]\\n\\n # Now get the output nodes which fall in iseg in iseg's segment tau space.\\n ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (iptau_segi[-1] - iptau_segi[0]) - 1\\n\\n # Create the interpolation matrix and add it to the blocks\\n L, _ = lagrange_matrices(istau_segi, ostau_segi)\\n L_blocks.append(L)\\n\\n self.interpolation_matrix = block_diag(*L_blocks)\\n r, c = np.nonzero(self.interpolation_matrix)\\n\\n output_num_nodes, input_num_nodes = self.interpolation_matrix.shape\\n\\n for (name, kwargs) in self._timeseries_outputs:\\n\\n input_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\\n input_name = 'input_values:{0}'.format(name)\\n self.add_input(input_name,\\n shape=(input_num_nodes,) + kwargs['shape'],\\n **input_kwargs)\\n\\n output_name = name\\n output_kwargs = {k: kwargs[k] for k in ('units', 'desc')}\\n output_kwargs['shape'] = (output_num_nodes,) + kwargs['shape']\\n self.add_output(output_name, **output_kwargs)\\n\\n self._vars.append((input_name, output_name, kwargs['shape']))\\n\\n size = np.prod(kwargs['shape'])\\n val_jac = np.zeros((output_num_nodes, size, input_num_nodes, size))\\n\\n for i in range(size):\\n val_jac[:, i, :, i] = self.interpolation_matrix\\n\\n val_jac = val_jac.reshape((output_num_nodes * size, input_num_nodes * size),\\n order='C')\\n\\n val_jac_rows, val_jac_cols = np.where(val_jac != 0)\\n\\n rs, cs = val_jac_rows, val_jac_cols\\n self.declare_partials(of=output_name,\\n wrt=input_name,\\n rows=rs, cols=cs, val=val_jac[rs, cs])\",\n \"def CII_vs_CO(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\\n fig,ax1 = plt.subplots()\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\\n # print('indices with L_CO < 1e0:')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\\n\\n # Observations\\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\\n K16_LCII = K16['[CII]158_Lsun']\\n K16_LCO = K16['CO(1-0)_Lsun']\\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\\\\n label='Mixed type galaxies [Kamenetzky+16]')\\n\\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\\n C15_LCII = C15['L_[CII]158']\\n C15_LCO = C15['L_CO(1-0)']\\n C15_Z = C15['Z']\\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\\n # if len(L_ul) > 0:\\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\\n\\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \\n\\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\\n CII_obs = CII_obs[index]\\n CO_obs = CO_obs[index]\\n\\n x = np.linspace(0, 7, 100)\\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\\\\n CII_obs.reshape(-1, 1))\\n L_fit = fit.predict(x.reshape(-1, 1))\\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\\n\\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\\n plt.colorbar(sc,label=r'log $\\\\langle$Z$\\\\rangle_{\\\\rm SFR}$ [Z$_{\\\\rm \\\\odot}$]')\\n\\n handles, labels = ax1.get_legend_handles_labels()\\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\\n\\n ax1.set_xlim([-3,6.2])\\n ax1.set_ylim([4,10])\\n\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)\",\n \"def CalculateG(g, s):\\n for i in range(len(y_train)):\\n g[i] = 1 - y_train[i]*s[i]\\n return g\",\n \"def plot():\\n xvals = np.arange(-50, 250, step=0.1)\\n\\n fig = plt.figure()\\n plt.suptitle(\\\"Gaussian with smooth transition to power law\\\")\\n\\n A0vals = [10, 11]\\n avals = [5*10**-3, 10**-3, 5*10**-4]\\n ttvals = [10., 50., 100.]\\n cvals = [-0.1, -0.9, -5./3., -4.]\\n offset = [-30, 0.0, 30]\\n\\n paramvals = [A0vals, avals, ttvals,cvals, offset]\\n titles, labels = return_parameter_names()\\n\\n nplots = len(paramvals)\\n\\n for i in range(nplots):\\n plt.subplot(nplots, 1, i+1)\\n vals = paramvals[i]\\n for j in range(len(vals)):\\n pset = list(default())\\n pset[i] = vals[j]\\n yvals=[]\\n ypower=[]\\n ypeak=[]\\n for x in xvals:\\n yvals.append(fitfunc(x, pset))\\n ypeak.append(logpeak(x,pset))\\n if x > 0:\\n ypower.append(logpowerlaw(x,pset))\\n label = labels[i] + \\\"=\\\"+str(vals[j])\\n plt.plot(xvals, yvals, label = label)\\n\\n plt.title(titles[i])\\n plt.legend()\\n\\n fig.set_size_inches(15, 30)\\n plt.savefig(\\\"graphs/misc/lightcurve_models.pdf\\\")\\n plt.close()\",\n \"def _gaussian_weighted_map_const_multi(\\n y_grid, x_grid, heatmap, points_y, points_x, boxes,\\n gaussian_denom_ratio):\\n num_instances, _ = _get_shape(boxes, 2)\\n height, width, num_keypoints = _get_shape(heatmap, 3)\\n\\n # [height, width, num_instances, num_keypoints].\\n # Note that we intentionally avoid using tf.newaxis as TfLite converter\\n # doesn't like it.\\n y_diff = (\\n tf.reshape(y_grid, [height, width, 1, 1]) -\\n tf.reshape(points_y, [1, 1, num_instances, num_keypoints]))\\n x_diff = (\\n tf.reshape(x_grid, [height, width, 1, 1]) -\\n tf.reshape(points_x, [1, 1, num_instances, num_keypoints]))\\n distance_square = y_diff * y_diff + x_diff * x_diff\\n\\n y_min, x_min, y_max, x_max = tf.split(boxes, 4, axis=1)\\n\\n # Make the mask with all 1.0 in the box regions.\\n # Shape: [height, width, num_instances]\\n in_boxes = tf.math.logical_and(\\n tf.math.logical_and(\\n tf.reshape(y_grid, [height, width, 1]) >= tf.reshape(\\n y_min, [1, 1, num_instances]),\\n tf.reshape(y_grid, [height, width, 1]) < tf.reshape(\\n y_max, [1, 1, num_instances])),\\n tf.math.logical_and(\\n tf.reshape(x_grid, [height, width, 1]) >= tf.reshape(\\n x_min, [1, 1, num_instances]),\\n tf.reshape(x_grid, [height, width, 1]) < tf.reshape(\\n x_max, [1, 1, num_instances])))\\n in_boxes = tf.cast(in_boxes, dtype=tf.float32)\\n\\n gaussian_denom = tf.cast(\\n tf.minimum(height, width), dtype=tf.float32) * gaussian_denom_ratio\\n # shape: [height, width, num_instances, num_keypoints]\\n gaussian_map = tf.exp((-1 * distance_square) / gaussian_denom)\\n return tf.expand_dims(heatmap, axis=2) * gaussian_map * tf.reshape(\\n in_boxes, [height, width, num_instances, 1])\",\n \"def gredo(x,y,guessx,guessy,guesspar):\\n\\n global BTRACK, GSTRUC, NPIX\\n \\n flag = True # do it unless proven wrong \\n \\n # FROM **ANY** PREVIOUS POSITION \\n prev, = np.where((BTRACK['x']==x) & (BTRACK['y']==y))\\n nprev = len(prev)\\n\\n if guesspar is None:\\n return False\\n tguesspar = guesspar\\n nguesspar = len(tguesspar) \\n ngg = nguesspar//3 \\n \\n # FROM **ANY** PREVIOUS POSITION \\n # We have used a guess from this position before \\n # but have the parameters changed sufficiently\\n nogaussians = True # no gaussians found by default\\n if (nprev > 0): \\n \\n # Looping through the previous ones \\n for i in range(nprev):\\n guesspar2 = BTRACK['data'][prev[i]]['guesspar']\\n \\n # Some gaussians found \\n if (guesspar2 is not None):\\n nogaussians = False\\n tpar = guesspar2\\n ntpar = len(tpar) \\n ntg = ntpar//3 # number of gaussians in this guess \\n \\n # Same number of gaussians \\n if (ntpar == nguesspar): \\n # Sorting, largest first \\n tpar2 = utils.gsort(tpar) \\n tguesspar2 = utils.gsort(tguesspar) \\n \\n # Fixing possible zeros that could ruin the ratio \\n dum = np.copy(tpar2)\\n bd, = np.where(dum == 0.) \\n if len(bd) > 0:\\n dum[bd] = 1e-5 \\n diff = np.abs(tpar2 - tguesspar2) \\n ratio = diff/np.abs(dum) \\n \\n # These differences are too small, NO redo \\n if (np.max(ratio) < 0.01): \\n return False\\n \\n # Some previous visits, but no Gaussians detected, redo=False\\n if nogaussians:\\n return False\\n \\n return flag\",\n \"def transition_function(grid, neighbourstates, neighbourcounts, decay_grid,\\n water_decay_grid):\\n\\n global water_counter\\n global ignition_grid\\n neighbourstates = np.array(neighbourstates)\\n init_grid = initial_grid.astype(int)\\n ig_grid = np.array(ignition_grid)\\n windspeed_ignition_modifiers = wind_speed_rvalue(\\\"NE\\\", 10)\\n new_ig_grid = []\\n for i, row in enumerate(grid):\\n new_ig_grid.append([\\n ignite(cell, neighbourstates[:, i, j],\\n windspeed_ignition_modifiers) for j, cell in enumerate(row)\\n ])\\n new_ig_grid = np.array(new_ig_grid)\\n started_to_burn = []\\n for i, row in enumerate(grid):\\n started_to_burn.append([\\n started_burning(cell, ig_grid[i, j], new_ig_grid[i, j])\\n for j, cell in enumerate(row)\\n ])\\n grid[started_to_burn] = START_BURN\\n ig_grid = np.add(new_ig_grid, ig_grid)\\n full_burn = []\\n for i, row in enumerate(grid):\\n full_burn.append([\\n fully_burning(cell, ig_grid[i, j], decay_grid[i, j])\\n for j, cell in enumerate(row)\\n ])\\n grid[full_burn] = BURNING\\n end_burning = []\\n for i, row in enumerate(grid):\\n end_burning.append([\\n ending_burn(cell, decay_grid[i, j], decay_values[int(\\n initial_grid[i, j])]) for j, cell in enumerate(row)\\n ])\\n grid[end_burning] = END_BURN\\n decay_grid[(grid == BURNING) | (grid == END_BURN)] -= 1\\n burnt_out = (decay_grid == 0) # find those which have decayed to 0\\n grid[(decay_grid == 0\\n )] = BURNT #set all that have decayed to zero to BURNT(7)\\n water_counter += 1\\n\\n if (water_counter == 100):\\n grid[120:160, 80:120] = initial_grid[120:160, 80:120]\\n water_decay_grid[(grid != LAKE)] -= 1 # take one off their decay value\\n grid[(water_decay_grid == 0)] = BURNT # switch their state to 5\\n ignition_grid = ig_grid\\n return grid\",\n \"def traces(mndata,Params,srate,imagepath):\\n\\t#plot high gamma traces\\n\\t#data should be bandpassed (todo)\\n\\t#resample to srate\\n\\tst = resample(Params[\\\"st\\\"],srate)\\n\\ten = resample(Params[\\\"en\\\"],srate)\\n\\tbl_en = resample(Params[\\\"bl_en\\\"],srate)\\n\\tbl_st = resample(Params[\\\"bl_st\\\"],srate)\\n\\tplot_tp = resample(Params[\\\"plot\\\"],srate)\\n\\tcue = resample(500,srate)\\n\\t\\n\\tcolors = ['red','orange','green','blue']\\n\\tx = np.array(range(st,en+1))\\n\\tf, (ax,ax2) = plt.subplots(1,2, sharex = False)\\n\\tax.axhline(y = 0,color = 'k',linewidth=2)\\n\\tax.axvline(x = 0,color='k',linewidth=2)\\n\\tax.axvline(x = cue,color = 'gray',linewidth = 2)\\n\\tax.axvline(x = cue+cue,color = 'gray',linewidth = 2)\\n\\tax.axvspan(cue, cue+cue, facecolor='0.5', alpha=0.25,label = 'cue')\\n\\n\\tfor j in range(len(Params[\\\"conditions\\\"])):\\n\\t\\tcondition = Params['conditions'][j]\\n\\t\\ty = mndata[condition]['data']\\n\\t\\tax.plot(x,y, label = condition,linewidth = 2,color = colors[j])\\n\\t\\n\\tax.set_ylim((-30,85))\\n\\tax.set_xlim(st,en)\\n\\tax.legend()\\n\\tax.xaxis.set_ticklabels(['', '0', '','500', '', '1000', '', '1500', '', '2000','','2500','', '3000'],minor=False)\\n\\tax.xaxis.set_ticks(range(st,en,plot_tp))\\n\\n\\tax.set_xlabel(\\\"time (ms)\\\")\\n\\tax.set_ylabel(\\\"% change baseline\\\")\\n\\tax.set_title('Analytic Amplitude - High Gamma (70-150Hz)', fontsize = 18)\\n\\n\\t#plot brain with elec location\\n\\t#brain = plt.imread(imagepath)\\n\\t#aa = pylab.mean(brain,2)\\n\\t#ax2.imshow(aa)\\n\\t#a2.gray()\\n\\n\\t#brain = Image.open(imagepath)\\n\\t#ax2.set_axis_off()\\n\\t#im = plt.imshow(brain, origin = 'lower')\\n\\n\\t#brain = _png.read_png(imagepath)\\n\\t#imagebox = OffsetImage(brain,zoom =5)\\n\\t#ab = AnnotationBbox(imagebox,)\\n\\n\\tim = Image.open(imagepath)\\n\\tax2.imshow(im,aspect = 'auto',origin = 'lower')\\n\\tax2.set_xlim((0,750))\\n\\tax2.set_title('Electrode Location',fontsize = 18)\\n\\n\\n\\n\\treturn f, (ax, ax2)\",\n \"def update_grhs():\\n init_gradient()\\n costs_per_batch = []\\n for i in range(n_train_batches):\\n c = update_gradient_batch(i,*args)\\n costs_per_batch.append(c)\\n return numpy.mean(costs_per_batch,axis=0)\",\n \"def flatNoiseCGH():\\n #Get data\\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/NoiseStudy/FlatMeasurements/'\\n d1,dx1 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas1.fits')\\n d2,dx2 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas2.fits')\\n p1,px1 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas1.fits')\\n p2,px2 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas2.fits')\\n p3,px3 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas1.fits')\\n p4,px4 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas2.fits')\\n\\n #Construct baseline power spectra\\n f1,pow1 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1)\\n f2,pow2 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1,axis=1)\\n \\n #Construct parroted power spectra\\n f3,pow3 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx1)\\n f4,pow4 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx2,axis=1)\\n f5,pow5 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx1)\\n f6,pow6 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx2,axis=1)\\n\\n #Plot\\n plt.loglog(f1,pow1/f1[0],label='Axial Baseline')\\n plt.loglog(f2,pow2/f2[0],label='Azimuthal Baseline')\\n plt.loglog(f3,pow3/f3[0],label='Pitch Axial')\\n plt.loglog(f4,pow4/f4[0],label='Pitch Azimuthal')\\n plt.loglog(f5,pow5/f5[0],label='Roll Axial')\\n plt.loglog(f6,pow6/f6[0],label='Roll Azimuthal')\\n plt.title('Residual Fringe Repeatability Impact')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n plt.legend(loc='lower left')\\n\\n return f1,pow1\",\n \"def compute_g_0(self, j):\\n #Compute variance and mean denominator (same denominator for both)\\n denominator = self.sigma2 * self.sigma_g_star_2[0, j] + (10**8) * self.sigma2\\n\\n numerator_mean = (10**8) * self.sigma2 * self.g_heat[1,j]\\n if (self.u_heat > self.temperatures[0]):\\n denominator = denominator + (10**8) * self.sigma_g_star_2[0, j] * ((self.temperatures[0] - self.u_heat )**2)\\n numerator_mean = numerator_mean + \\\\\\n (10**8) * self.sigma_g_star_2[0, j] * (self.temperatures[0] - self.u_heat) * (self.consumptions[0] - self.s[0,j] * self.kappa[self.daytypes[0]])\\n\\n #Mean\\n mean = numerator_mean / denominator\\n\\n #Compute variance numerator\\n variance_numerator = ((10**8) * self.sigma2 * self.sigma_g_star_2[0, j])\\n #Variance\\n variance = variance_numerator / denominator\\n\\n self.g_heat[0,j] = self.truncated_norm(-inf, 0, mean, variance)\",\n \"def swe(lon, lat, month):\\n\\n im = month - 1\\n\\n h0 = np.array( [ 8.37, 9.43, 10.74, 11.67, 11.80, 12.48,\\n 4.01, 1.08, 3.84, 6.24, 7.54, 8.00 ] )\\n a = np.array( [ -0.0270, 0.0058, 0.1618, 0.0841, -0.0043, 0.2084,\\n 0.0970, 0.0712, 0.0393, 0.1158, 0.0567, -0.0540 ] )\\n b = np.array( [ -0.3400, -0.1309, 0.0276, -0.1328, -0.4284, -0.5739,\\n -0.4930, -0.1450, -0.2107, -0.2803, -0.3201, -0.3650 ] )\\n c = np.array( [ -0.0319, 0.0017, 0.0213, 0.0081, -0.0380, -0.0468,\\n -0.0333, -0.0155, -0.0182, -0.0215, -0.0284, -0.0362 ] )\\n d = np.array( [ -0.0056, -0.0021, 0.0076, -0.0003, -0.0071, -0.0023,\\n -0.0026, 0.0014, -0.0053, 0.0015, -0.0032, -0.0112 ] )\\n e = np.array( [ -0.0005, -0.0072, -0.0125, -0.0301, -0.0063, -0.0253,\\n -0.0343, -0.0000, -0.0190, -0.0176, -0.0129, -0.0035 ] )\\n\\n x = (90. - lat) * np.cos( np.radians(lon) )\\n y = (90. - lat) * np.sin( np.radians(lon) )\\n\\n h = ( h0[im] + ( a[im] * x ) + ( b[im] * y ) + ( c[im] * x * y ) +\\n ( d[im] * x * x ) + ( e[im] * y * y ) )\\n\\n# h = np.where( h < 0., 0., h)\\n \\n return h\",\n \"def _get_sum_gaussian_image(s_gen, xs, ys, sdev, n=50):\\n m1, m2 = xs.min(), xs.max()\\n xx = np.linspace(m1, m2, n)\\n XX, YY = np.meshgrid(xx, xx)\\n XX, YY = [u.ravel()[np.newaxis, :] for u in [XX, YY]]\\n xs, ys, S_gen = [u[:, np.newaxis] for u in [xs, ys, s_gen]]\\n res = np.sum(\\n S_gen * np.exp(((xs - XX) ** 2 + (ys - YY) ** 2) /\\n (-2 * sdev ** 2)), axis=0)\\n return res.reshape(n, n)\",\n \"def garch(ω, α, β, n_out=1000):\\r\\n p = len(α)\\r\\n q = len(β)\\r\\n\\r\\n\\r\\n # Größe Einbrennphase\\r\\n n_pre = 1000\\r\\n n = n_pre + n_out\\r\\n\\r\\n\\r\\n # Sample noise\\r\\n ɛ = np.random.standard_normal(size=n)#st.t.rvs(df=5,size=n))\\r\\n #st.t.var(df=5))#np.random.normal(size=n)\\r\\n\\r\\n\\r\\n y = np.zeros(n)\\r\\n σ = np.zeros(n)\\r\\n\\r\\n #Start-Values\\r\\n for k in range(np.max([p, q])):\\r\\n σ[k] = (np.random.standard_normal())\\r\\n\\r\\n #Create GARCH data\\r\\n\\r\\n for k in range(np.max([p, q]), n):\\r\\n α_term = sum([α[i] * y[k-i-1]**2 for i in range(p)])\\r\\n β_term = sum([β[i] * ɛ[k-i-1]**2 for i in range(q)])\\r\\n σ[k] = np.sqrt(ω + α_term + β_term)\\r\\n y[k] = σ[k] * ɛ[k]\\r\\n\\r\\n\\r\\n return y[n_pre:]\",\n \"def CalcGreenFunctions(x, y, z, x_src_l, y_src_l, alpha, dire, Lambda_y, \\\\\\n gamma_l, c, omega, G_sen, dir_meas, dir_meas_deg, airloss_alpha, f, n):\\n \\n G = greens_fct(repmat(x_src_l, np.shape(x)[0],1), repmat(y_src_l, np.shape(y)[0],1), omega, c, \\\\\\n np.transpose(repmat(x, np.shape(x_src_l)[0], 1)), np.transpose(repmat(y, np.shape(y_src_l)[0], 1)), z)\\n\\n G = G_sen * G\\n \\n beta = np.arcsin((np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1)) \\\\\\n * np.sqrt((np.transpose(repmat(x, np.shape(x_src_l)[0], 1)) - \\\\\\n repmat(x_src_l, np.shape(x)[0], 1))**2 + \\\\\\n (np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1))**2)**(-1)) \\\\\\n + repmat(gamma_l, np.shape(x)[0], 1)\\n \\n # air attenuation\\n src_rec_dist = np.sqrt((np.transpose(repmat(x, np.shape(x_src_l)[0], 1)) - repmat(x_src_l, np.shape(x)[0], 1))**2 \\\\\\n + (np.transpose(repmat(y, np.shape(y_src_l)[0], 1)) - repmat(y_src_l, np.shape(y)[0], 1))**2)\\n \\n air_att = airloss_alpha * src_rec_dist\\n air_att = 10**(-air_att / 20)\\n G = G * air_att\\n \\n H_post = calc_directivity(dire, alpha, Lambda_y, beta, omega, c, f, dir_meas, dir_meas_deg, n)\\n\\n G = G * H_post\\n \\n return G\",\n \"def visualSignMap(phasemap1, phasemap2):\\r\\n\\r\\n if phasemap1.shape != phasemap2.shape:\\r\\n raise LookupError(\\\"'phasemap1' and 'phasemap2' should have same size.\\\")\\r\\n\\r\\n gradmap1 = np.gradient(phasemap1)\\r\\n gradmap2 = np.gradient(phasemap2)\\r\\n\\r\\n # gradmap1 = ni.filters.median_filter(gradmap1,100.)\\r\\n # gradmap2 = ni.filters.median_filter(gradmap2,100.)\\r\\n\\r\\n graddir1 = np.zeros(np.shape(gradmap1[0]))\\r\\n # gradmag1 = np.zeros(np.shape(gradmap1[0]))\\r\\n\\r\\n graddir2 = np.zeros(np.shape(gradmap2[0]))\\r\\n # gradmag2 = np.zeros(np.shape(gradmap2[0]))\\r\\n\\r\\n for i in range(phasemap1.shape[0]):\\r\\n for j in range(phasemap2.shape[1]):\\r\\n graddir1[i, j] = math.atan2(gradmap1[1][i, j], gradmap1[0][i, j])\\r\\n graddir2[i, j] = math.atan2(gradmap2[1][i, j], gradmap2[0][i, j])\\r\\n\\r\\n # gradmag1[i,j] = np.sqrt((gradmap1[1][i,j]**2)+(gradmap1[0][i,j]**2))\\r\\n # gradmag2[i,j] = np.sqrt((gradmap2[1][i,j]**2)+(gradmap2[0][i,j]**2))\\r\\n\\r\\n vdiff = np.multiply(np.exp(1j * graddir1), np.exp(-1j * graddir2))\\r\\n\\r\\n areamap = np.sin(np.angle(vdiff))\\r\\n\\r\\n return areamap\",\n \"def optimize_at_with_gd(y_true, output, thresholds, average):\\n opt = Adam()\\n opt.alpha = 1e-2\\n metric = calculate_f1(y_true, output, thresholds, average)\\n metric_asfo_epoch = [metric]\\n for i in range(100):\\n grads = calculate_at_gradient(y_true, output, thresholds, average)\\n # if i==0: print(\\\"grads:\\\", grads)\\n grads = [-e for e in grads]\\n thresholds = opt.GetNewParams(thresholds, grads)\\n metric = calculate_f1(y_true, output, thresholds, average)\\n if i % 50 == 0:\\n print('Iteration: {}, Score: {:.3f}, thresholds: {}'.format(\\n i, metric, np.array(thresholds)))\\n metric_asfo_epoch.append(metric)\\n\\n return metric, thresholds, metric_asfo_epoch\",\n \"def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\\n \\n random.seed(rand)\\n NGEN = ngen\\n MU = mu\\n LAMBDA = lamb\\n CXPB = cxpb\\n MUTPB = mutpb\\n \\n # Used for printing the results. It is the parameter that is changed one run from another\\n if param==\\\"rand\\\" or param==\\\"optimal\\\":\\n list_results=[rand]\\n elif param==\\\"mu\\\":\\n list_results=[mu]\\n elif param==\\\"lamb\\\":\\n list_results=[lamb]\\n elif param==\\\"cross\\\":\\n list_results=[cxpb]\\n elif param==\\\"mutate\\\":\\n list_results=[mutpb]\\n elif param==\\\"ngen\\\":\\n list_results=[ngen]\\n elif param==\\\"original\\\":\\n list_results=[0]\\n \\n # Initialization of the objects for the GA\\n pop = toolbox.population(n=MU)\\n hof = tools.ParetoFront()\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", np.mean, axis=0)\\n stats.register(\\\"std\\\", np.std, axis=0)\\n stats.register(\\\"min\\\", np.min, axis=0)\\n stats.register(\\\"max\\\", np.max, axis=0)\\n\\n # Run of the GA\\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\\n halloffame=hof,verbose=0)\\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\\n w = csv.DictWriter(f, logbook[0].keys())\\n w.writeheader()\\n for el in logbook:\\n w.writerow(el)\\n w.writerow({})\\n \\n # Takes the max fitness of the population from all of the runs\\n max_fit=0\\n max_gen=0\\n for elt in logbook:\\n if elt['max'][0]>max_fit:\\n max_fit=elt['max'][0]\\n max_gen=elt['gen']\\n list_results.append(max_fit)\\n list_results.append(max_gen)\\n \\n #TODO\\n# for ind in hof:\\n# dist = numpy.linalg.norm(a-b)\\n\\n print (\\\"{0} {1} {2} {3}\\\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\\n \\n return pop, stats, hof\",\n \"def _run_rbf_interpolation(out_dir, layer, bounds, function, smooth):\\n # if running scipy methods prepend root dir to out path\\n out_dir = OPJ(path_root, out_dir)\\n if not os.path.isdir(out_dir):\\n print(\\n os.path.abspath(out_dir),\\n ' does not exist, creating directory.\\\\n'\\n )\\n Path(out_dir).mkdir(parents=True, exist_ok=True)\\n\\n out_file = OPJ(\\n out_dir, \\n '{time_agg}.tiff'.format(time_agg=layer)\\n )\\n print(\\n '\\\\nInterpolating {g} point bias ratios for: {t}\\\\n'.\\\\\\n format(g=grid_var, t=layer),\\n 'Using the \\\"{}\\\" method\\\\n'.format(function),\\n 'Resolution (pixel size) of output raster: {} degrees'.format(res)\\n )\\n print( \\n 'GeoTIFF raster will be saved to: \\\\n',\\n os.path.abspath(out_file)\\n )\\n\\n\\n # get grid extent based on station locations in CSV\\n if not bounds:\\n bounds = get_subgrid_bounds(in_path, buffer=buffer, grid_res=CS) \\n lon_min, lon_max, lat_min, lat_max = bounds\\n # fix any minor adjustments to make raster fit gridMET fishnet extent\\n # if scale_factor=1 make sure raster pixels align exactly w/gridcells\\n # raster extent may exceed fishnet grid to fill gaps for zonal stats\\n if scale_factor:\\n nxcells = abs(lon_min-lon_max) / (CS*scale_factor)\\n nycells = abs(lat_min-lat_max) / (CS*scale_factor)\\n remainder_x = int(nxcells) - nxcells\\n remainder_y = int(nycells) - nycells\\n if abs(remainder_x) > CS:\\n remainder_x -= CS * (remainder_x / CS) \\n if abs(remainder_y) > CS:\\n remainder_y -= CS * (remainder_y / CS)\\n lon_min -= remainder_x\\n lon_max += CS\\n lat_min -= remainder_y\\n lat_min -= CS\\n \\n # check if layer is in summary CSV \\n existing_layers = pd.read_csv(in_path).columns\\n if not layer in existing_layers:\\n print('Column {} does not exist in input CSV:\\\\n {}'.format(\\n layer, in_path),\\n '\\\\nSkipping interpolation.'\\n )\\n return\\n \\n # get point station data from summary CSV\\n in_df = pd.read_csv(in_path, na_values=[-999])\\n lon_pts, lat_pts = in_df.STATION_LON.values, in_df.STATION_LAT.values\\n values = in_df[layer].values\\n \\n # mask out stations with missing data\\n if in_df[layer].isnull().sum() > 0:\\n mask = in_df[layer].notnull()\\n n_missing = in_df[layer].isna().sum()\\n # if one point or less data points exists exit\\n if len(mask) == n_missing or len(values) - n_missing == 1:\\n print('Missing sufficient point data for variable: {} {}'.\\\\\\n format(grid_var, layer),\\n '\\\\nNeed at least two stations with data, skipping.')\\n return\\n print('Warning:\\\\n',\\n 'Data missing for {} of {} stations for variable: {} {}'.\\\\\\n format(n_missing, len(values), grid_var, layer),\\n '\\\\nproceeding with interpolation.')\\n # get locations where ratio is not nan\\n values = values[mask]\\n lon_pts = lon_pts[mask]\\n lat_pts = lat_pts[mask]\\n\\n nx_cells = int(np.round(np.abs((lon_min - lon_max) / CS)))\\n ny_cells = int(np.round(np.abs((lat_min - lat_max) / CS)))\\n # rbf requires uniform grid (n X n) so \\n # extend short dimension and clip later \\n nx_cells_out = copy.copy(nx_cells)\\n ny_cells_out = copy.copy(ny_cells)\\n # gdal requires \\\"upper left\\\" corner coordinates\\n lat_max_out = copy.copy(lat_max)\\n lon_max_out = copy.copy(lon_max)\\n # extend short dimension to make square grid\\n if not nx_cells == ny_cells:\\n diff = np.abs(nx_cells - ny_cells)\\n if nx_cells > ny_cells:\\n lat_max += diff * CS\\n ny_cells += diff\\n else:\\n lon_max += diff * CS\\n nx_cells += diff\\n\\n if scale_factor == 1:\\n # make finer/coarse grid by scale factor\\n lons = np.linspace(lon_min, lon_max, \\n int(np.round(nx_cells/scale_factor))-1)\\n lats = np.linspace(lat_min, lat_max, \\n int(np.round(ny_cells/scale_factor))-1)\\n # extent for original created by spatial.build_subgrid\\n # add one to make sure raster covers full extent\\n lons_out = np.linspace(lon_min, lon_max_out, \\n int(np.round(nx_cells_out/scale_factor))-1)\\n lats_out = np.linspace(lat_min, lat_max_out, \\n int(np.round(ny_cells_out/scale_factor))-1)\\n\\n else:\\n # add one extra cell to cover grid buffer extent for upscaling\\n # raster extent always >= grid buffer\\n lons = np.linspace(lon_min, lon_max, \\n int(np.round(nx_cells/scale_factor)))\\n lats = np.linspace(lat_min, lat_max, \\n int(np.round(ny_cells/scale_factor)))\\n lons_out = np.linspace(lon_min, lon_max_out, \\n int(np.round(nx_cells_out/scale_factor)))\\n lats_out = np.linspace(lat_min, lat_max_out, \\n int(np.round(ny_cells_out/scale_factor)))\\n\\n # if function was 'linear_rbf' \\n function = function.replace('_rbf', '')\\n # make sampling square grid\\n XI, YI = np.meshgrid(lons, lats)\\n # apply rbf interpolation\\n rbf = Rbf(lon_pts, lat_pts, values, function=function, smooth=smooth)\\n ZI = rbf(XI, YI)\\n # clip to original extent, rbf array flips axes, and row order... \\n ZI_out = ZI[0:len(lats_out),0:len(lons_out)]\\n ZI_out = np.flip(ZI_out,axis=0)\\n\\n #### save scipy interpolated data as raster \\n pixel_size = CS * scale_factor\\n # number of pixels in each direction\\n x_size = len(lons_out)\\n y_size = len(lats_out)\\n # set geotransform info\\n gt = [\\n lon_min,\\n pixel_size,\\n 0,\\n lat_max_out,\\n 0,\\n -pixel_size\\n ]\\n # make geotiff raster\\n driver = gdal.GetDriverByName('GTiff')\\n ds = driver.Create(\\n out_file,\\n x_size, \\n y_size, \\n 1, \\n gdal.GDT_Float32, \\n )\\n # set projection geographic lat/lon WGS 84\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326)\\n ds.SetProjection(srs.ExportToWkt())\\n # assign spatial dimensions \\n ds.SetGeoTransform(gt)\\n outband = ds.GetRasterBand(1)\\n # save rbf interpolated array as geotiff raster close\\n outband.WriteArray(ZI_out)\\n ds = None\\n\\n # calc residuals add to shapefile and in_path CSV, move shape to out_dir\\n # only residuals for bias ratios, i.e. not for std dev, etc\\n if layer in InterpGdal.default_layers:\\n calc_pt_error(in_path, out_dir, layer, grid_var, \\n grid_id_name=grid_id_name\\n )\\n # calculate zonal statistics save means for each gridMET cell\\n if z_stats:\\n zonal_stats(in_path, out_file, grid_id_name=grid_id_name)\\n \\n # plot layer's interpolated residuals as bar plot witheach Wx station \\n # only produce residual plots for bias ratios, i.e. not for std dev, etc\\n if res_plot and layer in InterpGdal.default_layers:\\n layer = InterpGdal.var_residual_names.get(\\n layer, \\n layer.replace('mean','res')\\n )\\n y_label = 'residual (interpolated minus station value)'\\n title = 'layer: {} algorithm: {} (RBF) resolution: {} deg.'.format(\\n layer, function ,res)\\n res_plot_dir = Path(out_dir)/'residual_plots'\\n subtitle='parameters: smooth={}'.format(smooth)\\n source_file = Path(out_dir)/Path(in_path).name\\n\\n station_bar_plot(source_file, layer, out_dir=res_plot_dir, \\n y_label=y_label, title=title, subtitle=subtitle)\",\n \"def interpolating_model_DA(temp,grav,m_type='da2014'):\\n # PARAMETERS # \\n dir_models = basedir + '/WDModels_Koester.'+m_type+'_npy/'\\n if m_type==\\\"pier\\\":\\n teff=np.array([1500.,1750.,2000.,2250.,2500.,2750.,3000.,3250.,3500.,\\n 3750.,4000.,4250.,4500.,4750.,5000.,5250.,5500.,6000.,\\n 6500.,7000.,7500.,8000.,8500.,9000.,9500.,10000.,10500.,\\n 11000.,11500.,12000.,12500.,13000.,13500.,14000.,14500.,\\n 15000.,15500.,16000.,16500.,17000.,20000.,25000.,30000.,\\n 35000.,40000.,45000.,50000.,55000.,60000.,65000.,70000.,\\n 75000.,80000.,85000.,90000.])\\n logg=np.array([6.50,7.00,7.50,7.75,8.00,8.25,8.50,9.00,9.50])\\n elif m_type==\\\"da2014\\\":\\n teff=np.array([6000.,6250.,6500.,6750.,7000.,7250.,7500.,7750.,8000.,\\n 8250.,8500.,8750.,9000.,9250.,9500.,9750.,10000.,10100.,\\n 10200.,10250.,10300.,10400.,10500.,10600.,10700.,10750.,\\n 10800.,10900.,11000.,11100.,11200.,11250.,11300.,11400.,\\n 11500.,11600.,11700.,11750.,11800.,11900.,12000.,12100.,\\n 12200.,12250.,12300.,12400.,12500.,12600.,12700.,12750.,\\n 12800.,12900.,13000.,13500.,14000.,14250.,14500.,14750.,\\n 15000.,15250.,15500.,15750.,16000.,16250.,16500.,16750.,\\n 17000.,17250.,17500.,17750.,18000.,18250.,18500.,18750.,\\n 19000.,19250.,19500.,19750.,20000.,21000.,22000.,23000.,\\n 24000.,25000.,26000.,27000.,28000.,29000.,30000.,35000.,\\n 40000.,45000.,50000.,55000.,60000.,65000.,70000.,75000.,\\n 80000.,90000.,100000.])\\n logg=np.array([4.00,4.25,4.50,4.75,5.00,5.25,5.50,5.75,6.00,6.25,6.50,\\n 6.75,7.00,7.25,7.50,7.75,8.00,8.25,8.50,8.75,9.00,9.25,\\n 9.50])\\n if (m_type=='pier') & (temp<1500. or temp>90000. or grav<6.5 or grav>9.5): \\n return [],[]\\n elif (m_type=='da2014') & (temp<6000. or temp>100000. or grav<4.0 or grav>9.5): \\n return [],[]\\n # INTERPOLATION #\\n g1,g2 = np.max(logg[logg<=grav]),np.min(logg[logg>=grav])\\n if g1!=g2: g = (grav-g1)/(g2-g1)\\n else: g=0\\n t1,t2 = np.max(teff[teff<=temp]),np.min(teff[teff>=temp])\\n if t1!=t2: t = (temp-t1)/(t2-t1) \\n else: t=0\\t\\n if m_type =='da2014': models = ['da%06d_%d_2.7.npy'%(i, j*100) for i in [t1,t2] \\n for j in [g1,g2]]\\n else: models = ['WD_%.2f_%d.0.npy'%(j, i) for i in [t1,t2] for j in [g1,g2]]\\n try:\\n m11, m12 = np.load(dir_models+models[0]), np.load(dir_models+models[1])\\t\\n m21, m22 = np.load(dir_models+models[2]), np.load(dir_models+models[3])\\t\\n flux_i = (1-t)*(1-g)*m11[:,1]+t*(1-g)*m21[:,1]+t*g*m22[:,1]+(1-t)*g*m12[:,1]\\n return np.dstack((m11[:,0], flux_i))[0]\\n except: return [],[]\",\n \"def stamps(d_data='',**kwargs):\\n GR = glo.global_results()\\n if p.gal_index == 'all':\\n gal_indices = np.arange(GR.N_gal)\\n else:\\n gal_indices = p.gal_index\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n for gal_index in gal_indices:\\n\\n fig, ax = plt.subplots(figsize=(8,8))\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n simgas = gal_ob.particle_data.get_dataframe('simgas',d_data=d_data)\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop,pix_size_kpc=p.pix_size_kpc,scale=1.5)\\n\\n # Plot\\n ax.set_facecolor(\\\"black\\\")\\n Rmax = max_scale/2\\n if p.log:\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\\n map2D = np.log10(map2D)\\n if not p.log:\\n map2D[map2D < p.vmin] = p.vmin/2\\n map2D[map2D > p.vmax] = p.vmax\\n im = ax.imshow(map2D,\\\\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,vmax=p.vmax,cmap=p.cmap)\\n Rmax = p.R_max\\n ax.set_xlim([-Rmax,Rmax])\\n ax.set_ylim([-Rmax,Rmax])\\n ax.text(0.05,0.05,'G%i' % gal_index,\\\\\\n fontsize=55,transform=ax.transAxes,color='white')\\n ax.set_xticklabels([])\\n ax.set_yticklabels([])\\n ax.set_aspect('equal')\\n\\n #plt.gca().set_axis_off()\\n #plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, \\n # hspace = 0, wspace = 0)\\n #plt.margins(0,0)\\n #plt.gca().xaxis.set_major_locator(plt.NullLocator())\\n #plt.gca().yaxis.set_major_locator(plt.NullLocator())\\n if not os.path.isdir(p.d_plot + 'sim_data/stamps/'): os.mkdir(p.d_plot + 'sim_data/stamps/') \\n plt.savefig(p.d_plot + 'sim_data/stamps/%s%s_G%i.png' % (p.sim_name,p.sim_run,gal_index),\\\\\\n bbox_inches = 'tight', pad_inches = 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5963468","0.59558034","0.5918713","0.589341","0.5847284","0.58300716","0.5703463","0.5690351","0.5650076","0.5646748","0.56256956","0.55975646","0.5596329","0.5573423","0.55663466","0.55646265","0.5511411","0.55077195","0.5489869","0.54598117","0.5450311","0.54183257","0.54062396","0.5401389","0.53804684","0.5379006","0.53418905","0.5341473","0.53231657","0.53207844","0.53180736","0.530777","0.5291084","0.52750593","0.52524877","0.52524805","0.5248287","0.5247466","0.5245833","0.5242242","0.52342016","0.5232627","0.5224761","0.52233374","0.5218842","0.5217346","0.5212071","0.521084","0.5203079","0.51866645","0.5182905","0.5165376","0.5163047","0.5162325","0.51577514","0.51575476","0.51491517","0.5149011","0.5147609","0.51373905","0.5130344","0.5128186","0.5123508","0.5116181","0.5114735","0.5109679","0.5108474","0.5107884","0.5106447","0.5104243","0.50957733","0.5095492","0.50870675","0.50830495","0.50800985","0.50788385","0.50766855","0.507494","0.5061517","0.50512284","0.5050664","0.5048307","0.5032752","0.502368","0.50216216","0.5019701","0.50175","0.5012098","0.5008966","0.5007142","0.4997125","0.49920642","0.4991789","0.4986874","0.49857494","0.49822932","0.49792442","0.49782515","0.49728486","0.4971696","0.49710944"],"string":"[\n \"0.5963468\",\n \"0.59558034\",\n \"0.5918713\",\n \"0.589341\",\n \"0.5847284\",\n \"0.58300716\",\n \"0.5703463\",\n \"0.5690351\",\n \"0.5650076\",\n \"0.5646748\",\n \"0.56256956\",\n \"0.55975646\",\n \"0.5596329\",\n \"0.5573423\",\n \"0.55663466\",\n \"0.55646265\",\n \"0.5511411\",\n \"0.55077195\",\n \"0.5489869\",\n \"0.54598117\",\n \"0.5450311\",\n \"0.54183257\",\n \"0.54062396\",\n \"0.5401389\",\n \"0.53804684\",\n \"0.5379006\",\n \"0.53418905\",\n \"0.5341473\",\n \"0.53231657\",\n \"0.53207844\",\n \"0.53180736\",\n \"0.530777\",\n \"0.5291084\",\n \"0.52750593\",\n \"0.52524877\",\n \"0.52524805\",\n \"0.5248287\",\n \"0.5247466\",\n \"0.5245833\",\n \"0.5242242\",\n \"0.52342016\",\n \"0.5232627\",\n \"0.5224761\",\n \"0.52233374\",\n \"0.5218842\",\n \"0.5217346\",\n \"0.5212071\",\n \"0.521084\",\n \"0.5203079\",\n \"0.51866645\",\n \"0.5182905\",\n \"0.5165376\",\n \"0.5163047\",\n \"0.5162325\",\n \"0.51577514\",\n \"0.51575476\",\n \"0.51491517\",\n \"0.5149011\",\n \"0.5147609\",\n \"0.51373905\",\n \"0.5130344\",\n \"0.5128186\",\n \"0.5123508\",\n \"0.5116181\",\n \"0.5114735\",\n \"0.5109679\",\n \"0.5108474\",\n \"0.5107884\",\n \"0.5106447\",\n \"0.5104243\",\n \"0.50957733\",\n \"0.5095492\",\n \"0.50870675\",\n \"0.50830495\",\n \"0.50800985\",\n \"0.50788385\",\n \"0.50766855\",\n \"0.507494\",\n \"0.5061517\",\n \"0.50512284\",\n \"0.5050664\",\n \"0.5048307\",\n \"0.5032752\",\n \"0.502368\",\n \"0.50216216\",\n \"0.5019701\",\n \"0.50175\",\n \"0.5012098\",\n \"0.5008966\",\n \"0.5007142\",\n \"0.4997125\",\n \"0.49920642\",\n \"0.4991789\",\n \"0.4986874\",\n \"0.49857494\",\n \"0.49822932\",\n \"0.49792442\",\n \"0.49782515\",\n \"0.49728486\",\n \"0.4971696\",\n \"0.49710944\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7184,"cells":{"query":{"kind":"string","value":"Keep trying random points until it's mirror is far enough away"},"document":{"kind":"string","value":"def __generate_spawn_points(self):\n while True:\n p1x = random.randint(0, self.width - 1)\n p1y = random.randint(0, self.height - 1)\n p2x, p2y = self.__mirror(p1x, p1y)\n d_sq = (p1x - p2x)**2 + (p1y - p2y)**2\n if d_sq >= (self.width / 2)**2:\n break\n return (p1x, p1y), (p2x, p2y)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def random(self):\n adj = self.adjacent()\n self.switch(random.choice([pos for pos in adj if self.in_grid(pos) and pos != self.prev]))","def bc19_4_radius(randomize=False):\n moves = [\n Point(0, 1),\n Point(1, 0),\n Point(0, -1),\n Point(-1, 0),\n Point(1, 1),\n Point(1, -1),\n Point(-1, 1),\n Point(-1, -1),\n Point(2, 0),\n Point(0, 2),\n Point(0, -2),\n Point(-2, 0)\n ]\n\n if randomize:\n return random.shuffle(moves)\n else:\n return moves","def choose_next(self, round):\n return random.choice(self.possible_coords)","def random_walker_generator(rows, cols, negative=False):\n attempts = 0\n while True:\n steps = 0\n found_goal = False\n grid = np.zeros((rows, cols))\n # start on bottom row\n current = (rows - 1, random.randint(0, cols - 1))\n grid[current] = 1\n steps += 1\n visited = set(current)\n\n connection = 0\n\n neighbors = get_neighbors(current, grid, visited, similar_cells={1})\n while len(neighbors) > 0:\n for (neigh_x, neigh_y) in set(neighbors):\n # lookahead for neighbors neighbors\n lookahead = get_neighbors(\n (neigh_x, neigh_y), grid, visited, similar_cells={1})\n if len(lookahead) < 3: # contains neighbors with 1's\n # edge cases\n if neigh_x == 0 and random.random() >= 0.25:\n # chance of reaching goal at top\n continue\n elif ((neigh_y == 0 or neigh_y == rows - 1) and\n len(lookahead) == 2):\n continue\n else:\n neighbors.remove((neigh_x, neigh_y))\n\n if len(neighbors) == 0:\n # print (\"no more neighbors to pick\")\n break\n\n # time.sleep(0.15)\n # os.system(\"clear\")\n # draw_grid(grid)\n\n current = random.sample(neighbors, 1)[0] # pick a random neighbor\n # print (\"selected: \", current)\n grid[current] = 1\n steps += 1\n visited.add(current)\n if current[0] == 0: # top row\n # print (\"top row reached\")\n found_goal = True\n break\n neighbors = get_neighbors(current, grid, visited, similar_cells={1})\n\n if (found_goal and not negative) or (not found_goal and negative):\n # print (\"Succeeded after %d attempts\" % attempts)\n attempts = 0\n grid = apply_noise(grid)\n\n # hack\n # override above step counter, because the random noise\n # might have added more, shorter connections\n # we do this because network was picking up patterns\n # from making random noise not entirely random\n steps, connected = check_connections_length(grid)\n if connected and negative:\n continue\n\n # randomly flip grid upside down\n if random.random() <= 0.5:\n grid = np.flipud(grid)\n\n yield grid, steps, connected\n else:\n attempts += 1","def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)","def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)","def targetpoint(self, initpoint):\n while True:\n col = int(random.uniform(0, COLS))\n row = int(random.uniform(0, ROWS))\n if (row, col) != initpoint:\n break\n return (row, col)","def __random_movement(self):\n\t\tself.__steps += 1 \t\t# Increment after every frame\n\t\t# When __steps greater than threshold reverse the direction\n\t\t# and set threshold to a new random value\n\t\tif self.__steps >= self.__threshold_steps:\t\n\t\t\tif self.direction == 'RIGHT':\n\t\t\t\tself.move_left()\n\t\t\t\tself.direction = 'LEFT'\n\t\t\telse:\n\t\t\t\tself.move_right()\n\t\t\t\tself.direction = 'RIGHT'\n\t\t\tself.__threshold_steps = random.randint(25,50)\n\t\t\tself.__steps = 0\n\t\t# Confines the Donkeys movement to within the boundary \n\t\tself.__check_boundary()","def move(self):\n if self._z >= 75:\n a = random.random()\n print(str(a))\n if a < 0.2:\n self._z += 1\n if a > 0.2 and a < 0.9:\n self._z -= 1\n if a > 0.9:\n self._z = self._z\n else: \n self._z -= 1\n \n b = random.random()\n print(str(b))\n if b < 0.1:\n self._y += 1\n if b > 0.1 and b < 0.2:\n self._y -= 1\n if b > 0.2 and b < 0.25:\n self._x -= 1\n if b > 0.25:\n self._x += 1","def simulate_rerolling(p: float, n: int) -> int:\n\n counter = 0\n new_n = n\n while new_n > 0:\n for _ in range(new_n):\n ran = random.random()\n if ran < p:\n new_n -= 1\n counter += 1\n return counter","def MoveRandom(self):\n r = random.randint(0,3)\n if r == 0: self.x += 1\n elif r == 1: self.y += 1\n elif r == 2: self.x -= 1\n elif r == 3: self.y -= 1","def make_random_move(self):\n s=set()\n for i in range(self.height):\n for j in range(self.width):\n s.add((i,j))\n\n s=s-self.mines-self.moves_made\n if s==set(): return None\n return random.choice(list(s))\n #raise NotImplementedError","def topology_random_reconnect(self, probability):\n\t\tfor i in range(len(self.sites)):\n\t\t\tfor j in range(len(self.sites)):\n\t\t\t\tif (i != j) and (self.sites[j] in self.sites[i].neighbors):\n\t\t\t\t\tif numpy.random.rand() < probability / 2.0:\n\t\t\t\t\t\tchoice_list = [s for s in self.sites if not (s in self.sites[i].neighbors)]\n\t\t\t\t\t\tif len(choice_list) > 0:\n\t\t\t\t\t\t\tchoosed = numpy.random.choice(choice_list)\n\t\t\t\t\t\t\tself.sites[i].neighbors.remove(self.sites[j])\n\t\t\t\t\t\t\tself.sites[j].neighbors.remove(self.sites[i])\n\t\t\t\t\t\t\tself.sites[i].neighbors.append(choosed)\n\t\t\t\t\t\t\tchoosed.neighbors.append(self.sites[i])","def topology_random_connect(self, probability):\n\t\tfor i in range(len(self.sites) - 1):\n\t\t\tfor j in range(i + 1, len(self.sites)):\n\t\t\t\tif not (self.sites[j] in self.sites[i].neighbors):\n\t\t\t\t\tif numpy.random.rand() < probability:\n\t\t\t\t\t\tself.sites[i].neighbors.append(self.sites[j])\n\t\t\t\t\t\tself.sites[j].neighbors.append(self.sites[i])","def mutate_point_circ(mutated_genome):\n seed = random.randint(0,3)\n index = random.randint(0,max(0,len(mutated_genome)-1))\n if seed == 0:\n move_point_circ(mutated_genome,index)\n elif seed == 1:\n shift_point_circ(mutated_genome,index)\n elif seed == 2:\n move_radius_circ(mutated_genome,index)\n else: #seed == 3:\n shift_radius_circ(mutated_genome,index)","def shuffle(self):\n self.turn_by_deg(20)\n time.sleep(.25)\n self.fwd()\n time.sleep(1)\n self.stop()\n self.back()\n time.sleep(1)\n self.stop()\n self.turn_by_deg(-40)\n time.sleep(.25)\n self.fwd()\n time.sleep(1)\n self.back()\n time.sleep(1)\n self.stop()","def shuffle_points(mutated_genome,index):\n random.shuffle(mutated_genome[index][2])","def RandomCoordinate(): \r\n return ReturnRounded(np.random.uniform(-10,10))","def adjacent_linear(randomize=False):\n moves = [\n Point(0, 1),\n Point(1, 0),\n Point(0, -1),\n Point(-1, 0)\n ]\n\n if randomize:\n return random.shuffle(moves)\n else:\n return moves","def random_adjacent_tile(self):\n adj = self.adjacent()\n pos_list = [pos for pos in adj if self.in_grid(pos) and pos != self.prev]\n return random.choice(pos_list)","def _sample_epislon(self, cur_y, cur_z):\n old_loglik = self._loglik(cur_y, cur_z)\n old_epislon = self.epislon\n \n # modify the feature ownership matrix\n self.epislon = np.random.beta(1,1)\n new_loglik = self._loglik(cur_y, cur_z)\n move_prob = 1 / (1 + np.exp(old_loglik - new_loglik));\n if random.random() < move_prob:\n pass\n else:\n self.epislon = old_epislon","def shuffle(self): \n for x in range(12):\n self.right(primary=-60, counter=0)\n time.sleep(.1)\n self.left(primary=-60, counter=0)\n time.sleep(.1)\n self.stop()","def update(self):\n if self.x<0:\n self.x = 0\n\n if self.y <0:\n self.y = 0\n\n if bool(randint(0, 1))==True:\n if self.walker == True:\n self.x += randint(-2, 2)\n self.y += randint(-2, 2)","def on_epoch_end(self):\r\n if self.shuffle:\r\n np.random.shuffle(self.pathways)","def mutate_point_rect(mutated_genome):\n seed = random.randint(0,1)\n index = random.randint(0,max(0,len(mutated_genome)-1))\n if seed == 0:\n move_point_rect(mutated_genome,index)\n else: #seed == 1:\n shift_point_rect(mutated_genome,index)","def make_random_move(self):\n #completely random move\n all_moves = set(itertools.product(range(self.height), range(self.width)))\n moves_left = list(all_moves - self.mines - self.moves_made)\n if not moves_left:\n return None\n return random.choice(moves_left)","def mutate_random(self, point, population):\n other = Point(self.model.generate())\n other.evaluate(self.model)\n while other in population or other == point:\n other = Point(self.model.generate())\n other.evaluate(self.model)\n return other","def randomMove(board):\r\n go = True\r\n while go:\r\n y = random.randint(0, board.size - 1)\r\n x = random.randint(0, board.size - 1)\r\n go = not board.validMove((y, x))\r\n return (y, x)","def randomConnect(self):\n if self.Nc == 0:\n return\n else:\n possible_pairs = np.vstack(np.triu_indices(self.numMonomers,k=2)).T\n Nl = len(possible_pairs)\n selected = possible_pairs[np.random.choice(Nl,size=self.Nc,replace=False)].T\n self.connect(selected)","def random_location(self):\r\n\r\n while True:\r\n pt = (random.uniform(self.worldbox.tl[0], self.worldbox.br[0]),\r\n random.uniform(self.worldbox.tl[1], self.worldbox.br[1]))\r\n if not self.is_wall(pt) and not self.is_target(pt):\r\n return pt","def random_walk(n):\n x,y = 0,0\n for i in range(n):\n (dx,dy) = random.choice([(0,1),(1,0),(0,-1),(-1,0)])\n x += dx\n y+=dy\n return(x,y)","def shuffle_if_needed(self):\n if self.index >= len(self.wallpapers):\n self.shuffle()\n return True\n else:\n return False","def randomcorners():\n r = lambda x: random.randint(int(x*0.4), int(x*0.6))\n cx = r(gs.DEFAULTS['width'])\n cy = r(gs.DEFAULTS['height'])\n\n w = int(gs.DEFAULTS['width'] * random.random() * 0.2)\n h = int(gs.DEFAULTS['height'] * random.random() * 0.2)\n\n rcrns = [(cx-w, cy-h), (cx+w, cy-h), (cx+w, cy+h), (cx-w, cy+h)]\n random.shuffle(rcrns)\n\n return rcrns","def brute_force(savedPnts, unitRadius, point):\n for pnt in savedPnts:\n d = distance(pnt, point)\n if d < unitRadius: return False\n return True","def random_walk(lgca):\n # disarrange(lgca.nodes, axis=-1)\n relevant = (lgca.cell_density[lgca.nonborder] > 0) & \\\n (lgca.cell_density[lgca.nonborder] < lgca.K)\n coords = [a[relevant] for a in lgca.nonborder]\n for coord in zip(*coords):\n npr.shuffle(lgca.nodes[coord])","def cointoss():\n return random.random() < 0.5","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def bc19_9_radius(randomize=False):\n moves = [\n Point(0, 1),\n Point(1, 0),\n Point(0, -1),\n Point(-1, 0),\n Point(1, 1),\n Point(1, -1),\n Point(-1, 1),\n Point(-1, -1),\n Point(2, 0),\n Point(0, 2),\n Point(0, -2),\n Point(-2, 0),\n Point(0, 3),\n Point(1, 2),\n Point(2, 1),\n Point(3, 0),\n Point(2, -1),\n Point(1, -2),\n Point(0, -3),\n Point(-1, -2),\n Point(-2, -1),\n Point(-3, 0),\n Point(-2, 1),\n Point(-1, 2),\n Point(2, 2),\n Point(2, -2),\n Point(-2, 2),\n Point(-2, -2)\n ]\n\n if randomize:\n return random.shuffle(moves)\n else:\n return moves","def test_full_setup(n):\n for x in range(n):\n for y in range(n):\n Stitch(x,y)\n Stitch.stitches[(x,y)].vital = True if round(rnd.random()) == 1 else False","def uniform_random(self) -> None:\n\n size = self.circ_size\n random.seed(self.seed)\n\n gates = [self.h, self.x, self.y, self.z, self.s, self.t, self.cx]\n candidates = set(range(size))\n\n for i in range(size):\n for j in range(size):\n to_apply = random.choice(gates)\n\n num_qubits = 2 if to_apply == self.cx else 1\n targets = random.sample(candidates, num_qubits)\n to_apply(*targets)\n\n if self.meas: self.measure(self.qr, self.cr)","def generate(self):\n for i in range(4):\n random_first = randomize_first_box()\n self.randomize(random_first)\n for i in range(9):\n random_pos = randomize_position()\n self.randomize(random_pos)\n self.board.solve()","def stream():\n while True:\n yield random_point()","def switch_points(mutated_genome,index):\n point_index1 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n point_index2 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n temp = mutated_genome[index][2][point_index1]\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\n mutated_genome[index][2][point_index2] = temp","def totem_random():\n random_head()\n random_head()\n random_head()","def move_random(self, board):\n self.get_moves(board.board)\n return random.choice(self.available_moves)","def random_points_ascending_hillclimber(house, all_houses, waters, total_value_map):\n total_value_map_NEW = total_value_map\n\n # check in welke range het huis geplaats kan worden, niet kijkend naar water of andere \n rangex = MAXIMUM_WIDTH - house.width\n rangey = MAXIMUM_HEIGHT - house.length\n\n for x in range(100):\n # maak random x en y coördinaat\n randomizex = rangex * random()\n randomizey = rangey * random()\n\n # bewaar oude locaties\n tempx = house.bottom_left[0]\n tempy = house.bottom_left[1]\n \n # verander locatie\n bottom_left = (randomizex,randomizey)\n house.location(bottom_left)\n\n # als je je huis op nieuwe locatie kan plaatsen\n if place_house(house, all_houses, waters) == True:\n # bereken nieuw waarde map, waarin huis is verplaatst\n total_value_map_temp = 0\n for item in all_houses.values():\n for house in item:\n house.extra_meters()\n total_value_map_temp += house.totalprice()\n\n # als waarde met nieuwe locatie hoger is, verander deze\n if total_value_map_NEW < total_value_map_temp:\n total_value_map_NEW = total_value_map_temp\n # als waarde niet hoger is verander naar oude locatie en bereken weer totale waarde map\n else:\n bottom_left = (tempx,tempy)\n house.location(bottom_left)\n if place_house(house, all_houses, waters) == True:\n for item in all_houses.values():\n for houses in item:\n houses.extra_meters()\n houses.totalprice()\n # als huis niet geplaats kan worden, verander naar oude locatie en bereken weer totale waarde map\n else:\n bottom_left = (tempx,tempy)\n house.location(bottom_left)\n if place_house(house, all_houses, waters) == True:\n for item in all_houses.values():\n for houses in item:\n houses.extra_meters()\n houses.totalprice()\n\n return all_houses, total_value_map_NEW","def __random_pickup(self, guess):\n already_clustered = guess.sum(axis=0)\n while True:\n p1 = random.randint(0, guess.shape[1] - 1)\n p2 = random.randint(0, guess.shape[2] - 1)\n if not already_clustered[p1, p2]:\n return (p1, p2)","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def shuffle_pos(self, ):\n x, y = 0, 0\n while self.maze.structure[int(y / 40)][int(x / 40)] != \"0\" \\\n or (x, y) in self.forbiden_tulpes:\n x = random.randint(0, 14) * sprite_size\n y = random.randint(0, 14) * sprite_size\n self.forbiden_tulpes.append((x, y))\n return x, y","def pull(self):\n chance = np.random.uniform()\n return chance < self.winning_prob","def move_randomly(self, with_fight=False):\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\n rd.shuffle(delta)\n x, y = self.owner.pos\n while len(delta) > 0:\n dx, dy = delta.pop()\n if self.move_towards_position((x + dx, y + dy)):\n return","def step():\n x_rand = sample()\n x_nearest = new_nearest_neighbour(x_rand)\n x_new = steer(x_nearest, x_rand)\n if obstacle_free(x_nearest, x_new):\n X_near = new_neighbourhood(x_new)\n x_min = x_nearest\n c_min = x_nearest.cost + x_nearest.dist_to(x_new)\n for x_near in X_near:\n if obstacle_free(x_near, x_new) and (x_near.cost + x_near.dist_to(x_new) < c_min):\n x_min = x_near\n c_min = (x_near.cost + x_near.dist_to(x_new) < c_min)\n x_new_node = add_node(x_new, x_min, True)\n for x_near in X_near:\n if obstacle_free(x_near, x_new) and (x_new_node.cost + x_near.dist_to(x_new) < x_near.cost):\n x_near.change_parent(x_new_node)\n # Here I check for goal paths and draw the circle\n updated = False\n if shared.root_path:\n updated = goal_path_resolve(shared.root_path[0])\n updated = updated or goal_path_resolve(shared.nodes[-1])\n if updated:\n diameter = shared.root_path_length\n center = ((shared.root_path[0].x + shared.root_path[-1].x) / 2,\n (shared.root_path[0].y + shared.root_path[-1].y) / 2)\n if shared.region:\n shared.region.remove_from_batch()\n shared.region = ellipse.Ellipse(center[0], center[1], diameter)\n shared.region.add_to_batch()","def step_solution(self):\n import time, random\n time.sleep(1.0)\n print '(step_solution) Implement me!'\n return True if random.random() < 0.25 else False","def new_and_near(self):\n if self.prob and random.random() < self.prob:\n x_rand = self.goal_config\n else:\n x_rand = self.planning_env.sample_free()\n x_nearest_id, x_nearest = self.tree.GetNearestVertex(x_rand)\n x_new = self.steer(x_nearest, x_rand)\n # check if new point is in X_free and not already in V\n # if x_new in self.tree.vertices or not self.planning_env.state_validity_checker(x_new):\n if x_new in self.tree.vertices or not self.planning_env.collision_free(x_new, x_nearest):\n return None, None\n\n self.tree.samples_taken += 1\n return x_new, x_nearest","def choose_pos(self):\n s = self\n\n availablepos = []\n for dblock in s.pjs.dblocks:\n is_available = True\n\n for powerup in s.pjs.powerups:\n if powerup.rects[0].overlap(dblock.rects[0]):\n is_available = False\n break\n\n if is_available:\n availablepos.append(dblock.rpos)\n\n pos = random.randint(0, len(availablepos) - 1)\n s.rpos = availablepos[pos]","def wait(self):\n self.set_vals(spin=.2)\n nearest_deg = 0\n nearest_deg_dist = self.perim_dist + 1\n for i, x in enumerate(self.ranges):\n if (x != 0) and (x < nearest_deg_dist):\n nearest_deg = i\n nearest_deg_dist = x\n if nearest_deg_dist < self.perim_dist:\n nearest_deg = ((nearest_deg + 180) % 360) - 180\n self.center(degree=nearest_deg)\n self.current_state = \"follow\"","def direction_correction(self):\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))","def run(self):\n cell = self.grid.random_cell()\n unvisited = self.grid.size() - 1\n while unvisited > 0:\n neighbor = random.choice(cell.neighbors())\n\n if list(neighbor.links_list()) == []:\n cell.link(neighbor)\n unvisited -= 1\n\n cell = neighbor\n\n return self.grid","def random_walk_2(n):\n x,y=0,0\n for i in range(n):\n dx,dy = random.choice([(0,1), (0,-1),(1,0) ,(-1,0)])\n x+= dx\n y+= dy\n return (x,y)","def MovePoint(pos,points,s=0.5, k = 1000): \n \n x = [pos[0]] \n y = [pos[1]]\n rdm.seed(1) # comment out if you want randomness\n for i in range(k):\n n = len(points)\n r = int(rdm.random()*n)\n \n x.append(x[i] + (points[r][0] - x[i]) * s)\n y.append(y[i] + (points[r][1] - y[i]) * s)\n\n \n return(x,y)","def prepare_next_turn(grid):\n\tempties = get_empty_cells(grid)\n\ty,x = random.choice(empties)\n\tgrid[y][x] = 2 if random.random() < prob_2 else 4\n\treturn any_possible_moves(grid)","def new_tile(self):\n while True:\n random_row = random.randrange(self._grid_height)\n random_column = random.randrange(self._grid_width)\n if self._grid[random_row][random_column] == 0:\n self._grid[random_row][random_column] = random.choice([2] * 9 + [4])\n break","def neighbor(R, L, p):\n Rp = []\n for k in range(0,len(R)):\n epsilon = random.random()\n if (epsilon > p):\n Rp.append(R[k])\n else:\n randpoint = randVacantPoint(L)\n # update L according to the random\n # vacant point previously found.\n L[randpoint[0]][randpoint[1]]=1\n L[R[k][0]][R[k][1]]=0\n\n Rp.append(randpoint)\n \n return Rp","def changeState(self, xyPoints):\n nPts = len(xyPoints)\n ind0 = random.randint(1, nPts-1)\n ind1 = random.randint(1, nPts-1)\n while ind1 == ind0:\n ind1 = random.randint(1, nPts-1)\n # make copy of the sources to make sure the swap works correctly\n xyPoints[ind0], xyPoints[ind1] = tuple(xyPoints[ind1]), tuple(xyPoints[ind0])","def rs():\n return random.choice([-1,1])","def rs():\n return random.choice([-1,1])","def run(self):\n for cell in self.grid.each_cell():\n neighbors = []\n if cell.north:\n neighbors.append(cell.north)\n if cell.east:\n neighbors.append(cell.east)\n if neighbors:\n neighbor = random.choice(neighbors)\n if neighbor:\n cell.link(neighbor)\n return self.grid","def __generate_point_based_on_prob(self) -> Point:\n possible = False\n while not possible:\n # make the random decision based on a distribution (hot spots / different probabilities)\n prob_list = self.probability_distribution_grid.flatten()\n selected_index = np.random.choice(\n np.arange(0, len(prob_list)), p=prob_list)\n\n # get the indices of the cell (from the one array index)\n # width is the number of cells in x directions (it starts with cell 0/0) and is needed due to row-major order\n cell_x = int(selected_index % self.occupancy_map.info.width)\n cell_y = int(selected_index / self.occupancy_map.info.width)\n\n # get the real world coordinates (which represents the center of the cell)\n x = self.occupancy_map.info.origin.position.x + \\\n (cell_x + 0.5) * self.occupancy_map.info.resolution\n y = self.occupancy_map.info.origin.position.y + \\\n (cell_y + 0.5) * self.occupancy_map.info.resolution\n\n # Check if the actual cell is free of STATIC obstacles (not occupied)\n if not self.__cell_is_occupied(cell_x, cell_y):\n # Check for not occupied neighbors (the robot needs some space the reach it)\n if not self.__has_occupied_neighbors(cell_x, cell_y):\n # If actual spawning of dirt is enabled, then it should also be ensured that no other dirt object is already\n # at this position, because spawning a model in the same location of an already existing model can lead to problems\n if not self.prevent_duplicates or not self.__check_for_duplicates(Point(x, y, 0.0)):\n possible = True\n else:\n rospy.loginfo(\"*** WRAPPER MESSAGE ***\\n\\n\\tGenerated dirt at (%.2f | %.2f) was refused due to already \"\n \"active dirt at this position.\\n\\tGenerating next one...\\n\" % (x, y))\n else:\n rospy.loginfo(\"*** WRAPPER MESSAGE ***\\n\\n\\tGenerated dirt at (%.2f | %.2f) was refused due to occupied neighbor \"\n \"cells.\\n\\tGenerating next one...\\n\" % (x, y))\n else:\n rospy.loginfo(\"*** WRAPPER MESSAGE ***\\n\\n\\tGenerated dirt at (%.2f | %.2f) was refused due to occupied cell.\"\n \"\\n\\tGenerating next one...\\n\" % (x, y))\n return Point(x=x, y=y, z=0.0)","def possibleMovements(self,numIterations:int=50)->list[tuple]:\n x=random.randint(0,self._side-1); y=random.randint(0,self._side-1)\n possible_positions=[]\n positionsCovered=[(x,y)]\n for _ in range(numIterations):\n if x+2<self._side and y+1<self._side:\n possible_positions.append((x+2,y+1))\n \n if x+2<self._side and y-1<self._side and y-1>0:\n possible_positions.append((x+2,y-1))\n \n if x-2<self._side and y+1<self._side and x-2>0:\n possible_positions.append((x-2,y+1))\n \n if x-2<self._side and y-1<self._side and x-2>0 and y-1>0:\n possible_positions.append((x-2,y-1)) \n\n if x+1<self._side and y+2<self._side:\n possible_positions.append((x+1,y+2))\n \n if x+1<self._side and y-2<self._side and y-1>0:\n possible_positions.append((x+1,y-2))\n\n if x-1<self._side and y+2<self._side and x-1>0:\n possible_positions.append((x-1,y+2))\n \n if x-1<self._side and y-2<self._side and x-1>0 and y-2>0:\n possible_positions.append((x-1,y-2))\n\n newX,newY=random.choice(possible_positions) #choose randomly among the possible positions,and then repeat this \n x,y=newX,newY\n positionsCovered.append((newX,newY)) \n\n return positionsCovered","def random_pose(self):\n position = self._start\n while self[position].distance < np.sum(self._rooms.shape) * 2:\n position = np.array(\n [random.randrange(limit) for limit in self._rooms.shape]\n )\n direction = random.choice(self.exits(position))\n return (position, direction)","def randVacantPoint(L):\n pliste = vacantPoint(L)\n\n return pliste[random.randint(0, len(pliste)-1)]","def new_tile(self):\r\n # check if is zero or not\r\n new_tile_added = False\r\n # a list to 2 90% of the time and 4 10% of the time\r\n new_tile_list = [2,2,2,2,2,2,2,2,2,4]\r\n counter = 0\r\n while not new_tile_added:\r\n row_position = random.randrange(0,self.grid_height)\r\n col_position = random.randrange(0,self.grid_width)\r\n if self.grid[row_position][col_position] == 0:\r\n self.grid[row_position][col_position] = random.choice(new_tile_list)\r\n new_tile_added = True\r\n if counter > self.grid_width * self.grid_height:\r\n print 'you failed'\r\n break\r\n\r\n counter +=1","def mutate_point_poly(mutated_genome):\n seed = random.randint(0,7)\n index = random.randint(0,max(0,len(mutated_genome)-1))\n if len(mutated_genome[index][2]) < 3: seed = 0\n if seed == 0:\n insert_point(mutated_genome,index)\n elif seed == 1:\n remove_point(mutated_genome,index)\n elif seed == 2:\n switch_points(mutated_genome,index)\n elif seed == 3:\n shuffle_points(mutated_genome,index)\n elif seed == 4:\n move_point(mutated_genome,index)\n elif seed == 5:\n shift_point(mutated_genome,index)\n elif seed == 6:\n increment_point(mutated_genome,index)\n else: #seed == 7:\n decrement_point(mutated_genome,index)","def random_position(self):\n while True:\n h = random.randrange(0, self.height)\n w = random.randrange(0, self.width)\n if self.grid[h, w] == 0:\n return (h, w)","def _move_satisfy_random_constraint(self):\n secure_random = random.SystemRandom()\n done = False\n while not done:\n c = secure_random.choice(self.constraints)\n if self._is_constraint_violated(c):\n done = True\n # swap 2 wizards to move closer\n self._swap_wizards(c[random.randint(0, 1)], c[2])\n # with probability 0.5, swap the two border wizards\n if random.randint(0, 1) == 1:\n self._swap_wizards(c[0], c[1])\n if not done: print(\"Nothing to do...\")","def random_walk(turtle, distance, steps):\n turtle.color(randcolor(), randcolor())\n for step in range(0,steps):\n random_move(turtle, distance)\n gohome(turtle)","def randomMove(self, game):\n #time.sleep(0.25)\n return random.choice(game.get_all_legal_moves())","def takeNaiveMove():\r\n\tnotFound=True\r\n\twhile notFound:\r\n\t\tmove=random.randint(1,9)\r\n\t\tif validMove(move):\r\n\t\t\tnotFound=False\r\n\treturn move","def reroll(self):\n self._base_x = tf.random.uniform(\n (self._sample_count,),\n self.base_point_distribution._x_min,\n self.base_point_distribution._x_max\n )\n self._base_y = tf.random.uniform(\n (self._sample_count,),\n self.base_point_distribution._y_min,\n self.base_point_distribution._y_max\n )","def fill(self, random, matches_allowed=True, reset_cycles=True):\n if reset_cycles:\n self.cycles_remaining = 10000\n for y in range(self.height):\n for x in range(self.width):\n if self[x][y] is None:\n return self.fill_space(x,\n y,\n random,\n matches_allowed)\n return True","def click_aim(self, pos):\n x, y = pos\n if (self.x - x) ** 2 + (self.y - y) ** 2 <= self.r ** 2:\n self.color = random.choice(COLORS)\n self.x = randint(100, 1000)\n self.y = randint(100, 800)\n self.r = randint(50, 100)\n self.speed_x = randint(-200, 200)\n self.speed_y = randint(-200, 200)\n return True\n else:\n return False","def _generate_pores(self):\n logger.info(\"Place randomly located pores in the domain\")\n #Original Random Point Generator\n #coords = sp.rand(self._Np,3)*[self._Lx,self._Ly,self._Lz]\n #Seeding Code\n coords = np.zeros([self._Np,3])\n #reject points close to boundaries - if False there will be slightly more\n rejection = [False,False,True]\n for j in range(3):\n i = 0\n while i < self._Np:\n coord = np.random.uniform(0,1,1)\n if self._reject(coord) == rejection[j]:\n coords[i][j]=coord\n i += 1\n coords*=np.array([self._Lx,self._Ly,self._Lz])\n #Seeding Code\n #Uniform Random Generator\n #coords = np.array([np.random.uniform(0,self._Lx,self._Np),np.random.uniform(0,self._Ly,self._Np),np.random.uniform(0,self._Lz,self._Np)]).T\n\n self['pore.coords'] = coords\n logger.debug(\"End of method\")","def random_walk_2(n):\r\n x, y = 0, 0\r\n for i in range(n):\r\n (dx, dy) = random.choice([(0, 1), (0, -1), (1, 0), (-1, 0)])\r\n \r\n x += dx\r\n y += dy\r\n return (x, y)","def random_move(self):\n available_idx = self.get_empty_cells(self.game_board)\n return random.choice(available_idx)","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def randomize_trajectory(self):\n self.angle = randint(-360, 360)\n self.speed = randint(1, 5)/2.5","def _pick_random_angles_(self):\n movej_q = self._q_ref.copy()\n while True:\n reset_angles = self._rand_obj_.uniform(self._angles_low, self._angles_high)\n movej_q[self._joint_indices] = reset_angles\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(movej_q)\n if inside_buffer_bound:\n break\n return reset_angles, xyz","def random_walk(n, p):\n random_array = np.random.uniform(0, 1, n)\n left = random_array[random_array > p].size\n right = n - left\n \n return (right-left)","def get_random_points(n=5, scale=0.8, mindst=None, rec=0):\n mindst = mindst or .7 / n\n a = np.random.rand(n, 2)\n d = np.sqrt(np.sum(np.diff(ccw_sort(a), axis=0), axis=1) ** 2)\n if np.all(d >= mindst) or rec >= 200:\n return a * scale\n else:\n return get_random_points(n=n, scale=scale, mindst=mindst, rec=rec + 1)","def run(self):\n move_cmd = Twist()\n move_cmd.linear.x = 0\n move_cmd.angular.z = 0\n\n while not rospy.is_shutdown():\n # bump logic as previous psets\n if self.bump:\n self.bump = False\n # move backwards\n move_cmd.linear.x = LIN_SPEED * -1\n for i in range(5):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n rospy.sleep(1)\n\n # turn randomly in a random direction\n move_cmd.linear.x = 0\n move_cmd.angular.z = ROT_SPEED * ((-1)**random.randint(1,2))\n\n if self.bump == 0:\n move_cmd.angular.z = ROT_SPEED * (-1)\n elif self.bump == 2:\n move_cmd.angular.z = ROT_SPEED\n\n for i in range(random.randint(5,15)):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n rospy.sleep(1)\n\n move_cmd.angular.z = 0\n # if somethin in the screen is really close\n elif self.min_val < MIN_THRESHOLD:\n # make sure it's not the sock/leg warmer, and is actually an obstacle\n if self.obstacle_x <= self.x or self.obstacle_x >= self.x + self.w or abs(self.min_val - self.dist) > 0.1:\n move_cmd.linear.x = 0\n # turn away\n if self.obstacle_x > 320:\n move_cmd.angular.z = ROT_SPEED / 2\n else:\n move_cmd.angular.z = -ROT_SPEED / 2\n # self.min_val = 100\n for i in range(10):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n self.last_move = rospy.Time.now()\n else:\n rospy.loginfo(\"Perimeter \" + str(self.perimeter_size))\n rospy.loginfo(\"Distance is \" + str(self.dist))\n\n # normalize angle error to rot speed\n ang_error_norm = -float(self.ang_error) / 100\n\n # set min and max rot speed\n if ang_error_norm < -ROT_SPEED:\n ang_error_norm = -ROT_SPEED\n elif ang_error_norm > ROT_SPEED:\n ang_error_norm = ROT_SPEED\n\n move_cmd.angular.z = ang_error_norm\n\n if RACE == False:\n # normalize dist error to lin speed\n self.dist_error = self.dist - 0.5\n dist_error_norm = float(self.dist_error) / 2\n\n if dist_error_norm < 0:\n # if NaN (self.dist gets set to -1)\n if dist_error_norm > -0.7:\n self.lost = 0\n # if too close\n else:\n self.lost += 1\n # if it's been more than 2 seconds\n if rospy.Time.now() > self.last_move + rospy.Duration(2):\n dist_error_norm = 0\n # if been lost for a while rotate and beep\n if self.lost > 20:\n move_cmd.angular.z = ROT_SPEED / 4\n self.beep.publish(4)\n else:\n # continue as previous\n dist_error_norm = self.last_speed\n else:\n # set max lin speed\n if dist_error_norm > LIN_SPEED:\n dist_error_norm = LIN_SPEED\n\n # reset lost stats\n self.lost = 0\n self.last_speed = dist_error_norm\n self.last_move = rospy.Time.now()\n\n move_cmd.linear.x = dist_error_norm\n else:\n move_cmd.linear.x = LIN_SPEED\n\n self.cmd_vel.publish(move_cmd)","def mutate_point_wline(mutated_genome):\n seed = random.randint(0,7)\n index = random.randint(0,max(0,len(mutated_genome)-1))\n if len(mutated_genome[index][2]) < 1: seed = 0\n if seed == 0:\n insert_point_wline(mutated_genome,index)\n elif seed == 1:\n remove_point_wline(mutated_genome,index)\n elif seed == 2:\n switch_points_wline(mutated_genome,index)\n elif seed == 3:\n shuffle_points_wline(mutated_genome,index)\n elif seed == 4:\n move_point_wline(mutated_genome,index)\n elif seed == 5:\n shift_point_wline(mutated_genome,index)\n elif seed == 6:\n increment_point_wline(mutated_genome,index)\n else: #seed == 7:\n decrement_point_wline(mutated_genome,index)","def make_random_move(self):\n \n\n if len(self.moves_made) == 56:\n return None\n\n random_move = random.randrange(self.height), random.randrange(self.height)\n\n not_safe_moves = self.moves_made | self.mines\n\n while random_move in not_safe_moves:\n random_move = random.randrange(self.height), random.randrange(self.height)\n\n return random_move","def connectGreedy(self, random = False):\n if random:\n shuffle(self.houses)\n for house in self.houses:\n house.connectNearestBattery(self.batteries, self)\n self.calculateCosts()","def _random_walk(self, preference_vectors: sps.csr_matrix) -> np.ndarray:\n similarity_rank_vertices = preference_vectors\n nb_iteration = 0\n while True:\n previous_similarity_rank_vertices = similarity_rank_vertices\n if self.verbose:\n print(\"Step: {}\".format(nb_iteration + 1))\n\n similarity_rank_vertices = self.damping_factor * similarity_rank_vertices.dot(self._transition_matrix) + (\n 1 - self.damping_factor) * preference_vectors\n\n diff = np.sum(\n np.abs(similarity_rank_vertices - previous_similarity_rank_vertices))\n if nb_iteration > 0 and diff < self.minimal_random_walk_change_difference_value:\n if self.verbose:\n print(\"Converged with error: {:.6f}\".format(diff))\n break\n\n nb_iteration += 1\n\n if nb_iteration > self.max_iterations:\n if self.verbose:\n print(\"Random walk did not converge, current error: {:.6f}\".format(\n diff))\n break\n return similarity_rank_vertices.toarray()","def test_move_default_extra_steps(self):\n player = ss.ResilientPlayer()\n random.seed(2)\n player.move()\n random.seed(1)\n player.move()\n random.seed(2)\n player.move()\n assert player.position == 32","def confused(self, rand):\n return rand > 0","def fill_space(self, x, y, random, matches_allowed):\n rotation = random.randint(0, 3) * 90\n for _ in range(4):\n try:\n choices = self.choose_and_flip_extra_dominoes(\n random)\n for domino, is_flipped in choices:\n if self.cycles_remaining <= 0:\n return False\n self.cycles_remaining -= 1\n domino.rotate_to(rotation)\n self.add(domino, x, y)\n self.add_count += 1\n has_even_gaps = self.hasEvenGaps()\n if not has_even_gaps:\n self.remove(domino)\n break\n else:\n if is_flipped:\n domino.flip()\n if not matches_allowed and domino.hasMatch():\n pass\n else:\n if self.fill(random,\n matches_allowed,\n reset_cycles=False):\n return True\n self.remove(domino)\n except BadPositionError:\n pass\n rotation = (rotation + 90) % 360\n return False","def is_exhausted(self):\n return random.random() < 0.5","def realize(self):\n if numpy.random.rand() > self._P:\n self._w *= 0.0","def getARandomFreeSample(self, num, maxDimLens, dim):\n failTime=0;\n while( failTime < num ):\n rnd = [0] * dim;\n for i in range( 0, dim ):\n rnd[i] = randrange( 0, maxDimLens[i] );\n pass\n angles = self.mCSpace.map2UnscaledSpace( rnd );\n if( self.mCollisionMgr.ifCollide( angles ) ):\n continue;\n\n newSamp = True;\n\n grid = self.mSpacePartition.getContainingGrid( rnd );\n for sphere in grid.mContainer:\n if sphere.isInside( rnd, maxDimLens ):\n newSamp = False;\n failTime += 1\n break;\n\n #for sample in self.mDistSamples:\n # if sample.isInside( rnd, maxDimLens ):\n # newSamp = False;\n # failTime += 1\n # break;\n if newSamp:\n # randomly shoot rays to get the nearest distance to obstacles\n rayShooter = RayShooter( rnd, self.mCollisionMgr, self.mCSpace );\n dist = rayShooter.randShoot(50 * (dim-1));\n if math.fabs(dist) >= 1.0:\n newDistSamp = DistSample( rnd, dist );\n print \"failed times: {0}\".format( failTime );\n failTime=0;\n return newDistSamp;\n else:\n failTime += 1;\n\n return None;"],"string":"[\n \"def random(self):\\n adj = self.adjacent()\\n self.switch(random.choice([pos for pos in adj if self.in_grid(pos) and pos != self.prev]))\",\n \"def bc19_4_radius(randomize=False):\\n moves = [\\n Point(0, 1),\\n Point(1, 0),\\n Point(0, -1),\\n Point(-1, 0),\\n Point(1, 1),\\n Point(1, -1),\\n Point(-1, 1),\\n Point(-1, -1),\\n Point(2, 0),\\n Point(0, 2),\\n Point(0, -2),\\n Point(-2, 0)\\n ]\\n\\n if randomize:\\n return random.shuffle(moves)\\n else:\\n return moves\",\n \"def choose_next(self, round):\\n return random.choice(self.possible_coords)\",\n \"def random_walker_generator(rows, cols, negative=False):\\n attempts = 0\\n while True:\\n steps = 0\\n found_goal = False\\n grid = np.zeros((rows, cols))\\n # start on bottom row\\n current = (rows - 1, random.randint(0, cols - 1))\\n grid[current] = 1\\n steps += 1\\n visited = set(current)\\n\\n connection = 0\\n\\n neighbors = get_neighbors(current, grid, visited, similar_cells={1})\\n while len(neighbors) > 0:\\n for (neigh_x, neigh_y) in set(neighbors):\\n # lookahead for neighbors neighbors\\n lookahead = get_neighbors(\\n (neigh_x, neigh_y), grid, visited, similar_cells={1})\\n if len(lookahead) < 3: # contains neighbors with 1's\\n # edge cases\\n if neigh_x == 0 and random.random() >= 0.25:\\n # chance of reaching goal at top\\n continue\\n elif ((neigh_y == 0 or neigh_y == rows - 1) and\\n len(lookahead) == 2):\\n continue\\n else:\\n neighbors.remove((neigh_x, neigh_y))\\n\\n if len(neighbors) == 0:\\n # print (\\\"no more neighbors to pick\\\")\\n break\\n\\n # time.sleep(0.15)\\n # os.system(\\\"clear\\\")\\n # draw_grid(grid)\\n\\n current = random.sample(neighbors, 1)[0] # pick a random neighbor\\n # print (\\\"selected: \\\", current)\\n grid[current] = 1\\n steps += 1\\n visited.add(current)\\n if current[0] == 0: # top row\\n # print (\\\"top row reached\\\")\\n found_goal = True\\n break\\n neighbors = get_neighbors(current, grid, visited, similar_cells={1})\\n\\n if (found_goal and not negative) or (not found_goal and negative):\\n # print (\\\"Succeeded after %d attempts\\\" % attempts)\\n attempts = 0\\n grid = apply_noise(grid)\\n\\n # hack\\n # override above step counter, because the random noise\\n # might have added more, shorter connections\\n # we do this because network was picking up patterns\\n # from making random noise not entirely random\\n steps, connected = check_connections_length(grid)\\n if connected and negative:\\n continue\\n\\n # randomly flip grid upside down\\n if random.random() <= 0.5:\\n grid = np.flipud(grid)\\n\\n yield grid, steps, connected\\n else:\\n attempts += 1\",\n \"def random_step(self):\\n pos = [i for i in range(9) if self.grid[i] == 0]\\n move = random.choice(pos)\\n return self.step(move)\",\n \"def random_step(self):\\n pos = [i for i in range(9) if self.grid[i] == 0]\\n move = random.choice(pos)\\n return self.step(move)\",\n \"def targetpoint(self, initpoint):\\n while True:\\n col = int(random.uniform(0, COLS))\\n row = int(random.uniform(0, ROWS))\\n if (row, col) != initpoint:\\n break\\n return (row, col)\",\n \"def __random_movement(self):\\n\\t\\tself.__steps += 1 \\t\\t# Increment after every frame\\n\\t\\t# When __steps greater than threshold reverse the direction\\n\\t\\t# and set threshold to a new random value\\n\\t\\tif self.__steps >= self.__threshold_steps:\\t\\n\\t\\t\\tif self.direction == 'RIGHT':\\n\\t\\t\\t\\tself.move_left()\\n\\t\\t\\t\\tself.direction = 'LEFT'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.move_right()\\n\\t\\t\\t\\tself.direction = 'RIGHT'\\n\\t\\t\\tself.__threshold_steps = random.randint(25,50)\\n\\t\\t\\tself.__steps = 0\\n\\t\\t# Confines the Donkeys movement to within the boundary \\n\\t\\tself.__check_boundary()\",\n \"def move(self):\\n if self._z >= 75:\\n a = random.random()\\n print(str(a))\\n if a < 0.2:\\n self._z += 1\\n if a > 0.2 and a < 0.9:\\n self._z -= 1\\n if a > 0.9:\\n self._z = self._z\\n else: \\n self._z -= 1\\n \\n b = random.random()\\n print(str(b))\\n if b < 0.1:\\n self._y += 1\\n if b > 0.1 and b < 0.2:\\n self._y -= 1\\n if b > 0.2 and b < 0.25:\\n self._x -= 1\\n if b > 0.25:\\n self._x += 1\",\n \"def simulate_rerolling(p: float, n: int) -> int:\\n\\n counter = 0\\n new_n = n\\n while new_n > 0:\\n for _ in range(new_n):\\n ran = random.random()\\n if ran < p:\\n new_n -= 1\\n counter += 1\\n return counter\",\n \"def MoveRandom(self):\\n r = random.randint(0,3)\\n if r == 0: self.x += 1\\n elif r == 1: self.y += 1\\n elif r == 2: self.x -= 1\\n elif r == 3: self.y -= 1\",\n \"def make_random_move(self):\\n s=set()\\n for i in range(self.height):\\n for j in range(self.width):\\n s.add((i,j))\\n\\n s=s-self.mines-self.moves_made\\n if s==set(): return None\\n return random.choice(list(s))\\n #raise NotImplementedError\",\n \"def topology_random_reconnect(self, probability):\\n\\t\\tfor i in range(len(self.sites)):\\n\\t\\t\\tfor j in range(len(self.sites)):\\n\\t\\t\\t\\tif (i != j) and (self.sites[j] in self.sites[i].neighbors):\\n\\t\\t\\t\\t\\tif numpy.random.rand() < probability / 2.0:\\n\\t\\t\\t\\t\\t\\tchoice_list = [s for s in self.sites if not (s in self.sites[i].neighbors)]\\n\\t\\t\\t\\t\\t\\tif len(choice_list) > 0:\\n\\t\\t\\t\\t\\t\\t\\tchoosed = numpy.random.choice(choice_list)\\n\\t\\t\\t\\t\\t\\t\\tself.sites[i].neighbors.remove(self.sites[j])\\n\\t\\t\\t\\t\\t\\t\\tself.sites[j].neighbors.remove(self.sites[i])\\n\\t\\t\\t\\t\\t\\t\\tself.sites[i].neighbors.append(choosed)\\n\\t\\t\\t\\t\\t\\t\\tchoosed.neighbors.append(self.sites[i])\",\n \"def topology_random_connect(self, probability):\\n\\t\\tfor i in range(len(self.sites) - 1):\\n\\t\\t\\tfor j in range(i + 1, len(self.sites)):\\n\\t\\t\\t\\tif not (self.sites[j] in self.sites[i].neighbors):\\n\\t\\t\\t\\t\\tif numpy.random.rand() < probability:\\n\\t\\t\\t\\t\\t\\tself.sites[i].neighbors.append(self.sites[j])\\n\\t\\t\\t\\t\\t\\tself.sites[j].neighbors.append(self.sites[i])\",\n \"def mutate_point_circ(mutated_genome):\\n seed = random.randint(0,3)\\n index = random.randint(0,max(0,len(mutated_genome)-1))\\n if seed == 0:\\n move_point_circ(mutated_genome,index)\\n elif seed == 1:\\n shift_point_circ(mutated_genome,index)\\n elif seed == 2:\\n move_radius_circ(mutated_genome,index)\\n else: #seed == 3:\\n shift_radius_circ(mutated_genome,index)\",\n \"def shuffle(self):\\n self.turn_by_deg(20)\\n time.sleep(.25)\\n self.fwd()\\n time.sleep(1)\\n self.stop()\\n self.back()\\n time.sleep(1)\\n self.stop()\\n self.turn_by_deg(-40)\\n time.sleep(.25)\\n self.fwd()\\n time.sleep(1)\\n self.back()\\n time.sleep(1)\\n self.stop()\",\n \"def shuffle_points(mutated_genome,index):\\n random.shuffle(mutated_genome[index][2])\",\n \"def RandomCoordinate(): \\r\\n return ReturnRounded(np.random.uniform(-10,10))\",\n \"def adjacent_linear(randomize=False):\\n moves = [\\n Point(0, 1),\\n Point(1, 0),\\n Point(0, -1),\\n Point(-1, 0)\\n ]\\n\\n if randomize:\\n return random.shuffle(moves)\\n else:\\n return moves\",\n \"def random_adjacent_tile(self):\\n adj = self.adjacent()\\n pos_list = [pos for pos in adj if self.in_grid(pos) and pos != self.prev]\\n return random.choice(pos_list)\",\n \"def _sample_epislon(self, cur_y, cur_z):\\n old_loglik = self._loglik(cur_y, cur_z)\\n old_epislon = self.epislon\\n \\n # modify the feature ownership matrix\\n self.epislon = np.random.beta(1,1)\\n new_loglik = self._loglik(cur_y, cur_z)\\n move_prob = 1 / (1 + np.exp(old_loglik - new_loglik));\\n if random.random() < move_prob:\\n pass\\n else:\\n self.epislon = old_epislon\",\n \"def shuffle(self): \\n for x in range(12):\\n self.right(primary=-60, counter=0)\\n time.sleep(.1)\\n self.left(primary=-60, counter=0)\\n time.sleep(.1)\\n self.stop()\",\n \"def update(self):\\n if self.x<0:\\n self.x = 0\\n\\n if self.y <0:\\n self.y = 0\\n\\n if bool(randint(0, 1))==True:\\n if self.walker == True:\\n self.x += randint(-2, 2)\\n self.y += randint(-2, 2)\",\n \"def on_epoch_end(self):\\r\\n if self.shuffle:\\r\\n np.random.shuffle(self.pathways)\",\n \"def mutate_point_rect(mutated_genome):\\n seed = random.randint(0,1)\\n index = random.randint(0,max(0,len(mutated_genome)-1))\\n if seed == 0:\\n move_point_rect(mutated_genome,index)\\n else: #seed == 1:\\n shift_point_rect(mutated_genome,index)\",\n \"def make_random_move(self):\\n #completely random move\\n all_moves = set(itertools.product(range(self.height), range(self.width)))\\n moves_left = list(all_moves - self.mines - self.moves_made)\\n if not moves_left:\\n return None\\n return random.choice(moves_left)\",\n \"def mutate_random(self, point, population):\\n other = Point(self.model.generate())\\n other.evaluate(self.model)\\n while other in population or other == point:\\n other = Point(self.model.generate())\\n other.evaluate(self.model)\\n return other\",\n \"def randomMove(board):\\r\\n go = True\\r\\n while go:\\r\\n y = random.randint(0, board.size - 1)\\r\\n x = random.randint(0, board.size - 1)\\r\\n go = not board.validMove((y, x))\\r\\n return (y, x)\",\n \"def randomConnect(self):\\n if self.Nc == 0:\\n return\\n else:\\n possible_pairs = np.vstack(np.triu_indices(self.numMonomers,k=2)).T\\n Nl = len(possible_pairs)\\n selected = possible_pairs[np.random.choice(Nl,size=self.Nc,replace=False)].T\\n self.connect(selected)\",\n \"def random_location(self):\\r\\n\\r\\n while True:\\r\\n pt = (random.uniform(self.worldbox.tl[0], self.worldbox.br[0]),\\r\\n random.uniform(self.worldbox.tl[1], self.worldbox.br[1]))\\r\\n if not self.is_wall(pt) and not self.is_target(pt):\\r\\n return pt\",\n \"def random_walk(n):\\n x,y = 0,0\\n for i in range(n):\\n (dx,dy) = random.choice([(0,1),(1,0),(0,-1),(-1,0)])\\n x += dx\\n y+=dy\\n return(x,y)\",\n \"def shuffle_if_needed(self):\\n if self.index >= len(self.wallpapers):\\n self.shuffle()\\n return True\\n else:\\n return False\",\n \"def randomcorners():\\n r = lambda x: random.randint(int(x*0.4), int(x*0.6))\\n cx = r(gs.DEFAULTS['width'])\\n cy = r(gs.DEFAULTS['height'])\\n\\n w = int(gs.DEFAULTS['width'] * random.random() * 0.2)\\n h = int(gs.DEFAULTS['height'] * random.random() * 0.2)\\n\\n rcrns = [(cx-w, cy-h), (cx+w, cy-h), (cx+w, cy+h), (cx-w, cy+h)]\\n random.shuffle(rcrns)\\n\\n return rcrns\",\n \"def brute_force(savedPnts, unitRadius, point):\\n for pnt in savedPnts:\\n d = distance(pnt, point)\\n if d < unitRadius: return False\\n return True\",\n \"def random_walk(lgca):\\n # disarrange(lgca.nodes, axis=-1)\\n relevant = (lgca.cell_density[lgca.nonborder] > 0) & \\\\\\n (lgca.cell_density[lgca.nonborder] < lgca.K)\\n coords = [a[relevant] for a in lgca.nonborder]\\n for coord in zip(*coords):\\n npr.shuffle(lgca.nodes[coord])\",\n \"def cointoss():\\n return random.random() < 0.5\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def bc19_9_radius(randomize=False):\\n moves = [\\n Point(0, 1),\\n Point(1, 0),\\n Point(0, -1),\\n Point(-1, 0),\\n Point(1, 1),\\n Point(1, -1),\\n Point(-1, 1),\\n Point(-1, -1),\\n Point(2, 0),\\n Point(0, 2),\\n Point(0, -2),\\n Point(-2, 0),\\n Point(0, 3),\\n Point(1, 2),\\n Point(2, 1),\\n Point(3, 0),\\n Point(2, -1),\\n Point(1, -2),\\n Point(0, -3),\\n Point(-1, -2),\\n Point(-2, -1),\\n Point(-3, 0),\\n Point(-2, 1),\\n Point(-1, 2),\\n Point(2, 2),\\n Point(2, -2),\\n Point(-2, 2),\\n Point(-2, -2)\\n ]\\n\\n if randomize:\\n return random.shuffle(moves)\\n else:\\n return moves\",\n \"def test_full_setup(n):\\n for x in range(n):\\n for y in range(n):\\n Stitch(x,y)\\n Stitch.stitches[(x,y)].vital = True if round(rnd.random()) == 1 else False\",\n \"def uniform_random(self) -> None:\\n\\n size = self.circ_size\\n random.seed(self.seed)\\n\\n gates = [self.h, self.x, self.y, self.z, self.s, self.t, self.cx]\\n candidates = set(range(size))\\n\\n for i in range(size):\\n for j in range(size):\\n to_apply = random.choice(gates)\\n\\n num_qubits = 2 if to_apply == self.cx else 1\\n targets = random.sample(candidates, num_qubits)\\n to_apply(*targets)\\n\\n if self.meas: self.measure(self.qr, self.cr)\",\n \"def generate(self):\\n for i in range(4):\\n random_first = randomize_first_box()\\n self.randomize(random_first)\\n for i in range(9):\\n random_pos = randomize_position()\\n self.randomize(random_pos)\\n self.board.solve()\",\n \"def stream():\\n while True:\\n yield random_point()\",\n \"def switch_points(mutated_genome,index):\\n point_index1 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n point_index2 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\\n temp = mutated_genome[index][2][point_index1]\\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][point_index2]\\n mutated_genome[index][2][point_index2] = temp\",\n \"def totem_random():\\n random_head()\\n random_head()\\n random_head()\",\n \"def move_random(self, board):\\n self.get_moves(board.board)\\n return random.choice(self.available_moves)\",\n \"def random_points_ascending_hillclimber(house, all_houses, waters, total_value_map):\\n total_value_map_NEW = total_value_map\\n\\n # check in welke range het huis geplaats kan worden, niet kijkend naar water of andere \\n rangex = MAXIMUM_WIDTH - house.width\\n rangey = MAXIMUM_HEIGHT - house.length\\n\\n for x in range(100):\\n # maak random x en y coördinaat\\n randomizex = rangex * random()\\n randomizey = rangey * random()\\n\\n # bewaar oude locaties\\n tempx = house.bottom_left[0]\\n tempy = house.bottom_left[1]\\n \\n # verander locatie\\n bottom_left = (randomizex,randomizey)\\n house.location(bottom_left)\\n\\n # als je je huis op nieuwe locatie kan plaatsen\\n if place_house(house, all_houses, waters) == True:\\n # bereken nieuw waarde map, waarin huis is verplaatst\\n total_value_map_temp = 0\\n for item in all_houses.values():\\n for house in item:\\n house.extra_meters()\\n total_value_map_temp += house.totalprice()\\n\\n # als waarde met nieuwe locatie hoger is, verander deze\\n if total_value_map_NEW < total_value_map_temp:\\n total_value_map_NEW = total_value_map_temp\\n # als waarde niet hoger is verander naar oude locatie en bereken weer totale waarde map\\n else:\\n bottom_left = (tempx,tempy)\\n house.location(bottom_left)\\n if place_house(house, all_houses, waters) == True:\\n for item in all_houses.values():\\n for houses in item:\\n houses.extra_meters()\\n houses.totalprice()\\n # als huis niet geplaats kan worden, verander naar oude locatie en bereken weer totale waarde map\\n else:\\n bottom_left = (tempx,tempy)\\n house.location(bottom_left)\\n if place_house(house, all_houses, waters) == True:\\n for item in all_houses.values():\\n for houses in item:\\n houses.extra_meters()\\n houses.totalprice()\\n\\n return all_houses, total_value_map_NEW\",\n \"def __random_pickup(self, guess):\\n already_clustered = guess.sum(axis=0)\\n while True:\\n p1 = random.randint(0, guess.shape[1] - 1)\\n p2 = random.randint(0, guess.shape[2] - 1)\\n if not already_clustered[p1, p2]:\\n return (p1, p2)\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def shuffle_pos(self, ):\\n x, y = 0, 0\\n while self.maze.structure[int(y / 40)][int(x / 40)] != \\\"0\\\" \\\\\\n or (x, y) in self.forbiden_tulpes:\\n x = random.randint(0, 14) * sprite_size\\n y = random.randint(0, 14) * sprite_size\\n self.forbiden_tulpes.append((x, y))\\n return x, y\",\n \"def pull(self):\\n chance = np.random.uniform()\\n return chance < self.winning_prob\",\n \"def move_randomly(self, with_fight=False):\\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\\n rd.shuffle(delta)\\n x, y = self.owner.pos\\n while len(delta) > 0:\\n dx, dy = delta.pop()\\n if self.move_towards_position((x + dx, y + dy)):\\n return\",\n \"def step():\\n x_rand = sample()\\n x_nearest = new_nearest_neighbour(x_rand)\\n x_new = steer(x_nearest, x_rand)\\n if obstacle_free(x_nearest, x_new):\\n X_near = new_neighbourhood(x_new)\\n x_min = x_nearest\\n c_min = x_nearest.cost + x_nearest.dist_to(x_new)\\n for x_near in X_near:\\n if obstacle_free(x_near, x_new) and (x_near.cost + x_near.dist_to(x_new) < c_min):\\n x_min = x_near\\n c_min = (x_near.cost + x_near.dist_to(x_new) < c_min)\\n x_new_node = add_node(x_new, x_min, True)\\n for x_near in X_near:\\n if obstacle_free(x_near, x_new) and (x_new_node.cost + x_near.dist_to(x_new) < x_near.cost):\\n x_near.change_parent(x_new_node)\\n # Here I check for goal paths and draw the circle\\n updated = False\\n if shared.root_path:\\n updated = goal_path_resolve(shared.root_path[0])\\n updated = updated or goal_path_resolve(shared.nodes[-1])\\n if updated:\\n diameter = shared.root_path_length\\n center = ((shared.root_path[0].x + shared.root_path[-1].x) / 2,\\n (shared.root_path[0].y + shared.root_path[-1].y) / 2)\\n if shared.region:\\n shared.region.remove_from_batch()\\n shared.region = ellipse.Ellipse(center[0], center[1], diameter)\\n shared.region.add_to_batch()\",\n \"def step_solution(self):\\n import time, random\\n time.sleep(1.0)\\n print '(step_solution) Implement me!'\\n return True if random.random() < 0.25 else False\",\n \"def new_and_near(self):\\n if self.prob and random.random() < self.prob:\\n x_rand = self.goal_config\\n else:\\n x_rand = self.planning_env.sample_free()\\n x_nearest_id, x_nearest = self.tree.GetNearestVertex(x_rand)\\n x_new = self.steer(x_nearest, x_rand)\\n # check if new point is in X_free and not already in V\\n # if x_new in self.tree.vertices or not self.planning_env.state_validity_checker(x_new):\\n if x_new in self.tree.vertices or not self.planning_env.collision_free(x_new, x_nearest):\\n return None, None\\n\\n self.tree.samples_taken += 1\\n return x_new, x_nearest\",\n \"def choose_pos(self):\\n s = self\\n\\n availablepos = []\\n for dblock in s.pjs.dblocks:\\n is_available = True\\n\\n for powerup in s.pjs.powerups:\\n if powerup.rects[0].overlap(dblock.rects[0]):\\n is_available = False\\n break\\n\\n if is_available:\\n availablepos.append(dblock.rpos)\\n\\n pos = random.randint(0, len(availablepos) - 1)\\n s.rpos = availablepos[pos]\",\n \"def wait(self):\\n self.set_vals(spin=.2)\\n nearest_deg = 0\\n nearest_deg_dist = self.perim_dist + 1\\n for i, x in enumerate(self.ranges):\\n if (x != 0) and (x < nearest_deg_dist):\\n nearest_deg = i\\n nearest_deg_dist = x\\n if nearest_deg_dist < self.perim_dist:\\n nearest_deg = ((nearest_deg + 180) % 360) - 180\\n self.center(degree=nearest_deg)\\n self.current_state = \\\"follow\\\"\",\n \"def direction_correction(self):\\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))\",\n \"def run(self):\\n cell = self.grid.random_cell()\\n unvisited = self.grid.size() - 1\\n while unvisited > 0:\\n neighbor = random.choice(cell.neighbors())\\n\\n if list(neighbor.links_list()) == []:\\n cell.link(neighbor)\\n unvisited -= 1\\n\\n cell = neighbor\\n\\n return self.grid\",\n \"def random_walk_2(n):\\n x,y=0,0\\n for i in range(n):\\n dx,dy = random.choice([(0,1), (0,-1),(1,0) ,(-1,0)])\\n x+= dx\\n y+= dy\\n return (x,y)\",\n \"def MovePoint(pos,points,s=0.5, k = 1000): \\n \\n x = [pos[0]] \\n y = [pos[1]]\\n rdm.seed(1) # comment out if you want randomness\\n for i in range(k):\\n n = len(points)\\n r = int(rdm.random()*n)\\n \\n x.append(x[i] + (points[r][0] - x[i]) * s)\\n y.append(y[i] + (points[r][1] - y[i]) * s)\\n\\n \\n return(x,y)\",\n \"def prepare_next_turn(grid):\\n\\tempties = get_empty_cells(grid)\\n\\ty,x = random.choice(empties)\\n\\tgrid[y][x] = 2 if random.random() < prob_2 else 4\\n\\treturn any_possible_moves(grid)\",\n \"def new_tile(self):\\n while True:\\n random_row = random.randrange(self._grid_height)\\n random_column = random.randrange(self._grid_width)\\n if self._grid[random_row][random_column] == 0:\\n self._grid[random_row][random_column] = random.choice([2] * 9 + [4])\\n break\",\n \"def neighbor(R, L, p):\\n Rp = []\\n for k in range(0,len(R)):\\n epsilon = random.random()\\n if (epsilon > p):\\n Rp.append(R[k])\\n else:\\n randpoint = randVacantPoint(L)\\n # update L according to the random\\n # vacant point previously found.\\n L[randpoint[0]][randpoint[1]]=1\\n L[R[k][0]][R[k][1]]=0\\n\\n Rp.append(randpoint)\\n \\n return Rp\",\n \"def changeState(self, xyPoints):\\n nPts = len(xyPoints)\\n ind0 = random.randint(1, nPts-1)\\n ind1 = random.randint(1, nPts-1)\\n while ind1 == ind0:\\n ind1 = random.randint(1, nPts-1)\\n # make copy of the sources to make sure the swap works correctly\\n xyPoints[ind0], xyPoints[ind1] = tuple(xyPoints[ind1]), tuple(xyPoints[ind0])\",\n \"def rs():\\n return random.choice([-1,1])\",\n \"def rs():\\n return random.choice([-1,1])\",\n \"def run(self):\\n for cell in self.grid.each_cell():\\n neighbors = []\\n if cell.north:\\n neighbors.append(cell.north)\\n if cell.east:\\n neighbors.append(cell.east)\\n if neighbors:\\n neighbor = random.choice(neighbors)\\n if neighbor:\\n cell.link(neighbor)\\n return self.grid\",\n \"def __generate_point_based_on_prob(self) -> Point:\\n possible = False\\n while not possible:\\n # make the random decision based on a distribution (hot spots / different probabilities)\\n prob_list = self.probability_distribution_grid.flatten()\\n selected_index = np.random.choice(\\n np.arange(0, len(prob_list)), p=prob_list)\\n\\n # get the indices of the cell (from the one array index)\\n # width is the number of cells in x directions (it starts with cell 0/0) and is needed due to row-major order\\n cell_x = int(selected_index % self.occupancy_map.info.width)\\n cell_y = int(selected_index / self.occupancy_map.info.width)\\n\\n # get the real world coordinates (which represents the center of the cell)\\n x = self.occupancy_map.info.origin.position.x + \\\\\\n (cell_x + 0.5) * self.occupancy_map.info.resolution\\n y = self.occupancy_map.info.origin.position.y + \\\\\\n (cell_y + 0.5) * self.occupancy_map.info.resolution\\n\\n # Check if the actual cell is free of STATIC obstacles (not occupied)\\n if not self.__cell_is_occupied(cell_x, cell_y):\\n # Check for not occupied neighbors (the robot needs some space the reach it)\\n if not self.__has_occupied_neighbors(cell_x, cell_y):\\n # If actual spawning of dirt is enabled, then it should also be ensured that no other dirt object is already\\n # at this position, because spawning a model in the same location of an already existing model can lead to problems\\n if not self.prevent_duplicates or not self.__check_for_duplicates(Point(x, y, 0.0)):\\n possible = True\\n else:\\n rospy.loginfo(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\tGenerated dirt at (%.2f | %.2f) was refused due to already \\\"\\n \\\"active dirt at this position.\\\\n\\\\tGenerating next one...\\\\n\\\" % (x, y))\\n else:\\n rospy.loginfo(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\tGenerated dirt at (%.2f | %.2f) was refused due to occupied neighbor \\\"\\n \\\"cells.\\\\n\\\\tGenerating next one...\\\\n\\\" % (x, y))\\n else:\\n rospy.loginfo(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\tGenerated dirt at (%.2f | %.2f) was refused due to occupied cell.\\\"\\n \\\"\\\\n\\\\tGenerating next one...\\\\n\\\" % (x, y))\\n return Point(x=x, y=y, z=0.0)\",\n \"def possibleMovements(self,numIterations:int=50)->list[tuple]:\\n x=random.randint(0,self._side-1); y=random.randint(0,self._side-1)\\n possible_positions=[]\\n positionsCovered=[(x,y)]\\n for _ in range(numIterations):\\n if x+2<self._side and y+1<self._side:\\n possible_positions.append((x+2,y+1))\\n \\n if x+2<self._side and y-1<self._side and y-1>0:\\n possible_positions.append((x+2,y-1))\\n \\n if x-2<self._side and y+1<self._side and x-2>0:\\n possible_positions.append((x-2,y+1))\\n \\n if x-2<self._side and y-1<self._side and x-2>0 and y-1>0:\\n possible_positions.append((x-2,y-1)) \\n\\n if x+1<self._side and y+2<self._side:\\n possible_positions.append((x+1,y+2))\\n \\n if x+1<self._side and y-2<self._side and y-1>0:\\n possible_positions.append((x+1,y-2))\\n\\n if x-1<self._side and y+2<self._side and x-1>0:\\n possible_positions.append((x-1,y+2))\\n \\n if x-1<self._side and y-2<self._side and x-1>0 and y-2>0:\\n possible_positions.append((x-1,y-2))\\n\\n newX,newY=random.choice(possible_positions) #choose randomly among the possible positions,and then repeat this \\n x,y=newX,newY\\n positionsCovered.append((newX,newY)) \\n\\n return positionsCovered\",\n \"def random_pose(self):\\n position = self._start\\n while self[position].distance < np.sum(self._rooms.shape) * 2:\\n position = np.array(\\n [random.randrange(limit) for limit in self._rooms.shape]\\n )\\n direction = random.choice(self.exits(position))\\n return (position, direction)\",\n \"def randVacantPoint(L):\\n pliste = vacantPoint(L)\\n\\n return pliste[random.randint(0, len(pliste)-1)]\",\n \"def new_tile(self):\\r\\n # check if is zero or not\\r\\n new_tile_added = False\\r\\n # a list to 2 90% of the time and 4 10% of the time\\r\\n new_tile_list = [2,2,2,2,2,2,2,2,2,4]\\r\\n counter = 0\\r\\n while not new_tile_added:\\r\\n row_position = random.randrange(0,self.grid_height)\\r\\n col_position = random.randrange(0,self.grid_width)\\r\\n if self.grid[row_position][col_position] == 0:\\r\\n self.grid[row_position][col_position] = random.choice(new_tile_list)\\r\\n new_tile_added = True\\r\\n if counter > self.grid_width * self.grid_height:\\r\\n print 'you failed'\\r\\n break\\r\\n\\r\\n counter +=1\",\n \"def mutate_point_poly(mutated_genome):\\n seed = random.randint(0,7)\\n index = random.randint(0,max(0,len(mutated_genome)-1))\\n if len(mutated_genome[index][2]) < 3: seed = 0\\n if seed == 0:\\n insert_point(mutated_genome,index)\\n elif seed == 1:\\n remove_point(mutated_genome,index)\\n elif seed == 2:\\n switch_points(mutated_genome,index)\\n elif seed == 3:\\n shuffle_points(mutated_genome,index)\\n elif seed == 4:\\n move_point(mutated_genome,index)\\n elif seed == 5:\\n shift_point(mutated_genome,index)\\n elif seed == 6:\\n increment_point(mutated_genome,index)\\n else: #seed == 7:\\n decrement_point(mutated_genome,index)\",\n \"def random_position(self):\\n while True:\\n h = random.randrange(0, self.height)\\n w = random.randrange(0, self.width)\\n if self.grid[h, w] == 0:\\n return (h, w)\",\n \"def _move_satisfy_random_constraint(self):\\n secure_random = random.SystemRandom()\\n done = False\\n while not done:\\n c = secure_random.choice(self.constraints)\\n if self._is_constraint_violated(c):\\n done = True\\n # swap 2 wizards to move closer\\n self._swap_wizards(c[random.randint(0, 1)], c[2])\\n # with probability 0.5, swap the two border wizards\\n if random.randint(0, 1) == 1:\\n self._swap_wizards(c[0], c[1])\\n if not done: print(\\\"Nothing to do...\\\")\",\n \"def random_walk(turtle, distance, steps):\\n turtle.color(randcolor(), randcolor())\\n for step in range(0,steps):\\n random_move(turtle, distance)\\n gohome(turtle)\",\n \"def randomMove(self, game):\\n #time.sleep(0.25)\\n return random.choice(game.get_all_legal_moves())\",\n \"def takeNaiveMove():\\r\\n\\tnotFound=True\\r\\n\\twhile notFound:\\r\\n\\t\\tmove=random.randint(1,9)\\r\\n\\t\\tif validMove(move):\\r\\n\\t\\t\\tnotFound=False\\r\\n\\treturn move\",\n \"def reroll(self):\\n self._base_x = tf.random.uniform(\\n (self._sample_count,),\\n self.base_point_distribution._x_min,\\n self.base_point_distribution._x_max\\n )\\n self._base_y = tf.random.uniform(\\n (self._sample_count,),\\n self.base_point_distribution._y_min,\\n self.base_point_distribution._y_max\\n )\",\n \"def fill(self, random, matches_allowed=True, reset_cycles=True):\\n if reset_cycles:\\n self.cycles_remaining = 10000\\n for y in range(self.height):\\n for x in range(self.width):\\n if self[x][y] is None:\\n return self.fill_space(x,\\n y,\\n random,\\n matches_allowed)\\n return True\",\n \"def click_aim(self, pos):\\n x, y = pos\\n if (self.x - x) ** 2 + (self.y - y) ** 2 <= self.r ** 2:\\n self.color = random.choice(COLORS)\\n self.x = randint(100, 1000)\\n self.y = randint(100, 800)\\n self.r = randint(50, 100)\\n self.speed_x = randint(-200, 200)\\n self.speed_y = randint(-200, 200)\\n return True\\n else:\\n return False\",\n \"def _generate_pores(self):\\n logger.info(\\\"Place randomly located pores in the domain\\\")\\n #Original Random Point Generator\\n #coords = sp.rand(self._Np,3)*[self._Lx,self._Ly,self._Lz]\\n #Seeding Code\\n coords = np.zeros([self._Np,3])\\n #reject points close to boundaries - if False there will be slightly more\\n rejection = [False,False,True]\\n for j in range(3):\\n i = 0\\n while i < self._Np:\\n coord = np.random.uniform(0,1,1)\\n if self._reject(coord) == rejection[j]:\\n coords[i][j]=coord\\n i += 1\\n coords*=np.array([self._Lx,self._Ly,self._Lz])\\n #Seeding Code\\n #Uniform Random Generator\\n #coords = np.array([np.random.uniform(0,self._Lx,self._Np),np.random.uniform(0,self._Ly,self._Np),np.random.uniform(0,self._Lz,self._Np)]).T\\n\\n self['pore.coords'] = coords\\n logger.debug(\\\"End of method\\\")\",\n \"def random_walk_2(n):\\r\\n x, y = 0, 0\\r\\n for i in range(n):\\r\\n (dx, dy) = random.choice([(0, 1), (0, -1), (1, 0), (-1, 0)])\\r\\n \\r\\n x += dx\\r\\n y += dy\\r\\n return (x, y)\",\n \"def random_move(self):\\n available_idx = self.get_empty_cells(self.game_board)\\n return random.choice(available_idx)\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def randomize_trajectory(self):\\n self.angle = randint(-360, 360)\\n self.speed = randint(1, 5)/2.5\",\n \"def _pick_random_angles_(self):\\n movej_q = self._q_ref.copy()\\n while True:\\n reset_angles = self._rand_obj_.uniform(self._angles_low, self._angles_high)\\n movej_q[self._joint_indices] = reset_angles\\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(movej_q)\\n if inside_buffer_bound:\\n break\\n return reset_angles, xyz\",\n \"def random_walk(n, p):\\n random_array = np.random.uniform(0, 1, n)\\n left = random_array[random_array > p].size\\n right = n - left\\n \\n return (right-left)\",\n \"def get_random_points(n=5, scale=0.8, mindst=None, rec=0):\\n mindst = mindst or .7 / n\\n a = np.random.rand(n, 2)\\n d = np.sqrt(np.sum(np.diff(ccw_sort(a), axis=0), axis=1) ** 2)\\n if np.all(d >= mindst) or rec >= 200:\\n return a * scale\\n else:\\n return get_random_points(n=n, scale=scale, mindst=mindst, rec=rec + 1)\",\n \"def run(self):\\n move_cmd = Twist()\\n move_cmd.linear.x = 0\\n move_cmd.angular.z = 0\\n\\n while not rospy.is_shutdown():\\n # bump logic as previous psets\\n if self.bump:\\n self.bump = False\\n # move backwards\\n move_cmd.linear.x = LIN_SPEED * -1\\n for i in range(5):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n rospy.sleep(1)\\n\\n # turn randomly in a random direction\\n move_cmd.linear.x = 0\\n move_cmd.angular.z = ROT_SPEED * ((-1)**random.randint(1,2))\\n\\n if self.bump == 0:\\n move_cmd.angular.z = ROT_SPEED * (-1)\\n elif self.bump == 2:\\n move_cmd.angular.z = ROT_SPEED\\n\\n for i in range(random.randint(5,15)):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n rospy.sleep(1)\\n\\n move_cmd.angular.z = 0\\n # if somethin in the screen is really close\\n elif self.min_val < MIN_THRESHOLD:\\n # make sure it's not the sock/leg warmer, and is actually an obstacle\\n if self.obstacle_x <= self.x or self.obstacle_x >= self.x + self.w or abs(self.min_val - self.dist) > 0.1:\\n move_cmd.linear.x = 0\\n # turn away\\n if self.obstacle_x > 320:\\n move_cmd.angular.z = ROT_SPEED / 2\\n else:\\n move_cmd.angular.z = -ROT_SPEED / 2\\n # self.min_val = 100\\n for i in range(10):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n self.last_move = rospy.Time.now()\\n else:\\n rospy.loginfo(\\\"Perimeter \\\" + str(self.perimeter_size))\\n rospy.loginfo(\\\"Distance is \\\" + str(self.dist))\\n\\n # normalize angle error to rot speed\\n ang_error_norm = -float(self.ang_error) / 100\\n\\n # set min and max rot speed\\n if ang_error_norm < -ROT_SPEED:\\n ang_error_norm = -ROT_SPEED\\n elif ang_error_norm > ROT_SPEED:\\n ang_error_norm = ROT_SPEED\\n\\n move_cmd.angular.z = ang_error_norm\\n\\n if RACE == False:\\n # normalize dist error to lin speed\\n self.dist_error = self.dist - 0.5\\n dist_error_norm = float(self.dist_error) / 2\\n\\n if dist_error_norm < 0:\\n # if NaN (self.dist gets set to -1)\\n if dist_error_norm > -0.7:\\n self.lost = 0\\n # if too close\\n else:\\n self.lost += 1\\n # if it's been more than 2 seconds\\n if rospy.Time.now() > self.last_move + rospy.Duration(2):\\n dist_error_norm = 0\\n # if been lost for a while rotate and beep\\n if self.lost > 20:\\n move_cmd.angular.z = ROT_SPEED / 4\\n self.beep.publish(4)\\n else:\\n # continue as previous\\n dist_error_norm = self.last_speed\\n else:\\n # set max lin speed\\n if dist_error_norm > LIN_SPEED:\\n dist_error_norm = LIN_SPEED\\n\\n # reset lost stats\\n self.lost = 0\\n self.last_speed = dist_error_norm\\n self.last_move = rospy.Time.now()\\n\\n move_cmd.linear.x = dist_error_norm\\n else:\\n move_cmd.linear.x = LIN_SPEED\\n\\n self.cmd_vel.publish(move_cmd)\",\n \"def mutate_point_wline(mutated_genome):\\n seed = random.randint(0,7)\\n index = random.randint(0,max(0,len(mutated_genome)-1))\\n if len(mutated_genome[index][2]) < 1: seed = 0\\n if seed == 0:\\n insert_point_wline(mutated_genome,index)\\n elif seed == 1:\\n remove_point_wline(mutated_genome,index)\\n elif seed == 2:\\n switch_points_wline(mutated_genome,index)\\n elif seed == 3:\\n shuffle_points_wline(mutated_genome,index)\\n elif seed == 4:\\n move_point_wline(mutated_genome,index)\\n elif seed == 5:\\n shift_point_wline(mutated_genome,index)\\n elif seed == 6:\\n increment_point_wline(mutated_genome,index)\\n else: #seed == 7:\\n decrement_point_wline(mutated_genome,index)\",\n \"def make_random_move(self):\\n \\n\\n if len(self.moves_made) == 56:\\n return None\\n\\n random_move = random.randrange(self.height), random.randrange(self.height)\\n\\n not_safe_moves = self.moves_made | self.mines\\n\\n while random_move in not_safe_moves:\\n random_move = random.randrange(self.height), random.randrange(self.height)\\n\\n return random_move\",\n \"def connectGreedy(self, random = False):\\n if random:\\n shuffle(self.houses)\\n for house in self.houses:\\n house.connectNearestBattery(self.batteries, self)\\n self.calculateCosts()\",\n \"def _random_walk(self, preference_vectors: sps.csr_matrix) -> np.ndarray:\\n similarity_rank_vertices = preference_vectors\\n nb_iteration = 0\\n while True:\\n previous_similarity_rank_vertices = similarity_rank_vertices\\n if self.verbose:\\n print(\\\"Step: {}\\\".format(nb_iteration + 1))\\n\\n similarity_rank_vertices = self.damping_factor * similarity_rank_vertices.dot(self._transition_matrix) + (\\n 1 - self.damping_factor) * preference_vectors\\n\\n diff = np.sum(\\n np.abs(similarity_rank_vertices - previous_similarity_rank_vertices))\\n if nb_iteration > 0 and diff < self.minimal_random_walk_change_difference_value:\\n if self.verbose:\\n print(\\\"Converged with error: {:.6f}\\\".format(diff))\\n break\\n\\n nb_iteration += 1\\n\\n if nb_iteration > self.max_iterations:\\n if self.verbose:\\n print(\\\"Random walk did not converge, current error: {:.6f}\\\".format(\\n diff))\\n break\\n return similarity_rank_vertices.toarray()\",\n \"def test_move_default_extra_steps(self):\\n player = ss.ResilientPlayer()\\n random.seed(2)\\n player.move()\\n random.seed(1)\\n player.move()\\n random.seed(2)\\n player.move()\\n assert player.position == 32\",\n \"def confused(self, rand):\\n return rand > 0\",\n \"def fill_space(self, x, y, random, matches_allowed):\\n rotation = random.randint(0, 3) * 90\\n for _ in range(4):\\n try:\\n choices = self.choose_and_flip_extra_dominoes(\\n random)\\n for domino, is_flipped in choices:\\n if self.cycles_remaining <= 0:\\n return False\\n self.cycles_remaining -= 1\\n domino.rotate_to(rotation)\\n self.add(domino, x, y)\\n self.add_count += 1\\n has_even_gaps = self.hasEvenGaps()\\n if not has_even_gaps:\\n self.remove(domino)\\n break\\n else:\\n if is_flipped:\\n domino.flip()\\n if not matches_allowed and domino.hasMatch():\\n pass\\n else:\\n if self.fill(random,\\n matches_allowed,\\n reset_cycles=False):\\n return True\\n self.remove(domino)\\n except BadPositionError:\\n pass\\n rotation = (rotation + 90) % 360\\n return False\",\n \"def is_exhausted(self):\\n return random.random() < 0.5\",\n \"def realize(self):\\n if numpy.random.rand() > self._P:\\n self._w *= 0.0\",\n \"def getARandomFreeSample(self, num, maxDimLens, dim):\\n failTime=0;\\n while( failTime < num ):\\n rnd = [0] * dim;\\n for i in range( 0, dim ):\\n rnd[i] = randrange( 0, maxDimLens[i] );\\n pass\\n angles = self.mCSpace.map2UnscaledSpace( rnd );\\n if( self.mCollisionMgr.ifCollide( angles ) ):\\n continue;\\n\\n newSamp = True;\\n\\n grid = self.mSpacePartition.getContainingGrid( rnd );\\n for sphere in grid.mContainer:\\n if sphere.isInside( rnd, maxDimLens ):\\n newSamp = False;\\n failTime += 1\\n break;\\n\\n #for sample in self.mDistSamples:\\n # if sample.isInside( rnd, maxDimLens ):\\n # newSamp = False;\\n # failTime += 1\\n # break;\\n if newSamp:\\n # randomly shoot rays to get the nearest distance to obstacles\\n rayShooter = RayShooter( rnd, self.mCollisionMgr, self.mCSpace );\\n dist = rayShooter.randShoot(50 * (dim-1));\\n if math.fabs(dist) >= 1.0:\\n newDistSamp = DistSample( rnd, dist );\\n print \\\"failed times: {0}\\\".format( failTime );\\n failTime=0;\\n return newDistSamp;\\n else:\\n failTime += 1;\\n\\n return None;\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65802413","0.6314837","0.62275654","0.61400115","0.61373764","0.61373764","0.6013303","0.6008652","0.59597766","0.59432083","0.5940083","0.5926595","0.59224373","0.5892852","0.58895075","0.5842744","0.58392626","0.5830656","0.58206266","0.58205765","0.58132654","0.5799286","0.57960975","0.5771451","0.5762655","0.57625","0.5741609","0.57373685","0.57350034","0.57311213","0.5728878","0.57107115","0.57101923","0.57082707","0.5705172","0.5701551","0.56961244","0.5694103","0.5680299","0.5676493","0.56700134","0.56571877","0.5656566","0.5653499","0.56487125","0.5647044","0.5644442","0.56339383","0.5624072","0.56185436","0.56180525","0.5617316","0.56066066","0.5596827","0.55831885","0.55823624","0.5570801","0.5569943","0.5559909","0.5557711","0.5557707","0.5554068","0.5549027","0.55478495","0.5547419","0.5547419","0.5545641","0.5533221","0.552275","0.55213064","0.5520953","0.55201215","0.55191207","0.5516685","0.55055285","0.5505241","0.5502522","0.5500113","0.5497463","0.54929924","0.54928255","0.54922175","0.5483362","0.54824114","0.5477892","0.547556","0.54715097","0.54693866","0.5467904","0.5466916","0.5462227","0.54617566","0.54582584","0.5457853","0.5441424","0.5437863","0.5430049","0.54296374","0.5424456","0.54221714"],"string":"[\n \"0.65802413\",\n \"0.6314837\",\n \"0.62275654\",\n \"0.61400115\",\n \"0.61373764\",\n \"0.61373764\",\n \"0.6013303\",\n \"0.6008652\",\n \"0.59597766\",\n \"0.59432083\",\n \"0.5940083\",\n \"0.5926595\",\n \"0.59224373\",\n \"0.5892852\",\n \"0.58895075\",\n \"0.5842744\",\n \"0.58392626\",\n \"0.5830656\",\n \"0.58206266\",\n \"0.58205765\",\n \"0.58132654\",\n \"0.5799286\",\n \"0.57960975\",\n \"0.5771451\",\n \"0.5762655\",\n \"0.57625\",\n \"0.5741609\",\n \"0.57373685\",\n \"0.57350034\",\n \"0.57311213\",\n \"0.5728878\",\n \"0.57107115\",\n \"0.57101923\",\n \"0.57082707\",\n \"0.5705172\",\n \"0.5701551\",\n \"0.56961244\",\n \"0.5694103\",\n \"0.5680299\",\n \"0.5676493\",\n \"0.56700134\",\n \"0.56571877\",\n \"0.5656566\",\n \"0.5653499\",\n \"0.56487125\",\n \"0.5647044\",\n \"0.5644442\",\n \"0.56339383\",\n \"0.5624072\",\n \"0.56185436\",\n \"0.56180525\",\n \"0.5617316\",\n \"0.56066066\",\n \"0.5596827\",\n \"0.55831885\",\n \"0.55823624\",\n \"0.5570801\",\n \"0.5569943\",\n \"0.5559909\",\n \"0.5557711\",\n \"0.5557707\",\n \"0.5554068\",\n \"0.5549027\",\n \"0.55478495\",\n \"0.5547419\",\n \"0.5547419\",\n \"0.5545641\",\n \"0.5533221\",\n \"0.552275\",\n \"0.55213064\",\n \"0.5520953\",\n \"0.55201215\",\n \"0.55191207\",\n \"0.5516685\",\n \"0.55055285\",\n \"0.5505241\",\n \"0.5502522\",\n \"0.5500113\",\n \"0.5497463\",\n \"0.54929924\",\n \"0.54928255\",\n \"0.54922175\",\n \"0.5483362\",\n \"0.54824114\",\n \"0.5477892\",\n \"0.547556\",\n \"0.54715097\",\n \"0.54693866\",\n \"0.5467904\",\n \"0.5466916\",\n \"0.5462227\",\n \"0.54617566\",\n \"0.54582584\",\n \"0.5457853\",\n \"0.5441424\",\n \"0.5437863\",\n \"0.5430049\",\n \"0.54296374\",\n \"0.5424456\",\n \"0.54221714\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62696826"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":7185,"cells":{"query":{"kind":"string","value":"return orthanc object of study"},"document":{"kind":"string","value":"def get(self):\n return orthanc.study(self.orthanc_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def orthanc_studies(self):\n return [orthanc.study(x.orthanc_id) for x in self.studies]","def salome_study_init(theStudyId=0):\n\n global salome_study_initial\n global myStudyManager, myStudyId, myStudy, myStudyName\n global orb, lcc, naming_service, cm\n\n if salome_study_initial:\n salome_study_initial = 0\n\n orb, lcc, naming_service, cm = salome_kernel.salome_kernel_init()\n\n # get Study Manager reference\n if verbose(): print \"looking for studyManager ...\"\n obj = naming_service.Resolve('myStudyManager')\n myStudyManager = obj._narrow(SALOMEDS.StudyManager)\n if verbose(): print \"studyManager found\"\n\n # get active study Id, ref and name\n myStudyId = getActiveStudy(theStudyId)\n if verbose(): print \"myStudyId\",myStudyId\n myStudy = myStudyManager.GetStudyByID(myStudyId)\n myStudyName = myStudy._get_Name()\n\n return myStudyManager, myStudyId, myStudy, myStudyName","def __repr__(self):\n\n return \"<Study pmid=%d doi=%s title=%s year=%d>\" % (\n self.pmid, self.doi, self.title, self.year)","def clone(self, *args):\n return _SALOMERuntime.OutputStudyPort_clone(self, *args)","def demo_ortho_slicer():\n pl.clf()\n oslicer = OrthoSlicer(cut_coords=(0, 0, 0))\n from .anat_cache import _AnatCache\n map, affine, _ = _AnatCache.get_anat()\n oslicer.plot_map(map, affine, cmap=pl.cm.gray)\n return oslicer","def _createObj(self) -> None:\n phase_img = skimage.img_as_float(skimage.data.camera())[::-1, ::-1]\n mod_img = skimage.img_as_float(skimage.data.immunohistochemistry()[:, :, 0])[::-1, ::-1]\n mod = skimage.transform.resize(mod_img, self.shape,\n mode='wrap', preserve_range=True)\n phase = skimage.transform.resize(phase_img, self.shape,\n mode='wrap', preserve_range=True)\n\n # Setting the ranges\n phase = (phase - np.min(phase)) / (np.max(phase) - np.min(phase)) * self.phase_range\n mod = (mod - np.min(mod)) / (np.max(mod) - np.min(mod)) * self.mod_range\n\n # Centering the phase at 0.\n phase = np.angle(np.exp(1j * (phase - scipy.stats.circmean(phase))))\n obj = (mod * np.exp(1j * phase)).astype('complex64')\n self._setObjArrayValues(obj)","def phonology(request):\n\n perspective_cid = request.params.get('perspective_client_id')\n perspective_oid = request.params.get('perspective_object_id')\n\n # Checking if we have limits on number of computed results.\n\n limit = (None if 'limit' not in request.params else\n int(request.params.get('limit')))\n\n limit_exception = (None if 'limit_exception' not in request.params else\n int(request.params.get('limit_exception')))\n\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\n int(request.params.get('limit_no_vowel')))\n\n limit_result = (None if 'limit_result' not in request.params else\n int(request.params.get('limit_result')))\n\n # TODO: get perspective's translation and language it belongs to.\n\n # We get lexical entries of this perspective with markup'ed sounds.\n\n Sound = aliased(Entity, name = \"Sound\")\n PublishingSound = aliased(PublishingEntity, name = \"PublishingSound\")\n\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\n LexicalEntry.parent_client_id == perspective_cid,\n LexicalEntry.parent_object_id == perspective_oid,\n LexicalEntry.marked_for_deletion == False,\n Entity.parent_client_id == LexicalEntry.client_id,\n Entity.parent_object_id == LexicalEntry.object_id,\n Entity.marked_for_deletion == False,\n Entity.additional_metadata.contains({\"data_type\": \"praat markup\"}),\n PublishingEntity.client_id == Entity.client_id,\n PublishingEntity.object_id == Entity.object_id,\n PublishingEntity.published == True,\n PublishingEntity.accepted == True,\n Sound.client_id == Entity.self_client_id,\n Sound.object_id == Entity.self_object_id,\n Sound.marked_for_deletion == False,\n PublishingSound.client_id == Sound.client_id,\n PublishingSound.object_id == Sound.object_id,\n PublishingSound.published == True,\n PublishingSound.accepted == True))\n\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\n # then several hundred such lexical entries.\n\n exception_counter = 0\n no_vowel_counter = 0\n result_list = list()\n\n for index, row in enumerate(query.yield_per(100)):\n\n markup_url = row.Entity.content\n sound_url = row.Sound.content\n\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id)\n\n # Checking if we have cached result for this pair of sound/markup.\n\n cache_result = CACHE.get(cache_key)\n\n if cache_result == 'no_vowel':\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]: no vowels\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we have cached exception, we do the same as with absence of vowels, show its info and\n # continue.\n\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\n exception, traceback_string = cache_result[1:3]\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n '[CACHE {7}]: exception\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n log.debug(traceback_string)\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we actually have the result, we use it and continue.\n\n elif cache_result:\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in cache_result)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]:\\n{8}\\n{9}\\n{10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url, result_string))\n\n result_list.append(cache_result)\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n continue\n\n try:\n # Getting markup, checking for each tier if it needs to be processed.\n\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\n\n textgrid = pympi.Praat.TextGrid(xmax = 0)\n textgrid.from_file(\n io.BytesIO(markup_bytes),\n codec = chardet.detect(markup_bytes)['encoding'])\n\n tier_data_list = []\n vowel_flag = False\n\n for tier_number, tier_name in textgrid.get_tier_name_num():\n\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\n raw_interval_seq_list = [[]]\n\n # Splitting interval sequence on empty intervals.\n\n for raw_index, interval in enumerate(raw_interval_list):\n\n if len(interval[2].strip()) <= 0:\n if len(raw_interval_seq_list[-1]) > 0:\n raw_interval_seq_list.append([])\n\n else:\n raw_interval_seq_list[-1].append((raw_index, interval))\n\n if len(raw_interval_seq_list[-1]) <= 0:\n del raw_interval_seq_list[-1]\n\n # Selecting interval sequences for analysis, checking if we have unusual markup.\n \n interval_seq_list = []\n interval_idx_to_raw_idx = dict()\n\n unusual_markup_flag = False\n unusual_markup_list = []\n\n for raw_interval_seq in raw_interval_seq_list:\n\n interval_seq_list.append([])\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\n\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\n\n interval_text = interval[2].strip()\n\n # Accepting interval if its text contains at least one vowel, and is short enough or\n # is a valid phonetic transcription.\n\n transcription_check = re.fullmatch(transcription_re, interval_text)\n\n if (len(interval_text) > 0 and\n any(character in vowel_set for character in interval_text) and\n (len(interval_text) <= 2 or transcription_check)):\n\n interval_seq_list[-1].append(interval)\n\n sequence_index = len(interval_seq_list) - 1\n interval_index = len(interval_seq_list[-1]) - 1\n\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\n\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\n\n elif not transcription_check:\n\n unusual_markup_flag = True\n unusual_markup_list.append((raw_index, interval))\n\n transcription_list = [text for begin, end, text in raw_interval_list]\n transcription = ''.join(transcription_list)\n\n selected_list = [text\n for interval_list in interval_seq_list\n for begin, end, text in interval_list]\n\n selected = ''.join(selected_list)\n\n # If we have intervals with unusual markup, we report them.\n\n if unusual_markup_flag:\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' has interval(s) with unusual transcription text: '\n '{9} / {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\n\n # If the markup does not have any vowels, we note it and also report it.\n\n if all(character not in vowel_set for character in transcription):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' doesn\\'t have any vowel markup: {9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription_list))\n\n # It is also possible that while full transcription has vowels, intervals selected for\n # analysis do not. In that case we also note it and report it.\n\n elif not any(character in vowel_set for character in selected):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' intervals to be processed don\\'t have any vowel markup: '\n 'markup {9}, selected {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name,\n transcription_list, selected_list))\n\n # Otherwise we store tier data to be used during processing of the sound file.\n\n else:\n tier_data_list.append((tier_number, tier_name,\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\n interval_idx_to_raw_idx, transcription)))\n\n vowel_flag = True\n\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\n\n if not vowel_flag:\n\n CACHE.set(cache_key, 'no_vowel')\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\n\n sound = None\n with tempfile.NamedTemporaryFile() as temp_file:\n\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\n temp_file.write(sound_file.read())\n temp_file.flush()\n\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\n\n tier_result_list = []\n\n for tier_number, tier_name, tier_data in tier_data_list:\n\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\n tier_result_list.append((tier_number, tier_name, tier_data))\n continue\n\n # Analyzing vowel sounds of each interval sequence.\n\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\n transcription) = tier_data\n\n tier_result_list.append((tier_number, tier_name, []))\n\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\n raw_interval_seq_list, interval_seq_list)):\n\n if len(interval_list) <= 0:\n continue\n\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\n find_max_interval_praat(sound, interval_list)\n\n max_intensity_interval = interval_list[max_intensity_index]\n max_length_interval = interval_list[max_length_index]\n\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\n\n # Compiling results.\n\n max_length_str = '{0} {1:.3f} [{2}]'.format(\n max_length_interval[2], max_length,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\n\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\n max_intensity_interval[2],\n max_intensity,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\n\n tier_result_list[-1][2].append([\n ''.join(text for index, (begin, end, text) in raw_interval_list),\n max_length_str,\n '{0:.3f}'.format(max_length_f1_f2[0]),\n '{0:.3f}'.format(max_length_f1_f2[1]),\n max_intensity_str,\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\n '+' if max_intensity_index == max_length_index else '-'])\n\n # Saving result.\n\n result_list.append(tier_result_list)\n CACHE.set(cache_key, tier_result_list)\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\n '\\n{7}\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url, result_string))\n\n # Stopping earlier, if required.\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n except Exception as exception:\n\n #\n # NOTE\n #\n # Exceptional situations encountered so far:\n #\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\n #\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\n #\n # 2. Markup for one of the intervals contains a newline \"\\n\", and pympi fails to parse it.\n # Praat parses such files without problems.\n #\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\n #\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'exception\\n{7}\\n{8}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url))\n\n # if we encountered an exception, we show its info and remember not to try offending\n # sound/markup pair again.\n\n traceback_string = ''.join(traceback.format_exception(\n exception, exception, exception.__traceback__))[:-1]\n\n log.debug(traceback_string)\n\n CACHE.set(cache_key, ('exception', exception,\n traceback_string.replace('Traceback', 'CACHEd traceback')))\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\n perspective_cid, perspective_oid,\n len(result_list), '' if len(result_list) == 1 else 's',\n no_vowel_counter, exception_counter))\n\n # If we have no results, we indicate the situation and also show number of failures and number of\n # markups with no vowels.\n\n if not result_list:\n request.response.status = HTTPPreconditionFailed.code\n\n return {\n \"error\": \"no markups for this query\",\n \"exception_counter\": exception_counter,\n \"no_vowel_counter\": no_vowel_counter}\n\n # Otherwise we create and then serve Excel file.\n\n excel_book = xlwt.Workbook(encoding = \"utf-8\")\n sheet = excel_book.add_sheet(\"Sheet 1\")\n\n sheet.write(0, 0, 'Transcription')\n sheet.write(0, 1, 'Longest (seconds) interval')\n sheet.write(0, 2, 'F1 (Hz)')\n sheet.write(0, 3, 'F2 (Hz)')\n sheet.write(0, 4, 'Highest intensity (dB) interval')\n sheet.write(0, 5, 'F1 (Hz)')\n sheet.write(0, 6, 'F2 (Hz)')\n sheet.write(0, 7, 'Coincidence')\n\n row_counter = 1\n\n for tier_result_list in result_list:\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\n\n if tier_result_seq_list == 'no_vowel':\n continue\n\n for tier_data in tier_result_seq_list:\n for index, tier_data_str in enumerate(tier_data):\n sheet.write(row_counter, index, tier_data_str)\n\n row_counter += 1\n\n # Formatting column widths.\n\n sheet.col(0).width = 24 * 256\n sheet.col(1).width = 24 * 256\n sheet.col(2).width = 12 * 256\n sheet.col(3).width = 12 * 256\n sheet.col(4).width = 24 * 256\n sheet.col(5).width = 12 * 256\n sheet.col(6).width = 12 * 256\n sheet.col(7).width = 12 * 256\n\n excel_stream = io.BytesIO()\n excel_book.save(excel_stream)\n excel_stream.seek(0)\n\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\n # content-type.\n\n response = Response(content_type = 'application/vnd.ms-excel')\n\n response.app_iter = FileIter(excel_stream)\n response.headers['Content-Disposition'] = \"attachment; filename=phonology.xls\"\n\n return response","def get_o(self):\n return self.o","def __repr__(self):\n return \"{} ({}) <{}:{} pheno: {} study: {}\".format(\n self.id, self.snpid, self.chrom, self.pos, self.phenotype_desc,\n self.study.title\n )","def object(self):","def __str__(self):\r\n\r\n return 'Perspective(%s)' % self.id","def clone(self, *args):\n return _SALOMERuntime.InputStudyPort_clone(self, *args)","def get_main_object(tc):\n return Daal(tc)","def get_scn_obs_date(self, unq_id):\n import copy\n logger.debug(\"Creating Database Engine and Session.\")\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\n ses = session_sqlalc()\n logger.debug(\"Perform query to find scene.\")\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id).all()\n ses.close()\n scn_record = None\n if query_result is not None:\n if len(query_result) == 1:\n scn_record = query_result[0]\n else:\n logger.error(\n \"PID {0} has returned more than 1 scene - must be unique something really wrong.\".format(\n unq_id))\n raise EODataDownException(\n \"There was more than 1 scene which has been found - something has gone really wrong!\")\n else:\n logger.error(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n raise EODataDownException(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n return copy.copy(scn_record.BeginPosition)","def create_isa_study(brapi_study_id):\n brapi_study = get_brapi_study(brapi_study_id)\n this_study = Study(filename=\"s_\" + str(brapi_study_id) + \".txt\")\n this_study.identifier = brapi_study['studyDbId']\n if 'name' in brapi_study:\n this_study.title = brapi_study['name']\n elif 'studyName' in brapi_study:\n this_study.title = brapi_study['studyName']\n\n this_study.comments.append(Comment(name=\"Study Start Date\", value=brapi_study['startDate']))\n this_study.comments.append(Comment(name=\"Study End Date\", value=brapi_study['endDate']))\n if brapi_study['location'] is not None and brapi_study['location']['name'] is not None :\n this_study.comments.append(Comment(name=\"Study Geographical Location\",\n value=brapi_study['location']['name']))\n else:\n this_study.comments.append(Comment(name=\"Study Geographical Location\",value=\"\"))\n\n study_design = brapi_study['studyType']\n oa_st_design = OntologyAnnotation(term=study_design)\n this_study.design_descriptors = [oa_st_design]\n\n oref_tt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n oa_tt = OntologyAnnotation(term=\"phenotyping\", term_accession=\"\", term_source=oref_tt)\n oref_mt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n oa_mt = OntologyAnnotation(term=\"multi-technology\", term_accession=\"\", term_source=oref_mt)\n isa_assay_file = \"a_\" + str(brapi_study_id) + \".txt\"\n this_assay = Assay(measurement_type=oa_tt, technology_type=oa_mt, filename=isa_assay_file)\n this_study.assays.append(this_assay)\n\n return this_study","def get_study_info(self,std_id):\n raise NotImplementedError","def get_queryset(self):\n return Objective.objects.order_by('perspective')","def example_clinical_data(study_name, environment):\n\n odm = ODM(\"test system\")(\n ClinicalData(\"Mediflex\", \"DEV\")(\n SubjectData(\"MDSOL\", \"IJS TEST4\", transaction_type=\"Insert\")(\n StudyEventData(\"SUBJECT\")(\n FormData(\"EN\", transaction_type=\"Update\")(\n # Although Signature is ODM1.3.1 RWS does not support it inbound currently\n # RWSBuilders do support outbound generation of Signature at FormData level\n # Signature()(\n # UserRef(\"isparks\"),\n # LocationRef(\"MDSOL\"),\n # SignatureRef(\"APPROVED\"),\n # DateTimeStamp(datetime(2015, 9, 11, 10, 15, 22, 80))\n # ),\n ItemGroupData()(\n ItemData(\"SUBJINIT\", \"AAA\")(\n AuditRecord(edit_point=AuditRecord.EDIT_DATA_MANAGEMENT,\n used_imputation_method= False,\n identifier='X2011',\n include_file_oid=False)(\n UserRef(\"isparks\"),\n LocationRef(\"MDSOL\"),\n ReasonForChange(\"Data Entry Error\"),\n DateTimeStamp(datetime(2015, 9, 11, 10, 15, 22, 80))\n ),\n MdsolQuery(value=\"Subject initials should be 2 chars only.\", recipient=\"Site from System\",\n status=QueryStatusType.Open)\n ),\n ItemData(\"SUBJID\", '001')\n )\n )\n )\n )\n )\n )\n return odm","def __repr__(self):\n return f'ResidenciaModel(name={self.neighbourhood_group}, neighbourhood={self.room_type})'","def ortho(self):\r\n\r\n m11,m12,m13,m14,m21,m22,m23,m24,m31,m32,m33,m34,m41,m42,m43,m44 = self.mlist\r\n\r\n x = _vec3(m11, m21, m31)\r\n y = _vec3(m12, m22, m32)\r\n z = _vec3(m13, m23, m33)\r\n\r\n xl = x.length()\r\n xl*=xl\r\n y = y - ((x*y)/xl)*x\r\n z = z - ((x*z)/xl)*x\r\n\r\n yl = y.length()\r\n yl*=yl\r\n z = z - ((y*z)/yl)*y\r\n\r\n return mat4( x.x, y.x, z.x, m14,\r\n x.y, y.y, z.y, m24,\r\n x.z, y.z, z.z, m34,\r\n m41, m42, m43, m44)","def search_research_studies_with_observations():\n return ResearchStudy.where(struct={}).include('focus', Observation, reverse=True)","def get_objectives(self):\n return copy.deepcopy(self.objectives), self.gates_names","def get_scn_record(self, unq_id):\n logger.debug(\"Creating Database Engine and Session.\")\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\n ses = session_sqlalc()\n\n logger.debug(\"Perform query to find scene.\")\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id).all()\n ses.close()\n scn_record = None\n if query_result is not None:\n if len(query_result) == 1:\n scn_record = query_result[0]\n else:\n logger.error(\n \"PID {0} has returned more than 1 scene - must be unique something really wrong.\".format(unq_id))\n raise EODataDownException(\n \"There was more than 1 scene which has been found - something has gone really wrong!\")\n else:\n logger.error(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n raise EODataDownException(\"PID {0} has not returned a scene - check inputs.\".format(unq_id))\n return scn_record","def get_full_article(self):\n raise NotImplementedError","def get_rms(self):\r\n return self.rms.copy()","def substantiate():","def __init__(self, name=None, dss=28, date=None, project='SolarPatrol'):\n self.logger = logging.getLogger(logger.name+\".Observation\")\n DR.Observation.__init__(self, name=date, date=date, dss=dss, \n project=project)\n self.extended_init()\n \n #self.obs =Astronomy.Ephem.DSS(dss)\n #y,d = date.split('/')\n #self.year = int(y); self.DOY = int(d)\n #projdatapath, self.sessionpath, rawdatapath = \\\n # DR.get_obs_dirs(project, dss, self.year, self.DOY,\n # datafmt=None)","def create_general_object(self, x, y):\n return self.img[y-self.rad:y+self.rad, x-self.rad:x+self.rad]","def projection(self):\n pass","def init_od_sr(state_dict: Dict) -> SpectralResidual:\n od = SpectralResidual(threshold=state_dict['threshold'],\n window_amp=state_dict['window_amp'],\n window_local=state_dict['window_local'],\n n_est_points=state_dict['n_est_points'],\n n_grad_points=state_dict['n_grad_points'])\n return od","def orthopyroxene():\n\n rho = 3304.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 236.9; C[0,1] = 79.6; C[0,2] = 63.2; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 180.5; C[1,2] = 56.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 84.3; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 79.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 80.1\n\n return C, rho","def o(self, otra):\n return OEspecificacion(self, otra)","def model(self):","def model(self):","def model(self):","def model(self):","def model(self):","def create_scene(self):\n \n self.scene=soya.World()","def gen_obs(self):\n\n #grid, vis_mask = self.gen_obs_grid()\n\n # Encode the partially observable view into a numpy array\n image = self.grid.encode(self.agent_pos,self.drone_pos)\n\n #assert hasattr(self, 'mission'), \"environments must define a textual mission string\"\n\n # Observations are dictionaries containing:\n # - an image (partially observable view of the environment)\n # - the agent's direction/orientation (acting as a compass)\n # - a textual mission string (instructions for the agent)\n obs = {\n 'image': image,\n #'direction': self.agent_dir,\n 'mission': self.mission\n }\n obs=image\n #print(obs.shape)\n return self.render(mode='rgb_array')\n #return obs","def get_mapping(self, type = 'orthogonal', anchor_method = 'mutual_nn', max_anchors = None):\n # Method 1: Orthogonal projection that best macthes NN\n self.compute_scores(score_type='coupling') # TO refresh\n if anchor_method == 'mutual_nn':\n pseudo = self.find_mutual_nn()#[:100]\n elif anchor_method == 'all':\n translations, oov = self.generate_translations()\n pseudo = [(k,v[0]) for k,v in translations.items()]\n if max_anchors:\n pseudo = pseudo[:max_anchors]\n print('Finding orthogonal mapping with {} anchor points via {}'.format(len(pseudo), anchor_method))\n if anchor_method in ['mutual_nn', 'all']:\n idx_src = [self.src_word2ind[ws] for ws,_ in pseudo]\n idx_trg = [self.trg_word2ind[wt] for _,wt in pseudo]\n xs_nn = self.xs[idx_src]\n xt_nn = self.xt[idx_trg]\n P = orth_procrustes(xs_nn, xt_nn)\n elif anchor_method == 'barycenter':\n ot_emd = ot.da.EMDTransport()\n ot_emd.xs_ = self.xs\n ot_emd.xt_ = self.xt\n ot_emd.coupling_= self.coupling\n xt_hat = ot_emd.inverse_transform(Xt=self.xt) # Maps target to source space\n P = orth_procrustes(xt_hat, self.xt)\n return P","def getScene():\n #print \"servers direct scenes are \",soya.IDLER.scenes[:]\n \n return soya.IDLER.scenes[0]","def get_modeller_objects(self):\n return self._modpt, self.__edat.modpt, self.__libs.modpt","def get_representative_data_object(self, obj):\n if self.dim == 0:\n # In this way, obj can be a data object and this class can be\n # used even if the assignment is not between \"flattened components\"\n return obj\n else:\n nominal_index = self.nominal_index\n return obj[nominal_index]","def getAuthoredFromSymplectic(researcher_object):\r\n #description\r\n #checking\r\n # if not(researcher_object) or (researcher_object.symplectic_int_id is None): # int_id version\r\n if not(researcher_object) or (researcher_object.symplectic_id is None): # guid version\r\n return\r\n #symplectic api url and local file path\r\n # url = SYMPLECTIC_API_URL + 'users/' + str(researcher_object.symplectic_int_id) # int_id version\r\n url = SYMPLECTIC_API_URL + 'users/' + str(researcher_object.symplectic_id) # guid version\r\n # tmp_filename = SYMPLECTIC_LOCAL_XML_FOLDER + SYMPLECTIC_LOCAL_AUTH_FOLDER + str(researcher_object.symplectic_int_id) + '.xml' # int_id version\r\n tmp_filename = SYMPLECTIC_LOCAL_XML_FOLDER + SYMPLECTIC_LOCAL_AUTH_FOLDER + str(researcher_object.symplectic_id) + '.xml' # guid version\r\n #get xml document from symplectic api and store on hd\r\n (tmp_filename, http_headers,) = urllib.urlretrieve(url, tmp_filename)\r\n #parse xml file\r\n publications_etree = ElementTree(file=tmp_filename)\r\n #delete local file from hd\r\n #try:\r\n os.remove(tmp_filename)\r\n #except:\r\n #pass \r\n #publication elements are held in a subtree\r\n publications_subtree = publications_etree.find(SYMPLECTIC_NAMESPACE + 'publications')\r\n #check if any publication elements in subtree\r\n if publications_subtree is None or len(publications_subtree) < 1:\r\n return\r\n #now that we have their newest \"i authored that pub\" info, we can delete their old \"i authored that pub\" info\r\n researcher_object.remove_all_authored() \r\n #for each publication element in subtree\r\n for publication_element in publications_subtree.getchildren():\r\n SymplecticXMLAuthored.__createAuthoredObjFromUserPubElement(publication_element, researcher_object)","def composeWorkplaceOntology():\n\n import ossPyFuncs \n import pandas as pd\n \n #mysql query to extract full table from government organizations\n #certian table columns feature capital letters which cases uproblems\n postgreSql_selectQuery=\"SELECT * FROM us_gov_manual.us_govman_2019 ;\"\n #pass querry and obtain table\n govTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n\n #mysql query to obtain academic instutions\n postgreSql_selectQuery=\"SELECT institution FROM hipolabs.universities ;\"\n #pass querry and obtain table\n univTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2018_us1000;\"\n businesses1=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2019_us1000;\"\n businesses2=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2020_global2000;\"\n businesses3=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n\n #combine theinsitutions into a vector\n combinedSeries=[govTable['AgencyName'],univTable['institution'],businesses1['company'],businesses2['company'],businesses3['company']]\n #turn the multi item vector into a single series\n fullWordbank=pd.concat(combinedSeries)\n #turn that series into a pd dataframe\n wordbankTable=pd.DataFrame(fullWordbank.unique())\n\n return wordbankTable","def _orthogonal_init(self):\n # if is a conv layer, will need to reshape to fan in matrix,\n # which is of dimension\n # num input feature maps * filter height * filter width\n if(len(self.dims) > 2):\n rv_samp = np.random.randn(self.dims[2],\n self.dims[0] * self.dims[1] * self.dims[3])\n out_sigma = np.sqrt(1.0 / rv_samp.shape[1])\n # otherwise will be a densely connected layer\n else:\n rv_samp = np.random.randn(self.dims[0], self.dims[1])\n out_sigma = np.sqrt(1.0 / rv_samp.shape[0])\n # perform SVD\n U, _, V = np.linalg.svd(rv_samp, full_matrices=False) #pylint: disable=invalid-name\n # both U and V are orthoginal matricies, so will choose the one\n # that is the correct dimensions for our layer\n ortho_matrix = U if U.shape == rv_samp.shape else V\n # rescale so it is unit variance for each vector\n # print(\"std(q) = {}\".format(np.std(q)))\n ortho_norm = (ortho_matrix / np.std(ortho_matrix)) * out_sigma\n #print(\"std(qs) = {}\".format(np.std(qs)))\n #print(q.shape)\n return ortho_norm.reshape(self.dims).astype(np.float32)","def get_final_reconstruction(self):","def om(self):\n return self._om","def _get_observation(self, unseen=False):\n img_arr = p.getCameraImage(width=self._width,\n height=self._height,\n viewMatrix=self._view_matrix,\n projectionMatrix=self._proj_matrix)\n rgb = img_arr[2]\n depth = img_arr[3]\n min = 0.97\n max=1.0\n segmentation = img_arr[4]\n depth = np.reshape(depth, (self._height, self._width,1) )\n segmentation = np.reshape(segmentation, (self._height, self._width,1) )\n\n np_img_arr = np.reshape(rgb, (self._height, self._width, 4))\n np_img_arr = np_img_arr[:, :, :3].astype(np.float64)\n\n view_mat = np.asarray(self._view_matrix).reshape(4, 4)\n proj_mat = np.asarray(self._proj_matrix).reshape(4, 4)\n # pos = np.reshape(np.asarray(list(p.getBasePositionAndOrientation(self._objectUids[0])[0])+[1]), (4, 1))\n\n AABBs = np.zeros((len(self._objectUids), 2, 3))\n cls_ls = []\n \n for i, (_uid, _cls) in enumerate(zip(self._objectUids, self._objectClasses)):\n AABBs[i] = np.asarray(p.getAABB(_uid)).reshape(2, 3)\n cls_ls.append(NAME2IDX[_cls])\n\n # np.save('/home/tony/Desktop/obj_save/view_mat_'+str(self.img_save_cnt), view_mat)\n # np.save('/home/tony/Desktop/obj_save/proj_mat_'+str(self.img_save_cnt), proj_mat)\n # np.save('/home/tony/Desktop/obj_save/img_'+str(self.img_save_cnt), np_img_arr.astype(np.int16))\n # np.save('/home/tony/Desktop/obj_save/AABB_'+str(self.img_save_cnt), AABBs)\n # np.save('/home/tony/Desktop/obj_save/class_'+str(self.img_save_cnt), np.array(cls_ls))\n\n np.save(OUTPUT_DIR + '/image_' + str(self.img_save_cnt), np_img_arr.astype(np.int16))\n dets = np.zeros((AABBs.shape[0], 5))\n for i in range(AABBs.shape[0]):\n dets[i, :4] = self.get_2d_bbox(AABBs[i], view_mat, proj_mat, IM_HEIGHT, IM_WIDTH)\n dets[i, 4] = int(cls_ls[i])\n np.save(OUTPUT_DIR + '/annotation_'+str(self.img_save_cnt), dets)\n\n test = np.concatenate([np_img_arr[:, :, 0:2], segmentation], axis=-1)\n\n return test","def make_studyforrest_mockup(path):\n public = create(opj(path, 'public'), description=\"umbrella dataset\")\n # the following tries to capture the evolution of the project\n phase1 = public.create('phase1',\n description='old-style, no connection to RAW')\n structural = public.create('structural', description='anatomy')\n tnt = public.create('tnt', description='image templates')\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\n aligned = public.create('aligned', description='aligned image data')\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # new acquisition\n labet = create(opj(path, 'private', 'labet'), description=\"raw data ET\")\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\"raw data P2MRI\")\n phase2 = public.create('phase2',\n description='new-style, RAW connection')\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\n # add to derivatives\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n # never to be published media files\n media = create(opj(path, 'private', 'media'), description=\"raw data ET\")\n # assuming all annotations are in one dataset (in reality this is also\n # a superdatasets with about 10 subdatasets\n annot = public.create('annotations', description='stimulus annotation')\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\n # a few typical analysis datasets\n # (just doing 3, actual status quo is just shy of 10)\n # and also the real goal -> meta analysis\n metaanalysis = public.create('metaanalysis', description=\"analysis of analyses\")\n for i in range(1, 3):\n ana = public.create('analysis{}'.format(i),\n description='analysis{}'.format(i))\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # link to metaanalysis\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\n reckless='auto')\n # simulate change in an input (but not raw) dataset\n create_tree(\n aligned.path,\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\n aligned.save()\n # finally aggregate data\n aggregate = public.create('aggregate', description='aggregate data')\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n # the toplevel dataset is intentionally left dirty, to reflect the\n # most likely condition for the joint dataset to be in at any given\n # point in time","def orthographic_matrix(self) -> TransformationMatrixType:\n near, far = self._clipping[self.projection_mode.value]\n return orthographic_matrix(self.fov, self.aspect_ratio, near, far)","def __init__(self, parent):\n super(Demo4, self).__init__(parent)\n self.scenes = []\n self.draw_axes = True\n self.lighting = True\n self.current_scene = 0\n self.objects = []\n self.diffuse_light = [0.8, 0.8, 0.8, 1]","def __init__(self):\n self.name = \"Schaffer\"\n objectives = [o_sh_1, o_sh_2]\n decisions = [Decision(-10 ** 5, 10 ** 5)]\n Model.__init__(self, objectives, None, decisions)","def __createAuthoredObjFromUserPubElement(publication_element, researcher_object):\r\n #description\r\n #++++++PUBLICATION LITE++++++\r\n #check publication Element\r\n if publication_element is None:\r\n return \r\n #publication guid\r\n if publication_element is not None:\r\n guid = publication_element.get('id','')\r\n if guid == '':\r\n return\r\n #load Publication from db or create (flagged as needing refetch from symplectic) if doesnt exist\r\n publication_object = Publication.getOrCreatePublication(guid) \r\n #++++++BIBLIOGRAPHICRECORD LITE++++++\r\n #bibliographic-record element -> publication sub element (used to read XML)\r\n if publication_element is not None:\r\n #only ONE biblio element per publication will be returned when querying by User_id\r\n # this is in contrast to the multiple biblios elements per publication returned when querying by a Publication_guid\r\n biblio_element = publication_element.find(SYMPLECTIC_NAMESPACE + 'bibliographic-record')\r\n #biblio data-source\r\n if biblio_element is not None:\r\n data_source = biblio_element.get('data-source','')\r\n #load BibliographicRecord from db or create if doesnt exist (NB links biblio & publication)\r\n biblio_object = BibliographicRecord.getOrCreateBibliographicRecord(publication_object, data_source) \r\n #++++++AUTHORED++++++\r\n #authored preferences -> publication sub-elements (used to read XML)\r\n if publication_element is not None:\r\n preferences_element = publication_element.find(SYMPLECTIC_NAMESPACE + 'preferences-for-this-publication')\r\n #load Authored from db or create if doesnt exist (NB links authored & publication & researcher & bibliographic-record)\r\n authored_object = Authored.getOrCreateAuthored(publication_object, researcher_object, biblio_object)\r\n #preferences\r\n if preferences_element is not None:\r\n #Show this publication\r\n if preferences_element.get('visible','false') == 'true':\r\n authored_object.visible = True\r\n else:\r\n authored_object.visible = False\r\n #Favourite publication\r\n if preferences_element.get('is-a-favourite','false') == 'true':\r\n authored_object.is_a_favourite = True\r\n else:\r\n authored_object.is_a_favourite = False\r\n #Display order\r\n authored_object.reverse_sort_cue = preferences_element.get('reverse-sort-cue','')\r\n #Save Authored\r\n authored_object.save()","def analysis(self):\r\n return analysis.Analysis(self.parent, self.object_id)","def _make(self):\n\t\tself.scene.camera = self.camera\n\t\tself.camera.rotation_euler[0] = np.radians(np.random.randint(40, 100) +\n\t\t np.random.random())\n\t\tself.camera.rotation_euler[2] = np.radians(np.random.randint(0, 360) +\n\t\t np.random.random())\n\t\tprint([np.degrees(x) for x in self.camera.rotation_euler])","def visible_objects_and_duplis():\n\n for obj in context.visible_objects:\n if obj.type == 'MESH':\n if obj.modeling_cloth: \n yield (obj, obj.matrix_world.copy())","def visible_objects_and_duplis():\n\n for obj in context.visible_objects:\n if obj.type == 'MESH':\n if obj.modeling_cloth: \n yield (obj, obj.matrix_world.copy())","def test_to_from_scene(self): # pragma: lpy\n super(TestObjDict, self).test_to_from_scene(_as_obj=True)","def associated_coroot(self):","def __init__(self, before, after):\r\n self.M = cv2.getPerspectiveTransform(before, after)\r\n self.inverse_M = cv2.getPerspectiveTransform(after, before)","def topol (self):\n\n # child.connect(parent, parent_end, {child_start=0})\n # Distal (apical)\n self.dends['apical_trunk'].connect(self.soma, 1, 0)\n self.dends['apical_1'].connect(self.dends['apical_trunk'], 1, 0)\n self.dends['apical_2'].connect(self.dends['apical_1'], 1, 0)\n self.dends['apical_tuft'].connect(self.dends['apical_2'], 1, 0)\n\n # apical_oblique comes off distal end of apical_trunk\n self.dends['apical_oblique'].connect(self.dends['apical_trunk'], 1, 0)\n\n # Proximal (basal)\n self.dends['basal_1'].connect(self.soma, 0, 0)\n self.dends['basal_2'].connect(self.dends['basal_1'], 1, 0)\n self.dends['basal_3'].connect(self.dends['basal_1'], 1, 0)\n\n self.basic_shape() # translated from original hoc (2009 model)","def qa_tractography(stream_path,qa_out_path,brain_path):\n \n #Use window to visualize the streamlines\n r = window.renderer()\n \n #Load the streamline.trk file\n streamlines_mni_load = nib.streamlines.load(stream_path).streamlines\n streamlines_mni_in = Streamlines(streamlines_mni_load)\n streamlines_actor = actor.line(\n streamlines_mni_in,\n colormap.line_colors(streamlines_mni_in),\n lod_points=10000,\n depth_cue=True,\n linewidth=0.2,\n fake_tube=True,\n opacity=0.3,\n )\n \n r.add(streamlines_actor)\n #window.show(r)\n \n showmng = window.ShowManager(r)\n #window.record function can rotate the 3D-image, then get the snapshot of the specific angle. \n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,1,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Z_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (1,0,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Y_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,0,1),path_numbering= True,out_path = qa_out_path + '/Rotate_X_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n showmng.exit()\n combine_plot(qa_out_path,brain_path)","def create_antipodal_grasp_set(object):\n gripper_model = burg.gripper.Robotiq2F85()\n\n ags = burg.sampling.AntipodalGraspSampler()\n ags.only_grasp_from_above = True\n ags.mesh = object.object_type.mesh\n ags.gripper = gripper_model\n ags.n_orientations = 10\n ags.verbose = True\n ags.max_targets_per_ref_point = 2\n ags.no_contact_below_z = 0.01\n graspset, contacts, normals, approach_vectors = ags.sample(100)\n # gs.scores = ags.check_collisions(gs, use_width=False) # need to install python-fcl\n print('contacts.shape', contacts.shape)\n #burg.visualization.show_grasp_set([ags.mesh], graspset, gripper=gripper_model, use_width=False,\n # score_color_func=lambda s: [s, 1-s, 0], with_plane=True)\n \n \"\"\" print(\"graspset :\", len(graspset))\n print(\"contacts :\", contacts.shape)\n print(\"normales :\", normals.shape)\n print(\"angles :\", angles.shape) \"\"\"\n\n return graspset, contacts, normals, approach_vectors","def get_standardarticle(self):\n return self._standardarticle","def topdia(x):\r\n return Feature(x, \"TopDia\")","def visible_objects_and_duplis():\r\n \r\n for obj in context.visible_objects:\r\n if obj.type == 'MESH':\r\n yield (obj, obj.matrix_world.copy())\r\n \r\n if obj.dupli_type != 'NONE':\r\n obj.dupli_list_create(scene)\r\n for dob in obj.dupli_list:\r\n obj_dupli = dob.object\r\n if obj_dupli.type == 'MESH':\r\n yield (obj_dupli, dob.matrix.copy())\r\n \r\n obj.dupli_list_clear()","def __init__(self):\n self.name = \"Osyczka\"\n objectives = [ob_os_1, ob_os_2]\n constraints = [con_os_1, con_os_2, con_os_3, con_os_4, con_os_5, con_os_6]\n decisions = [Decision(0, 10), Decision(0, 10), Decision(1, 5), Decision(0, 6), Decision(1, 5), Decision(0, 10)]\n Model.__init__(self, objectives, constraints, decisions)","def create_observation(self):","def create_observation(self):","def build_dom():\n glass_thickness = 10 #mm\n size = 100 #mm\n # outside of the glass envelope\n outside_mesh = make.sphere(size)\n # inside of the glass envelope\n inside_mesh = make.sphere(size-glass_thickness)\n\n # outside solid with ice on the outside, and glass on the inside\n outside_solid = Solid(outside_mesh,glass,ice) \n\n inside_surface = r7081hqe_photocathode\n inside_color = 0x00ff00\n\n # construct the inside solid\n inside_solid = Solid(inside_mesh,vacuum,glass,surface=inside_surface,\n color=inside_color)\n\n # you can add solids and meshes!\n return outside_solid + inside_solid","def __init__(self):\n self.opening_scene = DungeonGate()\n # this list define the order of scenes in the corridor\n self.corridor_scenes = [GuardsRoom(), Cell(), Armory(), EmptyRoom(), Dormitory()]\n shuffle(self.corridor_scenes)\n self.explored_scenes = {\n \"GuardsRoom\": \"unexplored\",\n \"Cell\": \"unexplored\",\n \"Dormitory\": \"unexplored\",\n \"Armory\": \"unexplored\",\n \"EmptyRoom\": \"unexplored\",\n \"DungeonGate\": \"unexplored\"\n }","def mortality(self):\n pass","def output_projection(self):\n return self.projection(what='output')","def getCriticStation(analyzer):\n mayIn = model.getRankMay(analyzer,\"in\")\n mayOut=model.getRankMay(analyzer,\"out\")\n less=model.getRankMen(analyzer,\"LessPopular\")\n return (mayIn,mayOut,less)","def getOntologyItem(self, resource, oType=0):\n\n if isinstance(resource, int):\n resource = 'ontology/{0}/{1}'.format(resource, oType)\n\n res = self.getRequest(resource)\n onto = vsdModels.Ontology(**res)\n\n return onto","def lro(self) -> global___Snippet.Lro:","def view(self):\n view = self.empty_like()\n # Note that this is a shallow copy.\n view.sects = self.sects.copy()\n return view","def asr_model():\n\n asr_model = EncoderDecoderASR.from_hparams(\n source=\"speechbrain/asr-transformer-transformerlm-librispeech\"\n )\n return asr_model","def getRecord(self):\n logger.debug(\"Entering in ocentricWKT.getRecord\")\n\n\n record = {\n 'type':'OCENTRIC', \n OcentricMetadata.GEO_GCS_NAME.value: self.getGeoGcsName(),\n OcentricMetadata.DATUM_NAME.value: self.getDatumName(),\n OcentricMetadata.ELLIPSOIDE_NAME.value: self.getSpheroidName(),\n OcentricMetadata.RADIUS.value: self.getRadius(),\n OcentricMetadata.INVERSE_FLATTENING.value: self.getInverseFlattening(),\n OcentricMetadata.AUTHORITY_NAME.value: self.getAuthorityName(),\n OcentricMetadata.AUTHORITY_CODE.value: self.getAuthorityCode(), \n OcentricMetadata.LONGITUDE_ORDER.value: \"East\"\n }\n\n logger.debug(\"Exiting from ocentricWKT.getRecord\")\n return record","def to_povray(vis,world,properties={}):\n #patch on vapory\n patch_vapory()\n \n #camera\n mat=vis.view.camera.matrix()\n pos=mat[1]\n right=mat[0][0:3]\n up=mat[0][3:6]\n dir=op.mul(mat[0][6:9],-1)\n tgt=op.add(mat[1],dir)\n #scale\n fovy=vis.view.fov*vis.view.h/vis.view.w\n fovx=math.atan(vis.view.w*math.tan(fovy*math.pi/360.)/vis.view.h)*360./math.pi\n right=op.mul(right,-float(vis.view.w)/vis.view.h)\n #camera\n camera_params=['orthographic' if vis.view.orthogonal else 'perspective',\n 'location',[pos[0],pos[1],pos[2]],\n 'look_at',[tgt[0],tgt[1],tgt[2]],\n 'right',[right[0],right[1],right[2]],\n 'up',[up[0],up[1],up[2]],\n 'angle',fovx,\n 'sky',get_property(properties,[],\"sky\",[0.,0.,1.])]\n camera=vp.Camera(*camera_params)\n \n #tempfile\n tempfile=get_property(properties,[],\"tempfile\",None)\n tempfile_path=os.path.dirname(tempfile) if tempfile is not None else '.'\n if not os.path.exists(tempfile_path):\n os.mkdir(tempfile_path)\n \n #objects\n objects=[]\n objs=[o for o in properties[\"visualObjects\"]] if \"visualObjects\" in properties else []\n objs+=[world.terrain(i) for i in range(world.numTerrains())]\n objs+=[world.rigidObject(i) for i in range(world.numRigidObjects())]\n for r in range(world.numRobots()):\n objs+=[world.robot(r).link(i) for i in range(world.robot(r).numLinks())]\n for obj in objs:\n transient=get_property(properties,[obj],\"transient\",default=True)\n if transient:\n objects+=geometry_to_povray(obj.appearance(),obj.geometry(),obj,None,properties=properties)\n else: \n path=tempfile_path+'/'+obj.getName()+'.pov'\n if not os.path.exists(path):\n R,t=obj.geometry().getCurrentTransform()\n obj.geometry().setCurrentTransform([1,0,0,0,1,0,0,0,1],[0,0,0])\n geom=geometry_to_povray(obj.appearance(),obj.geometry(),obj,None,properties=properties)\n if len(geom)>1:\n file_content=vp.Union(*geom)\n elif len(geom)>0: \n file_content=vp.Object(*geom)\n else: file_content=None\n if file_content is not None:\n f=open(path,'w')\n f.write(str(file_content))\n f.close()\n obj.geometry().setCurrentTransform(R,t)\n else: path=None\n #include \n if path is not None:\n R,t=obj.geometry().getCurrentTransform()\n objects.append(vp.Object('#include \"%s\"'%path,\"matrix\",R+t))\n \n #light\n if \"lights\" in properties:\n objects+=properties[\"lights\"]\n \n #scene\n gsettings=[]\n scene=vp.Scene(camera=camera,\n objects=objects,\n included=get_property(properties,[],\"included\",[]),\n global_settings=get_property(properties,[],\"global_settings\",[]))\n try:\n #this works with later version of vapory\n return \\\n render_povstring(str(scene), \\\n outfile=get_property(properties,[],\"outfile\",None), \\\n width=vis.view.w,height=vis.view.h, \\\n quality=get_property(properties,[],\"quality\",None), \\\n antialiasing=get_property(properties,[],\"antialiasing\",0.3), \\\n remove_temp=get_property(properties,[],\"remove_temp\",False), \\\n show_window=get_property(properties,[],\"show_window\",False), \\\n tempfile=tempfile, \\\n includedirs=get_property(properties,[],\"includedirs\",None), \\\n output_alpha=get_property(properties,[],\"output_alpha\",True))\n except:\n #this works with earlier version of vapory\n return \\\n render_povstring(str(scene), \\\n outfile=get_property(properties,[],\"outfile\",None), \\\n width=vis.view.w,height=vis.view.h, \\\n quality=get_property(properties,[],\"quality\",None), \\\n antialiasing=get_property(properties,[],\"antialiasing\",0.3), \\\n remove_temp=get_property(properties,[],\"remove_temp\",False))","def __init__(self):\n self.rho=[]\n self.te=[]\n self.ti=[]\n self.ne=[]\n self.ni=[]\n self.ni1=[]\n self.ni2=[]\n self.ni3=[]\n self.vtor=[]\n self.zeff=[]\n\n self.nion=1\n self.Z=[]\n self.A=[]\n self.coll_mode=[]","def setup_orographic_enhancement_cube():\n data = np.array([[1, 1, 1], [0, 1, 0], [0, 0, 0], [0, 0, 0]], dtype=np.float32)\n orographic_enhancement_cube = set_up_variable_cube(\n data, name=\"orographic_enhancement\", units=\"mm/hr\", spatial_grid=\"equalarea\"\n )\n return orographic_enhancement_cube","def orthonormation_method(standardized_methods_cleaned):\n method_standardized_ortho = standardized_methods_cleaned.copy(deep=True)\n\n categories = method_standardized_ortho.columns.tolist()\n\n # Normation of the first category\n method_standardized_ortho[categories[0]] = method_standardized_ortho[categories[0]] / \\\n linalg.norm(method_standardized_ortho[categories[0]])\n\n # Normation of every following categories\n j = 0\n while j < len(categories):\n i = 0\n while i < j:\n # Calculates the orthogonal projection of j on each i and substraction of the projection from j\n method_standardized_ortho[categories[j]] = \\\n method_standardized_ortho[categories[j]] - method_standardized_ortho[categories[i]] * (\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[j]]) /\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[i]]))\n if linalg.norm(method_standardized_ortho[categories[j]]) == 0:\n # If after the projection, if the j columns is null it is droped (i.e it is linearly dependant with\n # the other columns) and the inner loop stops\n method_standardized_ortho.drop(method_standardized_ortho.columns[j], inplace=True, axis=1)\n categories.remove(categories[j])\n\n break\n else:\n # If the j column is not null, it is normed and the inner while loop keeps going\n method_standardized_ortho[categories[j]] = method_standardized_ortho[categories[j]] / \\\n (linalg.norm(method_standardized_ortho[categories[j]]))\n i += 1\n j += 1\n\n return method_standardized_ortho","def _get_ortho_path(self, **kwargs) -> Union[CloudPath, Path]:\n\n if self.product_type in self._proj_prod_type:\n ortho_name = f\"{self.condensed_name}_ortho.tif\"\n\n ortho_path, ortho_exists = self._get_out_path(ortho_name)\n if not ortho_exists:\n LOGGER.info(\n \"Manually orthorectified stack not given by the user. \"\n \"Reprojecting whole stack, this may take a while. \"\n \"(May be inaccurate on steep terrain, depending on the DEM resolution)\"\n )\n\n # Reproject and write on disk data\n dem_path = self._get_dem_path(**kwargs)\n with rasterio.open(str(self._get_tile_path(**kwargs))) as src:\n\n out_arr, meta = self._reproject(\n src.read(), src.meta, src.rpcs, dem_path, **kwargs\n )\n rasters_rio.write(out_arr, meta, ortho_path)\n\n else:\n ortho_path = self._get_tile_path(**kwargs)\n\n return ortho_path","def _get_viewer_container(self):\n self.viewer = self.traj._tempmol.draw3d(style='licorice')\n return self.viewer","def spatial(self):","def perspective(self):\n return self.container['perspective']","def model_cohort(cohort):\n model_traj = []\n for part in cohort:\n for traj in part.trajectories:\n if traj.filter is True:\n model_traj.append(\n modelling.model(x=traj.data.age.tolist(),\n y=traj.data.AF.tolist(),\n mutation=traj.mutation,\n variant_class=traj.variant_class,\n gene=traj.mutation.split()[0],\n id=part.id,\n p_key=traj.p_key))\n return model_traj","def get_contenu(self):\n return self.objets","def __init__(self, por): \n logger.debug(\"Entering in ocentricWKT constructor\") \n super(OcentricWKT, self).__init__(\n por.getElement(OcentricMetadata.GEO_GCS_NAME),\n por.getElement(OcentricMetadata.DATUM_NAME),\n por.getElement(OcentricMetadata.ELLIPSOIDE_NAME),\n por.getElement(OcentricMetadata.RADIUS),\n por.getElement(OcentricMetadata.INVERSE_FLATTENING),\n por.getElement(OcentricMetadata.AUTHORITY_NAME),\n por.getElement(OcentricMetadata.AUTHORITY_CODE)\n ) \n logger.debug(\"Exiting from ocentricWKT constructor\")","def get_study_by_pmid(cls, pmid):\n\n print \"Found study \", pmid\n study_obj = cls.query.filter(cls.pmid == pmid).first()\n return study_obj","def Omat(self):\n if self.standard:\n return np.matrix(((0, -1, 0), (0, 0, 1), (-1, 0, 0)))\n else:\n return np.matrix(((0, 0, 1), (0, 1, 0), (-1, 0, 0)))","def construct_data_LHS(model,num_pts,seed=None):\n num_dims = model.num_dims\n rv_trans = define_random_variable_transformation_hydromad(model)\n pts = latin_hypercube_design( num_pts, num_dims, seed )\n # returns points on [0,1] but need pts on [-1,1]\n pts = 2*pts-1.\n pts = rv_trans.map_from_canonical_distributions( pts )\n vals = model.evaluate_set( pts )\n numpy.savetxt( 'pts.txt', pts, delimiter = ',' )\n numpy.savetxt( 'vals.txt', vals, delimiter = ',' )","def __init__(self, settings, conflict):\n #generates the initial setting\n self.start_setting = random.choice(settings)\n self.current_setting = self.start_setting\n\n #gets a random character from possible setting characters as protagonist\n self.protag = random.choice(self.start_setting.characters)\n\n #selects as antagonist a random character that either the protagonist knows or is related\n #to the setting\n antaglist = list(set(self.protag.characters).union(self.start_setting.characters))\n self.antag = random.choice(antaglist)\n\n self.antag.set_protagonist(self.protag)\n self.protag.set_antagonist(self.antag)\n\n #lays out the basic story relations\n self.subsections = []\n\n for relation_outline in conflict.conflict_outline: \n subsection = Subsection() \n subsection.init_from_outline(relation_outline, self.protag, self.current_setting)\n self.subsections.append(subsection)\n\n #connects the rest of the story together\n self.details = []","def __init__(self):\n self.name = \"Kursawe\"\n objectives = [o_ku_1, o_ku_2]\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\n Model.__init__(self, objectives, None, decisions)","def objects(self):","def get_representation(self, reference):\n \n return Representation(data=self.get_data(reference), name=reference)","def clonar(self):\n return Tortuga(self.posicion, self.orientacion, self.pluma.clonar())","def make_multi_object_scene(self):\n multi1 = Scene3D()\n box = self.objects[0]\n box.set_location(1, 0, 0)\n box.set_size(0.4, 0.4, 0.1)\n multi1.add_object(box)\n\n box = self.objects[1]\n box.set_location(-1, 0, 0)\n multi1.add_object(box)\n\n self.scenes.append(multi1)"],"string":"[\n \"def orthanc_studies(self):\\n return [orthanc.study(x.orthanc_id) for x in self.studies]\",\n \"def salome_study_init(theStudyId=0):\\n\\n global salome_study_initial\\n global myStudyManager, myStudyId, myStudy, myStudyName\\n global orb, lcc, naming_service, cm\\n\\n if salome_study_initial:\\n salome_study_initial = 0\\n\\n orb, lcc, naming_service, cm = salome_kernel.salome_kernel_init()\\n\\n # get Study Manager reference\\n if verbose(): print \\\"looking for studyManager ...\\\"\\n obj = naming_service.Resolve('myStudyManager')\\n myStudyManager = obj._narrow(SALOMEDS.StudyManager)\\n if verbose(): print \\\"studyManager found\\\"\\n\\n # get active study Id, ref and name\\n myStudyId = getActiveStudy(theStudyId)\\n if verbose(): print \\\"myStudyId\\\",myStudyId\\n myStudy = myStudyManager.GetStudyByID(myStudyId)\\n myStudyName = myStudy._get_Name()\\n\\n return myStudyManager, myStudyId, myStudy, myStudyName\",\n \"def __repr__(self):\\n\\n return \\\"<Study pmid=%d doi=%s title=%s year=%d>\\\" % (\\n self.pmid, self.doi, self.title, self.year)\",\n \"def clone(self, *args):\\n return _SALOMERuntime.OutputStudyPort_clone(self, *args)\",\n \"def demo_ortho_slicer():\\n pl.clf()\\n oslicer = OrthoSlicer(cut_coords=(0, 0, 0))\\n from .anat_cache import _AnatCache\\n map, affine, _ = _AnatCache.get_anat()\\n oslicer.plot_map(map, affine, cmap=pl.cm.gray)\\n return oslicer\",\n \"def _createObj(self) -> None:\\n phase_img = skimage.img_as_float(skimage.data.camera())[::-1, ::-1]\\n mod_img = skimage.img_as_float(skimage.data.immunohistochemistry()[:, :, 0])[::-1, ::-1]\\n mod = skimage.transform.resize(mod_img, self.shape,\\n mode='wrap', preserve_range=True)\\n phase = skimage.transform.resize(phase_img, self.shape,\\n mode='wrap', preserve_range=True)\\n\\n # Setting the ranges\\n phase = (phase - np.min(phase)) / (np.max(phase) - np.min(phase)) * self.phase_range\\n mod = (mod - np.min(mod)) / (np.max(mod) - np.min(mod)) * self.mod_range\\n\\n # Centering the phase at 0.\\n phase = np.angle(np.exp(1j * (phase - scipy.stats.circmean(phase))))\\n obj = (mod * np.exp(1j * phase)).astype('complex64')\\n self._setObjArrayValues(obj)\",\n \"def phonology(request):\\n\\n perspective_cid = request.params.get('perspective_client_id')\\n perspective_oid = request.params.get('perspective_object_id')\\n\\n # Checking if we have limits on number of computed results.\\n\\n limit = (None if 'limit' not in request.params else\\n int(request.params.get('limit')))\\n\\n limit_exception = (None if 'limit_exception' not in request.params else\\n int(request.params.get('limit_exception')))\\n\\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\\n int(request.params.get('limit_no_vowel')))\\n\\n limit_result = (None if 'limit_result' not in request.params else\\n int(request.params.get('limit_result')))\\n\\n # TODO: get perspective's translation and language it belongs to.\\n\\n # We get lexical entries of this perspective with markup'ed sounds.\\n\\n Sound = aliased(Entity, name = \\\"Sound\\\")\\n PublishingSound = aliased(PublishingEntity, name = \\\"PublishingSound\\\")\\n\\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\\n LexicalEntry.parent_client_id == perspective_cid,\\n LexicalEntry.parent_object_id == perspective_oid,\\n LexicalEntry.marked_for_deletion == False,\\n Entity.parent_client_id == LexicalEntry.client_id,\\n Entity.parent_object_id == LexicalEntry.object_id,\\n Entity.marked_for_deletion == False,\\n Entity.additional_metadata.contains({\\\"data_type\\\": \\\"praat markup\\\"}),\\n PublishingEntity.client_id == Entity.client_id,\\n PublishingEntity.object_id == Entity.object_id,\\n PublishingEntity.published == True,\\n PublishingEntity.accepted == True,\\n Sound.client_id == Entity.self_client_id,\\n Sound.object_id == Entity.self_object_id,\\n Sound.marked_for_deletion == False,\\n PublishingSound.client_id == Sound.client_id,\\n PublishingSound.object_id == Sound.object_id,\\n PublishingSound.published == True,\\n PublishingSound.accepted == True))\\n\\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\\n # then several hundred such lexical entries.\\n\\n exception_counter = 0\\n no_vowel_counter = 0\\n result_list = list()\\n\\n for index, row in enumerate(query.yield_per(100)):\\n\\n markup_url = row.Entity.content\\n sound_url = row.Sound.content\\n\\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id)\\n\\n # Checking if we have cached result for this pair of sound/markup.\\n\\n cache_result = CACHE.get(cache_key)\\n\\n if cache_result == 'no_vowel':\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]: no vowels\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we have cached exception, we do the same as with absence of vowels, show its info and\\n # continue.\\n\\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\\n exception, traceback_string = cache_result[1:3]\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n '[CACHE {7}]: exception\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n log.debug(traceback_string)\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we actually have the result, we use it and continue.\\n\\n elif cache_result:\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in cache_result)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]:\\\\n{8}\\\\n{9}\\\\n{10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url, result_string))\\n\\n result_list.append(cache_result)\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n try:\\n # Getting markup, checking for each tier if it needs to be processed.\\n\\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\\n\\n textgrid = pympi.Praat.TextGrid(xmax = 0)\\n textgrid.from_file(\\n io.BytesIO(markup_bytes),\\n codec = chardet.detect(markup_bytes)['encoding'])\\n\\n tier_data_list = []\\n vowel_flag = False\\n\\n for tier_number, tier_name in textgrid.get_tier_name_num():\\n\\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\\n raw_interval_seq_list = [[]]\\n\\n # Splitting interval sequence on empty intervals.\\n\\n for raw_index, interval in enumerate(raw_interval_list):\\n\\n if len(interval[2].strip()) <= 0:\\n if len(raw_interval_seq_list[-1]) > 0:\\n raw_interval_seq_list.append([])\\n\\n else:\\n raw_interval_seq_list[-1].append((raw_index, interval))\\n\\n if len(raw_interval_seq_list[-1]) <= 0:\\n del raw_interval_seq_list[-1]\\n\\n # Selecting interval sequences for analysis, checking if we have unusual markup.\\n \\n interval_seq_list = []\\n interval_idx_to_raw_idx = dict()\\n\\n unusual_markup_flag = False\\n unusual_markup_list = []\\n\\n for raw_interval_seq in raw_interval_seq_list:\\n\\n interval_seq_list.append([])\\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\\n\\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\\n\\n interval_text = interval[2].strip()\\n\\n # Accepting interval if its text contains at least one vowel, and is short enough or\\n # is a valid phonetic transcription.\\n\\n transcription_check = re.fullmatch(transcription_re, interval_text)\\n\\n if (len(interval_text) > 0 and\\n any(character in vowel_set for character in interval_text) and\\n (len(interval_text) <= 2 or transcription_check)):\\n\\n interval_seq_list[-1].append(interval)\\n\\n sequence_index = len(interval_seq_list) - 1\\n interval_index = len(interval_seq_list[-1]) - 1\\n\\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\\n\\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\\n\\n elif not transcription_check:\\n\\n unusual_markup_flag = True\\n unusual_markup_list.append((raw_index, interval))\\n\\n transcription_list = [text for begin, end, text in raw_interval_list]\\n transcription = ''.join(transcription_list)\\n\\n selected_list = [text\\n for interval_list in interval_seq_list\\n for begin, end, text in interval_list]\\n\\n selected = ''.join(selected_list)\\n\\n # If we have intervals with unusual markup, we report them.\\n\\n if unusual_markup_flag:\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' has interval(s) with unusual transcription text: '\\n '{9} / {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\\n\\n # If the markup does not have any vowels, we note it and also report it.\\n\\n if all(character not in vowel_set for character in transcription):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' doesn\\\\'t have any vowel markup: {9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription_list))\\n\\n # It is also possible that while full transcription has vowels, intervals selected for\\n # analysis do not. In that case we also note it and report it.\\n\\n elif not any(character in vowel_set for character in selected):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' intervals to be processed don\\\\'t have any vowel markup: '\\n 'markup {9}, selected {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name,\\n transcription_list, selected_list))\\n\\n # Otherwise we store tier data to be used during processing of the sound file.\\n\\n else:\\n tier_data_list.append((tier_number, tier_name,\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\\n interval_idx_to_raw_idx, transcription)))\\n\\n vowel_flag = True\\n\\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\\n\\n if not vowel_flag:\\n\\n CACHE.set(cache_key, 'no_vowel')\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\\n\\n sound = None\\n with tempfile.NamedTemporaryFile() as temp_file:\\n\\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\\n temp_file.write(sound_file.read())\\n temp_file.flush()\\n\\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\\n\\n tier_result_list = []\\n\\n for tier_number, tier_name, tier_data in tier_data_list:\\n\\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\\n tier_result_list.append((tier_number, tier_name, tier_data))\\n continue\\n\\n # Analyzing vowel sounds of each interval sequence.\\n\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\\n transcription) = tier_data\\n\\n tier_result_list.append((tier_number, tier_name, []))\\n\\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\\n raw_interval_seq_list, interval_seq_list)):\\n\\n if len(interval_list) <= 0:\\n continue\\n\\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\\\\n find_max_interval_praat(sound, interval_list)\\n\\n max_intensity_interval = interval_list[max_intensity_index]\\n max_length_interval = interval_list[max_length_index]\\n\\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\\n\\n # Compiling results.\\n\\n max_length_str = '{0} {1:.3f} [{2}]'.format(\\n max_length_interval[2], max_length,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\\n\\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\\n max_intensity_interval[2],\\n max_intensity,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\\n\\n tier_result_list[-1][2].append([\\n ''.join(text for index, (begin, end, text) in raw_interval_list),\\n max_length_str,\\n '{0:.3f}'.format(max_length_f1_f2[0]),\\n '{0:.3f}'.format(max_length_f1_f2[1]),\\n max_intensity_str,\\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\\n '+' if max_intensity_index == max_length_index else '-'])\\n\\n # Saving result.\\n\\n result_list.append(tier_result_list)\\n CACHE.set(cache_key, tier_result_list)\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\\n '\\\\n{7}\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url, result_string))\\n\\n # Stopping earlier, if required.\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n except Exception as exception:\\n\\n #\\n # NOTE\\n #\\n # Exceptional situations encountered so far:\\n #\\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\\n #\\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\\n #\\n # 2. Markup for one of the intervals contains a newline \\\"\\\\n\\\", and pympi fails to parse it.\\n # Praat parses such files without problems.\\n #\\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\\n #\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'exception\\\\n{7}\\\\n{8}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url))\\n\\n # if we encountered an exception, we show its info and remember not to try offending\\n # sound/markup pair again.\\n\\n traceback_string = ''.join(traceback.format_exception(\\n exception, exception, exception.__traceback__))[:-1]\\n\\n log.debug(traceback_string)\\n\\n CACHE.set(cache_key, ('exception', exception,\\n traceback_string.replace('Traceback', 'CACHEd traceback')))\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\\n perspective_cid, perspective_oid,\\n len(result_list), '' if len(result_list) == 1 else 's',\\n no_vowel_counter, exception_counter))\\n\\n # If we have no results, we indicate the situation and also show number of failures and number of\\n # markups with no vowels.\\n\\n if not result_list:\\n request.response.status = HTTPPreconditionFailed.code\\n\\n return {\\n \\\"error\\\": \\\"no markups for this query\\\",\\n \\\"exception_counter\\\": exception_counter,\\n \\\"no_vowel_counter\\\": no_vowel_counter}\\n\\n # Otherwise we create and then serve Excel file.\\n\\n excel_book = xlwt.Workbook(encoding = \\\"utf-8\\\")\\n sheet = excel_book.add_sheet(\\\"Sheet 1\\\")\\n\\n sheet.write(0, 0, 'Transcription')\\n sheet.write(0, 1, 'Longest (seconds) interval')\\n sheet.write(0, 2, 'F1 (Hz)')\\n sheet.write(0, 3, 'F2 (Hz)')\\n sheet.write(0, 4, 'Highest intensity (dB) interval')\\n sheet.write(0, 5, 'F1 (Hz)')\\n sheet.write(0, 6, 'F2 (Hz)')\\n sheet.write(0, 7, 'Coincidence')\\n\\n row_counter = 1\\n\\n for tier_result_list in result_list:\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\\n\\n if tier_result_seq_list == 'no_vowel':\\n continue\\n\\n for tier_data in tier_result_seq_list:\\n for index, tier_data_str in enumerate(tier_data):\\n sheet.write(row_counter, index, tier_data_str)\\n\\n row_counter += 1\\n\\n # Formatting column widths.\\n\\n sheet.col(0).width = 24 * 256\\n sheet.col(1).width = 24 * 256\\n sheet.col(2).width = 12 * 256\\n sheet.col(3).width = 12 * 256\\n sheet.col(4).width = 24 * 256\\n sheet.col(5).width = 12 * 256\\n sheet.col(6).width = 12 * 256\\n sheet.col(7).width = 12 * 256\\n\\n excel_stream = io.BytesIO()\\n excel_book.save(excel_stream)\\n excel_stream.seek(0)\\n\\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\\n # content-type.\\n\\n response = Response(content_type = 'application/vnd.ms-excel')\\n\\n response.app_iter = FileIter(excel_stream)\\n response.headers['Content-Disposition'] = \\\"attachment; filename=phonology.xls\\\"\\n\\n return response\",\n \"def get_o(self):\\n return self.o\",\n \"def __repr__(self):\\n return \\\"{} ({}) <{}:{} pheno: {} study: {}\\\".format(\\n self.id, self.snpid, self.chrom, self.pos, self.phenotype_desc,\\n self.study.title\\n )\",\n \"def object(self):\",\n \"def __str__(self):\\r\\n\\r\\n return 'Perspective(%s)' % self.id\",\n \"def clone(self, *args):\\n return _SALOMERuntime.InputStudyPort_clone(self, *args)\",\n \"def get_main_object(tc):\\n return Daal(tc)\",\n \"def get_scn_obs_date(self, unq_id):\\n import copy\\n logger.debug(\\\"Creating Database Engine and Session.\\\")\\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\\n ses = session_sqlalc()\\n logger.debug(\\\"Perform query to find scene.\\\")\\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id).all()\\n ses.close()\\n scn_record = None\\n if query_result is not None:\\n if len(query_result) == 1:\\n scn_record = query_result[0]\\n else:\\n logger.error(\\n \\\"PID {0} has returned more than 1 scene - must be unique something really wrong.\\\".format(\\n unq_id))\\n raise EODataDownException(\\n \\\"There was more than 1 scene which has been found - something has gone really wrong!\\\")\\n else:\\n logger.error(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n raise EODataDownException(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n return copy.copy(scn_record.BeginPosition)\",\n \"def create_isa_study(brapi_study_id):\\n brapi_study = get_brapi_study(brapi_study_id)\\n this_study = Study(filename=\\\"s_\\\" + str(brapi_study_id) + \\\".txt\\\")\\n this_study.identifier = brapi_study['studyDbId']\\n if 'name' in brapi_study:\\n this_study.title = brapi_study['name']\\n elif 'studyName' in brapi_study:\\n this_study.title = brapi_study['studyName']\\n\\n this_study.comments.append(Comment(name=\\\"Study Start Date\\\", value=brapi_study['startDate']))\\n this_study.comments.append(Comment(name=\\\"Study End Date\\\", value=brapi_study['endDate']))\\n if brapi_study['location'] is not None and brapi_study['location']['name'] is not None :\\n this_study.comments.append(Comment(name=\\\"Study Geographical Location\\\",\\n value=brapi_study['location']['name']))\\n else:\\n this_study.comments.append(Comment(name=\\\"Study Geographical Location\\\",value=\\\"\\\"))\\n\\n study_design = brapi_study['studyType']\\n oa_st_design = OntologyAnnotation(term=study_design)\\n this_study.design_descriptors = [oa_st_design]\\n\\n oref_tt = OntologySource(name=\\\"OBI\\\", description=\\\"Ontology for Biomedical Investigation\\\")\\n oa_tt = OntologyAnnotation(term=\\\"phenotyping\\\", term_accession=\\\"\\\", term_source=oref_tt)\\n oref_mt = OntologySource(name=\\\"OBI\\\", description=\\\"Ontology for Biomedical Investigation\\\")\\n oa_mt = OntologyAnnotation(term=\\\"multi-technology\\\", term_accession=\\\"\\\", term_source=oref_mt)\\n isa_assay_file = \\\"a_\\\" + str(brapi_study_id) + \\\".txt\\\"\\n this_assay = Assay(measurement_type=oa_tt, technology_type=oa_mt, filename=isa_assay_file)\\n this_study.assays.append(this_assay)\\n\\n return this_study\",\n \"def get_study_info(self,std_id):\\n raise NotImplementedError\",\n \"def get_queryset(self):\\n return Objective.objects.order_by('perspective')\",\n \"def example_clinical_data(study_name, environment):\\n\\n odm = ODM(\\\"test system\\\")(\\n ClinicalData(\\\"Mediflex\\\", \\\"DEV\\\")(\\n SubjectData(\\\"MDSOL\\\", \\\"IJS TEST4\\\", transaction_type=\\\"Insert\\\")(\\n StudyEventData(\\\"SUBJECT\\\")(\\n FormData(\\\"EN\\\", transaction_type=\\\"Update\\\")(\\n # Although Signature is ODM1.3.1 RWS does not support it inbound currently\\n # RWSBuilders do support outbound generation of Signature at FormData level\\n # Signature()(\\n # UserRef(\\\"isparks\\\"),\\n # LocationRef(\\\"MDSOL\\\"),\\n # SignatureRef(\\\"APPROVED\\\"),\\n # DateTimeStamp(datetime(2015, 9, 11, 10, 15, 22, 80))\\n # ),\\n ItemGroupData()(\\n ItemData(\\\"SUBJINIT\\\", \\\"AAA\\\")(\\n AuditRecord(edit_point=AuditRecord.EDIT_DATA_MANAGEMENT,\\n used_imputation_method= False,\\n identifier='X2011',\\n include_file_oid=False)(\\n UserRef(\\\"isparks\\\"),\\n LocationRef(\\\"MDSOL\\\"),\\n ReasonForChange(\\\"Data Entry Error\\\"),\\n DateTimeStamp(datetime(2015, 9, 11, 10, 15, 22, 80))\\n ),\\n MdsolQuery(value=\\\"Subject initials should be 2 chars only.\\\", recipient=\\\"Site from System\\\",\\n status=QueryStatusType.Open)\\n ),\\n ItemData(\\\"SUBJID\\\", '001')\\n )\\n )\\n )\\n )\\n )\\n )\\n return odm\",\n \"def __repr__(self):\\n return f'ResidenciaModel(name={self.neighbourhood_group}, neighbourhood={self.room_type})'\",\n \"def ortho(self):\\r\\n\\r\\n m11,m12,m13,m14,m21,m22,m23,m24,m31,m32,m33,m34,m41,m42,m43,m44 = self.mlist\\r\\n\\r\\n x = _vec3(m11, m21, m31)\\r\\n y = _vec3(m12, m22, m32)\\r\\n z = _vec3(m13, m23, m33)\\r\\n\\r\\n xl = x.length()\\r\\n xl*=xl\\r\\n y = y - ((x*y)/xl)*x\\r\\n z = z - ((x*z)/xl)*x\\r\\n\\r\\n yl = y.length()\\r\\n yl*=yl\\r\\n z = z - ((y*z)/yl)*y\\r\\n\\r\\n return mat4( x.x, y.x, z.x, m14,\\r\\n x.y, y.y, z.y, m24,\\r\\n x.z, y.z, z.z, m34,\\r\\n m41, m42, m43, m44)\",\n \"def search_research_studies_with_observations():\\n return ResearchStudy.where(struct={}).include('focus', Observation, reverse=True)\",\n \"def get_objectives(self):\\n return copy.deepcopy(self.objectives), self.gates_names\",\n \"def get_scn_record(self, unq_id):\\n logger.debug(\\\"Creating Database Engine and Session.\\\")\\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\\n ses = session_sqlalc()\\n\\n logger.debug(\\\"Perform query to find scene.\\\")\\n query_result = ses.query(EDDSentinel1ASF).filter(EDDSentinel1ASF.PID == unq_id).all()\\n ses.close()\\n scn_record = None\\n if query_result is not None:\\n if len(query_result) == 1:\\n scn_record = query_result[0]\\n else:\\n logger.error(\\n \\\"PID {0} has returned more than 1 scene - must be unique something really wrong.\\\".format(unq_id))\\n raise EODataDownException(\\n \\\"There was more than 1 scene which has been found - something has gone really wrong!\\\")\\n else:\\n logger.error(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n raise EODataDownException(\\\"PID {0} has not returned a scene - check inputs.\\\".format(unq_id))\\n return scn_record\",\n \"def get_full_article(self):\\n raise NotImplementedError\",\n \"def get_rms(self):\\r\\n return self.rms.copy()\",\n \"def substantiate():\",\n \"def __init__(self, name=None, dss=28, date=None, project='SolarPatrol'):\\n self.logger = logging.getLogger(logger.name+\\\".Observation\\\")\\n DR.Observation.__init__(self, name=date, date=date, dss=dss, \\n project=project)\\n self.extended_init()\\n \\n #self.obs =Astronomy.Ephem.DSS(dss)\\n #y,d = date.split('/')\\n #self.year = int(y); self.DOY = int(d)\\n #projdatapath, self.sessionpath, rawdatapath = \\\\\\n # DR.get_obs_dirs(project, dss, self.year, self.DOY,\\n # datafmt=None)\",\n \"def create_general_object(self, x, y):\\n return self.img[y-self.rad:y+self.rad, x-self.rad:x+self.rad]\",\n \"def projection(self):\\n pass\",\n \"def init_od_sr(state_dict: Dict) -> SpectralResidual:\\n od = SpectralResidual(threshold=state_dict['threshold'],\\n window_amp=state_dict['window_amp'],\\n window_local=state_dict['window_local'],\\n n_est_points=state_dict['n_est_points'],\\n n_grad_points=state_dict['n_grad_points'])\\n return od\",\n \"def orthopyroxene():\\n\\n rho = 3304.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 236.9; C[0,1] = 79.6; C[0,2] = 63.2; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 180.5; C[1,2] = 56.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 84.3; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 79.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 80.1\\n\\n return C, rho\",\n \"def o(self, otra):\\n return OEspecificacion(self, otra)\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def create_scene(self):\\n \\n self.scene=soya.World()\",\n \"def gen_obs(self):\\n\\n #grid, vis_mask = self.gen_obs_grid()\\n\\n # Encode the partially observable view into a numpy array\\n image = self.grid.encode(self.agent_pos,self.drone_pos)\\n\\n #assert hasattr(self, 'mission'), \\\"environments must define a textual mission string\\\"\\n\\n # Observations are dictionaries containing:\\n # - an image (partially observable view of the environment)\\n # - the agent's direction/orientation (acting as a compass)\\n # - a textual mission string (instructions for the agent)\\n obs = {\\n 'image': image,\\n #'direction': self.agent_dir,\\n 'mission': self.mission\\n }\\n obs=image\\n #print(obs.shape)\\n return self.render(mode='rgb_array')\\n #return obs\",\n \"def get_mapping(self, type = 'orthogonal', anchor_method = 'mutual_nn', max_anchors = None):\\n # Method 1: Orthogonal projection that best macthes NN\\n self.compute_scores(score_type='coupling') # TO refresh\\n if anchor_method == 'mutual_nn':\\n pseudo = self.find_mutual_nn()#[:100]\\n elif anchor_method == 'all':\\n translations, oov = self.generate_translations()\\n pseudo = [(k,v[0]) for k,v in translations.items()]\\n if max_anchors:\\n pseudo = pseudo[:max_anchors]\\n print('Finding orthogonal mapping with {} anchor points via {}'.format(len(pseudo), anchor_method))\\n if anchor_method in ['mutual_nn', 'all']:\\n idx_src = [self.src_word2ind[ws] for ws,_ in pseudo]\\n idx_trg = [self.trg_word2ind[wt] for _,wt in pseudo]\\n xs_nn = self.xs[idx_src]\\n xt_nn = self.xt[idx_trg]\\n P = orth_procrustes(xs_nn, xt_nn)\\n elif anchor_method == 'barycenter':\\n ot_emd = ot.da.EMDTransport()\\n ot_emd.xs_ = self.xs\\n ot_emd.xt_ = self.xt\\n ot_emd.coupling_= self.coupling\\n xt_hat = ot_emd.inverse_transform(Xt=self.xt) # Maps target to source space\\n P = orth_procrustes(xt_hat, self.xt)\\n return P\",\n \"def getScene():\\n #print \\\"servers direct scenes are \\\",soya.IDLER.scenes[:]\\n \\n return soya.IDLER.scenes[0]\",\n \"def get_modeller_objects(self):\\n return self._modpt, self.__edat.modpt, self.__libs.modpt\",\n \"def get_representative_data_object(self, obj):\\n if self.dim == 0:\\n # In this way, obj can be a data object and this class can be\\n # used even if the assignment is not between \\\"flattened components\\\"\\n return obj\\n else:\\n nominal_index = self.nominal_index\\n return obj[nominal_index]\",\n \"def getAuthoredFromSymplectic(researcher_object):\\r\\n #description\\r\\n #checking\\r\\n # if not(researcher_object) or (researcher_object.symplectic_int_id is None): # int_id version\\r\\n if not(researcher_object) or (researcher_object.symplectic_id is None): # guid version\\r\\n return\\r\\n #symplectic api url and local file path\\r\\n # url = SYMPLECTIC_API_URL + 'users/' + str(researcher_object.symplectic_int_id) # int_id version\\r\\n url = SYMPLECTIC_API_URL + 'users/' + str(researcher_object.symplectic_id) # guid version\\r\\n # tmp_filename = SYMPLECTIC_LOCAL_XML_FOLDER + SYMPLECTIC_LOCAL_AUTH_FOLDER + str(researcher_object.symplectic_int_id) + '.xml' # int_id version\\r\\n tmp_filename = SYMPLECTIC_LOCAL_XML_FOLDER + SYMPLECTIC_LOCAL_AUTH_FOLDER + str(researcher_object.symplectic_id) + '.xml' # guid version\\r\\n #get xml document from symplectic api and store on hd\\r\\n (tmp_filename, http_headers,) = urllib.urlretrieve(url, tmp_filename)\\r\\n #parse xml file\\r\\n publications_etree = ElementTree(file=tmp_filename)\\r\\n #delete local file from hd\\r\\n #try:\\r\\n os.remove(tmp_filename)\\r\\n #except:\\r\\n #pass \\r\\n #publication elements are held in a subtree\\r\\n publications_subtree = publications_etree.find(SYMPLECTIC_NAMESPACE + 'publications')\\r\\n #check if any publication elements in subtree\\r\\n if publications_subtree is None or len(publications_subtree) < 1:\\r\\n return\\r\\n #now that we have their newest \\\"i authored that pub\\\" info, we can delete their old \\\"i authored that pub\\\" info\\r\\n researcher_object.remove_all_authored() \\r\\n #for each publication element in subtree\\r\\n for publication_element in publications_subtree.getchildren():\\r\\n SymplecticXMLAuthored.__createAuthoredObjFromUserPubElement(publication_element, researcher_object)\",\n \"def composeWorkplaceOntology():\\n\\n import ossPyFuncs \\n import pandas as pd\\n \\n #mysql query to extract full table from government organizations\\n #certian table columns feature capital letters which cases uproblems\\n postgreSql_selectQuery=\\\"SELECT * FROM us_gov_manual.us_govman_2019 ;\\\"\\n #pass querry and obtain table\\n govTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n\\n #mysql query to obtain academic instutions\\n postgreSql_selectQuery=\\\"SELECT institution FROM hipolabs.universities ;\\\"\\n #pass querry and obtain table\\n univTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2018_us1000;\\\"\\n businesses1=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2019_us1000;\\\"\\n businesses2=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2020_global2000;\\\"\\n businesses3=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n\\n #combine theinsitutions into a vector\\n combinedSeries=[govTable['AgencyName'],univTable['institution'],businesses1['company'],businesses2['company'],businesses3['company']]\\n #turn the multi item vector into a single series\\n fullWordbank=pd.concat(combinedSeries)\\n #turn that series into a pd dataframe\\n wordbankTable=pd.DataFrame(fullWordbank.unique())\\n\\n return wordbankTable\",\n \"def _orthogonal_init(self):\\n # if is a conv layer, will need to reshape to fan in matrix,\\n # which is of dimension\\n # num input feature maps * filter height * filter width\\n if(len(self.dims) > 2):\\n rv_samp = np.random.randn(self.dims[2],\\n self.dims[0] * self.dims[1] * self.dims[3])\\n out_sigma = np.sqrt(1.0 / rv_samp.shape[1])\\n # otherwise will be a densely connected layer\\n else:\\n rv_samp = np.random.randn(self.dims[0], self.dims[1])\\n out_sigma = np.sqrt(1.0 / rv_samp.shape[0])\\n # perform SVD\\n U, _, V = np.linalg.svd(rv_samp, full_matrices=False) #pylint: disable=invalid-name\\n # both U and V are orthoginal matricies, so will choose the one\\n # that is the correct dimensions for our layer\\n ortho_matrix = U if U.shape == rv_samp.shape else V\\n # rescale so it is unit variance for each vector\\n # print(\\\"std(q) = {}\\\".format(np.std(q)))\\n ortho_norm = (ortho_matrix / np.std(ortho_matrix)) * out_sigma\\n #print(\\\"std(qs) = {}\\\".format(np.std(qs)))\\n #print(q.shape)\\n return ortho_norm.reshape(self.dims).astype(np.float32)\",\n \"def get_final_reconstruction(self):\",\n \"def om(self):\\n return self._om\",\n \"def _get_observation(self, unseen=False):\\n img_arr = p.getCameraImage(width=self._width,\\n height=self._height,\\n viewMatrix=self._view_matrix,\\n projectionMatrix=self._proj_matrix)\\n rgb = img_arr[2]\\n depth = img_arr[3]\\n min = 0.97\\n max=1.0\\n segmentation = img_arr[4]\\n depth = np.reshape(depth, (self._height, self._width,1) )\\n segmentation = np.reshape(segmentation, (self._height, self._width,1) )\\n\\n np_img_arr = np.reshape(rgb, (self._height, self._width, 4))\\n np_img_arr = np_img_arr[:, :, :3].astype(np.float64)\\n\\n view_mat = np.asarray(self._view_matrix).reshape(4, 4)\\n proj_mat = np.asarray(self._proj_matrix).reshape(4, 4)\\n # pos = np.reshape(np.asarray(list(p.getBasePositionAndOrientation(self._objectUids[0])[0])+[1]), (4, 1))\\n\\n AABBs = np.zeros((len(self._objectUids), 2, 3))\\n cls_ls = []\\n \\n for i, (_uid, _cls) in enumerate(zip(self._objectUids, self._objectClasses)):\\n AABBs[i] = np.asarray(p.getAABB(_uid)).reshape(2, 3)\\n cls_ls.append(NAME2IDX[_cls])\\n\\n # np.save('/home/tony/Desktop/obj_save/view_mat_'+str(self.img_save_cnt), view_mat)\\n # np.save('/home/tony/Desktop/obj_save/proj_mat_'+str(self.img_save_cnt), proj_mat)\\n # np.save('/home/tony/Desktop/obj_save/img_'+str(self.img_save_cnt), np_img_arr.astype(np.int16))\\n # np.save('/home/tony/Desktop/obj_save/AABB_'+str(self.img_save_cnt), AABBs)\\n # np.save('/home/tony/Desktop/obj_save/class_'+str(self.img_save_cnt), np.array(cls_ls))\\n\\n np.save(OUTPUT_DIR + '/image_' + str(self.img_save_cnt), np_img_arr.astype(np.int16))\\n dets = np.zeros((AABBs.shape[0], 5))\\n for i in range(AABBs.shape[0]):\\n dets[i, :4] = self.get_2d_bbox(AABBs[i], view_mat, proj_mat, IM_HEIGHT, IM_WIDTH)\\n dets[i, 4] = int(cls_ls[i])\\n np.save(OUTPUT_DIR + '/annotation_'+str(self.img_save_cnt), dets)\\n\\n test = np.concatenate([np_img_arr[:, :, 0:2], segmentation], axis=-1)\\n\\n return test\",\n \"def make_studyforrest_mockup(path):\\n public = create(opj(path, 'public'), description=\\\"umbrella dataset\\\")\\n # the following tries to capture the evolution of the project\\n phase1 = public.create('phase1',\\n description='old-style, no connection to RAW')\\n structural = public.create('structural', description='anatomy')\\n tnt = public.create('tnt', description='image templates')\\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\\n aligned = public.create('aligned', description='aligned image data')\\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # new acquisition\\n labet = create(opj(path, 'private', 'labet'), description=\\\"raw data ET\\\")\\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\\\"raw data P2MRI\\\")\\n phase2 = public.create('phase2',\\n description='new-style, RAW connection')\\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\\n # add to derivatives\\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n # never to be published media files\\n media = create(opj(path, 'private', 'media'), description=\\\"raw data ET\\\")\\n # assuming all annotations are in one dataset (in reality this is also\\n # a superdatasets with about 10 subdatasets\\n annot = public.create('annotations', description='stimulus annotation')\\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\\n # a few typical analysis datasets\\n # (just doing 3, actual status quo is just shy of 10)\\n # and also the real goal -> meta analysis\\n metaanalysis = public.create('metaanalysis', description=\\\"analysis of analyses\\\")\\n for i in range(1, 3):\\n ana = public.create('analysis{}'.format(i),\\n description='analysis{}'.format(i))\\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # link to metaanalysis\\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\\n reckless='auto')\\n # simulate change in an input (but not raw) dataset\\n create_tree(\\n aligned.path,\\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\\n aligned.save()\\n # finally aggregate data\\n aggregate = public.create('aggregate', description='aggregate data')\\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n # the toplevel dataset is intentionally left dirty, to reflect the\\n # most likely condition for the joint dataset to be in at any given\\n # point in time\",\n \"def orthographic_matrix(self) -> TransformationMatrixType:\\n near, far = self._clipping[self.projection_mode.value]\\n return orthographic_matrix(self.fov, self.aspect_ratio, near, far)\",\n \"def __init__(self, parent):\\n super(Demo4, self).__init__(parent)\\n self.scenes = []\\n self.draw_axes = True\\n self.lighting = True\\n self.current_scene = 0\\n self.objects = []\\n self.diffuse_light = [0.8, 0.8, 0.8, 1]\",\n \"def __init__(self):\\n self.name = \\\"Schaffer\\\"\\n objectives = [o_sh_1, o_sh_2]\\n decisions = [Decision(-10 ** 5, 10 ** 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def __createAuthoredObjFromUserPubElement(publication_element, researcher_object):\\r\\n #description\\r\\n #++++++PUBLICATION LITE++++++\\r\\n #check publication Element\\r\\n if publication_element is None:\\r\\n return \\r\\n #publication guid\\r\\n if publication_element is not None:\\r\\n guid = publication_element.get('id','')\\r\\n if guid == '':\\r\\n return\\r\\n #load Publication from db or create (flagged as needing refetch from symplectic) if doesnt exist\\r\\n publication_object = Publication.getOrCreatePublication(guid) \\r\\n #++++++BIBLIOGRAPHICRECORD LITE++++++\\r\\n #bibliographic-record element -> publication sub element (used to read XML)\\r\\n if publication_element is not None:\\r\\n #only ONE biblio element per publication will be returned when querying by User_id\\r\\n # this is in contrast to the multiple biblios elements per publication returned when querying by a Publication_guid\\r\\n biblio_element = publication_element.find(SYMPLECTIC_NAMESPACE + 'bibliographic-record')\\r\\n #biblio data-source\\r\\n if biblio_element is not None:\\r\\n data_source = biblio_element.get('data-source','')\\r\\n #load BibliographicRecord from db or create if doesnt exist (NB links biblio & publication)\\r\\n biblio_object = BibliographicRecord.getOrCreateBibliographicRecord(publication_object, data_source) \\r\\n #++++++AUTHORED++++++\\r\\n #authored preferences -> publication sub-elements (used to read XML)\\r\\n if publication_element is not None:\\r\\n preferences_element = publication_element.find(SYMPLECTIC_NAMESPACE + 'preferences-for-this-publication')\\r\\n #load Authored from db or create if doesnt exist (NB links authored & publication & researcher & bibliographic-record)\\r\\n authored_object = Authored.getOrCreateAuthored(publication_object, researcher_object, biblio_object)\\r\\n #preferences\\r\\n if preferences_element is not None:\\r\\n #Show this publication\\r\\n if preferences_element.get('visible','false') == 'true':\\r\\n authored_object.visible = True\\r\\n else:\\r\\n authored_object.visible = False\\r\\n #Favourite publication\\r\\n if preferences_element.get('is-a-favourite','false') == 'true':\\r\\n authored_object.is_a_favourite = True\\r\\n else:\\r\\n authored_object.is_a_favourite = False\\r\\n #Display order\\r\\n authored_object.reverse_sort_cue = preferences_element.get('reverse-sort-cue','')\\r\\n #Save Authored\\r\\n authored_object.save()\",\n \"def analysis(self):\\r\\n return analysis.Analysis(self.parent, self.object_id)\",\n \"def _make(self):\\n\\t\\tself.scene.camera = self.camera\\n\\t\\tself.camera.rotation_euler[0] = np.radians(np.random.randint(40, 100) +\\n\\t\\t np.random.random())\\n\\t\\tself.camera.rotation_euler[2] = np.radians(np.random.randint(0, 360) +\\n\\t\\t np.random.random())\\n\\t\\tprint([np.degrees(x) for x in self.camera.rotation_euler])\",\n \"def visible_objects_and_duplis():\\n\\n for obj in context.visible_objects:\\n if obj.type == 'MESH':\\n if obj.modeling_cloth: \\n yield (obj, obj.matrix_world.copy())\",\n \"def visible_objects_and_duplis():\\n\\n for obj in context.visible_objects:\\n if obj.type == 'MESH':\\n if obj.modeling_cloth: \\n yield (obj, obj.matrix_world.copy())\",\n \"def test_to_from_scene(self): # pragma: lpy\\n super(TestObjDict, self).test_to_from_scene(_as_obj=True)\",\n \"def associated_coroot(self):\",\n \"def __init__(self, before, after):\\r\\n self.M = cv2.getPerspectiveTransform(before, after)\\r\\n self.inverse_M = cv2.getPerspectiveTransform(after, before)\",\n \"def topol (self):\\n\\n # child.connect(parent, parent_end, {child_start=0})\\n # Distal (apical)\\n self.dends['apical_trunk'].connect(self.soma, 1, 0)\\n self.dends['apical_1'].connect(self.dends['apical_trunk'], 1, 0)\\n self.dends['apical_2'].connect(self.dends['apical_1'], 1, 0)\\n self.dends['apical_tuft'].connect(self.dends['apical_2'], 1, 0)\\n\\n # apical_oblique comes off distal end of apical_trunk\\n self.dends['apical_oblique'].connect(self.dends['apical_trunk'], 1, 0)\\n\\n # Proximal (basal)\\n self.dends['basal_1'].connect(self.soma, 0, 0)\\n self.dends['basal_2'].connect(self.dends['basal_1'], 1, 0)\\n self.dends['basal_3'].connect(self.dends['basal_1'], 1, 0)\\n\\n self.basic_shape() # translated from original hoc (2009 model)\",\n \"def qa_tractography(stream_path,qa_out_path,brain_path):\\n \\n #Use window to visualize the streamlines\\n r = window.renderer()\\n \\n #Load the streamline.trk file\\n streamlines_mni_load = nib.streamlines.load(stream_path).streamlines\\n streamlines_mni_in = Streamlines(streamlines_mni_load)\\n streamlines_actor = actor.line(\\n streamlines_mni_in,\\n colormap.line_colors(streamlines_mni_in),\\n lod_points=10000,\\n depth_cue=True,\\n linewidth=0.2,\\n fake_tube=True,\\n opacity=0.3,\\n )\\n \\n r.add(streamlines_actor)\\n #window.show(r)\\n \\n showmng = window.ShowManager(r)\\n #window.record function can rotate the 3D-image, then get the snapshot of the specific angle. \\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,1,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Z_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (1,0,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Y_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,0,1),path_numbering= True,out_path = qa_out_path + '/Rotate_X_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n showmng.exit()\\n combine_plot(qa_out_path,brain_path)\",\n \"def create_antipodal_grasp_set(object):\\n gripper_model = burg.gripper.Robotiq2F85()\\n\\n ags = burg.sampling.AntipodalGraspSampler()\\n ags.only_grasp_from_above = True\\n ags.mesh = object.object_type.mesh\\n ags.gripper = gripper_model\\n ags.n_orientations = 10\\n ags.verbose = True\\n ags.max_targets_per_ref_point = 2\\n ags.no_contact_below_z = 0.01\\n graspset, contacts, normals, approach_vectors = ags.sample(100)\\n # gs.scores = ags.check_collisions(gs, use_width=False) # need to install python-fcl\\n print('contacts.shape', contacts.shape)\\n #burg.visualization.show_grasp_set([ags.mesh], graspset, gripper=gripper_model, use_width=False,\\n # score_color_func=lambda s: [s, 1-s, 0], with_plane=True)\\n \\n \\\"\\\"\\\" print(\\\"graspset :\\\", len(graspset))\\n print(\\\"contacts :\\\", contacts.shape)\\n print(\\\"normales :\\\", normals.shape)\\n print(\\\"angles :\\\", angles.shape) \\\"\\\"\\\"\\n\\n return graspset, contacts, normals, approach_vectors\",\n \"def get_standardarticle(self):\\n return self._standardarticle\",\n \"def topdia(x):\\r\\n return Feature(x, \\\"TopDia\\\")\",\n \"def visible_objects_and_duplis():\\r\\n \\r\\n for obj in context.visible_objects:\\r\\n if obj.type == 'MESH':\\r\\n yield (obj, obj.matrix_world.copy())\\r\\n \\r\\n if obj.dupli_type != 'NONE':\\r\\n obj.dupli_list_create(scene)\\r\\n for dob in obj.dupli_list:\\r\\n obj_dupli = dob.object\\r\\n if obj_dupli.type == 'MESH':\\r\\n yield (obj_dupli, dob.matrix.copy())\\r\\n \\r\\n obj.dupli_list_clear()\",\n \"def __init__(self):\\n self.name = \\\"Osyczka\\\"\\n objectives = [ob_os_1, ob_os_2]\\n constraints = [con_os_1, con_os_2, con_os_3, con_os_4, con_os_5, con_os_6]\\n decisions = [Decision(0, 10), Decision(0, 10), Decision(1, 5), Decision(0, 6), Decision(1, 5), Decision(0, 10)]\\n Model.__init__(self, objectives, constraints, decisions)\",\n \"def create_observation(self):\",\n \"def create_observation(self):\",\n \"def build_dom():\\n glass_thickness = 10 #mm\\n size = 100 #mm\\n # outside of the glass envelope\\n outside_mesh = make.sphere(size)\\n # inside of the glass envelope\\n inside_mesh = make.sphere(size-glass_thickness)\\n\\n # outside solid with ice on the outside, and glass on the inside\\n outside_solid = Solid(outside_mesh,glass,ice) \\n\\n inside_surface = r7081hqe_photocathode\\n inside_color = 0x00ff00\\n\\n # construct the inside solid\\n inside_solid = Solid(inside_mesh,vacuum,glass,surface=inside_surface,\\n color=inside_color)\\n\\n # you can add solids and meshes!\\n return outside_solid + inside_solid\",\n \"def __init__(self):\\n self.opening_scene = DungeonGate()\\n # this list define the order of scenes in the corridor\\n self.corridor_scenes = [GuardsRoom(), Cell(), Armory(), EmptyRoom(), Dormitory()]\\n shuffle(self.corridor_scenes)\\n self.explored_scenes = {\\n \\\"GuardsRoom\\\": \\\"unexplored\\\",\\n \\\"Cell\\\": \\\"unexplored\\\",\\n \\\"Dormitory\\\": \\\"unexplored\\\",\\n \\\"Armory\\\": \\\"unexplored\\\",\\n \\\"EmptyRoom\\\": \\\"unexplored\\\",\\n \\\"DungeonGate\\\": \\\"unexplored\\\"\\n }\",\n \"def mortality(self):\\n pass\",\n \"def output_projection(self):\\n return self.projection(what='output')\",\n \"def getCriticStation(analyzer):\\n mayIn = model.getRankMay(analyzer,\\\"in\\\")\\n mayOut=model.getRankMay(analyzer,\\\"out\\\")\\n less=model.getRankMen(analyzer,\\\"LessPopular\\\")\\n return (mayIn,mayOut,less)\",\n \"def getOntologyItem(self, resource, oType=0):\\n\\n if isinstance(resource, int):\\n resource = 'ontology/{0}/{1}'.format(resource, oType)\\n\\n res = self.getRequest(resource)\\n onto = vsdModels.Ontology(**res)\\n\\n return onto\",\n \"def lro(self) -> global___Snippet.Lro:\",\n \"def view(self):\\n view = self.empty_like()\\n # Note that this is a shallow copy.\\n view.sects = self.sects.copy()\\n return view\",\n \"def asr_model():\\n\\n asr_model = EncoderDecoderASR.from_hparams(\\n source=\\\"speechbrain/asr-transformer-transformerlm-librispeech\\\"\\n )\\n return asr_model\",\n \"def getRecord(self):\\n logger.debug(\\\"Entering in ocentricWKT.getRecord\\\")\\n\\n\\n record = {\\n 'type':'OCENTRIC', \\n OcentricMetadata.GEO_GCS_NAME.value: self.getGeoGcsName(),\\n OcentricMetadata.DATUM_NAME.value: self.getDatumName(),\\n OcentricMetadata.ELLIPSOIDE_NAME.value: self.getSpheroidName(),\\n OcentricMetadata.RADIUS.value: self.getRadius(),\\n OcentricMetadata.INVERSE_FLATTENING.value: self.getInverseFlattening(),\\n OcentricMetadata.AUTHORITY_NAME.value: self.getAuthorityName(),\\n OcentricMetadata.AUTHORITY_CODE.value: self.getAuthorityCode(), \\n OcentricMetadata.LONGITUDE_ORDER.value: \\\"East\\\"\\n }\\n\\n logger.debug(\\\"Exiting from ocentricWKT.getRecord\\\")\\n return record\",\n \"def to_povray(vis,world,properties={}):\\n #patch on vapory\\n patch_vapory()\\n \\n #camera\\n mat=vis.view.camera.matrix()\\n pos=mat[1]\\n right=mat[0][0:3]\\n up=mat[0][3:6]\\n dir=op.mul(mat[0][6:9],-1)\\n tgt=op.add(mat[1],dir)\\n #scale\\n fovy=vis.view.fov*vis.view.h/vis.view.w\\n fovx=math.atan(vis.view.w*math.tan(fovy*math.pi/360.)/vis.view.h)*360./math.pi\\n right=op.mul(right,-float(vis.view.w)/vis.view.h)\\n #camera\\n camera_params=['orthographic' if vis.view.orthogonal else 'perspective',\\n 'location',[pos[0],pos[1],pos[2]],\\n 'look_at',[tgt[0],tgt[1],tgt[2]],\\n 'right',[right[0],right[1],right[2]],\\n 'up',[up[0],up[1],up[2]],\\n 'angle',fovx,\\n 'sky',get_property(properties,[],\\\"sky\\\",[0.,0.,1.])]\\n camera=vp.Camera(*camera_params)\\n \\n #tempfile\\n tempfile=get_property(properties,[],\\\"tempfile\\\",None)\\n tempfile_path=os.path.dirname(tempfile) if tempfile is not None else '.'\\n if not os.path.exists(tempfile_path):\\n os.mkdir(tempfile_path)\\n \\n #objects\\n objects=[]\\n objs=[o for o in properties[\\\"visualObjects\\\"]] if \\\"visualObjects\\\" in properties else []\\n objs+=[world.terrain(i) for i in range(world.numTerrains())]\\n objs+=[world.rigidObject(i) for i in range(world.numRigidObjects())]\\n for r in range(world.numRobots()):\\n objs+=[world.robot(r).link(i) for i in range(world.robot(r).numLinks())]\\n for obj in objs:\\n transient=get_property(properties,[obj],\\\"transient\\\",default=True)\\n if transient:\\n objects+=geometry_to_povray(obj.appearance(),obj.geometry(),obj,None,properties=properties)\\n else: \\n path=tempfile_path+'/'+obj.getName()+'.pov'\\n if not os.path.exists(path):\\n R,t=obj.geometry().getCurrentTransform()\\n obj.geometry().setCurrentTransform([1,0,0,0,1,0,0,0,1],[0,0,0])\\n geom=geometry_to_povray(obj.appearance(),obj.geometry(),obj,None,properties=properties)\\n if len(geom)>1:\\n file_content=vp.Union(*geom)\\n elif len(geom)>0: \\n file_content=vp.Object(*geom)\\n else: file_content=None\\n if file_content is not None:\\n f=open(path,'w')\\n f.write(str(file_content))\\n f.close()\\n obj.geometry().setCurrentTransform(R,t)\\n else: path=None\\n #include \\n if path is not None:\\n R,t=obj.geometry().getCurrentTransform()\\n objects.append(vp.Object('#include \\\"%s\\\"'%path,\\\"matrix\\\",R+t))\\n \\n #light\\n if \\\"lights\\\" in properties:\\n objects+=properties[\\\"lights\\\"]\\n \\n #scene\\n gsettings=[]\\n scene=vp.Scene(camera=camera,\\n objects=objects,\\n included=get_property(properties,[],\\\"included\\\",[]),\\n global_settings=get_property(properties,[],\\\"global_settings\\\",[]))\\n try:\\n #this works with later version of vapory\\n return \\\\\\n render_povstring(str(scene), \\\\\\n outfile=get_property(properties,[],\\\"outfile\\\",None), \\\\\\n width=vis.view.w,height=vis.view.h, \\\\\\n quality=get_property(properties,[],\\\"quality\\\",None), \\\\\\n antialiasing=get_property(properties,[],\\\"antialiasing\\\",0.3), \\\\\\n remove_temp=get_property(properties,[],\\\"remove_temp\\\",False), \\\\\\n show_window=get_property(properties,[],\\\"show_window\\\",False), \\\\\\n tempfile=tempfile, \\\\\\n includedirs=get_property(properties,[],\\\"includedirs\\\",None), \\\\\\n output_alpha=get_property(properties,[],\\\"output_alpha\\\",True))\\n except:\\n #this works with earlier version of vapory\\n return \\\\\\n render_povstring(str(scene), \\\\\\n outfile=get_property(properties,[],\\\"outfile\\\",None), \\\\\\n width=vis.view.w,height=vis.view.h, \\\\\\n quality=get_property(properties,[],\\\"quality\\\",None), \\\\\\n antialiasing=get_property(properties,[],\\\"antialiasing\\\",0.3), \\\\\\n remove_temp=get_property(properties,[],\\\"remove_temp\\\",False))\",\n \"def __init__(self):\\n self.rho=[]\\n self.te=[]\\n self.ti=[]\\n self.ne=[]\\n self.ni=[]\\n self.ni1=[]\\n self.ni2=[]\\n self.ni3=[]\\n self.vtor=[]\\n self.zeff=[]\\n\\n self.nion=1\\n self.Z=[]\\n self.A=[]\\n self.coll_mode=[]\",\n \"def setup_orographic_enhancement_cube():\\n data = np.array([[1, 1, 1], [0, 1, 0], [0, 0, 0], [0, 0, 0]], dtype=np.float32)\\n orographic_enhancement_cube = set_up_variable_cube(\\n data, name=\\\"orographic_enhancement\\\", units=\\\"mm/hr\\\", spatial_grid=\\\"equalarea\\\"\\n )\\n return orographic_enhancement_cube\",\n \"def orthonormation_method(standardized_methods_cleaned):\\n method_standardized_ortho = standardized_methods_cleaned.copy(deep=True)\\n\\n categories = method_standardized_ortho.columns.tolist()\\n\\n # Normation of the first category\\n method_standardized_ortho[categories[0]] = method_standardized_ortho[categories[0]] / \\\\\\n linalg.norm(method_standardized_ortho[categories[0]])\\n\\n # Normation of every following categories\\n j = 0\\n while j < len(categories):\\n i = 0\\n while i < j:\\n # Calculates the orthogonal projection of j on each i and substraction of the projection from j\\n method_standardized_ortho[categories[j]] = \\\\\\n method_standardized_ortho[categories[j]] - method_standardized_ortho[categories[i]] * (\\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[j]]) /\\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[i]]))\\n if linalg.norm(method_standardized_ortho[categories[j]]) == 0:\\n # If after the projection, if the j columns is null it is droped (i.e it is linearly dependant with\\n # the other columns) and the inner loop stops\\n method_standardized_ortho.drop(method_standardized_ortho.columns[j], inplace=True, axis=1)\\n categories.remove(categories[j])\\n\\n break\\n else:\\n # If the j column is not null, it is normed and the inner while loop keeps going\\n method_standardized_ortho[categories[j]] = method_standardized_ortho[categories[j]] / \\\\\\n (linalg.norm(method_standardized_ortho[categories[j]]))\\n i += 1\\n j += 1\\n\\n return method_standardized_ortho\",\n \"def _get_ortho_path(self, **kwargs) -> Union[CloudPath, Path]:\\n\\n if self.product_type in self._proj_prod_type:\\n ortho_name = f\\\"{self.condensed_name}_ortho.tif\\\"\\n\\n ortho_path, ortho_exists = self._get_out_path(ortho_name)\\n if not ortho_exists:\\n LOGGER.info(\\n \\\"Manually orthorectified stack not given by the user. \\\"\\n \\\"Reprojecting whole stack, this may take a while. \\\"\\n \\\"(May be inaccurate on steep terrain, depending on the DEM resolution)\\\"\\n )\\n\\n # Reproject and write on disk data\\n dem_path = self._get_dem_path(**kwargs)\\n with rasterio.open(str(self._get_tile_path(**kwargs))) as src:\\n\\n out_arr, meta = self._reproject(\\n src.read(), src.meta, src.rpcs, dem_path, **kwargs\\n )\\n rasters_rio.write(out_arr, meta, ortho_path)\\n\\n else:\\n ortho_path = self._get_tile_path(**kwargs)\\n\\n return ortho_path\",\n \"def _get_viewer_container(self):\\n self.viewer = self.traj._tempmol.draw3d(style='licorice')\\n return self.viewer\",\n \"def spatial(self):\",\n \"def perspective(self):\\n return self.container['perspective']\",\n \"def model_cohort(cohort):\\n model_traj = []\\n for part in cohort:\\n for traj in part.trajectories:\\n if traj.filter is True:\\n model_traj.append(\\n modelling.model(x=traj.data.age.tolist(),\\n y=traj.data.AF.tolist(),\\n mutation=traj.mutation,\\n variant_class=traj.variant_class,\\n gene=traj.mutation.split()[0],\\n id=part.id,\\n p_key=traj.p_key))\\n return model_traj\",\n \"def get_contenu(self):\\n return self.objets\",\n \"def __init__(self, por): \\n logger.debug(\\\"Entering in ocentricWKT constructor\\\") \\n super(OcentricWKT, self).__init__(\\n por.getElement(OcentricMetadata.GEO_GCS_NAME),\\n por.getElement(OcentricMetadata.DATUM_NAME),\\n por.getElement(OcentricMetadata.ELLIPSOIDE_NAME),\\n por.getElement(OcentricMetadata.RADIUS),\\n por.getElement(OcentricMetadata.INVERSE_FLATTENING),\\n por.getElement(OcentricMetadata.AUTHORITY_NAME),\\n por.getElement(OcentricMetadata.AUTHORITY_CODE)\\n ) \\n logger.debug(\\\"Exiting from ocentricWKT constructor\\\")\",\n \"def get_study_by_pmid(cls, pmid):\\n\\n print \\\"Found study \\\", pmid\\n study_obj = cls.query.filter(cls.pmid == pmid).first()\\n return study_obj\",\n \"def Omat(self):\\n if self.standard:\\n return np.matrix(((0, -1, 0), (0, 0, 1), (-1, 0, 0)))\\n else:\\n return np.matrix(((0, 0, 1), (0, 1, 0), (-1, 0, 0)))\",\n \"def construct_data_LHS(model,num_pts,seed=None):\\n num_dims = model.num_dims\\n rv_trans = define_random_variable_transformation_hydromad(model)\\n pts = latin_hypercube_design( num_pts, num_dims, seed )\\n # returns points on [0,1] but need pts on [-1,1]\\n pts = 2*pts-1.\\n pts = rv_trans.map_from_canonical_distributions( pts )\\n vals = model.evaluate_set( pts )\\n numpy.savetxt( 'pts.txt', pts, delimiter = ',' )\\n numpy.savetxt( 'vals.txt', vals, delimiter = ',' )\",\n \"def __init__(self, settings, conflict):\\n #generates the initial setting\\n self.start_setting = random.choice(settings)\\n self.current_setting = self.start_setting\\n\\n #gets a random character from possible setting characters as protagonist\\n self.protag = random.choice(self.start_setting.characters)\\n\\n #selects as antagonist a random character that either the protagonist knows or is related\\n #to the setting\\n antaglist = list(set(self.protag.characters).union(self.start_setting.characters))\\n self.antag = random.choice(antaglist)\\n\\n self.antag.set_protagonist(self.protag)\\n self.protag.set_antagonist(self.antag)\\n\\n #lays out the basic story relations\\n self.subsections = []\\n\\n for relation_outline in conflict.conflict_outline: \\n subsection = Subsection() \\n subsection.init_from_outline(relation_outline, self.protag, self.current_setting)\\n self.subsections.append(subsection)\\n\\n #connects the rest of the story together\\n self.details = []\",\n \"def __init__(self):\\n self.name = \\\"Kursawe\\\"\\n objectives = [o_ku_1, o_ku_2]\\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def objects(self):\",\n \"def get_representation(self, reference):\\n \\n return Representation(data=self.get_data(reference), name=reference)\",\n \"def clonar(self):\\n return Tortuga(self.posicion, self.orientacion, self.pluma.clonar())\",\n \"def make_multi_object_scene(self):\\n multi1 = Scene3D()\\n box = self.objects[0]\\n box.set_location(1, 0, 0)\\n box.set_size(0.4, 0.4, 0.1)\\n multi1.add_object(box)\\n\\n box = self.objects[1]\\n box.set_location(-1, 0, 0)\\n multi1.add_object(box)\\n\\n self.scenes.append(multi1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6370708","0.5323299","0.51920646","0.5153776","0.5107432","0.5070821","0.5055284","0.49911162","0.49496976","0.49446896","0.49231794","0.49001023","0.4827991","0.48052597","0.4804953","0.47864586","0.4774238","0.47692737","0.4762107","0.4743235","0.47330493","0.47329876","0.47263598","0.47207245","0.471975","0.47196102","0.4711031","0.47081006","0.4705919","0.4705121","0.46896866","0.46767405","0.467562","0.467562","0.467562","0.467562","0.467562","0.4668627","0.46669814","0.46645162","0.46600243","0.46508408","0.46411917","0.46197522","0.46158427","0.4600766","0.4581928","0.45757353","0.45709148","0.4558139","0.4552361","0.45523325","0.45490208","0.45481914","0.45446768","0.4542857","0.45420247","0.45420247","0.4538394","0.45294303","0.45262632","0.4512127","0.45084658","0.4507053","0.45067167","0.45038936","0.45034894","0.45025218","0.45025107","0.45025107","0.45011085","0.4496832","0.44852245","0.44844362","0.4483251","0.44815874","0.4478691","0.44748443","0.4474844","0.44666067","0.4463685","0.44575936","0.44568217","0.44553483","0.44490966","0.4446391","0.44451025","0.44448847","0.44425008","0.44392443","0.44297513","0.44254318","0.4425175","0.4418159","0.44138345","0.44079903","0.4402327","0.44020832","0.44019854","0.43975428"],"string":"[\n \"0.6370708\",\n \"0.5323299\",\n \"0.51920646\",\n \"0.5153776\",\n \"0.5107432\",\n \"0.5070821\",\n \"0.5055284\",\n \"0.49911162\",\n \"0.49496976\",\n \"0.49446896\",\n \"0.49231794\",\n \"0.49001023\",\n \"0.4827991\",\n \"0.48052597\",\n \"0.4804953\",\n \"0.47864586\",\n \"0.4774238\",\n \"0.47692737\",\n \"0.4762107\",\n \"0.4743235\",\n \"0.47330493\",\n \"0.47329876\",\n \"0.47263598\",\n \"0.47207245\",\n \"0.471975\",\n \"0.47196102\",\n \"0.4711031\",\n \"0.47081006\",\n \"0.4705919\",\n \"0.4705121\",\n \"0.46896866\",\n \"0.46767405\",\n \"0.467562\",\n \"0.467562\",\n \"0.467562\",\n \"0.467562\",\n \"0.467562\",\n \"0.4668627\",\n \"0.46669814\",\n \"0.46645162\",\n \"0.46600243\",\n \"0.46508408\",\n \"0.46411917\",\n \"0.46197522\",\n \"0.46158427\",\n \"0.4600766\",\n \"0.4581928\",\n \"0.45757353\",\n \"0.45709148\",\n \"0.4558139\",\n \"0.4552361\",\n \"0.45523325\",\n \"0.45490208\",\n \"0.45481914\",\n \"0.45446768\",\n \"0.4542857\",\n \"0.45420247\",\n \"0.45420247\",\n \"0.4538394\",\n \"0.45294303\",\n \"0.45262632\",\n \"0.4512127\",\n \"0.45084658\",\n \"0.4507053\",\n \"0.45067167\",\n \"0.45038936\",\n \"0.45034894\",\n \"0.45025218\",\n \"0.45025107\",\n \"0.45025107\",\n \"0.45011085\",\n \"0.4496832\",\n \"0.44852245\",\n \"0.44844362\",\n \"0.4483251\",\n \"0.44815874\",\n \"0.4478691\",\n \"0.44748443\",\n \"0.4474844\",\n \"0.44666067\",\n \"0.4463685\",\n \"0.44575936\",\n \"0.44568217\",\n \"0.44553483\",\n \"0.44490966\",\n \"0.4446391\",\n \"0.44451025\",\n \"0.44448847\",\n \"0.44425008\",\n \"0.44392443\",\n \"0.44297513\",\n \"0.44254318\",\n \"0.4425175\",\n \"0.4418159\",\n \"0.44138345\",\n \"0.44079903\",\n \"0.4402327\",\n \"0.44020832\",\n \"0.44019854\",\n \"0.43975428\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7259197"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7186,"cells":{"query":{"kind":"string","value":"return orthanc objects of studies"},"document":{"kind":"string","value":"def orthanc_studies(self):\n return [orthanc.study(x.orthanc_id) for x in self.studies]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get(self):\n return orthanc.study(self.orthanc_id)","def studies(self):\n return self._study_queryset","def DumpStudies():\n for name in myStudyManager.GetOpenStudies():\n s=myStudyManager.GetStudyByName(name)\n print \"study:\",name, s._get_StudyId()\n DumpStudy(s)","def search_research_studies_with_observations():\n return ResearchStudy.where(struct={}).include('focus', Observation, reverse=True)","def objects_rst(self):\n return [_.as_rst for _ in self.objects]","def visible_objects_and_duplis():\n\n for obj in context.visible_objects:\n if obj.type == 'MESH':\n if obj.modeling_cloth: \n yield (obj, obj.matrix_world.copy())","def visible_objects_and_duplis():\n\n for obj in context.visible_objects:\n if obj.type == 'MESH':\n if obj.modeling_cloth: \n yield (obj, obj.matrix_world.copy())","def visible_objects_and_duplis():\r\n \r\n for obj in context.visible_objects:\r\n if obj.type == 'MESH':\r\n yield (obj, obj.matrix_world.copy())\r\n \r\n if obj.dupli_type != 'NONE':\r\n obj.dupli_list_create(scene)\r\n for dob in obj.dupli_list:\r\n obj_dupli = dob.object\r\n if obj_dupli.type == 'MESH':\r\n yield (obj_dupli, dob.matrix.copy())\r\n \r\n obj.dupli_list_clear()","def anvil_research_studies_with_observations(anvil_server, search_research_studies_with_observations):\n return search_research_studies_with_observations.perform_resources(anvil_server.server)","def objects(self):","def get_administerable_studies_by_name():\n researcher_admin = get_session_researcher()\n if researcher_admin.site_admin:\n studies = Study.get_all_studies_by_name()\n else:\n studies = researcher_admin.get_administered_studies_by_name()\n return studies","def get_queryset(self):\n return Objective.objects.order_by('perspective')","def model_cohort(cohort):\n model_traj = []\n for part in cohort:\n for traj in part.trajectories:\n if traj.filter is True:\n model_traj.append(\n modelling.model(x=traj.data.age.tolist(),\n y=traj.data.AF.tolist(),\n mutation=traj.mutation,\n variant_class=traj.variant_class,\n gene=traj.mutation.split()[0],\n id=part.id,\n p_key=traj.p_key))\n return model_traj","def visible_objects_and_duplis():\n\n for obj in context.visible_objects:\n if obj.type == 'MESH':\n yield (obj, obj.matrix_world.copy())\n\n if obj.instance_type != 'NONE':\n obj.dupli_list_create(scene)\n for dob in obj.dupli_list:\n obj_dupli = dob.object\n if obj_dupli.type == 'MESH':\n yield (obj_dupli, dob.matrix.copy())\n\n obj.dupli_list_clear()","def get_objectives(self):\n return copy.deepcopy(self.objectives), self.gates_names","def objets_uniques(self):\n objets = []\n for membre in self.membres:\n for objet in membre.equipe:\n if objet.unique:\n objets.append(objet)\n objets.extend(objet.prototype.objets_contenus(objet))\n if membre.tenu and membre.tenu.unique:\n objet = membre.tenu\n objets.append(objet)\n objets.extend(objet.prototype.objets_contenus(objet))\n\n return objets","def all_research_studies_with_observations(aggregated_server, search_research_studies_with_observations):\n return search_research_studies_with_observations.perform_resources(aggregated_server.server)","def composeWorkplaceOntology():\n\n import ossPyFuncs \n import pandas as pd\n \n #mysql query to extract full table from government organizations\n #certian table columns feature capital letters which cases uproblems\n postgreSql_selectQuery=\"SELECT * FROM us_gov_manual.us_govman_2019 ;\"\n #pass querry and obtain table\n govTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n\n #mysql query to obtain academic instutions\n postgreSql_selectQuery=\"SELECT institution FROM hipolabs.universities ;\"\n #pass querry and obtain table\n univTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2018_us1000;\"\n businesses1=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2019_us1000;\"\n businesses2=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n \n postgreSql_selectQuery=\"SELECT company FROM forbes.fortune2020_global2000;\"\n businesses3=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\n\n #combine theinsitutions into a vector\n combinedSeries=[govTable['AgencyName'],univTable['institution'],businesses1['company'],businesses2['company'],businesses3['company']]\n #turn the multi item vector into a single series\n fullWordbank=pd.concat(combinedSeries)\n #turn that series into a pd dataframe\n wordbankTable=pd.DataFrame(fullWordbank.unique())\n\n return wordbankTable","def phonology(request):\n\n perspective_cid = request.params.get('perspective_client_id')\n perspective_oid = request.params.get('perspective_object_id')\n\n # Checking if we have limits on number of computed results.\n\n limit = (None if 'limit' not in request.params else\n int(request.params.get('limit')))\n\n limit_exception = (None if 'limit_exception' not in request.params else\n int(request.params.get('limit_exception')))\n\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\n int(request.params.get('limit_no_vowel')))\n\n limit_result = (None if 'limit_result' not in request.params else\n int(request.params.get('limit_result')))\n\n # TODO: get perspective's translation and language it belongs to.\n\n # We get lexical entries of this perspective with markup'ed sounds.\n\n Sound = aliased(Entity, name = \"Sound\")\n PublishingSound = aliased(PublishingEntity, name = \"PublishingSound\")\n\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\n LexicalEntry.parent_client_id == perspective_cid,\n LexicalEntry.parent_object_id == perspective_oid,\n LexicalEntry.marked_for_deletion == False,\n Entity.parent_client_id == LexicalEntry.client_id,\n Entity.parent_object_id == LexicalEntry.object_id,\n Entity.marked_for_deletion == False,\n Entity.additional_metadata.contains({\"data_type\": \"praat markup\"}),\n PublishingEntity.client_id == Entity.client_id,\n PublishingEntity.object_id == Entity.object_id,\n PublishingEntity.published == True,\n PublishingEntity.accepted == True,\n Sound.client_id == Entity.self_client_id,\n Sound.object_id == Entity.self_object_id,\n Sound.marked_for_deletion == False,\n PublishingSound.client_id == Sound.client_id,\n PublishingSound.object_id == Sound.object_id,\n PublishingSound.published == True,\n PublishingSound.accepted == True))\n\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\n # then several hundred such lexical entries.\n\n exception_counter = 0\n no_vowel_counter = 0\n result_list = list()\n\n for index, row in enumerate(query.yield_per(100)):\n\n markup_url = row.Entity.content\n sound_url = row.Sound.content\n\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id)\n\n # Checking if we have cached result for this pair of sound/markup.\n\n cache_result = CACHE.get(cache_key)\n\n if cache_result == 'no_vowel':\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]: no vowels\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we have cached exception, we do the same as with absence of vowels, show its info and\n # continue.\n\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\n exception, traceback_string = cache_result[1:3]\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n '[CACHE {7}]: exception\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n log.debug(traceback_string)\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we actually have the result, we use it and continue.\n\n elif cache_result:\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in cache_result)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]:\\n{8}\\n{9}\\n{10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url, result_string))\n\n result_list.append(cache_result)\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n continue\n\n try:\n # Getting markup, checking for each tier if it needs to be processed.\n\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\n\n textgrid = pympi.Praat.TextGrid(xmax = 0)\n textgrid.from_file(\n io.BytesIO(markup_bytes),\n codec = chardet.detect(markup_bytes)['encoding'])\n\n tier_data_list = []\n vowel_flag = False\n\n for tier_number, tier_name in textgrid.get_tier_name_num():\n\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\n raw_interval_seq_list = [[]]\n\n # Splitting interval sequence on empty intervals.\n\n for raw_index, interval in enumerate(raw_interval_list):\n\n if len(interval[2].strip()) <= 0:\n if len(raw_interval_seq_list[-1]) > 0:\n raw_interval_seq_list.append([])\n\n else:\n raw_interval_seq_list[-1].append((raw_index, interval))\n\n if len(raw_interval_seq_list[-1]) <= 0:\n del raw_interval_seq_list[-1]\n\n # Selecting interval sequences for analysis, checking if we have unusual markup.\n \n interval_seq_list = []\n interval_idx_to_raw_idx = dict()\n\n unusual_markup_flag = False\n unusual_markup_list = []\n\n for raw_interval_seq in raw_interval_seq_list:\n\n interval_seq_list.append([])\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\n\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\n\n interval_text = interval[2].strip()\n\n # Accepting interval if its text contains at least one vowel, and is short enough or\n # is a valid phonetic transcription.\n\n transcription_check = re.fullmatch(transcription_re, interval_text)\n\n if (len(interval_text) > 0 and\n any(character in vowel_set for character in interval_text) and\n (len(interval_text) <= 2 or transcription_check)):\n\n interval_seq_list[-1].append(interval)\n\n sequence_index = len(interval_seq_list) - 1\n interval_index = len(interval_seq_list[-1]) - 1\n\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\n\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\n\n elif not transcription_check:\n\n unusual_markup_flag = True\n unusual_markup_list.append((raw_index, interval))\n\n transcription_list = [text for begin, end, text in raw_interval_list]\n transcription = ''.join(transcription_list)\n\n selected_list = [text\n for interval_list in interval_seq_list\n for begin, end, text in interval_list]\n\n selected = ''.join(selected_list)\n\n # If we have intervals with unusual markup, we report them.\n\n if unusual_markup_flag:\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' has interval(s) with unusual transcription text: '\n '{9} / {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\n\n # If the markup does not have any vowels, we note it and also report it.\n\n if all(character not in vowel_set for character in transcription):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' doesn\\'t have any vowel markup: {9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription_list))\n\n # It is also possible that while full transcription has vowels, intervals selected for\n # analysis do not. In that case we also note it and report it.\n\n elif not any(character in vowel_set for character in selected):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' intervals to be processed don\\'t have any vowel markup: '\n 'markup {9}, selected {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name,\n transcription_list, selected_list))\n\n # Otherwise we store tier data to be used during processing of the sound file.\n\n else:\n tier_data_list.append((tier_number, tier_name,\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\n interval_idx_to_raw_idx, transcription)))\n\n vowel_flag = True\n\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\n\n if not vowel_flag:\n\n CACHE.set(cache_key, 'no_vowel')\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\n\n sound = None\n with tempfile.NamedTemporaryFile() as temp_file:\n\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\n temp_file.write(sound_file.read())\n temp_file.flush()\n\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\n\n tier_result_list = []\n\n for tier_number, tier_name, tier_data in tier_data_list:\n\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\n tier_result_list.append((tier_number, tier_name, tier_data))\n continue\n\n # Analyzing vowel sounds of each interval sequence.\n\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\n transcription) = tier_data\n\n tier_result_list.append((tier_number, tier_name, []))\n\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\n raw_interval_seq_list, interval_seq_list)):\n\n if len(interval_list) <= 0:\n continue\n\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\n find_max_interval_praat(sound, interval_list)\n\n max_intensity_interval = interval_list[max_intensity_index]\n max_length_interval = interval_list[max_length_index]\n\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\n\n # Compiling results.\n\n max_length_str = '{0} {1:.3f} [{2}]'.format(\n max_length_interval[2], max_length,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\n\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\n max_intensity_interval[2],\n max_intensity,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\n\n tier_result_list[-1][2].append([\n ''.join(text for index, (begin, end, text) in raw_interval_list),\n max_length_str,\n '{0:.3f}'.format(max_length_f1_f2[0]),\n '{0:.3f}'.format(max_length_f1_f2[1]),\n max_intensity_str,\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\n '+' if max_intensity_index == max_length_index else '-'])\n\n # Saving result.\n\n result_list.append(tier_result_list)\n CACHE.set(cache_key, tier_result_list)\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\n '\\n{7}\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url, result_string))\n\n # Stopping earlier, if required.\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n except Exception as exception:\n\n #\n # NOTE\n #\n # Exceptional situations encountered so far:\n #\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\n #\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\n #\n # 2. Markup for one of the intervals contains a newline \"\\n\", and pympi fails to parse it.\n # Praat parses such files without problems.\n #\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\n #\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'exception\\n{7}\\n{8}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url))\n\n # if we encountered an exception, we show its info and remember not to try offending\n # sound/markup pair again.\n\n traceback_string = ''.join(traceback.format_exception(\n exception, exception, exception.__traceback__))[:-1]\n\n log.debug(traceback_string)\n\n CACHE.set(cache_key, ('exception', exception,\n traceback_string.replace('Traceback', 'CACHEd traceback')))\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\n perspective_cid, perspective_oid,\n len(result_list), '' if len(result_list) == 1 else 's',\n no_vowel_counter, exception_counter))\n\n # If we have no results, we indicate the situation and also show number of failures and number of\n # markups with no vowels.\n\n if not result_list:\n request.response.status = HTTPPreconditionFailed.code\n\n return {\n \"error\": \"no markups for this query\",\n \"exception_counter\": exception_counter,\n \"no_vowel_counter\": no_vowel_counter}\n\n # Otherwise we create and then serve Excel file.\n\n excel_book = xlwt.Workbook(encoding = \"utf-8\")\n sheet = excel_book.add_sheet(\"Sheet 1\")\n\n sheet.write(0, 0, 'Transcription')\n sheet.write(0, 1, 'Longest (seconds) interval')\n sheet.write(0, 2, 'F1 (Hz)')\n sheet.write(0, 3, 'F2 (Hz)')\n sheet.write(0, 4, 'Highest intensity (dB) interval')\n sheet.write(0, 5, 'F1 (Hz)')\n sheet.write(0, 6, 'F2 (Hz)')\n sheet.write(0, 7, 'Coincidence')\n\n row_counter = 1\n\n for tier_result_list in result_list:\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\n\n if tier_result_seq_list == 'no_vowel':\n continue\n\n for tier_data in tier_result_seq_list:\n for index, tier_data_str in enumerate(tier_data):\n sheet.write(row_counter, index, tier_data_str)\n\n row_counter += 1\n\n # Formatting column widths.\n\n sheet.col(0).width = 24 * 256\n sheet.col(1).width = 24 * 256\n sheet.col(2).width = 12 * 256\n sheet.col(3).width = 12 * 256\n sheet.col(4).width = 24 * 256\n sheet.col(5).width = 12 * 256\n sheet.col(6).width = 12 * 256\n sheet.col(7).width = 12 * 256\n\n excel_stream = io.BytesIO()\n excel_book.save(excel_stream)\n excel_stream.seek(0)\n\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\n # content-type.\n\n response = Response(content_type = 'application/vnd.ms-excel')\n\n response.app_iter = FileIter(excel_stream)\n response.headers['Content-Disposition'] = \"attachment; filename=phonology.xls\"\n\n return response","def getHierarchies():","def getHierarchies():","def iterate_studies(self, start, end):\n pass","def get_queryset(self):\n return Initiative.objects.filter(objective__perspective__description='Financial').order_by('objective')","def _get_all_oshapes(self):\n an_iname = self.node_list[0]\n an_inode = self.builder.nodes[an_iname]\n an_ishape = an_inode.oshapes['loc']\n \n return {'main' : an_ishape,\n 'loc' : an_ishape,\n 'cov' : an_ishape + [an_ishape[-1]]}","def findAtypicalTerms(self):\n self.atypicalTermsDict = collections.OrderedDict()\n distanceList = list()\n distance = 0\n for key in self.summaryFilteredDict:\n partitionName = str(key).split(\" :\")[0]\n partition = voc.getPartition(partitionName)\n modNames = partition.getModNames()\n currentModality = str(key).split(\": \")[1]\n indexCurrentModality = modNames.index(currentModality)\n coverCurrentModality = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,partitionName + \" : \" + currentModality) #cover(v',R)\n if coverCurrentModality > 0:\n for modality in partition.getModalities():\n coverModality = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,partitionName + \" : \" + modality.getName()) # cover(v,R)\n if modality.isTrapeziumModality():\n indexModality = modNames.index(modality.getName())\n distance = abs(indexCurrentModality - indexModality) / (partition.getNbModalities() - 1) #d(v,v')\n elif modality.isEnumModality():\n if (modality.getName() == currentModality):\n distance = 0\n else:\n distance = 1\n distanceList.append(min(distance, 1 - coverCurrentModality, coverModality)) # min(d(v,v'),cover(v,R),1-cover(v',R))\n self.atypicalTermsDict[partitionName + \" : \" + currentModality] = max(distanceList) # D(v',R)\n distanceList = list()","def all_objects():\n objs = {}\n objs['Section'] = list(h.all_sec())\n objs['Segment'] = []\n for sec in objs['Section']:\n objs['Segment'].extend(list(sec.allseg()))\n objs['PointProcess'] = []\n for seg in objs['Segment']:\n objs['PointProcess'].extend(list(seg.point_processes()))\n \n return objs","def demo_ortho_slicer():\n pl.clf()\n oslicer = OrthoSlicer(cut_coords=(0, 0, 0))\n from .anat_cache import _AnatCache\n map, affine, _ = _AnatCache.get_anat()\n oslicer.plot_map(map, affine, cmap=pl.cm.gray)\n return oslicer","def open_iossifov_neuron_cohort():\n logging.info('getting Iossifov et al Neuron 2012 cohort')\n s1 = pandas.read_excel(supp_s1_url, sheet_name='SNV.v4.1-normlized')\n s2 = pandas.read_excel(supp_s2_url, sheet_name='suppLGKTable')\n s3 = pandas.read_excel(supp_s3_url, sheet_name='ID.v4.1-normlized')\n \n fam_ids = list(s1.quadId) + list(s2.quadId) + list(s3.quadId)\n members = list(s1.inChild) + list(s2.inChild) + list(s3.inChild)\n \n sex = ['M', 'F']\n affected = ['aut', 'sib']\n possible = list(itertools.product(affected, sex))\n study = ['10.1016/j.neuron.2012.04.009']\n \n persons = set()\n for fam, children in zip(fam_ids, members):\n for affected, sex in possible:\n string = f'{affected}{sex}'\n if string in children:\n status = ['unaffected'] if affected != 'aut' else ['HP:0000717']\n member = 's1' if affected != 'aut' else 'p1'\n sex = 'female' if sex == 'F' else 'male'\n person_id = f'{fam}.{member}|asd_cohorts'\n \n person = Person(person_id, sex, status, study)\n persons.add(person)\n \n return persons","def make_openalex_dataset(dataset: ObservatoryDataset) -> List[dict]:\n\n result = []\n for paper in dataset.papers:\n entry = {\n \"id\": str(paper.id),\n \"doi\": f\"https://doi.org/{paper.doi}\",\n \"cited_by_count\": len(paper.cited_by),\n \"concepts\": [\n {\"id\": str(fos.id), \"display_name\": fos.name, \"level\": fos.level} for fos in paper.fields_of_study\n ],\n \"authorships\": [\n {\n \"author\": {\n \"id\": str(author.id),\n \"display_name\": author.name,\n },\n \"institutions\": [\n {\n \"id\": str(author.institution.id),\n \"ror\": author.institution.ror_id,\n \"display_name\": author.institution.name,\n \"country_code\": author.institution.country_code,\n \"type\": author.institution.types,\n }\n ],\n }\n for author in paper.authors\n ],\n }\n result.append(entry)\n\n return result","def salome_study_init(theStudyId=0):\n\n global salome_study_initial\n global myStudyManager, myStudyId, myStudy, myStudyName\n global orb, lcc, naming_service, cm\n\n if salome_study_initial:\n salome_study_initial = 0\n\n orb, lcc, naming_service, cm = salome_kernel.salome_kernel_init()\n\n # get Study Manager reference\n if verbose(): print \"looking for studyManager ...\"\n obj = naming_service.Resolve('myStudyManager')\n myStudyManager = obj._narrow(SALOMEDS.StudyManager)\n if verbose(): print \"studyManager found\"\n\n # get active study Id, ref and name\n myStudyId = getActiveStudy(theStudyId)\n if verbose(): print \"myStudyId\",myStudyId\n myStudy = myStudyManager.GetStudyByID(myStudyId)\n myStudyName = myStudy._get_Name()\n\n return myStudyManager, myStudyId, myStudy, myStudyName","def dbgap_research_studies_with_observations(dbgap_server, search_research_studies_with_observations):\n return search_research_studies_with_observations.perform_resources(dbgap_server.server)","def get_soma_objects(self):\n\n msg_store = MessageStoreProxy(database=\"soma2data\", collection=\"soma2\")\n objs = msg_store.query(SOMA2Object._type, message_query={\"map_name\":self.soma_map,\"config\":self.soma_conf})\n print \"queried soma2 objects >> \", objs\n self.soma_objects = ce.get_soma_objects()\n print \"hard coded objects >> \", [self.soma_objects[r].keys() for r in self.soma_objects.keys()]","def examples(self):\n for obj_ind in range(len(self.objects)):\n yield self.get_object_intent_by_index(obj_ind)","def orthonormation_method(standardized_methods_cleaned):\n method_standardized_ortho = standardized_methods_cleaned.copy(deep=True)\n\n categories = method_standardized_ortho.columns.tolist()\n\n # Normation of the first category\n method_standardized_ortho[categories[0]] = method_standardized_ortho[categories[0]] / \\\n linalg.norm(method_standardized_ortho[categories[0]])\n\n # Normation of every following categories\n j = 0\n while j < len(categories):\n i = 0\n while i < j:\n # Calculates the orthogonal projection of j on each i and substraction of the projection from j\n method_standardized_ortho[categories[j]] = \\\n method_standardized_ortho[categories[j]] - method_standardized_ortho[categories[i]] * (\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[j]]) /\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[i]]))\n if linalg.norm(method_standardized_ortho[categories[j]]) == 0:\n # If after the projection, if the j columns is null it is droped (i.e it is linearly dependant with\n # the other columns) and the inner loop stops\n method_standardized_ortho.drop(method_standardized_ortho.columns[j], inplace=True, axis=1)\n categories.remove(categories[j])\n\n break\n else:\n # If the j column is not null, it is normed and the inner while loop keeps going\n method_standardized_ortho[categories[j]] = method_standardized_ortho[categories[j]] / \\\n (linalg.norm(method_standardized_ortho[categories[j]]))\n i += 1\n j += 1\n\n return method_standardized_ortho","def test_ResearchStudy_Observations(all_research_studies_with_observations):\n all_observations = {r.relativePath(): r for r in all_research_studies_with_observations if\n r.__class__.__name__ == 'Observation'}\n all_research_studies = {r.relativePath(): r for r in all_research_studies_with_observations if\n r.__class__.__name__ == 'ResearchStudy'}\n research_studies_missing_observations = dict(**all_research_studies)\n for observation in all_observations.values():\n del research_studies_missing_observations[observation.focus[0].reference]\n assert len(\n research_studies_missing_observations) == 0, f\"ResearchStudies missing Observations {[r.id for r in research_studies_missing_observations.values()]}\"","def clarify_objects(self): \n dict_cxt = dict(list(zip(list(map(tuple, self)), self.objects)))\n table = list(map(list, list(dict_cxt.keys())))\n objects = list(dict_cxt.values())\n return Context(table, objects, self.attributes)","def generate_voc(self):\n\n observations = [\"walk\", \"shop\", \"clean\", \"tennis\", \"read\"]\n states = [\"sunny\", \"rainy\", \"snowy\"]\n\n # Sort them alphabetically, just to be on the safe side\n observations.sort()\n states.sort()\n\n return (observations, states)","def variations():","def get_all_object_names(self):\n o_objects = []\n for s in [\"Non Model\", \"Solids\", \"Unclassified\", \"Sheets\", \"Lines\"]:\n o_objects += self.design.modeler.get_objects_in_group(s)\n return o_objects","def get_representation(self, syl):\n \n first = syl[0]\n second = syl[1]\n \n return [self.representation[first], self.representation[second]]","def _get_all_oshapes(self):\n iseq_name = self.node_list[0]\n iseq = self.builder.nodes[iseq_name]\n iseq_mainshape = iseq.oshapes['main']\n \n return {'main' : iseq_mainshape,\n 'loc' : iseq_mainshape,\n 'invscaled' : iseq_mainshape + [iseq_mainshape[-1]],\n 'invscaleoffd' : iseq_mainshape + [iseq_mainshape[-1]]}","def _get_objects(\n self, model: Model, old_objects: List[Viewable], doc: Document,\n root: Model, comm: Optional[Comm] = None\n ):\n from ..pane.base import RerenderError, panel\n new_models, old_models = [], []\n for i, pane in enumerate(self.objects):\n pane = panel(pane)\n self.objects[i] = pane\n\n for obj in old_objects:\n if obj not in self.objects:\n obj._cleanup(root)\n\n current_objects = list(self.objects)\n ref = root.ref['id']\n for i, pane in enumerate(self.objects):\n if pane in old_objects and ref in pane._models:\n child, _ = pane._models[root.ref['id']]\n old_models.append(child)\n else:\n try:\n child = pane._get_model(doc, root, model, comm)\n except RerenderError as e:\n if e.layout is not None and e.layout is not self:\n raise e\n e.layout = None\n return self._get_objects(model, current_objects[:i], doc, root, comm)\n new_models.append(child)\n return new_models, old_models","def get_queryset(self):\n return Initiative.objects.filter(objective__perspective__description='Processes').order_by('objective')","def get_source_studies(self):\n return list(set([trait.source_dataset.source_study_version.study for trait in self.get_all_source_traits()]))","def get_queryset(self):\n return Objective.objects.filter(perspective__description='Financial').order_by('code')","def getLandsatCollection():\n ## standardize band names\n bn8 = ['B1', 'B2', 'B3', 'B4', 'B6', 'pixel_qa', 'B5', 'B7']\n bn7 = ['B1', 'B1', 'B2', 'B3', 'B5', 'pixel_qa', 'B4', 'B7']\n bn5 = ['B1', 'B1', 'B2', 'B3', 'B5', 'pixel_qa', 'B4', 'B7']\n bns = ['uBlue', 'Blue', 'Green', 'Red', 'Swir1', 'BQA', 'Nir', 'Swir2']\n\n # create a merged collection from landsat 5, 7, and 8\n ls5 = ee.ImageCollection(\"LANDSAT/LT05/C01/T1_SR\").select(bn5, bns)\n\n ls7 = (ee.ImageCollection(\"LANDSAT/LE07/C01/T1_SR\")\n .filterDate('1999-04-15', '2003-05-30')\n .select(bn7, bns))\n\n ls8 = ee.ImageCollection(\"LANDSAT/LC08/C01/T1_SR\").select(bn8, bns)\n\n merged = ls5.merge(ls7).merge(ls8)\n\n return(merged)","def get_modeller_objects(self):\n return self._modpt, self.__edat.modpt, self.__libs.modpt","def sngl_obj_evo(self, lacking):\n prob, algo = self.probinit('jde', 0)\n l = list()\n u = 6+(self.N-3)*4\n for i in range(lacking):\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\n for j in range(u):\n archi.evolve(5)\n stdout.write(\"\\r{0} / {1}\".format(i*u+j+1, lacking*u))\n stdout.flush()\n tmp = [isl for isl in archi]\n tmp.sort(key = lambda x: x.population.champion.f[0]);\n l.append(tmp[0].population.champion)\n stdout.write(\" Done. \")\n return l, prob","def phenotypes(self):\n\t\treturn Phenotype.PhenotypesByPatient(self.id, self.host)","def get_designs(self):","def _get_subobjects(self) -> Iterable[SymbolicObject]:\n\n return self._subobjects","def test_returns_all_studies_with_archived_tagged_traits(self):\n tag = TagFactory.create()\n tagged_traits = []\n for study in self.studies:\n tmp = TaggedTraitFactory.create(trait__source_dataset__source_study_version__study=study,\n archived=True, tag=tag)\n tagged_traits.append(tmp)\n get_data = {'q': ''}\n response = self.client.get(self.get_url(), get_data)\n pks = get_autocomplete_view_ids(response)\n self.assertEqual(sorted([study.pk for study in self.studies]), sorted(pks))","def _orthogonal_init(self):\n # if is a conv layer, will need to reshape to fan in matrix,\n # which is of dimension\n # num input feature maps * filter height * filter width\n if(len(self.dims) > 2):\n rv_samp = np.random.randn(self.dims[2],\n self.dims[0] * self.dims[1] * self.dims[3])\n out_sigma = np.sqrt(1.0 / rv_samp.shape[1])\n # otherwise will be a densely connected layer\n else:\n rv_samp = np.random.randn(self.dims[0], self.dims[1])\n out_sigma = np.sqrt(1.0 / rv_samp.shape[0])\n # perform SVD\n U, _, V = np.linalg.svd(rv_samp, full_matrices=False) #pylint: disable=invalid-name\n # both U and V are orthoginal matricies, so will choose the one\n # that is the correct dimensions for our layer\n ortho_matrix = U if U.shape == rv_samp.shape else V\n # rescale so it is unit variance for each vector\n # print(\"std(q) = {}\".format(np.std(q)))\n ortho_norm = (ortho_matrix / np.std(ortho_matrix)) * out_sigma\n #print(\"std(qs) = {}\".format(np.std(qs)))\n #print(q.shape)\n return ortho_norm.reshape(self.dims).astype(np.float32)","def undulations(**kwargs):\n\n\t#---parameters\n\tsn = kwargs['sn']\n\twork = kwargs['workspace']\n\tcalc = kwargs['calc']\n\tupname = 'lipid_abstractor'\n\tgrid_spacing = calc['specs']['grid_spacing']\n\tvecs = datmerge(kwargs,upname,'vecs')\n\tnframes = int(np.sum(datmerge(kwargs,upname,'nframes')))\n\ttrajectory = datmerge(kwargs,upname,'points')\n\tattrs,result = {},{}\n\t#---! hacking through error with monolayer separation\n\ttry: monolayer_indices = kwargs['upstream'][upname+'0']['monolayer_indices']\n\texcept: monolayer_indices = kwargs['upstream'][upname]['monolayer_indices']\n\t#---choose grid dimensions\n\tgrid = np.array([round(i) for i in np.mean(vecs,axis=0)/grid_spacing])[:2]\n\t#---! removed timeseries from result for new version of omnicalc\n\t#---parallel\n\tmesh = [[],[]]\n\tfor mn in range(2):\n\t\tstart = time.time()\n\t\tmesh[mn] = Parallel(n_jobs=work.nprocs,verbose=0,require='sharedmem')(\n\t\t\tdelayed(makemesh_regular)(\n\t\t\t\ttrajectory[fr][np.where(monolayer_indices==mn)],vecs[fr],grid)\n\t\t\tfor fr in framelooper(nframes,start=start,text='monolayer %d, frame'%mn))\n\tchecktime()\n\n\t#---pack\n\tresult['mesh'] = np.array(mesh)\n\tresult['grid'] = np.array(grid)\n\tresult['nframes'] = np.array(nframes)\n\tresult['vecs'] = vecs\n\tattrs['grid_spacing'] = grid_spacing\n\treturn result,attrs","def latents(self):\n self.assert_sampled()\n return self._representations","def _get_halo(self,i):\n if self._order is False:\n if self._subs is True:\n #this needs to be tested again on a snapshot that is not ordered!\n x = Halo(i, self, self.base, np.where(np.in1d(self.base['iord'], self.ids[self._subhalodat['sub_off'][i]:self._subhalodat['sub_off'][i]+self._subhalodat['sub_len'][i]] )))\n else:\n x = Halo(i, self, self.base, np.where(np.in1d(self.base['iord'], self.ids[self._halodat['group_off'][i]:self._halodat['group_off'][i]+self._halodat['group_len'][i]] )))\n \n else:\n if self._subs is False: #to use groups as halos:\n x = Halo(i, self, self.base, self.ids[self._halodat['group_off'][i]:self._halodat['group_off'][i]+self._halodat['group_len'][i]] ) \n else:\n x=Halo(i, self, self.base, self.ids[self._subhalodat['sub_off'][i]:self._subhalodat['sub_off'][i]+self._subhalodat['sub_len'][i]] )\n \n x._descriptor = \"halo_\"+str(i)\n x.properties.update(self.get_halo_properties(i))\n return x","def kids_first_research_studies_with_observations(caplog, kids_first_server, search_research_studies_with_observations):\n # caplog.set_level(logging.DEBUG)\n return search_research_studies_with_observations.perform_resources(kids_first_server.server)","def get_session_researcher_study_ids():\n session_researcher = get_session_researcher()\n if session_researcher.site_admin:\n return Study.objects.exclude(deleted=True).values_list(\"id\", flat=True)\n else:\n return session_researcher.study_relations.filter(study__deleted=False).values_list(\"study__id\", flat=True)","def make_objects(self):\n pass","def representations(self):\n self.assert_sampled()\n return self._representations","def GetObjects(self): \r\n return self.model.GetObjects()","def get_queryset(self):\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')","def samples(self):\n samples = set()\n for trio in self.trios:\n if trio.child is None or trio.mother is None or trio.father is None:\n continue\n samples.add(trio.father)\n samples.add(trio.mother)\n samples.add(trio.child)\n return list(samples)","def newAnalyzer():\n analyzer = {'crimes': None,\n 'dateIndex': None,\n 'autors': None,\n 'instrumentalness': None,\n 'tempo':None,\n 'liveness':None,\n 'speechiness':None,\n 'danceability':None,\n 'valence':None,\n 'loudness':None,\n 'acousticness':None,\n 'energy':None,\n 'generos':None\n }\n\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n\n analyzer['autors'] = om.newMap(omaptype='RBT',\n comparefunction=compareAUTOR)\n\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['tempo'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['liveness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['danceability'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n analyzer['valence'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['loudness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['energy'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n\n analyzer['generos']= m.newMap(11,\n maptype='CHAINING',\n loadfactor=4.0)\n \n return analyzer","def orbit(self):\n return [x for x in TransitiveIdeal(attrcall('simple_reflections'), [self])]","def structures(self):\n pdb = self.name\n residues = self.__residues__(pdb)\n return Structure(list(residues), pdb=pdb)","def getCriticStation(analyzer):\n mayIn = model.getRankMay(analyzer,\"in\")\n mayOut=model.getRankMay(analyzer,\"out\")\n less=model.getRankMen(analyzer,\"LessPopular\")\n return (mayIn,mayOut,less)","def top_sources_male(args: Dict[str, Any]) -> List[object]:\n query = [\n {\n \"$match\": {\n \"body\": {\"$ne\": \"\"},\n \"quotesUpdated\": {\"$exists\": True},\n \"outlet\": {\"$in\": args[\"outlets\"]},\n \"publishedAt\": {\n \"$gte\": args[\"begin_date\"],\n \"$lt\": args[\"end_date\"] + timedelta(days=1),\n },\n }\n },\n {\"$project\": {\"outlet\": 1.0, \"sourcesMale\": 1.0}},\n {\"$unwind\": {\"path\": \"$sourcesMale\", \"preserveNullAndEmptyArrays\": False}},\n {\"$group\": {\"_id\": \"$sourcesMale\", \"count\": {\"$sum\": 1.0}}},\n {\"$sort\": {\"count\": args[\"sort\"]}},\n {\"$limit\": args[\"limit\"]},\n ]\n return query","def __init__(self):\n self.rho=[]\n self.te=[]\n self.ti=[]\n self.ne=[]\n self.ni=[]\n self.ni1=[]\n self.ni2=[]\n self.ni3=[]\n self.vtor=[]\n self.zeff=[]\n\n self.nion=1\n self.Z=[]\n self.A=[]\n self.coll_mode=[]","def get_queryset(self):\n return Initiative.objects.order_by('objective')","def sdc_to_distributions(self, mysdc):\n if \"right\" in mysdc[\"verb\"]:\n D_mat = transpose(self.T_mat_right)\n elif \"left\" in mysdc[\"verb\"]:\n D_mat = transpose(self.T_mat_left)\n else:\n D_mat = transpose(self.T_mat_str)\n \n T_mat = ones([len(D_mat), len(D_mat[0])])*1.0\n \n \n if mysdc[\"sr\"] != None and len(mysdc[\"landmarks\"]) > 0 and self.use_spatial_relations:\n\n sr_i = self.sr_class.engineToIdx(mysdc[\"sr\"])\n SR_mat = self.srel_mat[sr_i,:,:,:]\n L_mat = self.get_prob_landmark_given_sdc_modifiers(mysdc)\n L_mat_entropy = entropy(L_mat)\n print \"using spatial relations\", str(mysdc)\n if L_mat_entropy > 2 and False:\n SR_mat = None\n L_mat = None\n else:\n SR_mat = None\n L_mat = None\n \n if mysdc[\"landmark\"] != None:\n O_mat = self.O_mat[:,self.names_to_index[mysdc[\"landmark\"]]]\n else:\n O_mat = None\n \n return O_mat, T_mat, SR_mat, L_mat, D_mat","def merge(cls, analyses):\r\n raise NotImplementedError()","def substantiate():","def hispaniola_models():\n return [\n ('GFS (6-hr steps, 7 days)', 'gfs'),\n ('WRF-PR (1-hr steps, 2 days)', 'wrfpr'),\n ]","def _get_orthogonal_states(self, and_state):\n if and_state not in self.and_states:\n states = and_state.get_orthogonal_states()\n states.insert(0, and_state)\n self.and_states[and_state] = states\n return self.and_states[and_state]","def serotype_escherichia(metadata, analysistype):\n for sample in metadata:\n # Initialise negative results to be overwritten when necessary\n sample[analysistype].best_o_pid = '-'\n sample[analysistype].o_genes = ['-']\n sample[analysistype].o_set = ['-']\n sample[analysistype].best_h_pid = '-'\n sample[analysistype].h_genes = ['-']\n sample[analysistype].h_set = ['-']\n if sample.general.bestassemblyfile != 'NA':\n if sample.general.closestrefseqgenus in ['Escherichia', 'Shigella']:\n o = dict()\n h = dict()\n for result, percentid in sample[analysistype].blastresults.items():\n if 'O' in result.split('_')[-1]:\n o.update({result: float(percentid)})\n if 'H' in result.split('_')[-1]:\n h.update({result: float(percentid)})\n # O\n try:\n sorted_o = sorted(o.items(), key=operator.itemgetter(1), reverse=True)\n sample[analysistype].best_o_pid = str(sorted_o[0][1])\n\n sample[analysistype].o_genes = [gene for gene, pid in o.items()\n if str(pid) == sample[analysistype].best_o_pid]\n sample[analysistype].o_set = \\\n list(set(gene.split('_')[-1] for gene in sample[analysistype].o_genes))\n except (KeyError, IndexError):\n pass\n # H\n try:\n sorted_h = sorted(h.items(), key=operator.itemgetter(1), reverse=True)\n sample[analysistype].best_h_pid = str(sorted_h[0][1])\n sample[analysistype].h_genes = [gene for gene, pid in h.items()\n if str(pid) == sample[analysistype].best_h_pid]\n sample[analysistype].h_set = \\\n list(set(gene.split('_')[-1] for gene in sample[analysistype].h_genes))\n except (KeyError, IndexError):\n pass\n return metadata","def build_from_landscape(self, landscape):\n assert(self.models_loaded)\n\n self.farms=list()\n for p in landscape.premises:\n f=self.farm_models[p.production_type].clone(p.name, p.size)\n self.farms.append(f)\n\n #### airborne\n if len(self.spread_models)>0:\n a_spread_model=next (iter (self.spread_models.values()))\n special_factor=a_spread_model.herd_factor(landscape.premises)\n for v in self.spread_models.values():\n v.set_special_factor(special_factor)\n self.airborne=list()\n for aidx, bidx in itertools.permutations(range(len(self.farms)), 2):\n a=self.farms[aidx]\n b=self.farms[bidx]\n from_type=landscape.premises[aidx].production_type\n to_type=landscape.premises[bidx].production_type\n dx=landscape.distances[aidx, bidx]\n air_model=self.spread_models[(from_type, to_type)].clone(\n a, b, dx)\n self.airborne.append(air_model)\n else:\n self.airborne=list()\n\n ### Indirect and Direct Contact\n self.indirect=list()\n if len(self.contact_models)>0 and self.use_indirect:\n for ind_idx in range(len(self.farms)):\n f=self.farms[ind_idx]\n p=landscape.premises[ind_idx]\n for cm in self.contact_models[p.production_type]:\n im=cm.clone(self.farms, landscape, ind_idx)\n self.indirect.append(im)","def toggled_objects(self):\n\t\tobjects = []\n\t\tfor i in range(len(self.object_list)):\n\t\t\tif(self.toggles[i]):\n\t\t\t\tnext_object = (self.object_list[i], self.quantities[i])\n\t\t\t\tobjects.append(next_object)\n\t\treturn objects","def get_queryset(self):\n return Initiative.objects.filter(objective__perspective__description='Customer').order_by('objective')","def get_common_food(cls):\n objs = cls.objects\n return objs","def get_objects(si, args):\n # Get datacenter object.\n datacenter_list = si.content.rootFolder.childEntity\n \"\"\"\n if args.datacenter_name:\n datacenter_obj = get_obj_in_list(args.datacenter_name, datacenter_list)\n else:\n \"\"\"\n datacenter_obj = datacenter_list[0]\n\n # Get datastore object.\n datastore_list = datacenter_obj.datastoreFolder.childEntity\n \"\"\"if args.datastore_name:\n datastore_obj = get_obj_in_list(args.datastore_name, datastore_list)\n elif len(datastore_list) > 0:\"\"\"\n datastore_obj = datastore_list[0]\n #else:\n # print \"No datastores found in DC (%s).\" % datacenter_obj.name\n\n # Get cluster object.\n cluster_list = datacenter_obj.hostFolder.childEntity\n \"\"\"if args.cluster_name:\n cluster_obj = get_obj_in_list(args.cluster_name, cluster_list)\n elif len(cluster_list) > 0:\"\"\"\n cluster_obj = cluster_list[0]\n #else:\n # print \"No clusters found in DC (%s).\" % datacenter_obj.name\n\n # Generate resource pool.\n resource_pool_obj = cluster_obj.resourcePool\n\n return {\"datacenter\": datacenter_obj,\n \"datastore\": datastore_obj\n ,\"resource pool\": resource_pool_obj}","def getAncestors():","def get_objects(self, image=None):\n output_dict = self.run_inference_for_single_image(image)\n return output_dict, self.category_index","def find_own_objects(cs):\n own_objects = {}\n for con in cs:\n own_objects[con] = []\n for obj in con.extent:\n own_objects[con].append(obj)\n for sub_con in cs:\n if sub_con.extent < con.extent and\\\n obj in sub_con.extent:\n own_objects[con].pop()\n break\n return own_objects","def get_variations_urls(self, obj):\n\n # Initiate return object\n return_object = {}\n\n # Get the field of the object\n field = obj.field\n\n # A lot of ifs going around, first check if it has the field variations\n if hasattr(field, 'variations'):\n # Get the variations\n variations = field.variations\n # Go through the variations dict\n for key in variations.keys():\n # Just to be sure if the stdimage object has it stored in the obj\n if hasattr(obj, key):\n # get the by stdimage properties\n field_obj = getattr(obj, key, None)\n if field_obj and hasattr(field_obj, 'url'):\n # store it, with the name of the variation type into our return object\n return_object[key] = super(StdImageFieldSerializer, self).to_representation(field_obj)\n\n # Also include the original (if possible)\n try:\n if hasattr(obj, 'url'):\n return_object['original'] = super(StdImageFieldSerializer, self).to_representation(obj)\n except ValueError:\n pass\n\n return return_object","def writeOrthologSets(outfile, nexus,\n extract_species,\n extract_gene,\n options,\n reference_tree=None,\n method=\"strict\",\n outgroups=None):\n\n ######################################################################\n # build species set to compare\n sets = []\n species = options.column2org\n nspecies = len(species)\n\n if options.enumeration == \"monophyletic\":\n if reference_tree:\n for members, h1, h2 in TreeTools.GetSubsets(reference_tree):\n if len(members) > 1:\n sets.append(members)\n else:\n raise \"please specify a species tree for monophyletic enumeration\"\n\n elif options.enumeration == \"exhaustive\":\n for x in range(2, len(species)):\n sets += list(SetTools.xuniqueCombinations(species, x))\n sets.append(species)\n\n elif options.enumeration == \"pairwise\":\n\n for x in range(len(species) - 1):\n for y in range(x + 1, len(species)):\n sets.append((species[x], species[y]))\n\n elif options.enumeration == \"full\":\n sets.append(species)\n\n elif options.enumeration == \"lineage\":\n for s in species:\n sets.append((s,))\n\n elif options.enumeration == \"explicit\":\n for x in range(2, len(options.species_set)):\n sets += list(SetTools.xuniqueCombinations(options.species_set, x))\n sets.append(options.species_set)\n\n ######################################################################\n # build sets with positional information\n xsets = []\n map_frozenset2set = {}\n for x in range(len(sets)):\n ss = frozenset(map(lambda x: options.org2column[x], sets[x]))\n xsets.append(ss)\n map_frozenset2set[ss] = x\n\n ######################################################################\n # collect outgroups\n if outgroups:\n noutgroups = set()\n for x in outgroups:\n noutgroups.add(options.org2column[x])\n else:\n noutgroups = None\n\n ######################################################################\n # loop over each tree and set\n # I did not see a way to loop a tree once for all sets without doing\n # complicated counting. The problem is that counting has to be stopped\n # at different tree heights for different sets.\n ninput, noutput, nempty, nskipped = 0, 0, 0, 0\n\n counts = [0] * len(sets)\n\n options.stdout.write(\n \"nspecies\\tname\\tid\\tcluster\\tpattern\\t%s\\tnode_id\\tmembers\\n\" % \"\\t\".join(species))\n\n cluster_id = 0\n nerrors = 0\n\n for tree in nexus.trees:\n\n ninput += 1\n ntotal_tree = 0\n\n if options.loglevel >= 3:\n options.stdlog.write(\"# processing tree %s\\n\" % tree.name)\n\n if options.reroot:\n rerootTree(tree, extract_species, options)\n\n for c in range(len(xsets)):\n # numbered species set: 0,1,...\n sn = xsets[c]\n # literal species set: species1, species2, ...\n sl = sets[c]\n\n ortholog_nodes = getOrthologNodes(tree, sn, options, selector=method,\n outgroups=noutgroups)\n ntotal_tree += len(ortholog_nodes)\n\n n = 0\n\n pattern = buildPattern(nspecies, sn)\n\n # check for inconsistent partitions (the same gene in different\n # ortholog clusters) within the current tree\n found_genes = set()\n ortho_sets = set()\n\n # reverse ortholog_node - work in top-down manner.\n ortholog_nodes.reverse()\n\n for node_id, members in ortholog_nodes:\n n += 1\n cluster_id += 1\n\n otus = filter(\n lambda x: extract_species(x) in sl, tree.get_taxa(node_id))\n genes = set(map(extract_gene, otus))\n\n if found_genes.intersection(genes):\n\n # only take largest cluster for lineage specific\n # duplications\n if method == \"lineage\":\n continue\n\n if frozenset(genes) in ortho_sets:\n nskipped += 1\n if options.loglevel >= 1:\n options.stdlog.write(\"# %s: cluster %i: redundant node: %i - skipped because already present: %s\\n\" %\n (tree.name, n, node_id, str(found_genes.intersection(genes))))\n else:\n nerrors += 1\n if options.loglevel >= 1:\n options.stdlog.write(\"# %s: cluster %i: inconsistent node: %i - the same gene in different clusters: %s\\n\" %\n (tree.name, n, node_id, str(found_genes.intersection(genes))))\n\n found_genes = found_genes.union(genes)\n ortho_sets.add(frozenset(genes))\n\n xpattern = buildPattern(nspecies, sn, members)\n\n options.stdout.write(\"%i\\t%s\\t%i\\t%i\\t%s\\t%s\\t%i\\t%s\\n\" % (len(sl),\n tree.name,\n n,\n cluster_id,\n \"\".join(\n pattern),\n \"\\t\".join(\n xpattern),\n node_id,\n \";\".join(otus)))\n\n counts[c] += n\n\n if ntotal_tree == 0:\n nempty += 1\n else:\n noutput += 1\n\n if options.loglevel >= 1:\n options.stdout.write(\"# ninput=%i, nempty=%i, noutput=%i, nskipped=%i, nerrors=%i\\n\" % (\n ninput, nempty, noutput, nskipped, nerrors))\n\n # write summary information\n\n if options.filename_summary:\n outfile = open(options.filename_summary, \"w\")\n else:\n outfile = options.stdout\n outfile.write(\"//\\n\")\n\n outfile.write(\"cluster\\tpattern\\tcounts\\t%s\\n\" % (\"\\t\".join(species)))\n\n for c in range(len(xsets)):\n pattern = buildPattern(nspecies, xsets[c])\n outfile.write(\"%i\\t%s\\t%i\\t%s\\n\" % (c,\n \"\".join(pattern),\n counts[c],\n \"\\t\".join(pattern)))\n\n if outfile != options.stdout:\n outfile.close()","def detect_objects(snap):\n client = vision.ImageAnnotatorClient()\n print(snap)\n\n with open(snap, 'rb') as im_file:\n content = im_file.read()\n image = vision.Image(content=content)\n\n objects = client.object_localization(image=image).localized_object_annotations\n\n print(f\"Found {len(objects)} objects\")\n [print(f\"{objet.name} : {round(objet.score*100,2)}\") for objet in objects]\n \n return objects","def create_objects(self, user, instrument):\n created_ids = set() # IDs for created objects.\n\n # ONLY applicable to Likert Questions.\n qtypes = {t.lower() for t, q in instrument.questions}\n if 'likertquestion' not in qtypes and len(qtypes) != 1:\n raise ValueError(\"A SurveyResult is only valid for LikertQuestions\")\n\n questions = [q for qt, q in instrument.questions]\n if len(questions) % 2 != 0:\n # Maybe survey is incomplete?\n raise ValueError(\"Instruments must have even number of questions\")\n\n # TODO: There's probably a nicer way to do this :-/\n # Split the questions into two subscales; they should be ordered\n # correctly by default.\n middle = int(len(questions) - len(questions) / 2)\n a, b = questions[:middle], questions[middle:]\n\n try:\n for q1, q2 in zip(a, b):\n # Q1 - Q2; discard anything less than zero (keep 0)\n # We need the user's responses to these questions.\n r1 = q1.likertresponse_set.filter(user=user).latest()\n r2 = q2.likertresponse_set.filter(user=user).latest()\n score = max(r1.selected_option - r2.selected_option, 0)\n labels = list(set(q1.labels + q2.labels))\n\n obj = self.create(\n user=user,\n instrument=instrument,\n score=score,\n labels=labels\n )\n created_ids.add(obj.id)\n except ObjectDoesNotExist:\n return self.get_queryset().none()\n\n return self.get_queryset().filter(id__in=created_ids)","def generate_asteroid_perspectives(self) -> list:\n for asteroid in self.asteroids:\n asteroid.generate_perspective()","def make_studyforrest_mockup(path):\n public = create(opj(path, 'public'), description=\"umbrella dataset\")\n # the following tries to capture the evolution of the project\n phase1 = public.create('phase1',\n description='old-style, no connection to RAW')\n structural = public.create('structural', description='anatomy')\n tnt = public.create('tnt', description='image templates')\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\n aligned = public.create('aligned', description='aligned image data')\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # new acquisition\n labet = create(opj(path, 'private', 'labet'), description=\"raw data ET\")\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\"raw data P2MRI\")\n phase2 = public.create('phase2',\n description='new-style, RAW connection')\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\n # add to derivatives\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n # never to be published media files\n media = create(opj(path, 'private', 'media'), description=\"raw data ET\")\n # assuming all annotations are in one dataset (in reality this is also\n # a superdatasets with about 10 subdatasets\n annot = public.create('annotations', description='stimulus annotation')\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\n # a few typical analysis datasets\n # (just doing 3, actual status quo is just shy of 10)\n # and also the real goal -> meta analysis\n metaanalysis = public.create('metaanalysis', description=\"analysis of analyses\")\n for i in range(1, 3):\n ana = public.create('analysis{}'.format(i),\n description='analysis{}'.format(i))\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # link to metaanalysis\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\n reckless='auto')\n # simulate change in an input (but not raw) dataset\n create_tree(\n aligned.path,\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\n aligned.save()\n # finally aggregate data\n aggregate = public.create('aggregate', description='aggregate data')\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n # the toplevel dataset is intentionally left dirty, to reflect the\n # most likely condition for the joint dataset to be in at any given\n # point in time","def get_activations_from_studies(cls, studies):\n\n activations = cls.query.filter(\n cls.pmid.in_(studies), cls.location_id < 81925).all()\n\n return activations","def _get_orientations(self):\n for atom in self.invarioms:\n atom.get_orientation()","def view_all_tracks_as_pairs(self):\n self.command(\"viewaspairs\")","def observations(store, loqusdb, case_obj, variant_obj):\n composite_id = (\"{this[chromosome]}_{this[position]}_{this[reference]}_\"\n \"{this[alternative]}\".format(this=variant_obj))\n obs_data = loqusdb.get_variant({'_id': composite_id}) or {}\n obs_data['total'] = loqusdb.case_count()\n\n obs_data['cases'] = []\n institute_id = variant_obj['institute']\n for case_id in obs_data.get('families', []):\n if case_id != variant_obj['case_id'] and case_id.startswith(institute_id):\n other_variant = store.variant(variant_obj['variant_id'], case_id=case_id)\n other_case = store.case(case_id)\n obs_data['cases'].append(dict(case=other_case, variant=other_variant))\n\n return obs_data","def get_stone_tests():\n stone_tests = []\n for rotation in stones_and_potions.possible_rotations():\n for sm in stones_and_potions.possible_stone_maps():\n stone_tests.append(\n ([(stones_and_potions.unalign(sm.apply_inverse(l), rotation), l)\n for l in stones_and_potions.possible_latent_stones()],\n unity_python_conversion.to_stone_unity_properties,\n functools.partial(from_stone_unity_properties, rotation=rotation),\n lambda x: x, _make_tuple))\n return stone_tests","def rtsobjects():\n pass","def skill_pedagogic_ressources(request, type, slug):\n\n if type == 'skill':\n base = get_object_or_404(Skill, code=slug)\n elif type == 'section':\n base = get_object_or_404(Section, id=slug)\n else:\n # type == 'coder'\n base = get_object_or_404(CodeR, id=slug)\n\n personal_resource = base.resource.filter(section=\"personal_resource\")\n lesson_resource = base.resource.filter(section=\"lesson_resource\")\n exercice_resource = base.resource.filter(section=\"exercice_resource\")\n other_resource = base.resource.filter(section=\"other_resource\")\n\n exercice_resource_sesamath = list()\n lesson_resource_sesamath = list()\n lesson_resource_khanacademy = list()\n\n sori_skills_lesson_resources = list()\n sori_skills_exercice_resources = list()\n sori_skills_other_resources = list()\n\n sori_skills_lesson_resource_sesamath = list()\n sori_skills_lesson_resource_khanacademy = list()\n sori_skills_exercice_resource_sesamath = list()\n\n sori_coder_lesson_resources = list()\n sori_coder_exercice_resources = list()\n sori_coder_other_resources = list()\n\n sori_coder_lesson_resource_sesamath = list()\n sori_coder_lesson_resource_khanacademy = list()\n sori_coder_exercice_resource_sesamath = list()\n\n # Sorting the different type of resources by category (personal, lesson, exercice or other)\n # and by type (khanacademy, sesamath or other)\n\n for exo in lesson_resource:\n if exo.content.get('from') and exo.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\n lesson_resource_sesamath.append([exo.pk, resource])\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\n\n elif exo.content.get('from') and exo.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=exo.content['referenced'])\n lesson_resource_khanacademy.append([exo.pk, resource])\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\n\n for exo in exercice_resource:\n if exo.content.get('from') and exo.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\n exercice_resource_sesamath.append([exo.pk, resource])\n exercice_resource = exercice_resource.exclude(pk=exo.pk)\n\n # Do the same operation for similar or identical resources :\n # Some Skill or CodeR can be similar or identical to other Skill or CodeR\n # We have to display the resources of those as well\n\n # We achieve this only if we have either a Skill or a CodeR.\n # If it is a Section, we don't display the similar/identical resources\n # First step : is base a Skill or a CodeR ?\n if isinstance(base, Skill):\n # Begin with the Skills, same step is done for CodeR a bit further\n\n # Retrieve all the similar and identical Relations for Skills\n skills_from = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], from_skill=base.id))\n skills_to = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], to_skill=base.id))\n\n # This list will gather all the similar or 'identic' Skills\n # (similar_or_identic = sori)\n sori_skills = list()\n for skill_from in skills_from:\n sori_skills.append(skill_from.to_skill)\n for skill_to in skills_to:\n sori_skills.append(skill_to.from_skill)\n\n # Get the associated resources for these Skills\n # We need to keep them separated by section to ease their use in the template\n\n for skill in sori_skills:\n lesson = skill.resource.filter(section=\"lesson_resource\")\n if lesson:\n sori_skills_lesson_resources.append([skill, lesson])\n exercice = skill.resource.filter(section=\"exercice_resource\")\n if exercice:\n sori_skills_exercice_resources.append([skill, exercice])\n other = skill.resource.filter(section=\"other_resource\")\n if other:\n sori_skills_other_resources.append([skill, other])\n\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\n for skill in sori_skills_lesson_resources:\n # Lists to regroup resources together\n sesamath_list = list()\n khan_list = list()\n\n for res in skill[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n elif res.content.get('from') and res.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\n khan_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not skill[1]:\n sori_skills_lesson_resources.remove(skill)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_skills_lesson_resource_sesamath.append([skill[0], sesamath_list])\n if khan_list:\n sori_skills_lesson_resource_khanacademy.append([skill[0], khan_list])\n\n # sori_skills_exercice_resources has resources of different types, we need to distinguish them\n for skill in sori_skills_exercice_resources:\n # List to regroup resources together\n sesamath_list = list()\n\n for res in skill[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n skill[1] = skill[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not skill[1]:\n sori_skills_exercice_resources.remove(skill)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_skills_exercice_resource_sesamath.append([skill[0], sesamath_list])\n\n if isinstance(base, CodeR) or isinstance(base, Skill):\n # Repeat this operation for the CodeR\n # TODO : Group these steps as a function for Skill and CodeR\n # Retrieve all the CodeR related to the current Skill or to the current CodeR\n\n if isinstance(base, Skill):\n related_coder = CodeR.objects.filter(skill=base.id)\n else:\n coder_from = CodeR_relations.objects.filter(\n Q(relation_type__in=['similar_to', 'identic_to'], from_coder=base.id))\n coder_to = CodeR_relations.objects.filter(\n Q(relation_type__in=['similar_to', 'identic_to'], to_coder=base.id))\n\n # This list will gather all the similar or 'identic' CodeR\n related_coder = list()\n for coder_from in coder_from:\n related_coder.append(coder_from.to_coder)\n for coder_to in coder_to:\n related_coder.append(coder_to.from_coder)\n # Case where we look for CodeR similar or identic to a CodeR\n\n # Get the associated resources for these CodeR\n\n for coder in related_coder:\n lesson = coder.resource.filter(section=\"lesson_resource\")\n if lesson:\n sori_coder_lesson_resources.append([coder, lesson])\n exercice = coder.resource.filter(section=\"exercice_resource\")\n if exercice:\n sori_coder_exercice_resources.append([coder, exercice])\n other = coder.resource.filter(section=\"other_resource\")\n if other:\n sori_coder_other_resources.append([coder, other])\n\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\n for coder in sori_coder_lesson_resources:\n # Lists to regroup resources together\n sesamath_list = list()\n khan_list = list()\n\n for res in coder[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n elif res.content.get('from') and res.content['from'] == \"skills_khanacademyvideoskill\":\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\n khan_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the Skill, we remove it from the list\n if not coder[1]:\n sori_coder_lesson_resources.remove(coder)\n\n # Once resources are sorted, we can add them with the associated Skill\n if sesamath_list:\n sori_coder_lesson_resource_sesamath.append([coder[0], sesamath_list])\n if khan_list:\n sori_coder_lesson_resource_khanacademy.append([coder[0], khan_list])\n\n # sori_coder_exercice_resources has resources of different types, we need to distinguish them\n for coder in sori_coder_exercice_resources:\n # List to regroup resources together\n sesamath_list = list()\n\n for res in coder[1]:\n if res.content.get('from') and res.content['from'] == \"skills_sesamathskill\":\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\n sesamath_list.append([res.pk, resource])\n coder[1] = coder[1].exclude(pk=res.pk)\n\n # If we have emptied the ressources in the CodeR, we remove it from the list\n if not coder[1]:\n sori_coder_exercice_resources.remove(coder)\n\n # Once resources are sorted, we can add them with the associated CodeR\n if sesamath_list:\n sori_coder_exercice_resource_sesamath.append([coder[0], sesamath_list])\n\n\n sesamath_references_manuals = Sesamath.objects.filter(ressource_kind__iexact=\"Manuel\")\n sesamath_references_cahiers = Sesamath.objects.filter(ressource_kind__iexact=\"Cahier\")\n\n return render(request, \"professor/skill/update_pedagogical_resources.haml\", {\n \"sesamath_references_manuals\": sesamath_references_manuals,\n \"sesamath_references_cahier\": sesamath_references_cahiers,\n \"base\": base,\n \"personal_resources\": personal_resource,\n \"other_resources\": other_resource,\n \"exercice_resources\": exercice_resource,\n \"exercice_resource_sesamath\": exercice_resource_sesamath,\n \"lesson_resources\": lesson_resource,\n \"lesson_resource_sesamath\": lesson_resource_sesamath,\n \"lesson_resource_khanacademy\": lesson_resource_khanacademy,\n \"type\": type,\n \"sori_skills_exercice_resources\": sori_skills_exercice_resources,\n \"sori_skills_lesson_resources\": sori_skills_lesson_resources,\n \"sori_skills_other_resources\": sori_skills_other_resources,\n \"sori_skills_lesson_resource_sesamath\": sori_skills_lesson_resource_sesamath,\n \"sori_skills_lesson_resource_khanacademy\": sori_skills_lesson_resource_khanacademy,\n \"sori_skills_exercice_resource_sesamath\": sori_skills_exercice_resource_sesamath,\n \"sori_coder_exercice_resources\": sori_coder_exercice_resources,\n \"sori_coder_lesson_resources\": sori_coder_lesson_resources,\n \"sori_coder_other_resources\": sori_coder_other_resources,\n \"sori_coder_lesson_resource_sesamath\": sori_coder_lesson_resource_sesamath,\n \"sori_coder_lesson_resource_khanacademy\": sori_coder_lesson_resource_khanacademy,\n \"sori_coder_exercice_resource_sesamath\": sori_coder_exercice_resource_sesamath,\n })","def triples():","def get_contenu(self):\n return self.objets","def analogies(self, queries):\n pass"],"string":"[\n \"def get(self):\\n return orthanc.study(self.orthanc_id)\",\n \"def studies(self):\\n return self._study_queryset\",\n \"def DumpStudies():\\n for name in myStudyManager.GetOpenStudies():\\n s=myStudyManager.GetStudyByName(name)\\n print \\\"study:\\\",name, s._get_StudyId()\\n DumpStudy(s)\",\n \"def search_research_studies_with_observations():\\n return ResearchStudy.where(struct={}).include('focus', Observation, reverse=True)\",\n \"def objects_rst(self):\\n return [_.as_rst for _ in self.objects]\",\n \"def visible_objects_and_duplis():\\n\\n for obj in context.visible_objects:\\n if obj.type == 'MESH':\\n if obj.modeling_cloth: \\n yield (obj, obj.matrix_world.copy())\",\n \"def visible_objects_and_duplis():\\n\\n for obj in context.visible_objects:\\n if obj.type == 'MESH':\\n if obj.modeling_cloth: \\n yield (obj, obj.matrix_world.copy())\",\n \"def visible_objects_and_duplis():\\r\\n \\r\\n for obj in context.visible_objects:\\r\\n if obj.type == 'MESH':\\r\\n yield (obj, obj.matrix_world.copy())\\r\\n \\r\\n if obj.dupli_type != 'NONE':\\r\\n obj.dupli_list_create(scene)\\r\\n for dob in obj.dupli_list:\\r\\n obj_dupli = dob.object\\r\\n if obj_dupli.type == 'MESH':\\r\\n yield (obj_dupli, dob.matrix.copy())\\r\\n \\r\\n obj.dupli_list_clear()\",\n \"def anvil_research_studies_with_observations(anvil_server, search_research_studies_with_observations):\\n return search_research_studies_with_observations.perform_resources(anvil_server.server)\",\n \"def objects(self):\",\n \"def get_administerable_studies_by_name():\\n researcher_admin = get_session_researcher()\\n if researcher_admin.site_admin:\\n studies = Study.get_all_studies_by_name()\\n else:\\n studies = researcher_admin.get_administered_studies_by_name()\\n return studies\",\n \"def get_queryset(self):\\n return Objective.objects.order_by('perspective')\",\n \"def model_cohort(cohort):\\n model_traj = []\\n for part in cohort:\\n for traj in part.trajectories:\\n if traj.filter is True:\\n model_traj.append(\\n modelling.model(x=traj.data.age.tolist(),\\n y=traj.data.AF.tolist(),\\n mutation=traj.mutation,\\n variant_class=traj.variant_class,\\n gene=traj.mutation.split()[0],\\n id=part.id,\\n p_key=traj.p_key))\\n return model_traj\",\n \"def visible_objects_and_duplis():\\n\\n for obj in context.visible_objects:\\n if obj.type == 'MESH':\\n yield (obj, obj.matrix_world.copy())\\n\\n if obj.instance_type != 'NONE':\\n obj.dupli_list_create(scene)\\n for dob in obj.dupli_list:\\n obj_dupli = dob.object\\n if obj_dupli.type == 'MESH':\\n yield (obj_dupli, dob.matrix.copy())\\n\\n obj.dupli_list_clear()\",\n \"def get_objectives(self):\\n return copy.deepcopy(self.objectives), self.gates_names\",\n \"def objets_uniques(self):\\n objets = []\\n for membre in self.membres:\\n for objet in membre.equipe:\\n if objet.unique:\\n objets.append(objet)\\n objets.extend(objet.prototype.objets_contenus(objet))\\n if membre.tenu and membre.tenu.unique:\\n objet = membre.tenu\\n objets.append(objet)\\n objets.extend(objet.prototype.objets_contenus(objet))\\n\\n return objets\",\n \"def all_research_studies_with_observations(aggregated_server, search_research_studies_with_observations):\\n return search_research_studies_with_observations.perform_resources(aggregated_server.server)\",\n \"def composeWorkplaceOntology():\\n\\n import ossPyFuncs \\n import pandas as pd\\n \\n #mysql query to extract full table from government organizations\\n #certian table columns feature capital letters which cases uproblems\\n postgreSql_selectQuery=\\\"SELECT * FROM us_gov_manual.us_govman_2019 ;\\\"\\n #pass querry and obtain table\\n govTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n\\n #mysql query to obtain academic instutions\\n postgreSql_selectQuery=\\\"SELECT institution FROM hipolabs.universities ;\\\"\\n #pass querry and obtain table\\n univTable=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2018_us1000;\\\"\\n businesses1=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2019_us1000;\\\"\\n businesses2=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n \\n postgreSql_selectQuery=\\\"SELECT company FROM forbes.fortune2020_global2000;\\\"\\n businesses3=ossPyFuncs.queryToPDTable(postgreSql_selectQuery)\\n\\n #combine theinsitutions into a vector\\n combinedSeries=[govTable['AgencyName'],univTable['institution'],businesses1['company'],businesses2['company'],businesses3['company']]\\n #turn the multi item vector into a single series\\n fullWordbank=pd.concat(combinedSeries)\\n #turn that series into a pd dataframe\\n wordbankTable=pd.DataFrame(fullWordbank.unique())\\n\\n return wordbankTable\",\n \"def phonology(request):\\n\\n perspective_cid = request.params.get('perspective_client_id')\\n perspective_oid = request.params.get('perspective_object_id')\\n\\n # Checking if we have limits on number of computed results.\\n\\n limit = (None if 'limit' not in request.params else\\n int(request.params.get('limit')))\\n\\n limit_exception = (None if 'limit_exception' not in request.params else\\n int(request.params.get('limit_exception')))\\n\\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\\n int(request.params.get('limit_no_vowel')))\\n\\n limit_result = (None if 'limit_result' not in request.params else\\n int(request.params.get('limit_result')))\\n\\n # TODO: get perspective's translation and language it belongs to.\\n\\n # We get lexical entries of this perspective with markup'ed sounds.\\n\\n Sound = aliased(Entity, name = \\\"Sound\\\")\\n PublishingSound = aliased(PublishingEntity, name = \\\"PublishingSound\\\")\\n\\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\\n LexicalEntry.parent_client_id == perspective_cid,\\n LexicalEntry.parent_object_id == perspective_oid,\\n LexicalEntry.marked_for_deletion == False,\\n Entity.parent_client_id == LexicalEntry.client_id,\\n Entity.parent_object_id == LexicalEntry.object_id,\\n Entity.marked_for_deletion == False,\\n Entity.additional_metadata.contains({\\\"data_type\\\": \\\"praat markup\\\"}),\\n PublishingEntity.client_id == Entity.client_id,\\n PublishingEntity.object_id == Entity.object_id,\\n PublishingEntity.published == True,\\n PublishingEntity.accepted == True,\\n Sound.client_id == Entity.self_client_id,\\n Sound.object_id == Entity.self_object_id,\\n Sound.marked_for_deletion == False,\\n PublishingSound.client_id == Sound.client_id,\\n PublishingSound.object_id == Sound.object_id,\\n PublishingSound.published == True,\\n PublishingSound.accepted == True))\\n\\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\\n # then several hundred such lexical entries.\\n\\n exception_counter = 0\\n no_vowel_counter = 0\\n result_list = list()\\n\\n for index, row in enumerate(query.yield_per(100)):\\n\\n markup_url = row.Entity.content\\n sound_url = row.Sound.content\\n\\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id)\\n\\n # Checking if we have cached result for this pair of sound/markup.\\n\\n cache_result = CACHE.get(cache_key)\\n\\n if cache_result == 'no_vowel':\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]: no vowels\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we have cached exception, we do the same as with absence of vowels, show its info and\\n # continue.\\n\\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\\n exception, traceback_string = cache_result[1:3]\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n '[CACHE {7}]: exception\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n log.debug(traceback_string)\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we actually have the result, we use it and continue.\\n\\n elif cache_result:\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in cache_result)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]:\\\\n{8}\\\\n{9}\\\\n{10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url, result_string))\\n\\n result_list.append(cache_result)\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n try:\\n # Getting markup, checking for each tier if it needs to be processed.\\n\\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\\n\\n textgrid = pympi.Praat.TextGrid(xmax = 0)\\n textgrid.from_file(\\n io.BytesIO(markup_bytes),\\n codec = chardet.detect(markup_bytes)['encoding'])\\n\\n tier_data_list = []\\n vowel_flag = False\\n\\n for tier_number, tier_name in textgrid.get_tier_name_num():\\n\\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\\n raw_interval_seq_list = [[]]\\n\\n # Splitting interval sequence on empty intervals.\\n\\n for raw_index, interval in enumerate(raw_interval_list):\\n\\n if len(interval[2].strip()) <= 0:\\n if len(raw_interval_seq_list[-1]) > 0:\\n raw_interval_seq_list.append([])\\n\\n else:\\n raw_interval_seq_list[-1].append((raw_index, interval))\\n\\n if len(raw_interval_seq_list[-1]) <= 0:\\n del raw_interval_seq_list[-1]\\n\\n # Selecting interval sequences for analysis, checking if we have unusual markup.\\n \\n interval_seq_list = []\\n interval_idx_to_raw_idx = dict()\\n\\n unusual_markup_flag = False\\n unusual_markup_list = []\\n\\n for raw_interval_seq in raw_interval_seq_list:\\n\\n interval_seq_list.append([])\\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\\n\\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\\n\\n interval_text = interval[2].strip()\\n\\n # Accepting interval if its text contains at least one vowel, and is short enough or\\n # is a valid phonetic transcription.\\n\\n transcription_check = re.fullmatch(transcription_re, interval_text)\\n\\n if (len(interval_text) > 0 and\\n any(character in vowel_set for character in interval_text) and\\n (len(interval_text) <= 2 or transcription_check)):\\n\\n interval_seq_list[-1].append(interval)\\n\\n sequence_index = len(interval_seq_list) - 1\\n interval_index = len(interval_seq_list[-1]) - 1\\n\\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\\n\\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\\n\\n elif not transcription_check:\\n\\n unusual_markup_flag = True\\n unusual_markup_list.append((raw_index, interval))\\n\\n transcription_list = [text for begin, end, text in raw_interval_list]\\n transcription = ''.join(transcription_list)\\n\\n selected_list = [text\\n for interval_list in interval_seq_list\\n for begin, end, text in interval_list]\\n\\n selected = ''.join(selected_list)\\n\\n # If we have intervals with unusual markup, we report them.\\n\\n if unusual_markup_flag:\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' has interval(s) with unusual transcription text: '\\n '{9} / {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\\n\\n # If the markup does not have any vowels, we note it and also report it.\\n\\n if all(character not in vowel_set for character in transcription):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' doesn\\\\'t have any vowel markup: {9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription_list))\\n\\n # It is also possible that while full transcription has vowels, intervals selected for\\n # analysis do not. In that case we also note it and report it.\\n\\n elif not any(character in vowel_set for character in selected):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' intervals to be processed don\\\\'t have any vowel markup: '\\n 'markup {9}, selected {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name,\\n transcription_list, selected_list))\\n\\n # Otherwise we store tier data to be used during processing of the sound file.\\n\\n else:\\n tier_data_list.append((tier_number, tier_name,\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\\n interval_idx_to_raw_idx, transcription)))\\n\\n vowel_flag = True\\n\\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\\n\\n if not vowel_flag:\\n\\n CACHE.set(cache_key, 'no_vowel')\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\\n\\n sound = None\\n with tempfile.NamedTemporaryFile() as temp_file:\\n\\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\\n temp_file.write(sound_file.read())\\n temp_file.flush()\\n\\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\\n\\n tier_result_list = []\\n\\n for tier_number, tier_name, tier_data in tier_data_list:\\n\\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\\n tier_result_list.append((tier_number, tier_name, tier_data))\\n continue\\n\\n # Analyzing vowel sounds of each interval sequence.\\n\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\\n transcription) = tier_data\\n\\n tier_result_list.append((tier_number, tier_name, []))\\n\\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\\n raw_interval_seq_list, interval_seq_list)):\\n\\n if len(interval_list) <= 0:\\n continue\\n\\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\\\\n find_max_interval_praat(sound, interval_list)\\n\\n max_intensity_interval = interval_list[max_intensity_index]\\n max_length_interval = interval_list[max_length_index]\\n\\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\\n\\n # Compiling results.\\n\\n max_length_str = '{0} {1:.3f} [{2}]'.format(\\n max_length_interval[2], max_length,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\\n\\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\\n max_intensity_interval[2],\\n max_intensity,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\\n\\n tier_result_list[-1][2].append([\\n ''.join(text for index, (begin, end, text) in raw_interval_list),\\n max_length_str,\\n '{0:.3f}'.format(max_length_f1_f2[0]),\\n '{0:.3f}'.format(max_length_f1_f2[1]),\\n max_intensity_str,\\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\\n '+' if max_intensity_index == max_length_index else '-'])\\n\\n # Saving result.\\n\\n result_list.append(tier_result_list)\\n CACHE.set(cache_key, tier_result_list)\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\\n '\\\\n{7}\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url, result_string))\\n\\n # Stopping earlier, if required.\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n except Exception as exception:\\n\\n #\\n # NOTE\\n #\\n # Exceptional situations encountered so far:\\n #\\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\\n #\\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\\n #\\n # 2. Markup for one of the intervals contains a newline \\\"\\\\n\\\", and pympi fails to parse it.\\n # Praat parses such files without problems.\\n #\\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\\n #\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'exception\\\\n{7}\\\\n{8}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url))\\n\\n # if we encountered an exception, we show its info and remember not to try offending\\n # sound/markup pair again.\\n\\n traceback_string = ''.join(traceback.format_exception(\\n exception, exception, exception.__traceback__))[:-1]\\n\\n log.debug(traceback_string)\\n\\n CACHE.set(cache_key, ('exception', exception,\\n traceback_string.replace('Traceback', 'CACHEd traceback')))\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\\n perspective_cid, perspective_oid,\\n len(result_list), '' if len(result_list) == 1 else 's',\\n no_vowel_counter, exception_counter))\\n\\n # If we have no results, we indicate the situation and also show number of failures and number of\\n # markups with no vowels.\\n\\n if not result_list:\\n request.response.status = HTTPPreconditionFailed.code\\n\\n return {\\n \\\"error\\\": \\\"no markups for this query\\\",\\n \\\"exception_counter\\\": exception_counter,\\n \\\"no_vowel_counter\\\": no_vowel_counter}\\n\\n # Otherwise we create and then serve Excel file.\\n\\n excel_book = xlwt.Workbook(encoding = \\\"utf-8\\\")\\n sheet = excel_book.add_sheet(\\\"Sheet 1\\\")\\n\\n sheet.write(0, 0, 'Transcription')\\n sheet.write(0, 1, 'Longest (seconds) interval')\\n sheet.write(0, 2, 'F1 (Hz)')\\n sheet.write(0, 3, 'F2 (Hz)')\\n sheet.write(0, 4, 'Highest intensity (dB) interval')\\n sheet.write(0, 5, 'F1 (Hz)')\\n sheet.write(0, 6, 'F2 (Hz)')\\n sheet.write(0, 7, 'Coincidence')\\n\\n row_counter = 1\\n\\n for tier_result_list in result_list:\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\\n\\n if tier_result_seq_list == 'no_vowel':\\n continue\\n\\n for tier_data in tier_result_seq_list:\\n for index, tier_data_str in enumerate(tier_data):\\n sheet.write(row_counter, index, tier_data_str)\\n\\n row_counter += 1\\n\\n # Formatting column widths.\\n\\n sheet.col(0).width = 24 * 256\\n sheet.col(1).width = 24 * 256\\n sheet.col(2).width = 12 * 256\\n sheet.col(3).width = 12 * 256\\n sheet.col(4).width = 24 * 256\\n sheet.col(5).width = 12 * 256\\n sheet.col(6).width = 12 * 256\\n sheet.col(7).width = 12 * 256\\n\\n excel_stream = io.BytesIO()\\n excel_book.save(excel_stream)\\n excel_stream.seek(0)\\n\\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\\n # content-type.\\n\\n response = Response(content_type = 'application/vnd.ms-excel')\\n\\n response.app_iter = FileIter(excel_stream)\\n response.headers['Content-Disposition'] = \\\"attachment; filename=phonology.xls\\\"\\n\\n return response\",\n \"def getHierarchies():\",\n \"def getHierarchies():\",\n \"def iterate_studies(self, start, end):\\n pass\",\n \"def get_queryset(self):\\n return Initiative.objects.filter(objective__perspective__description='Financial').order_by('objective')\",\n \"def _get_all_oshapes(self):\\n an_iname = self.node_list[0]\\n an_inode = self.builder.nodes[an_iname]\\n an_ishape = an_inode.oshapes['loc']\\n \\n return {'main' : an_ishape,\\n 'loc' : an_ishape,\\n 'cov' : an_ishape + [an_ishape[-1]]}\",\n \"def findAtypicalTerms(self):\\n self.atypicalTermsDict = collections.OrderedDict()\\n distanceList = list()\\n distance = 0\\n for key in self.summaryFilteredDict:\\n partitionName = str(key).split(\\\" :\\\")[0]\\n partition = voc.getPartition(partitionName)\\n modNames = partition.getModNames()\\n currentModality = str(key).split(\\\": \\\")[1]\\n indexCurrentModality = modNames.index(currentModality)\\n coverCurrentModality = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,partitionName + \\\" : \\\" + currentModality) #cover(v',R)\\n if coverCurrentModality > 0:\\n for modality in partition.getModalities():\\n coverModality = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,partitionName + \\\" : \\\" + modality.getName()) # cover(v,R)\\n if modality.isTrapeziumModality():\\n indexModality = modNames.index(modality.getName())\\n distance = abs(indexCurrentModality - indexModality) / (partition.getNbModalities() - 1) #d(v,v')\\n elif modality.isEnumModality():\\n if (modality.getName() == currentModality):\\n distance = 0\\n else:\\n distance = 1\\n distanceList.append(min(distance, 1 - coverCurrentModality, coverModality)) # min(d(v,v'),cover(v,R),1-cover(v',R))\\n self.atypicalTermsDict[partitionName + \\\" : \\\" + currentModality] = max(distanceList) # D(v',R)\\n distanceList = list()\",\n \"def all_objects():\\n objs = {}\\n objs['Section'] = list(h.all_sec())\\n objs['Segment'] = []\\n for sec in objs['Section']:\\n objs['Segment'].extend(list(sec.allseg()))\\n objs['PointProcess'] = []\\n for seg in objs['Segment']:\\n objs['PointProcess'].extend(list(seg.point_processes()))\\n \\n return objs\",\n \"def demo_ortho_slicer():\\n pl.clf()\\n oslicer = OrthoSlicer(cut_coords=(0, 0, 0))\\n from .anat_cache import _AnatCache\\n map, affine, _ = _AnatCache.get_anat()\\n oslicer.plot_map(map, affine, cmap=pl.cm.gray)\\n return oslicer\",\n \"def open_iossifov_neuron_cohort():\\n logging.info('getting Iossifov et al Neuron 2012 cohort')\\n s1 = pandas.read_excel(supp_s1_url, sheet_name='SNV.v4.1-normlized')\\n s2 = pandas.read_excel(supp_s2_url, sheet_name='suppLGKTable')\\n s3 = pandas.read_excel(supp_s3_url, sheet_name='ID.v4.1-normlized')\\n \\n fam_ids = list(s1.quadId) + list(s2.quadId) + list(s3.quadId)\\n members = list(s1.inChild) + list(s2.inChild) + list(s3.inChild)\\n \\n sex = ['M', 'F']\\n affected = ['aut', 'sib']\\n possible = list(itertools.product(affected, sex))\\n study = ['10.1016/j.neuron.2012.04.009']\\n \\n persons = set()\\n for fam, children in zip(fam_ids, members):\\n for affected, sex in possible:\\n string = f'{affected}{sex}'\\n if string in children:\\n status = ['unaffected'] if affected != 'aut' else ['HP:0000717']\\n member = 's1' if affected != 'aut' else 'p1'\\n sex = 'female' if sex == 'F' else 'male'\\n person_id = f'{fam}.{member}|asd_cohorts'\\n \\n person = Person(person_id, sex, status, study)\\n persons.add(person)\\n \\n return persons\",\n \"def make_openalex_dataset(dataset: ObservatoryDataset) -> List[dict]:\\n\\n result = []\\n for paper in dataset.papers:\\n entry = {\\n \\\"id\\\": str(paper.id),\\n \\\"doi\\\": f\\\"https://doi.org/{paper.doi}\\\",\\n \\\"cited_by_count\\\": len(paper.cited_by),\\n \\\"concepts\\\": [\\n {\\\"id\\\": str(fos.id), \\\"display_name\\\": fos.name, \\\"level\\\": fos.level} for fos in paper.fields_of_study\\n ],\\n \\\"authorships\\\": [\\n {\\n \\\"author\\\": {\\n \\\"id\\\": str(author.id),\\n \\\"display_name\\\": author.name,\\n },\\n \\\"institutions\\\": [\\n {\\n \\\"id\\\": str(author.institution.id),\\n \\\"ror\\\": author.institution.ror_id,\\n \\\"display_name\\\": author.institution.name,\\n \\\"country_code\\\": author.institution.country_code,\\n \\\"type\\\": author.institution.types,\\n }\\n ],\\n }\\n for author in paper.authors\\n ],\\n }\\n result.append(entry)\\n\\n return result\",\n \"def salome_study_init(theStudyId=0):\\n\\n global salome_study_initial\\n global myStudyManager, myStudyId, myStudy, myStudyName\\n global orb, lcc, naming_service, cm\\n\\n if salome_study_initial:\\n salome_study_initial = 0\\n\\n orb, lcc, naming_service, cm = salome_kernel.salome_kernel_init()\\n\\n # get Study Manager reference\\n if verbose(): print \\\"looking for studyManager ...\\\"\\n obj = naming_service.Resolve('myStudyManager')\\n myStudyManager = obj._narrow(SALOMEDS.StudyManager)\\n if verbose(): print \\\"studyManager found\\\"\\n\\n # get active study Id, ref and name\\n myStudyId = getActiveStudy(theStudyId)\\n if verbose(): print \\\"myStudyId\\\",myStudyId\\n myStudy = myStudyManager.GetStudyByID(myStudyId)\\n myStudyName = myStudy._get_Name()\\n\\n return myStudyManager, myStudyId, myStudy, myStudyName\",\n \"def dbgap_research_studies_with_observations(dbgap_server, search_research_studies_with_observations):\\n return search_research_studies_with_observations.perform_resources(dbgap_server.server)\",\n \"def get_soma_objects(self):\\n\\n msg_store = MessageStoreProxy(database=\\\"soma2data\\\", collection=\\\"soma2\\\")\\n objs = msg_store.query(SOMA2Object._type, message_query={\\\"map_name\\\":self.soma_map,\\\"config\\\":self.soma_conf})\\n print \\\"queried soma2 objects >> \\\", objs\\n self.soma_objects = ce.get_soma_objects()\\n print \\\"hard coded objects >> \\\", [self.soma_objects[r].keys() for r in self.soma_objects.keys()]\",\n \"def examples(self):\\n for obj_ind in range(len(self.objects)):\\n yield self.get_object_intent_by_index(obj_ind)\",\n \"def orthonormation_method(standardized_methods_cleaned):\\n method_standardized_ortho = standardized_methods_cleaned.copy(deep=True)\\n\\n categories = method_standardized_ortho.columns.tolist()\\n\\n # Normation of the first category\\n method_standardized_ortho[categories[0]] = method_standardized_ortho[categories[0]] / \\\\\\n linalg.norm(method_standardized_ortho[categories[0]])\\n\\n # Normation of every following categories\\n j = 0\\n while j < len(categories):\\n i = 0\\n while i < j:\\n # Calculates the orthogonal projection of j on each i and substraction of the projection from j\\n method_standardized_ortho[categories[j]] = \\\\\\n method_standardized_ortho[categories[j]] - method_standardized_ortho[categories[i]] * (\\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[j]]) /\\n sum(method_standardized_ortho[categories[i]] * method_standardized_ortho[categories[i]]))\\n if linalg.norm(method_standardized_ortho[categories[j]]) == 0:\\n # If after the projection, if the j columns is null it is droped (i.e it is linearly dependant with\\n # the other columns) and the inner loop stops\\n method_standardized_ortho.drop(method_standardized_ortho.columns[j], inplace=True, axis=1)\\n categories.remove(categories[j])\\n\\n break\\n else:\\n # If the j column is not null, it is normed and the inner while loop keeps going\\n method_standardized_ortho[categories[j]] = method_standardized_ortho[categories[j]] / \\\\\\n (linalg.norm(method_standardized_ortho[categories[j]]))\\n i += 1\\n j += 1\\n\\n return method_standardized_ortho\",\n \"def test_ResearchStudy_Observations(all_research_studies_with_observations):\\n all_observations = {r.relativePath(): r for r in all_research_studies_with_observations if\\n r.__class__.__name__ == 'Observation'}\\n all_research_studies = {r.relativePath(): r for r in all_research_studies_with_observations if\\n r.__class__.__name__ == 'ResearchStudy'}\\n research_studies_missing_observations = dict(**all_research_studies)\\n for observation in all_observations.values():\\n del research_studies_missing_observations[observation.focus[0].reference]\\n assert len(\\n research_studies_missing_observations) == 0, f\\\"ResearchStudies missing Observations {[r.id for r in research_studies_missing_observations.values()]}\\\"\",\n \"def clarify_objects(self): \\n dict_cxt = dict(list(zip(list(map(tuple, self)), self.objects)))\\n table = list(map(list, list(dict_cxt.keys())))\\n objects = list(dict_cxt.values())\\n return Context(table, objects, self.attributes)\",\n \"def generate_voc(self):\\n\\n observations = [\\\"walk\\\", \\\"shop\\\", \\\"clean\\\", \\\"tennis\\\", \\\"read\\\"]\\n states = [\\\"sunny\\\", \\\"rainy\\\", \\\"snowy\\\"]\\n\\n # Sort them alphabetically, just to be on the safe side\\n observations.sort()\\n states.sort()\\n\\n return (observations, states)\",\n \"def variations():\",\n \"def get_all_object_names(self):\\n o_objects = []\\n for s in [\\\"Non Model\\\", \\\"Solids\\\", \\\"Unclassified\\\", \\\"Sheets\\\", \\\"Lines\\\"]:\\n o_objects += self.design.modeler.get_objects_in_group(s)\\n return o_objects\",\n \"def get_representation(self, syl):\\n \\n first = syl[0]\\n second = syl[1]\\n \\n return [self.representation[first], self.representation[second]]\",\n \"def _get_all_oshapes(self):\\n iseq_name = self.node_list[0]\\n iseq = self.builder.nodes[iseq_name]\\n iseq_mainshape = iseq.oshapes['main']\\n \\n return {'main' : iseq_mainshape,\\n 'loc' : iseq_mainshape,\\n 'invscaled' : iseq_mainshape + [iseq_mainshape[-1]],\\n 'invscaleoffd' : iseq_mainshape + [iseq_mainshape[-1]]}\",\n \"def _get_objects(\\n self, model: Model, old_objects: List[Viewable], doc: Document,\\n root: Model, comm: Optional[Comm] = None\\n ):\\n from ..pane.base import RerenderError, panel\\n new_models, old_models = [], []\\n for i, pane in enumerate(self.objects):\\n pane = panel(pane)\\n self.objects[i] = pane\\n\\n for obj in old_objects:\\n if obj not in self.objects:\\n obj._cleanup(root)\\n\\n current_objects = list(self.objects)\\n ref = root.ref['id']\\n for i, pane in enumerate(self.objects):\\n if pane in old_objects and ref in pane._models:\\n child, _ = pane._models[root.ref['id']]\\n old_models.append(child)\\n else:\\n try:\\n child = pane._get_model(doc, root, model, comm)\\n except RerenderError as e:\\n if e.layout is not None and e.layout is not self:\\n raise e\\n e.layout = None\\n return self._get_objects(model, current_objects[:i], doc, root, comm)\\n new_models.append(child)\\n return new_models, old_models\",\n \"def get_queryset(self):\\n return Initiative.objects.filter(objective__perspective__description='Processes').order_by('objective')\",\n \"def get_source_studies(self):\\n return list(set([trait.source_dataset.source_study_version.study for trait in self.get_all_source_traits()]))\",\n \"def get_queryset(self):\\n return Objective.objects.filter(perspective__description='Financial').order_by('code')\",\n \"def getLandsatCollection():\\n ## standardize band names\\n bn8 = ['B1', 'B2', 'B3', 'B4', 'B6', 'pixel_qa', 'B5', 'B7']\\n bn7 = ['B1', 'B1', 'B2', 'B3', 'B5', 'pixel_qa', 'B4', 'B7']\\n bn5 = ['B1', 'B1', 'B2', 'B3', 'B5', 'pixel_qa', 'B4', 'B7']\\n bns = ['uBlue', 'Blue', 'Green', 'Red', 'Swir1', 'BQA', 'Nir', 'Swir2']\\n\\n # create a merged collection from landsat 5, 7, and 8\\n ls5 = ee.ImageCollection(\\\"LANDSAT/LT05/C01/T1_SR\\\").select(bn5, bns)\\n\\n ls7 = (ee.ImageCollection(\\\"LANDSAT/LE07/C01/T1_SR\\\")\\n .filterDate('1999-04-15', '2003-05-30')\\n .select(bn7, bns))\\n\\n ls8 = ee.ImageCollection(\\\"LANDSAT/LC08/C01/T1_SR\\\").select(bn8, bns)\\n\\n merged = ls5.merge(ls7).merge(ls8)\\n\\n return(merged)\",\n \"def get_modeller_objects(self):\\n return self._modpt, self.__edat.modpt, self.__libs.modpt\",\n \"def sngl_obj_evo(self, lacking):\\n prob, algo = self.probinit('jde', 0)\\n l = list()\\n u = 6+(self.N-3)*4\\n for i in range(lacking):\\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\\n for j in range(u):\\n archi.evolve(5)\\n stdout.write(\\\"\\\\r{0} / {1}\\\".format(i*u+j+1, lacking*u))\\n stdout.flush()\\n tmp = [isl for isl in archi]\\n tmp.sort(key = lambda x: x.population.champion.f[0]);\\n l.append(tmp[0].population.champion)\\n stdout.write(\\\" Done. \\\")\\n return l, prob\",\n \"def phenotypes(self):\\n\\t\\treturn Phenotype.PhenotypesByPatient(self.id, self.host)\",\n \"def get_designs(self):\",\n \"def _get_subobjects(self) -> Iterable[SymbolicObject]:\\n\\n return self._subobjects\",\n \"def test_returns_all_studies_with_archived_tagged_traits(self):\\n tag = TagFactory.create()\\n tagged_traits = []\\n for study in self.studies:\\n tmp = TaggedTraitFactory.create(trait__source_dataset__source_study_version__study=study,\\n archived=True, tag=tag)\\n tagged_traits.append(tmp)\\n get_data = {'q': ''}\\n response = self.client.get(self.get_url(), get_data)\\n pks = get_autocomplete_view_ids(response)\\n self.assertEqual(sorted([study.pk for study in self.studies]), sorted(pks))\",\n \"def _orthogonal_init(self):\\n # if is a conv layer, will need to reshape to fan in matrix,\\n # which is of dimension\\n # num input feature maps * filter height * filter width\\n if(len(self.dims) > 2):\\n rv_samp = np.random.randn(self.dims[2],\\n self.dims[0] * self.dims[1] * self.dims[3])\\n out_sigma = np.sqrt(1.0 / rv_samp.shape[1])\\n # otherwise will be a densely connected layer\\n else:\\n rv_samp = np.random.randn(self.dims[0], self.dims[1])\\n out_sigma = np.sqrt(1.0 / rv_samp.shape[0])\\n # perform SVD\\n U, _, V = np.linalg.svd(rv_samp, full_matrices=False) #pylint: disable=invalid-name\\n # both U and V are orthoginal matricies, so will choose the one\\n # that is the correct dimensions for our layer\\n ortho_matrix = U if U.shape == rv_samp.shape else V\\n # rescale so it is unit variance for each vector\\n # print(\\\"std(q) = {}\\\".format(np.std(q)))\\n ortho_norm = (ortho_matrix / np.std(ortho_matrix)) * out_sigma\\n #print(\\\"std(qs) = {}\\\".format(np.std(qs)))\\n #print(q.shape)\\n return ortho_norm.reshape(self.dims).astype(np.float32)\",\n \"def undulations(**kwargs):\\n\\n\\t#---parameters\\n\\tsn = kwargs['sn']\\n\\twork = kwargs['workspace']\\n\\tcalc = kwargs['calc']\\n\\tupname = 'lipid_abstractor'\\n\\tgrid_spacing = calc['specs']['grid_spacing']\\n\\tvecs = datmerge(kwargs,upname,'vecs')\\n\\tnframes = int(np.sum(datmerge(kwargs,upname,'nframes')))\\n\\ttrajectory = datmerge(kwargs,upname,'points')\\n\\tattrs,result = {},{}\\n\\t#---! hacking through error with monolayer separation\\n\\ttry: monolayer_indices = kwargs['upstream'][upname+'0']['monolayer_indices']\\n\\texcept: monolayer_indices = kwargs['upstream'][upname]['monolayer_indices']\\n\\t#---choose grid dimensions\\n\\tgrid = np.array([round(i) for i in np.mean(vecs,axis=0)/grid_spacing])[:2]\\n\\t#---! removed timeseries from result for new version of omnicalc\\n\\t#---parallel\\n\\tmesh = [[],[]]\\n\\tfor mn in range(2):\\n\\t\\tstart = time.time()\\n\\t\\tmesh[mn] = Parallel(n_jobs=work.nprocs,verbose=0,require='sharedmem')(\\n\\t\\t\\tdelayed(makemesh_regular)(\\n\\t\\t\\t\\ttrajectory[fr][np.where(monolayer_indices==mn)],vecs[fr],grid)\\n\\t\\t\\tfor fr in framelooper(nframes,start=start,text='monolayer %d, frame'%mn))\\n\\tchecktime()\\n\\n\\t#---pack\\n\\tresult['mesh'] = np.array(mesh)\\n\\tresult['grid'] = np.array(grid)\\n\\tresult['nframes'] = np.array(nframes)\\n\\tresult['vecs'] = vecs\\n\\tattrs['grid_spacing'] = grid_spacing\\n\\treturn result,attrs\",\n \"def latents(self):\\n self.assert_sampled()\\n return self._representations\",\n \"def _get_halo(self,i):\\n if self._order is False:\\n if self._subs is True:\\n #this needs to be tested again on a snapshot that is not ordered!\\n x = Halo(i, self, self.base, np.where(np.in1d(self.base['iord'], self.ids[self._subhalodat['sub_off'][i]:self._subhalodat['sub_off'][i]+self._subhalodat['sub_len'][i]] )))\\n else:\\n x = Halo(i, self, self.base, np.where(np.in1d(self.base['iord'], self.ids[self._halodat['group_off'][i]:self._halodat['group_off'][i]+self._halodat['group_len'][i]] )))\\n \\n else:\\n if self._subs is False: #to use groups as halos:\\n x = Halo(i, self, self.base, self.ids[self._halodat['group_off'][i]:self._halodat['group_off'][i]+self._halodat['group_len'][i]] ) \\n else:\\n x=Halo(i, self, self.base, self.ids[self._subhalodat['sub_off'][i]:self._subhalodat['sub_off'][i]+self._subhalodat['sub_len'][i]] )\\n \\n x._descriptor = \\\"halo_\\\"+str(i)\\n x.properties.update(self.get_halo_properties(i))\\n return x\",\n \"def kids_first_research_studies_with_observations(caplog, kids_first_server, search_research_studies_with_observations):\\n # caplog.set_level(logging.DEBUG)\\n return search_research_studies_with_observations.perform_resources(kids_first_server.server)\",\n \"def get_session_researcher_study_ids():\\n session_researcher = get_session_researcher()\\n if session_researcher.site_admin:\\n return Study.objects.exclude(deleted=True).values_list(\\\"id\\\", flat=True)\\n else:\\n return session_researcher.study_relations.filter(study__deleted=False).values_list(\\\"study__id\\\", flat=True)\",\n \"def make_objects(self):\\n pass\",\n \"def representations(self):\\n self.assert_sampled()\\n return self._representations\",\n \"def GetObjects(self): \\r\\n return self.model.GetObjects()\",\n \"def get_queryset(self):\\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')\",\n \"def samples(self):\\n samples = set()\\n for trio in self.trios:\\n if trio.child is None or trio.mother is None or trio.father is None:\\n continue\\n samples.add(trio.father)\\n samples.add(trio.mother)\\n samples.add(trio.child)\\n return list(samples)\",\n \"def newAnalyzer():\\n analyzer = {'crimes': None,\\n 'dateIndex': None,\\n 'autors': None,\\n 'instrumentalness': None,\\n 'tempo':None,\\n 'liveness':None,\\n 'speechiness':None,\\n 'danceability':None,\\n 'valence':None,\\n 'loudness':None,\\n 'acousticness':None,\\n 'energy':None,\\n 'generos':None\\n }\\n\\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n\\n analyzer['autors'] = om.newMap(omaptype='RBT',\\n comparefunction=compareAUTOR)\\n\\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n analyzer['valence'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['loudness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['energy'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n\\n analyzer['generos']= m.newMap(11,\\n maptype='CHAINING',\\n loadfactor=4.0)\\n \\n return analyzer\",\n \"def orbit(self):\\n return [x for x in TransitiveIdeal(attrcall('simple_reflections'), [self])]\",\n \"def structures(self):\\n pdb = self.name\\n residues = self.__residues__(pdb)\\n return Structure(list(residues), pdb=pdb)\",\n \"def getCriticStation(analyzer):\\n mayIn = model.getRankMay(analyzer,\\\"in\\\")\\n mayOut=model.getRankMay(analyzer,\\\"out\\\")\\n less=model.getRankMen(analyzer,\\\"LessPopular\\\")\\n return (mayIn,mayOut,less)\",\n \"def top_sources_male(args: Dict[str, Any]) -> List[object]:\\n query = [\\n {\\n \\\"$match\\\": {\\n \\\"body\\\": {\\\"$ne\\\": \\\"\\\"},\\n \\\"quotesUpdated\\\": {\\\"$exists\\\": True},\\n \\\"outlet\\\": {\\\"$in\\\": args[\\\"outlets\\\"]},\\n \\\"publishedAt\\\": {\\n \\\"$gte\\\": args[\\\"begin_date\\\"],\\n \\\"$lt\\\": args[\\\"end_date\\\"] + timedelta(days=1),\\n },\\n }\\n },\\n {\\\"$project\\\": {\\\"outlet\\\": 1.0, \\\"sourcesMale\\\": 1.0}},\\n {\\\"$unwind\\\": {\\\"path\\\": \\\"$sourcesMale\\\", \\\"preserveNullAndEmptyArrays\\\": False}},\\n {\\\"$group\\\": {\\\"_id\\\": \\\"$sourcesMale\\\", \\\"count\\\": {\\\"$sum\\\": 1.0}}},\\n {\\\"$sort\\\": {\\\"count\\\": args[\\\"sort\\\"]}},\\n {\\\"$limit\\\": args[\\\"limit\\\"]},\\n ]\\n return query\",\n \"def __init__(self):\\n self.rho=[]\\n self.te=[]\\n self.ti=[]\\n self.ne=[]\\n self.ni=[]\\n self.ni1=[]\\n self.ni2=[]\\n self.ni3=[]\\n self.vtor=[]\\n self.zeff=[]\\n\\n self.nion=1\\n self.Z=[]\\n self.A=[]\\n self.coll_mode=[]\",\n \"def get_queryset(self):\\n return Initiative.objects.order_by('objective')\",\n \"def sdc_to_distributions(self, mysdc):\\n if \\\"right\\\" in mysdc[\\\"verb\\\"]:\\n D_mat = transpose(self.T_mat_right)\\n elif \\\"left\\\" in mysdc[\\\"verb\\\"]:\\n D_mat = transpose(self.T_mat_left)\\n else:\\n D_mat = transpose(self.T_mat_str)\\n \\n T_mat = ones([len(D_mat), len(D_mat[0])])*1.0\\n \\n \\n if mysdc[\\\"sr\\\"] != None and len(mysdc[\\\"landmarks\\\"]) > 0 and self.use_spatial_relations:\\n\\n sr_i = self.sr_class.engineToIdx(mysdc[\\\"sr\\\"])\\n SR_mat = self.srel_mat[sr_i,:,:,:]\\n L_mat = self.get_prob_landmark_given_sdc_modifiers(mysdc)\\n L_mat_entropy = entropy(L_mat)\\n print \\\"using spatial relations\\\", str(mysdc)\\n if L_mat_entropy > 2 and False:\\n SR_mat = None\\n L_mat = None\\n else:\\n SR_mat = None\\n L_mat = None\\n \\n if mysdc[\\\"landmark\\\"] != None:\\n O_mat = self.O_mat[:,self.names_to_index[mysdc[\\\"landmark\\\"]]]\\n else:\\n O_mat = None\\n \\n return O_mat, T_mat, SR_mat, L_mat, D_mat\",\n \"def merge(cls, analyses):\\r\\n raise NotImplementedError()\",\n \"def substantiate():\",\n \"def hispaniola_models():\\n return [\\n ('GFS (6-hr steps, 7 days)', 'gfs'),\\n ('WRF-PR (1-hr steps, 2 days)', 'wrfpr'),\\n ]\",\n \"def _get_orthogonal_states(self, and_state):\\n if and_state not in self.and_states:\\n states = and_state.get_orthogonal_states()\\n states.insert(0, and_state)\\n self.and_states[and_state] = states\\n return self.and_states[and_state]\",\n \"def serotype_escherichia(metadata, analysistype):\\n for sample in metadata:\\n # Initialise negative results to be overwritten when necessary\\n sample[analysistype].best_o_pid = '-'\\n sample[analysistype].o_genes = ['-']\\n sample[analysistype].o_set = ['-']\\n sample[analysistype].best_h_pid = '-'\\n sample[analysistype].h_genes = ['-']\\n sample[analysistype].h_set = ['-']\\n if sample.general.bestassemblyfile != 'NA':\\n if sample.general.closestrefseqgenus in ['Escherichia', 'Shigella']:\\n o = dict()\\n h = dict()\\n for result, percentid in sample[analysistype].blastresults.items():\\n if 'O' in result.split('_')[-1]:\\n o.update({result: float(percentid)})\\n if 'H' in result.split('_')[-1]:\\n h.update({result: float(percentid)})\\n # O\\n try:\\n sorted_o = sorted(o.items(), key=operator.itemgetter(1), reverse=True)\\n sample[analysistype].best_o_pid = str(sorted_o[0][1])\\n\\n sample[analysistype].o_genes = [gene for gene, pid in o.items()\\n if str(pid) == sample[analysistype].best_o_pid]\\n sample[analysistype].o_set = \\\\\\n list(set(gene.split('_')[-1] for gene in sample[analysistype].o_genes))\\n except (KeyError, IndexError):\\n pass\\n # H\\n try:\\n sorted_h = sorted(h.items(), key=operator.itemgetter(1), reverse=True)\\n sample[analysistype].best_h_pid = str(sorted_h[0][1])\\n sample[analysistype].h_genes = [gene for gene, pid in h.items()\\n if str(pid) == sample[analysistype].best_h_pid]\\n sample[analysistype].h_set = \\\\\\n list(set(gene.split('_')[-1] for gene in sample[analysistype].h_genes))\\n except (KeyError, IndexError):\\n pass\\n return metadata\",\n \"def build_from_landscape(self, landscape):\\n assert(self.models_loaded)\\n\\n self.farms=list()\\n for p in landscape.premises:\\n f=self.farm_models[p.production_type].clone(p.name, p.size)\\n self.farms.append(f)\\n\\n #### airborne\\n if len(self.spread_models)>0:\\n a_spread_model=next (iter (self.spread_models.values()))\\n special_factor=a_spread_model.herd_factor(landscape.premises)\\n for v in self.spread_models.values():\\n v.set_special_factor(special_factor)\\n self.airborne=list()\\n for aidx, bidx in itertools.permutations(range(len(self.farms)), 2):\\n a=self.farms[aidx]\\n b=self.farms[bidx]\\n from_type=landscape.premises[aidx].production_type\\n to_type=landscape.premises[bidx].production_type\\n dx=landscape.distances[aidx, bidx]\\n air_model=self.spread_models[(from_type, to_type)].clone(\\n a, b, dx)\\n self.airborne.append(air_model)\\n else:\\n self.airborne=list()\\n\\n ### Indirect and Direct Contact\\n self.indirect=list()\\n if len(self.contact_models)>0 and self.use_indirect:\\n for ind_idx in range(len(self.farms)):\\n f=self.farms[ind_idx]\\n p=landscape.premises[ind_idx]\\n for cm in self.contact_models[p.production_type]:\\n im=cm.clone(self.farms, landscape, ind_idx)\\n self.indirect.append(im)\",\n \"def toggled_objects(self):\\n\\t\\tobjects = []\\n\\t\\tfor i in range(len(self.object_list)):\\n\\t\\t\\tif(self.toggles[i]):\\n\\t\\t\\t\\tnext_object = (self.object_list[i], self.quantities[i])\\n\\t\\t\\t\\tobjects.append(next_object)\\n\\t\\treturn objects\",\n \"def get_queryset(self):\\n return Initiative.objects.filter(objective__perspective__description='Customer').order_by('objective')\",\n \"def get_common_food(cls):\\n objs = cls.objects\\n return objs\",\n \"def get_objects(si, args):\\n # Get datacenter object.\\n datacenter_list = si.content.rootFolder.childEntity\\n \\\"\\\"\\\"\\n if args.datacenter_name:\\n datacenter_obj = get_obj_in_list(args.datacenter_name, datacenter_list)\\n else:\\n \\\"\\\"\\\"\\n datacenter_obj = datacenter_list[0]\\n\\n # Get datastore object.\\n datastore_list = datacenter_obj.datastoreFolder.childEntity\\n \\\"\\\"\\\"if args.datastore_name:\\n datastore_obj = get_obj_in_list(args.datastore_name, datastore_list)\\n elif len(datastore_list) > 0:\\\"\\\"\\\"\\n datastore_obj = datastore_list[0]\\n #else:\\n # print \\\"No datastores found in DC (%s).\\\" % datacenter_obj.name\\n\\n # Get cluster object.\\n cluster_list = datacenter_obj.hostFolder.childEntity\\n \\\"\\\"\\\"if args.cluster_name:\\n cluster_obj = get_obj_in_list(args.cluster_name, cluster_list)\\n elif len(cluster_list) > 0:\\\"\\\"\\\"\\n cluster_obj = cluster_list[0]\\n #else:\\n # print \\\"No clusters found in DC (%s).\\\" % datacenter_obj.name\\n\\n # Generate resource pool.\\n resource_pool_obj = cluster_obj.resourcePool\\n\\n return {\\\"datacenter\\\": datacenter_obj,\\n \\\"datastore\\\": datastore_obj\\n ,\\\"resource pool\\\": resource_pool_obj}\",\n \"def getAncestors():\",\n \"def get_objects(self, image=None):\\n output_dict = self.run_inference_for_single_image(image)\\n return output_dict, self.category_index\",\n \"def find_own_objects(cs):\\n own_objects = {}\\n for con in cs:\\n own_objects[con] = []\\n for obj in con.extent:\\n own_objects[con].append(obj)\\n for sub_con in cs:\\n if sub_con.extent < con.extent and\\\\\\n obj in sub_con.extent:\\n own_objects[con].pop()\\n break\\n return own_objects\",\n \"def get_variations_urls(self, obj):\\n\\n # Initiate return object\\n return_object = {}\\n\\n # Get the field of the object\\n field = obj.field\\n\\n # A lot of ifs going around, first check if it has the field variations\\n if hasattr(field, 'variations'):\\n # Get the variations\\n variations = field.variations\\n # Go through the variations dict\\n for key in variations.keys():\\n # Just to be sure if the stdimage object has it stored in the obj\\n if hasattr(obj, key):\\n # get the by stdimage properties\\n field_obj = getattr(obj, key, None)\\n if field_obj and hasattr(field_obj, 'url'):\\n # store it, with the name of the variation type into our return object\\n return_object[key] = super(StdImageFieldSerializer, self).to_representation(field_obj)\\n\\n # Also include the original (if possible)\\n try:\\n if hasattr(obj, 'url'):\\n return_object['original'] = super(StdImageFieldSerializer, self).to_representation(obj)\\n except ValueError:\\n pass\\n\\n return return_object\",\n \"def writeOrthologSets(outfile, nexus,\\n extract_species,\\n extract_gene,\\n options,\\n reference_tree=None,\\n method=\\\"strict\\\",\\n outgroups=None):\\n\\n ######################################################################\\n # build species set to compare\\n sets = []\\n species = options.column2org\\n nspecies = len(species)\\n\\n if options.enumeration == \\\"monophyletic\\\":\\n if reference_tree:\\n for members, h1, h2 in TreeTools.GetSubsets(reference_tree):\\n if len(members) > 1:\\n sets.append(members)\\n else:\\n raise \\\"please specify a species tree for monophyletic enumeration\\\"\\n\\n elif options.enumeration == \\\"exhaustive\\\":\\n for x in range(2, len(species)):\\n sets += list(SetTools.xuniqueCombinations(species, x))\\n sets.append(species)\\n\\n elif options.enumeration == \\\"pairwise\\\":\\n\\n for x in range(len(species) - 1):\\n for y in range(x + 1, len(species)):\\n sets.append((species[x], species[y]))\\n\\n elif options.enumeration == \\\"full\\\":\\n sets.append(species)\\n\\n elif options.enumeration == \\\"lineage\\\":\\n for s in species:\\n sets.append((s,))\\n\\n elif options.enumeration == \\\"explicit\\\":\\n for x in range(2, len(options.species_set)):\\n sets += list(SetTools.xuniqueCombinations(options.species_set, x))\\n sets.append(options.species_set)\\n\\n ######################################################################\\n # build sets with positional information\\n xsets = []\\n map_frozenset2set = {}\\n for x in range(len(sets)):\\n ss = frozenset(map(lambda x: options.org2column[x], sets[x]))\\n xsets.append(ss)\\n map_frozenset2set[ss] = x\\n\\n ######################################################################\\n # collect outgroups\\n if outgroups:\\n noutgroups = set()\\n for x in outgroups:\\n noutgroups.add(options.org2column[x])\\n else:\\n noutgroups = None\\n\\n ######################################################################\\n # loop over each tree and set\\n # I did not see a way to loop a tree once for all sets without doing\\n # complicated counting. The problem is that counting has to be stopped\\n # at different tree heights for different sets.\\n ninput, noutput, nempty, nskipped = 0, 0, 0, 0\\n\\n counts = [0] * len(sets)\\n\\n options.stdout.write(\\n \\\"nspecies\\\\tname\\\\tid\\\\tcluster\\\\tpattern\\\\t%s\\\\tnode_id\\\\tmembers\\\\n\\\" % \\\"\\\\t\\\".join(species))\\n\\n cluster_id = 0\\n nerrors = 0\\n\\n for tree in nexus.trees:\\n\\n ninput += 1\\n ntotal_tree = 0\\n\\n if options.loglevel >= 3:\\n options.stdlog.write(\\\"# processing tree %s\\\\n\\\" % tree.name)\\n\\n if options.reroot:\\n rerootTree(tree, extract_species, options)\\n\\n for c in range(len(xsets)):\\n # numbered species set: 0,1,...\\n sn = xsets[c]\\n # literal species set: species1, species2, ...\\n sl = sets[c]\\n\\n ortholog_nodes = getOrthologNodes(tree, sn, options, selector=method,\\n outgroups=noutgroups)\\n ntotal_tree += len(ortholog_nodes)\\n\\n n = 0\\n\\n pattern = buildPattern(nspecies, sn)\\n\\n # check for inconsistent partitions (the same gene in different\\n # ortholog clusters) within the current tree\\n found_genes = set()\\n ortho_sets = set()\\n\\n # reverse ortholog_node - work in top-down manner.\\n ortholog_nodes.reverse()\\n\\n for node_id, members in ortholog_nodes:\\n n += 1\\n cluster_id += 1\\n\\n otus = filter(\\n lambda x: extract_species(x) in sl, tree.get_taxa(node_id))\\n genes = set(map(extract_gene, otus))\\n\\n if found_genes.intersection(genes):\\n\\n # only take largest cluster for lineage specific\\n # duplications\\n if method == \\\"lineage\\\":\\n continue\\n\\n if frozenset(genes) in ortho_sets:\\n nskipped += 1\\n if options.loglevel >= 1:\\n options.stdlog.write(\\\"# %s: cluster %i: redundant node: %i - skipped because already present: %s\\\\n\\\" %\\n (tree.name, n, node_id, str(found_genes.intersection(genes))))\\n else:\\n nerrors += 1\\n if options.loglevel >= 1:\\n options.stdlog.write(\\\"# %s: cluster %i: inconsistent node: %i - the same gene in different clusters: %s\\\\n\\\" %\\n (tree.name, n, node_id, str(found_genes.intersection(genes))))\\n\\n found_genes = found_genes.union(genes)\\n ortho_sets.add(frozenset(genes))\\n\\n xpattern = buildPattern(nspecies, sn, members)\\n\\n options.stdout.write(\\\"%i\\\\t%s\\\\t%i\\\\t%i\\\\t%s\\\\t%s\\\\t%i\\\\t%s\\\\n\\\" % (len(sl),\\n tree.name,\\n n,\\n cluster_id,\\n \\\"\\\".join(\\n pattern),\\n \\\"\\\\t\\\".join(\\n xpattern),\\n node_id,\\n \\\";\\\".join(otus)))\\n\\n counts[c] += n\\n\\n if ntotal_tree == 0:\\n nempty += 1\\n else:\\n noutput += 1\\n\\n if options.loglevel >= 1:\\n options.stdout.write(\\\"# ninput=%i, nempty=%i, noutput=%i, nskipped=%i, nerrors=%i\\\\n\\\" % (\\n ninput, nempty, noutput, nskipped, nerrors))\\n\\n # write summary information\\n\\n if options.filename_summary:\\n outfile = open(options.filename_summary, \\\"w\\\")\\n else:\\n outfile = options.stdout\\n outfile.write(\\\"//\\\\n\\\")\\n\\n outfile.write(\\\"cluster\\\\tpattern\\\\tcounts\\\\t%s\\\\n\\\" % (\\\"\\\\t\\\".join(species)))\\n\\n for c in range(len(xsets)):\\n pattern = buildPattern(nspecies, xsets[c])\\n outfile.write(\\\"%i\\\\t%s\\\\t%i\\\\t%s\\\\n\\\" % (c,\\n \\\"\\\".join(pattern),\\n counts[c],\\n \\\"\\\\t\\\".join(pattern)))\\n\\n if outfile != options.stdout:\\n outfile.close()\",\n \"def detect_objects(snap):\\n client = vision.ImageAnnotatorClient()\\n print(snap)\\n\\n with open(snap, 'rb') as im_file:\\n content = im_file.read()\\n image = vision.Image(content=content)\\n\\n objects = client.object_localization(image=image).localized_object_annotations\\n\\n print(f\\\"Found {len(objects)} objects\\\")\\n [print(f\\\"{objet.name} : {round(objet.score*100,2)}\\\") for objet in objects]\\n \\n return objects\",\n \"def create_objects(self, user, instrument):\\n created_ids = set() # IDs for created objects.\\n\\n # ONLY applicable to Likert Questions.\\n qtypes = {t.lower() for t, q in instrument.questions}\\n if 'likertquestion' not in qtypes and len(qtypes) != 1:\\n raise ValueError(\\\"A SurveyResult is only valid for LikertQuestions\\\")\\n\\n questions = [q for qt, q in instrument.questions]\\n if len(questions) % 2 != 0:\\n # Maybe survey is incomplete?\\n raise ValueError(\\\"Instruments must have even number of questions\\\")\\n\\n # TODO: There's probably a nicer way to do this :-/\\n # Split the questions into two subscales; they should be ordered\\n # correctly by default.\\n middle = int(len(questions) - len(questions) / 2)\\n a, b = questions[:middle], questions[middle:]\\n\\n try:\\n for q1, q2 in zip(a, b):\\n # Q1 - Q2; discard anything less than zero (keep 0)\\n # We need the user's responses to these questions.\\n r1 = q1.likertresponse_set.filter(user=user).latest()\\n r2 = q2.likertresponse_set.filter(user=user).latest()\\n score = max(r1.selected_option - r2.selected_option, 0)\\n labels = list(set(q1.labels + q2.labels))\\n\\n obj = self.create(\\n user=user,\\n instrument=instrument,\\n score=score,\\n labels=labels\\n )\\n created_ids.add(obj.id)\\n except ObjectDoesNotExist:\\n return self.get_queryset().none()\\n\\n return self.get_queryset().filter(id__in=created_ids)\",\n \"def generate_asteroid_perspectives(self) -> list:\\n for asteroid in self.asteroids:\\n asteroid.generate_perspective()\",\n \"def make_studyforrest_mockup(path):\\n public = create(opj(path, 'public'), description=\\\"umbrella dataset\\\")\\n # the following tries to capture the evolution of the project\\n phase1 = public.create('phase1',\\n description='old-style, no connection to RAW')\\n structural = public.create('structural', description='anatomy')\\n tnt = public.create('tnt', description='image templates')\\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\\n aligned = public.create('aligned', description='aligned image data')\\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # new acquisition\\n labet = create(opj(path, 'private', 'labet'), description=\\\"raw data ET\\\")\\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\\\"raw data P2MRI\\\")\\n phase2 = public.create('phase2',\\n description='new-style, RAW connection')\\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\\n # add to derivatives\\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n # never to be published media files\\n media = create(opj(path, 'private', 'media'), description=\\\"raw data ET\\\")\\n # assuming all annotations are in one dataset (in reality this is also\\n # a superdatasets with about 10 subdatasets\\n annot = public.create('annotations', description='stimulus annotation')\\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\\n # a few typical analysis datasets\\n # (just doing 3, actual status quo is just shy of 10)\\n # and also the real goal -> meta analysis\\n metaanalysis = public.create('metaanalysis', description=\\\"analysis of analyses\\\")\\n for i in range(1, 3):\\n ana = public.create('analysis{}'.format(i),\\n description='analysis{}'.format(i))\\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # link to metaanalysis\\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\\n reckless='auto')\\n # simulate change in an input (but not raw) dataset\\n create_tree(\\n aligned.path,\\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\\n aligned.save()\\n # finally aggregate data\\n aggregate = public.create('aggregate', description='aggregate data')\\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n # the toplevel dataset is intentionally left dirty, to reflect the\\n # most likely condition for the joint dataset to be in at any given\\n # point in time\",\n \"def get_activations_from_studies(cls, studies):\\n\\n activations = cls.query.filter(\\n cls.pmid.in_(studies), cls.location_id < 81925).all()\\n\\n return activations\",\n \"def _get_orientations(self):\\n for atom in self.invarioms:\\n atom.get_orientation()\",\n \"def view_all_tracks_as_pairs(self):\\n self.command(\\\"viewaspairs\\\")\",\n \"def observations(store, loqusdb, case_obj, variant_obj):\\n composite_id = (\\\"{this[chromosome]}_{this[position]}_{this[reference]}_\\\"\\n \\\"{this[alternative]}\\\".format(this=variant_obj))\\n obs_data = loqusdb.get_variant({'_id': composite_id}) or {}\\n obs_data['total'] = loqusdb.case_count()\\n\\n obs_data['cases'] = []\\n institute_id = variant_obj['institute']\\n for case_id in obs_data.get('families', []):\\n if case_id != variant_obj['case_id'] and case_id.startswith(institute_id):\\n other_variant = store.variant(variant_obj['variant_id'], case_id=case_id)\\n other_case = store.case(case_id)\\n obs_data['cases'].append(dict(case=other_case, variant=other_variant))\\n\\n return obs_data\",\n \"def get_stone_tests():\\n stone_tests = []\\n for rotation in stones_and_potions.possible_rotations():\\n for sm in stones_and_potions.possible_stone_maps():\\n stone_tests.append(\\n ([(stones_and_potions.unalign(sm.apply_inverse(l), rotation), l)\\n for l in stones_and_potions.possible_latent_stones()],\\n unity_python_conversion.to_stone_unity_properties,\\n functools.partial(from_stone_unity_properties, rotation=rotation),\\n lambda x: x, _make_tuple))\\n return stone_tests\",\n \"def rtsobjects():\\n pass\",\n \"def skill_pedagogic_ressources(request, type, slug):\\n\\n if type == 'skill':\\n base = get_object_or_404(Skill, code=slug)\\n elif type == 'section':\\n base = get_object_or_404(Section, id=slug)\\n else:\\n # type == 'coder'\\n base = get_object_or_404(CodeR, id=slug)\\n\\n personal_resource = base.resource.filter(section=\\\"personal_resource\\\")\\n lesson_resource = base.resource.filter(section=\\\"lesson_resource\\\")\\n exercice_resource = base.resource.filter(section=\\\"exercice_resource\\\")\\n other_resource = base.resource.filter(section=\\\"other_resource\\\")\\n\\n exercice_resource_sesamath = list()\\n lesson_resource_sesamath = list()\\n lesson_resource_khanacademy = list()\\n\\n sori_skills_lesson_resources = list()\\n sori_skills_exercice_resources = list()\\n sori_skills_other_resources = list()\\n\\n sori_skills_lesson_resource_sesamath = list()\\n sori_skills_lesson_resource_khanacademy = list()\\n sori_skills_exercice_resource_sesamath = list()\\n\\n sori_coder_lesson_resources = list()\\n sori_coder_exercice_resources = list()\\n sori_coder_other_resources = list()\\n\\n sori_coder_lesson_resource_sesamath = list()\\n sori_coder_lesson_resource_khanacademy = list()\\n sori_coder_exercice_resource_sesamath = list()\\n\\n # Sorting the different type of resources by category (personal, lesson, exercice or other)\\n # and by type (khanacademy, sesamath or other)\\n\\n for exo in lesson_resource:\\n if exo.content.get('from') and exo.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\\n lesson_resource_sesamath.append([exo.pk, resource])\\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\\n\\n elif exo.content.get('from') and exo.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=exo.content['referenced'])\\n lesson_resource_khanacademy.append([exo.pk, resource])\\n lesson_resource = lesson_resource.exclude(pk=exo.pk)\\n\\n for exo in exercice_resource:\\n if exo.content.get('from') and exo.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=exo.content['referenced'])\\n exercice_resource_sesamath.append([exo.pk, resource])\\n exercice_resource = exercice_resource.exclude(pk=exo.pk)\\n\\n # Do the same operation for similar or identical resources :\\n # Some Skill or CodeR can be similar or identical to other Skill or CodeR\\n # We have to display the resources of those as well\\n\\n # We achieve this only if we have either a Skill or a CodeR.\\n # If it is a Section, we don't display the similar/identical resources\\n # First step : is base a Skill or a CodeR ?\\n if isinstance(base, Skill):\\n # Begin with the Skills, same step is done for CodeR a bit further\\n\\n # Retrieve all the similar and identical Relations for Skills\\n skills_from = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], from_skill=base.id))\\n skills_to = Relations.objects.filter(Q(relation_type__in=['similar_to', 'identic_to'], to_skill=base.id))\\n\\n # This list will gather all the similar or 'identic' Skills\\n # (similar_or_identic = sori)\\n sori_skills = list()\\n for skill_from in skills_from:\\n sori_skills.append(skill_from.to_skill)\\n for skill_to in skills_to:\\n sori_skills.append(skill_to.from_skill)\\n\\n # Get the associated resources for these Skills\\n # We need to keep them separated by section to ease their use in the template\\n\\n for skill in sori_skills:\\n lesson = skill.resource.filter(section=\\\"lesson_resource\\\")\\n if lesson:\\n sori_skills_lesson_resources.append([skill, lesson])\\n exercice = skill.resource.filter(section=\\\"exercice_resource\\\")\\n if exercice:\\n sori_skills_exercice_resources.append([skill, exercice])\\n other = skill.resource.filter(section=\\\"other_resource\\\")\\n if other:\\n sori_skills_other_resources.append([skill, other])\\n\\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\\n for skill in sori_skills_lesson_resources:\\n # Lists to regroup resources together\\n sesamath_list = list()\\n khan_list = list()\\n\\n for res in skill[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n elif res.content.get('from') and res.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\\n khan_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not skill[1]:\\n sori_skills_lesson_resources.remove(skill)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_skills_lesson_resource_sesamath.append([skill[0], sesamath_list])\\n if khan_list:\\n sori_skills_lesson_resource_khanacademy.append([skill[0], khan_list])\\n\\n # sori_skills_exercice_resources has resources of different types, we need to distinguish them\\n for skill in sori_skills_exercice_resources:\\n # List to regroup resources together\\n sesamath_list = list()\\n\\n for res in skill[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n skill[1] = skill[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not skill[1]:\\n sori_skills_exercice_resources.remove(skill)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_skills_exercice_resource_sesamath.append([skill[0], sesamath_list])\\n\\n if isinstance(base, CodeR) or isinstance(base, Skill):\\n # Repeat this operation for the CodeR\\n # TODO : Group these steps as a function for Skill and CodeR\\n # Retrieve all the CodeR related to the current Skill or to the current CodeR\\n\\n if isinstance(base, Skill):\\n related_coder = CodeR.objects.filter(skill=base.id)\\n else:\\n coder_from = CodeR_relations.objects.filter(\\n Q(relation_type__in=['similar_to', 'identic_to'], from_coder=base.id))\\n coder_to = CodeR_relations.objects.filter(\\n Q(relation_type__in=['similar_to', 'identic_to'], to_coder=base.id))\\n\\n # This list will gather all the similar or 'identic' CodeR\\n related_coder = list()\\n for coder_from in coder_from:\\n related_coder.append(coder_from.to_coder)\\n for coder_to in coder_to:\\n related_coder.append(coder_to.from_coder)\\n # Case where we look for CodeR similar or identic to a CodeR\\n\\n # Get the associated resources for these CodeR\\n\\n for coder in related_coder:\\n lesson = coder.resource.filter(section=\\\"lesson_resource\\\")\\n if lesson:\\n sori_coder_lesson_resources.append([coder, lesson])\\n exercice = coder.resource.filter(section=\\\"exercice_resource\\\")\\n if exercice:\\n sori_coder_exercice_resources.append([coder, exercice])\\n other = coder.resource.filter(section=\\\"other_resource\\\")\\n if other:\\n sori_coder_other_resources.append([coder, other])\\n\\n # sori_skills_lesson_resources has resources of different types, we need to distinguish them\\n for coder in sori_coder_lesson_resources:\\n # Lists to regroup resources together\\n sesamath_list = list()\\n khan_list = list()\\n\\n for res in coder[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n elif res.content.get('from') and res.content['from'] == \\\"skills_khanacademyvideoskill\\\":\\n resource = get_object_or_404(KhanAcademy, pk=res.content['referenced'])\\n khan_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the Skill, we remove it from the list\\n if not coder[1]:\\n sori_coder_lesson_resources.remove(coder)\\n\\n # Once resources are sorted, we can add them with the associated Skill\\n if sesamath_list:\\n sori_coder_lesson_resource_sesamath.append([coder[0], sesamath_list])\\n if khan_list:\\n sori_coder_lesson_resource_khanacademy.append([coder[0], khan_list])\\n\\n # sori_coder_exercice_resources has resources of different types, we need to distinguish them\\n for coder in sori_coder_exercice_resources:\\n # List to regroup resources together\\n sesamath_list = list()\\n\\n for res in coder[1]:\\n if res.content.get('from') and res.content['from'] == \\\"skills_sesamathskill\\\":\\n resource = get_object_or_404(Sesamath, pk=res.content['referenced'])\\n sesamath_list.append([res.pk, resource])\\n coder[1] = coder[1].exclude(pk=res.pk)\\n\\n # If we have emptied the ressources in the CodeR, we remove it from the list\\n if not coder[1]:\\n sori_coder_exercice_resources.remove(coder)\\n\\n # Once resources are sorted, we can add them with the associated CodeR\\n if sesamath_list:\\n sori_coder_exercice_resource_sesamath.append([coder[0], sesamath_list])\\n\\n\\n sesamath_references_manuals = Sesamath.objects.filter(ressource_kind__iexact=\\\"Manuel\\\")\\n sesamath_references_cahiers = Sesamath.objects.filter(ressource_kind__iexact=\\\"Cahier\\\")\\n\\n return render(request, \\\"professor/skill/update_pedagogical_resources.haml\\\", {\\n \\\"sesamath_references_manuals\\\": sesamath_references_manuals,\\n \\\"sesamath_references_cahier\\\": sesamath_references_cahiers,\\n \\\"base\\\": base,\\n \\\"personal_resources\\\": personal_resource,\\n \\\"other_resources\\\": other_resource,\\n \\\"exercice_resources\\\": exercice_resource,\\n \\\"exercice_resource_sesamath\\\": exercice_resource_sesamath,\\n \\\"lesson_resources\\\": lesson_resource,\\n \\\"lesson_resource_sesamath\\\": lesson_resource_sesamath,\\n \\\"lesson_resource_khanacademy\\\": lesson_resource_khanacademy,\\n \\\"type\\\": type,\\n \\\"sori_skills_exercice_resources\\\": sori_skills_exercice_resources,\\n \\\"sori_skills_lesson_resources\\\": sori_skills_lesson_resources,\\n \\\"sori_skills_other_resources\\\": sori_skills_other_resources,\\n \\\"sori_skills_lesson_resource_sesamath\\\": sori_skills_lesson_resource_sesamath,\\n \\\"sori_skills_lesson_resource_khanacademy\\\": sori_skills_lesson_resource_khanacademy,\\n \\\"sori_skills_exercice_resource_sesamath\\\": sori_skills_exercice_resource_sesamath,\\n \\\"sori_coder_exercice_resources\\\": sori_coder_exercice_resources,\\n \\\"sori_coder_lesson_resources\\\": sori_coder_lesson_resources,\\n \\\"sori_coder_other_resources\\\": sori_coder_other_resources,\\n \\\"sori_coder_lesson_resource_sesamath\\\": sori_coder_lesson_resource_sesamath,\\n \\\"sori_coder_lesson_resource_khanacademy\\\": sori_coder_lesson_resource_khanacademy,\\n \\\"sori_coder_exercice_resource_sesamath\\\": sori_coder_exercice_resource_sesamath,\\n })\",\n \"def triples():\",\n \"def get_contenu(self):\\n return self.objets\",\n \"def analogies(self, queries):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6256025","0.5748354","0.5600851","0.5587314","0.5289076","0.52684903","0.52684903","0.5218291","0.50912505","0.509042","0.50746655","0.50711817","0.50640666","0.5047204","0.49905527","0.49653897","0.49444157","0.49329725","0.49328998","0.4929676","0.4929676","0.49144045","0.48689863","0.48640102","0.48568565","0.48278075","0.48118252","0.47862917","0.4778137","0.4765602","0.47376904","0.47371423","0.4712948","0.47079098","0.4690761","0.46817285","0.46692193","0.46649206","0.46633402","0.46618327","0.46478578","0.46388152","0.4627976","0.46255767","0.46221846","0.46071997","0.4599627","0.4599293","0.45980915","0.45943493","0.45882478","0.45799354","0.45698762","0.45679364","0.45601118","0.45581642","0.455512","0.45536482","0.45489037","0.45432234","0.45428252","0.45411226","0.4537223","0.45338848","0.4532763","0.45249236","0.45103616","0.45084417","0.45019335","0.44992322","0.44932643","0.44907832","0.44900984","0.44881555","0.4486375","0.44840714","0.44582695","0.4452424","0.4450995","0.44497752","0.44487044","0.44430786","0.4440595","0.4435284","0.44301262","0.4421716","0.44188485","0.4417894","0.44126633","0.44124982","0.44105592","0.44101116","0.4404261","0.44027844","0.4400198","0.4399984","0.4397268","0.43954667","0.4394178","0.43939963"],"string":"[\n \"0.6256025\",\n \"0.5748354\",\n \"0.5600851\",\n \"0.5587314\",\n \"0.5289076\",\n \"0.52684903\",\n \"0.52684903\",\n \"0.5218291\",\n \"0.50912505\",\n \"0.509042\",\n \"0.50746655\",\n \"0.50711817\",\n \"0.50640666\",\n \"0.5047204\",\n \"0.49905527\",\n \"0.49653897\",\n \"0.49444157\",\n \"0.49329725\",\n \"0.49328998\",\n \"0.4929676\",\n \"0.4929676\",\n \"0.49144045\",\n \"0.48689863\",\n \"0.48640102\",\n \"0.48568565\",\n \"0.48278075\",\n \"0.48118252\",\n \"0.47862917\",\n \"0.4778137\",\n \"0.4765602\",\n \"0.47376904\",\n \"0.47371423\",\n \"0.4712948\",\n \"0.47079098\",\n \"0.4690761\",\n \"0.46817285\",\n \"0.46692193\",\n \"0.46649206\",\n \"0.46633402\",\n \"0.46618327\",\n \"0.46478578\",\n \"0.46388152\",\n \"0.4627976\",\n \"0.46255767\",\n \"0.46221846\",\n \"0.46071997\",\n \"0.4599627\",\n \"0.4599293\",\n \"0.45980915\",\n \"0.45943493\",\n \"0.45882478\",\n \"0.45799354\",\n \"0.45698762\",\n \"0.45679364\",\n \"0.45601118\",\n \"0.45581642\",\n \"0.455512\",\n \"0.45536482\",\n \"0.45489037\",\n \"0.45432234\",\n \"0.45428252\",\n \"0.45411226\",\n \"0.4537223\",\n \"0.45338848\",\n \"0.4532763\",\n \"0.45249236\",\n \"0.45103616\",\n \"0.45084417\",\n \"0.45019335\",\n \"0.44992322\",\n \"0.44932643\",\n \"0.44907832\",\n \"0.44900984\",\n \"0.44881555\",\n \"0.4486375\",\n \"0.44840714\",\n \"0.44582695\",\n \"0.4452424\",\n \"0.4450995\",\n \"0.44497752\",\n \"0.44487044\",\n \"0.44430786\",\n \"0.4440595\",\n \"0.4435284\",\n \"0.44301262\",\n \"0.4421716\",\n \"0.44188485\",\n \"0.4417894\",\n \"0.44126633\",\n \"0.44124982\",\n \"0.44105592\",\n \"0.44101116\",\n \"0.4404261\",\n \"0.44027844\",\n \"0.4400198\",\n \"0.4399984\",\n \"0.4397268\",\n \"0.43954667\",\n \"0.4394178\",\n \"0.43939963\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7746891"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7187,"cells":{"query":{"kind":"string","value":"Returns all numbers below N, that is a multiple of M."},"document":{"kind":"string","value":"def findMultiples(M, N):\n\tnumbers = []\n\n\tfor i in range(N):\n\t\tif(i + 1 == N):\n\t\t\tbreak\n\t\tif(((i + 1) % M) == 0):\n\t\t\tnumbers.append(i+1)\n\n\treturn numbers"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sixn(m):\n if m <= 2:\n return ()\n if m > 2:\n yield 2\n if m > 3:\n yield 3\n for n in count(1):\n x = 6 * n - 1\n y = x + 2\n if x < m:\n yield x\n else:\n break\n if y < m:\n yield y\n else:\n break","def islice(n, m):\n npiece = int(math.ceil(1.0*n/m))\n for i in range(npiece):\n if (i+1)*m > n:\n yield i, i*m, n\n else:\n yield i, i*m, (i+1)*m","def choose(n, m):\n assert n >= m, \"Cannot choose {0} elements from {1}\".format(m, n)\n result = 1\n for n_i, m_i in zip(range(n, m, -1), range(1, m+1)):\n result *= n_i\n result /= m_i\n return result","def evenly_select(N, M):\n if N == M:\n return np.ones(N, dtype=int)\n assert N > M\n if M > N/2:\n cut = np.ones(N, dtype=int)\n q, r = divmod(N, N-M)\n indices = [q*i + min(i, r) for i in range(N-M)]\n cut[indices] = False\n else:\n cut = np.zeros(N, dtype=int)\n q, r = divmod(N, M)\n indices = [q*i + min(i, r) for i in range(M)]\n cut[indices] = True\n\n return cut","def cashflow_times(n, m):\n return [i for i in range(m * n+1) if i != 0 ]","def find_multiple(self, num):\n result = dict()\n for n in range(1, num+1):\n temp = self.find_prime_divisors(n)\n result.update({k:v for k,v in temp.items() if k not in result or result[k] < v})\n return reduce(operator.mul, (pow(k, v) for k,v in result.items()))","def mult(n,m):\n result = 0\n\n if m == 0 or n == 0:\n result = 0\n\n elif n > 0:\n for x in range(n):\n result = result + m\n else:\n for x in range(-n):\n result = result - m\n return result","def get_multiples(ratio, n):\n ls = [ratio ** i for i in range(n)]\n return ls","def hart(N):\n m = 2\n i = 1\n while not is_square(m):\n s = isqrt(N * i) + 1\n m = pow(s, 2, N)\n i += 1\n t = isqrt(m)\n g = gcd(s - t, N)\n return g, N // g","def partition(n, m, discard= False):\n steps = range(0, 1 + n, m)\n yield from zip(steps, steps[1:])\n if n % m and not discard:\n yield n - (n % m), n","def slice(n, m):\n chunks = []\n for piece in islice(n, m):\n chunks.append(piece)\n return chunks","def find_powers(n):\n # find_powers(6) --> [1, 2, 3, 4]\n return list(takewhile(lambda x: len(str(n**x)) == x, count(1)))","def wrap(x, m, M):\n diff = M - m\n while x > M:\n x = x - diff\n while x < m:\n x = x + diff\n return x","def algorithm_h(n, m):\n partition = [1]*m\n partition[0] = n - m + 1\n\n while True:\n yield partition[:]\n if partition[1] < partition[0] - 1:\n partition[0] -= 1\n partition[1] += 1\n else:\n j = 2\n s = partition[0] + partition[1] - 1\n while j < m and partition[j] >= partition[0] - 1:\n s += partition[j]\n j += 1\n if j >= m:\n return\n replacement = partition[j] + 1\n partition[j] = replacement\n j -= 1\n while j > 0:\n partition[j] = replacement\n s -= replacement\n j -= 1\n partition[0] = s","def evenly_select(n, m):\n if n == m:\n return np.ones(n, dtype=int)\n assert n > m\n if m > n/2:\n cut = np.ones(n, dtype=int)\n q, r = divmod(n, n - m)\n indices = [q * i + min(i, r) for i in range(n - m)]\n cut[indices] = False\n else:\n cut = np.zeros(n, dtype=int)\n q, r = divmod(n, m)\n indices = [q * i + min(i, r) for i in range(m)]\n cut[indices] = True\n\n return cut","def is_multiple(n,m):\n return n % m == 0","def subdim(number):\n res = []\n for i in [2, 3, 4, 5, 6, 7, 8, 9, 10]:\n res.append(number % i)\n if number % i == 0:\n n = i\n m = number // i\n return n, m\n if not 0 in res:\n return subdim(number + 1)","def ideal_fanin(k, m):\n fanin = 0\n needed = k\n for select in range(3, m + 1, 2):\n combinations = list(itertools.combinations(range(m), select))\n if len(combinations) <= needed:\n fanin += int(math.ceil(float(len(combinations) * select) / m))\n needed -= len(combinations)\n else:\n fanin += int(math.ceil(float(needed * select) / m))\n needed = 0\n if not needed:\n break\n return fanin","def bruteForce_MC(N,M):\n hewlist = np.zeros(M)\n for i in range(M):\n x = createDist(N)\n x = np.abs(x-np.mean(x))\n x.sort()\n hewlist[i] = np.median(x)*2.\n return np.mean(hewlist), np.std(hewlist)","def pick_chosen_points(m, n):\r\n return [i * n // m + n // (2 * m) for i in range(m)]","def get_k(self, n, m):\n k = m/n * log(2)\n return int(k)","def solution1(n):\n res = []\n while n > 0:\n m = int(math.floor(math.sqrt(n))**2)\n res.append(m)\n n -= m\n return res","def maiores(lista, n):\n numeros = [lista for lista in lista if lista > n]\n return numeros","def uniform_sample(n, m):\n interval = m / n\n indices = [0]\n index = 0.0\n while True:\n index += interval\n if index >= m - 1:\n indices.append(int(m - 1))\n break\n else:\n indices.append(int(index))\n\n return np.array(indices)","def non_mcnugget():\n nugget = [0, 6, 9, 20]\n mcnugget = set([6, 9, 20])\n\n while True:\n mcnugget = set([m+n for m in mcnugget for n in nugget])\n\n for m in mcnugget:\n found = all([m+j in mcnugget for j in range(6)])\n if found:\n return [k for k in range(1, m) if k not in mcnugget]","def countm(m):\n nfound=0\n\n for i in range(1,m+1):\n for jpk in range(2,(2*i)+1):\n d1=i*i+(jpk)*(jpk) \n if(checkpsq(d1)): \n if(jpk<=i):\n factor=jpk/2 \n else:\n factor=((2*i-jpk)+2)/2 \n nfound=nfound+factor\n\n return nfound","def sum_n_m(n, m):\n total = 0\n for i in range(n, m+1):\n total += i\n return total","def power(x, m, n):\n a = 1\n while m > 0:\n if m % 2 == 1:\n a=(a*x)%n\n x=(x*x)%n\n m//=2\n return a","def tri(N, M=None, k=0, dtype=float):\r\n if M is None: M = N\r\n m = greater_equal(subtract.outer(arange(N), arange(M)),-k)\r\n return m.astype(dtype)","def bartlett(M):\n if M < 1:\n return array([])\n if M == 1:\n return ones(1, float)\n n = arange(0,M)\n return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))","def solve(n=10):\n return sum(M_N_S(n, d)[2] for d in range(10))","def workersNeeded(k, m):\n # formula: k/m\n from math import ceil\n return ceil(float(k)/float(m))","def liste_N_nb_premier(N):\n liste = []\n i = 0\n while len(liste) < N:\n if is_prime(i):\n liste.append(i)\n i += 1\n return liste","def getDivisors(n):","def solve(total):\n\n if not isnatural(total):\n raise ValueError(\"El numero tiene que ser natural\")\n\n suplimit = total//2 + 2\n for m in range(1, suplimit):\n for n in range(m, suplimit):\n suma = (n-m+1)*(m+n) // 2\n if suma < total:\n continue\n elif suma == total:\n yield (m, n)\n break","def findKthNumber(self, m: int, n: int, k: int) -> int:\n l, r = 1, m * n\n while l < r:\n mid = l + ((r - l) >> 1)\n\n # Check if there are k or more values that are less than mid.\n # For each row, its elements look like 1*i, 2*i, ... n*i, so the\n # largest number that is less than x will be x // i. But if x is\n # too large for the current row, the total count for this row\n # will be n instead.\n if sum(min(mid // r, n) for r in range(1, m + 1)) >= k:\n # mid is our candidate.\n r = mid\n else:\n l = mid + 1\n\n return l","def main(n=20):\n return functools.reduce(lcm, range(1, 20))","def get_set(dim, maximum):\n\n i = 0\n numbers = []\n while i**2 <= maximum:\n n = i**2\n counter = 0\n while n <= maximum and counter < dim:\n numbers += [i**2]\n n += i**2\n counter += 1\n i += 1\n return numbers","def splitArray(self, nums: List[int], m: int) -> int:\n l = max(nums)\n r = sum(nums)\n ans = r\n\n while l <= r:\n mid = (l + r) // 2\n range_sum = 0\n range_sum_count = 1\n for i in range(len(nums)):\n if (range_sum + nums[i] > mid):\n range_sum = nums[i]\n range_sum_count += 1\n else:\n range_sum += nums[i]\n if range_sum_count <= m:\n ans = min(ans, mid)\n r = mid - 1\n else:\n l = mid + 1\n return ans","def get_divisors(n, includeN=True):\n lower_divisors, upper_divisors = [], []\n i = 1\n while i * i <= n:\n if n % i == 0:\n lower_divisors.append(i)\n if i != n // i:\n upper_divisors.append(n//i)\n i += 1\n upper_divisors = upper_divisors[::-1]\n if not includeN:\n upper_divisors.pop()\n return lower_divisors + upper_divisors","def gen_num(lim=10000):\n n = 1\n yield 2\n yield 3\n while 6 * n + 1 <= lim:\n yield 6 * n - 1\n yield 6 * n + 1\n n += 1","def combination(num_m: int, num_n: int) -> int:\n if num_m < num_n:\n return 0\n\n tmp_m = 1\n tmp_n = 1\n tmp_cnt = 0\n for i in range(num_n, 0, -1):\n tmp_n *= i\n tmp_m *= num_m - tmp_cnt\n tmp_cnt += 1\n\n return tmp_m / tmp_n","def smallest_multiple(n):\n return reduce(lowest_common_multiple, range(1, n+1))","def primfact(e):\n for n in range(2, e):\n for x in range(2, n):\n if n % x == 0:\n break\n else:\n print n,","def solution3(n):\n res = []\n while n > 0:\n m = int(math.sqrt(n))**2\n res.append(m)\n n -= m\n return res","def m(self):\n\t\tn = 0\n\t\ti = self.k0\n\t\twhile 1:\n\t\t\tif i > self.j:\n\t\t\t\treturn n\n\t\t\tif not self.cons(i):\n\t\t\t\tbreak\n\t\t\ti = i + 1\n\t\ti = i + 1\n\t\twhile 1:\n\t\t\twhile 1:\n\t\t\t\tif i > self.j:\n\t\t\t\t\treturn n\n\t\t\t\tif self.cons(i):\n\t\t\t\t\tbreak\n\t\t\t\ti = i + 1\n\t\t\ti = i + 1\n\t\t\tn = n + 1\n\t\t\twhile 1:\n\t\t\t\tif i > self.j:\n\t\t\t\t\treturn n\n\t\t\t\tif not self.cons(i):\n\t\t\t\t\tbreak\n\t\t\t\ti = i + 1\n\t\t\ti = i + 1","def listes(Mi, Ma, N):\n if N == 0:\n yield []\n elif Ma - Mi < N:\n yield None\n else:\n # Avec Mi + 1\n for reste in listes(Mi + 1, Ma, N - 1):\n if reste != None:\n yield [Mi + 1] + reste\n \n # Sans Mi + 1\n for reste in listes(Mi + 1, Ma, N):\n if reste != None:\n yield reste","def get_integers(n: int) -> int:\n l = [int(sqrt(n))]\n val = l[0] * l[0]\n index = 0\n while val != n:\n val = sum([x*x for x in l])\n if val > n:\n l[index] -= 1\n elif val < n:\n index += 1\n l.append(l[index - 1])\n\n return len(l)","def _P(m):\n P = np.zeros((m**2,m**2), dtype=np.int64)\n for i in range(1, m**2 + 1):\n j = 1 + m*((i - 1) % m) + (i - 1)//m\n P[i-1, j-1] = 1\n return P","def dice(n, m):\n return Lea.interval(1, m).cprodTimes(n)","def divisors(n):\r\n numbers = []\r\n for i in xrange(1, n+1):\r\n if n % i == 0:\r\n numbers.append(i)\r\n return numbers","def between_m_and_n_ver(seq, m, n):\r\n num_AB = 0\r\n for item in seq:\r\n if np.array_equal(item, Quantifier.AB):\r\n num_AB += 1\r\n return Quantifier.T if (m <= num_AB and num_AB <= n) else Quantifier.F","def proper_divisors(n):\r\n numbers = []\r\n for i in xrange(1, n):\r\n if n % i == 0:\r\n numbers.append(i)\r\n \r\n return numbers","def get_3_5_sum(num):\n nums = range(num)\n mults = [num for num in nums if (num % 3 == 0 or num % 5 == 0)]\n return sum(mults)","def getNPrime(num):\n prime_numbers = []\n for i in range(num):\n if isPrime(i + 1):\n prime_numbers.append(i)\n return prime_numbers","def factor(N):\n\n factors = []\n sqrtN = math.sqrt(N)\n for x in range(2, int(sqrtN)+1):\n (d, r) = divmod(N, x)\n if r == 0:\n factors.append(x)\n if x != d: factors.append(d)\n return [1, N] + factors","def _get_l1_mlist(n):\n global _l1_bucket_max\n if n == 1:\n mlist = [1]\n elif n <= _l1_bucket_max:\n mlist = [n, 2*n, n*n]\n else:\n mlist = [n, 2*n] #just to try\n return mlist","def how_many_5(numbers):\r\n # Modify example to take argument that specifies threshold\r\n return sum( 1 for number in numbers if number > 5 )","def primes_below(n):\n L, M = [2], [x for x in range(3, int(n), 2)]\n if n <= 2:\n print('There are no primes below 2')\n return None\n for i in range(3, int(n), 2):\n if M[i // 2 - 1] != 0 and is_prime(i):\n L.append(i)\n for j in range(i, int(n), 2 * i):\n M[j // 2 - 1] = 0\n return L","def sum_divisibles(limit):\n res = [x for x in range(limit) if x % 3 == 0 or x % 5 == 0]\n return sum(res)","def tourney_prob(k, N, m):\n\n if N < m:\n print \"The second argument cannot be smaller than the third one.\"\n sys.exit()\n\n if m < 1 or k <= 0:\n return 0.0\n elif m == 1:\n return 1.0 / N\n else:\n return float(N - k) * m / (N * (m - 1)) * tourney_prob(k, N - 1, m - 1)","def splitArray(self, nums: List[int], m: int) -> int:\n low, high, res = max(nums), sum(nums), -1\n while low <= high:\n pivot=(low+high)//2\n if self.isValid(nums,m,pivot):\n res, high = pivot, pivot - 1\n else:\n low = pivot + 1\n return res","def MM(x,N,n,l,t=0):\n Mat = np.zeros([2**l,2**l])\n for iii in range(N):\n Mat[iii,(x**n * iii)%N] = 1\n return Mat","def choices(N, M):\n #yield from itertools.combinations(range(2, M+1), N)\n # Use stars and bars method\n allbars = itertools.combinations( range(1,N+M-1), M-2 )\n for bars in allbars:\n bars = [0] + list(bars) + [N+M-1]\n yi = [ y-x for x, y in zip(bars, bars[1:]) ]\n yield [ y-1 for y in yi ]","def bruteForcePopulation(N):\n return list(itertools.permutations(range(N), N))","def primes(m):\n if m <= 2:\n return ()\n sieve = [True] * m\n for i in sixn(m):\n if sieve[i]:\n yield i\n for mult in range(i * i, m, i):\n sieve[mult] = False","def choose_m_n(li,min,max):\n n_items = random.randrange(min,max+1)\n if n_items == 0:\n return [ ]\n sample=random.sample(li,n_items) # Should it be sorted?\n return sample","def Samples(n=6, m=1000):\n t = [Sample(n) for i in range(m)]\n return t","def _two_of_m(m):\n return (m * (m - 1) / 2)","def multiply(m, n):\n if n == 1:\n return m\n else:\n return m + multiply(m, n - 1)","def count_partitions(n, m):\n if n == 0:\n return 1\n elif n < 0:\n return 0\n elif m == 0:\n return 0\n else:\n with_m = count_partitions(n - m, m)\n without_m = count_partitions(n, m - 1)\n return with_m + without_m","def solve(limit):\n upper_limit = ceil(sqrt(limit - 2**4 - 2**3))\n p_list = PrimeList(upper_limit)\n\n num_set = set()\n for x in p_list:\n val = limit - 2**4 - x**3\n if val < 0: continue\n lim = ceil(sqrt(val))\n for y in takewhile(lambda i: i<lim, p_list):\n val = limit - min(x,y)**4 - max(x,y)**3\n if val < 0: continue\n lim = ceil(sqrt(val))\n for z in takewhile(lambda i: i<lim, p_list):\n\n for a,b,c in permutations([x,y,z]):\n ans = a**2 + b**3 + c**4\n if ans > limit: continue\n num_set.add(ans)\n if a ==b and b == c: break\n\n return len(num_set)","def count_partitions(n, m):\n # print(n, m)\n if n == 0:\n return 1\n elif n < 0:\n return 0\n elif m == 0:\n return 0\n else:\n return count_partitions(n-m, m) + count_partitions(n, m//2)","def make_evaluation_grids(W, M, N):\n nu = (np.arange(W * M, dtype=float) + 0.5) / (2 * M)\n x = np.arange(N + 1, dtype=float) / (2 * N)\n return nu, x","def batches(l, n):\n for i in range(0, l, n):\n yield range(i,min(l,i+n))","def ip(M, N):\n\n return M ^ N","def gp(M, N):\n\n return M*N","def _power(self, a, n, m):\n res = 1\n while n != 0:\n if n % 2 != 0:\n res *= a\n res %= m\n n -= 1\n else:\n a *= a\n a %= m\n n //= 2\n return res","def numbers():\n for number in range(1, 76):\n yield number","def get_k(M):\n k = np.arange(1,M+1)*np.pi/(M+1) # vector of all possible quasimomenta\n return k","def sum_mult(number, limit):\n limit = limit - 1\n limit = (limit - (limit%number))/number\n return (number * limit * (limit+1))/2","def primes_lt3(N):\n\t# test every number less than N/2\n\tprimes = [ i for i in xrange(2,N)\n\t\t\t\t if not any( ( i % p == 0 for p in xrange(2,int(sqrt(i))+1) ) )]\n\n\treturn primes","def primes_lt3(N):\n\t# test every number less than N/2\n\tprimes = [ i for i in xrange(2,N)\n\t\t\t\t if not any( ( i % p == 0 for p in xrange(2,int(sqrt(i))+1) ) )]\n\n\treturn primes","def prbs(m, n):\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5","def MM_n(N, data):\n out = np.zeros(len(data))\n\n for j in range(N):\n out[j] = np.average(data[:j+1])\n for (j,d) in enumerate(data[N-1:]):\n out[j+N-1] = np.average(data[j:j+N])\n\n return out","def divisior(n: int) -> list:\n j = [n]\n for d in range(n+1): #loop bis n\n d > 0","def amicable_numbers(n):\n amicables = []\n sumDivisors = {}\n for i in range(1, n):\n divisors = proper_divisors(i)\n sumDivisors[i] = sum(divisors)\n for i in range(1, n):\n sumDivisorsOfi = sumDivisors[i]\n if sumDivisorsOfi < n:\n compare = sumDivisors[sumDivisorsOfi]\n if compare == i and sumDivisorsOfi != i:\n amicables.append(i)\n return amicables","def generate_numbers():\n\n nums = []\n\n for i in range(0, 12):\n for j in range(1, 12):\n nums.append(i / j)\n\n return list(set(nums))","def cycles(n, m):\n if n <= 2:\n return 1\n k = 2\n tk = 3\n pk = 2\n while k < n - 1:\n tk1 = (2 * pk * tk * tk) % m\n pk1 = (2 * pk * pk * tk) % m\n tk = tk1\n pk = pk1\n k += 1\n return (pk * pk * pk) % m","def sum_amicable(limit):\n\n def find_amicable_pair(n):\n check_n= 0\n potential_half = 0\n for i in range(1,n):\n if n % i == 0:\n potential_half += i\n for i in range(1, potential_half):\n if potential_half % i == 0:\n check_n += i\n if check_n == n and n != potential_half: # exclude self amicable\n result.append(n)\n result.append(potential_half)\n\n result = []\n for num in range(1, limit):\n if num not in result:\n find_amicable_pair(num)\n return sum(result)","def skip_mul(n):\n if n <= 0:\n return 1\n else:\n return n * skip_mul(n - 2)","def ModExp(n, k, m):\n a = list(bin(k))[2:]\n a.reverse()\n s = 1\n for i in a:\n if i == '1':\n s = (s * n) % m\n n = (n * n) % m\n return s","def problem3(m, n):\n # TODO: Implement and test this function.\n # We supplied some tests in test_problem3 above.","def divisors(n):\n return [x for x in range(1, n) if n % x == 0]","def f(n):\n\tfor i in range(101, n):\n\t\tif (i % 21 == 0):\n\t\t\treturn i","def fast_power(a, n, m): # (a ^ n) % m\n result = 1\n value = a\n power = n\n while power > 0:\n if power % 2 == 1:\n result = result * value\n result %= m\n value = value * value\n value %= m\n power = power//2\n return result","def eratosthenes_npo(limit):\n if isinstance(limit, (int, float)):\n limit = int(limit)\n else:\n raise ValueError\n mask = np.ones(limit//2, dtype=np.bool)\n for i in range(3, int(limit**0.5)+1, 2):\n if mask[i//2]:\n mask[i*i//2::i] = False\n return np.r_[2, 2*np.nonzero(mask)[0][1::]+1]","def perfect_power(n: int):\n k = 2\n max_k = round(n ** (1/2))\n while True:\n m = round(n ** (1/k))\n if m ** k == n:\n return [m, k]\n elif k > max_k:\n return None\n else:\n k += 1","def prime_numbers(limit):\n primes = [2, 3, 5]\n for p in primes:\n yield p\n n = 5\n count = 3\n last_idx = -1\n sqrd_prime = 0\n while count <= limit:\n n += 2\n if n > sqrd_prime:\n last_idx += 1\n sqrd_prime = primes[last_idx] ** 2\n is_prime = True\n for i in range(1, last_idx + 1):\n p = primes[i]\n if n % p == 0:\n is_prime = False\n break\n if is_prime:\n count += 1\n primes.append(n)\n yield n","def prime_factorization(n):\r\n result = []\r\n for i in xrange(2, n+1):\r\n s = 0;\r\n while n / float(i) == floor(n/float(i)):\r\n n = n / float(i)\r\n s += 1\r\n if s > 0:\r\n for k in range(s):\r\n result.append(i)\r\n if n == 1:\r\n return result"],"string":"[\n \"def sixn(m):\\n if m <= 2:\\n return ()\\n if m > 2:\\n yield 2\\n if m > 3:\\n yield 3\\n for n in count(1):\\n x = 6 * n - 1\\n y = x + 2\\n if x < m:\\n yield x\\n else:\\n break\\n if y < m:\\n yield y\\n else:\\n break\",\n \"def islice(n, m):\\n npiece = int(math.ceil(1.0*n/m))\\n for i in range(npiece):\\n if (i+1)*m > n:\\n yield i, i*m, n\\n else:\\n yield i, i*m, (i+1)*m\",\n \"def choose(n, m):\\n assert n >= m, \\\"Cannot choose {0} elements from {1}\\\".format(m, n)\\n result = 1\\n for n_i, m_i in zip(range(n, m, -1), range(1, m+1)):\\n result *= n_i\\n result /= m_i\\n return result\",\n \"def evenly_select(N, M):\\n if N == M:\\n return np.ones(N, dtype=int)\\n assert N > M\\n if M > N/2:\\n cut = np.ones(N, dtype=int)\\n q, r = divmod(N, N-M)\\n indices = [q*i + min(i, r) for i in range(N-M)]\\n cut[indices] = False\\n else:\\n cut = np.zeros(N, dtype=int)\\n q, r = divmod(N, M)\\n indices = [q*i + min(i, r) for i in range(M)]\\n cut[indices] = True\\n\\n return cut\",\n \"def cashflow_times(n, m):\\n return [i for i in range(m * n+1) if i != 0 ]\",\n \"def find_multiple(self, num):\\n result = dict()\\n for n in range(1, num+1):\\n temp = self.find_prime_divisors(n)\\n result.update({k:v for k,v in temp.items() if k not in result or result[k] < v})\\n return reduce(operator.mul, (pow(k, v) for k,v in result.items()))\",\n \"def mult(n,m):\\n result = 0\\n\\n if m == 0 or n == 0:\\n result = 0\\n\\n elif n > 0:\\n for x in range(n):\\n result = result + m\\n else:\\n for x in range(-n):\\n result = result - m\\n return result\",\n \"def get_multiples(ratio, n):\\n ls = [ratio ** i for i in range(n)]\\n return ls\",\n \"def hart(N):\\n m = 2\\n i = 1\\n while not is_square(m):\\n s = isqrt(N * i) + 1\\n m = pow(s, 2, N)\\n i += 1\\n t = isqrt(m)\\n g = gcd(s - t, N)\\n return g, N // g\",\n \"def partition(n, m, discard= False):\\n steps = range(0, 1 + n, m)\\n yield from zip(steps, steps[1:])\\n if n % m and not discard:\\n yield n - (n % m), n\",\n \"def slice(n, m):\\n chunks = []\\n for piece in islice(n, m):\\n chunks.append(piece)\\n return chunks\",\n \"def find_powers(n):\\n # find_powers(6) --> [1, 2, 3, 4]\\n return list(takewhile(lambda x: len(str(n**x)) == x, count(1)))\",\n \"def wrap(x, m, M):\\n diff = M - m\\n while x > M:\\n x = x - diff\\n while x < m:\\n x = x + diff\\n return x\",\n \"def algorithm_h(n, m):\\n partition = [1]*m\\n partition[0] = n - m + 1\\n\\n while True:\\n yield partition[:]\\n if partition[1] < partition[0] - 1:\\n partition[0] -= 1\\n partition[1] += 1\\n else:\\n j = 2\\n s = partition[0] + partition[1] - 1\\n while j < m and partition[j] >= partition[0] - 1:\\n s += partition[j]\\n j += 1\\n if j >= m:\\n return\\n replacement = partition[j] + 1\\n partition[j] = replacement\\n j -= 1\\n while j > 0:\\n partition[j] = replacement\\n s -= replacement\\n j -= 1\\n partition[0] = s\",\n \"def evenly_select(n, m):\\n if n == m:\\n return np.ones(n, dtype=int)\\n assert n > m\\n if m > n/2:\\n cut = np.ones(n, dtype=int)\\n q, r = divmod(n, n - m)\\n indices = [q * i + min(i, r) for i in range(n - m)]\\n cut[indices] = False\\n else:\\n cut = np.zeros(n, dtype=int)\\n q, r = divmod(n, m)\\n indices = [q * i + min(i, r) for i in range(m)]\\n cut[indices] = True\\n\\n return cut\",\n \"def is_multiple(n,m):\\n return n % m == 0\",\n \"def subdim(number):\\n res = []\\n for i in [2, 3, 4, 5, 6, 7, 8, 9, 10]:\\n res.append(number % i)\\n if number % i == 0:\\n n = i\\n m = number // i\\n return n, m\\n if not 0 in res:\\n return subdim(number + 1)\",\n \"def ideal_fanin(k, m):\\n fanin = 0\\n needed = k\\n for select in range(3, m + 1, 2):\\n combinations = list(itertools.combinations(range(m), select))\\n if len(combinations) <= needed:\\n fanin += int(math.ceil(float(len(combinations) * select) / m))\\n needed -= len(combinations)\\n else:\\n fanin += int(math.ceil(float(needed * select) / m))\\n needed = 0\\n if not needed:\\n break\\n return fanin\",\n \"def bruteForce_MC(N,M):\\n hewlist = np.zeros(M)\\n for i in range(M):\\n x = createDist(N)\\n x = np.abs(x-np.mean(x))\\n x.sort()\\n hewlist[i] = np.median(x)*2.\\n return np.mean(hewlist), np.std(hewlist)\",\n \"def pick_chosen_points(m, n):\\r\\n return [i * n // m + n // (2 * m) for i in range(m)]\",\n \"def get_k(self, n, m):\\n k = m/n * log(2)\\n return int(k)\",\n \"def solution1(n):\\n res = []\\n while n > 0:\\n m = int(math.floor(math.sqrt(n))**2)\\n res.append(m)\\n n -= m\\n return res\",\n \"def maiores(lista, n):\\n numeros = [lista for lista in lista if lista > n]\\n return numeros\",\n \"def uniform_sample(n, m):\\n interval = m / n\\n indices = [0]\\n index = 0.0\\n while True:\\n index += interval\\n if index >= m - 1:\\n indices.append(int(m - 1))\\n break\\n else:\\n indices.append(int(index))\\n\\n return np.array(indices)\",\n \"def non_mcnugget():\\n nugget = [0, 6, 9, 20]\\n mcnugget = set([6, 9, 20])\\n\\n while True:\\n mcnugget = set([m+n for m in mcnugget for n in nugget])\\n\\n for m in mcnugget:\\n found = all([m+j in mcnugget for j in range(6)])\\n if found:\\n return [k for k in range(1, m) if k not in mcnugget]\",\n \"def countm(m):\\n nfound=0\\n\\n for i in range(1,m+1):\\n for jpk in range(2,(2*i)+1):\\n d1=i*i+(jpk)*(jpk) \\n if(checkpsq(d1)): \\n if(jpk<=i):\\n factor=jpk/2 \\n else:\\n factor=((2*i-jpk)+2)/2 \\n nfound=nfound+factor\\n\\n return nfound\",\n \"def sum_n_m(n, m):\\n total = 0\\n for i in range(n, m+1):\\n total += i\\n return total\",\n \"def power(x, m, n):\\n a = 1\\n while m > 0:\\n if m % 2 == 1:\\n a=(a*x)%n\\n x=(x*x)%n\\n m//=2\\n return a\",\n \"def tri(N, M=None, k=0, dtype=float):\\r\\n if M is None: M = N\\r\\n m = greater_equal(subtract.outer(arange(N), arange(M)),-k)\\r\\n return m.astype(dtype)\",\n \"def bartlett(M):\\n if M < 1:\\n return array([])\\n if M == 1:\\n return ones(1, float)\\n n = arange(0,M)\\n return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))\",\n \"def solve(n=10):\\n return sum(M_N_S(n, d)[2] for d in range(10))\",\n \"def workersNeeded(k, m):\\n # formula: k/m\\n from math import ceil\\n return ceil(float(k)/float(m))\",\n \"def liste_N_nb_premier(N):\\n liste = []\\n i = 0\\n while len(liste) < N:\\n if is_prime(i):\\n liste.append(i)\\n i += 1\\n return liste\",\n \"def getDivisors(n):\",\n \"def solve(total):\\n\\n if not isnatural(total):\\n raise ValueError(\\\"El numero tiene que ser natural\\\")\\n\\n suplimit = total//2 + 2\\n for m in range(1, suplimit):\\n for n in range(m, suplimit):\\n suma = (n-m+1)*(m+n) // 2\\n if suma < total:\\n continue\\n elif suma == total:\\n yield (m, n)\\n break\",\n \"def findKthNumber(self, m: int, n: int, k: int) -> int:\\n l, r = 1, m * n\\n while l < r:\\n mid = l + ((r - l) >> 1)\\n\\n # Check if there are k or more values that are less than mid.\\n # For each row, its elements look like 1*i, 2*i, ... n*i, so the\\n # largest number that is less than x will be x // i. But if x is\\n # too large for the current row, the total count for this row\\n # will be n instead.\\n if sum(min(mid // r, n) for r in range(1, m + 1)) >= k:\\n # mid is our candidate.\\n r = mid\\n else:\\n l = mid + 1\\n\\n return l\",\n \"def main(n=20):\\n return functools.reduce(lcm, range(1, 20))\",\n \"def get_set(dim, maximum):\\n\\n i = 0\\n numbers = []\\n while i**2 <= maximum:\\n n = i**2\\n counter = 0\\n while n <= maximum and counter < dim:\\n numbers += [i**2]\\n n += i**2\\n counter += 1\\n i += 1\\n return numbers\",\n \"def splitArray(self, nums: List[int], m: int) -> int:\\n l = max(nums)\\n r = sum(nums)\\n ans = r\\n\\n while l <= r:\\n mid = (l + r) // 2\\n range_sum = 0\\n range_sum_count = 1\\n for i in range(len(nums)):\\n if (range_sum + nums[i] > mid):\\n range_sum = nums[i]\\n range_sum_count += 1\\n else:\\n range_sum += nums[i]\\n if range_sum_count <= m:\\n ans = min(ans, mid)\\n r = mid - 1\\n else:\\n l = mid + 1\\n return ans\",\n \"def get_divisors(n, includeN=True):\\n lower_divisors, upper_divisors = [], []\\n i = 1\\n while i * i <= n:\\n if n % i == 0:\\n lower_divisors.append(i)\\n if i != n // i:\\n upper_divisors.append(n//i)\\n i += 1\\n upper_divisors = upper_divisors[::-1]\\n if not includeN:\\n upper_divisors.pop()\\n return lower_divisors + upper_divisors\",\n \"def gen_num(lim=10000):\\n n = 1\\n yield 2\\n yield 3\\n while 6 * n + 1 <= lim:\\n yield 6 * n - 1\\n yield 6 * n + 1\\n n += 1\",\n \"def combination(num_m: int, num_n: int) -> int:\\n if num_m < num_n:\\n return 0\\n\\n tmp_m = 1\\n tmp_n = 1\\n tmp_cnt = 0\\n for i in range(num_n, 0, -1):\\n tmp_n *= i\\n tmp_m *= num_m - tmp_cnt\\n tmp_cnt += 1\\n\\n return tmp_m / tmp_n\",\n \"def smallest_multiple(n):\\n return reduce(lowest_common_multiple, range(1, n+1))\",\n \"def primfact(e):\\n for n in range(2, e):\\n for x in range(2, n):\\n if n % x == 0:\\n break\\n else:\\n print n,\",\n \"def solution3(n):\\n res = []\\n while n > 0:\\n m = int(math.sqrt(n))**2\\n res.append(m)\\n n -= m\\n return res\",\n \"def m(self):\\n\\t\\tn = 0\\n\\t\\ti = self.k0\\n\\t\\twhile 1:\\n\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\treturn n\\n\\t\\t\\tif not self.cons(i):\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ti = i + 1\\n\\t\\ti = i + 1\\n\\t\\twhile 1:\\n\\t\\t\\twhile 1:\\n\\t\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\t\\treturn n\\n\\t\\t\\t\\tif self.cons(i):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\ti = i + 1\\n\\t\\t\\ti = i + 1\\n\\t\\t\\tn = n + 1\\n\\t\\t\\twhile 1:\\n\\t\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\t\\treturn n\\n\\t\\t\\t\\tif not self.cons(i):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\ti = i + 1\\n\\t\\t\\ti = i + 1\",\n \"def listes(Mi, Ma, N):\\n if N == 0:\\n yield []\\n elif Ma - Mi < N:\\n yield None\\n else:\\n # Avec Mi + 1\\n for reste in listes(Mi + 1, Ma, N - 1):\\n if reste != None:\\n yield [Mi + 1] + reste\\n \\n # Sans Mi + 1\\n for reste in listes(Mi + 1, Ma, N):\\n if reste != None:\\n yield reste\",\n \"def get_integers(n: int) -> int:\\n l = [int(sqrt(n))]\\n val = l[0] * l[0]\\n index = 0\\n while val != n:\\n val = sum([x*x for x in l])\\n if val > n:\\n l[index] -= 1\\n elif val < n:\\n index += 1\\n l.append(l[index - 1])\\n\\n return len(l)\",\n \"def _P(m):\\n P = np.zeros((m**2,m**2), dtype=np.int64)\\n for i in range(1, m**2 + 1):\\n j = 1 + m*((i - 1) % m) + (i - 1)//m\\n P[i-1, j-1] = 1\\n return P\",\n \"def dice(n, m):\\n return Lea.interval(1, m).cprodTimes(n)\",\n \"def divisors(n):\\r\\n numbers = []\\r\\n for i in xrange(1, n+1):\\r\\n if n % i == 0:\\r\\n numbers.append(i)\\r\\n return numbers\",\n \"def between_m_and_n_ver(seq, m, n):\\r\\n num_AB = 0\\r\\n for item in seq:\\r\\n if np.array_equal(item, Quantifier.AB):\\r\\n num_AB += 1\\r\\n return Quantifier.T if (m <= num_AB and num_AB <= n) else Quantifier.F\",\n \"def proper_divisors(n):\\r\\n numbers = []\\r\\n for i in xrange(1, n):\\r\\n if n % i == 0:\\r\\n numbers.append(i)\\r\\n \\r\\n return numbers\",\n \"def get_3_5_sum(num):\\n nums = range(num)\\n mults = [num for num in nums if (num % 3 == 0 or num % 5 == 0)]\\n return sum(mults)\",\n \"def getNPrime(num):\\n prime_numbers = []\\n for i in range(num):\\n if isPrime(i + 1):\\n prime_numbers.append(i)\\n return prime_numbers\",\n \"def factor(N):\\n\\n factors = []\\n sqrtN = math.sqrt(N)\\n for x in range(2, int(sqrtN)+1):\\n (d, r) = divmod(N, x)\\n if r == 0:\\n factors.append(x)\\n if x != d: factors.append(d)\\n return [1, N] + factors\",\n \"def _get_l1_mlist(n):\\n global _l1_bucket_max\\n if n == 1:\\n mlist = [1]\\n elif n <= _l1_bucket_max:\\n mlist = [n, 2*n, n*n]\\n else:\\n mlist = [n, 2*n] #just to try\\n return mlist\",\n \"def how_many_5(numbers):\\r\\n # Modify example to take argument that specifies threshold\\r\\n return sum( 1 for number in numbers if number > 5 )\",\n \"def primes_below(n):\\n L, M = [2], [x for x in range(3, int(n), 2)]\\n if n <= 2:\\n print('There are no primes below 2')\\n return None\\n for i in range(3, int(n), 2):\\n if M[i // 2 - 1] != 0 and is_prime(i):\\n L.append(i)\\n for j in range(i, int(n), 2 * i):\\n M[j // 2 - 1] = 0\\n return L\",\n \"def sum_divisibles(limit):\\n res = [x for x in range(limit) if x % 3 == 0 or x % 5 == 0]\\n return sum(res)\",\n \"def tourney_prob(k, N, m):\\n\\n if N < m:\\n print \\\"The second argument cannot be smaller than the third one.\\\"\\n sys.exit()\\n\\n if m < 1 or k <= 0:\\n return 0.0\\n elif m == 1:\\n return 1.0 / N\\n else:\\n return float(N - k) * m / (N * (m - 1)) * tourney_prob(k, N - 1, m - 1)\",\n \"def splitArray(self, nums: List[int], m: int) -> int:\\n low, high, res = max(nums), sum(nums), -1\\n while low <= high:\\n pivot=(low+high)//2\\n if self.isValid(nums,m,pivot):\\n res, high = pivot, pivot - 1\\n else:\\n low = pivot + 1\\n return res\",\n \"def MM(x,N,n,l,t=0):\\n Mat = np.zeros([2**l,2**l])\\n for iii in range(N):\\n Mat[iii,(x**n * iii)%N] = 1\\n return Mat\",\n \"def choices(N, M):\\n #yield from itertools.combinations(range(2, M+1), N)\\n # Use stars and bars method\\n allbars = itertools.combinations( range(1,N+M-1), M-2 )\\n for bars in allbars:\\n bars = [0] + list(bars) + [N+M-1]\\n yi = [ y-x for x, y in zip(bars, bars[1:]) ]\\n yield [ y-1 for y in yi ]\",\n \"def bruteForcePopulation(N):\\n return list(itertools.permutations(range(N), N))\",\n \"def primes(m):\\n if m <= 2:\\n return ()\\n sieve = [True] * m\\n for i in sixn(m):\\n if sieve[i]:\\n yield i\\n for mult in range(i * i, m, i):\\n sieve[mult] = False\",\n \"def choose_m_n(li,min,max):\\n n_items = random.randrange(min,max+1)\\n if n_items == 0:\\n return [ ]\\n sample=random.sample(li,n_items) # Should it be sorted?\\n return sample\",\n \"def Samples(n=6, m=1000):\\n t = [Sample(n) for i in range(m)]\\n return t\",\n \"def _two_of_m(m):\\n return (m * (m - 1) / 2)\",\n \"def multiply(m, n):\\n if n == 1:\\n return m\\n else:\\n return m + multiply(m, n - 1)\",\n \"def count_partitions(n, m):\\n if n == 0:\\n return 1\\n elif n < 0:\\n return 0\\n elif m == 0:\\n return 0\\n else:\\n with_m = count_partitions(n - m, m)\\n without_m = count_partitions(n, m - 1)\\n return with_m + without_m\",\n \"def solve(limit):\\n upper_limit = ceil(sqrt(limit - 2**4 - 2**3))\\n p_list = PrimeList(upper_limit)\\n\\n num_set = set()\\n for x in p_list:\\n val = limit - 2**4 - x**3\\n if val < 0: continue\\n lim = ceil(sqrt(val))\\n for y in takewhile(lambda i: i<lim, p_list):\\n val = limit - min(x,y)**4 - max(x,y)**3\\n if val < 0: continue\\n lim = ceil(sqrt(val))\\n for z in takewhile(lambda i: i<lim, p_list):\\n\\n for a,b,c in permutations([x,y,z]):\\n ans = a**2 + b**3 + c**4\\n if ans > limit: continue\\n num_set.add(ans)\\n if a ==b and b == c: break\\n\\n return len(num_set)\",\n \"def count_partitions(n, m):\\n # print(n, m)\\n if n == 0:\\n return 1\\n elif n < 0:\\n return 0\\n elif m == 0:\\n return 0\\n else:\\n return count_partitions(n-m, m) + count_partitions(n, m//2)\",\n \"def make_evaluation_grids(W, M, N):\\n nu = (np.arange(W * M, dtype=float) + 0.5) / (2 * M)\\n x = np.arange(N + 1, dtype=float) / (2 * N)\\n return nu, x\",\n \"def batches(l, n):\\n for i in range(0, l, n):\\n yield range(i,min(l,i+n))\",\n \"def ip(M, N):\\n\\n return M ^ N\",\n \"def gp(M, N):\\n\\n return M*N\",\n \"def _power(self, a, n, m):\\n res = 1\\n while n != 0:\\n if n % 2 != 0:\\n res *= a\\n res %= m\\n n -= 1\\n else:\\n a *= a\\n a %= m\\n n //= 2\\n return res\",\n \"def numbers():\\n for number in range(1, 76):\\n yield number\",\n \"def get_k(M):\\n k = np.arange(1,M+1)*np.pi/(M+1) # vector of all possible quasimomenta\\n return k\",\n \"def sum_mult(number, limit):\\n limit = limit - 1\\n limit = (limit - (limit%number))/number\\n return (number * limit * (limit+1))/2\",\n \"def primes_lt3(N):\\n\\t# test every number less than N/2\\n\\tprimes = [ i for i in xrange(2,N)\\n\\t\\t\\t\\t if not any( ( i % p == 0 for p in xrange(2,int(sqrt(i))+1) ) )]\\n\\n\\treturn primes\",\n \"def primes_lt3(N):\\n\\t# test every number less than N/2\\n\\tprimes = [ i for i in xrange(2,N)\\n\\t\\t\\t\\t if not any( ( i % p == 0 for p in xrange(2,int(sqrt(i))+1) ) )]\\n\\n\\treturn primes\",\n \"def prbs(m, n):\\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5\",\n \"def MM_n(N, data):\\n out = np.zeros(len(data))\\n\\n for j in range(N):\\n out[j] = np.average(data[:j+1])\\n for (j,d) in enumerate(data[N-1:]):\\n out[j+N-1] = np.average(data[j:j+N])\\n\\n return out\",\n \"def divisior(n: int) -> list:\\n j = [n]\\n for d in range(n+1): #loop bis n\\n d > 0\",\n \"def amicable_numbers(n):\\n amicables = []\\n sumDivisors = {}\\n for i in range(1, n):\\n divisors = proper_divisors(i)\\n sumDivisors[i] = sum(divisors)\\n for i in range(1, n):\\n sumDivisorsOfi = sumDivisors[i]\\n if sumDivisorsOfi < n:\\n compare = sumDivisors[sumDivisorsOfi]\\n if compare == i and sumDivisorsOfi != i:\\n amicables.append(i)\\n return amicables\",\n \"def generate_numbers():\\n\\n nums = []\\n\\n for i in range(0, 12):\\n for j in range(1, 12):\\n nums.append(i / j)\\n\\n return list(set(nums))\",\n \"def cycles(n, m):\\n if n <= 2:\\n return 1\\n k = 2\\n tk = 3\\n pk = 2\\n while k < n - 1:\\n tk1 = (2 * pk * tk * tk) % m\\n pk1 = (2 * pk * pk * tk) % m\\n tk = tk1\\n pk = pk1\\n k += 1\\n return (pk * pk * pk) % m\",\n \"def sum_amicable(limit):\\n\\n def find_amicable_pair(n):\\n check_n= 0\\n potential_half = 0\\n for i in range(1,n):\\n if n % i == 0:\\n potential_half += i\\n for i in range(1, potential_half):\\n if potential_half % i == 0:\\n check_n += i\\n if check_n == n and n != potential_half: # exclude self amicable\\n result.append(n)\\n result.append(potential_half)\\n\\n result = []\\n for num in range(1, limit):\\n if num not in result:\\n find_amicable_pair(num)\\n return sum(result)\",\n \"def skip_mul(n):\\n if n <= 0:\\n return 1\\n else:\\n return n * skip_mul(n - 2)\",\n \"def ModExp(n, k, m):\\n a = list(bin(k))[2:]\\n a.reverse()\\n s = 1\\n for i in a:\\n if i == '1':\\n s = (s * n) % m\\n n = (n * n) % m\\n return s\",\n \"def problem3(m, n):\\n # TODO: Implement and test this function.\\n # We supplied some tests in test_problem3 above.\",\n \"def divisors(n):\\n return [x for x in range(1, n) if n % x == 0]\",\n \"def f(n):\\n\\tfor i in range(101, n):\\n\\t\\tif (i % 21 == 0):\\n\\t\\t\\treturn i\",\n \"def fast_power(a, n, m): # (a ^ n) % m\\n result = 1\\n value = a\\n power = n\\n while power > 0:\\n if power % 2 == 1:\\n result = result * value\\n result %= m\\n value = value * value\\n value %= m\\n power = power//2\\n return result\",\n \"def eratosthenes_npo(limit):\\n if isinstance(limit, (int, float)):\\n limit = int(limit)\\n else:\\n raise ValueError\\n mask = np.ones(limit//2, dtype=np.bool)\\n for i in range(3, int(limit**0.5)+1, 2):\\n if mask[i//2]:\\n mask[i*i//2::i] = False\\n return np.r_[2, 2*np.nonzero(mask)[0][1::]+1]\",\n \"def perfect_power(n: int):\\n k = 2\\n max_k = round(n ** (1/2))\\n while True:\\n m = round(n ** (1/k))\\n if m ** k == n:\\n return [m, k]\\n elif k > max_k:\\n return None\\n else:\\n k += 1\",\n \"def prime_numbers(limit):\\n primes = [2, 3, 5]\\n for p in primes:\\n yield p\\n n = 5\\n count = 3\\n last_idx = -1\\n sqrd_prime = 0\\n while count <= limit:\\n n += 2\\n if n > sqrd_prime:\\n last_idx += 1\\n sqrd_prime = primes[last_idx] ** 2\\n is_prime = True\\n for i in range(1, last_idx + 1):\\n p = primes[i]\\n if n % p == 0:\\n is_prime = False\\n break\\n if is_prime:\\n count += 1\\n primes.append(n)\\n yield n\",\n \"def prime_factorization(n):\\r\\n result = []\\r\\n for i in xrange(2, n+1):\\r\\n s = 0;\\r\\n while n / float(i) == floor(n/float(i)):\\r\\n n = n / float(i)\\r\\n s += 1\\r\\n if s > 0:\\r\\n for k in range(s):\\r\\n result.append(i)\\r\\n if n == 1:\\r\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67994684","0.67914283","0.6521023","0.64013743","0.632049","0.62614083","0.6100065","0.6055581","0.6036513","0.6017747","0.60122454","0.60112625","0.59835833","0.59610224","0.59551555","0.59433913","0.5900685","0.587324","0.5860821","0.5820013","0.58147126","0.58118325","0.58117676","0.58067805","0.5791375","0.5788349","0.5780253","0.5773223","0.5770476","0.5767918","0.5759007","0.5741875","0.57391894","0.57294613","0.57218325","0.5710505","0.5699357","0.56893206","0.56803834","0.5676526","0.56713736","0.56521595","0.5642624","0.5642587","0.5642252","0.56401104","0.56388783","0.5626682","0.56264246","0.56111056","0.56066567","0.55986536","0.5594367","0.55916905","0.55900174","0.55831313","0.55812496","0.55482745","0.5545712","0.55340123","0.5531779","0.55310553","0.5528484","0.55244267","0.5520194","0.551607","0.5513164","0.5496783","0.5495809","0.54915017","0.5488247","0.5476103","0.5455536","0.54501975","0.5437969","0.5436581","0.5434888","0.54267466","0.54249406","0.542101","0.54186136","0.5398261","0.5398261","0.5397383","0.5392915","0.5377719","0.53676444","0.5362202","0.53610384","0.5353305","0.5349506","0.5343859","0.53376096","0.53353226","0.53339803","0.5333947","0.5330843","0.53298694","0.5325","0.53225803"],"string":"[\n \"0.67994684\",\n \"0.67914283\",\n \"0.6521023\",\n \"0.64013743\",\n \"0.632049\",\n \"0.62614083\",\n \"0.6100065\",\n \"0.6055581\",\n \"0.6036513\",\n \"0.6017747\",\n \"0.60122454\",\n \"0.60112625\",\n \"0.59835833\",\n \"0.59610224\",\n \"0.59551555\",\n \"0.59433913\",\n \"0.5900685\",\n \"0.587324\",\n \"0.5860821\",\n \"0.5820013\",\n \"0.58147126\",\n \"0.58118325\",\n \"0.58117676\",\n \"0.58067805\",\n \"0.5791375\",\n \"0.5788349\",\n \"0.5780253\",\n \"0.5773223\",\n \"0.5770476\",\n \"0.5767918\",\n \"0.5759007\",\n \"0.5741875\",\n \"0.57391894\",\n \"0.57294613\",\n \"0.57218325\",\n \"0.5710505\",\n \"0.5699357\",\n \"0.56893206\",\n \"0.56803834\",\n \"0.5676526\",\n \"0.56713736\",\n \"0.56521595\",\n \"0.5642624\",\n \"0.5642587\",\n \"0.5642252\",\n \"0.56401104\",\n \"0.56388783\",\n \"0.5626682\",\n \"0.56264246\",\n \"0.56111056\",\n \"0.56066567\",\n \"0.55986536\",\n \"0.5594367\",\n \"0.55916905\",\n \"0.55900174\",\n \"0.55831313\",\n \"0.55812496\",\n \"0.55482745\",\n \"0.5545712\",\n \"0.55340123\",\n \"0.5531779\",\n \"0.55310553\",\n \"0.5528484\",\n \"0.55244267\",\n \"0.5520194\",\n \"0.551607\",\n \"0.5513164\",\n \"0.5496783\",\n \"0.5495809\",\n \"0.54915017\",\n \"0.5488247\",\n \"0.5476103\",\n \"0.5455536\",\n \"0.54501975\",\n \"0.5437969\",\n \"0.5436581\",\n \"0.5434888\",\n \"0.54267466\",\n \"0.54249406\",\n \"0.542101\",\n \"0.54186136\",\n \"0.5398261\",\n \"0.5398261\",\n \"0.5397383\",\n \"0.5392915\",\n \"0.5377719\",\n \"0.53676444\",\n \"0.5362202\",\n \"0.53610384\",\n \"0.5353305\",\n \"0.5349506\",\n \"0.5343859\",\n \"0.53376096\",\n \"0.53353226\",\n \"0.53339803\",\n \"0.5333947\",\n \"0.5330843\",\n \"0.53298694\",\n \"0.5325\",\n \"0.53225803\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7943605"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7188,"cells":{"query":{"kind":"string","value":"Check if object is exactly an instance of specified class"},"document":{"kind":"string","value":"def is_same_class(obj, a_class):\n return type(obj) == a_class"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.isKindOfClass(self))\n else:\n return False","def is_same_class(obj, a_class):\n return isinstance(obj, a_class)","def is_same_class(obj, a_class):\n if type(obj) is a_class:\n return True\n else:\n return False","def is_same_class(obj, a_class):\n return type(obj) is a_class","def is_same_class(obj, a_class):\n return type(obj) is a_class","def is_same_class(obj, a_class):\n return(type(obj) is a_class)","def is_same_class(obj, a_class):\n\n if type(obj) is a_class:\n return True\n return False","def is_same_class(obj, a_class):\n return (type(obj) is a_class)","def is_same_class(obj, a_class):\n if type(obj) == a_class:\n return True\n else:\n return False","def match(self, cls):\n return isinstance(self, cls)","def is_same_class(obj, a_class):\n return(type(obj) == a_class)","def is_kind_of_class(obj, a_class):\n return(isinstance(obj, a_class))","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n if isinstance(obj, a_class):\n return True\n return False","def is_same_class(obj, a_class):\n return (type(obj) == a_class)","def is_kind_of_class(obj, a_class):\n return (isinstance(obj, a_class))","def is_kind_of_class(obj, a_class):\n if isinstance(obj, a_class):\n return True\n else:\n return False","def is_kind_of_class(obj, a_class):\n if isinstance(obj, a_class):\n return True\n else:\n return False","def is_kind_of_class(obj, a_class):\n if isinstance(obj, a_class) is True:\n return True\n else:\n return False","def isclassinstance(object):\n if not hasattr(object, \"__class__\"):\n return False\n if isbuiltin(object.__class__):\n return False\n return True","def is_kind_of_class(obj, a_class):\n\n return isinstance(obj, a_class)","def is_kind_of_class(obj, a_class):\n\n return (isinstance(obj, a_class))","def check_class(instance, type):\n\tif not issubclass(instance, type):\n\t\traise TypeError('Subclass expected type {0}, but got: {1}', type(type), type(instance))","def is_kind_of_class(obj, a_class):\n\n if isinstance(obj, a_class):\n return True\n else:\n return False","def class_is(cls: Class) -> bool:\n pass","def isclass(object):\r\n return isinstance(object, (type, types.ClassType))","def is_instance(instance, expected_types):\n for expected_type in expected_types:\n if isinstance(instance, expected_type):\n return True\n\n return False","def assert_is_instance(self, obj, cls, msg=\"\"):\r\n assert isinstance(obj, cls)","def test_is_instance(self):\n self.assertIsInstance(self.obj, Rectangle, \"created obj is not an \" +\n \"instance of Rectangle class.\")","def is_instance(self, thing: Any) -> bool:\n return isinstance(thing, self.underlying)","def is_dataclass_instance(obj: Any) -> bool:\n return dataclasses.is_dataclass(obj) and not isinstance(obj, type)","def inherits_from(obj, a_class):\n if issubclass(type(obj), a_class) and not type(obj) == a_class:\n return True\n else:\n return False","def verify_is_instance(self, obj, cls, msg=\"\"):\r\n try:\r\n self.assert_is_instance(obj, cls, msg)\r\n except AssertionError, e:\r\n if msg:\r\n m = \"%s:\\n%s\" % (msg, str(e))\r\n else:\r\n m = str(e)\r\n self.verification_erorrs.append(m)","def test_isclassinstance():\n class MockClass:\n pass\n\n # Since Python3, everything is a class, so this means nothing (?)\n assert isclassinstance(0)\n assert isclassinstance(1.0)\n assert isclassinstance(complex(2.0))\n assert isclassinstance('foo')\n assert isclassinstance([])\n assert isclassinstance(())\n assert isclassinstance(range(6))\n assert isclassinstance(bytes(7))\n assert isclassinstance(bytearray())\n assert isclassinstance(memoryview(b'nine'))\n assert isclassinstance(set())\n assert isclassinstance(frozenset())\n assert isclassinstance({})\n assert isclassinstance(None)\n assert isclassinstance(MockClass())","def inherits_from(obj, a_class):\n if issubclass(type(obj), a_class):\n if type(obj) is not a_class:\n return True\n return False","def inherits_from(obj, a_class):\n if isinstance(type(obj), a_class) and type(obj) != a_class:\n return True\n return False","def inherits_from(obj, a_class):\n\n if issubclass(type(obj), a_class) and type(obj) != a_class:\n return True\n return False","def obj_is_in_class(obj: unrealsdk.UObject, in_class: str) -> bool:\n return bool(obj.Class == unrealsdk.FindClass(in_class))","def inherits_from(obj, a_class):\n\n if isinstance(obj, a_class) and type(obj) is not a_class:\n return True\n\n return False","def issubclass(obj, cls):\r\n if isinstance(obj, Assert):\r\n obj = obj.obj\r\n return assert_(issubclass(obj, cls),\r\n 'not issubclass(%s, %s)' % (_repr(obj), _repr(cls)))","def __instancecheck__(cls, instance):\r\n # Inline the cache checking when it's simple.\r\n subclass = getattr(instance, '__class__', None)\r\n if subclass in cls._abc_cache:\r\n return True\r\n subtype = type(instance)\r\n if subtype is subclass or subclass is None:\r\n if (cls._abc_negative_cache_version ==\r\n ABCMeta._abc_invalidation_counter and\r\n subtype in cls._abc_negative_cache):\r\n return False\r\n # Fall back to the subclass check.\r\n return cls.__subclasscheck__(subtype)\r\n return (cls.__subclasscheck__(subclass) or\r\n cls.__subclasscheck__(subtype))","def _isinstance(self, instance, raise_error=True):\n\n if isinstance(instance, self.__model__):\n return True\n elif raise_error:\n raise ValueError('{} is not of type {}.'.format(\n instance, self.__model__,\n ))\n else:\n return False","def assertIsInstance(self, obj, cls, msg=None):\r\n if not _is_instance(obj, cls):\r\n standardMsg = '%s is not an instance of %r' % (safe_repr(obj), cls)\r\n self.fail(self._formatMessage(msg, standardMsg))","def instance_of(cls):\n def check(value):\n return (\n isinstance(value, cls),\n u\"{value!r} is instance of {actual!s}, required {required!s}\".format(\n value=value,\n actual=fullyQualifiedName(type(value)),\n required=fullyQualifiedName(cls),\n ),\n )\n return check","def is_instance_of_type(object_a, type_a):\n\n return is_type_subclass_of_type(type(object_a), type_a)","def inherits_from(obj, a_class):\n if type(obj) is not a_class:\n return(issubclass(type(obj), a_class))\n else:\n return False","def has_exactly_type(obj, tpe):\r\n return type(obj) == tpe","def inherits_from(obj, a_class):\n if type(obj) == a_class:\n return False\n return issubclass(type(obj), a_class)","def inherits_from(obj, a_class):\n if type(obj) is not a_class and issubclass(type(obj), a_class):\n return True\n else:\n return False","def inherits_from(obj, a_class):\n return issubclass(type(obj), a_class) and type(obj) != a_class","def __is_type_instance( self, instance_type ):\n for index, instance in enumerate(INSTANCE_TYPES):\n if instance == instance_type:\n return True\n return False","def inherits_from(obj, a_class):\n if a_class == type(obj):\n return False\n return isinstance(obj, a_class)","def inherits_from(obj, a_class):\n return(issubclass(type(obj), a_class) and type(obj) != a_class)","def inherits_from(obj, a_class):\n return isinstance(obj, a_class) and type(obj) is not a_class","def inherits_from(obj, a_class):\n return isinstance(obj, a_class) and type(obj) is not a_class","def inherits_from(obj, a_class):\n return isinstance(obj, a_class) and type(obj) != a_class","def inherits_from(obj, a_class):\n return isinstance(obj, a_class) and type(obj) != a_class","def inherits_from(obj, a_class):\n\n return isinstance(obj, a_class) and type(obj) is not a_class","def test_issubclass(self):\n self.assertTrue(issubclass(self.rev.__class__, BaseModel), True)","def inherits_from(obj, a_class):\n return (isinstance(obj, a_class) and type(obj) != a_class)","def _is_dataclass_instance(obj):\n return hasattr(type(obj), '__dataclass_fields__')","def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.conformsToProtocol(self))\n else:\n return False","def __instancecheck__(self, obj: object) -> NoReturn:\n raise TypeError(\"isinstance() argument 2 cannot be a \"\n \"parameterized generic\")","def assert_is_not_instance(self, obj, cls, msg=\"\"):\r\n assert not isinstance(obj, cls)","def inherits_from(obj, a_class):\n return ((issubclass(type(obj), a_class)) and type(obj) != a_class)","def _check(self, class_):\r\n\r\n if isinstance(class_, (types.FunctionType, types.LambdaType,\r\n types.ClassType, types.InstanceType)):\r\n return False\r\n if not hasattr(class_, '__dict__'):\r\n if not hasattr(class_, '__slots__'):\r\n return False\r\n return True","def isclass(object):\n if not inspect.isclass(object):\n return False\n if isbuiltin(object):\n return False\n return type not in inspect.getmro(object)","def exactly(base_cls):\n\n @meta\n def check(cls):\n return cls is base_cls\n\n return check","def verify_type(self, obj):\n return isinstance(obj, self.type_)","def needs_unique_instance(type_):\n return type_ in unique_instance_types","def isNestedInstance(obj, cl):\n tree = []\n for k in cl.__subclasses__():\n tree += k.__subclasses__()\n tree += cl.__subclasses__() + [cl]\n return issubclass(obj.__class__, tuple(tree))","def check_instance(self):\n self.assertIsInstance(self.amenity_1, amenity)\n self.assertIsInstance(self.amenity_2, amenity)","def _isinstancetype(an_obj):\n if an_obj is None: return False\n if not PY3K:\n return isinstance(an_obj, types.InstanceType)\n typstr = str(type(an_obj))\n # the following logic works, as PyRAF users expect, in both v2 and v3\n return typstr==\"<type 'instance'>\" or \\\n (typstr.startswith(\"<class '\") and ('.' in typstr))","def _isinstance(self, obj, raise_error=True):\n rv = isinstance(obj, self.__model__)\n if not rv and raise_error:\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\n return rv","def check_type(instance, type):\n\tif not isinstance(instance, type):\n\t\traise TypeError('Instance expected type {0}, but got: {1}', type(type), type(instance))","def is_object(value, class_name):\n\n return isinstance(value, getattr(schema, class_name))","def test_is_instance(self):\n self.assertTrue(isinstance(self.new_review, Review))","def isinstancemethod(cls, obj):\n return _isinstancemethod(cls, obj)","def is_icecube_class(obj: Any) -> bool:\n classname = str(type(obj))\n return \"icecube.\" in classname","def is_type(obj):\n return type(obj) is type or type(obj) is types.ClassType","def issubclass_(arg1, arg2):\n try:\n return issubclass(arg1, arg2)\n except TypeError:\n return False","def assert_isinstance(object, class_or_tuple):\n assert isinstance(\n object, class_or_tuple\n ), \"unexpected instance type, expected=%s, actual=%s\" % (\n class_or_tuple,\n type(object),\n )\n return object","def safe_isinstance(obj, class_path_str):\n # this function is copy-paste from the code of the SHAP Python library\n # Copyright (c) 2018 Scott Lundberg\n if isinstance(class_path_str, str):\n class_path_strs = [class_path_str]\n elif isinstance(class_path_str, list) or isinstance(class_path_str, tuple):\n class_path_strs = class_path_str\n else:\n class_path_strs = ['']\n\n # try each module path in order\n for class_path_str in class_path_strs:\n if \".\" not in class_path_str:\n raise ValueError(\"class_path_str must be a string or list of strings specifying a full \\\n module path to a class. Eg, 'sklearn.ensemble.RandomForestRegressor'\")\n\n # Splits on last occurence of \".\"\n module_name, class_name = class_path_str.rsplit(\".\", 1)\n\n # here we don't check further if the model is not imported, since we shouldn't have\n # an object of that types passed to us if the model the type is from has never been\n # imported. (and we don't want to import lots of new modules for no reason)\n if module_name not in sys.modules:\n continue\n\n module = sys.modules[module_name]\n\n #Get class\n _class = getattr(module, class_name, None)\n\n if _class is None:\n continue\n\n if isinstance(obj, _class):\n return True\n\n return False","def issubclass_safe(value, type_):\n try:\n return issubclass(value, type_)\n except (TypeError, AttributeError):\n # Cannot perform issubclass on some types\n return False","def issubclass(cls1, cls2):\n\n if cls1 == cls2:\n return 1\n\n for base in cls1.bases:\n for base_cls in lobj(base, strict=1):\n if base_cls == cls2:\n return 1\n else:\n return issubclass(base_cls, cls2)\n return 0","def test_instance(self):\n b = Review()\n self.assertIsInstance(b, Review)\n self.assertTrue(issubclass(type(b), BaseModel))","def match(cls, kind: 'dsl.Any') -> bool:\n return isinstance(kind, cls)","def is_child_class(obj, classinfo):\n try:\n return issubclass(obj, classinfo)\n except TypeError:\n return None","def _class(self, class_):\r\n\r\n if class_:\r\n if hasattr(class_, '__mro__'):\r\n #this is a class\r\n return class_\r\n else:\r\n #this is an instance\r\n return type(class_)","def test_issubclass(self):\n self.assertTrue(issubclass(User()), BaseModel)","def is_ctypes_instance(obj):\n return issubclass(type(obj), ctypes.Structure) or issubclass(type(obj), ctypes.Union)","def is_instance_of(self, mu, env, obj_idx, class_idx):\n obj = self.get_reference(obj_idx)\n if not isinstance(obj, jobject):\n raise ValueError('Expected a jobject.')\n\n clazz = self.get_reference(class_idx)\n if not isinstance(clazz, jclass):\n raise ValueError('Expected a jclass.')\n\n # TODO: Casting check (?)\n\n return JNI_TRUE if obj.value.jvm_id == clazz.value.jvm_id else JNI_FALSE","def _is_this_color(cls, obj: Any) -> bool:\n\n return type(obj) is cls","def is_same_type_as_other(cls, other):\r\n return isinstance(other, cls)"],"string":"[\n \"def __instancecheck__(self, instance):\\n\\n if isinstance(instance, ObjCInstance):\\n return bool(instance.isKindOfClass(self))\\n else:\\n return False\",\n \"def is_same_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_same_class(obj, a_class):\\n if type(obj) is a_class:\\n return True\\n else:\\n return False\",\n \"def is_same_class(obj, a_class):\\n return type(obj) is a_class\",\n \"def is_same_class(obj, a_class):\\n return type(obj) is a_class\",\n \"def is_same_class(obj, a_class):\\n return(type(obj) is a_class)\",\n \"def is_same_class(obj, a_class):\\n\\n if type(obj) is a_class:\\n return True\\n return False\",\n \"def is_same_class(obj, a_class):\\n return (type(obj) is a_class)\",\n \"def is_same_class(obj, a_class):\\n if type(obj) == a_class:\\n return True\\n else:\\n return False\",\n \"def match(self, cls):\\n return isinstance(self, cls)\",\n \"def is_same_class(obj, a_class):\\n return(type(obj) == a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return(isinstance(obj, a_class))\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n if isinstance(obj, a_class):\\n return True\\n return False\",\n \"def is_same_class(obj, a_class):\\n return (type(obj) == a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n return (isinstance(obj, a_class))\",\n \"def is_kind_of_class(obj, a_class):\\n if isinstance(obj, a_class):\\n return True\\n else:\\n return False\",\n \"def is_kind_of_class(obj, a_class):\\n if isinstance(obj, a_class):\\n return True\\n else:\\n return False\",\n \"def is_kind_of_class(obj, a_class):\\n if isinstance(obj, a_class) is True:\\n return True\\n else:\\n return False\",\n \"def isclassinstance(object):\\n if not hasattr(object, \\\"__class__\\\"):\\n return False\\n if isbuiltin(object.__class__):\\n return False\\n return True\",\n \"def is_kind_of_class(obj, a_class):\\n\\n return isinstance(obj, a_class)\",\n \"def is_kind_of_class(obj, a_class):\\n\\n return (isinstance(obj, a_class))\",\n \"def check_class(instance, type):\\n\\tif not issubclass(instance, type):\\n\\t\\traise TypeError('Subclass expected type {0}, but got: {1}', type(type), type(instance))\",\n \"def is_kind_of_class(obj, a_class):\\n\\n if isinstance(obj, a_class):\\n return True\\n else:\\n return False\",\n \"def class_is(cls: Class) -> bool:\\n pass\",\n \"def isclass(object):\\r\\n return isinstance(object, (type, types.ClassType))\",\n \"def is_instance(instance, expected_types):\\n for expected_type in expected_types:\\n if isinstance(instance, expected_type):\\n return True\\n\\n return False\",\n \"def assert_is_instance(self, obj, cls, msg=\\\"\\\"):\\r\\n assert isinstance(obj, cls)\",\n \"def test_is_instance(self):\\n self.assertIsInstance(self.obj, Rectangle, \\\"created obj is not an \\\" +\\n \\\"instance of Rectangle class.\\\")\",\n \"def is_instance(self, thing: Any) -> bool:\\n return isinstance(thing, self.underlying)\",\n \"def is_dataclass_instance(obj: Any) -> bool:\\n return dataclasses.is_dataclass(obj) and not isinstance(obj, type)\",\n \"def inherits_from(obj, a_class):\\n if issubclass(type(obj), a_class) and not type(obj) == a_class:\\n return True\\n else:\\n return False\",\n \"def verify_is_instance(self, obj, cls, msg=\\\"\\\"):\\r\\n try:\\r\\n self.assert_is_instance(obj, cls, msg)\\r\\n except AssertionError, e:\\r\\n if msg:\\r\\n m = \\\"%s:\\\\n%s\\\" % (msg, str(e))\\r\\n else:\\r\\n m = str(e)\\r\\n self.verification_erorrs.append(m)\",\n \"def test_isclassinstance():\\n class MockClass:\\n pass\\n\\n # Since Python3, everything is a class, so this means nothing (?)\\n assert isclassinstance(0)\\n assert isclassinstance(1.0)\\n assert isclassinstance(complex(2.0))\\n assert isclassinstance('foo')\\n assert isclassinstance([])\\n assert isclassinstance(())\\n assert isclassinstance(range(6))\\n assert isclassinstance(bytes(7))\\n assert isclassinstance(bytearray())\\n assert isclassinstance(memoryview(b'nine'))\\n assert isclassinstance(set())\\n assert isclassinstance(frozenset())\\n assert isclassinstance({})\\n assert isclassinstance(None)\\n assert isclassinstance(MockClass())\",\n \"def inherits_from(obj, a_class):\\n if issubclass(type(obj), a_class):\\n if type(obj) is not a_class:\\n return True\\n return False\",\n \"def inherits_from(obj, a_class):\\n if isinstance(type(obj), a_class) and type(obj) != a_class:\\n return True\\n return False\",\n \"def inherits_from(obj, a_class):\\n\\n if issubclass(type(obj), a_class) and type(obj) != a_class:\\n return True\\n return False\",\n \"def obj_is_in_class(obj: unrealsdk.UObject, in_class: str) -> bool:\\n return bool(obj.Class == unrealsdk.FindClass(in_class))\",\n \"def inherits_from(obj, a_class):\\n\\n if isinstance(obj, a_class) and type(obj) is not a_class:\\n return True\\n\\n return False\",\n \"def issubclass(obj, cls):\\r\\n if isinstance(obj, Assert):\\r\\n obj = obj.obj\\r\\n return assert_(issubclass(obj, cls),\\r\\n 'not issubclass(%s, %s)' % (_repr(obj), _repr(cls)))\",\n \"def __instancecheck__(cls, instance):\\r\\n # Inline the cache checking when it's simple.\\r\\n subclass = getattr(instance, '__class__', None)\\r\\n if subclass in cls._abc_cache:\\r\\n return True\\r\\n subtype = type(instance)\\r\\n if subtype is subclass or subclass is None:\\r\\n if (cls._abc_negative_cache_version ==\\r\\n ABCMeta._abc_invalidation_counter and\\r\\n subtype in cls._abc_negative_cache):\\r\\n return False\\r\\n # Fall back to the subclass check.\\r\\n return cls.__subclasscheck__(subtype)\\r\\n return (cls.__subclasscheck__(subclass) or\\r\\n cls.__subclasscheck__(subtype))\",\n \"def _isinstance(self, instance, raise_error=True):\\n\\n if isinstance(instance, self.__model__):\\n return True\\n elif raise_error:\\n raise ValueError('{} is not of type {}.'.format(\\n instance, self.__model__,\\n ))\\n else:\\n return False\",\n \"def assertIsInstance(self, obj, cls, msg=None):\\r\\n if not _is_instance(obj, cls):\\r\\n standardMsg = '%s is not an instance of %r' % (safe_repr(obj), cls)\\r\\n self.fail(self._formatMessage(msg, standardMsg))\",\n \"def instance_of(cls):\\n def check(value):\\n return (\\n isinstance(value, cls),\\n u\\\"{value!r} is instance of {actual!s}, required {required!s}\\\".format(\\n value=value,\\n actual=fullyQualifiedName(type(value)),\\n required=fullyQualifiedName(cls),\\n ),\\n )\\n return check\",\n \"def is_instance_of_type(object_a, type_a):\\n\\n return is_type_subclass_of_type(type(object_a), type_a)\",\n \"def inherits_from(obj, a_class):\\n if type(obj) is not a_class:\\n return(issubclass(type(obj), a_class))\\n else:\\n return False\",\n \"def has_exactly_type(obj, tpe):\\r\\n return type(obj) == tpe\",\n \"def inherits_from(obj, a_class):\\n if type(obj) == a_class:\\n return False\\n return issubclass(type(obj), a_class)\",\n \"def inherits_from(obj, a_class):\\n if type(obj) is not a_class and issubclass(type(obj), a_class):\\n return True\\n else:\\n return False\",\n \"def inherits_from(obj, a_class):\\n return issubclass(type(obj), a_class) and type(obj) != a_class\",\n \"def __is_type_instance( self, instance_type ):\\n for index, instance in enumerate(INSTANCE_TYPES):\\n if instance == instance_type:\\n return True\\n return False\",\n \"def inherits_from(obj, a_class):\\n if a_class == type(obj):\\n return False\\n return isinstance(obj, a_class)\",\n \"def inherits_from(obj, a_class):\\n return(issubclass(type(obj), a_class) and type(obj) != a_class)\",\n \"def inherits_from(obj, a_class):\\n return isinstance(obj, a_class) and type(obj) is not a_class\",\n \"def inherits_from(obj, a_class):\\n return isinstance(obj, a_class) and type(obj) is not a_class\",\n \"def inherits_from(obj, a_class):\\n return isinstance(obj, a_class) and type(obj) != a_class\",\n \"def inherits_from(obj, a_class):\\n return isinstance(obj, a_class) and type(obj) != a_class\",\n \"def inherits_from(obj, a_class):\\n\\n return isinstance(obj, a_class) and type(obj) is not a_class\",\n \"def test_issubclass(self):\\n self.assertTrue(issubclass(self.rev.__class__, BaseModel), True)\",\n \"def inherits_from(obj, a_class):\\n return (isinstance(obj, a_class) and type(obj) != a_class)\",\n \"def _is_dataclass_instance(obj):\\n return hasattr(type(obj), '__dataclass_fields__')\",\n \"def __instancecheck__(self, instance):\\n\\n if isinstance(instance, ObjCInstance):\\n return bool(instance.conformsToProtocol(self))\\n else:\\n return False\",\n \"def __instancecheck__(self, obj: object) -> NoReturn:\\n raise TypeError(\\\"isinstance() argument 2 cannot be a \\\"\\n \\\"parameterized generic\\\")\",\n \"def assert_is_not_instance(self, obj, cls, msg=\\\"\\\"):\\r\\n assert not isinstance(obj, cls)\",\n \"def inherits_from(obj, a_class):\\n return ((issubclass(type(obj), a_class)) and type(obj) != a_class)\",\n \"def _check(self, class_):\\r\\n\\r\\n if isinstance(class_, (types.FunctionType, types.LambdaType,\\r\\n types.ClassType, types.InstanceType)):\\r\\n return False\\r\\n if not hasattr(class_, '__dict__'):\\r\\n if not hasattr(class_, '__slots__'):\\r\\n return False\\r\\n return True\",\n \"def isclass(object):\\n if not inspect.isclass(object):\\n return False\\n if isbuiltin(object):\\n return False\\n return type not in inspect.getmro(object)\",\n \"def exactly(base_cls):\\n\\n @meta\\n def check(cls):\\n return cls is base_cls\\n\\n return check\",\n \"def verify_type(self, obj):\\n return isinstance(obj, self.type_)\",\n \"def needs_unique_instance(type_):\\n return type_ in unique_instance_types\",\n \"def isNestedInstance(obj, cl):\\n tree = []\\n for k in cl.__subclasses__():\\n tree += k.__subclasses__()\\n tree += cl.__subclasses__() + [cl]\\n return issubclass(obj.__class__, tuple(tree))\",\n \"def check_instance(self):\\n self.assertIsInstance(self.amenity_1, amenity)\\n self.assertIsInstance(self.amenity_2, amenity)\",\n \"def _isinstancetype(an_obj):\\n if an_obj is None: return False\\n if not PY3K:\\n return isinstance(an_obj, types.InstanceType)\\n typstr = str(type(an_obj))\\n # the following logic works, as PyRAF users expect, in both v2 and v3\\n return typstr==\\\"<type 'instance'>\\\" or \\\\\\n (typstr.startswith(\\\"<class '\\\") and ('.' in typstr))\",\n \"def _isinstance(self, obj, raise_error=True):\\n rv = isinstance(obj, self.__model__)\\n if not rv and raise_error:\\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\\n return rv\",\n \"def check_type(instance, type):\\n\\tif not isinstance(instance, type):\\n\\t\\traise TypeError('Instance expected type {0}, but got: {1}', type(type), type(instance))\",\n \"def is_object(value, class_name):\\n\\n return isinstance(value, getattr(schema, class_name))\",\n \"def test_is_instance(self):\\n self.assertTrue(isinstance(self.new_review, Review))\",\n \"def isinstancemethod(cls, obj):\\n return _isinstancemethod(cls, obj)\",\n \"def is_icecube_class(obj: Any) -> bool:\\n classname = str(type(obj))\\n return \\\"icecube.\\\" in classname\",\n \"def is_type(obj):\\n return type(obj) is type or type(obj) is types.ClassType\",\n \"def issubclass_(arg1, arg2):\\n try:\\n return issubclass(arg1, arg2)\\n except TypeError:\\n return False\",\n \"def assert_isinstance(object, class_or_tuple):\\n assert isinstance(\\n object, class_or_tuple\\n ), \\\"unexpected instance type, expected=%s, actual=%s\\\" % (\\n class_or_tuple,\\n type(object),\\n )\\n return object\",\n \"def safe_isinstance(obj, class_path_str):\\n # this function is copy-paste from the code of the SHAP Python library\\n # Copyright (c) 2018 Scott Lundberg\\n if isinstance(class_path_str, str):\\n class_path_strs = [class_path_str]\\n elif isinstance(class_path_str, list) or isinstance(class_path_str, tuple):\\n class_path_strs = class_path_str\\n else:\\n class_path_strs = ['']\\n\\n # try each module path in order\\n for class_path_str in class_path_strs:\\n if \\\".\\\" not in class_path_str:\\n raise ValueError(\\\"class_path_str must be a string or list of strings specifying a full \\\\\\n module path to a class. Eg, 'sklearn.ensemble.RandomForestRegressor'\\\")\\n\\n # Splits on last occurence of \\\".\\\"\\n module_name, class_name = class_path_str.rsplit(\\\".\\\", 1)\\n\\n # here we don't check further if the model is not imported, since we shouldn't have\\n # an object of that types passed to us if the model the type is from has never been\\n # imported. (and we don't want to import lots of new modules for no reason)\\n if module_name not in sys.modules:\\n continue\\n\\n module = sys.modules[module_name]\\n\\n #Get class\\n _class = getattr(module, class_name, None)\\n\\n if _class is None:\\n continue\\n\\n if isinstance(obj, _class):\\n return True\\n\\n return False\",\n \"def issubclass_safe(value, type_):\\n try:\\n return issubclass(value, type_)\\n except (TypeError, AttributeError):\\n # Cannot perform issubclass on some types\\n return False\",\n \"def issubclass(cls1, cls2):\\n\\n if cls1 == cls2:\\n return 1\\n\\n for base in cls1.bases:\\n for base_cls in lobj(base, strict=1):\\n if base_cls == cls2:\\n return 1\\n else:\\n return issubclass(base_cls, cls2)\\n return 0\",\n \"def test_instance(self):\\n b = Review()\\n self.assertIsInstance(b, Review)\\n self.assertTrue(issubclass(type(b), BaseModel))\",\n \"def match(cls, kind: 'dsl.Any') -> bool:\\n return isinstance(kind, cls)\",\n \"def is_child_class(obj, classinfo):\\n try:\\n return issubclass(obj, classinfo)\\n except TypeError:\\n return None\",\n \"def _class(self, class_):\\r\\n\\r\\n if class_:\\r\\n if hasattr(class_, '__mro__'):\\r\\n #this is a class\\r\\n return class_\\r\\n else:\\r\\n #this is an instance\\r\\n return type(class_)\",\n \"def test_issubclass(self):\\n self.assertTrue(issubclass(User()), BaseModel)\",\n \"def is_ctypes_instance(obj):\\n return issubclass(type(obj), ctypes.Structure) or issubclass(type(obj), ctypes.Union)\",\n \"def is_instance_of(self, mu, env, obj_idx, class_idx):\\n obj = self.get_reference(obj_idx)\\n if not isinstance(obj, jobject):\\n raise ValueError('Expected a jobject.')\\n\\n clazz = self.get_reference(class_idx)\\n if not isinstance(clazz, jclass):\\n raise ValueError('Expected a jclass.')\\n\\n # TODO: Casting check (?)\\n\\n return JNI_TRUE if obj.value.jvm_id == clazz.value.jvm_id else JNI_FALSE\",\n \"def _is_this_color(cls, obj: Any) -> bool:\\n\\n return type(obj) is cls\",\n \"def is_same_type_as_other(cls, other):\\r\\n return isinstance(other, cls)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7938733","0.7921352","0.7915792","0.79037","0.79037","0.7898302","0.7889888","0.7864915","0.7833402","0.78156614","0.778558","0.77762145","0.77750516","0.77750516","0.77750516","0.77750516","0.77750516","0.77750516","0.777345","0.7769582","0.7744331","0.7743631","0.7743631","0.7735183","0.77251005","0.7699322","0.7695979","0.76958835","0.7689502","0.76553655","0.76337165","0.7629859","0.7572446","0.7460568","0.74496037","0.7399509","0.7320281","0.73165","0.73128164","0.7312458","0.7310445","0.72889775","0.72826624","0.72494704","0.72482854","0.72039306","0.7198594","0.7180114","0.71746033","0.716997","0.71595746","0.71568716","0.7151595","0.71453476","0.7141164","0.71212023","0.7120686","0.7114234","0.7099861","0.7099861","0.70642793","0.70642793","0.7054249","0.7054011","0.7025262","0.7024464","0.7017139","0.7015372","0.6998749","0.6988215","0.6987929","0.6980995","0.6976936","0.6918344","0.6915231","0.69132537","0.6889717","0.6880734","0.68571705","0.68527997","0.6840351","0.6806334","0.678912","0.6768781","0.6719238","0.6696865","0.66788745","0.6653535","0.6651278","0.66483116","0.6645451","0.6640547","0.6624231","0.66150194","0.6597071","0.65900713","0.6588198","0.6563817","0.6559196"],"string":"[\n \"0.7938733\",\n \"0.7921352\",\n \"0.7915792\",\n \"0.79037\",\n \"0.79037\",\n \"0.7898302\",\n \"0.7889888\",\n \"0.7864915\",\n \"0.7833402\",\n \"0.78156614\",\n \"0.778558\",\n \"0.77762145\",\n \"0.77750516\",\n \"0.77750516\",\n \"0.77750516\",\n \"0.77750516\",\n \"0.77750516\",\n \"0.77750516\",\n \"0.777345\",\n \"0.7769582\",\n \"0.7744331\",\n \"0.7743631\",\n \"0.7743631\",\n \"0.7735183\",\n \"0.77251005\",\n \"0.7699322\",\n \"0.7695979\",\n \"0.76958835\",\n \"0.7689502\",\n \"0.76553655\",\n \"0.76337165\",\n \"0.7629859\",\n \"0.7572446\",\n \"0.7460568\",\n \"0.74496037\",\n \"0.7399509\",\n \"0.7320281\",\n \"0.73165\",\n \"0.73128164\",\n \"0.7312458\",\n \"0.7310445\",\n \"0.72889775\",\n \"0.72826624\",\n \"0.72494704\",\n \"0.72482854\",\n \"0.72039306\",\n \"0.7198594\",\n \"0.7180114\",\n \"0.71746033\",\n \"0.716997\",\n \"0.71595746\",\n \"0.71568716\",\n \"0.7151595\",\n \"0.71453476\",\n \"0.7141164\",\n \"0.71212023\",\n \"0.7120686\",\n \"0.7114234\",\n \"0.7099861\",\n \"0.7099861\",\n \"0.70642793\",\n \"0.70642793\",\n \"0.7054249\",\n \"0.7054011\",\n \"0.7025262\",\n \"0.7024464\",\n \"0.7017139\",\n \"0.7015372\",\n \"0.6998749\",\n \"0.6988215\",\n \"0.6987929\",\n \"0.6980995\",\n \"0.6976936\",\n \"0.6918344\",\n \"0.6915231\",\n \"0.69132537\",\n \"0.6889717\",\n \"0.6880734\",\n \"0.68571705\",\n \"0.68527997\",\n \"0.6840351\",\n \"0.6806334\",\n \"0.678912\",\n \"0.6768781\",\n \"0.6719238\",\n \"0.6696865\",\n \"0.66788745\",\n \"0.6653535\",\n \"0.6651278\",\n \"0.66483116\",\n \"0.6645451\",\n \"0.6640547\",\n \"0.6624231\",\n \"0.66150194\",\n \"0.6597071\",\n \"0.65900713\",\n \"0.6588198\",\n \"0.6563817\",\n \"0.6559196\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7816911"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":7189,"cells":{"query":{"kind":"string","value":"setup any state tied to the execution of the given method in a class. setup_method is invoked for every test method of a class."},"document":{"kind":"string","value":"def setup_method(self, objectCreation):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setup_method(self, method):\n pass","def setup_method(self, method):\n pass","def _decorate_setup(self, setup_method):\n @wraps(setup_method)\n def setup_method_wrapper(*args, **kwargs):\n \"\"\"setup method wrapper.\n\n * Locks the required resources for the test.\n * Executes the original setUp method.\n * Upon exception, finalizes the resources.\n \"\"\"\n if self.is_main:\n if isinstance(self.result, Result):\n skip_reason = self.result.shouldSkip(self)\n\n if skip_reason is not None:\n self.skip_sub_components(skip_reason)\n self.skipTest(skip_reason)\n\n else:\n if self.mode in (MODE_CRITICAL, MODE_OPTIONAL):\n siblings = list(self.parent)\n for component in siblings:\n if component is self:\n break\n\n if component.is_failing():\n self.skip_sub_components(\n self.PREVIOUS_FAILED_MESSAGE)\n self.skipTest(self.PREVIOUS_FAILED_MESSAGE)\n\n try:\n self.request_resources(self.get_resource_requests(),\n use_previous=True)\n\n self.override_resource_loggers()\n\n except Exception as err:\n if isinstance(err, ServerError):\n self.skip_sub_components(self.NO_RESOURCES_MESSAGE)\n\n raise\n\n try:\n if not self.IS_COMPLEX:\n self._set_parameters(override_previous=False,\n **{input_name: value.default\n for (input_name, value) in\n iteritems(self.get_inputs())\n if value.is_optional()})\n\n for pipe_name, pipe in iteritems(self._pipes):\n if pipe_name in self.get_inputs():\n setattr(self, pipe_name, pipe.get_value(self))\n\n if not self.is_main:\n # Validate all required inputs were passed\n self.validate_inputs()\n\n setup_method(*args, **kwargs)\n if isinstance(self.result, Result):\n self.result.setupFinished(self)\n\n except Exception:\n self.release_resources(dirty=True)\n raise\n\n return setup_method_wrapper","def setup_method(self, method):\n self.hass = get_test_home_assistant()","def setup_method(self, test_method):\n self.wo_obj = TestCases()\n self.global_config, self.test_args = self.wo_obj.get_config_data(test_method=test_method.__name__)","def setup_method(self, method):\n save_settings()\n set_known_good_settings()","def setup_method(cls):\n seed()","def setup_method(self, method):\n self.hass = get_test_home_assistant()\n self.calls = []\n\n @callback\n def record_call(service):\n \"\"\"Track function calls..\"\"\"\n self.calls.append(service)\n\n self.hass.services.register('test', 'automation', record_call)","def setup_method(self, method):\n self.hass = get_test_home_assistant()\n self.port = mock.MagicMock()\n self.switch = mfi.MfiSwitch(self.port)","def setupClass(cls):\n print(\"In setUpClass()...\")","def setup_method(self, method):\r\n self.cf = self.config = saunter.ConfigWrapper.ConfigWrapper()\r\n\r\n self.current_method_name = method.__name__\r\n\r\n browser = self.cf[\"browsers\"][self.cf[\"saunter\"][\"default_browser\"]]\r\n if browser[\"type\"][0] == \"*\":\r\n browser = browser[\"type\"] = browser[\"type\"][1:]\r\n\r\n profile = None\r\n if browser[\"type\"] == 'firefox':\r\n if browser[\"profiles\"][sys.platform]:\r\n profile_path = os.path.join(self.cf[\"saunter\"][\"base\"], 'support', 'profiles', browser[\"profiles\"][sys.platform])\r\n elif browser[\"profiles\"][\"profile\"]:\r\n profile_path = os.path.join(self.cf[\"saunter\"][\"base\"], 'support', 'profiles', browser[\"profiles\"][\"profile\"])\r\n else:\r\n profile_path = None\r\n\r\n if profile_path:\r\n if os.path.isdir(profile_path):\r\n profile = FirefoxProfile(profile_path)\r\n else:\r\n raise ProfileNotFound(\"Profile not found at %s\" % profile_path)\r\n\r\n if \"saucelabs\" in browser and browser[\"saucelabs\"][\"ondemand\"]:\r\n desired_capabilities = {\r\n \"platform\": self.cf[\"sauceLabs\"][\"os\"],\r\n \"browserName\": self.cf[\"sauceLabs\"][\"browser\"],\r\n \"version\": self.cf.get(\"SauceLabs\", \"browser_version\"),\r\n \"name\": method.__name__\r\n }\r\n if desired_capabilities[\"browserName\"][0] == \"*\":\r\n desired_capabilities[\"browserName\"] = desired_capabilities[\"browserName\"][1:]\r\n if desired_capabilities[\"platform\"] in os_map:\r\n desired_capabilities[\"platform\"] = os_map[desired_capabilities[\"platform\"]]\r\n\r\n if self.cf.has_option(\"SauceLabs\", \"selenium_version\"):\r\n desired_capabilities['selenium-version'] = self.cf.get('SauceLabs', 'selenium_version')\r\n\r\n command_executor = \"http://%s:%s@ondemand.saucelabs.com:80/wd/hub\" % (self.cf.get(\"SauceLabs\", \"username\"), self.cf.get(\"SauceLabs\", \"key\"))\r\n else:\r\n desired_capabilities = capabilities_map[browser[\"type\"]]\r\n\r\n if browser[\"proxy\"][\"type\"] and browser[\"proxy\"][\"type\"].lower() == \"browsermob\":\r\n from browsermobproxy import Client\r\n self.client = Client(self.cf.get(\"Proxy\", \"proxy_url\"))\r\n self.client.add_to_webdriver_capabilities(desired_capabilities)\r\n\r\n if \"is grid\" in self.cf[\"selenium\"] and self.cf[\"selenium\"][\"executor\"][\"is grid\"]:\r\n if browser[\"grid filters\"][\"platform\"]:\r\n desired_capabilities[\"platform\"] = browser[\"grid filters\"][\"platform\"].upper()\r\n if browser[\"grid filters\"][\"version\"]:\r\n desired_capabilities[\"platform\"] = str(browser[\"grid filters\"][\"version\"])\r\n\r\n command_executor = \"http://%s:%s/wd/hub\" % (self.cf[\"selenium\"][\"executor\"][\"host\"], self.cf[\"selenium\"][\"executor\"][\"port\"])\r\n\r\n self.driver = WebDriver(desired_capabilities=desired_capabilities, command_executor=command_executor, browser_profile=profile)\r\n\r\n self.verificationErrors = []\r\n self.matchers = Matchers(self.driver, self.verificationErrors)\r\n\r\n if \"saucelabs\" in self.cf[\"browsers\"][self.cf[\"saunter\"][\"default_browser\"]] and self.cf[\"browsers\"][self.cf[\"saunter\"][\"default_browser\"]][\"saucelabs\"][\"ondemand\"]:\r\n self.sauce_session = self.driver.session_id\r\n\r\n self._screenshot_number = 1","def setUpClass(cls):\n cls.state_f = inspect.getmembers(State, inspect.isfunction)","def setup_method_wrapper(*args, **kwargs):\n if self.is_main:\n if isinstance(self.result, Result):\n skip_reason = self.result.shouldSkip(self)\n\n if skip_reason is not None:\n self.skip_sub_components(skip_reason)\n self.skipTest(skip_reason)\n\n else:\n if self.mode in (MODE_CRITICAL, MODE_OPTIONAL):\n siblings = list(self.parent)\n for component in siblings:\n if component is self:\n break\n\n if component.is_failing():\n self.skip_sub_components(\n self.PREVIOUS_FAILED_MESSAGE)\n self.skipTest(self.PREVIOUS_FAILED_MESSAGE)\n\n try:\n self.request_resources(self.get_resource_requests(),\n use_previous=True)\n\n self.override_resource_loggers()\n\n except Exception as err:\n if isinstance(err, ServerError):\n self.skip_sub_components(self.NO_RESOURCES_MESSAGE)\n\n raise\n\n try:\n if not self.IS_COMPLEX:\n self._set_parameters(override_previous=False,\n **{input_name: value.default\n for (input_name, value) in\n iteritems(self.get_inputs())\n if value.is_optional()})\n\n for pipe_name, pipe in iteritems(self._pipes):\n if pipe_name in self.get_inputs():\n setattr(self, pipe_name, pipe.get_value(self))\n\n if not self.is_main:\n # Validate all required inputs were passed\n self.validate_inputs()\n\n setup_method(*args, **kwargs)\n if isinstance(self.result, Result):\n self.result.setupFinished(self)\n\n except Exception:\n self.release_resources(dirty=True)\n raise","def setup_method(self, method):\n super().setup_method(method)\n\n self.CONFIG = FakeEnv.generate_data()\n\n class MyEnv(Env):\n ENVIRON = self.CONFIG\n\n self.env = MyEnv()","def __run_test_methods(self):\n for test_method in self.runnable_test_methods():\n\n result = TestResult(test_method)\n test_method.im_self.test_result = result\n\n try:\n self._method_level = True # Flag that we're currently running method-level stuff (rather than class-level)\n\n # run \"on-run\" callbacks. eg/ print out the test method name\n for callback in self.__callbacks[self.EVENT_ON_RUN_TEST_METHOD]:\n callback(result.to_dict())\n result.start()\n\n if self.__class_level_failure:\n result.end_in_failure(self.__class_level_failure)\n elif self.__class_level_error:\n result.end_in_error(self.__class_level_error)\n else:\n # first, run setup fixtures\n self._stage = self.STAGE_SETUP\n def _setup_block():\n for fixture_method in self.setup_fixtures + [ self.setUp ]:\n fixture_method()\n self.__execute_block_recording_exceptions(_setup_block, result)\n\n def _run_test_block():\n # then run the test method itself, assuming setup was successful\n self._stage = self.STAGE_TEST_METHOD\n if not result.complete:\n self.__execute_block_recording_exceptions(test_method, result)\n\n def _setup_teardown_block():\n self.__enter_context_managers(self.setup_teardown_fixtures, _run_test_block)\n\n # then run any setup_teardown fixtures, assuming setup was successful.\n if not result.complete:\n self.__execute_block_recording_exceptions(_setup_teardown_block, result)\n\n # finally, run the teardown phase\n self._stage = self.STAGE_TEARDOWN\n def _teardown_block():\n for fixture_method in [ self.tearDown ] + self.teardown_fixtures:\n fixture_method()\n self.__execute_block_recording_exceptions(_teardown_block, result)\n\n # if nothing's gone wrong, it's not about to start\n if not result.complete:\n result.end_in_success()\n except (KeyboardInterrupt, SystemExit):\n result.end_in_interruption(sys.exc_info())\n raise\n finally:\n for callback in self.__callbacks[self.EVENT_ON_COMPLETE_TEST_METHOD]:\n callback(result.to_dict())\n\n self._method_level = False\n\n if not result.success:\n self.failure_count += 1\n if self.failure_limit and self.failure_count >= self.failure_limit:\n return","def setup_method(self, method):\n self.cmds = []","def setup_method(self) -> None:\n self.client = Mock()","def setUpClass(cls):\n cls.setup_log()\n cls.setup_conn()\n cls.setup_cache()\n cls.setup_params()","def setup_method(self):\n self.hass = get_test_home_assistant()","def setUp(self):\n # use self.attribute to keep anything which needs to be accessed later\n print('setUp method\\n')","def setup_method(self):\n self.ae = None","def setup_method(self) -> None:\n super(TestProfiles, self).setup_method()\n if os.getenv('SETUP_METHOD') is None:\n self.custom.setup_method(self)","def setup_method(self, method):\n self.old_environ = os.environ\n os.environ = Env.ENVIRON = FakeEnv.generate_data()\n self.env = Env()","def __run_class_setup_fixtures(self):\n self.__run_class_fixtures(\n self.STAGE_CLASS_SETUP,\n self.class_setup_fixtures + [ self.classSetUp ],\n self.EVENT_ON_RUN_CLASS_SETUP_METHOD,\n self.EVENT_ON_COMPLETE_CLASS_SETUP_METHOD,\n )","def setup_method(self, method):\n self.user1 = UserFactory()\n self.user2 = UserFactory()\n self.budget1 = BudgetFactory(name=\"test1\", creator=self.user1)\n self.budget2 = BudgetFactory(name=\"budget0\", creator=self.user1)\n self.budget3 = BudgetFactory(name=\"third\", creator=self.user2)\n self.budget4 = BudgetFactory(name=\"fourth\", creator=self.user2, users=[self.user1])\n TransactionFactory(budget=self.budget1, amount=Decimal('30'), user=self.user1, category='test')\n TransactionFactory(budget=self.budget1, amount=Decimal('12.20'), user=self.user1)\n TransactionFactory(budget=self.budget1, amount=Decimal('-34.85'), user=self.user1)\n TransactionFactory(budget=self.budget4, user=self.user1)\n TransactionFactory(budget=self.budget4, user=self.user2)\n TransactionFactory(budget=self.budget4, user=self.user2)","def setup_method(self, method):\n if _debug: TestAnnexJCodec._debug(\"setup_method %r\", method)\n\n # minature trapped stack\n self.client = TrappedClient()\n self.codec = AnnexJCodec()\n self.server = TrappedServer()\n bind(self.client, self.codec, self.server)","def setup_method(self):\n self.hass = get_test_home_assistant()\n self.mock_mqtt = mock_mqtt_component(self.hass)","def setup_method(self, method):\n self.opp = get_test_open_peer_power()","def setup_method(self, method):\n self.opp = get_test_open_peer_power()","def test_class_started(self, cls):","def setUpClass(cls) -> None:\n super().setUpClass()\n # add any additional setup needed here\n return","def setup_method(self, _method, filename=__file__):\n super().setup_method(_method=_method, filename=filename)","def setUpClass(cls):\r\n print(\"Setting up class for tests!\")\r\n print(\"==========================\")","def setup_class(cls):\n # ns.assert_true(False, \"setup_class run\")\n print('setup_class\\n')","def setup_method(self):\n self.ti_helper = TIHelper(self.indicator_type, self.indicator_field_arg)\n self.ti = self.ti_helper.ti\n self.tcex = self.ti_helper.tcex","def setUpClass(cls):\n # Establish the session for each test method\n cls.session = CutlassTestConfig.get_session()\n\n cls.util = CutlassTestUtil()","def setUpClass(cls):\n # Establish the session for each test method\n cls.session = CutlassTestConfig.get_session()\n cls.util = CutlassTestUtil()","def setUpClass(cls):\n # Establish the session for each test method\n cls.session = CutlassTestConfig.get_session()\n cls.util = CutlassTestUtil()","def setupClass(cls):\n cls._tmp_dir = tempfile.mkdtemp()\n cls.test_filepath = os.path.join( cls._tmp_dir, \"test_data.h5\" )\n cls._generate_testdata_h5(cls.test_filepath)\n cls.server_proc, cls.shutdown_event = cls._start_mockserver( cls.test_filepath, same_process=True )\n cls.client_connection = httplib.HTTPConnection( \"localhost:8000\" )","def setUpClass(cls) -> None:\n helper_funcs.setup_mock_files() # setup mock files\n cls.module, cls.ffi = helper_funcs.load(cls._filenames, cls._function_names,\n header_includes=cls.helper_include,\n compiled_file_end=\"helper_func\")","def setUpClass(self):\n\n self.test_a_summarize()\n self.test_b_store()\n self.test_c_get_existing_records()\n self.test_d_remove_database()","def setUpClass(cls):\n log.debug('setUpClass of {0} called...'.format(cls))\n # Check to see if the environment that this class is initialised for\n # matches the project path (if specified). If the environment was\n # initialised by the ChipTools internal loaded then skip these steps.\n if cls._environment_type != 'chiptools':\n if cls._loaded_path != cls.project:\n project = Project()\n project.load_project(cls.project)\n # Using external test runner (such as Nosetests) to execute this\n # test. The test environment therefore needs to be loaded from\n # the Project instance:\n simulator, root, libs = cls.get_environment(project)\n cls._loaded_path = cls.project\n cls._simulator = simulator\n cls._simulation_root = root\n cls._simulation_libraries = libs\n cls._environment_type = 'external'\n # Compile the design if required (simulators with caching will\n # perform this step once).\n cls._simulator.compile_project(\n includes=cls._simulation_libraries\n )\n log.debug(\n '...finished initialising environment for {0}'.format(cls)\n )\n if cls._environment_type is None:\n raise EnvironmentError(\n 'The simulation environment for this TestCase is not ' +\n 'initialised so the test cases cannot be executed. ' +\n 'If you are running this test directly ensure that ' +\n 'the TestCase class has a \"project\" attribute which ' +\n 'holds a path to a valid ChipTools project file.'\n )","def setUp(self):\n GlusterBaseClass.setUp.im_func(self)\n self.test_method_complete = False","def setUpClass(cls):\n # Call the setUpClass method(s) of the parent class(es)","def setUpClass(cls):\n pass","def setUpClass(cls):\n pass","def setUpClass(cls):\n pass","def setUpClass(cls):\n pass","def setup_stage(method):\n def decorator(self):\n name = method.func_name\n if should_run_stage(name):\n say(\"Setup.%s\" % name)\n method(self)\n set_stage(name)\n decorator.__doc__ = method.__doc__\n return decorator","def setUpClass(cls):\r\n pass","def setUp(self):\n self._class = State()\n self._class2 = State()\n self._name = \"State\"","def setUpClass(cls) -> None:\n stack_name = TestStateMachine.get_and_verify_stack_name()\n\n client = boto3.client(\"cloudformation\")\n response = client.list_stack_resources(StackName=stack_name)\n resources = response[\"StackResourceSummaries\"]\n state_machine_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"TransactionProcessorStateMachine\"\n ]\n transaction_table_purchase_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"PurchaseTable\"\n ]\n transaction_table_refund_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"RefundTable\"\n ]\n transaction_table_error_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"ErrorTable\"\n ]\n lambda_function_purchase_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"PurchaseFunction\"\n ]\n lambda_function_validator_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"ValidatorFunction\"\n ]\n lambda_function_refund_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"RefundFunction\"\n ]\n lambda_function_error_resources = [\n resource for resource in resources if resource[\"LogicalResourceId\"] == \"ErrorFunction\"\n ]\n\n if not state_machine_resources:\n raise Exception(\"Cannot find TransactionProcessorStateMachine\")\n if not transaction_table_purchase_resources:\n raise Exception(\"Cannot find DDBPurchaseTable\")\n if not transaction_table_refund_resources:\n raise Exception(\"Cannot find DDBRefundTable\")\n if not transaction_table_error_resources:\n raise Exception(\"Cannot find DDBErrorTable\")\n if not lambda_function_error_resources:\n raise Exception(\"Cannot find ErrorFunction\")\n if not lambda_function_purchase_resources:\n raise Exception(\"Cannot find PurchaseFunction\")\n if not lambda_function_refund_resources:\n raise Exception(\"Cannot find RefundFunction\")\n if not lambda_function_validator_resources:\n raise Exception(\"Cannot find ValidatorFunction\")\n\n cls.state_machine_arn = state_machine_resources[0][\"PhysicalResourceId\"]\n cls.transaction_table_purchase = transaction_table_purchase_resources[0][\"PhysicalResourceId\"]\n cls.transaction_table_refund = transaction_table_refund_resources[0][\"PhysicalResourceId\"]\n cls.transaction_table_error = transaction_table_error_resources[0][\"PhysicalResourceId\"]\n cls.lambda_function_validator = lambda_function_validator_resources[0][\"PhysicalResourceId\"]\n cls.lambda_function_purchase = lambda_function_purchase_resources[0][\"PhysicalResourceId\"]\n cls.lambda_function_refund = lambda_function_refund_resources[0][\"PhysicalResourceId\"]\n cls.lambda_function_error = lambda_function_error_resources[0][\"PhysicalResourceId\"]","def run(self):\n\n self.__run_class_setup_fixtures()\n self.__enter_context_managers(self.class_setup_teardown_fixtures, self.__run_test_methods)\n self.__run_class_teardown_fixtures()","def setup_class(klass):","def setup_class(klass):","def setUp(self):\n print('Calling \\'setUp\\'')","def test_set_up(self):\n # set_up has been called for the test suite\n # (see runtests) so there is not much that\n # can be tested\n with patch('react.utils.shortcuts.set_up', autospec=True) as s:\n utils.set_up()\n s.assert_called_once_with()","def __init_fixture_methods(self):\n # init our self.(class_setup|setup|teardown|class_teardown)_fixtures lists\n for fixture_type in fixture_types:\n setattr(self, \"%s_fixtures\" % fixture_type, [])\n\n # for setup methods, we want oldest class first. for teardowns, we want newest class first\n hierarchy = list(reversed(type(self).mro()))\n for cls in hierarchy[1:]:\n # mixins on TestCase instances that derive from, say, object, won't be set up properly\n if hasattr(cls, '_fixture_methods'):\n # the metaclass stored the class's fixtures in a _fixture_methods instance variable\n for fixture_type, fixture_methods in cls._fixture_methods.iteritems():\n bound_fixture_methods = [instancemethod(func, self, self.__class__) for func in fixture_methods]\n if fixture_type.endswith('setup'):\n # for setup methods, we want methods defined further back in the\n # class hierarchy to execute first\n getattr(self, \"%s_fixtures\" % fixture_type).extend(bound_fixture_methods)\n else:\n # for teardown methods though, we want the opposite\n setattr(self, \"%s_fixtures\" % fixture_type, bound_fixture_methods + getattr(self, \"%s_fixtures\" % fixture_type))","def setup_class(self):\n # Initialize instance variable(s)\n self.n2vc = None\n\n # Track internal state for each test run\n self.state = {}\n\n # Parse the test's descriptors\n self.nsd = get_descriptor(self.NSD_YAML)\n self.vnfd = get_descriptor(self.VNFD_YAML)\n\n self.ns_name = self.nsd['name']\n self.vnf_name = self.vnfd['name']\n\n self.charms = {}\n self.parse_vnf_descriptor()\n assert self.charms is not {}\n\n # Track artifacts, like compiled charms, that will need to be removed\n self.artifacts = {}\n\n # Build the charm(s) needed for this test\n for charm in self.get_charm_names():\n # debug(\"Building charm {}\".format(charm))\n self.get_charm(charm)\n\n # A bit of a hack, in order to allow the N2VC callback to run parallel\n # to pytest. Test(s) should wait for this flag to change to False\n # before returning.\n self._running = True\n self._stopping = False","def setup_method(self, method):\n super().setup_method(method)\n\n Env.ENVIRON = {}\n self.env.read_env(\n Path(__file__, is_file=True)('test_env.txt'),\n PATH_VAR=Path(__file__, is_file=True).__root__\n )","def setUpClass(self):","def setUpClass(cls):\n cls.student_f = inspect.getmembers(Student, inspect.isfunction)","def _set_up():\n repl._setUp = self.setUp","def setup_class(cls):","def setup_class(cls):","def setup():\n\n self.zorp_mock = Mock()\n\n for name, func in six.iteritems(self._get_zorp_mock_methods()):\n self.zorp_mock.server.registry.put(name, func)\n\n self.zorp_mock.start()","def __call__(self, result=None):\n self._pre_setup()\n super(TestCase, self).__call__(result)\n self._post_tearDown()","def teardown_method(self, method) -> None:","def XXsetUp(self):\n print(\"FooTest:setUp_:begin\")\n ## do something...\n print(\"FooTest:setUp_:end\")","def XXsetUp(self):\n print(\"FooTest:setUp_:begin\")\n ## do something...\n print(\"FooTest:setUp_:end\")","def __init__(self):\n self.setup_called = False","def setUp(self):\n self.p_state = p_state","def setUp(self):\n raise NotImplementedError","def setup(self):\n raise NotImplementedError(\"Need to be implemented in subclasses\")","def setUp(self):\n log.debug('TestModule.setUp')\n if self.module is None:\n self.module = _import(self.moduleName, [self.path], self.conf)\n log.debug('Imported %s from %s on %s', self.module,\n self.moduleName, self.path)\n if hasattr(self.module, '__path__'):\n names = ['setupPackage', 'setUpPackage', 'setup_package']\n else:\n names = ['setupModule', 'setUpModule', 'setup_module']\n names += ['setUp', 'setup']\n try_run(self.module, names)","def setup_class(self):\n pass","def setUpClass(cls):\n super(CharmOperationTest, cls).setUpClass()","def setUpClass(cls):\n super(BaseTest, cls).setUpClass()\n try:\n cls.configure()\n cls.execute()\n except:\n cls.annihilate()\n raise","def setUpClass(cls, *_):\n super(PagingTest, cls).setUpClass()\n warnings.simplefilter('ignore')\n cls.service_manager = create_service_manager([], ['classifier'])\n cls._tbl_num = cls.service_manager.get_table_num(Classifier.APP_NAME)\n\n testing_controller_reference = Future()\n classifier_reference = Future()\n test_setup = TestSetup(\n apps=[\n PipelinedController.Classifier,\n PipelinedController.Testing,\n PipelinedController.StartupFlows,\n ],\n references={\n PipelinedController.Classifier:\n classifier_reference,\n PipelinedController.Testing:\n testing_controller_reference,\n PipelinedController.StartupFlows:\n Future(),\n },\n config={\n 'bridge_name': cls.BRIDGE,\n 'bridge_ip_address': cls.BRIDGE_IP,\n 'internal_ip_subnet': '192.168.0.0/16',\n 'ovs_gtp_port_number': 32768,\n 'ovs_mtr_port_number': 15577,\n 'ovs_internal_sampling_port_number': 15578,\n 'ovs_internal_sampling_fwd_tbl_number': 201,\n 'ovs_internal_conntrack_port_number': 15579,\n 'ovs_internal_conntrack_fwd_tbl_number': 202,\n 'clean_restart': True,\n 'paging_timeout': 30,\n 'classifier_controller_id': 5,\n 'enable_nat': True,\n 'ovs_uplink_port_name': \"patch-up\",\n },\n mconfig=PipelineD(),\n loop=None,\n service_manager=cls.service_manager,\n integ_test=False,\n rpc_stubs={'sessiond_setinterface': MagicMock()},\n )\n\n BridgeTools.create_bridge(cls.BRIDGE, cls.IFACE)\n cls.thread = start_ryu_app_thread(test_setup)\n cls.classifier_controller = classifier_reference.result()\n cls.testing_controller = testing_controller_reference.result()","def setUp(self):\n self._control = test_control.PauseFailControl()\n self._digest_pool = logging_pool.pool(test_constants.POOL_SIZE)\n self._digest = _digest.digest(\n _stock_service.STOCK_TEST_SERVICE, self._control, self._digest_pool)\n\n generic_stub, dynamic_stubs, self._memo = self.implementation.instantiate(\n self._digest.methods, self._digest.event_method_implementations, None)\n self._invoker = self.invoker_constructor.construct_invoker(\n generic_stub, dynamic_stubs, self._digest.methods)","def setup_method(self):\n self.project_dir = os.path.join(DIR, 'test-project')\n self.e2e = E2EEnv(self.project_dir)","def setup_method(self):\n self.ti_helper = TIHelper(self.group_type, required_fields=self.required_fields)\n self.ti = self.ti_helper.ti\n self.tcex = self.ti_helper.tcex","def make_set_up(set_up=None):\n def _do_set_up(obj):\n if set_up:\n return set_up(obj)\n return obj.setUp()\n return _do_set_up","def setUp(self, up):\n\t\tself.up = up","def setUpClass(cls):\n cls._tmpdir = tempfile.mkdtemp()\n if (not os.path.isdir(cls._tmpdir)):\n raise Exception(\"Failed to create tempdir to use for dump tests\")\n try:\n cls.extraSetUpClass()\n except Exception:\n pass","def before_run_tests(cls):\n pass","def setup(self) -> None:\n pass","def setup(self) -> None:\n pass","def setup(self) -> None:\n pass","def setUpClass(cls):\r\n # Clear out any existing modulestores,\r\n # which will cause them to be re-created\r\n # the next time they are accessed.\r\n clear_existing_modulestores()\r\n TestCase.setUpClass()","def setup_method(self,method):\n from importlib import reload\n reload(bt_temp)\n self.transport_obj = bt_temp.ns.modelList[0].levelModelList[0]\n self.bdm2_obj = self.transport_obj.velocityPostProcessor.vpp_algorithms[0]\n self._setRelativePath()","def test_setup(self):\n with pytest.raises(NotImplementedError):\n self.behaviour.setup()","def setup_class(cls):\n cls.mock_get_patcher = patch('project.services.requests.get')\n cls.mock_get = cls.mock_get_patcher.start()","def setup(self):\n raise NotImplementedError","def setup(self):\n raise NotImplementedError","def setup(self):\n raise NotImplementedError","def setup(self):\n raise NotImplementedError","def __call__(self, result=None):\n try:\n self._pre_setup()\n super(TestCase, self).__call__(result)\n finally:\n self._post_teardown()","def setup(self) -> None:","def setUp(self):\n\t\tprint(\"\\n-------------------------------------\\nIn Test_RMT_Util:\", self._testMethodName)"],"string":"[\n \"def setup_method(self, method):\\n pass\",\n \"def setup_method(self, method):\\n pass\",\n \"def _decorate_setup(self, setup_method):\\n @wraps(setup_method)\\n def setup_method_wrapper(*args, **kwargs):\\n \\\"\\\"\\\"setup method wrapper.\\n\\n * Locks the required resources for the test.\\n * Executes the original setUp method.\\n * Upon exception, finalizes the resources.\\n \\\"\\\"\\\"\\n if self.is_main:\\n if isinstance(self.result, Result):\\n skip_reason = self.result.shouldSkip(self)\\n\\n if skip_reason is not None:\\n self.skip_sub_components(skip_reason)\\n self.skipTest(skip_reason)\\n\\n else:\\n if self.mode in (MODE_CRITICAL, MODE_OPTIONAL):\\n siblings = list(self.parent)\\n for component in siblings:\\n if component is self:\\n break\\n\\n if component.is_failing():\\n self.skip_sub_components(\\n self.PREVIOUS_FAILED_MESSAGE)\\n self.skipTest(self.PREVIOUS_FAILED_MESSAGE)\\n\\n try:\\n self.request_resources(self.get_resource_requests(),\\n use_previous=True)\\n\\n self.override_resource_loggers()\\n\\n except Exception as err:\\n if isinstance(err, ServerError):\\n self.skip_sub_components(self.NO_RESOURCES_MESSAGE)\\n\\n raise\\n\\n try:\\n if not self.IS_COMPLEX:\\n self._set_parameters(override_previous=False,\\n **{input_name: value.default\\n for (input_name, value) in\\n iteritems(self.get_inputs())\\n if value.is_optional()})\\n\\n for pipe_name, pipe in iteritems(self._pipes):\\n if pipe_name in self.get_inputs():\\n setattr(self, pipe_name, pipe.get_value(self))\\n\\n if not self.is_main:\\n # Validate all required inputs were passed\\n self.validate_inputs()\\n\\n setup_method(*args, **kwargs)\\n if isinstance(self.result, Result):\\n self.result.setupFinished(self)\\n\\n except Exception:\\n self.release_resources(dirty=True)\\n raise\\n\\n return setup_method_wrapper\",\n \"def setup_method(self, method):\\n self.hass = get_test_home_assistant()\",\n \"def setup_method(self, test_method):\\n self.wo_obj = TestCases()\\n self.global_config, self.test_args = self.wo_obj.get_config_data(test_method=test_method.__name__)\",\n \"def setup_method(self, method):\\n save_settings()\\n set_known_good_settings()\",\n \"def setup_method(cls):\\n seed()\",\n \"def setup_method(self, method):\\n self.hass = get_test_home_assistant()\\n self.calls = []\\n\\n @callback\\n def record_call(service):\\n \\\"\\\"\\\"Track function calls..\\\"\\\"\\\"\\n self.calls.append(service)\\n\\n self.hass.services.register('test', 'automation', record_call)\",\n \"def setup_method(self, method):\\n self.hass = get_test_home_assistant()\\n self.port = mock.MagicMock()\\n self.switch = mfi.MfiSwitch(self.port)\",\n \"def setupClass(cls):\\n print(\\\"In setUpClass()...\\\")\",\n \"def setup_method(self, method):\\r\\n self.cf = self.config = saunter.ConfigWrapper.ConfigWrapper()\\r\\n\\r\\n self.current_method_name = method.__name__\\r\\n\\r\\n browser = self.cf[\\\"browsers\\\"][self.cf[\\\"saunter\\\"][\\\"default_browser\\\"]]\\r\\n if browser[\\\"type\\\"][0] == \\\"*\\\":\\r\\n browser = browser[\\\"type\\\"] = browser[\\\"type\\\"][1:]\\r\\n\\r\\n profile = None\\r\\n if browser[\\\"type\\\"] == 'firefox':\\r\\n if browser[\\\"profiles\\\"][sys.platform]:\\r\\n profile_path = os.path.join(self.cf[\\\"saunter\\\"][\\\"base\\\"], 'support', 'profiles', browser[\\\"profiles\\\"][sys.platform])\\r\\n elif browser[\\\"profiles\\\"][\\\"profile\\\"]:\\r\\n profile_path = os.path.join(self.cf[\\\"saunter\\\"][\\\"base\\\"], 'support', 'profiles', browser[\\\"profiles\\\"][\\\"profile\\\"])\\r\\n else:\\r\\n profile_path = None\\r\\n\\r\\n if profile_path:\\r\\n if os.path.isdir(profile_path):\\r\\n profile = FirefoxProfile(profile_path)\\r\\n else:\\r\\n raise ProfileNotFound(\\\"Profile not found at %s\\\" % profile_path)\\r\\n\\r\\n if \\\"saucelabs\\\" in browser and browser[\\\"saucelabs\\\"][\\\"ondemand\\\"]:\\r\\n desired_capabilities = {\\r\\n \\\"platform\\\": self.cf[\\\"sauceLabs\\\"][\\\"os\\\"],\\r\\n \\\"browserName\\\": self.cf[\\\"sauceLabs\\\"][\\\"browser\\\"],\\r\\n \\\"version\\\": self.cf.get(\\\"SauceLabs\\\", \\\"browser_version\\\"),\\r\\n \\\"name\\\": method.__name__\\r\\n }\\r\\n if desired_capabilities[\\\"browserName\\\"][0] == \\\"*\\\":\\r\\n desired_capabilities[\\\"browserName\\\"] = desired_capabilities[\\\"browserName\\\"][1:]\\r\\n if desired_capabilities[\\\"platform\\\"] in os_map:\\r\\n desired_capabilities[\\\"platform\\\"] = os_map[desired_capabilities[\\\"platform\\\"]]\\r\\n\\r\\n if self.cf.has_option(\\\"SauceLabs\\\", \\\"selenium_version\\\"):\\r\\n desired_capabilities['selenium-version'] = self.cf.get('SauceLabs', 'selenium_version')\\r\\n\\r\\n command_executor = \\\"http://%s:%s@ondemand.saucelabs.com:80/wd/hub\\\" % (self.cf.get(\\\"SauceLabs\\\", \\\"username\\\"), self.cf.get(\\\"SauceLabs\\\", \\\"key\\\"))\\r\\n else:\\r\\n desired_capabilities = capabilities_map[browser[\\\"type\\\"]]\\r\\n\\r\\n if browser[\\\"proxy\\\"][\\\"type\\\"] and browser[\\\"proxy\\\"][\\\"type\\\"].lower() == \\\"browsermob\\\":\\r\\n from browsermobproxy import Client\\r\\n self.client = Client(self.cf.get(\\\"Proxy\\\", \\\"proxy_url\\\"))\\r\\n self.client.add_to_webdriver_capabilities(desired_capabilities)\\r\\n\\r\\n if \\\"is grid\\\" in self.cf[\\\"selenium\\\"] and self.cf[\\\"selenium\\\"][\\\"executor\\\"][\\\"is grid\\\"]:\\r\\n if browser[\\\"grid filters\\\"][\\\"platform\\\"]:\\r\\n desired_capabilities[\\\"platform\\\"] = browser[\\\"grid filters\\\"][\\\"platform\\\"].upper()\\r\\n if browser[\\\"grid filters\\\"][\\\"version\\\"]:\\r\\n desired_capabilities[\\\"platform\\\"] = str(browser[\\\"grid filters\\\"][\\\"version\\\"])\\r\\n\\r\\n command_executor = \\\"http://%s:%s/wd/hub\\\" % (self.cf[\\\"selenium\\\"][\\\"executor\\\"][\\\"host\\\"], self.cf[\\\"selenium\\\"][\\\"executor\\\"][\\\"port\\\"])\\r\\n\\r\\n self.driver = WebDriver(desired_capabilities=desired_capabilities, command_executor=command_executor, browser_profile=profile)\\r\\n\\r\\n self.verificationErrors = []\\r\\n self.matchers = Matchers(self.driver, self.verificationErrors)\\r\\n\\r\\n if \\\"saucelabs\\\" in self.cf[\\\"browsers\\\"][self.cf[\\\"saunter\\\"][\\\"default_browser\\\"]] and self.cf[\\\"browsers\\\"][self.cf[\\\"saunter\\\"][\\\"default_browser\\\"]][\\\"saucelabs\\\"][\\\"ondemand\\\"]:\\r\\n self.sauce_session = self.driver.session_id\\r\\n\\r\\n self._screenshot_number = 1\",\n \"def setUpClass(cls):\\n cls.state_f = inspect.getmembers(State, inspect.isfunction)\",\n \"def setup_method_wrapper(*args, **kwargs):\\n if self.is_main:\\n if isinstance(self.result, Result):\\n skip_reason = self.result.shouldSkip(self)\\n\\n if skip_reason is not None:\\n self.skip_sub_components(skip_reason)\\n self.skipTest(skip_reason)\\n\\n else:\\n if self.mode in (MODE_CRITICAL, MODE_OPTIONAL):\\n siblings = list(self.parent)\\n for component in siblings:\\n if component is self:\\n break\\n\\n if component.is_failing():\\n self.skip_sub_components(\\n self.PREVIOUS_FAILED_MESSAGE)\\n self.skipTest(self.PREVIOUS_FAILED_MESSAGE)\\n\\n try:\\n self.request_resources(self.get_resource_requests(),\\n use_previous=True)\\n\\n self.override_resource_loggers()\\n\\n except Exception as err:\\n if isinstance(err, ServerError):\\n self.skip_sub_components(self.NO_RESOURCES_MESSAGE)\\n\\n raise\\n\\n try:\\n if not self.IS_COMPLEX:\\n self._set_parameters(override_previous=False,\\n **{input_name: value.default\\n for (input_name, value) in\\n iteritems(self.get_inputs())\\n if value.is_optional()})\\n\\n for pipe_name, pipe in iteritems(self._pipes):\\n if pipe_name in self.get_inputs():\\n setattr(self, pipe_name, pipe.get_value(self))\\n\\n if not self.is_main:\\n # Validate all required inputs were passed\\n self.validate_inputs()\\n\\n setup_method(*args, **kwargs)\\n if isinstance(self.result, Result):\\n self.result.setupFinished(self)\\n\\n except Exception:\\n self.release_resources(dirty=True)\\n raise\",\n \"def setup_method(self, method):\\n super().setup_method(method)\\n\\n self.CONFIG = FakeEnv.generate_data()\\n\\n class MyEnv(Env):\\n ENVIRON = self.CONFIG\\n\\n self.env = MyEnv()\",\n \"def __run_test_methods(self):\\n for test_method in self.runnable_test_methods():\\n\\n result = TestResult(test_method)\\n test_method.im_self.test_result = result\\n\\n try:\\n self._method_level = True # Flag that we're currently running method-level stuff (rather than class-level)\\n\\n # run \\\"on-run\\\" callbacks. eg/ print out the test method name\\n for callback in self.__callbacks[self.EVENT_ON_RUN_TEST_METHOD]:\\n callback(result.to_dict())\\n result.start()\\n\\n if self.__class_level_failure:\\n result.end_in_failure(self.__class_level_failure)\\n elif self.__class_level_error:\\n result.end_in_error(self.__class_level_error)\\n else:\\n # first, run setup fixtures\\n self._stage = self.STAGE_SETUP\\n def _setup_block():\\n for fixture_method in self.setup_fixtures + [ self.setUp ]:\\n fixture_method()\\n self.__execute_block_recording_exceptions(_setup_block, result)\\n\\n def _run_test_block():\\n # then run the test method itself, assuming setup was successful\\n self._stage = self.STAGE_TEST_METHOD\\n if not result.complete:\\n self.__execute_block_recording_exceptions(test_method, result)\\n\\n def _setup_teardown_block():\\n self.__enter_context_managers(self.setup_teardown_fixtures, _run_test_block)\\n\\n # then run any setup_teardown fixtures, assuming setup was successful.\\n if not result.complete:\\n self.__execute_block_recording_exceptions(_setup_teardown_block, result)\\n\\n # finally, run the teardown phase\\n self._stage = self.STAGE_TEARDOWN\\n def _teardown_block():\\n for fixture_method in [ self.tearDown ] + self.teardown_fixtures:\\n fixture_method()\\n self.__execute_block_recording_exceptions(_teardown_block, result)\\n\\n # if nothing's gone wrong, it's not about to start\\n if not result.complete:\\n result.end_in_success()\\n except (KeyboardInterrupt, SystemExit):\\n result.end_in_interruption(sys.exc_info())\\n raise\\n finally:\\n for callback in self.__callbacks[self.EVENT_ON_COMPLETE_TEST_METHOD]:\\n callback(result.to_dict())\\n\\n self._method_level = False\\n\\n if not result.success:\\n self.failure_count += 1\\n if self.failure_limit and self.failure_count >= self.failure_limit:\\n return\",\n \"def setup_method(self, method):\\n self.cmds = []\",\n \"def setup_method(self) -> None:\\n self.client = Mock()\",\n \"def setUpClass(cls):\\n cls.setup_log()\\n cls.setup_conn()\\n cls.setup_cache()\\n cls.setup_params()\",\n \"def setup_method(self):\\n self.hass = get_test_home_assistant()\",\n \"def setUp(self):\\n # use self.attribute to keep anything which needs to be accessed later\\n print('setUp method\\\\n')\",\n \"def setup_method(self):\\n self.ae = None\",\n \"def setup_method(self) -> None:\\n super(TestProfiles, self).setup_method()\\n if os.getenv('SETUP_METHOD') is None:\\n self.custom.setup_method(self)\",\n \"def setup_method(self, method):\\n self.old_environ = os.environ\\n os.environ = Env.ENVIRON = FakeEnv.generate_data()\\n self.env = Env()\",\n \"def __run_class_setup_fixtures(self):\\n self.__run_class_fixtures(\\n self.STAGE_CLASS_SETUP,\\n self.class_setup_fixtures + [ self.classSetUp ],\\n self.EVENT_ON_RUN_CLASS_SETUP_METHOD,\\n self.EVENT_ON_COMPLETE_CLASS_SETUP_METHOD,\\n )\",\n \"def setup_method(self, method):\\n self.user1 = UserFactory()\\n self.user2 = UserFactory()\\n self.budget1 = BudgetFactory(name=\\\"test1\\\", creator=self.user1)\\n self.budget2 = BudgetFactory(name=\\\"budget0\\\", creator=self.user1)\\n self.budget3 = BudgetFactory(name=\\\"third\\\", creator=self.user2)\\n self.budget4 = BudgetFactory(name=\\\"fourth\\\", creator=self.user2, users=[self.user1])\\n TransactionFactory(budget=self.budget1, amount=Decimal('30'), user=self.user1, category='test')\\n TransactionFactory(budget=self.budget1, amount=Decimal('12.20'), user=self.user1)\\n TransactionFactory(budget=self.budget1, amount=Decimal('-34.85'), user=self.user1)\\n TransactionFactory(budget=self.budget4, user=self.user1)\\n TransactionFactory(budget=self.budget4, user=self.user2)\\n TransactionFactory(budget=self.budget4, user=self.user2)\",\n \"def setup_method(self, method):\\n if _debug: TestAnnexJCodec._debug(\\\"setup_method %r\\\", method)\\n\\n # minature trapped stack\\n self.client = TrappedClient()\\n self.codec = AnnexJCodec()\\n self.server = TrappedServer()\\n bind(self.client, self.codec, self.server)\",\n \"def setup_method(self):\\n self.hass = get_test_home_assistant()\\n self.mock_mqtt = mock_mqtt_component(self.hass)\",\n \"def setup_method(self, method):\\n self.opp = get_test_open_peer_power()\",\n \"def setup_method(self, method):\\n self.opp = get_test_open_peer_power()\",\n \"def test_class_started(self, cls):\",\n \"def setUpClass(cls) -> None:\\n super().setUpClass()\\n # add any additional setup needed here\\n return\",\n \"def setup_method(self, _method, filename=__file__):\\n super().setup_method(_method=_method, filename=filename)\",\n \"def setUpClass(cls):\\r\\n print(\\\"Setting up class for tests!\\\")\\r\\n print(\\\"==========================\\\")\",\n \"def setup_class(cls):\\n # ns.assert_true(False, \\\"setup_class run\\\")\\n print('setup_class\\\\n')\",\n \"def setup_method(self):\\n self.ti_helper = TIHelper(self.indicator_type, self.indicator_field_arg)\\n self.ti = self.ti_helper.ti\\n self.tcex = self.ti_helper.tcex\",\n \"def setUpClass(cls):\\n # Establish the session for each test method\\n cls.session = CutlassTestConfig.get_session()\\n\\n cls.util = CutlassTestUtil()\",\n \"def setUpClass(cls):\\n # Establish the session for each test method\\n cls.session = CutlassTestConfig.get_session()\\n cls.util = CutlassTestUtil()\",\n \"def setUpClass(cls):\\n # Establish the session for each test method\\n cls.session = CutlassTestConfig.get_session()\\n cls.util = CutlassTestUtil()\",\n \"def setupClass(cls):\\n cls._tmp_dir = tempfile.mkdtemp()\\n cls.test_filepath = os.path.join( cls._tmp_dir, \\\"test_data.h5\\\" )\\n cls._generate_testdata_h5(cls.test_filepath)\\n cls.server_proc, cls.shutdown_event = cls._start_mockserver( cls.test_filepath, same_process=True )\\n cls.client_connection = httplib.HTTPConnection( \\\"localhost:8000\\\" )\",\n \"def setUpClass(cls) -> None:\\n helper_funcs.setup_mock_files() # setup mock files\\n cls.module, cls.ffi = helper_funcs.load(cls._filenames, cls._function_names,\\n header_includes=cls.helper_include,\\n compiled_file_end=\\\"helper_func\\\")\",\n \"def setUpClass(self):\\n\\n self.test_a_summarize()\\n self.test_b_store()\\n self.test_c_get_existing_records()\\n self.test_d_remove_database()\",\n \"def setUpClass(cls):\\n log.debug('setUpClass of {0} called...'.format(cls))\\n # Check to see if the environment that this class is initialised for\\n # matches the project path (if specified). If the environment was\\n # initialised by the ChipTools internal loaded then skip these steps.\\n if cls._environment_type != 'chiptools':\\n if cls._loaded_path != cls.project:\\n project = Project()\\n project.load_project(cls.project)\\n # Using external test runner (such as Nosetests) to execute this\\n # test. The test environment therefore needs to be loaded from\\n # the Project instance:\\n simulator, root, libs = cls.get_environment(project)\\n cls._loaded_path = cls.project\\n cls._simulator = simulator\\n cls._simulation_root = root\\n cls._simulation_libraries = libs\\n cls._environment_type = 'external'\\n # Compile the design if required (simulators with caching will\\n # perform this step once).\\n cls._simulator.compile_project(\\n includes=cls._simulation_libraries\\n )\\n log.debug(\\n '...finished initialising environment for {0}'.format(cls)\\n )\\n if cls._environment_type is None:\\n raise EnvironmentError(\\n 'The simulation environment for this TestCase is not ' +\\n 'initialised so the test cases cannot be executed. ' +\\n 'If you are running this test directly ensure that ' +\\n 'the TestCase class has a \\\"project\\\" attribute which ' +\\n 'holds a path to a valid ChipTools project file.'\\n )\",\n \"def setUp(self):\\n GlusterBaseClass.setUp.im_func(self)\\n self.test_method_complete = False\",\n \"def setUpClass(cls):\\n # Call the setUpClass method(s) of the parent class(es)\",\n \"def setUpClass(cls):\\n pass\",\n \"def setUpClass(cls):\\n pass\",\n \"def setUpClass(cls):\\n pass\",\n \"def setUpClass(cls):\\n pass\",\n \"def setup_stage(method):\\n def decorator(self):\\n name = method.func_name\\n if should_run_stage(name):\\n say(\\\"Setup.%s\\\" % name)\\n method(self)\\n set_stage(name)\\n decorator.__doc__ = method.__doc__\\n return decorator\",\n \"def setUpClass(cls):\\r\\n pass\",\n \"def setUp(self):\\n self._class = State()\\n self._class2 = State()\\n self._name = \\\"State\\\"\",\n \"def setUpClass(cls) -> None:\\n stack_name = TestStateMachine.get_and_verify_stack_name()\\n\\n client = boto3.client(\\\"cloudformation\\\")\\n response = client.list_stack_resources(StackName=stack_name)\\n resources = response[\\\"StackResourceSummaries\\\"]\\n state_machine_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"TransactionProcessorStateMachine\\\"\\n ]\\n transaction_table_purchase_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"PurchaseTable\\\"\\n ]\\n transaction_table_refund_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"RefundTable\\\"\\n ]\\n transaction_table_error_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"ErrorTable\\\"\\n ]\\n lambda_function_purchase_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"PurchaseFunction\\\"\\n ]\\n lambda_function_validator_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"ValidatorFunction\\\"\\n ]\\n lambda_function_refund_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"RefundFunction\\\"\\n ]\\n lambda_function_error_resources = [\\n resource for resource in resources if resource[\\\"LogicalResourceId\\\"] == \\\"ErrorFunction\\\"\\n ]\\n\\n if not state_machine_resources:\\n raise Exception(\\\"Cannot find TransactionProcessorStateMachine\\\")\\n if not transaction_table_purchase_resources:\\n raise Exception(\\\"Cannot find DDBPurchaseTable\\\")\\n if not transaction_table_refund_resources:\\n raise Exception(\\\"Cannot find DDBRefundTable\\\")\\n if not transaction_table_error_resources:\\n raise Exception(\\\"Cannot find DDBErrorTable\\\")\\n if not lambda_function_error_resources:\\n raise Exception(\\\"Cannot find ErrorFunction\\\")\\n if not lambda_function_purchase_resources:\\n raise Exception(\\\"Cannot find PurchaseFunction\\\")\\n if not lambda_function_refund_resources:\\n raise Exception(\\\"Cannot find RefundFunction\\\")\\n if not lambda_function_validator_resources:\\n raise Exception(\\\"Cannot find ValidatorFunction\\\")\\n\\n cls.state_machine_arn = state_machine_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.transaction_table_purchase = transaction_table_purchase_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.transaction_table_refund = transaction_table_refund_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.transaction_table_error = transaction_table_error_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.lambda_function_validator = lambda_function_validator_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.lambda_function_purchase = lambda_function_purchase_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.lambda_function_refund = lambda_function_refund_resources[0][\\\"PhysicalResourceId\\\"]\\n cls.lambda_function_error = lambda_function_error_resources[0][\\\"PhysicalResourceId\\\"]\",\n \"def run(self):\\n\\n self.__run_class_setup_fixtures()\\n self.__enter_context_managers(self.class_setup_teardown_fixtures, self.__run_test_methods)\\n self.__run_class_teardown_fixtures()\",\n \"def setup_class(klass):\",\n \"def setup_class(klass):\",\n \"def setUp(self):\\n print('Calling \\\\'setUp\\\\'')\",\n \"def test_set_up(self):\\n # set_up has been called for the test suite\\n # (see runtests) so there is not much that\\n # can be tested\\n with patch('react.utils.shortcuts.set_up', autospec=True) as s:\\n utils.set_up()\\n s.assert_called_once_with()\",\n \"def __init_fixture_methods(self):\\n # init our self.(class_setup|setup|teardown|class_teardown)_fixtures lists\\n for fixture_type in fixture_types:\\n setattr(self, \\\"%s_fixtures\\\" % fixture_type, [])\\n\\n # for setup methods, we want oldest class first. for teardowns, we want newest class first\\n hierarchy = list(reversed(type(self).mro()))\\n for cls in hierarchy[1:]:\\n # mixins on TestCase instances that derive from, say, object, won't be set up properly\\n if hasattr(cls, '_fixture_methods'):\\n # the metaclass stored the class's fixtures in a _fixture_methods instance variable\\n for fixture_type, fixture_methods in cls._fixture_methods.iteritems():\\n bound_fixture_methods = [instancemethod(func, self, self.__class__) for func in fixture_methods]\\n if fixture_type.endswith('setup'):\\n # for setup methods, we want methods defined further back in the\\n # class hierarchy to execute first\\n getattr(self, \\\"%s_fixtures\\\" % fixture_type).extend(bound_fixture_methods)\\n else:\\n # for teardown methods though, we want the opposite\\n setattr(self, \\\"%s_fixtures\\\" % fixture_type, bound_fixture_methods + getattr(self, \\\"%s_fixtures\\\" % fixture_type))\",\n \"def setup_class(self):\\n # Initialize instance variable(s)\\n self.n2vc = None\\n\\n # Track internal state for each test run\\n self.state = {}\\n\\n # Parse the test's descriptors\\n self.nsd = get_descriptor(self.NSD_YAML)\\n self.vnfd = get_descriptor(self.VNFD_YAML)\\n\\n self.ns_name = self.nsd['name']\\n self.vnf_name = self.vnfd['name']\\n\\n self.charms = {}\\n self.parse_vnf_descriptor()\\n assert self.charms is not {}\\n\\n # Track artifacts, like compiled charms, that will need to be removed\\n self.artifacts = {}\\n\\n # Build the charm(s) needed for this test\\n for charm in self.get_charm_names():\\n # debug(\\\"Building charm {}\\\".format(charm))\\n self.get_charm(charm)\\n\\n # A bit of a hack, in order to allow the N2VC callback to run parallel\\n # to pytest. Test(s) should wait for this flag to change to False\\n # before returning.\\n self._running = True\\n self._stopping = False\",\n \"def setup_method(self, method):\\n super().setup_method(method)\\n\\n Env.ENVIRON = {}\\n self.env.read_env(\\n Path(__file__, is_file=True)('test_env.txt'),\\n PATH_VAR=Path(__file__, is_file=True).__root__\\n )\",\n \"def setUpClass(self):\",\n \"def setUpClass(cls):\\n cls.student_f = inspect.getmembers(Student, inspect.isfunction)\",\n \"def _set_up():\\n repl._setUp = self.setUp\",\n \"def setup_class(cls):\",\n \"def setup_class(cls):\",\n \"def setup():\\n\\n self.zorp_mock = Mock()\\n\\n for name, func in six.iteritems(self._get_zorp_mock_methods()):\\n self.zorp_mock.server.registry.put(name, func)\\n\\n self.zorp_mock.start()\",\n \"def __call__(self, result=None):\\n self._pre_setup()\\n super(TestCase, self).__call__(result)\\n self._post_tearDown()\",\n \"def teardown_method(self, method) -> None:\",\n \"def XXsetUp(self):\\n print(\\\"FooTest:setUp_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:setUp_:end\\\")\",\n \"def XXsetUp(self):\\n print(\\\"FooTest:setUp_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:setUp_:end\\\")\",\n \"def __init__(self):\\n self.setup_called = False\",\n \"def setUp(self):\\n self.p_state = p_state\",\n \"def setUp(self):\\n raise NotImplementedError\",\n \"def setup(self):\\n raise NotImplementedError(\\\"Need to be implemented in subclasses\\\")\",\n \"def setUp(self):\\n log.debug('TestModule.setUp')\\n if self.module is None:\\n self.module = _import(self.moduleName, [self.path], self.conf)\\n log.debug('Imported %s from %s on %s', self.module,\\n self.moduleName, self.path)\\n if hasattr(self.module, '__path__'):\\n names = ['setupPackage', 'setUpPackage', 'setup_package']\\n else:\\n names = ['setupModule', 'setUpModule', 'setup_module']\\n names += ['setUp', 'setup']\\n try_run(self.module, names)\",\n \"def setup_class(self):\\n pass\",\n \"def setUpClass(cls):\\n super(CharmOperationTest, cls).setUpClass()\",\n \"def setUpClass(cls):\\n super(BaseTest, cls).setUpClass()\\n try:\\n cls.configure()\\n cls.execute()\\n except:\\n cls.annihilate()\\n raise\",\n \"def setUpClass(cls, *_):\\n super(PagingTest, cls).setUpClass()\\n warnings.simplefilter('ignore')\\n cls.service_manager = create_service_manager([], ['classifier'])\\n cls._tbl_num = cls.service_manager.get_table_num(Classifier.APP_NAME)\\n\\n testing_controller_reference = Future()\\n classifier_reference = Future()\\n test_setup = TestSetup(\\n apps=[\\n PipelinedController.Classifier,\\n PipelinedController.Testing,\\n PipelinedController.StartupFlows,\\n ],\\n references={\\n PipelinedController.Classifier:\\n classifier_reference,\\n PipelinedController.Testing:\\n testing_controller_reference,\\n PipelinedController.StartupFlows:\\n Future(),\\n },\\n config={\\n 'bridge_name': cls.BRIDGE,\\n 'bridge_ip_address': cls.BRIDGE_IP,\\n 'internal_ip_subnet': '192.168.0.0/16',\\n 'ovs_gtp_port_number': 32768,\\n 'ovs_mtr_port_number': 15577,\\n 'ovs_internal_sampling_port_number': 15578,\\n 'ovs_internal_sampling_fwd_tbl_number': 201,\\n 'ovs_internal_conntrack_port_number': 15579,\\n 'ovs_internal_conntrack_fwd_tbl_number': 202,\\n 'clean_restart': True,\\n 'paging_timeout': 30,\\n 'classifier_controller_id': 5,\\n 'enable_nat': True,\\n 'ovs_uplink_port_name': \\\"patch-up\\\",\\n },\\n mconfig=PipelineD(),\\n loop=None,\\n service_manager=cls.service_manager,\\n integ_test=False,\\n rpc_stubs={'sessiond_setinterface': MagicMock()},\\n )\\n\\n BridgeTools.create_bridge(cls.BRIDGE, cls.IFACE)\\n cls.thread = start_ryu_app_thread(test_setup)\\n cls.classifier_controller = classifier_reference.result()\\n cls.testing_controller = testing_controller_reference.result()\",\n \"def setUp(self):\\n self._control = test_control.PauseFailControl()\\n self._digest_pool = logging_pool.pool(test_constants.POOL_SIZE)\\n self._digest = _digest.digest(\\n _stock_service.STOCK_TEST_SERVICE, self._control, self._digest_pool)\\n\\n generic_stub, dynamic_stubs, self._memo = self.implementation.instantiate(\\n self._digest.methods, self._digest.event_method_implementations, None)\\n self._invoker = self.invoker_constructor.construct_invoker(\\n generic_stub, dynamic_stubs, self._digest.methods)\",\n \"def setup_method(self):\\n self.project_dir = os.path.join(DIR, 'test-project')\\n self.e2e = E2EEnv(self.project_dir)\",\n \"def setup_method(self):\\n self.ti_helper = TIHelper(self.group_type, required_fields=self.required_fields)\\n self.ti = self.ti_helper.ti\\n self.tcex = self.ti_helper.tcex\",\n \"def make_set_up(set_up=None):\\n def _do_set_up(obj):\\n if set_up:\\n return set_up(obj)\\n return obj.setUp()\\n return _do_set_up\",\n \"def setUp(self, up):\\n\\t\\tself.up = up\",\n \"def setUpClass(cls):\\n cls._tmpdir = tempfile.mkdtemp()\\n if (not os.path.isdir(cls._tmpdir)):\\n raise Exception(\\\"Failed to create tempdir to use for dump tests\\\")\\n try:\\n cls.extraSetUpClass()\\n except Exception:\\n pass\",\n \"def before_run_tests(cls):\\n pass\",\n \"def setup(self) -> None:\\n pass\",\n \"def setup(self) -> None:\\n pass\",\n \"def setup(self) -> None:\\n pass\",\n \"def setUpClass(cls):\\r\\n # Clear out any existing modulestores,\\r\\n # which will cause them to be re-created\\r\\n # the next time they are accessed.\\r\\n clear_existing_modulestores()\\r\\n TestCase.setUpClass()\",\n \"def setup_method(self,method):\\n from importlib import reload\\n reload(bt_temp)\\n self.transport_obj = bt_temp.ns.modelList[0].levelModelList[0]\\n self.bdm2_obj = self.transport_obj.velocityPostProcessor.vpp_algorithms[0]\\n self._setRelativePath()\",\n \"def test_setup(self):\\n with pytest.raises(NotImplementedError):\\n self.behaviour.setup()\",\n \"def setup_class(cls):\\n cls.mock_get_patcher = patch('project.services.requests.get')\\n cls.mock_get = cls.mock_get_patcher.start()\",\n \"def setup(self):\\n raise NotImplementedError\",\n \"def setup(self):\\n raise NotImplementedError\",\n \"def setup(self):\\n raise NotImplementedError\",\n \"def setup(self):\\n raise NotImplementedError\",\n \"def __call__(self, result=None):\\n try:\\n self._pre_setup()\\n super(TestCase, self).__call__(result)\\n finally:\\n self._post_teardown()\",\n \"def setup(self) -> None:\",\n \"def setUp(self):\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_RMT_Util:\\\", self._testMethodName)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75784695","0.75784695","0.74024475","0.7322949","0.72970635","0.72218513","0.70287365","0.6968257","0.6819937","0.67959106","0.6794511","0.6696449","0.66864085","0.6648191","0.6600183","0.659615","0.6559962","0.65412855","0.65281886","0.6489964","0.6485201","0.6484623","0.64716375","0.6440813","0.6377603","0.63452506","0.6340291","0.63207495","0.63207495","0.63141537","0.63056284","0.63035023","0.629433","0.6284603","0.62840784","0.6275843","0.6251558","0.6251558","0.6229501","0.6195655","0.619454","0.617918","0.6170356","0.6157203","0.6136336","0.6136336","0.6136336","0.6136336","0.61154723","0.6093295","0.60331804","0.6031173","0.6027659","0.5994838","0.5994838","0.59663004","0.5955467","0.59321225","0.5924233","0.59034735","0.59006816","0.58982927","0.5886498","0.58788615","0.58788615","0.58711207","0.58684015","0.58506244","0.5845662","0.5845662","0.5843906","0.5839412","0.5837896","0.58251226","0.5807423","0.5805195","0.58020914","0.5796929","0.57835203","0.57796985","0.57793236","0.57732785","0.57702404","0.57690096","0.57603365","0.57466257","0.5733444","0.5733444","0.5733444","0.5718833","0.5715744","0.57118237","0.57117844","0.5701201","0.5701201","0.5701201","0.5701201","0.57008207","0.569702","0.56968194"],"string":"[\n \"0.75784695\",\n \"0.75784695\",\n \"0.74024475\",\n \"0.7322949\",\n \"0.72970635\",\n \"0.72218513\",\n \"0.70287365\",\n \"0.6968257\",\n \"0.6819937\",\n \"0.67959106\",\n \"0.6794511\",\n \"0.6696449\",\n \"0.66864085\",\n \"0.6648191\",\n \"0.6600183\",\n \"0.659615\",\n \"0.6559962\",\n \"0.65412855\",\n \"0.65281886\",\n \"0.6489964\",\n \"0.6485201\",\n \"0.6484623\",\n \"0.64716375\",\n \"0.6440813\",\n \"0.6377603\",\n \"0.63452506\",\n \"0.6340291\",\n \"0.63207495\",\n \"0.63207495\",\n \"0.63141537\",\n \"0.63056284\",\n \"0.63035023\",\n \"0.629433\",\n \"0.6284603\",\n \"0.62840784\",\n \"0.6275843\",\n \"0.6251558\",\n \"0.6251558\",\n \"0.6229501\",\n \"0.6195655\",\n \"0.619454\",\n \"0.617918\",\n \"0.6170356\",\n \"0.6157203\",\n \"0.6136336\",\n \"0.6136336\",\n \"0.6136336\",\n \"0.6136336\",\n \"0.61154723\",\n \"0.6093295\",\n \"0.60331804\",\n \"0.6031173\",\n \"0.6027659\",\n \"0.5994838\",\n \"0.5994838\",\n \"0.59663004\",\n \"0.5955467\",\n \"0.59321225\",\n \"0.5924233\",\n \"0.59034735\",\n \"0.59006816\",\n \"0.58982927\",\n \"0.5886498\",\n \"0.58788615\",\n \"0.58788615\",\n \"0.58711207\",\n \"0.58684015\",\n \"0.58506244\",\n \"0.5845662\",\n \"0.5845662\",\n \"0.5843906\",\n \"0.5839412\",\n \"0.5837896\",\n \"0.58251226\",\n \"0.5807423\",\n \"0.5805195\",\n \"0.58020914\",\n \"0.5796929\",\n \"0.57835203\",\n \"0.57796985\",\n \"0.57793236\",\n \"0.57732785\",\n \"0.57702404\",\n \"0.57690096\",\n \"0.57603365\",\n \"0.57466257\",\n \"0.5733444\",\n \"0.5733444\",\n \"0.5733444\",\n \"0.5718833\",\n \"0.5715744\",\n \"0.57118237\",\n \"0.57117844\",\n \"0.5701201\",\n \"0.5701201\",\n \"0.5701201\",\n \"0.5701201\",\n \"0.57008207\",\n \"0.569702\",\n \"0.56968194\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61271554"},"document_rank":{"kind":"string","value":"48"}}},{"rowIdx":7190,"cells":{"query":{"kind":"string","value":"teardown any state that was previously setup with a setup_method call."},"document":{"kind":"string","value":"def teardown_method(self, objectCreation):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def teardown_method(self, method) -> None:","def teardown_method(self, method):\n pass","def teardown_method(self, method):\n pass","def teardown_method(self, test_method):\n self.wo_obj = None\n self.config_data = None","def teardown_method(self):","def teardown_method(self, method):\n restore_settings()","def _teardown(self):\n # No-op base implementation","def teardown(self,**kwargs):\n pass","def teardown(self):\n raise NotImplementedError","def teardown(self):\n raise NotImplementedError","def teardown(self) -> None:\n pass","def teardown(self) -> None:\n pass","def teardown(self):\n pass","def teardown(self):\n pass","def teardown(self):\n pass","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self):\n pass # pylint: disable=unnecessary-pass","def teardown(self):","def teardown(self):","def teardown(self):","def teardown_method(self):\n world.clear_paths()\n print(\"\\nEnd of tests in: %s\\n-------------------\\n\" % __name__)\n self.bigml = {}","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self):\n if self.ae:\n self.ae.shutdown()","def teardown_method(self, method):\n self.opp.stop()","def teardown_method(self, method):\n self.opp.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown_method(self, method):\n self.hass.stop()","def teardown(self):\n del self.testInst, self.dname\n\n return","def teardown(self):\n self.tcex.log.trace('teardown')","def teardown_class(self):\n self._tester = None\n self._sut = None","def teardown(self):\n del self.testInst, self.dname, self.test_vals, self.test_fracs\n\n return","def teardown(self, event):\n pass","def teardown_method(self) -> None:\n if os.getenv('TEARDOWN_METHOD') is None:\n self.custom.teardown_method(self)\n super(TestProfiles, self).teardown_method()","def teardown_class(self):\n pass","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def teardown(self):\n\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\n\n return","def _teardown(self) -> None:\n try:\n if (\n self._config\n and \"restore_state\" in self._config[\"core\"]\n and self._config[\"core\"][\"restore_state\"]\n ):\n self._save_state()\n except Exception as e:\n logger.warning(\"Unexpected error while saving state: %s\", str(e))","def teardown_method(self):\n self.hass.stop()","def teardown_method(self):\n self.hass.stop()","def teardown_method(self):\n self.hass.stop()","def teardown_method(self, method):\n assert self.old_environ is not None\n os.environ = self.old_environ","def teardown_method(self, method):\n os.chdir(self.old_pwd)\n shutil.rmtree(self.test_workspace, True)","def teardown():\n\n self.zorp_mock.stop()","def teardown(self):\n del self.testC, self.testI, self.bounds\n\n return","def teardown(self):\n\n del self.testInst, self.test_bins, self.test_label, self.test_data\n del self.out_keys, self.out_data\n\n return","def teardown_provider(self):\n pass","def teardown(self):\n # self.in_kwargs, self.ref_time,\n del self.warn_msgs, self.war\n return","def tearDown(self):\n test_env_teardown()","def tearDown(self):\n self.teardown_beets()","def teardown_method(self, method):\n # Remove user file and user\n if self.user is not None:\n try:\n path = self.user._User__filename()\n os.remove(path)\n except OSError:\n pass\n del self.user\n\n # Restore original user\n self.request.cookies = self.saved_cookie\n self.request.user = self.saved_user\n\n # Remove user name to id cache, or next test will fail\n caching.CacheEntry(self.request, 'user', 'name2id', scope='wiki').remove()\n try:\n del self.request.cfg.cache.name2id\n except:\n pass","def teardown(self):\n del self.testC, self.bounds, self.one_d_vars, self.unequal_one_d_vars\n del self.testI\n\n return","def _tear_down():\n repl._tearDown = self.tearDown","def teardown(self):\n del self.testInst, self.bounds1, self.bounds2\n\n return","def test_teardown(self):\n with pytest.raises(NotImplementedError):\n self.behaviour.teardown()","def teardown_class(klass):","def teardown_class(klass):","def teardown_method(self, method):\n\n db = _get_db()\n db.drop_collection('user_stat')\n db.drop_collection('user')\n disconnect(alias='test')","def teardown_class(cls):","def teardown_class(cls):","def teardown_class(cls):","def teardown(cls):\n del cls.user\n del cls.console","def teardown(self, recursive=None):\n log_method_call(self, self.name, status=self.status,\n controllable=self.controllable)\n # we don't really care about the return value of _preTeardown here.\n # see comment just above mddeactivate call\n self._preTeardown(recursive=recursive)\n\n # Since BIOS RAID sets (containers in mdraid terminology) never change\n # there is no need to stop them and later restart them. Not stopping\n # (and thus also not starting) them also works around bug 523334\n if self.type == \"mdcontainer\" or self.type == \"mdbiosraidarray\":\n return\n\n if self.isDisk:\n # treat arrays whose members are disks as partitionable disks\n return\n\n # We don't really care what the array's state is. If the device\n # file exists, we want to deactivate it. mdraid has too many\n # states.\n if self.exists and os.path.exists(self.path):\n mdraid.mddeactivate(self.path)\n\n self._postTeardown(recursive=recursive)","def teardown(self, rc):\n pass","def teardown(self) -> BaseStep:\n if self.session is not None:\n self.session.close()\n self.is_initialized = False\n\n return self","def teardown(self, log, info):\n raise NotImplementedError","def teardown(self, exception):","def tearDown(self):\n\n self._tear_down()","def teardown_class(cls):\n pass","def teardown_class(cls):\n pass","def tearDown(self):\n pass\n # teardown called after each test\n # e.g. maybe write test results to some text file","def teardown(self) -> None:\n self._unregister_service()\n self._unregister_agent()","def tear_down(self):\n verdict, msg = TestStepEngine.tear_down(self)\n self._device.inject_device_log(\"i\", \"ACS_TESTCASE\", \"TEARDOWN: %s\" % self._name)\n return verdict, msg","async def _teardown(self, commit: bool = None):","def teardown(self):\n for cls in self.implements:\n getattr(self, cls.name).teardown()","def teardown(self):\n if self.ae:\n self.ae.shutdown()","def teardown(self):\n for mr in self.mrs:\n mr.restore_pretest(pretest=mr.pretest_info)","def teardown(self):\n\n\t\tself.shutdown = True\n\t\tself.terminate_process()","def teardown(self):\n del self.testInst, self.bounds1, self.bounds2, self.long_bins\n del self.mlt_bins\n\n return","def teardown_test(self):\n self.log.info('Tearing down the test case')\n self.iperf_server.stop()\n self.access_point.bridge.teardown(self.brconfigs)\n self.access_point.close()\n wputils.reset_host_interface(self.pkt_sender.interface)\n self.mon.usb('on')","def teardown(self):\n\n self.dummy.set_current()\n self.endpoints.lock()","def tearDown(self):\n reset()","def teardown_method(self, method):\r\n if hasattr(self, \"config\"):\r\n if \"saucelabs\" in self.cf[\"browsers\"][self.cf[\"saunter\"][\"default_browser\"]] and not self.cf[\"browsers\"][self.cf[\"saunter\"][\"default_browser\"]][\"saucelabs\"][\"ondemand\"]:\r\n self.take_named_screenshot(\"final\")\r\n\r\n if hasattr(self, \"driver\"):\r\n self.driver.quit()","def teardown(cls):\n del cls.my_object","def tearDown(self):\n super(TestCase, self).tearDown()\n self._context.check_done()","def teardown_class(cls):\n cls._unpatch_logger()\n cls.multiplexer.disconnect()\n cls.decision_maker.stop()","def test_teardown(self):\n with pytest.raises(NotImplementedError):\n self.handler.teardown()","def teardown_application(self):\n pass","def tearDown(self):\n self.testbed.deactivate()","def tearDown(self):\n self.testbed.deactivate()"],"string":"[\n \"def teardown_method(self, method) -> None:\",\n \"def teardown_method(self, method):\\n pass\",\n \"def teardown_method(self, method):\\n pass\",\n \"def teardown_method(self, test_method):\\n self.wo_obj = None\\n self.config_data = None\",\n \"def teardown_method(self):\",\n \"def teardown_method(self, method):\\n restore_settings()\",\n \"def _teardown(self):\\n # No-op base implementation\",\n \"def teardown(self,**kwargs):\\n pass\",\n \"def teardown(self):\\n raise NotImplementedError\",\n \"def teardown(self):\\n raise NotImplementedError\",\n \"def teardown(self) -> None:\\n pass\",\n \"def teardown(self) -> None:\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self):\\n pass # pylint: disable=unnecessary-pass\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown_method(self):\\n world.clear_paths()\\n print(\\\"\\\\nEnd of tests in: %s\\\\n-------------------\\\\n\\\" % __name__)\\n self.bigml = {}\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown_method(self, method):\\n self.opp.stop()\",\n \"def teardown_method(self, method):\\n self.opp.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n self.hass.stop()\",\n \"def teardown(self):\\n del self.testInst, self.dname\\n\\n return\",\n \"def teardown(self):\\n self.tcex.log.trace('teardown')\",\n \"def teardown_class(self):\\n self._tester = None\\n self._sut = None\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_vals, self.test_fracs\\n\\n return\",\n \"def teardown(self, event):\\n pass\",\n \"def teardown_method(self) -> None:\\n if os.getenv('TEARDOWN_METHOD') is None:\\n self.custom.teardown_method(self)\\n super(TestProfiles, self).teardown_method()\",\n \"def teardown_class(self):\\n pass\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def teardown(self):\\n\\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\\n\\n return\",\n \"def _teardown(self) -> None:\\n try:\\n if (\\n self._config\\n and \\\"restore_state\\\" in self._config[\\\"core\\\"]\\n and self._config[\\\"core\\\"][\\\"restore_state\\\"]\\n ):\\n self._save_state()\\n except Exception as e:\\n logger.warning(\\\"Unexpected error while saving state: %s\\\", str(e))\",\n \"def teardown_method(self):\\n self.hass.stop()\",\n \"def teardown_method(self):\\n self.hass.stop()\",\n \"def teardown_method(self):\\n self.hass.stop()\",\n \"def teardown_method(self, method):\\n assert self.old_environ is not None\\n os.environ = self.old_environ\",\n \"def teardown_method(self, method):\\n os.chdir(self.old_pwd)\\n shutil.rmtree(self.test_workspace, True)\",\n \"def teardown():\\n\\n self.zorp_mock.stop()\",\n \"def teardown(self):\\n del self.testC, self.testI, self.bounds\\n\\n return\",\n \"def teardown(self):\\n\\n del self.testInst, self.test_bins, self.test_label, self.test_data\\n del self.out_keys, self.out_data\\n\\n return\",\n \"def teardown_provider(self):\\n pass\",\n \"def teardown(self):\\n # self.in_kwargs, self.ref_time,\\n del self.warn_msgs, self.war\\n return\",\n \"def tearDown(self):\\n test_env_teardown()\",\n \"def tearDown(self):\\n self.teardown_beets()\",\n \"def teardown_method(self, method):\\n # Remove user file and user\\n if self.user is not None:\\n try:\\n path = self.user._User__filename()\\n os.remove(path)\\n except OSError:\\n pass\\n del self.user\\n\\n # Restore original user\\n self.request.cookies = self.saved_cookie\\n self.request.user = self.saved_user\\n\\n # Remove user name to id cache, or next test will fail\\n caching.CacheEntry(self.request, 'user', 'name2id', scope='wiki').remove()\\n try:\\n del self.request.cfg.cache.name2id\\n except:\\n pass\",\n \"def teardown(self):\\n del self.testC, self.bounds, self.one_d_vars, self.unequal_one_d_vars\\n del self.testI\\n\\n return\",\n \"def _tear_down():\\n repl._tearDown = self.tearDown\",\n \"def teardown(self):\\n del self.testInst, self.bounds1, self.bounds2\\n\\n return\",\n \"def test_teardown(self):\\n with pytest.raises(NotImplementedError):\\n self.behaviour.teardown()\",\n \"def teardown_class(klass):\",\n \"def teardown_class(klass):\",\n \"def teardown_method(self, method):\\n\\n db = _get_db()\\n db.drop_collection('user_stat')\\n db.drop_collection('user')\\n disconnect(alias='test')\",\n \"def teardown_class(cls):\",\n \"def teardown_class(cls):\",\n \"def teardown_class(cls):\",\n \"def teardown(cls):\\n del cls.user\\n del cls.console\",\n \"def teardown(self, recursive=None):\\n log_method_call(self, self.name, status=self.status,\\n controllable=self.controllable)\\n # we don't really care about the return value of _preTeardown here.\\n # see comment just above mddeactivate call\\n self._preTeardown(recursive=recursive)\\n\\n # Since BIOS RAID sets (containers in mdraid terminology) never change\\n # there is no need to stop them and later restart them. Not stopping\\n # (and thus also not starting) them also works around bug 523334\\n if self.type == \\\"mdcontainer\\\" or self.type == \\\"mdbiosraidarray\\\":\\n return\\n\\n if self.isDisk:\\n # treat arrays whose members are disks as partitionable disks\\n return\\n\\n # We don't really care what the array's state is. If the device\\n # file exists, we want to deactivate it. mdraid has too many\\n # states.\\n if self.exists and os.path.exists(self.path):\\n mdraid.mddeactivate(self.path)\\n\\n self._postTeardown(recursive=recursive)\",\n \"def teardown(self, rc):\\n pass\",\n \"def teardown(self) -> BaseStep:\\n if self.session is not None:\\n self.session.close()\\n self.is_initialized = False\\n\\n return self\",\n \"def teardown(self, log, info):\\n raise NotImplementedError\",\n \"def teardown(self, exception):\",\n \"def tearDown(self):\\n\\n self._tear_down()\",\n \"def teardown_class(cls):\\n pass\",\n \"def teardown_class(cls):\\n pass\",\n \"def tearDown(self):\\n pass\\n # teardown called after each test\\n # e.g. maybe write test results to some text file\",\n \"def teardown(self) -> None:\\n self._unregister_service()\\n self._unregister_agent()\",\n \"def tear_down(self):\\n verdict, msg = TestStepEngine.tear_down(self)\\n self._device.inject_device_log(\\\"i\\\", \\\"ACS_TESTCASE\\\", \\\"TEARDOWN: %s\\\" % self._name)\\n return verdict, msg\",\n \"async def _teardown(self, commit: bool = None):\",\n \"def teardown(self):\\n for cls in self.implements:\\n getattr(self, cls.name).teardown()\",\n \"def teardown(self):\\n if self.ae:\\n self.ae.shutdown()\",\n \"def teardown(self):\\n for mr in self.mrs:\\n mr.restore_pretest(pretest=mr.pretest_info)\",\n \"def teardown(self):\\n\\n\\t\\tself.shutdown = True\\n\\t\\tself.terminate_process()\",\n \"def teardown(self):\\n del self.testInst, self.bounds1, self.bounds2, self.long_bins\\n del self.mlt_bins\\n\\n return\",\n \"def teardown_test(self):\\n self.log.info('Tearing down the test case')\\n self.iperf_server.stop()\\n self.access_point.bridge.teardown(self.brconfigs)\\n self.access_point.close()\\n wputils.reset_host_interface(self.pkt_sender.interface)\\n self.mon.usb('on')\",\n \"def teardown(self):\\n\\n self.dummy.set_current()\\n self.endpoints.lock()\",\n \"def tearDown(self):\\n reset()\",\n \"def teardown_method(self, method):\\r\\n if hasattr(self, \\\"config\\\"):\\r\\n if \\\"saucelabs\\\" in self.cf[\\\"browsers\\\"][self.cf[\\\"saunter\\\"][\\\"default_browser\\\"]] and not self.cf[\\\"browsers\\\"][self.cf[\\\"saunter\\\"][\\\"default_browser\\\"]][\\\"saucelabs\\\"][\\\"ondemand\\\"]:\\r\\n self.take_named_screenshot(\\\"final\\\")\\r\\n\\r\\n if hasattr(self, \\\"driver\\\"):\\r\\n self.driver.quit()\",\n \"def teardown(cls):\\n del cls.my_object\",\n \"def tearDown(self):\\n super(TestCase, self).tearDown()\\n self._context.check_done()\",\n \"def teardown_class(cls):\\n cls._unpatch_logger()\\n cls.multiplexer.disconnect()\\n cls.decision_maker.stop()\",\n \"def test_teardown(self):\\n with pytest.raises(NotImplementedError):\\n self.handler.teardown()\",\n \"def teardown_application(self):\\n pass\",\n \"def tearDown(self):\\n self.testbed.deactivate()\",\n \"def tearDown(self):\\n self.testbed.deactivate()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79968005","0.78846675","0.78846675","0.7871643","0.7792453","0.7720631","0.7659778","0.7625319","0.757913","0.757913","0.75738186","0.75738186","0.7567817","0.7567817","0.7567817","0.7565642","0.7565642","0.7565642","0.7450825","0.7437996","0.7437996","0.7437996","0.7431355","0.7428631","0.7428631","0.7428631","0.7428631","0.7428631","0.7428631","0.7366032","0.7366032","0.7304177","0.7304177","0.7304177","0.7304177","0.7304177","0.7304177","0.72788274","0.7175022","0.71385556","0.71075046","0.7091692","0.7056069","0.70300037","0.7023737","0.7023737","0.69832927","0.6955219","0.69526285","0.69526285","0.69526285","0.6932532","0.6914876","0.69015354","0.6883374","0.6854549","0.679873","0.67654216","0.67536587","0.6738352","0.6716742","0.67105925","0.67047703","0.6701911","0.668103","0.6680603","0.6680603","0.6670707","0.66693103","0.66693103","0.66693103","0.66567045","0.66522944","0.663639","0.6627201","0.6607731","0.6575961","0.65752053","0.65742815","0.65742815","0.6570281","0.65686333","0.6557653","0.6547724","0.6524568","0.6519039","0.64895844","0.6482584","0.64744186","0.64699376","0.64681727","0.6467692","0.6464617","0.6460315","0.6458118","0.64537394","0.6439651","0.6438113","0.64334685","0.64334685"],"string":"[\n \"0.79968005\",\n \"0.78846675\",\n \"0.78846675\",\n \"0.7871643\",\n \"0.7792453\",\n \"0.7720631\",\n \"0.7659778\",\n \"0.7625319\",\n \"0.757913\",\n \"0.757913\",\n \"0.75738186\",\n \"0.75738186\",\n \"0.7567817\",\n \"0.7567817\",\n \"0.7567817\",\n \"0.7565642\",\n \"0.7565642\",\n \"0.7565642\",\n \"0.7450825\",\n \"0.7437996\",\n \"0.7437996\",\n \"0.7437996\",\n \"0.7431355\",\n \"0.7428631\",\n \"0.7428631\",\n \"0.7428631\",\n \"0.7428631\",\n \"0.7428631\",\n \"0.7428631\",\n \"0.7366032\",\n \"0.7366032\",\n \"0.7304177\",\n \"0.7304177\",\n \"0.7304177\",\n \"0.7304177\",\n \"0.7304177\",\n \"0.7304177\",\n \"0.72788274\",\n \"0.7175022\",\n \"0.71385556\",\n \"0.71075046\",\n \"0.7091692\",\n \"0.7056069\",\n \"0.70300037\",\n \"0.7023737\",\n \"0.7023737\",\n \"0.69832927\",\n \"0.6955219\",\n \"0.69526285\",\n \"0.69526285\",\n \"0.69526285\",\n \"0.6932532\",\n \"0.6914876\",\n \"0.69015354\",\n \"0.6883374\",\n \"0.6854549\",\n \"0.679873\",\n \"0.67654216\",\n \"0.67536587\",\n \"0.6738352\",\n \"0.6716742\",\n \"0.67105925\",\n \"0.67047703\",\n \"0.6701911\",\n \"0.668103\",\n \"0.6680603\",\n \"0.6680603\",\n \"0.6670707\",\n \"0.66693103\",\n \"0.66693103\",\n \"0.66693103\",\n \"0.66567045\",\n \"0.66522944\",\n \"0.663639\",\n \"0.6627201\",\n \"0.6607731\",\n \"0.6575961\",\n \"0.65752053\",\n \"0.65742815\",\n \"0.65742815\",\n \"0.6570281\",\n \"0.65686333\",\n \"0.6557653\",\n \"0.6547724\",\n \"0.6524568\",\n \"0.6519039\",\n \"0.64895844\",\n \"0.6482584\",\n \"0.64744186\",\n \"0.64699376\",\n \"0.64681727\",\n \"0.6467692\",\n \"0.6464617\",\n \"0.6460315\",\n \"0.6458118\",\n \"0.64537394\",\n \"0.6439651\",\n \"0.6438113\",\n \"0.64334685\",\n \"0.64334685\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71103287"},"document_rank":{"kind":"string","value":"40"}}},{"rowIdx":7191,"cells":{"query":{"kind":"string","value":"Stop the simulator thread."},"document":{"kind":"string","value":"def stop(self):\n self._stop_event.set()\n super().stop()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def stop(self):\n self._Thread__stop()","def stop(self):\n\n self.stop_thread = True","def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)","def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)","def stop(self) -> None:\n # set the stop event, then cancel the timer\n self._stop_event.set()\n\n # wait for the runthread to finish\n try:\n self.join()\n except RuntimeError:\n pass","def stop_thread(self):\n t, e = self.workers[0]\n e = e.set() # put event to set True for stop thread\n del self.workers[0]","def stop(self):\n logging.info(\"Shutting down thread...\")\n self.disconnect()\n self.running = False","def stop(self):\n self.running = False\n self.join()","def stop(self):\n self.thread.join()","def stop(self):\n self._run = False\n self.IA.stop()","def stop(self):\n debug(\"CBA4.__worker_thread.stop()\")\n self.__run = False\n #end stop()","def stop(self):\n self.controller.stop()","def stop(self) -> None:\n assert self.is_alive(), \"Thread not alive\"\n self.stopped.set()","def stop(self):\r\n with self._lock:\r\n self._stopped = True\r\n self.join()","def stop(self):\n\t\tself._run_flag = False\n\t\tself.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self._run_flag = False\n self.wait()","def stop(self):\n self.__running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def stop(self):\n self.send_stop()\n self.join()","def stop(self):\n self._running = False","def stop(self):\n self._running = False","def stop_thread(self):\n self.running = False\n if self.run_thread.is_alive():\n self.run_thread.join()","def stop(self) -> None:\n self._running = False","def stop(self):\r\n self.stopped = True\r\n time.sleep(1)","def stop(self):\n with self.stop_lock:\n self._stop_event.set()","def stop(self):\n if self._server_thread is None:\n return\n self._stopping.set()\n self._server_thread = None\n self._stopped.wait()","async def stop(self) -> None:\n if self.id:\n await self._client.stop_thread(self.id)\n\n self._is_running = False\n self._id = 0","def stop(self):\n self._stopped.set()\n if self._timer:\n self._timer.cancel()\n self._timer = None","def stop(self):\n self.api.stop()","def stop(self):\n with self.stop_event_lock:\n self.stop_event.set()\n self.clean_up()","def stop(self):\n self._stop = True\n self.wake_up()","def stop(self):\n self._run = False","def stop(self):\n if self.thread_state is True:\n os.system('qq stop')\n self.thread_state = False","def stop(self):\n\n self.detach()\n self._pi.stop()\n self._stopped = True","def stop(self) -> None:\n ...","def _stop(self):\n self._pi.stop()","def stop(self):\n self.stopped = True","def stop(self):\n self.stopped = True","def stop(self):\n self._stop_event.set()","def stop() -> None:","def stop(self):\n self._stop_flag = True","def stop(self):\r\n self.stopped = True","def stop(self):\r\n self._stop.set()","def _stop(self) -> None:\n self._stopped.set()","def _stop(self, terminate_sim: bool = True):\n steppable_registry = CompuCellSetup.persistent_globals.steppable_registry\n steppable_registry.on_stop()","def stop(self):\n self._log.info(\"Stopping\")\n self._running.clear()","def stop(self):\n self._schedule(0, 0)\n self._started = False","def slot_stop(self):\n\n self.thread.working = False","def stop_threading(self):\n if self.algorithm_thread != None:\n self.running = False\n self.algorithm_thread.join()\n self.algorithm_thread = None\n if self.step_event.is_set():\n self.step_event.clear()","def stop(self) :\n raise NotImplementedError(\"stop not implemented\")","def stop(self, code=None):\n\n if not self.running:\n return\n\n self._running = False\n\n self.fire(stopped(self))\n\n if self.root._executing_thread is None:\n for _ in range(3):\n self.tick()\n\n if code is not None:\n raise SystemExit(code)","async def stop(self):\n await self._backend.stop()","def stop(self):\r\n self.stopped = True\r\n #self.worker.join()\r\n self.FPSThread.join()","def stop(self):\n self.should_run = False\n if self.is_alive():\n self.join()","async def stop(self):\n self._stopped.set()","def stop(self):\n self._stop_signal = True","def stop(self):\n\n self.__stop_threads = True\n self.__new_bus_Socket.close()\n self.__bus_stations_Socket.close()\n print(f\"stopped {self}\")","def stop(self):\r\n self.stopped = True\r\n time.sleep(3)","def stop(self):\n with self._lock:\n if not self.stopped():\n self._started = None\n getattr(self.factory, 'stop_' + self.class_name())(self)","def stop():","def stop():","def stop():","def stop():","async def _stop(self) -> None:\n self._stopped.set()","def stop(self) -> None:\n pass","def stop(self):\n\n self.keep_running = False","def stop(self):\n self.stopped = True","def stop(self):\r\n raise NotImplementedError('method stop() is not implemented')","def stopController(self):\n self.running = False","def stop(self):\n if self.running:\n self._unschedule_all()\n self.loop.stop()","def stop(self):\n self._stop.set()","def stop (self):\n pass","def stop (self):\n pass","def stop(self):\n self.stop_recognising.set()\n self.thread.join()","def stop(self):\n GameEngine().stop()\n self.on_stop()","def stop(self):\n if self.debug:\n print(\"%s stop\" % self.name)\n self.force_exit()","def stop(self):\n self.scion_sh('stop')","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n pass","def stop(self):\n self.stop_event.set()","def stop(self):\n self.stop_event.set()"],"string":"[\n \"def stop(self):\\n self._Thread__stop()\",\n \"def stop(self):\\n\\n self.stop_thread = True\",\n \"def stop(self):\\n self._state.transit(sitcpy.THREAD_STOPPING)\",\n \"def stop(self):\\n self._state.transit(sitcpy.THREAD_STOPPING)\",\n \"def stop(self) -> None:\\n # set the stop event, then cancel the timer\\n self._stop_event.set()\\n\\n # wait for the runthread to finish\\n try:\\n self.join()\\n except RuntimeError:\\n pass\",\n \"def stop_thread(self):\\n t, e = self.workers[0]\\n e = e.set() # put event to set True for stop thread\\n del self.workers[0]\",\n \"def stop(self):\\n logging.info(\\\"Shutting down thread...\\\")\\n self.disconnect()\\n self.running = False\",\n \"def stop(self):\\n self.running = False\\n self.join()\",\n \"def stop(self):\\n self.thread.join()\",\n \"def stop(self):\\n self._run = False\\n self.IA.stop()\",\n \"def stop(self):\\n debug(\\\"CBA4.__worker_thread.stop()\\\")\\n self.__run = False\\n #end stop()\",\n \"def stop(self):\\n self.controller.stop()\",\n \"def stop(self) -> None:\\n assert self.is_alive(), \\\"Thread not alive\\\"\\n self.stopped.set()\",\n \"def stop(self):\\r\\n with self._lock:\\r\\n self._stopped = True\\r\\n self.join()\",\n \"def stop(self):\\n\\t\\tself._run_flag = False\\n\\t\\tself.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self._run_flag = False\\n self.wait()\",\n \"def stop(self):\\n self.__running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\n self.send_stop()\\n self.join()\",\n \"def stop(self):\\n self._running = False\",\n \"def stop(self):\\n self._running = False\",\n \"def stop_thread(self):\\n self.running = False\\n if self.run_thread.is_alive():\\n self.run_thread.join()\",\n \"def stop(self) -> None:\\n self._running = False\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(1)\",\n \"def stop(self):\\n with self.stop_lock:\\n self._stop_event.set()\",\n \"def stop(self):\\n if self._server_thread is None:\\n return\\n self._stopping.set()\\n self._server_thread = None\\n self._stopped.wait()\",\n \"async def stop(self) -> None:\\n if self.id:\\n await self._client.stop_thread(self.id)\\n\\n self._is_running = False\\n self._id = 0\",\n \"def stop(self):\\n self._stopped.set()\\n if self._timer:\\n self._timer.cancel()\\n self._timer = None\",\n \"def stop(self):\\n self.api.stop()\",\n \"def stop(self):\\n with self.stop_event_lock:\\n self.stop_event.set()\\n self.clean_up()\",\n \"def stop(self):\\n self._stop = True\\n self.wake_up()\",\n \"def stop(self):\\n self._run = False\",\n \"def stop(self):\\n if self.thread_state is True:\\n os.system('qq stop')\\n self.thread_state = False\",\n \"def stop(self):\\n\\n self.detach()\\n self._pi.stop()\\n self._stopped = True\",\n \"def stop(self) -> None:\\n ...\",\n \"def _stop(self):\\n self._pi.stop()\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\n self._stop_event.set()\",\n \"def stop() -> None:\",\n \"def stop(self):\\n self._stop_flag = True\",\n \"def stop(self):\\r\\n self.stopped = True\",\n \"def stop(self):\\r\\n self._stop.set()\",\n \"def _stop(self) -> None:\\n self._stopped.set()\",\n \"def _stop(self, terminate_sim: bool = True):\\n steppable_registry = CompuCellSetup.persistent_globals.steppable_registry\\n steppable_registry.on_stop()\",\n \"def stop(self):\\n self._log.info(\\\"Stopping\\\")\\n self._running.clear()\",\n \"def stop(self):\\n self._schedule(0, 0)\\n self._started = False\",\n \"def slot_stop(self):\\n\\n self.thread.working = False\",\n \"def stop_threading(self):\\n if self.algorithm_thread != None:\\n self.running = False\\n self.algorithm_thread.join()\\n self.algorithm_thread = None\\n if self.step_event.is_set():\\n self.step_event.clear()\",\n \"def stop(self) :\\n raise NotImplementedError(\\\"stop not implemented\\\")\",\n \"def stop(self, code=None):\\n\\n if not self.running:\\n return\\n\\n self._running = False\\n\\n self.fire(stopped(self))\\n\\n if self.root._executing_thread is None:\\n for _ in range(3):\\n self.tick()\\n\\n if code is not None:\\n raise SystemExit(code)\",\n \"async def stop(self):\\n await self._backend.stop()\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n #self.worker.join()\\r\\n self.FPSThread.join()\",\n \"def stop(self):\\n self.should_run = False\\n if self.is_alive():\\n self.join()\",\n \"async def stop(self):\\n self._stopped.set()\",\n \"def stop(self):\\n self._stop_signal = True\",\n \"def stop(self):\\n\\n self.__stop_threads = True\\n self.__new_bus_Socket.close()\\n self.__bus_stations_Socket.close()\\n print(f\\\"stopped {self}\\\")\",\n \"def stop(self):\\r\\n self.stopped = True\\r\\n time.sleep(3)\",\n \"def stop(self):\\n with self._lock:\\n if not self.stopped():\\n self._started = None\\n getattr(self.factory, 'stop_' + self.class_name())(self)\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"async def _stop(self) -> None:\\n self._stopped.set()\",\n \"def stop(self) -> None:\\n pass\",\n \"def stop(self):\\n\\n self.keep_running = False\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\r\\n raise NotImplementedError('method stop() is not implemented')\",\n \"def stopController(self):\\n self.running = False\",\n \"def stop(self):\\n if self.running:\\n self._unschedule_all()\\n self.loop.stop()\",\n \"def stop(self):\\n self._stop.set()\",\n \"def stop (self):\\n pass\",\n \"def stop (self):\\n pass\",\n \"def stop(self):\\n self.stop_recognising.set()\\n self.thread.join()\",\n \"def stop(self):\\n GameEngine().stop()\\n self.on_stop()\",\n \"def stop(self):\\n if self.debug:\\n print(\\\"%s stop\\\" % self.name)\\n self.force_exit()\",\n \"def stop(self):\\n self.scion_sh('stop')\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n pass\",\n \"def stop(self):\\n self.stop_event.set()\",\n \"def stop(self):\\n self.stop_event.set()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81179816","0.796208","0.7771914","0.7771914","0.76687425","0.76618785","0.76455426","0.76039106","0.75676537","0.75364685","0.7525308","0.7504455","0.7481357","0.7431703","0.7421729","0.7421202","0.7421202","0.7421202","0.7421202","0.7421202","0.7421202","0.7415426","0.740481","0.740481","0.740481","0.740481","0.740481","0.738109","0.738109","0.73742974","0.7366818","0.7366818","0.7357696","0.7328492","0.73261267","0.73089004","0.7293952","0.72899014","0.7276056","0.7268756","0.72519207","0.7237337","0.7237328","0.7234576","0.72280896","0.7218432","0.72137105","0.7194043","0.7194043","0.7191958","0.7190089","0.71898365","0.718394","0.71806604","0.7180066","0.71652025","0.7162806","0.71539694","0.71496737","0.71447766","0.7136844","0.71352494","0.7124793","0.71209615","0.71130204","0.7110021","0.7106346","0.71061146","0.71025616","0.7091283","0.7086847","0.7086847","0.7086847","0.7086847","0.70839256","0.70829076","0.7081245","0.7078081","0.70766515","0.707508","0.7072506","0.70709103","0.7065228","0.7065228","0.7058199","0.70555866","0.7044494","0.7044036","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040721","0.7040067","0.7040067"],"string":"[\n \"0.81179816\",\n \"0.796208\",\n \"0.7771914\",\n \"0.7771914\",\n \"0.76687425\",\n \"0.76618785\",\n \"0.76455426\",\n \"0.76039106\",\n \"0.75676537\",\n \"0.75364685\",\n \"0.7525308\",\n \"0.7504455\",\n \"0.7481357\",\n \"0.7431703\",\n \"0.7421729\",\n \"0.7421202\",\n \"0.7421202\",\n \"0.7421202\",\n \"0.7421202\",\n \"0.7421202\",\n \"0.7421202\",\n \"0.7415426\",\n \"0.740481\",\n \"0.740481\",\n \"0.740481\",\n \"0.740481\",\n \"0.740481\",\n \"0.738109\",\n \"0.738109\",\n \"0.73742974\",\n \"0.7366818\",\n \"0.7366818\",\n \"0.7357696\",\n \"0.7328492\",\n \"0.73261267\",\n \"0.73089004\",\n \"0.7293952\",\n \"0.72899014\",\n \"0.7276056\",\n \"0.7268756\",\n \"0.72519207\",\n \"0.7237337\",\n \"0.7237328\",\n \"0.7234576\",\n \"0.72280896\",\n \"0.7218432\",\n \"0.72137105\",\n \"0.7194043\",\n \"0.7194043\",\n \"0.7191958\",\n \"0.7190089\",\n \"0.71898365\",\n \"0.718394\",\n \"0.71806604\",\n \"0.7180066\",\n \"0.71652025\",\n \"0.7162806\",\n \"0.71539694\",\n \"0.71496737\",\n \"0.71447766\",\n \"0.7136844\",\n \"0.71352494\",\n \"0.7124793\",\n \"0.71209615\",\n \"0.71130204\",\n \"0.7110021\",\n \"0.7106346\",\n \"0.71061146\",\n \"0.71025616\",\n \"0.7091283\",\n \"0.7086847\",\n \"0.7086847\",\n \"0.7086847\",\n \"0.7086847\",\n \"0.70839256\",\n \"0.70829076\",\n \"0.7081245\",\n \"0.7078081\",\n \"0.70766515\",\n \"0.707508\",\n \"0.7072506\",\n \"0.70709103\",\n \"0.7065228\",\n \"0.7065228\",\n \"0.7058199\",\n \"0.70555866\",\n \"0.7044494\",\n \"0.7044036\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040721\",\n \"0.7040067\",\n \"0.7040067\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7192,"cells":{"query":{"kind":"string","value":"Returns true if the logged in user is active"},"document":{"kind":"string","value":"def is_active(self):\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_active(self):\n return self.status == ACTIVE_USER","def is_active(self):\n return self.user.is_active","def is_active_user(self):\n\n return self.is_active","def check_active(self, user):\r\n if not self.require_active:\r\n # Ignore & move on.\r\n return True\r\n\r\n return user.is_active","def is_active():\n return True","def active(self):\n if self._active is not None:\n return self._active\n # Try to get it from the userprofile\n try:\n self._active = self.userprofile.user.is_active\n except UserProfile.DoesNotExist:\n # User profile does not exist.\n # The default value for active is False.\n self._active = False\n return self._active","def is_active(self) -> bool:\r\n return self.active","def is_active(self) -> bool:\n return self.active == \"active\"","def is_active(self) -> bool:\n return self.__is_active","def is_active(self) -> bool:","def activate_user(self, user):\n if not user.active:\n user.active = True\n return True\n return False","def logged_in(self):\n return self.auth.get_user_by_session() is not None","def is_active(self) -> bool:\n return self._is_active","def logged_in(self):\n return self.user is not None","def is_active(self):\r\n return True","def test_func(self):\n return self.request.user.is_active # any active user","def is_active(self) -> bool:\n if not self.expires_at:\n return False\n return self.expires_at > datetime.datetime.now()","def IsActive(self):\n return True","def has_permission(self, request):\n return request.user.is_active","def has_permission(self, request):\n return request.user.is_active","def has_permission(self, request):\n return request.user.is_active","def is_active(self):\n return self.active","def is_active(self):\n return self.active","def is_active(self):\n return self.active","def is_active(self):\n return self.active","def is_active(self):\n return self._is_active","def is_active(self):\n return self._is_active","def is_active(self):\n return self._is_active","def is_active(self):\r\n return self.active","def user_is_activated(self, user_name):\n return not self._simultanious_log_ins and \\\n user_name in self._active_users_names","def active(self) -> bool:\n return pulumi.get(self, \"active\")","def logged_in():\n\n if current_user.is_authenticated:\n return True\n\n return False","def is_logged_in():\n return 'user' in session","def is_logged_in(self) -> bool:\n return self.id is not None and self.username is not None","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def has_permission(self, request):\n\t\treturn request.user.is_active","def is_registered(self):\n if self.user == getpass.getuser():\n return True\n else:\n return False","def is_logged_in(self):\n return self.__is_logged_in","def is_active(self):\n return self._active","def _checkUserInactive(username,self):\r\n active = False\r\n user = _findUser(username)\r\n \r\n if user is not None:\r\n active = user.getIsActive()\r\n \r\n return active","def isLoggedIn(self):\n session = self.getSession()\n if session is not None:\n return True\n return False","def is_authenticated(self):\n return self.user is not None and self.state == AuthenticationOptions.authenticated","def logged_in(self) -> bool:\n return self._logged_in","def IsActive(self):\r\n\r\n return self.active","def is_logged_in(self):\n return self.cookie is not None","def test_activate_active_user(self):\n activate_user(self.user, self.request)\n self.assertEqual(self.user.is_active, True)","def _can_login(self):\n return all([self.user.is_active, self.status, self.status_detail == \"active\"])","def is_active(self):\n return self._is_record_status(self.ACTIVE)","def active(self) -> bool:\n return self.relay(\"active\")","def check_user_exists(self):\n is_exists = False\n if auth.UserInfo.objects.filter(\n user_id__username=self.username,\n is_active=True).exists():\n is_exists = True\n return is_exists","def is_logged_in(self):\n return self.router.token is not None","def is_logged_in(self, username):\n if username in self.users:\n return self.users[username].is_logged\n return False","def logged_in(request):\n return request.current_user is not None","def user_in_session():\n return 'user_id' in login_session","def is_logged_in():\n logged_in = \"uid\" in session\n if logged_in:\n user = api.user.get_user(uid=session[\"uid\"])\n if not user or (user.get(\"disabled\", False) is True):\n logout()\n return False\n return logged_in","def activate_user(self, user):\n if not user.active:\n user.active = True\n # noinspection PyUnresolvedReferences\n self.save(user)\n return True\n\n return","def active(self) -> bool:\n return self._active","def is_active(self):\n return self.type_id in ACTIVE_STATES","def active(self) -> bool:\n return self.orchestration_status == \"Active\"","def active(self):\n\n return True","def validUser(self):\n if self.state == SessionStates.LOGGED_OUT:\n return False\n\n # if self.user == None:\n # return False\n return True","def test_func(self):\n if not self.request.user.is_authenticated:\n return False\n if self.request.user.is_staff:\n return True\n return self.get_user() == self.request.user","def user_auth(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n return True\n return False","def is_activated(self):\n return self._activated","def is_active(self):\n with self._lock:\n return self._robot is not None","def users_active(self):\n return self.users(\"inactive == NO\")","def is_active(self):\n if self.wallet < 25:\n return True\n else:\n return False","def logged_in(self):\n return self._token is not None","def is_authenticated(self):\n return True #self.authenticated","def is_logged_in(session):\n return 'user' in session","def is_logged_in(self, params):\n email = self.credentials.get('email', '')\n password = self.credentials.get('password', '')\n if email != '' and password != '':\n return False\n return self.netflix_session.is_logged_in(account=self.credentials)","def is_active(self):\n with settings(hide('running', 'stdout', 'stderr', 'warnings'), warn_only=True):\n return self._action('is-active').succeeded","def is_authenticated(self):\n return self.ping() is not None","def isActive(self):\n return self.data.active","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def is_integrated(user):\n if user.is_authenticated() and user.is_active:\n return NokiaUser.objects.filter(user=user).exists()\n return False","def is_authenticated(self):\r\n return self.authenticated","def active(self) -> pulumi.Input[bool]:\n return pulumi.get(self, \"active\")","def active(self) -> pulumi.Output[bool]:\n return pulumi.get(self, \"active\")","def is_active(self):\n current_time = datetime.now().hour\n early = self.active[0]\n late = self.active[-1]\n return early <= current_time < late","def is_active(self):\n if self.state != 'checkout':\n return True \n if self.state == 'checkout':\n return False","def is_active(self) -> bool:\n return not any((self.is_ancillary, self.is_removed, self.is_system))"],"string":"[\n \"def is_active(self):\\n return self.status == ACTIVE_USER\",\n \"def is_active(self):\\n return self.user.is_active\",\n \"def is_active_user(self):\\n\\n return self.is_active\",\n \"def check_active(self, user):\\r\\n if not self.require_active:\\r\\n # Ignore & move on.\\r\\n return True\\r\\n\\r\\n return user.is_active\",\n \"def is_active():\\n return True\",\n \"def active(self):\\n if self._active is not None:\\n return self._active\\n # Try to get it from the userprofile\\n try:\\n self._active = self.userprofile.user.is_active\\n except UserProfile.DoesNotExist:\\n # User profile does not exist.\\n # The default value for active is False.\\n self._active = False\\n return self._active\",\n \"def is_active(self) -> bool:\\r\\n return self.active\",\n \"def is_active(self) -> bool:\\n return self.active == \\\"active\\\"\",\n \"def is_active(self) -> bool:\\n return self.__is_active\",\n \"def is_active(self) -> bool:\",\n \"def activate_user(self, user):\\n if not user.active:\\n user.active = True\\n return True\\n return False\",\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def is_active(self) -> bool:\\n return self._is_active\",\n \"def logged_in(self):\\n return self.user is not None\",\n \"def is_active(self):\\r\\n return True\",\n \"def test_func(self):\\n return self.request.user.is_active # any active user\",\n \"def is_active(self) -> bool:\\n if not self.expires_at:\\n return False\\n return self.expires_at > datetime.datetime.now()\",\n \"def IsActive(self):\\n return True\",\n \"def has_permission(self, request):\\n return request.user.is_active\",\n \"def has_permission(self, request):\\n return request.user.is_active\",\n \"def has_permission(self, request):\\n return request.user.is_active\",\n \"def is_active(self):\\n return self.active\",\n \"def is_active(self):\\n return self.active\",\n \"def is_active(self):\\n return self.active\",\n \"def is_active(self):\\n return self.active\",\n \"def is_active(self):\\n return self._is_active\",\n \"def is_active(self):\\n return self._is_active\",\n \"def is_active(self):\\n return self._is_active\",\n \"def is_active(self):\\r\\n return self.active\",\n \"def user_is_activated(self, user_name):\\n return not self._simultanious_log_ins and \\\\\\n user_name in self._active_users_names\",\n \"def active(self) -> bool:\\n return pulumi.get(self, \\\"active\\\")\",\n \"def logged_in():\\n\\n if current_user.is_authenticated:\\n return True\\n\\n return False\",\n \"def is_logged_in():\\n return 'user' in session\",\n \"def is_logged_in(self) -> bool:\\n return self.id is not None and self.username is not None\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def has_permission(self, request):\\n\\t\\treturn request.user.is_active\",\n \"def is_registered(self):\\n if self.user == getpass.getuser():\\n return True\\n else:\\n return False\",\n \"def is_logged_in(self):\\n return self.__is_logged_in\",\n \"def is_active(self):\\n return self._active\",\n \"def _checkUserInactive(username,self):\\r\\n active = False\\r\\n user = _findUser(username)\\r\\n \\r\\n if user is not None:\\r\\n active = user.getIsActive()\\r\\n \\r\\n return active\",\n \"def isLoggedIn(self):\\n session = self.getSession()\\n if session is not None:\\n return True\\n return False\",\n \"def is_authenticated(self):\\n return self.user is not None and self.state == AuthenticationOptions.authenticated\",\n \"def logged_in(self) -> bool:\\n return self._logged_in\",\n \"def IsActive(self):\\r\\n\\r\\n return self.active\",\n \"def is_logged_in(self):\\n return self.cookie is not None\",\n \"def test_activate_active_user(self):\\n activate_user(self.user, self.request)\\n self.assertEqual(self.user.is_active, True)\",\n \"def _can_login(self):\\n return all([self.user.is_active, self.status, self.status_detail == \\\"active\\\"])\",\n \"def is_active(self):\\n return self._is_record_status(self.ACTIVE)\",\n \"def active(self) -> bool:\\n return self.relay(\\\"active\\\")\",\n \"def check_user_exists(self):\\n is_exists = False\\n if auth.UserInfo.objects.filter(\\n user_id__username=self.username,\\n is_active=True).exists():\\n is_exists = True\\n return is_exists\",\n \"def is_logged_in(self):\\n return self.router.token is not None\",\n \"def is_logged_in(self, username):\\n if username in self.users:\\n return self.users[username].is_logged\\n return False\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def user_in_session():\\n return 'user_id' in login_session\",\n \"def is_logged_in():\\n logged_in = \\\"uid\\\" in session\\n if logged_in:\\n user = api.user.get_user(uid=session[\\\"uid\\\"])\\n if not user or (user.get(\\\"disabled\\\", False) is True):\\n logout()\\n return False\\n return logged_in\",\n \"def activate_user(self, user):\\n if not user.active:\\n user.active = True\\n # noinspection PyUnresolvedReferences\\n self.save(user)\\n return True\\n\\n return\",\n \"def active(self) -> bool:\\n return self._active\",\n \"def is_active(self):\\n return self.type_id in ACTIVE_STATES\",\n \"def active(self) -> bool:\\n return self.orchestration_status == \\\"Active\\\"\",\n \"def active(self):\\n\\n return True\",\n \"def validUser(self):\\n if self.state == SessionStates.LOGGED_OUT:\\n return False\\n\\n # if self.user == None:\\n # return False\\n return True\",\n \"def test_func(self):\\n if not self.request.user.is_authenticated:\\n return False\\n if self.request.user.is_staff:\\n return True\\n return self.get_user() == self.request.user\",\n \"def user_auth(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n return True\\n return False\",\n \"def is_activated(self):\\n return self._activated\",\n \"def is_active(self):\\n with self._lock:\\n return self._robot is not None\",\n \"def users_active(self):\\n return self.users(\\\"inactive == NO\\\")\",\n \"def is_active(self):\\n if self.wallet < 25:\\n return True\\n else:\\n return False\",\n \"def logged_in(self):\\n return self._token is not None\",\n \"def is_authenticated(self):\\n return True #self.authenticated\",\n \"def is_logged_in(session):\\n return 'user' in session\",\n \"def is_logged_in(self, params):\\n email = self.credentials.get('email', '')\\n password = self.credentials.get('password', '')\\n if email != '' and password != '':\\n return False\\n return self.netflix_session.is_logged_in(account=self.credentials)\",\n \"def is_active(self):\\n with settings(hide('running', 'stdout', 'stderr', 'warnings'), warn_only=True):\\n return self._action('is-active').succeeded\",\n \"def is_authenticated(self):\\n return self.ping() is not None\",\n \"def isActive(self):\\n return self.data.active\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def is_integrated(user):\\n if user.is_authenticated() and user.is_active:\\n return NokiaUser.objects.filter(user=user).exists()\\n return False\",\n \"def is_authenticated(self):\\r\\n return self.authenticated\",\n \"def active(self) -> pulumi.Input[bool]:\\n return pulumi.get(self, \\\"active\\\")\",\n \"def active(self) -> pulumi.Output[bool]:\\n return pulumi.get(self, \\\"active\\\")\",\n \"def is_active(self):\\n current_time = datetime.now().hour\\n early = self.active[0]\\n late = self.active[-1]\\n return early <= current_time < late\",\n \"def is_active(self):\\n if self.state != 'checkout':\\n return True \\n if self.state == 'checkout':\\n return False\",\n \"def is_active(self) -> bool:\\n return not any((self.is_ancillary, self.is_removed, self.is_system))\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.88505435\",\n \"0.8798741\",\n \"0.8700004\",\n \"0.854557\",\n \"0.78350693\",\n \"0.7738377\",\n \"0.7705481\",\n \"0.76957625\",\n \"0.76723784\",\n \"0.7669489\",\n \"0.7668266\",\n \"0.76530737\",\n \"0.7633366\",\n \"0.76033133\",\n \"0.75975317\",\n \"0.7560072\",\n \"0.7550248\",\n \"0.7520325\",\n \"0.75200886\",\n \"0.75200886\",\n \"0.75200886\",\n \"0.7513665\",\n \"0.7513665\",\n \"0.7513665\",\n \"0.7513665\",\n \"0.74734646\",\n \"0.74734646\",\n \"0.74734646\",\n \"0.7452469\",\n \"0.74136424\",\n \"0.740429\",\n \"0.7385657\",\n \"0.7370619\",\n \"0.7370392\",\n \"0.73412925\",\n \"0.7323043\",\n \"0.73216283\",\n \"0.7320729\",\n \"0.7293575\",\n \"0.7265245\",\n \"0.7214026\",\n \"0.7208764\",\n \"0.7164602\",\n \"0.71411747\",\n \"0.71212655\",\n \"0.71150506\",\n \"0.7070638\",\n \"0.70642847\",\n \"0.70632637\",\n \"0.7063135\",\n \"0.70619744\",\n \"0.7055995\",\n \"0.70344687\",\n \"0.6987697\",\n \"0.69707084\",\n \"0.69520235\",\n \"0.6949824\",\n 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self.auth.get_user_by_session() is not None","def isLoggedIn(self):\n session = self.getSession()\n if session is not None:\n return True\n return False","def is_logged_in(self) -> bool:\n return self.id is not None and self.username is not None","def logged_in(self):\n return self.user is not None","def validUser(self):\n if self.state == SessionStates.LOGGED_OUT:\n return False\n\n # if self.user == None:\n # return False\n return True","def is_logged_in():\n return 'user' in session","def check_user_logged():\n global user\n if 'user' not in session:\n return False\n else:\n user = session.get('user')\n return user['username'] != ''","def logged_in():\n\n if current_user.is_authenticated:\n return True\n\n return False","def is_logged_in():\n logged_in = \"uid\" in session\n if logged_in:\n user = api.user.get_user(uid=session[\"uid\"])\n if not user or (user.get(\"disabled\", False) is True):\n logout()\n return False\n return logged_in","def is_logged_in(self):\n return self.cookie is not None","def is_logged_in(self):\n return self.__is_logged_in","def is_logged_in(self):\n return self.router.token is not None","def require_login(self):\n\tif users.get_current_user():\n\t return True\n\telse:\n\t self.redirect(users.create_login_url(self.request.uri))\n\t return False","def is_logged_in():\n _has_cookie = util.web.has_cookie('pass')\n if _has_cookie:\n _is_expired = util.web.is_cookie_expired('pass')\n if _is_expired:\n return False\n return True\n return False","def logged_in(self) -> bool:\n return self._logged_in","def is_logged_in(session):\n return 'user' in session","def logged_in(request):\n return request.current_user is not None","def test_func(self):\n if not self.request.user.is_authenticated:\n return False\n if self.request.user.is_staff:\n return True\n return self.get_user() == self.request.user","def is_authenticated(self):\n return False","def logged_in(self):\n return self._token is not None","def user_in_session():\n return 'user_id' in login_session","def is_logged_in() -> bool:\n is_dev_login_disabled = SETTINGS.DEV_LOGIN_DISABLED and is_localhost()\n return bool(is_dev_login_disabled or is_logged_in_user())","def is_valid(self):\n return self.user.is_authenticated","def is_logged_in(self, username):\n if username in self.users:\n return self.users[username].is_logged\n return False","def is_regular_user(user):\n return user.is_authenticated()","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_authenticated(self):\n return True","def is_logged_in(self, params):\n email = self.credentials.get('email', '')\n password = self.credentials.get('password', '')\n if email != '' and password != '':\n return False\n return self.netflix_session.is_logged_in(account=self.credentials)","def is_authenticated(self):\n return self.user is not None and self.state == AuthenticationOptions.authenticated","def logged():\n if session.login==1:\n return True\n else:\n return False","def is_authenticated(self):\n return True #self.authenticated","def is_authenticated(self, request, **kwargs):\r\n return True","def is_registered(self):\n if self.user == getpass.getuser():\n return True\n else:\n return False","def check_user(self):\n try:\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\n return True\n else:\n return False\n except:\n return False","def is_authenticated(self):\n return self.ping() is not None","def has_user(self):\n\t\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0","def is_admin(self):\n if not self.current_user:\n return False\n else:\n return self.current_user in [\"1\"]","def authenticated(self):\n # We don't support authentication yet\n return False","def isLoggedOut(self):\n return self.sess is None or self.sess.isNew()","def user_auth(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n return True\n return False","def is_authenticated(self):\n return bool(get_auth_token())","def is_authenticated(self):\r\n return self.authenticated","def is_logged(html):\n soup = BeautifulSoup(html, \"html.parser\")\n\n if soup.find('div', {'id': 'user_information'}) is None:\n return False\n return True","def is_authenticated(self) -> bool:\n return self.requester.uuid is not None","def check_user(self):\n try:\n if self.get_customer()[0][0] == self.dni:\n return True\n else:\n return False\n except:\n return False","def is_authenticated(self):\n result = self.lpass(\"lpass status\")\n\n if \"Logged in as\" in result.output:\n return True\n\n return False","def check_authentication():\r\n\r\n #TODO: Reservation based authentication\r\n try:\r\n authenticated_user()\r\n except Exception as e:\r\n return e\r\n\r\n return True","def is_logged_in(self, login_url: str) -> bool:\n self.d('Check login - %s', login_url)\n if not login_url:\n return False\n res = self.get(login_url, allow_redirects=False)\n if res.status_code == 302:\n self.i('Is logged in')\n return True\n self.i('Is not logged in')\n return False","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def is_user_authenticated(request):\n return request.session.session_key","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authorized(self) -> bool:\n\t\tif \"access_token\" in session:\n\t\t\tif session.get(\"access_token\") is not None:\n\t\t\t\tif \"user\" in session:\n\t\t\t\t\treturn True\n\t\treturn False","def check_login(request, username=None):\r\n if request.user is None:\r\n return False\r\n\r\n # if we have a username we're told to check against, make sure the\r\n # username matches\r\n if username is not None and username != request.user.username:\r\n return False\r\n\r\n return True","def is_correct_user(self, login, password):\n pass","def is_authenticated(self):\n return True","def check_user_and_login(self) -> Response:\n pass","def is_authenticated(self):\n return bool(self._authenticated)","def isAdmin():\n\tif 'username' in session and session['username'] == 'admin':\n\t\treturn True\n\telse:\n\t\treturn False","def is_allowed_to_submit(request):\n return not settings.REQUIRE_LOGIN or request.user.is_authenticated()","def confirm_login_allowed(self, user):\n # if the user has been disabled due to incorrect\n # password retries or other.\n if not user.is_active:\n return False; \n return True","def check(self):\n\n us = ServiceLocator.resolve(ServiceLocator.USERS)\n\n user_session = self.get()\n user = self.get_user()\n\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)","def is_active_user(self):\n\n return self.is_active","def can_log_in_without_cas(self):\n return self.password is not None and self.password != \"\"","def is_user_context(context):\n if not context:\n return False\n if context.is_admin:\n return False\n if not context.user_id or not context.project_id:\n return False\n return True","def is_user_context(context):\n if not context:\n return False\n if context.is_admin:\n return False\n if not context.user_id or not context.project_id:\n return False\n return True","def is_user_context(context):\n if not context:\n return False\n if context.is_admin:\n return False\n if not context.user_id or not context.project_id:\n return False\n return True","def is_authenticated_CSC_user():\n key = SAML_ATTRIBUTES['CSC_username']\n if executing_travis():\n return False\n return True if 'samlUserdata' in session and len(session.get('samlUserdata', None)) > 0 and key in session.get('samlUserdata', None) else False","def _is_user_context(context):\n if not context:\n return False\n if context.is_admin:\n return False\n if not context.user_id or not context.project_id:\n return False\n return True","def _is_user_context(context):\n if not context:\n return False\n if context.is_admin:\n return False\n if not context.user_id or not context.project_id:\n return False\n return True","def is_user_authenticated(user):\r\n\r\n if DJ_VERSION[0] > 1:\r\n return user.is_authenticated\r\n else:\r\n return user.is_authenticated()","def has_username(self):\n return self.username is not None","def authenticated():\n if 'user_id' in session and 'access_token' in session:\n user = db_session.query(User).filter_by(id=session['user_id']).first()\n\n if user:\n return user.access_token == session['access_token']\n return False","def valid_credentials(self):\n path = '/api/session-user'\n url = '{}{}'.format(self._url_base, path)\n response, content = super(DSBaseService, self)._request(url,\n headers=self._headers(with_content_type=False))\n return int(response['status']) == 200","def authenticated(self):\n return self.token is not None","def is_logged_in(self, email):\n wait_for_element(self.browser, self.expand_left_panel)\n expand_panel = self.browser.find_element(*self.expand_left_panel)\n custom_click(self.browser, expand_panel)\n wait_for(lambda: len(self.browser.find_element(*self.logged_user).text) > 0, delay=1, num_sec=5)\n user_logged_in = self.browser.find_element(*self.logged_user).text\n if user_logged_in == email:\n return True\n else:\n raise Exception('Failed to login by user => {}'.format(email))","def user_auth_inst(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n inst = UserInformation.objects.get(user=user)\n if(inst.user_instructor):\n return True\n return False","def is_user_context(context):\n if not context or not isinstance(context, RequestContext):\n return False\n if context.is_admin:\n return False\n return True","def is_authenticated(self) -> bool:\n return self._authenticated","def is_anonymous():\n return False","def is_authenticated(self):\n if self._token is None:\n self.authenticate()\n\n return self._token is not None","def check(self):\n self.log.debug('Testing for a valid login session')\n # If our cookie jar is empty, we obviously don't have a valid login session\n if not len(self.cookiejar):\n return False\n\n # Test our login session and make sure it's still active\n return requests.get(self.TEST_URL, cookies=self.cookiejar).status_code == 200","def is_authenticated(self):\n if not self.token:\n return False\n\n try:\n self.lookup_token()\n return True\n except Forbidden:\n return False\n except InvalidPath:\n return False\n except InvalidRequest:\n return False","def logged_in(browser: RoboBrowser):\n login_div = browser.find('div', content=\"Login\")\n return True if not login_div else False"],"string":"[\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def isLoggedIn(self):\\n session = self.getSession()\\n if session is not None:\\n return True\\n return False\",\n \"def is_logged_in(self) -> bool:\\n return self.id is not None and self.username is not None\",\n \"def logged_in(self):\\n return self.user is not None\",\n \"def validUser(self):\\n if self.state == SessionStates.LOGGED_OUT:\\n return False\\n\\n # if self.user == None:\\n # return False\\n return True\",\n \"def is_logged_in():\\n return 'user' in session\",\n \"def check_user_logged():\\n global user\\n if 'user' not in session:\\n return False\\n else:\\n user = session.get('user')\\n return user['username'] != ''\",\n \"def logged_in():\\n\\n if current_user.is_authenticated:\\n return True\\n\\n return False\",\n \"def is_logged_in():\\n logged_in = \\\"uid\\\" in session\\n if logged_in:\\n user = api.user.get_user(uid=session[\\\"uid\\\"])\\n if not user or (user.get(\\\"disabled\\\", False) is True):\\n logout()\\n return False\\n return logged_in\",\n \"def is_logged_in(self):\\n return self.cookie is not None\",\n \"def is_logged_in(self):\\n return self.__is_logged_in\",\n \"def is_logged_in(self):\\n return self.router.token is not None\",\n \"def require_login(self):\\n\\tif users.get_current_user():\\n\\t return True\\n\\telse:\\n\\t self.redirect(users.create_login_url(self.request.uri))\\n\\t return False\",\n \"def is_logged_in():\\n _has_cookie = util.web.has_cookie('pass')\\n if _has_cookie:\\n _is_expired = util.web.is_cookie_expired('pass')\\n if _is_expired:\\n return False\\n return True\\n return False\",\n \"def logged_in(self) -> bool:\\n return self._logged_in\",\n \"def is_logged_in(session):\\n return 'user' in session\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def test_func(self):\\n if not self.request.user.is_authenticated:\\n return False\\n if self.request.user.is_staff:\\n return True\\n return self.get_user() == self.request.user\",\n \"def is_authenticated(self):\\n return False\",\n \"def logged_in(self):\\n return self._token is not None\",\n \"def user_in_session():\\n return 'user_id' in login_session\",\n \"def is_logged_in() -> bool:\\n is_dev_login_disabled = SETTINGS.DEV_LOGIN_DISABLED and is_localhost()\\n return bool(is_dev_login_disabled or is_logged_in_user())\",\n \"def is_valid(self):\\n return self.user.is_authenticated\",\n \"def is_logged_in(self, username):\\n if username in self.users:\\n return self.users[username].is_logged\\n return False\",\n \"def is_regular_user(user):\\n return user.is_authenticated()\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_logged_in(self, params):\\n email = self.credentials.get('email', '')\\n password = self.credentials.get('password', '')\\n if email != '' and password != '':\\n return False\\n return self.netflix_session.is_logged_in(account=self.credentials)\",\n \"def is_authenticated(self):\\n return self.user is not None and self.state == AuthenticationOptions.authenticated\",\n \"def logged():\\n if session.login==1:\\n return True\\n else:\\n return False\",\n \"def is_authenticated(self):\\n return True #self.authenticated\",\n \"def is_authenticated(self, request, **kwargs):\\r\\n return True\",\n \"def is_registered(self):\\n if self.user == getpass.getuser():\\n return True\\n else:\\n return False\",\n \"def check_user(self):\\n try:\\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def is_authenticated(self):\\n return self.ping() is not None\",\n \"def has_user(self):\\n\\t\\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0\",\n \"def is_admin(self):\\n if not self.current_user:\\n return False\\n else:\\n return self.current_user in [\\\"1\\\"]\",\n \"def authenticated(self):\\n # We don't support authentication yet\\n return False\",\n \"def isLoggedOut(self):\\n return self.sess is None or self.sess.isNew()\",\n \"def user_auth(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n return True\\n return False\",\n \"def is_authenticated(self):\\n return bool(get_auth_token())\",\n \"def is_authenticated(self):\\r\\n return self.authenticated\",\n \"def is_logged(html):\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n\\n if soup.find('div', {'id': 'user_information'}) is None:\\n return False\\n return True\",\n \"def is_authenticated(self) -> bool:\\n return self.requester.uuid is not None\",\n \"def check_user(self):\\n try:\\n if self.get_customer()[0][0] == self.dni:\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def is_authenticated(self):\\n result = self.lpass(\\\"lpass status\\\")\\n\\n if \\\"Logged in as\\\" in result.output:\\n return True\\n\\n return False\",\n \"def check_authentication():\\r\\n\\r\\n #TODO: Reservation based authentication\\r\\n try:\\r\\n authenticated_user()\\r\\n except Exception as e:\\r\\n return e\\r\\n\\r\\n return True\",\n \"def is_logged_in(self, login_url: str) -> bool:\\n self.d('Check login - %s', login_url)\\n if not login_url:\\n return False\\n res = self.get(login_url, allow_redirects=False)\\n if res.status_code == 302:\\n self.i('Is logged in')\\n return True\\n self.i('Is not logged in')\\n return False\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def is_user_authenticated(request):\\n return request.session.session_key\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authorized(self) -> bool:\\n\\t\\tif \\\"access_token\\\" in session:\\n\\t\\t\\tif session.get(\\\"access_token\\\") is not None:\\n\\t\\t\\t\\tif \\\"user\\\" in session:\\n\\t\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def check_login(request, username=None):\\r\\n if request.user is None:\\r\\n return False\\r\\n\\r\\n # if we have a username we're told to check against, make sure the\\r\\n # username matches\\r\\n if username is not None and username != request.user.username:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def is_correct_user(self, login, password):\\n pass\",\n \"def is_authenticated(self):\\n return True\",\n \"def check_user_and_login(self) -> Response:\\n pass\",\n \"def is_authenticated(self):\\n return bool(self._authenticated)\",\n \"def isAdmin():\\n\\tif 'username' in session and session['username'] == 'admin':\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"def is_allowed_to_submit(request):\\n return not settings.REQUIRE_LOGIN or request.user.is_authenticated()\",\n \"def confirm_login_allowed(self, user):\\n # if the user has been disabled due to incorrect\\n # password retries or other.\\n if not user.is_active:\\n return False; \\n return True\",\n \"def check(self):\\n\\n us = ServiceLocator.resolve(ServiceLocator.USERS)\\n\\n user_session = self.get()\\n user = self.get_user()\\n\\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)\",\n \"def is_active_user(self):\\n\\n return self.is_active\",\n \"def can_log_in_without_cas(self):\\n return self.password is not None and self.password != \\\"\\\"\",\n \"def is_user_context(context):\\n if not context:\\n return False\\n if context.is_admin:\\n return False\\n if not context.user_id or not context.project_id:\\n return False\\n return True\",\n \"def is_user_context(context):\\n if not context:\\n return False\\n if context.is_admin:\\n return False\\n if not context.user_id or not context.project_id:\\n return False\\n return True\",\n \"def is_user_context(context):\\n if not context:\\n return False\\n if context.is_admin:\\n return False\\n if not context.user_id or not context.project_id:\\n return False\\n return True\",\n \"def is_authenticated_CSC_user():\\n key = SAML_ATTRIBUTES['CSC_username']\\n if executing_travis():\\n return False\\n return True if 'samlUserdata' in session and len(session.get('samlUserdata', None)) > 0 and key in session.get('samlUserdata', None) else False\",\n \"def _is_user_context(context):\\n if not context:\\n return False\\n if context.is_admin:\\n return False\\n if not context.user_id or not context.project_id:\\n return False\\n return True\",\n \"def _is_user_context(context):\\n if not context:\\n return False\\n if context.is_admin:\\n return False\\n if not context.user_id or not context.project_id:\\n return False\\n return True\",\n \"def is_user_authenticated(user):\\r\\n\\r\\n if DJ_VERSION[0] > 1:\\r\\n return user.is_authenticated\\r\\n else:\\r\\n return user.is_authenticated()\",\n \"def has_username(self):\\n return self.username is not None\",\n \"def authenticated():\\n if 'user_id' in session and 'access_token' in session:\\n user = db_session.query(User).filter_by(id=session['user_id']).first()\\n\\n if user:\\n return user.access_token == session['access_token']\\n return False\",\n \"def valid_credentials(self):\\n path = '/api/session-user'\\n url = '{}{}'.format(self._url_base, path)\\n response, content = super(DSBaseService, self)._request(url,\\n headers=self._headers(with_content_type=False))\\n return int(response['status']) == 200\",\n \"def authenticated(self):\\n return self.token is not None\",\n \"def is_logged_in(self, email):\\n wait_for_element(self.browser, self.expand_left_panel)\\n expand_panel = self.browser.find_element(*self.expand_left_panel)\\n custom_click(self.browser, expand_panel)\\n wait_for(lambda: len(self.browser.find_element(*self.logged_user).text) > 0, delay=1, num_sec=5)\\n user_logged_in = self.browser.find_element(*self.logged_user).text\\n if user_logged_in == email:\\n return True\\n else:\\n raise Exception('Failed to login by user => {}'.format(email))\",\n \"def user_auth_inst(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n inst = UserInformation.objects.get(user=user)\\n if(inst.user_instructor):\\n return True\\n return False\",\n \"def is_user_context(context):\\n if not context or not isinstance(context, RequestContext):\\n return False\\n if context.is_admin:\\n return False\\n return True\",\n \"def is_authenticated(self) -> bool:\\n return self._authenticated\",\n \"def is_anonymous():\\n return False\",\n \"def is_authenticated(self):\\n if self._token is None:\\n self.authenticate()\\n\\n return self._token is not None\",\n \"def check(self):\\n self.log.debug('Testing for a valid login session')\\n # If our cookie jar is empty, we obviously don't have a valid login session\\n if not len(self.cookiejar):\\n return False\\n\\n # Test our login session and make sure it's still active\\n return requests.get(self.TEST_URL, cookies=self.cookiejar).status_code == 200\",\n \"def is_authenticated(self):\\n if not self.token:\\n return False\\n\\n try:\\n self.lookup_token()\\n return True\\n except Forbidden:\\n return False\\n except InvalidPath:\\n return False\\n except InvalidRequest:\\n return False\",\n \"def logged_in(browser: RoboBrowser):\\n login_div = browser.find('div', content=\\\"Login\\\")\\n return True if not login_div else False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85514784","0.8487751","0.8463887","0.84311163","0.83854413","0.83422846","0.8227351","0.81986076","0.8189232","0.81029665","0.80995315","0.8037724","0.79774845","0.7907793","0.78765494","0.7856657","0.7833945","0.7820103","0.7789442","0.77781737","0.77136534","0.76728445","0.7649724","0.7607182","0.76053685","0.75882924","0.75333154","0.75333154","0.75333154","0.75333154","0.75333154","0.75333154","0.75333154","0.74919665","0.74563384","0.7451827","0.74273866","0.7421799","0.7336918","0.7330698","0.7294168","0.7268295","0.72512484","0.7248372","0.7243039","0.72335947","0.7210918","0.72059184","0.71585065","0.7157258","0.7152786","0.71364725","0.71009403","0.7087075","0.706841","0.7049101","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7043798","0.7042321","0.7037053","0.7026143","0.7022888","0.6991125","0.6978641","0.6977515","0.6972748","0.69697255","0.6961067","0.69610333","0.69396436","0.6905875","0.6905875","0.6905875","0.689996","0.6897613","0.6897613","0.68971425","0.68928164","0.6891332","0.6869483","0.6859305","0.6853357","0.683687","0.67950356","0.6765828","0.6761971","0.6740324","0.67367065","0.6732457","0.67310834"],"string":"[\n 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in"},"document":{"kind":"string","value":"def is_authenticated(self):\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def logged_in():\n\n if current_user.is_authenticated:\n return True\n\n return False","def is_logged_in():\n return 'user' in session","def logged_in(self):\n return self.auth.get_user_by_session() is not None","def isLoggedIn(self):\n session = self.getSession()\n if session is not None:\n return True\n return False","def is_logged_in(self) -> bool:\n return self.id is not None and self.username is not None","def logged_in(self) -> bool:\n return self._logged_in","def is_logged_in(self):\n return self.__is_logged_in","def logged_in(self):\n return self.user is not None","def is_logged_in(session):\n return 'user' in session","def is_logged_in():\n logged_in = \"uid\" in session\n if logged_in:\n user = api.user.get_user(uid=session[\"uid\"])\n if not user or (user.get(\"disabled\", False) is True):\n logout()\n return False\n return logged_in","def is_logged_in(self):\n return self.cookie is not None","def logged_in(request):\n return request.current_user is not None","def is_logged_in(self):\n return self.router.token is not None","def check_user_logged():\n global user\n if 'user' not in session:\n return False\n else:\n user = session.get('user')\n return user['username'] != ''","def is_logged_in():\n _has_cookie = util.web.has_cookie('pass')\n if _has_cookie:\n _is_expired = util.web.is_cookie_expired('pass')\n if _is_expired:\n return False\n return True\n return False","def is_logged_in(self, username):\n if username in self.users:\n return self.users[username].is_logged\n return False","def is_logged_in() -> bool:\n is_dev_login_disabled = SETTINGS.DEV_LOGIN_DISABLED and is_localhost()\n return bool(is_dev_login_disabled or is_logged_in_user())","def is_logged_in(self, params):\n email = self.credentials.get('email', '')\n password = self.credentials.get('password', '')\n if email != '' and password != '':\n return False\n return self.netflix_session.is_logged_in(account=self.credentials)","def is_authenticated(self):\n return self.user is not None and self.state == AuthenticationOptions.authenticated","def logged_in(self):\n return self._token is not None","def user_in_session():\n return 'user_id' in login_session","def authenticated():\n if 'user_id' in session and 'access_token' in session:\n user = db_session.query(User).filter_by(id=session['user_id']).first()\n\n if user:\n return user.access_token == session['access_token']\n return False","def is_user_authenticated(user):\r\n\r\n if DJ_VERSION[0] > 1:\r\n return user.is_authenticated\r\n else:\r\n return user.is_authenticated()","def logged():\n if session.login==1:\n return True\n else:\n return False","def is_authenticated(self):\n result = self.lpass(\"lpass status\")\n\n if \"Logged in as\" in result.output:\n return True\n\n return False","def is_authenticated(self):\n return bool(get_auth_token())","def is_user_authenticated(request):\n return request.session.session_key","def is_authenticated(self):\n return True #self.authenticated","def is_authenticated(self, request, **kwargs):\r\n return True","def is_authenticated(self):\r\n return self.authenticated","def is_regular_user(user):\n return user.is_authenticated()","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_authenticated(self):\n return self.authenticated","def is_logged_in(self, login_url: str) -> bool:\n self.d('Check login - %s', login_url)\n if not login_url:\n return False\n res = self.get(login_url, allow_redirects=False)\n if res.status_code == 302:\n self.i('Is logged in')\n return True\n self.i('Is not logged in')\n return False","def is_authenticated(self):\n return bool(self._authenticated)","def validUser(self):\n if self.state == SessionStates.LOGGED_OUT:\n return False\n\n # if self.user == None:\n # return False\n return True","def is_authenticated(self) -> bool:\n return self._authenticated","def require_login(self):\n\tif users.get_current_user():\n\t return True\n\telse:\n\t self.redirect(users.create_login_url(self.request.uri))\n\t return False","def is_valid(self):\n return self.user.is_authenticated","def is_authenticated(self):\n return self.ping() is not None","def test_func(self):\n if not self.request.user.is_authenticated:\n return False\n if self.request.user.is_staff:\n return True\n return self.get_user() == self.request.user","def is_authenticated_CSC_user():\n key = SAML_ATTRIBUTES['CSC_username']\n if executing_travis():\n return False\n return True if 'samlUserdata' in session and len(session.get('samlUserdata', None)) > 0 and key in session.get('samlUserdata', None) else False","def is_authenticated(self):\n return True","def is_authorized(self) -> bool:\n\t\tif \"access_token\" in session:\n\t\t\tif session.get(\"access_token\") is not None:\n\t\t\t\tif \"user\" in session:\n\t\t\t\t\treturn True\n\t\treturn False","def is_user_authenticated(self, user_id, auth_token):\n\n user = self.user_db[user_id]\n if user['token'] == auth_token:\n return True\n else:\n return False","def user_auth(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n return True\n return False","def is_authenticated(self):\n return False","def is_registered(self):\n if self.user == getpass.getuser():\n return True\n else:\n return False","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def is_authenticated(self) -> bool:\n return self.requester.uuid is not None","def is_logged_in(self, email):\n wait_for_element(self.browser, self.expand_left_panel)\n expand_panel = self.browser.find_element(*self.expand_left_panel)\n custom_click(self.browser, expand_panel)\n wait_for(lambda: len(self.browser.find_element(*self.logged_user).text) > 0, delay=1, num_sec=5)\n user_logged_in = self.browser.find_element(*self.logged_user).text\n if user_logged_in == email:\n return True\n else:\n raise Exception('Failed to login by user => {}'.format(email))","def is_active_user(self):\n\n return self.is_active","def check(self):\n\n us = ServiceLocator.resolve(ServiceLocator.USERS)\n\n user_session = self.get()\n user = self.get_user()\n\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)","def check_user(self):\n try:\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\n return True\n else:\n return False\n except:\n return False","def is_authenticated(self):\n if self._token is None:\n self.authenticate()\n\n return self._token is not None","def authenticated(self):\n # We don't support authentication yet\n return False","def is_active(self):\n return self.user.is_active","def logged_in(self, use_page=None):\n # allow page soup to be passed as argument to make get_soup calling this function faster\n if use_page is None: soup = self.get_soup(\"overview\")\n else: soup = use_page\n\n found = soup.find(\"meta\", {\"name\": \"ogame-player-name\"})\n if found is None: return False\n if str(found[\"content\"]) == self.username: return True","def is_logged_in_user(consumer=None) -> bool:\n if not consumer:\n consumer = get_consumer()\n return bool(\n not consumer.get(\"X-Anonymous-Consumer\") and consumer.get(\"X-Consumer-Id\")\n )","def authenticated(self):\n return self.token is not None","def is_logged_in(session):\n if 'tid' in session:\n return {'success': 1, 'message': '你处于登录状态.', \n 'teamname': session['teamname'], 'is_zju_user': session['is_zju_user']}\n else:\n return {\"success\": 0, \"message\": \"你并未处于登录状态.\"}","def logged_in(browser: RoboBrowser):\n login_div = browser.find('div', content=\"Login\")\n return True if not login_div else False","def acctLogin(self):\n loggedIn = False\n if self.acctObj.login():\n loggedIn = True\n else:\n logger.warning(\"{} Authentication failed\".format(self))\n self.acctObj = None\n\n if loggedIn:\n return True\n return False","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def _can_login(self):\n return all([self.user.is_active, self.status, self.status_detail == \"active\"])","def is_authenticated(self, request, **kwargs):\r\n from django.contrib.auth import get_user_model\r\n User = get_user_model()\r\n\r\n try:\r\n username, api_key = self.extract_credentials(request)\r\n except ValueError:\r\n return self._unauthorized()\r\n\r\n if not username or not api_key:\r\n return self._unauthorized()\r\n\r\n try:\r\n user = User.objects.get_by_natural_key(username)\r\n except (User.DoesNotExist, User.MultipleObjectsReturned):\r\n return self._unauthorized()\r\n\r\n if not self.check_active(user):\r\n return False\r\n\r\n request.user = user\r\n return self.get_key(user, api_key)","def user_logged_in():\n if not session.get('user_id'):\n return \"nope\", 401\n else:\n return \"yep\", 200","def is_authenticated(self, request, **kwargs):\r\n if not request.META.get('HTTP_AUTHORIZATION'):\r\n return self._unauthorized()\r\n\r\n try:\r\n (auth_type, data) = request.META['HTTP_AUTHORIZATION'].split()\r\n if auth_type.lower() != 'basic':\r\n return self._unauthorized()\r\n user_pass = base64.b64decode(data)\r\n except:\r\n return self._unauthorized()\r\n\r\n bits = user_pass.split(':', 1)\r\n\r\n if len(bits) != 2:\r\n return self._unauthorized()\r\n\r\n if self.backend:\r\n user = self.backend.authenticate(\r\n username=bits[0], password=bits[1])\r\n else:\r\n user = authenticate(username=bits[0], password=bits[1])\r\n\r\n if user is None:\r\n return self._unauthorized()\r\n\r\n if not self.check_active(user):\r\n return False\r\n\r\n request.user = user\r\n return True","def is_authenticated(connection, window_info, kwargs):\n return window_info and window_info.is_authenticated","def check_user_and_login(self) -> Response:\n pass","def is_authenticated(self):\n if not self.token:\n return False\n\n try:\n self.lookup_token()\n return True\n except Forbidden:\n return False\n except InvalidPath:\n return False\n except InvalidRequest:\n return False","def log_in(self, password: str) -> bool:\n return self._password == password","def check_login(request, username=None):\r\n if request.user is None:\r\n return False\r\n\r\n # if we have a username we're told to check against, make sure the\r\n # username matches\r\n if username is not None and username != request.user.username:\r\n return False\r\n\r\n return True","def is_active(self):\n return self.status == ACTIVE_USER","def has_user(self):\n\t\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0","def loginState(self, user_data):\n\t\tif self.db.request(\"getOne\", user_data):\n\t\t\treturn True;\n\t\telse:\n\t\t\treturn False;","def is_logged_in(soup):\n logged_in = False\n login_link = soup.findAll(\"a\",{\"accesskey\":\"x\"})\n if len(login_link) > 0: \n link = login_link[0].attrs['href'] \n if link.find(\"logout\") > 0:\n logged_in = True\n return logged_in","def check_user(self,username, password):\n safe_input = (username, password)\n vals = self.cur.execute(\"SELECT Username, Password FROM Users WHERE Username=? AND Password=?\",safe_input).fetchone()\n if vals:\n logging.info('%s was authenticated', username)\n return True\n else:\n logging.info('Failed login for %s', username)\n return False","def check_authentication():\r\n\r\n #TODO: Reservation based authentication\r\n try:\r\n authenticated_user()\r\n except Exception as e:\r\n return e\r\n\r\n return True","def is_correct_user(self, login, password):\n pass","def is_logged_in (self, account):\n is_logged_in = False\n # load cookies\n account_hash = self._generate_account_hash(account=account)\n if self._load_cookies(filename=self.cookie_path + '_' + account_hash) == False:\n return False\n if self._load_data(filename=self.data_path + '_' + account_hash) == False:\n # load the profiles page (to verify the user)\n response = self._session_get(component='profiles')\n\n # parse out the needed inline information\n only_script_tags = SoupStrainer('script')\n page_soup = BeautifulSoup(response.text, 'html.parser', parse_only=only_script_tags)\n page_data = self._parse_page_contents(page_soup=page_soup)\n\n # check if the cookie is still valid\n for item in page_data:\n if 'profilesList' in dict(item).keys():\n if item['profilesList']['summary']['length'] >= 1:\n is_logged_in = True\n return is_logged_in\n return True","def has_object_permission(self, request, view, account):\n if request.user.is_authenticated():\n if request.user.is_staff:\n return True\n return account.username == request.user.username\n return False","def is_authenticated(self, request, **kwargs):\n logging.debug(request.META)\n if not request.META.get('HTTP_AUTHORIZATION'):\n logging.error('HTTP_AUTHORIZATION not found')\n return self._unauthorized()\n\n try:\n logging.debug('start split')\n (auth_type, data) = request.META['HTTP_AUTHORIZATION'].split()\n if auth_type.lower() != 'basic':\n logging.error('not basic')\n return self._unauthorized()\n user_pass = base64.b64decode(data).decode('utf-8')\n except:\n logging.error('plit issue')\n return self._unauthorized()\n\n bits = user_pass.split(':', 1)\n logging.debug(bits)\n if len(bits) != 2:\n logging.error('len bits issue')\n return self._unauthorized()\n\n if self.backend:\n user = self.backend.authenticate(username=bits[0], password=bits[1])\n else:\n user = authenticate(username=bits[0], password=bits[1])\n\n if user is None:\n logging.debug('username password is not correct.')\n return self._unauthorized()\n\n if not self.check_active(user):\n logging.debug('user is not active')\n return False\n request.user = user\n return True"],"string":"[\n \"def logged_in():\\n\\n if current_user.is_authenticated:\\n return True\\n\\n return False\",\n \"def is_logged_in():\\n return 'user' in session\",\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def isLoggedIn(self):\\n session = self.getSession()\\n if session is not None:\\n return True\\n return False\",\n \"def is_logged_in(self) -> bool:\\n return self.id is not None and self.username is not None\",\n \"def logged_in(self) -> bool:\\n return self._logged_in\",\n \"def is_logged_in(self):\\n return self.__is_logged_in\",\n \"def logged_in(self):\\n return self.user is not None\",\n \"def is_logged_in(session):\\n return 'user' in session\",\n \"def is_logged_in():\\n logged_in = \\\"uid\\\" in session\\n if logged_in:\\n user = api.user.get_user(uid=session[\\\"uid\\\"])\\n if not user or (user.get(\\\"disabled\\\", False) is True):\\n logout()\\n return False\\n return logged_in\",\n \"def is_logged_in(self):\\n return self.cookie is not None\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def is_logged_in(self):\\n return self.router.token is not None\",\n \"def check_user_logged():\\n global user\\n if 'user' not in session:\\n return False\\n else:\\n user = session.get('user')\\n return user['username'] != ''\",\n \"def is_logged_in():\\n _has_cookie = util.web.has_cookie('pass')\\n if _has_cookie:\\n _is_expired = util.web.is_cookie_expired('pass')\\n if _is_expired:\\n return False\\n return True\\n return False\",\n \"def is_logged_in(self, username):\\n if username in self.users:\\n return self.users[username].is_logged\\n return False\",\n \"def is_logged_in() -> bool:\\n is_dev_login_disabled = SETTINGS.DEV_LOGIN_DISABLED and is_localhost()\\n return bool(is_dev_login_disabled or is_logged_in_user())\",\n \"def is_logged_in(self, params):\\n email = self.credentials.get('email', '')\\n password = self.credentials.get('password', '')\\n if email != '' and password != '':\\n return False\\n return self.netflix_session.is_logged_in(account=self.credentials)\",\n \"def is_authenticated(self):\\n return self.user is not None and self.state == AuthenticationOptions.authenticated\",\n \"def logged_in(self):\\n return self._token is not None\",\n \"def user_in_session():\\n return 'user_id' in login_session\",\n \"def authenticated():\\n if 'user_id' in session and 'access_token' in session:\\n user = db_session.query(User).filter_by(id=session['user_id']).first()\\n\\n if user:\\n return user.access_token == session['access_token']\\n return False\",\n \"def is_user_authenticated(user):\\r\\n\\r\\n if DJ_VERSION[0] > 1:\\r\\n return user.is_authenticated\\r\\n else:\\r\\n return user.is_authenticated()\",\n \"def logged():\\n if session.login==1:\\n return True\\n else:\\n return False\",\n \"def is_authenticated(self):\\n result = self.lpass(\\\"lpass status\\\")\\n\\n if \\\"Logged in as\\\" in result.output:\\n return True\\n\\n return False\",\n \"def is_authenticated(self):\\n return bool(get_auth_token())\",\n \"def is_user_authenticated(request):\\n return request.session.session_key\",\n \"def is_authenticated(self):\\n return True #self.authenticated\",\n \"def is_authenticated(self, request, **kwargs):\\r\\n return True\",\n \"def is_authenticated(self):\\r\\n return self.authenticated\",\n \"def is_regular_user(user):\\n return user.is_authenticated()\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_authenticated(self):\\n return self.authenticated\",\n \"def is_logged_in(self, login_url: str) -> bool:\\n self.d('Check login - %s', login_url)\\n if not login_url:\\n return False\\n res = self.get(login_url, allow_redirects=False)\\n if res.status_code == 302:\\n self.i('Is logged in')\\n return True\\n self.i('Is not logged in')\\n return False\",\n \"def is_authenticated(self):\\n return bool(self._authenticated)\",\n \"def validUser(self):\\n if self.state == SessionStates.LOGGED_OUT:\\n return False\\n\\n # if self.user == None:\\n # return False\\n return True\",\n \"def is_authenticated(self) -> bool:\\n return self._authenticated\",\n \"def require_login(self):\\n\\tif users.get_current_user():\\n\\t return True\\n\\telse:\\n\\t self.redirect(users.create_login_url(self.request.uri))\\n\\t return False\",\n \"def is_valid(self):\\n return self.user.is_authenticated\",\n \"def is_authenticated(self):\\n return self.ping() is not None\",\n \"def test_func(self):\\n if not self.request.user.is_authenticated:\\n return False\\n if self.request.user.is_staff:\\n return True\\n return self.get_user() == self.request.user\",\n \"def is_authenticated_CSC_user():\\n key = SAML_ATTRIBUTES['CSC_username']\\n if executing_travis():\\n return False\\n return True if 'samlUserdata' in session and len(session.get('samlUserdata', None)) > 0 and key in session.get('samlUserdata', None) else False\",\n \"def is_authenticated(self):\\n return True\",\n \"def is_authorized(self) -> bool:\\n\\t\\tif \\\"access_token\\\" in session:\\n\\t\\t\\tif session.get(\\\"access_token\\\") is not None:\\n\\t\\t\\t\\tif \\\"user\\\" in session:\\n\\t\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def is_user_authenticated(self, user_id, auth_token):\\n\\n user = self.user_db[user_id]\\n if user['token'] == auth_token:\\n return True\\n else:\\n return False\",\n \"def user_auth(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n return True\\n return False\",\n \"def is_authenticated(self):\\n return False\",\n \"def is_registered(self):\\n if self.user == getpass.getuser():\\n return True\\n else:\\n return False\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def is_authenticated(self) -> bool:\\n return self.requester.uuid is not None\",\n \"def is_logged_in(self, email):\\n wait_for_element(self.browser, self.expand_left_panel)\\n expand_panel = self.browser.find_element(*self.expand_left_panel)\\n custom_click(self.browser, expand_panel)\\n wait_for(lambda: len(self.browser.find_element(*self.logged_user).text) > 0, delay=1, num_sec=5)\\n user_logged_in = self.browser.find_element(*self.logged_user).text\\n if user_logged_in == email:\\n return True\\n else:\\n raise Exception('Failed to login by user => {}'.format(email))\",\n \"def is_active_user(self):\\n\\n return self.is_active\",\n \"def check(self):\\n\\n us = ServiceLocator.resolve(ServiceLocator.USERS)\\n\\n user_session = self.get()\\n user = self.get_user()\\n\\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)\",\n \"def check_user(self):\\n try:\\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def is_authenticated(self):\\n if self._token is None:\\n self.authenticate()\\n\\n return self._token is not None\",\n \"def authenticated(self):\\n # We don't support authentication yet\\n return False\",\n \"def is_active(self):\\n return self.user.is_active\",\n \"def logged_in(self, use_page=None):\\n # allow page soup to be passed as argument to make get_soup calling this function faster\\n if use_page is None: soup = self.get_soup(\\\"overview\\\")\\n else: soup = use_page\\n\\n found = soup.find(\\\"meta\\\", {\\\"name\\\": \\\"ogame-player-name\\\"})\\n if found is None: return False\\n if str(found[\\\"content\\\"]) == self.username: return True\",\n \"def is_logged_in_user(consumer=None) -> bool:\\n if not consumer:\\n consumer = get_consumer()\\n return bool(\\n not consumer.get(\\\"X-Anonymous-Consumer\\\") and consumer.get(\\\"X-Consumer-Id\\\")\\n )\",\n \"def authenticated(self):\\n return self.token is not None\",\n \"def is_logged_in(session):\\n if 'tid' in session:\\n return {'success': 1, 'message': '你处于登录状态.', \\n 'teamname': session['teamname'], 'is_zju_user': session['is_zju_user']}\\n else:\\n return {\\\"success\\\": 0, \\\"message\\\": \\\"你并未处于登录状态.\\\"}\",\n \"def logged_in(browser: RoboBrowser):\\n login_div = browser.find('div', content=\\\"Login\\\")\\n return True if not login_div else False\",\n \"def acctLogin(self):\\n loggedIn = False\\n if self.acctObj.login():\\n loggedIn = True\\n else:\\n logger.warning(\\\"{} Authentication failed\\\".format(self))\\n self.acctObj = None\\n\\n if loggedIn:\\n return True\\n return False\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def _can_login(self):\\n return all([self.user.is_active, self.status, self.status_detail == \\\"active\\\"])\",\n \"def is_authenticated(self, request, **kwargs):\\r\\n from django.contrib.auth import get_user_model\\r\\n User = get_user_model()\\r\\n\\r\\n try:\\r\\n username, api_key = self.extract_credentials(request)\\r\\n except ValueError:\\r\\n return self._unauthorized()\\r\\n\\r\\n if not username or not api_key:\\r\\n return self._unauthorized()\\r\\n\\r\\n try:\\r\\n user = User.objects.get_by_natural_key(username)\\r\\n except (User.DoesNotExist, User.MultipleObjectsReturned):\\r\\n return self._unauthorized()\\r\\n\\r\\n if not self.check_active(user):\\r\\n return False\\r\\n\\r\\n request.user = user\\r\\n return self.get_key(user, api_key)\",\n \"def user_logged_in():\\n if not session.get('user_id'):\\n return \\\"nope\\\", 401\\n else:\\n return \\\"yep\\\", 200\",\n \"def is_authenticated(self, request, **kwargs):\\r\\n if not request.META.get('HTTP_AUTHORIZATION'):\\r\\n return self._unauthorized()\\r\\n\\r\\n try:\\r\\n (auth_type, data) = request.META['HTTP_AUTHORIZATION'].split()\\r\\n if auth_type.lower() != 'basic':\\r\\n return self._unauthorized()\\r\\n user_pass = base64.b64decode(data)\\r\\n except:\\r\\n return self._unauthorized()\\r\\n\\r\\n bits = user_pass.split(':', 1)\\r\\n\\r\\n if len(bits) != 2:\\r\\n return self._unauthorized()\\r\\n\\r\\n if self.backend:\\r\\n user = self.backend.authenticate(\\r\\n username=bits[0], password=bits[1])\\r\\n else:\\r\\n user = authenticate(username=bits[0], password=bits[1])\\r\\n\\r\\n if user is None:\\r\\n return self._unauthorized()\\r\\n\\r\\n if not self.check_active(user):\\r\\n return False\\r\\n\\r\\n request.user = user\\r\\n return True\",\n \"def is_authenticated(connection, window_info, kwargs):\\n return window_info and window_info.is_authenticated\",\n \"def check_user_and_login(self) -> Response:\\n pass\",\n \"def is_authenticated(self):\\n if not self.token:\\n return False\\n\\n try:\\n self.lookup_token()\\n return True\\n except Forbidden:\\n return False\\n except InvalidPath:\\n return False\\n except InvalidRequest:\\n return False\",\n \"def log_in(self, password: str) -> bool:\\n return self._password == password\",\n \"def check_login(request, username=None):\\r\\n if request.user is None:\\r\\n return False\\r\\n\\r\\n # if we have a username we're told to check against, make sure the\\r\\n # username matches\\r\\n if username is not None and username != request.user.username:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def is_active(self):\\n return self.status == ACTIVE_USER\",\n \"def has_user(self):\\n\\t\\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0\",\n \"def loginState(self, user_data):\\n\\t\\tif self.db.request(\\\"getOne\\\", user_data):\\n\\t\\t\\treturn True;\\n\\t\\telse:\\n\\t\\t\\treturn False;\",\n \"def is_logged_in(soup):\\n logged_in = False\\n login_link = soup.findAll(\\\"a\\\",{\\\"accesskey\\\":\\\"x\\\"})\\n if len(login_link) > 0: \\n link = login_link[0].attrs['href'] \\n if link.find(\\\"logout\\\") > 0:\\n logged_in = True\\n return logged_in\",\n \"def check_user(self,username, password):\\n safe_input = (username, password)\\n vals = self.cur.execute(\\\"SELECT Username, Password FROM Users WHERE Username=? AND Password=?\\\",safe_input).fetchone()\\n if vals:\\n logging.info('%s was authenticated', username)\\n return True\\n else:\\n logging.info('Failed login for %s', username)\\n return False\",\n \"def check_authentication():\\r\\n\\r\\n #TODO: Reservation based authentication\\r\\n try:\\r\\n authenticated_user()\\r\\n except Exception as e:\\r\\n return e\\r\\n\\r\\n return True\",\n \"def is_correct_user(self, login, password):\\n pass\",\n \"def is_logged_in (self, account):\\n is_logged_in = False\\n # load cookies\\n account_hash = self._generate_account_hash(account=account)\\n if self._load_cookies(filename=self.cookie_path + '_' + account_hash) == False:\\n return False\\n if self._load_data(filename=self.data_path + '_' + account_hash) == False:\\n # load the profiles page (to verify the user)\\n response = self._session_get(component='profiles')\\n\\n # parse out the needed inline information\\n only_script_tags = SoupStrainer('script')\\n page_soup = BeautifulSoup(response.text, 'html.parser', parse_only=only_script_tags)\\n page_data = self._parse_page_contents(page_soup=page_soup)\\n\\n # check if the cookie is still valid\\n for item in page_data:\\n if 'profilesList' in dict(item).keys():\\n if item['profilesList']['summary']['length'] >= 1:\\n is_logged_in = True\\n return is_logged_in\\n return True\",\n \"def has_object_permission(self, request, view, account):\\n if request.user.is_authenticated():\\n if request.user.is_staff:\\n return True\\n return account.username == request.user.username\\n return False\",\n \"def is_authenticated(self, request, **kwargs):\\n logging.debug(request.META)\\n if not request.META.get('HTTP_AUTHORIZATION'):\\n logging.error('HTTP_AUTHORIZATION not found')\\n return self._unauthorized()\\n\\n try:\\n logging.debug('start split')\\n (auth_type, data) = request.META['HTTP_AUTHORIZATION'].split()\\n if auth_type.lower() != 'basic':\\n logging.error('not basic')\\n return self._unauthorized()\\n user_pass = base64.b64decode(data).decode('utf-8')\\n except:\\n logging.error('plit issue')\\n return self._unauthorized()\\n\\n bits = user_pass.split(':', 1)\\n logging.debug(bits)\\n if len(bits) != 2:\\n logging.error('len bits issue')\\n return self._unauthorized()\\n\\n if self.backend:\\n user = self.backend.authenticate(username=bits[0], password=bits[1])\\n else:\\n user = authenticate(username=bits[0], password=bits[1])\\n\\n if user is None:\\n logging.debug('username password is not correct.')\\n return self._unauthorized()\\n\\n if not self.check_active(user):\\n logging.debug('user is not active')\\n return False\\n request.user = user\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.88950294","0.8790608","0.8753903","0.8581064","0.8567044","0.84687513","0.84203804","0.8413966","0.8383255","0.8209496","0.82089865","0.8092678","0.80382335","0.8021618","0.79841244","0.7928396","0.7890733","0.78188694","0.77794355","0.7776155","0.77729654","0.77462757","0.7742831","0.76829624","0.7680131","0.76035655","0.7601533","0.7595474","0.757267","0.75625646","0.74810857","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7467262","0.7443127","0.73699677","0.73590964","0.73545504","0.7349122","0.7270624","0.72696877","0.7243561","0.7232548","0.7187476","0.7183464","0.7171943","0.71561635","0.7140426","0.71366656","0.7119026","0.7116422","0.70415527","0.70003116","0.6989533","0.6988987","0.6959796","0.691174","0.69069386","0.68759996","0.6860933","0.6851553","0.6811386","0.6789085","0.67846954","0.6782115","0.6781466","0.6765767","0.6749032","0.67484796","0.6746666","0.67227983","0.6682899","0.66697824","0.66676223","0.6652874","0.66503","0.66355574","0.6625005","0.65868074","0.6575347","0.6555238","0.65547025","0.6550267","0.6548364"],"string":"[\n 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False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_admin(user):\n return user.is_authenticated and user.id == app.config.get('ADMIN')","def is_admin_user(self):\n if \"is_admin\" in self._properties and self.is_admin == 'YES':\n return True\n return False","def isAdmin():\n\tif 'username' in session and session['username'] == 'admin':\n\t\treturn True\n\telse:\n\t\treturn False","def is_admin(self):\n if not self.current_user:\n return False\n else:\n return self.current_user in [\"1\"]","def user_is_admin(user):\n return user in admins","def isAdmin(self, user):\r\n if user.id in self.admins:\r\n return True\r\n return False","def is_admin(self, user):\n return user.name in self.admins","def is_admin(self):\n if self.user is None:\n return False\n if unicode(self.user._id) in self.barcamp.admins:\n return True\n if self.user.is_admin:\n return True\n return False","def check_is_admin(current_user):\n return current_user['isAdmin'] == True","def is_current_user_admin():\n return (os.environ.get('USER_IS_ADMIN', '0')) == \"1\"","def is_admin(self) -> bool:\n return self._is_admin","def isAdmin(user):\n return isUserType(user, Admin)","def is_admin(self):\r\n return self.admin","def is_admin(self):\n return self.admin","def is_admin(self,user):\n if user.is_superuser:\n return True\n\n if user.groups.filter(name=self.admin_group_name).count() > 0:\n return True\n else:\n return False","def admin_flag(user_id):\n user = User.query.filter_by(id=user_id).first()\n if user.is_admin:\n return True\n return False","def is_admin(ctx) -> bool:\n return db.user(ctx.author).is_admin","def is_admin():\n user = users.get_current_user()\n if user:\n if users.is_current_user_admin() or is_local_admin():\n return 'Yes, you are admin'\n else:\n return \"No, you don't admin\"\n else:\n return \"You not logged in\"","def is_admin(self, user) -> bool:\n return (\n user.is_superuser\n or user.groups.filter(pk=self.admins_group.pk).exists()\n )","def check_admin() -> bool:\n return ctypes.windll.shell32.IsUserAnAdmin() == 1","def is_user_admin(request):\n return request.user.is_superuser","def is_admin(self):\n return self._is_admin","def is_admin(self):\n return self._is_admin","def is_admin(self) -> bool:\n\n return current_app.config[\"AUTH_ROLE_ADMIN\"] in [\n role.name for role in self.get_user_roles()\n ]","def is_user_admin(self, user):\n return user == self.created_by","def is_admin(username: str) -> bool:\n db = get_db()\n return int(db.get_user_by_name(username)[\"is_admin\"]) == 1","def is_admin(self):\n if self.is_main_admin:\n return True\n if self.user is not None and self.barcamp is not None:\n if unicode(self.user._id) in self.barcamp.admins:\n return True\n return False","def get_is_admin(self, obj):\n try:\n user = self.context.get('request').user\n except Exception:\n # raise serializers.ValidationError('Could not access request.user')\n return False\n if user == obj.admin:\n return True\n else:\n return False","def is_main_admin(self):\n if self.user is None:\n return False\n return self.user.has_permission(\"admin\")","def is_admin():\n # TODO(felipemonteiro): Make this more robust via a context is admin\n # lookup.\n return CONF.patrole.rbac_test_role == CONF.identity.admin_role","def admin(self):\n if self.is_admin:\n return True\n return False","def is_superuser(self):\n sesh = self.get_session()\n return sesh.curr_role == 'admin'","def user_is_admin(userobj):\n from .node import Node\n from .subject import Subject\n from .period import Period\n from .assignment import Assignment\n return user_is_basenodeadmin(userobj, Node, Subject, Period, Assignment)","def is_admin(user):\n return user.groups.filter(name='Profesores').exists()","def is_admin(self):\n return Role.query.get(2) in self.roles","def is_admin(self, username):\n con = dbcon()\n cur = con.cursor()\n cur.execute(\"SELECT * FROM my_users WHERE username=%(username)s\",\\\n {\"username\":username})\n res = cur.fetchone()\n if res[5].lower() == 'admin':\n return True\n return False","def check_admin(self, user: TelegramController.User = None, id: str = None):\n\n if id == None:\n id = user.id\n\n return md5((str(id) + \"admin\").encode()).hexdigest() in self.__admins","def is_admin():\n if platform_is(WINDOWS):\n return windll.shell32.IsUserAnAdmin()\n return os.getuid() == 0","def check_admin_session(self):\n for session in vms.get_vm_sessions(vm_name=self.vm_name):\n if (\n session.get_console_user()\n and\n session.get_user().get_user_name().startswith(\"admin\")\n ):\n return True\n return False","def is_admin(user):\n return get_organisations_as_admin(user).count() > 0","def is_not_admin(user):\n return not user.is_superuser","def is_admin(self):\n return False","def admin_user_exists(self):\n try:\n User.objects.get(username='admin')\n except User.DoesNotExist:\n return False\n\n return True","def isAdmin(self, nick):\n\t\tif nick in self.config[\"admins\"]:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","def is_admin(author):\n if str(author).lower() in config[\"admins\"]:\n return True\n return False","def is_superuser(self):\n return self.is_admin","def is_user_cloud_admin(self):\n user = users.get_current_user()\n if not user:\n return False\n try:\n user_info = self.get_by_id(UserInfo, user.email())\n if user_info:\n return user_info.is_user_cloud_admin\n else:\n return False\n except Exception as err:\n logging.exception(err)\n return False","def is_admin(self, username): #WORKS\n done = self.cur.execute(\"SELECT username FROM admins WHERE username=\\\"{}\\\"\".format(username))\n if done == 0: # If query is unsuccessful, username is not an administrator.\n return False\n else:\n return True","def is_accessible(self):\n return current_user.is_authenticated and current_user.role == 'admin'","def is_accessible(self):\n return current_user.is_authenticated and current_user.role == 'admin'","def is_admin(context):\n request = context[\"request\"]\n url = resolve(request.path)\n context['is_admin'] = False\n return url.app_name == 'admin'","def _check_admin_only(self, request):\r\n api_key = request.params.get(self.api_field, None)\r\n\r\n if request.user is None:\r\n user = self.user_fetcher(api_key=api_key)\r\n else:\r\n user = request.user\r\n\r\n if user is not None and user.is_admin:\r\n request.user = user\r\n return True","def is_administrator(self):\n return self.can(Permission.ADMIN)","def is_administrator(self):\n return self.can(Permission.ADMIN)","def check_admin():\n if not current_user.is_admin:\n abort(403)","def check_admin():\n if not current_user.is_admin:\n abort(403)","def check_admin():\r\n if not current_user.is_admin:\r\n abort(403)","def is_administrator(self):\n return self.rol == ProfileRoles.ADMINISTRATOR or self.user.is_staff","def is_admin():\n # type: () -> bool\n current_os_name = os.name\n\n # Works with XP SP2 +\n if current_os_name == \"nt\":\n try:\n return IsUserAnAdmin()\n except Exception:\n raise EnvironmentError(\"Cannot check admin privileges\")\n elif current_os_name == \"posix\":\n # Check for root on Posix\n # os.getuid only exists on postix OSes\n # pylint: disable=E1101 (no-member)\n return os.getuid() == 0\n else:\n raise EnvironmentError(\n \"OS does not seem to be supported for admin check. OS: {}\".format(\n current_os_name\n )\n )","async def is_admin(ctx):\n member = ctx.message.author\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\n if aRole in member.roles or member.id == 715048392408956950: return True","def is_administrator(self):\n return False","def check_admin():\n\tif not current_user.is_admin:\n\t\tabort(403)","def is_staff(self):\r\n return self.is_admin","def is_admin(self, user):\n return (acl.action_allowed(self.request, 'OperatorDashboard', '*') or\n acl.action_allowed(self.request, 'Feed', 'Curate'))","def check_is_admin(cookie):\n return ';admin=true;' in cookie","def IsCorpUserOrAdmin(self):\n user_email = auth_util.GetUserEmail()\n return ((user_email and user_email.endswith('@google.com')) or\n auth_util.IsCurrentUserAdmin())","def user_is_nodeadmin(userobj):\n from .node import Node\n return user_is_basenodeadmin(userobj, Node)","def test_is_admin_user(self):\n admin = User.objects.get(email='testadminuser@test.com')\n self.assertEqual(admin.is_staff, True)","def user_is_admin_or_superadmin(userobj):\n if userobj.is_superuser:\n return True\n else:\n return user_is_admin(userobj)","def is_staff(self):\n return self.is_admin","def is_staff(self):\n return self.is_admin","def check_is_admin(self, wormhole: str, user: int):\n query = \"SELECT 1 FROM wormhole_admin WHERE name = ? AND admin = ?\"\n query_res = self.bot.db_query(query, (wormhole, user))\n return len(query_res) > 0","def is_accessible(self):\n if login.current_user.is_authenticated:\n return login.current_user.is_admin()\n return False","def is_staff(self) -> bool:\n return self.is_admin","def check_is_admin(context):\n\n init()\n # the target is user-self\n target = default_target(context)\n return _ENFORCER.authorize('context_is_admin', target, context)","def login_as_admin():\n users.loginAsUser(\n config.VDC_ADMIN_USER, config.VDC_ADMIN_DOMAIN,\n config.VDC_PASSWORD, filter=False\n )\n return True","def verify_user(self):\n verified = False\n if self.user.role.role_name == \"Admin\":\n verified = True\n\n return verified","def check_is_admin(context):\n init()\n credentials = context.to_policy_values()\n target = credentials\n return _ENFORCER.authorize('admin_required', target, credentials)","def is_staff(self):\n\t\treturn self.is_admin","def admin(ctx):\n return ctx.message.author.permissions_in(ctx.channel).administrator","def user_is_assignmentadmin(userobj):\n from .assignment import Assignment\n return user_is_basenodeadmin(userobj, Assignment)","def is_admin(member: Union[discord.Member, discord.User]) -> bool:\n if not isinstance(member, discord.Member):\n return False\n return any([role.id in config.BOT_ADMIN_ROLES for role in member.roles])","def check_is_admin(context):\n init()\n\n #the target is user-self\n credentials = context.to_dict()\n target = credentials\n\n return policy.check('context_is_admin', target, credentials)","def can_edit_user(user):\n\tu = current_user._get_current_object()\n\treturn u==user or u.is_admin()","def validate_admin(self, request):\n\n self.validate_login(request)\n\n if request.session['id'] not in self.admins:\n handler.logHelper.log_it_visit(request, __name__ + '.validate_admin', authorized=False)\n raise PermissionDenied('You need to be an admin to access this page.')","def is_admin(self, is_admin):\n\n self._is_admin = is_admin","def check_admin(self, *args, **kwargs):\n if not users.is_current_user_admin():\n self.response.write(\n '<div style=\"padding-top: 200px; height:178px; width: 500px; color: white; margin: 0 auto; font-size: 52px; text-align: center; background: url(\\'http://3.bp.blogspot.com/_d_q1e2dFExM/TNWbWrJJ7xI/AAAAAAAAAjU/JnjBiTSA1xg/s1600/Bank+Vault.jpg\\')\">Forbidden Access <a style=\\'color: white;\\' href=\\'%s\\'>Login</a></div>' %\n users.create_login_url(self.request.path_url + self.request.query_string))\n return\n else:\n return handler(self, *args, **kwargs)","def user_is_periodadmin(userobj):\n from .period import Period\n return user_is_basenodeadmin(userobj, Period)","def get_editable(self, user):\n return user.get('role') == 'admin'","def has_super_access():\n current_user = frappe.get_doc('User', frappe.session.user)\n roles = set([role.role for role in current_user.roles])\n return bool(roles & {'Administrator', 'Instructor', 'Education Manager', 'System Manager', 'Academic User'})","def has_access_to_admin_console(self):\n return self.is_superuser or self.has_perm('user.can_view_admin_console')","async def assert_user_is_admin(auth: Auth, requester: Requester) -> None:\n is_admin = await auth.is_server_admin(requester)\n if not is_admin:\n raise AuthError(HTTPStatus.FORBIDDEN, \"You are not a server admin\")","def authorized(self, user):\n\n return self.admin.id.getUnhashed() == user.id.getUnhashed()","def has_admins(cls, server, bucket=None):\n\t\tres = cls.find_on({'type': 'user', 'admin': True}, server, bucket)\n\t\treturn True if res and len(res) > 0 else False","def test_func(self, user):\n return self.get_object().admin == user","def is_staff(self):\n # Simplest possible answer: All admins are staff\n return self.is_admin","def is_staff(self):\n # Simplest possible answer: All admins are staff\n return self.is_admin","def is_staff(self):\n # Simplest possible answer: All admins are staff\n return self.is_admin","def get_is_admin():\n try:\n return ctypes.windll.shell32.IsUserAnAdmin()\n except:\n return \"Could not get the UAC level.\"","def is_user_allowed(self, user):\n return user.is_staff"],"string":"[\n \"def is_admin(user):\\n return user.is_authenticated and user.id == app.config.get('ADMIN')\",\n \"def is_admin_user(self):\\n if \\\"is_admin\\\" in self._properties and self.is_admin == 'YES':\\n return True\\n return False\",\n \"def isAdmin():\\n\\tif 'username' in session and session['username'] == 'admin':\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"def is_admin(self):\\n if not self.current_user:\\n return False\\n else:\\n return self.current_user in [\\\"1\\\"]\",\n \"def user_is_admin(user):\\n return user in admins\",\n \"def isAdmin(self, user):\\r\\n if user.id in self.admins:\\r\\n return True\\r\\n return False\",\n \"def is_admin(self, user):\\n return user.name in self.admins\",\n \"def is_admin(self):\\n if self.user is None:\\n return False\\n if unicode(self.user._id) in self.barcamp.admins:\\n return True\\n if self.user.is_admin:\\n return True\\n return False\",\n \"def check_is_admin(current_user):\\n return current_user['isAdmin'] == True\",\n \"def is_current_user_admin():\\n return (os.environ.get('USER_IS_ADMIN', '0')) == \\\"1\\\"\",\n \"def is_admin(self) -> bool:\\n return self._is_admin\",\n \"def isAdmin(user):\\n return isUserType(user, Admin)\",\n \"def is_admin(self):\\r\\n return self.admin\",\n \"def is_admin(self):\\n return self.admin\",\n \"def is_admin(self,user):\\n if user.is_superuser:\\n return True\\n\\n if user.groups.filter(name=self.admin_group_name).count() > 0:\\n return True\\n else:\\n return False\",\n \"def admin_flag(user_id):\\n user = User.query.filter_by(id=user_id).first()\\n if user.is_admin:\\n return True\\n return False\",\n \"def is_admin(ctx) -> bool:\\n return db.user(ctx.author).is_admin\",\n \"def is_admin():\\n user = users.get_current_user()\\n if user:\\n if users.is_current_user_admin() or is_local_admin():\\n return 'Yes, you are admin'\\n else:\\n return \\\"No, you don't admin\\\"\\n else:\\n return \\\"You not logged in\\\"\",\n \"def is_admin(self, user) -> bool:\\n return (\\n user.is_superuser\\n or user.groups.filter(pk=self.admins_group.pk).exists()\\n )\",\n \"def check_admin() -> bool:\\n return ctypes.windll.shell32.IsUserAnAdmin() == 1\",\n \"def is_user_admin(request):\\n return request.user.is_superuser\",\n \"def is_admin(self):\\n return self._is_admin\",\n \"def is_admin(self):\\n return self._is_admin\",\n \"def is_admin(self) -> bool:\\n\\n return current_app.config[\\\"AUTH_ROLE_ADMIN\\\"] in [\\n role.name for role in self.get_user_roles()\\n ]\",\n \"def is_user_admin(self, user):\\n return user == self.created_by\",\n \"def is_admin(username: str) -> bool:\\n db = get_db()\\n return int(db.get_user_by_name(username)[\\\"is_admin\\\"]) == 1\",\n \"def is_admin(self):\\n if self.is_main_admin:\\n return True\\n if self.user is not None and self.barcamp is not None:\\n if unicode(self.user._id) in self.barcamp.admins:\\n return True\\n return False\",\n \"def get_is_admin(self, obj):\\n try:\\n user = self.context.get('request').user\\n except Exception:\\n # raise serializers.ValidationError('Could not access request.user')\\n return False\\n if user == obj.admin:\\n return True\\n else:\\n return False\",\n \"def is_main_admin(self):\\n if self.user is None:\\n return False\\n return self.user.has_permission(\\\"admin\\\")\",\n \"def is_admin():\\n # TODO(felipemonteiro): Make this more robust via a context is admin\\n # lookup.\\n return CONF.patrole.rbac_test_role == CONF.identity.admin_role\",\n \"def admin(self):\\n if self.is_admin:\\n return True\\n return False\",\n \"def is_superuser(self):\\n sesh = self.get_session()\\n return sesh.curr_role == 'admin'\",\n \"def user_is_admin(userobj):\\n from .node import Node\\n from .subject import Subject\\n from .period import Period\\n from .assignment import Assignment\\n return user_is_basenodeadmin(userobj, Node, Subject, Period, Assignment)\",\n \"def is_admin(user):\\n return user.groups.filter(name='Profesores').exists()\",\n \"def is_admin(self):\\n return Role.query.get(2) in self.roles\",\n \"def is_admin(self, username):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM my_users WHERE username=%(username)s\\\",\\\\\\n {\\\"username\\\":username})\\n res = cur.fetchone()\\n if res[5].lower() == 'admin':\\n return True\\n return False\",\n \"def check_admin(self, user: TelegramController.User = None, id: str = None):\\n\\n if id == None:\\n id = user.id\\n\\n return md5((str(id) + \\\"admin\\\").encode()).hexdigest() in self.__admins\",\n \"def is_admin():\\n if platform_is(WINDOWS):\\n return windll.shell32.IsUserAnAdmin()\\n return os.getuid() == 0\",\n \"def check_admin_session(self):\\n for session in vms.get_vm_sessions(vm_name=self.vm_name):\\n if (\\n session.get_console_user()\\n and\\n session.get_user().get_user_name().startswith(\\\"admin\\\")\\n ):\\n return True\\n return False\",\n \"def is_admin(user):\\n return get_organisations_as_admin(user).count() > 0\",\n \"def is_not_admin(user):\\n return not user.is_superuser\",\n \"def is_admin(self):\\n return False\",\n \"def admin_user_exists(self):\\n try:\\n User.objects.get(username='admin')\\n except User.DoesNotExist:\\n return False\\n\\n return True\",\n \"def isAdmin(self, nick):\\n\\t\\tif nick in self.config[\\\"admins\\\"]:\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def is_admin(author):\\n if str(author).lower() in config[\\\"admins\\\"]:\\n return True\\n return False\",\n \"def is_superuser(self):\\n return self.is_admin\",\n \"def is_user_cloud_admin(self):\\n user = users.get_current_user()\\n if not user:\\n return False\\n try:\\n user_info = self.get_by_id(UserInfo, user.email())\\n if user_info:\\n return user_info.is_user_cloud_admin\\n else:\\n return False\\n except Exception as err:\\n logging.exception(err)\\n return False\",\n \"def is_admin(self, username): #WORKS\\n done = self.cur.execute(\\\"SELECT username FROM admins WHERE username=\\\\\\\"{}\\\\\\\"\\\".format(username))\\n if done == 0: # If query is unsuccessful, username is not an administrator.\\n return False\\n else:\\n return True\",\n \"def is_accessible(self):\\n return current_user.is_authenticated and current_user.role == 'admin'\",\n \"def is_accessible(self):\\n return current_user.is_authenticated and current_user.role == 'admin'\",\n \"def is_admin(context):\\n request = context[\\\"request\\\"]\\n url = resolve(request.path)\\n context['is_admin'] = False\\n return url.app_name == 'admin'\",\n \"def _check_admin_only(self, request):\\r\\n api_key = request.params.get(self.api_field, None)\\r\\n\\r\\n if request.user is None:\\r\\n user = self.user_fetcher(api_key=api_key)\\r\\n else:\\r\\n user = request.user\\r\\n\\r\\n if user is not None and user.is_admin:\\r\\n request.user = user\\r\\n return True\",\n \"def is_administrator(self):\\n return self.can(Permission.ADMIN)\",\n \"def is_administrator(self):\\n return self.can(Permission.ADMIN)\",\n \"def check_admin():\\n if not current_user.is_admin:\\n abort(403)\",\n \"def check_admin():\\n if not current_user.is_admin:\\n abort(403)\",\n \"def check_admin():\\r\\n if not current_user.is_admin:\\r\\n abort(403)\",\n \"def is_administrator(self):\\n return self.rol == ProfileRoles.ADMINISTRATOR or self.user.is_staff\",\n \"def is_admin():\\n # type: () -> bool\\n current_os_name = os.name\\n\\n # Works with XP SP2 +\\n if current_os_name == \\\"nt\\\":\\n try:\\n return IsUserAnAdmin()\\n except Exception:\\n raise EnvironmentError(\\\"Cannot check admin privileges\\\")\\n elif current_os_name == \\\"posix\\\":\\n # Check for root on Posix\\n # os.getuid only exists on postix OSes\\n # pylint: disable=E1101 (no-member)\\n return os.getuid() == 0\\n else:\\n raise EnvironmentError(\\n \\\"OS does not seem to be supported for admin check. OS: {}\\\".format(\\n current_os_name\\n )\\n )\",\n \"async def is_admin(ctx):\\n member = ctx.message.author\\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\\n if aRole in member.roles or member.id == 715048392408956950: return True\",\n \"def is_administrator(self):\\n return False\",\n \"def check_admin():\\n\\tif not current_user.is_admin:\\n\\t\\tabort(403)\",\n \"def is_staff(self):\\r\\n return self.is_admin\",\n \"def is_admin(self, user):\\n return (acl.action_allowed(self.request, 'OperatorDashboard', '*') or\\n acl.action_allowed(self.request, 'Feed', 'Curate'))\",\n \"def check_is_admin(cookie):\\n return ';admin=true;' in cookie\",\n \"def IsCorpUserOrAdmin(self):\\n user_email = auth_util.GetUserEmail()\\n return ((user_email and user_email.endswith('@google.com')) or\\n auth_util.IsCurrentUserAdmin())\",\n \"def user_is_nodeadmin(userobj):\\n from .node import Node\\n return user_is_basenodeadmin(userobj, Node)\",\n \"def test_is_admin_user(self):\\n admin = User.objects.get(email='testadminuser@test.com')\\n self.assertEqual(admin.is_staff, True)\",\n \"def user_is_admin_or_superadmin(userobj):\\n if userobj.is_superuser:\\n return True\\n else:\\n return user_is_admin(userobj)\",\n \"def is_staff(self):\\n return self.is_admin\",\n \"def is_staff(self):\\n return self.is_admin\",\n \"def check_is_admin(self, wormhole: str, user: int):\\n query = \\\"SELECT 1 FROM wormhole_admin WHERE name = ? AND admin = ?\\\"\\n query_res = self.bot.db_query(query, (wormhole, user))\\n return len(query_res) > 0\",\n \"def is_accessible(self):\\n if login.current_user.is_authenticated:\\n return login.current_user.is_admin()\\n return False\",\n \"def is_staff(self) -> bool:\\n return self.is_admin\",\n \"def check_is_admin(context):\\n\\n init()\\n # the target is user-self\\n target = default_target(context)\\n return _ENFORCER.authorize('context_is_admin', target, context)\",\n \"def login_as_admin():\\n users.loginAsUser(\\n config.VDC_ADMIN_USER, config.VDC_ADMIN_DOMAIN,\\n config.VDC_PASSWORD, filter=False\\n )\\n return True\",\n \"def verify_user(self):\\n verified = False\\n if self.user.role.role_name == \\\"Admin\\\":\\n verified = True\\n\\n return verified\",\n \"def check_is_admin(context):\\n init()\\n credentials = context.to_policy_values()\\n target = credentials\\n return _ENFORCER.authorize('admin_required', target, credentials)\",\n \"def is_staff(self):\\n\\t\\treturn self.is_admin\",\n \"def admin(ctx):\\n return ctx.message.author.permissions_in(ctx.channel).administrator\",\n \"def user_is_assignmentadmin(userobj):\\n from .assignment import Assignment\\n return user_is_basenodeadmin(userobj, Assignment)\",\n \"def is_admin(member: Union[discord.Member, discord.User]) -> bool:\\n if not isinstance(member, discord.Member):\\n return False\\n return any([role.id in config.BOT_ADMIN_ROLES for role in member.roles])\",\n \"def check_is_admin(context):\\n init()\\n\\n #the target is user-self\\n credentials = context.to_dict()\\n target = credentials\\n\\n return policy.check('context_is_admin', target, credentials)\",\n \"def can_edit_user(user):\\n\\tu = current_user._get_current_object()\\n\\treturn u==user or u.is_admin()\",\n \"def validate_admin(self, request):\\n\\n self.validate_login(request)\\n\\n if request.session['id'] not in self.admins:\\n handler.logHelper.log_it_visit(request, __name__ + '.validate_admin', authorized=False)\\n raise PermissionDenied('You need to be an admin to access this page.')\",\n \"def is_admin(self, is_admin):\\n\\n self._is_admin = is_admin\",\n \"def check_admin(self, *args, **kwargs):\\n if not users.is_current_user_admin():\\n self.response.write(\\n '<div style=\\\"padding-top: 200px; height:178px; width: 500px; color: white; margin: 0 auto; font-size: 52px; text-align: center; background: url(\\\\'http://3.bp.blogspot.com/_d_q1e2dFExM/TNWbWrJJ7xI/AAAAAAAAAjU/JnjBiTSA1xg/s1600/Bank+Vault.jpg\\\\')\\\">Forbidden Access <a style=\\\\'color: white;\\\\' href=\\\\'%s\\\\'>Login</a></div>' %\\n users.create_login_url(self.request.path_url + self.request.query_string))\\n return\\n else:\\n return handler(self, *args, **kwargs)\",\n \"def user_is_periodadmin(userobj):\\n from .period import Period\\n return user_is_basenodeadmin(userobj, Period)\",\n \"def get_editable(self, user):\\n return user.get('role') == 'admin'\",\n \"def has_super_access():\\n current_user = frappe.get_doc('User', frappe.session.user)\\n roles = set([role.role for role in current_user.roles])\\n return bool(roles & {'Administrator', 'Instructor', 'Education Manager', 'System Manager', 'Academic User'})\",\n \"def has_access_to_admin_console(self):\\n return self.is_superuser or self.has_perm('user.can_view_admin_console')\",\n \"async def assert_user_is_admin(auth: Auth, requester: Requester) -> None:\\n is_admin = await auth.is_server_admin(requester)\\n if not is_admin:\\n raise AuthError(HTTPStatus.FORBIDDEN, \\\"You are not a server admin\\\")\",\n \"def authorized(self, user):\\n\\n return self.admin.id.getUnhashed() == user.id.getUnhashed()\",\n \"def has_admins(cls, server, bucket=None):\\n\\t\\tres = cls.find_on({'type': 'user', 'admin': True}, server, bucket)\\n\\t\\treturn True if res and len(res) > 0 else False\",\n \"def test_func(self, user):\\n return self.get_object().admin == user\",\n \"def is_staff(self):\\n # Simplest possible answer: All admins are staff\\n return self.is_admin\",\n \"def is_staff(self):\\n # Simplest possible answer: All admins are staff\\n return self.is_admin\",\n \"def is_staff(self):\\n # Simplest possible answer: All admins are staff\\n return self.is_admin\",\n \"def get_is_admin():\\n try:\\n return ctypes.windll.shell32.IsUserAnAdmin()\\n except:\\n return \\\"Could not get the UAC level.\\\"\",\n \"def is_user_allowed(self, user):\\n return user.is_staff\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.88923126\",\n \"0.8880638\",\n \"0.8843605\",\n \"0.87530965\",\n \"0.87412965\",\n \"0.86163646\",\n \"0.8612412\",\n \"0.85848016\",\n \"0.84613305\",\n \"0.8420776\",\n \"0.84073126\",\n \"0.840208\",\n \"0.8374834\",\n \"0.83690375\",\n \"0.835545\",\n \"0.8350487\",\n \"0.8346385\",\n \"0.8346304\",\n \"0.83000356\",\n \"0.8297389\",\n \"0.828466\",\n \"0.8266288\",\n \"0.8266288\",\n \"0.8252766\",\n \"0.8195621\",\n \"0.818433\",\n \"0.81677186\",\n \"0.8152489\",\n \"0.81507516\",\n \"0.81432587\",\n \"0.8110384\",\n \"0.80539495\",\n \"0.7957466\",\n \"0.79545826\",\n \"0.79240423\",\n \"0.7910742\",\n \"0.7864869\",\n \"0.7851695\",\n \"0.7850405\",\n \"0.7845059\",\n \"0.7843409\",\n \"0.7834\",\n \"0.7832082\",\n \"0.77894855\",\n \"0.7776684\",\n \"0.7759556\",\n \"0.77120674\",\n \"0.77060527\",\n \"0.7678168\",\n \"0.7678168\",\n \"0.76574785\",\n \"0.76312387\",\n \"0.7630629\",\n \"0.7630629\",\n \"0.7615884\",\n \"0.7615884\",\n \"0.76106846\",\n \"0.760987\",\n 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get_id(self):\n return self.id"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_user_id(self):\n return self.id_user","def get_id(self):\n return self.user_id","def get_id(self) -> int:\n return self.user_id","def id(self) -> int:\n return self.user.id","def user_id(self):\n return self.status.user[\"id\"]","def get_user_id(self):\n raise NotImplementedError","def get_id(self): \n\t\treturn (self.user_id)","def user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"user_id\")","def user_id(self):\n return self._user_id","def user_id(self):\n return self._user_id","def user_id(self):\n return self._user_id","def user_id(self):\n return self._user_id","def user_id(self):\n return self._user_id","def user_id(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"user_id\")","def user_id(self):\n return json_loads(self.user_json).get('id')","def user_id(self) -> str:\n return self._user_id","def user_id(self) -> str:\n return self._user_id","def user_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_id\")","def get_id(self):\n return self.username","def get_id(self):\n return self.username","def get_id(self):\n return self.username","def user_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_id\")","def user_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_id\")","def get_id(self):\r\n return self.username","def user_id(self):\n # type: () -> string_types\n return self._user_id","def user_id(self):\n return lamin_user_settings().id","def get_user_id():\n user_id = session.get(\"user_id\")\n return user_id if user_id else None","def get_user_id(self, user):\n\n found_user = self.search(user)\n\n if found_user:\n return found_user[\"data\"][0][\"id\"]\n else:\n raise UserNotFound(\"User \" + user + \" not found.\")","def _get_user_id(self, user: Optional[Dict[str, Any]]) -> Optional[str]:\n return user[\"id\"] if user and \"id\" in user else None","def get_user(self):\n return str(self.request.user.id)","def getUserID(self):\n\t\treturn self.UserID","def get_user_id(self, details, response):\n return details[\"user_id\"]","def get_user_id():\n csc_name = get_user_csc_name()\n if csc_name:\n return csc_name\n haka_id = get_user_haka_identifier()\n if haka_id:\n return haka_id\n return None","def getId(self):\n return self.getUserName()","def getId(self):\n return self.getUserName()","def get_userid(self):\n user_id = \"\"\n if self.is_valid():\n user_id = self.__httprequest.session[\"lti_user_id\"]\n return user_id","def get_user_id(self, details, response):\n return response['uid']","def get_current_user_id():\n user = get_current_user()\n return user.pk if user and user.is_authenticated else None","def get_id(self, username):\n\n users_list = self.get_list()\n for user_info in users_list:\n if user_info['username'] == username:\n return user_info['id']\n # return None\n raise UserNotFoundException(\"User {0} not found\".format(username))","def get_user(id):\n pass","async def user_id(\n event,\n user_id: ('user_id', 'Get the id of an other user?', 'user') = None,\n):\n if user_id is None:\n user_id = event.user.id\n \n return str(user_id)","def get_id(self):\n return self.uid","def user_id(self) -> str:\n return self.app_config()[\"metadata.user.id\"]","def get_new_id(self) -> str:\n user = self.get_template(list_entries=False)\n return user.id","def get_user_id(self, details, response):\n return details['username']","def get_userid():\n return _userid()","def get_user_id(self, session, **kwargs):\n return None","def get_user_primary_key(self, request):\r\n try:\r\n return request.user.pk\r\n except AttributeError:\r\n return ''","def userid(self):\n mtool = getToolByName(self.context, 'portal_membership')\n return mtool.getAuthenticatedMember().getId()","def identify(cls, user_id):\n return cls.query.get(user_id)","def user_id(self):\n return text_type(hash(self.username))","def get_user_id():\n return os.getuid()","def get_current_user(self):\n\n if self._user_id:\n return self._user_id\n endpoint = \"/me\"\n response = self._send(endpoint, \"GET\")\n user_id = response.json()[\"id\"]\n self._user_id = user_id\n return user_id","def get_user_id(username):\n if not user_exists(username):\n return -1\n sql = \"SELECT id \" \\\n \"FROM users \" \\\n \"WHERE username=:username\"\n result = db.session.execute(sql, {\"username\": username})\n user_id = result.fetchone()[0]\n return user_id","def get_user_id(self):\r\n message = self.q(css='BODY').text[0].strip()\r\n match = re.search(r' user_id ([^$]+)$', message)\r\n return match.groups()[0] if match else None","def user_id_str(self):\n return str(self.status.user['id'])","def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","def created_by_user_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"created_by_user_id\")","def get_test_id(self):\n test_username = \"testusername\"\n test_user_id = db.session.query(User.id).filter_by(username = test_username).first()\n print(test_user_id)\n return test_user_id","def get_user_id(username):\n # rv = query_db('select user_id from user where username = ?',\n # [username], one=True)\n # return rv[0] if rv else None\n # db = get_db()\n # print \"LOOKHERE UID\"\n rv = mongo.db.users.find_one({'username': username}, {'_id': []})\n print rv['_id']\n return rv['_id'] if rv else None","def get_user_id(username):\n rv = query_db('select user_id from user where username = %s',\n [username], one=True)\n return rv[0] if rv else None","def get_current_user_id(data):\n current_user = api.user.get_current()\n if current_user:\n return current_user.getId()\n return None","def _get_user_id(username):\n user_id = select(u.user_id for u in UserInformationData if u.username == username).first()\n\n return user_id","def get_user_id(username):\n rv = query_db('select user_id from user where username = ?',\n [username], one=True)\n return rv[0] if rv else None","def get_user_id(username):\n rv = query_db('select user_id from user where username = ?',\n [username], one=True)\n return rv[0] if rv else None","def get_user_id(username):\n rv = query_db('select user_id from user where username = ?',\n [username], one=True)\n return rv[0] if rv else None","def get_user_id(self, details, response):\n return response.get(\"sub\")","def get_userid(self):\n return util.kbase_env.user","def get_accessible_user_id(self):\n ### DATABASE CODE GOES HERE\n return 1","def get_identifier(self, request):\n return request.user.username","def get_user_id(self, name):\n if '@' in name:\n params = {\n 'method': 'flickr.people.findByEmail',\n 'find_email': name\n }\n else:\n params = {\n 'method': 'flickr.people.findByUsername',\n 'username': name\n }\n response = self.oauth_session.get(self.API_ENDPOINT, params=params)\n json_response = response.json()\n if json_response['stat'] == 'fail':\n raise FlickrError(json_response['message'])\n return json_response['user']['id']","def get_identifier(self, request):\r\n return request.user.username","def __int__(self):\r\n return self.userid","def created_by_user_id(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"created_by_user_id\")","def get_user_id(username):\n rv = query_db('select user_id from users where username = ?',\n [username], one=True)\n return rv[0] if rv else None","def get_user_id(email):\n try:\n user = session.query(User).filter_by(email=email).one()\n return user.id\n except:\n return None","def get_user_id(username):\n rv = query_db('SELECT user_id FROM users where username = ?', \n [username], one=True)\n return rv[0] if rv else None","def getUserID(email):\r\n try:\r\n user = session.query(User_info).filter_by(email=email).one()\r\n return user.id\r\n except Exception as e:\r\n return None","def get_id(self):\n return self.email","def get_id(self):\n return self.email","def get_user_id(sdk, username):\n users = sdk.users.get_by_username(username)[\"users\"]\n if not users:\n raise UserDoesNotExistError(username)\n return users[0][\"userUid\"]","def _getLoggedinUserId(self):\n securityManager = getSecurityManager()\n return securityManager.getUser()._login","def _get_user_id(self, name):\n try:\n apiResponse = twitchAPI.twitchAPIGet(\"/users\", {\"login\": name}) #Try to get user id from API\n userID = apiResponse[\"data\"][0][\"id\"]\n except (KeyError, APIUnavailable):\n userID = input(\"Please enter the user id of the user: \")\n except IndexError: #If Twitch API does not return user id\n print(\"That user does not exist on Twitch.\")\n userID = False\n return(userID)","def user_huid(self) -> Optional[UUID]:\n return self.user.user_huid","def getUserID(email):\r\n try:\r\n session = DBSession()\r\n return session.query(User).filter_by(email=email).one().id\r\n except:\r\n return None","def get_user_id(email):\n try:\n user = session.query(User).filter_by(email=email).one()\n return user.id\n except Exception as e:\n print 'No user found for ' + email + ': ' + str(e)\n return None","def user_id_from_username(self, username: str) -> int:\n return int(self.user_info_by_username(username).pk)","def get_user_id(session, email):\n try:\n user = session.query(User).filter_by(email=email).one()\n return user.id\n\n except BaseException:\n return None","def get_id(self, email):\n\n query = self._db.User.select(self._db.User.c.email == email)\n query = query.with_only_columns([self._db.User.c.id_, ])\n\n record = query.execute().fetchone()\n return record[0]","def users_user_id_get(userId): # noqa: E501\n base.check_session()\n return _cleanuser(_finduser(userId))","def get_user_id(email):\n try:\n user = session.query(User).filter_by(email=email).one()\n return user.id\n except NoResultFound:\n return -1\n except MultipleResultsFound:\n return -1","def get_userid_profile(db, user_id):\n return db['user'].find_one({'_id': user_id})","def getUserID(email):\n try:\n user = session.query(User).filter_by(email=email).one()\n return user.id\n except:\n return None","def fetch_current_user_id(s):","def _get_user_id(self):\n auth_response = self._slack_client.api_call(\"auth.test\")\n\n if auth_response.get(\"ok\") is not None and auth_response[\"ok\"]:\n bot_id = auth_response[\"user_id\"]\n logger.info(\"Connected to slack with user id: {}\".format(bot_id))\n return bot_id\n else:\n raise PermissionError(auth_response[\"error\"])","def get_userid(email, name):\n user = session.query(User).filter_by(email=email).first()\n\n if user:\n return user.id\n else:\n user = User(email=email, name=name)\n session.add(user)\n session.commit()\n return user.id"],"string":"[\n \"def get_user_id(self):\\n return self.id_user\",\n \"def get_id(self):\\n return self.user_id\",\n \"def get_id(self) -> int:\\n return self.user_id\",\n \"def id(self) -> int:\\n return self.user.id\",\n \"def user_id(self):\\n return self.status.user[\\\"id\\\"]\",\n \"def get_user_id(self):\\n raise NotImplementedError\",\n \"def get_id(self): \\n\\t\\treturn (self.user_id)\",\n \"def user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self):\\n return self._user_id\",\n \"def user_id(self):\\n return self._user_id\",\n \"def user_id(self):\\n return self._user_id\",\n \"def user_id(self):\\n return self._user_id\",\n \"def user_id(self):\\n return self._user_id\",\n \"def user_id(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self):\\n return json_loads(self.user_json).get('id')\",\n \"def user_id(self) -> str:\\n return self._user_id\",\n \"def user_id(self) -> str:\\n return self._user_id\",\n \"def user_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def get_id(self):\\n return self.username\",\n \"def get_id(self):\\n return self.username\",\n \"def get_id(self):\\n return self.username\",\n \"def user_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def user_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_id\\\")\",\n \"def get_id(self):\\r\\n return self.username\",\n \"def user_id(self):\\n # type: () -> string_types\\n return self._user_id\",\n \"def user_id(self):\\n return lamin_user_settings().id\",\n \"def get_user_id():\\n user_id = session.get(\\\"user_id\\\")\\n return user_id if user_id else None\",\n \"def get_user_id(self, user):\\n\\n found_user = self.search(user)\\n\\n if found_user:\\n return found_user[\\\"data\\\"][0][\\\"id\\\"]\\n else:\\n raise UserNotFound(\\\"User \\\" + user + \\\" not found.\\\")\",\n \"def _get_user_id(self, user: Optional[Dict[str, Any]]) -> Optional[str]:\\n return user[\\\"id\\\"] if user and \\\"id\\\" in user else None\",\n \"def get_user(self):\\n return str(self.request.user.id)\",\n \"def getUserID(self):\\n\\t\\treturn self.UserID\",\n \"def get_user_id(self, details, response):\\n return details[\\\"user_id\\\"]\",\n \"def get_user_id():\\n csc_name = get_user_csc_name()\\n if csc_name:\\n return csc_name\\n haka_id = get_user_haka_identifier()\\n if haka_id:\\n return haka_id\\n return None\",\n \"def getId(self):\\n return self.getUserName()\",\n \"def getId(self):\\n return self.getUserName()\",\n \"def get_userid(self):\\n user_id = \\\"\\\"\\n if self.is_valid():\\n user_id = self.__httprequest.session[\\\"lti_user_id\\\"]\\n return user_id\",\n \"def get_user_id(self, details, response):\\n return response['uid']\",\n \"def get_current_user_id():\\n user = get_current_user()\\n return user.pk if user and user.is_authenticated else None\",\n \"def get_id(self, username):\\n\\n users_list = self.get_list()\\n for user_info in users_list:\\n if user_info['username'] == username:\\n return user_info['id']\\n # return None\\n raise UserNotFoundException(\\\"User {0} not found\\\".format(username))\",\n \"def get_user(id):\\n pass\",\n \"async def user_id(\\n event,\\n user_id: ('user_id', 'Get the id of an other user?', 'user') = None,\\n):\\n if user_id is None:\\n user_id = event.user.id\\n \\n return str(user_id)\",\n \"def get_id(self):\\n return self.uid\",\n \"def user_id(self) -> str:\\n return self.app_config()[\\\"metadata.user.id\\\"]\",\n \"def get_new_id(self) -> str:\\n user = self.get_template(list_entries=False)\\n return user.id\",\n \"def get_user_id(self, details, response):\\n return details['username']\",\n \"def get_userid():\\n return _userid()\",\n \"def get_user_id(self, session, **kwargs):\\n return None\",\n \"def get_user_primary_key(self, request):\\r\\n try:\\r\\n return request.user.pk\\r\\n except AttributeError:\\r\\n return ''\",\n \"def userid(self):\\n mtool = getToolByName(self.context, 'portal_membership')\\n return mtool.getAuthenticatedMember().getId()\",\n \"def identify(cls, user_id):\\n return cls.query.get(user_id)\",\n \"def user_id(self):\\n return text_type(hash(self.username))\",\n \"def get_user_id():\\n return os.getuid()\",\n \"def get_current_user(self):\\n\\n if self._user_id:\\n return self._user_id\\n endpoint = \\\"/me\\\"\\n response = self._send(endpoint, \\\"GET\\\")\\n user_id = response.json()[\\\"id\\\"]\\n self._user_id = user_id\\n return user_id\",\n \"def get_user_id(username):\\n if not user_exists(username):\\n return -1\\n sql = \\\"SELECT id \\\" \\\\\\n \\\"FROM users \\\" \\\\\\n \\\"WHERE username=:username\\\"\\n result = db.session.execute(sql, {\\\"username\\\": username})\\n user_id = result.fetchone()[0]\\n return user_id\",\n \"def get_user_id(self):\\r\\n message = self.q(css='BODY').text[0].strip()\\r\\n match = re.search(r' user_id ([^$]+)$', message)\\r\\n return match.groups()[0] if match else None\",\n \"def user_id_str(self):\\n return str(self.status.user['id'])\",\n \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"def created_by_user_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"created_by_user_id\\\")\",\n \"def get_test_id(self):\\n test_username = \\\"testusername\\\"\\n test_user_id = db.session.query(User.id).filter_by(username = test_username).first()\\n print(test_user_id)\\n return test_user_id\",\n \"def get_user_id(username):\\n # rv = query_db('select user_id from user where username = ?',\\n # [username], one=True)\\n # return rv[0] if rv else None\\n # db = get_db()\\n # print \\\"LOOKHERE UID\\\"\\n rv = mongo.db.users.find_one({'username': username}, {'_id': []})\\n print rv['_id']\\n return rv['_id'] if rv else None\",\n \"def get_user_id(username):\\n rv = query_db('select user_id from user where username = %s',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def get_current_user_id(data):\\n current_user = api.user.get_current()\\n if current_user:\\n return current_user.getId()\\n return None\",\n \"def _get_user_id(username):\\n user_id = select(u.user_id for u in UserInformationData if u.username == username).first()\\n\\n return user_id\",\n \"def get_user_id(username):\\n rv = query_db('select user_id from user where username = ?',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def get_user_id(username):\\n rv = query_db('select user_id from user where username = ?',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def get_user_id(username):\\n rv = query_db('select user_id from user where username = ?',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def get_user_id(self, details, response):\\n return response.get(\\\"sub\\\")\",\n \"def get_userid(self):\\n return util.kbase_env.user\",\n \"def get_accessible_user_id(self):\\n ### DATABASE CODE GOES HERE\\n return 1\",\n \"def get_identifier(self, request):\\n return request.user.username\",\n \"def get_user_id(self, name):\\n if '@' in name:\\n params = {\\n 'method': 'flickr.people.findByEmail',\\n 'find_email': name\\n }\\n else:\\n params = {\\n 'method': 'flickr.people.findByUsername',\\n 'username': name\\n }\\n response = self.oauth_session.get(self.API_ENDPOINT, params=params)\\n json_response = response.json()\\n if json_response['stat'] == 'fail':\\n raise FlickrError(json_response['message'])\\n return json_response['user']['id']\",\n \"def get_identifier(self, request):\\r\\n return request.user.username\",\n \"def __int__(self):\\r\\n return self.userid\",\n \"def created_by_user_id(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"created_by_user_id\\\")\",\n \"def get_user_id(username):\\n rv = query_db('select user_id from users where username = ?',\\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def get_user_id(email):\\n try:\\n user = session.query(User).filter_by(email=email).one()\\n return user.id\\n except:\\n return None\",\n \"def get_user_id(username):\\n rv = query_db('SELECT user_id FROM users where username = ?', \\n [username], one=True)\\n return rv[0] if rv else None\",\n \"def getUserID(email):\\r\\n try:\\r\\n user = session.query(User_info).filter_by(email=email).one()\\r\\n return user.id\\r\\n except Exception as e:\\r\\n return None\",\n \"def get_id(self):\\n return self.email\",\n \"def get_id(self):\\n return self.email\",\n \"def get_user_id(sdk, username):\\n users = sdk.users.get_by_username(username)[\\\"users\\\"]\\n if not users:\\n raise UserDoesNotExistError(username)\\n return users[0][\\\"userUid\\\"]\",\n \"def _getLoggedinUserId(self):\\n securityManager = getSecurityManager()\\n return securityManager.getUser()._login\",\n \"def _get_user_id(self, name):\\n try:\\n apiResponse = twitchAPI.twitchAPIGet(\\\"/users\\\", {\\\"login\\\": name}) #Try to get user id from API\\n userID = apiResponse[\\\"data\\\"][0][\\\"id\\\"]\\n except (KeyError, APIUnavailable):\\n userID = input(\\\"Please enter the user id of the user: \\\")\\n except IndexError: #If Twitch API does not return user id\\n print(\\\"That user does not exist on Twitch.\\\")\\n userID = False\\n return(userID)\",\n \"def user_huid(self) -> Optional[UUID]:\\n return self.user.user_huid\",\n \"def getUserID(email):\\r\\n try:\\r\\n session = DBSession()\\r\\n return session.query(User).filter_by(email=email).one().id\\r\\n except:\\r\\n return None\",\n \"def get_user_id(email):\\n try:\\n user = session.query(User).filter_by(email=email).one()\\n return user.id\\n except Exception as e:\\n print 'No user found for ' + email + ': ' + str(e)\\n return None\",\n \"def user_id_from_username(self, username: str) -> int:\\n return int(self.user_info_by_username(username).pk)\",\n \"def get_user_id(session, email):\\n try:\\n user = session.query(User).filter_by(email=email).one()\\n return user.id\\n\\n except BaseException:\\n return None\",\n \"def get_id(self, email):\\n\\n query = self._db.User.select(self._db.User.c.email == email)\\n query = query.with_only_columns([self._db.User.c.id_, ])\\n\\n record = query.execute().fetchone()\\n return record[0]\",\n \"def users_user_id_get(userId): # noqa: E501\\n base.check_session()\\n return _cleanuser(_finduser(userId))\",\n \"def get_user_id(email):\\n try:\\n user = session.query(User).filter_by(email=email).one()\\n return user.id\\n except NoResultFound:\\n return -1\\n except MultipleResultsFound:\\n return -1\",\n \"def get_userid_profile(db, user_id):\\n return db['user'].find_one({'_id': user_id})\",\n \"def getUserID(email):\\n try:\\n user = session.query(User).filter_by(email=email).one()\\n return user.id\\n except:\\n return None\",\n \"def fetch_current_user_id(s):\",\n \"def _get_user_id(self):\\n auth_response = self._slack_client.api_call(\\\"auth.test\\\")\\n\\n if auth_response.get(\\\"ok\\\") is not None and auth_response[\\\"ok\\\"]:\\n bot_id = auth_response[\\\"user_id\\\"]\\n logger.info(\\\"Connected to slack with user id: {}\\\".format(bot_id))\\n return bot_id\\n else:\\n raise PermissionError(auth_response[\\\"error\\\"])\",\n \"def get_userid(email, name):\\n user = session.query(User).filter_by(email=email).first()\\n\\n if user:\\n return user.id\\n else:\\n user = User(email=email, name=name)\\n session.add(user)\\n session.commit()\\n return user.id\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.87912023","0.8627861","0.8567077","0.8435428","0.8355682","0.82900876","0.8260854","0.8231441","0.8231441","0.82138103","0.82138103","0.82138103","0.82138103","0.82138103","0.82041234","0.8196352","0.8126279","0.8126279","0.8086236","0.8086236","0.8080791","0.80761266","0.80761266","0.80761266","0.8062972","0.8039875","0.8039875","0.8039875","0.80239123","0.80071855","0.79140306","0.7910011","0.78571516","0.7794613","0.7771452","0.7726183","0.7688242","0.7682307","0.76499367","0.76499367","0.75971687","0.759037","0.756036","0.75532013","0.74888813","0.74830496","0.7479268","0.74687225","0.7456848","0.74101037","0.7383293","0.7336175","0.7328559","0.7325287","0.73055106","0.73017174","0.728428","0.7263137","0.72628903","0.72549087","0.7239469","0.72363293","0.7233222","0.72272843","0.7222717","0.72158676","0.71981007","0.7195517","0.7189305","0.7189305","0.7189305","0.7179366","0.7156229","0.7149648","0.71459216","0.7141641","0.7132005","0.71245825","0.7086691","0.70813453","0.7075369","0.7072656","0.70664865","0.70620054","0.70620054","0.7012691","0.7009709","0.7004867","0.70023096","0.6999746","0.69848096","0.6981063","0.6980187","0.6979085","0.6974829","0.6973623","0.6961824","0.69615555","0.69606","0.69439876","0.69330126"],"string":"[\n \"0.87912023\",\n \"0.8627861\",\n \"0.8567077\",\n \"0.8435428\",\n \"0.8355682\",\n \"0.82900876\",\n \"0.8260854\",\n \"0.8231441\",\n \"0.8231441\",\n \"0.82138103\",\n \"0.82138103\",\n \"0.82138103\",\n \"0.82138103\",\n \"0.82138103\",\n \"0.82041234\",\n \"0.8196352\",\n \"0.8126279\",\n \"0.8126279\",\n \"0.8086236\",\n \"0.8086236\",\n \"0.8080791\",\n \"0.80761266\",\n \"0.80761266\",\n \"0.80761266\",\n \"0.8062972\",\n \"0.8039875\",\n \"0.8039875\",\n \"0.8039875\",\n \"0.80239123\",\n \"0.80071855\",\n \"0.79140306\",\n \"0.7910011\",\n \"0.78571516\",\n \"0.7794613\",\n \"0.7771452\",\n \"0.7726183\",\n \"0.7688242\",\n \"0.7682307\",\n \"0.76499367\",\n \"0.76499367\",\n \"0.75971687\",\n \"0.759037\",\n \"0.756036\",\n \"0.75532013\",\n \"0.74888813\",\n \"0.74830496\",\n \"0.7479268\",\n \"0.74687225\",\n \"0.7456848\",\n \"0.74101037\",\n \"0.7383293\",\n \"0.7336175\",\n \"0.7328559\",\n \"0.7325287\",\n \"0.73055106\",\n \"0.73017174\",\n \"0.728428\",\n \"0.7263137\",\n \"0.72628903\",\n \"0.72549087\",\n \"0.7239469\",\n \"0.72363293\",\n \"0.7233222\",\n \"0.72272843\",\n \"0.7222717\",\n \"0.72158676\",\n \"0.71981007\",\n \"0.7195517\",\n \"0.7189305\",\n \"0.7189305\",\n \"0.7189305\",\n \"0.7179366\",\n \"0.7156229\",\n \"0.7149648\",\n \"0.71459216\",\n \"0.7141641\",\n \"0.7132005\",\n \"0.71245825\",\n \"0.7086691\",\n \"0.70813453\",\n \"0.7075369\",\n \"0.7072656\",\n \"0.70664865\",\n \"0.70620054\",\n \"0.70620054\",\n \"0.7012691\",\n \"0.7009709\",\n \"0.7004867\",\n \"0.70023096\",\n \"0.6999746\",\n \"0.69848096\",\n \"0.6981063\",\n \"0.6980187\",\n \"0.6979085\",\n \"0.6974829\",\n \"0.6973623\",\n \"0.6961824\",\n \"0.69615555\",\n \"0.69606\",\n \"0.69439876\",\n \"0.69330126\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7197,"cells":{"query":{"kind":"string","value":"Returns the logged in user after adding the wanted user to his followers list"},"document":{"kind":"string","value":"def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def follow_user(cls, user, following):\r\n pass","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def follow_user(cls, user, following):\n pass","def author_following(self):\n\t\tpass","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def actor_user(self):\n if self.actor:\n return User.objects.get(id=self.actor['id'])","def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\n user_followers = list([_InstagramUser(x) for x in followers])\n return user_followers","def follow(self, followerId, followeeId):\n\n # 把 followeeId append到他的 follow 属性中\n if followerId == followeeId: # 不能自己关注自己\n return\n # 实例化一个user(followerID)\n follower = UserInfo()\n follower.user_id = followerId \n follower.follows.append(followeeId) \n self.user_pool[followerId] = follower","def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\n # If no uid was specified, use the authenticated user's uid\n if uid == 0:\n uid = self.uid\n\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\n user_followings = list([_InstagramUser(x) for x in followings])\n return user_followings","def follow(self, followerId: int, followeeId: int) -> None:\n self.user_followed[followerId].append(followeeId)","def follow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$push': {'follows': whom_id}})\n flash('You are now following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\n else:\n redis_obj.delete(str(g.user['_id']))\n print \"Invalidating cache after Follow\"\n return redirect(url_for('user_timeline', username=username))","def follow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are now following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def followUser(following):\n\n cur, user_id, con = initialise(3, True)\n cur.execute(\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\", (user_id, following))\n finish(con)","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId: return\n self.users[followerId].add(followeeId)","def follow(self, followerId: int, followeeId: int) -> None:\n if followerId == followeeId:\n return\n if followerId not in self.users.keys():\n self.users[followerId] = user()\n if followeeId not in self.users.keys():\n self.users[followeeId] = user()\n self.users[followerId].followees[followeeId] = self.users[followeeId]","def get_one_user():","def add_follow(follow_id):\n\n want_to_follow_user = User.query.get_or_404(follow_id)\n if want_to_follow_user.private:\n # =========== NEED TO IMPLEMENT ====================\n # send them a request to follow\n want_to_follow_user.from_users.append(g.user) \n db.session.commit()\n flash(\"Your request has been sent\", \"success\")\n return redirect(f\"/users/{g.user.id}/following\")\n\n g.user.following.append(want_to_follow_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def get_user(self):\n return self.get('users/self')","def get_user(self):\n return self.user","def get_user(self):\n return self.user","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.users:\n self._create_user(followeeId)\n if followerId not in self.users:\n self._create_user(followerId)\n self.users[followerId].add(followeeId)","def getFollowers():\n\n cur, user_id = initialise(3)\n cur.execute(\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\", [user_id])\n tempFollowers = cur.fetchall()\n followers = []\n for follower in tempFollowers:\n followers.append(follower[0])\n return followers","def follow(whomUserName,whoUserName):\n\n whomuser = query_db('select * from user where username = ?',\n [whomUserName], one=True)\n whouser = query_db('select * from user where username = ?',\n [whoUserName], one=True)\n\n\n followed = query_db('''select 1 from follower where\n follower.who_id = ? and follower.whom_id = ?''',\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\n\n if whouser is None:\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\n\n if whomuser is None:\n return jsonify({'message':'User getting followed does not exist yet'}),404\n\n if not followed:\n db = get_db()\n\n db.execute('''insert into follower (\n who_id, whom_id) values (?, ?)''',\n [whouser['user_id'], whomuser['user_id']])\n db.commit()\n flash('Operation successful')\n return jsonify({'message': 'Successfully following'}), 201\n else:\n return jsonify({'message':'Specified user is already following another user'}),403","def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","def follow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.followList.get(followerId, [followerId]):\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\n # print(self.followList)","def follow(self, follower, followee):\n pass","def follow(current_user,user_id):\n if request.method == \"POST\":\n #follee = request.get_json('user_id')\n if User.query.filter_by(userid= user_id):\n follow = Follows(userid =user_id, follower_id =current_user.userid)\n db.session.add(follow)\n db.session.commit()\n return jsonify({'message' :'You are now following'})\n return jsonify({'message' :'User doesnt exist..Try again'})\n return jsonify({'errors' : 'Method Invalid'})","def get_current_user(self):\n user_id = self.get_secure_cookie(\"blogdemo_user\")\n if not user_id: return None\n user = self.db.get(\"SELECT * FROM authors WHERE id = %s\", int(user_id))\n return user","def get_user(self):\n return None","def userFollowers(nick):\n if (len(nick) != 1):\n print \"Has d'introduir només un nick\"\n return\n i.userFollow(nick[0])","def users_followers(user_id):\n\n user = User.query.get_or_404(user_id)\n return render_template('users/followers.html', user=user)","def get_user(self):\n raise NotImplementedError","def follow_friend():\n print \"followuser\"\n username = request.args.get('username')\n print \"JSON Data\", username\n # username= req_data[username]\n whom_id = get_user_id(username)\n print \"whom_id:\", whom_id\n if whom_id is None:\n abort(404)\n follow_query(whom_id)\n flash('You are now following \"%s\"' % username)\n name = {'name of following user': username}\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def author(self):\r\n return self.user","def user(self):\n if self._user is None:\n pk, full_name = self.owner.split(',')\n pk = int(pk)\n self._user = User.objects.get(pk=pk)\n return self._user","def add_following(self, user_id):\n sleep(360) # too much follows => function ban\n self.following.append(user_id)\n return perform_with_ran_delay(self.instagram.follow, user_id)","def me():\n return current_user.get()","async def get_user(event):\n if event.reply_to_msg_id:\n previous_message = await event.get_reply_message()\n replied_user = await event.client(GetFullUserRequest(previous_message.from_id))\n else:\n user = event.pattern_match.group(1)\n if user.isnumeric():\n user = int(user)\n\n if not user:\n self_user = await event.client.get_me()\n user = self_user.id\n\n if event.message.entities is not None:\n probable_user_mention_entity = event.message.entities[0]\n\n if isinstance(probable_user_mention_entity, MessageEntityMentionName):\n user_id = probable_user_mention_entity.user_id\n replied_user = await event.client(GetFullUserRequest(user_id))\n return replied_user\n try:\n user_object = await event.client.get_entity(user)\n replied_user = await event.client(GetFullUserRequest(user_object.id))\n\n except (TypeError, ValueError):\n await event.edit(\"`I don't slap aliens, they ugly AF !!`\")\n return None\n\n return replied_user","async def get_user(event):\n if event.reply_to_msg_id:\n previous_message = await event.get_reply_message()\n replied_user = await event.client(\n GetFullUserRequest(previous_message.sender_id)\n )\n else:\n user = event.pattern_match.group(1)\n\n if user.isnumeric():\n user = int(user)\n\n if not user:\n self_user = await event.client.get_me()\n user = self_user.id\n\n if event.message.entities is not None:\n probable_user_mention_entity = event.message.entities[0]\n\n if isinstance(probable_user_mention_entity, MessageEntityMentionName):\n user_id = probable_user_mention_entity.user_id\n replied_user = await event.client(GetFullUserRequest(user_id))\n return replied_user\n try:\n user_object = await event.client.get_entity(user)\n replied_user = await event.client(GetFullUserRequest(user_object.id))\n\n except (TypeError, ValueError):\n await event.edit(\"`I don't slap aliens, they ugly AF !!`\")\n return None\n\n return replied_user","def target_user(self):\n return self.request.user","def get(self, request):\n # Retrieve the user from the request if they have been authenticated\n current_user = request.user\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n # Return the follower details for the current user\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['MY_FOLLOWERS_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_200_OK\n )","def user(self, uid):","def get_user(self):\n return str(self.request.user.id)","def add_to_following(sender, instance, created, **kwargs):\r\n sender_= instance.sender\r\n receiver_ = instance.receiver\r\n if instance.status == 'accepted':\r\n sender_.following.add(receiver_.user)","def get_user(self):\n if \"user\" not in self._data:\n self._data[\"user\"] = User.objects.get(pk=self.kwargs[\"user_id\"])\n return self._data[\"user\"]","def add_user_to_g():\n \n if CURRENT_USER in session:\n g.user = User.query.get(session[CURRENT_USER])\n\n else:\n g.user = None","def user(self):\n return self.owner.user","def follow(self, user):\n if not self.is_following(user):\n f = Follow(follower=self, followed=user)\n db.session.add(f)","def getToUser(self):\n return self.toUser","def add(self, user: U) -> None:\n ...","def show_following(user_id):\n\n\n user = User.query.get_or_404(user_id)\n return render_template('users/following.html', user=user)","def get_current_user(self):\n user_id = self.get_secure_cookie(\"blogdemo_user\")\n if not user_id: return None\n user = self.db.bucket('authors').get(str(user_id))\n if not user.exists: return None\n return user.data","def add_follow(follow_id):\n followed_user = User.query.get_or_404(follow_id)\n if not g.user or g.user.id == follow_id or followed_user.is_blocking(g.user):\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n g.user.following.append(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def follow(self, followerId, followeeId):\r\n if followerId != followeeId:\r\n self.follows[followerId].add(followeeId)","def get_user(current_user):\n for user in user_db:\n if user['email'] == current_user:\n return user","def user(self):\n pass","def checkUsers(u1, u2):\n if type(u1) == int:\n try:\n user1 = mt.User.objects.filter(Id = u1).get()\n except:\n\n return None\n else:\n user1 = u1\n try:\n user2 = mt.User.objects.filter(Id = u2).get()\n except:\n return user1\n mt.follows(\n followee= user1,\n follower= user2\n ).save()\n return user1","def post(self, request, username):\n\n # Retrieve the user from the user table if the user exists\n try:\n user_details = User.objects.get(username=username)\n current_user = request.user\n # If a user is trying to follow themselves then stop the request\n if user_details.profile.id == current_user.profile.id:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['CANNOT_FOLLOW_SELF']},\n status=status.HTTP_400_BAD_REQUEST\n )\n # Otherwise follow the author the current user has indicated\n current_user.profile.follows.add(user_details.profile)\n\n # notify user of new follower\n send_notifications(request,\n notification_type=\"user_followed\",\n instance=current_user,\n recipients=[user_details])\n\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['USER_FOLLOW_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_201_CREATED\n )\n except User.DoesNotExist:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\n status=status.HTTP_404_NOT_FOUND\n )","def users_followers(user_id):\n\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n user = User.query.get_or_404(user_id)\n users_blocking = [block.user_blocking_id for block in Blocks.query.all() if block.user_being_blocked_id == g.user.id]\n likes = [message for message in user.likes if message.user_id not in users_blocking]\n return render_template('users/followers.html', user=user, likes=likes)","def get_follow(self):\n return self.follow","def following_and_storing(self, user_obj):\n if self.following(user_obj['user']):\n self.monitored_users.append({'user': user_obj['user'], 'username': user_obj['username'],\n 'followDate': datetime.now().timestamp()})","def get_following_by_user(request):\n response, status_code = get_followings(request)\n if status_code != 200:\n return JsonResponse(response, status=status_code)\n serialize_data = FollowingSerializer(response, many=False).data\n return JsonResponse(serialize_data, status=status_code, safe=False)","def followed_by(self, user_id):\n\n url = \"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\".format(user_id, self.access_token)\n\n request = requests.get(url)\n return request.json()","def get_current_user(self):\n return None","def user_following_gql(self, user_id: int, amount: int = 0) -> list:\n user_id = int(user_id)\n end_cursor = None\n users = []\n variables = {\n \"id\": user_id,\n \"include_reel\": True,\n \"fetch_mutual\": False,\n \"first\": 24\n }\n while True:\n if end_cursor:\n variables[\"after\"] = end_cursor\n data = self.public_graphql_request(\n variables, query_hash=\"e7e2f4da4b02303f74f0841279e52d76\"\n )\n if not data[\"user\"] and not users:\n raise UserNotFound(user_id=user_id, **data)\n page_info = json_value(\n data, \"user\", \"edge_follow\", \"page_info\", default={}\n )\n edges = json_value(\n data, \"user\", \"edge_follow\", \"edges\", default=[]\n )\n for edge in edges:\n users.append(extract_user_short(edge[\"node\"]))\n end_cursor = page_info.get(\"end_cursor\")\n if not page_info.get(\"has_next_page\") or not end_cursor:\n break\n if amount and len(users) >= amount:\n break\n # time.sleep(sleep)\n if amount:\n users = users[:amount]\n return users","def getUser(self):\n user = users.get_current_user()\n if not user:\n self.redirect(users.create_login_url(self.request.uri))\n else:\n return user","def get_followers(user):\n if user.has_key('followers_list'):\n pass\n else:\n if user.has_key('followers_count'):\n if user['followers_count'] > 4999:\n pages = user['followers_count'] / 5000\n f_list = []\n for page in range(pages):\n try:\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n f_list = friends_list + f_list\n time.sleep(60)\n user['followers_list'] = f_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(f_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)\n else:\n try:\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\n friends_list = []\n for follower in follower_set:\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\n friends_list.append(follower.GetId())\n user['followers_list'] = friends_list\n twitter_users.update({'id': user['id']}, user)\n print \"\\n\\nGot %s followers out of %s listed\" % (len(friends_list), user['followers_count'])\n except Exception, e:\n print str(e)\n time.sleep(60)","def current_user(self, **kwargs):\n return self.me(**kwargs)","def followed_by(self):\r\n return relationships.FollowedBy(self)","def get_current_user(self):\n return self.current_user","def addUser(self, user):\r\n self.users.append(user)\r\n return len(self.users)-1","def getFollowers():\n followers = []\n next_max_id = True\n while next_max_id:\n # first iteration hack\n if next_max_id is True:\n next_max_id = ''\n\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\n return \"You have currently \"+str(len(followers))+\" Followers on Instagram.\"","def follow_user(self, user):\n self.nav_user(user)\n follow_button = self.driver.find_element_by_xpath(\n \"//button[contains(text(), 'Follow')]\")\n follow_button.click()\n time.sleep(1)\n self.driver.get(self.base_url)","def user_follow_users(self, ids=None, **kwargs):\n return self._put(\n API.MY_FOLLOWING.value, type=\"user\", ids=\",\".join(ids or []), **kwargs\n )","def get_current_user(request): \n user_id = request.session.get('user_id', False)\n \n return Member.objects.get(id=user_id)","def select_user_following(args):\n is_parameter_exists([\n constants.ID\n ], args)\n\n # Request User\n request_user = args[constants.USER]\n\n # Requested User ID\n requested_user_id = args[constants.ID]\n\n # Page Number\n page_number = 1 if constants.PAGE_NUMBER not in args else int(args[constants.PAGE_NUMBER])\n\n # Check User Id\n if not User.objects.filter(id=requested_user_id).exists():\n raise ApiError(constants.NOT_EXIST_OBJECT)\n\n # Following QuerySet\n queryset = UserFollow.objects.filter(following_user=requested_user_id).values_list('followed_user', flat=True)\n\n # User Ids\n user_ids = get_results_from_queryset(queryset, 10, page_number)\n\n # is_finished\n is_finished = not user_ids.has_next()\n\n # Filter Query\n filter_query = Q(id__in=user_ids)\n\n # Users\n users, _, _ = __get_users(filter_query, request_user, 10)\n\n return users, page_number, is_finished","def get_user(request: Request):\n return request.user","def follow(self, followerId, followeeId):\n if followerId in self.follows:\n self.follows[followerId].add(followeeId)\n else:\n self.follows[followerId] = set([followeeId])","def add_user_to_g():\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def get_followings(request):\n user_id = request.GET.get(\"user_id\")\n if not user_id:\n return {\"error\": \"User Id should be provided\"}, 400\n following_data = Following.objects.filter(user_profile_id=user_id, is_active=True).first()\n return following_data, 200","def created_user(self):\n return self._created_user","def get_user_id(self):\n return self.id_user","def owner(self) -> discord.User:\n if self.config.owner_id:\n return self.get_user(self.config.owner_id)\n if self.owner_ids:\n return self.get_user(self.config.owner_ids[0])\n return None","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_object(self):\n return self.request.user","def get_user(id):\n pass","def followers(congressDict, twitterAPI):\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\n least = most\n for name in congressDict:\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\n numFollowers = tempAPI._json['followers_count']\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\n most = tempAPI\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\n least = tempAPI\n return [most._json[\"name\"], least._json[\"name\"]]","def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","def follow_user(self:'InstaClient', user:str, nav_to_user:bool=True):\n # Check User Vadility\n profile = self.get_profile(user)\n if not profile:\n raise InvalidUserError(user)\n\n # Navigate to User Page\n self._nav_user(user, check_user=False)\n \n if self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.MESSAGE_USER_BTN))):\n # User already followed\n pass\n else:\n follow_button = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.FOLLOW_BTN)), url=ClientUrls.NAV_USER.format(user))\n self._press_button(follow_button)\n profile.requested_by_viewer = True\n return profile","def get_object(self):\n return User.objects.get(username=self.request.user.username)","def add_user(self, attrs):\n pass","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def user_follow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n if user_id in self._users_following.get(self.user_id, []):\n self.logger.debug(\"User %s already followed\", user_id)\n return False\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/create/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following.pop(self.user_id) # reset\n return result[\"friendship_status\"][\"following\"] is True","def get_queryset(self, *args, **kwargs):\n following_username = self.kwargs.get(self.look_url_kwarg)\n following_users = FollowUser.objects.filter(\n following_username=following_username)\n\n return following_users","def my_get_user(users_list, user_id):\n for user in users_list:\n if user.get(\"user_id\") == user_id:\n return user\n return None","def user(self):\n return self._push.get('user', None)"],"string":"[\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def actor_user(self):\\n if self.actor:\\n return User.objects.get(id=self.actor['id'])\",\n \"def _user_follower_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followers: List[Dict[str, Any]] = self.api.getTotalFollowers(uid)\\n user_followers = list([_InstagramUser(x) for x in followers])\\n return user_followers\",\n \"def follow(self, followerId, followeeId):\\n\\n # 把 followeeId append到他的 follow 属性中\\n if followerId == followeeId: # 不能自己关注自己\\n return\\n # 实例化一个user(followerID)\\n follower = UserInfo()\\n follower.user_id = followerId \\n follower.follows.append(followeeId) \\n self.user_pool[followerId] = follower\",\n \"def _user_following_info(self, uid: int = 0) -> List[_InstagramUser]:\\n # If no uid was specified, use the authenticated user's uid\\n if uid == 0:\\n uid = self.uid\\n\\n followings: List[Dict[str, Any]] = self.api.getTotalFollowings(uid)\\n user_followings = list([_InstagramUser(x) for x in followings])\\n return user_followings\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n self.user_followed[followerId].append(followeeId)\",\n \"def follow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$push': {'follows': whom_id}})\\n flash('You are now following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(r.get(user_ID))))\\n else:\\n redis_obj.delete(str(g.user['_id']))\\n print \\\"Invalidating cache after Follow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def follow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('insert into follower (who_id, whom_id) values (?, ?)',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are now following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def followUser(following):\\n\\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"INSERT INTO followers (user, following) VALUES ((SELECT username FROM users WHERE id = ?), ?)\\\", (user_id, following))\\n finish(con)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId: return\\n self.users[followerId].add(followeeId)\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followerId == followeeId:\\n return\\n if followerId not in self.users.keys():\\n self.users[followerId] = user()\\n if followeeId not in self.users.keys():\\n self.users[followeeId] = user()\\n self.users[followerId].followees[followeeId] = self.users[followeeId]\",\n \"def get_one_user():\",\n \"def add_follow(follow_id):\\n\\n want_to_follow_user = User.query.get_or_404(follow_id)\\n if want_to_follow_user.private:\\n # =========== NEED TO IMPLEMENT ====================\\n # send them a request to follow\\n want_to_follow_user.from_users.append(g.user) \\n db.session.commit()\\n flash(\\\"Your request has been sent\\\", \\\"success\\\")\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\\n\\n g.user.following.append(want_to_follow_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def get_user(self):\\n return self.get('users/self')\",\n \"def get_user(self):\\n return self.user\",\n \"def get_user(self):\\n return self.user\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.users:\\n self._create_user(followeeId)\\n if followerId not in self.users:\\n self._create_user(followerId)\\n self.users[followerId].add(followeeId)\",\n \"def getFollowers():\\n\\n cur, user_id = initialise(3)\\n cur.execute(\\\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\\\", [user_id])\\n tempFollowers = cur.fetchall()\\n followers = []\\n for follower in tempFollowers:\\n followers.append(follower[0])\\n return followers\",\n \"def follow(whomUserName,whoUserName):\\n\\n whomuser = query_db('select * from user where username = ?',\\n [whomUserName], one=True)\\n whouser = query_db('select * from user where username = ?',\\n [whoUserName], one=True)\\n\\n\\n followed = query_db('''select 1 from follower where\\n follower.who_id = ? and follower.whom_id = ?''',\\n [whouser['user_id'], whomuser['user_id']],one=True) is not None\\n\\n if whouser is None:\\n return jsonify({'message':'User trying to follow another user which does not exist'}),404\\n\\n if whomuser is None:\\n return jsonify({'message':'User getting followed does not exist yet'}),404\\n\\n if not followed:\\n db = get_db()\\n\\n db.execute('''insert into follower (\\n who_id, whom_id) values (?, ?)''',\\n [whouser['user_id'], whomuser['user_id']])\\n db.commit()\\n flash('Operation successful')\\n return jsonify({'message': 'Successfully following'}), 201\\n else:\\n return jsonify({'message':'Specified user is already following another user'}),403\",\n \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"def follow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.followList.get(followerId, [followerId]):\\n self.followList[followerId] = self.followList.get(followerId, [followerId]) + [followeeId]\\n # print(self.followList)\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def follow(current_user,user_id):\\n if request.method == \\\"POST\\\":\\n #follee = request.get_json('user_id')\\n if User.query.filter_by(userid= user_id):\\n follow = Follows(userid =user_id, follower_id =current_user.userid)\\n db.session.add(follow)\\n db.session.commit()\\n return jsonify({'message' :'You are now following'})\\n return jsonify({'message' :'User doesnt exist..Try again'})\\n return jsonify({'errors' : 'Method Invalid'})\",\n \"def get_current_user(self):\\n user_id = self.get_secure_cookie(\\\"blogdemo_user\\\")\\n if not user_id: return None\\n user = self.db.get(\\\"SELECT * FROM authors WHERE id = %s\\\", int(user_id))\\n return user\",\n \"def get_user(self):\\n return None\",\n \"def userFollowers(nick):\\n if (len(nick) != 1):\\n print \\\"Has d'introduir només un nick\\\"\\n return\\n i.userFollow(nick[0])\",\n \"def users_followers(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('users/followers.html', user=user)\",\n \"def get_user(self):\\n raise NotImplementedError\",\n \"def follow_friend():\\n print \\\"followuser\\\"\\n username = request.args.get('username')\\n print \\\"JSON Data\\\", username\\n # username= req_data[username]\\n whom_id = get_user_id(username)\\n print \\\"whom_id:\\\", whom_id\\n if whom_id is None:\\n abort(404)\\n follow_query(whom_id)\\n flash('You are now following \\\"%s\\\"' % username)\\n name = {'name of following user': username}\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def author(self):\\r\\n return self.user\",\n \"def user(self):\\n if self._user is None:\\n pk, full_name = self.owner.split(',')\\n pk = int(pk)\\n self._user = User.objects.get(pk=pk)\\n return self._user\",\n \"def add_following(self, user_id):\\n sleep(360) # too much follows => function ban\\n self.following.append(user_id)\\n return perform_with_ran_delay(self.instagram.follow, user_id)\",\n \"def me():\\n return current_user.get()\",\n \"async def get_user(event):\\n if event.reply_to_msg_id:\\n previous_message = await event.get_reply_message()\\n replied_user = await event.client(GetFullUserRequest(previous_message.from_id))\\n else:\\n user = event.pattern_match.group(1)\\n if user.isnumeric():\\n user = int(user)\\n\\n if not user:\\n self_user = await event.client.get_me()\\n user = self_user.id\\n\\n if event.message.entities is not None:\\n probable_user_mention_entity = event.message.entities[0]\\n\\n if isinstance(probable_user_mention_entity, MessageEntityMentionName):\\n user_id = probable_user_mention_entity.user_id\\n replied_user = await event.client(GetFullUserRequest(user_id))\\n return replied_user\\n try:\\n user_object = await event.client.get_entity(user)\\n replied_user = await event.client(GetFullUserRequest(user_object.id))\\n\\n except (TypeError, ValueError):\\n await event.edit(\\\"`I don't slap aliens, they ugly AF !!`\\\")\\n return None\\n\\n return replied_user\",\n \"async def get_user(event):\\n if event.reply_to_msg_id:\\n previous_message = await event.get_reply_message()\\n replied_user = await event.client(\\n GetFullUserRequest(previous_message.sender_id)\\n )\\n else:\\n user = event.pattern_match.group(1)\\n\\n if user.isnumeric():\\n user = int(user)\\n\\n if not user:\\n self_user = await event.client.get_me()\\n user = self_user.id\\n\\n if event.message.entities is not None:\\n probable_user_mention_entity = event.message.entities[0]\\n\\n if isinstance(probable_user_mention_entity, MessageEntityMentionName):\\n user_id = probable_user_mention_entity.user_id\\n replied_user = await event.client(GetFullUserRequest(user_id))\\n return replied_user\\n try:\\n user_object = await event.client.get_entity(user)\\n replied_user = await event.client(GetFullUserRequest(user_object.id))\\n\\n except (TypeError, ValueError):\\n await event.edit(\\\"`I don't slap aliens, they ugly AF !!`\\\")\\n return None\\n\\n return replied_user\",\n \"def target_user(self):\\n return self.request.user\",\n \"def get(self, request):\\n # Retrieve the user from the request if they have been authenticated\\n current_user = request.user\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n # Return the follower details for the current user\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['MY_FOLLOWERS_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_200_OK\\n )\",\n \"def user(self, uid):\",\n \"def get_user(self):\\n return str(self.request.user.id)\",\n \"def add_to_following(sender, instance, created, **kwargs):\\r\\n sender_= instance.sender\\r\\n receiver_ = instance.receiver\\r\\n if instance.status == 'accepted':\\r\\n sender_.following.add(receiver_.user)\",\n \"def get_user(self):\\n if \\\"user\\\" not in self._data:\\n self._data[\\\"user\\\"] = User.objects.get(pk=self.kwargs[\\\"user_id\\\"])\\n return self._data[\\\"user\\\"]\",\n \"def add_user_to_g():\\n \\n if CURRENT_USER in session:\\n g.user = User.query.get(session[CURRENT_USER])\\n\\n else:\\n g.user = None\",\n \"def user(self):\\n return self.owner.user\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n f = Follow(follower=self, followed=user)\\n db.session.add(f)\",\n \"def getToUser(self):\\n return self.toUser\",\n \"def add(self, user: U) -> None:\\n ...\",\n \"def show_following(user_id):\\n\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('users/following.html', user=user)\",\n \"def get_current_user(self):\\n user_id = self.get_secure_cookie(\\\"blogdemo_user\\\")\\n if not user_id: return None\\n user = self.db.bucket('authors').get(str(user_id))\\n if not user.exists: return None\\n return user.data\",\n \"def add_follow(follow_id):\\n followed_user = User.query.get_or_404(follow_id)\\n if not g.user or g.user.id == follow_id or followed_user.is_blocking(g.user):\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n g.user.following.append(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def follow(self, followerId, followeeId):\\r\\n if followerId != followeeId:\\r\\n self.follows[followerId].add(followeeId)\",\n \"def get_user(current_user):\\n for user in user_db:\\n if user['email'] == current_user:\\n return user\",\n \"def user(self):\\n pass\",\n \"def checkUsers(u1, u2):\\n if type(u1) == int:\\n try:\\n user1 = mt.User.objects.filter(Id = u1).get()\\n except:\\n\\n return None\\n else:\\n user1 = u1\\n try:\\n user2 = mt.User.objects.filter(Id = u2).get()\\n except:\\n return user1\\n mt.follows(\\n followee= user1,\\n follower= user2\\n ).save()\\n return user1\",\n \"def post(self, request, username):\\n\\n # Retrieve the user from the user table if the user exists\\n try:\\n user_details = User.objects.get(username=username)\\n current_user = request.user\\n # If a user is trying to follow themselves then stop the request\\n if user_details.profile.id == current_user.profile.id:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['CANNOT_FOLLOW_SELF']},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Otherwise follow the author the current user has indicated\\n current_user.profile.follows.add(user_details.profile)\\n\\n # notify user of new follower\\n send_notifications(request,\\n notification_type=\\\"user_followed\\\",\\n instance=current_user,\\n recipients=[user_details])\\n\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['USER_FOLLOW_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_201_CREATED\\n )\\n except User.DoesNotExist:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\\n status=status.HTTP_404_NOT_FOUND\\n )\",\n \"def users_followers(user_id):\\n\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n user = User.query.get_or_404(user_id)\\n users_blocking = [block.user_blocking_id for block in Blocks.query.all() if block.user_being_blocked_id == g.user.id]\\n likes = [message for message in user.likes if message.user_id not in users_blocking]\\n return render_template('users/followers.html', user=user, likes=likes)\",\n \"def get_follow(self):\\n return self.follow\",\n \"def following_and_storing(self, user_obj):\\n if self.following(user_obj['user']):\\n self.monitored_users.append({'user': user_obj['user'], 'username': user_obj['username'],\\n 'followDate': datetime.now().timestamp()})\",\n \"def get_following_by_user(request):\\n response, status_code = get_followings(request)\\n if status_code != 200:\\n return JsonResponse(response, status=status_code)\\n serialize_data = FollowingSerializer(response, many=False).data\\n return JsonResponse(serialize_data, status=status_code, safe=False)\",\n \"def followed_by(self, user_id):\\n\\n url = \\\"https://api.instagram.com/v1/users/{0}/followed-by?access_token={1}\\\".format(user_id, self.access_token)\\n\\n request = requests.get(url)\\n return request.json()\",\n \"def get_current_user(self):\\n return None\",\n \"def user_following_gql(self, user_id: int, amount: int = 0) -> list:\\n user_id = int(user_id)\\n end_cursor = None\\n users = []\\n variables = {\\n \\\"id\\\": user_id,\\n \\\"include_reel\\\": True,\\n \\\"fetch_mutual\\\": False,\\n \\\"first\\\": 24\\n }\\n while True:\\n if end_cursor:\\n variables[\\\"after\\\"] = end_cursor\\n data = self.public_graphql_request(\\n variables, query_hash=\\\"e7e2f4da4b02303f74f0841279e52d76\\\"\\n )\\n if not data[\\\"user\\\"] and not users:\\n raise UserNotFound(user_id=user_id, **data)\\n page_info = json_value(\\n data, \\\"user\\\", \\\"edge_follow\\\", \\\"page_info\\\", default={}\\n )\\n edges = json_value(\\n data, \\\"user\\\", \\\"edge_follow\\\", \\\"edges\\\", default=[]\\n )\\n for edge in edges:\\n users.append(extract_user_short(edge[\\\"node\\\"]))\\n end_cursor = page_info.get(\\\"end_cursor\\\")\\n if not page_info.get(\\\"has_next_page\\\") or not end_cursor:\\n break\\n if amount and len(users) >= amount:\\n break\\n # time.sleep(sleep)\\n if amount:\\n users = users[:amount]\\n return users\",\n \"def getUser(self):\\n user = users.get_current_user()\\n if not user:\\n self.redirect(users.create_login_url(self.request.uri))\\n else:\\n return user\",\n \"def get_followers(user):\\n if user.has_key('followers_list'):\\n pass\\n else:\\n if user.has_key('followers_count'):\\n if user['followers_count'] > 4999:\\n pages = user['followers_count'] / 5000\\n f_list = []\\n for page in range(pages):\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], cursor=page, count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n f_list = friends_list + f_list\\n time.sleep(60)\\n user['followers_list'] = f_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(f_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\\n else:\\n try:\\n follower_set = api.GetFollowers(user_id=user['id'], count=5000)\\n friends_list = []\\n for follower in follower_set:\\n twitter_users.update({'id':follower.GetId()},follower.AsDict(),upsert=True)\\n friends_list.append(follower.GetId())\\n user['followers_list'] = friends_list\\n twitter_users.update({'id': user['id']}, user)\\n print \\\"\\\\n\\\\nGot %s followers out of %s listed\\\" % (len(friends_list), user['followers_count'])\\n except Exception, e:\\n print str(e)\\n time.sleep(60)\",\n \"def current_user(self, **kwargs):\\n return self.me(**kwargs)\",\n \"def followed_by(self):\\r\\n return relationships.FollowedBy(self)\",\n \"def get_current_user(self):\\n return self.current_user\",\n \"def addUser(self, user):\\r\\n self.users.append(user)\\r\\n return len(self.users)-1\",\n \"def getFollowers():\\n followers = []\\n next_max_id = True\\n while next_max_id:\\n # first iteration hack\\n if next_max_id is True:\\n next_max_id = ''\\n\\n _ = GetInstagramAnswer.igApi.getUserFollowers(GetInstagramAnswer.igApi.username_id, maxid=next_max_id)\\n followers.extend(GetInstagramAnswer.igApi.LastJson.get('users',[]))\\n next_max_id = GetInstagramAnswer.igApi.LastJson.get('next_max_id','')\\n return \\\"You have currently \\\"+str(len(followers))+\\\" Followers on Instagram.\\\"\",\n \"def follow_user(self, user):\\n self.nav_user(user)\\n follow_button = self.driver.find_element_by_xpath(\\n \\\"//button[contains(text(), 'Follow')]\\\")\\n follow_button.click()\\n time.sleep(1)\\n self.driver.get(self.base_url)\",\n \"def user_follow_users(self, ids=None, **kwargs):\\n return self._put(\\n API.MY_FOLLOWING.value, type=\\\"user\\\", ids=\\\",\\\".join(ids or []), **kwargs\\n )\",\n \"def get_current_user(request): \\n user_id = request.session.get('user_id', False)\\n \\n return Member.objects.get(id=user_id)\",\n \"def select_user_following(args):\\n is_parameter_exists([\\n constants.ID\\n ], args)\\n\\n # Request User\\n request_user = args[constants.USER]\\n\\n # Requested User ID\\n requested_user_id = args[constants.ID]\\n\\n # Page Number\\n page_number = 1 if constants.PAGE_NUMBER not in args else int(args[constants.PAGE_NUMBER])\\n\\n # Check User Id\\n if not User.objects.filter(id=requested_user_id).exists():\\n raise ApiError(constants.NOT_EXIST_OBJECT)\\n\\n # Following QuerySet\\n queryset = UserFollow.objects.filter(following_user=requested_user_id).values_list('followed_user', flat=True)\\n\\n # User Ids\\n user_ids = get_results_from_queryset(queryset, 10, page_number)\\n\\n # is_finished\\n is_finished = not user_ids.has_next()\\n\\n # Filter Query\\n filter_query = Q(id__in=user_ids)\\n\\n # Users\\n users, _, _ = __get_users(filter_query, request_user, 10)\\n\\n return users, page_number, is_finished\",\n \"def get_user(request: Request):\\n return request.user\",\n \"def follow(self, followerId, followeeId):\\n if followerId in self.follows:\\n self.follows[followerId].add(followeeId)\\n else:\\n self.follows[followerId] = set([followeeId])\",\n \"def add_user_to_g():\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def get_followings(request):\\n user_id = request.GET.get(\\\"user_id\\\")\\n if not user_id:\\n return {\\\"error\\\": \\\"User Id should be provided\\\"}, 400\\n following_data = Following.objects.filter(user_profile_id=user_id, is_active=True).first()\\n return following_data, 200\",\n \"def created_user(self):\\n return self._created_user\",\n \"def get_user_id(self):\\n return self.id_user\",\n \"def owner(self) -> discord.User:\\n if self.config.owner_id:\\n return self.get_user(self.config.owner_id)\\n if self.owner_ids:\\n return self.get_user(self.config.owner_ids[0])\\n return None\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_object(self):\\n return self.request.user\",\n \"def get_user(id):\\n pass\",\n \"def followers(congressDict, twitterAPI):\\n most = twitterAPI.get_user(list(congressDict.items())[0][1]) # Choose an arbitrary starting point from the dictionary and assign it their user details.\\n least = most\\n for name in congressDict:\\n tempAPI = twitterAPI.get_user(congressDict[name]) # Get the user details of each congress members' twitter handle.\\n numFollowers = tempAPI._json['followers_count']\\n if (numFollowers > most._json['followers_count']): # If the follower count is greater than most, replace the user details with current one.\\n most = tempAPI\\n elif (numFollowers < least._json['followers_count']): # If the follower count is lower than least, replace the user details with the current one.\\n least = tempAPI\\n return [most._json[\\\"name\\\"], least._json[\\\"name\\\"]]\",\n \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"def follow_user(self:'InstaClient', user:str, nav_to_user:bool=True):\\n # Check User Vadility\\n profile = self.get_profile(user)\\n if not profile:\\n raise InvalidUserError(user)\\n\\n # Navigate to User Page\\n self._nav_user(user, check_user=False)\\n \\n if self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.MESSAGE_USER_BTN))):\\n # User already followed\\n pass\\n else:\\n follow_button = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.FOLLOW_BTN)), url=ClientUrls.NAV_USER.format(user))\\n self._press_button(follow_button)\\n profile.requested_by_viewer = True\\n return profile\",\n \"def get_object(self):\\n return User.objects.get(username=self.request.user.username)\",\n \"def add_user(self, attrs):\\n pass\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def user_follow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n if user_id in self._users_following.get(self.user_id, []):\\n self.logger.debug(\\\"User %s already followed\\\", user_id)\\n return False\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/create/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following.pop(self.user_id) # reset\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is True\",\n \"def get_queryset(self, *args, **kwargs):\\n following_username = self.kwargs.get(self.look_url_kwarg)\\n following_users = FollowUser.objects.filter(\\n following_username=following_username)\\n\\n return following_users\",\n \"def my_get_user(users_list, user_id):\\n for user in users_list:\\n if user.get(\\\"user_id\\\") == user_id:\\n return user\\n return None\",\n \"def user(self):\\n return self._push.get('user', None)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7044998","0.69360614","0.6906444","0.6870493","0.67471176","0.66945994","0.66936237","0.668267","0.6638689","0.65982777","0.6567574","0.65394175","0.6528088","0.6518445","0.64401454","0.6416909","0.6398086","0.6381833","0.63553584","0.63553584","0.6350465","0.63263625","0.6305007","0.6303222","0.62997025","0.62849957","0.62846375","0.62629235","0.6206243","0.61844134","0.618406","0.6180341","0.61708814","0.61486816","0.61389786","0.61306214","0.6129847","0.61106503","0.6109269","0.61015093","0.608964","0.6080447","0.60637003","0.60471314","0.60341406","0.6014736","0.6014412","0.6006301","0.6005864","0.59997535","0.5997092","0.5995834","0.59919614","0.59889936","0.59850556","0.5979546","0.5979161","0.5977076","0.59769106","0.59752184","0.59337616","0.5922739","0.59219813","0.5915432","0.5913267","0.59120286","0.590383","0.5896157","0.5894813","0.5888959","0.5888887","0.58881044","0.58880395","0.588536","0.58826625","0.587243","0.5867339","0.5862555","0.5861763","0.58578426","0.5849514","0.58492357","0.5848579","0.58481103","0.58481103","0.58481103","0.58481103","0.58481103","0.58471817","0.58458203","0.581197","0.5811311","0.5809334","0.5806933","0.5805576","0.58052397","0.58023626","0.58008116","0.5799232"],"string":"[\n \"0.7044998\",\n \"0.69360614\",\n \"0.6906444\",\n \"0.6870493\",\n \"0.67471176\",\n \"0.66945994\",\n \"0.66936237\",\n \"0.668267\",\n \"0.6638689\",\n \"0.65982777\",\n \"0.6567574\",\n \"0.65394175\",\n \"0.6528088\",\n \"0.6518445\",\n \"0.64401454\",\n \"0.6416909\",\n \"0.6398086\",\n \"0.6381833\",\n \"0.63553584\",\n \"0.63553584\",\n \"0.6350465\",\n \"0.63263625\",\n \"0.6305007\",\n \"0.6303222\",\n \"0.62997025\",\n \"0.62849957\",\n \"0.62846375\",\n \"0.62629235\",\n \"0.6206243\",\n \"0.61844134\",\n \"0.618406\",\n \"0.6180341\",\n \"0.61708814\",\n \"0.61486816\",\n \"0.61389786\",\n \"0.61306214\",\n \"0.6129847\",\n \"0.61106503\",\n \"0.6109269\",\n \"0.61015093\",\n \"0.608964\",\n \"0.6080447\",\n \"0.60637003\",\n \"0.60471314\",\n \"0.60341406\",\n \"0.6014736\",\n \"0.6014412\",\n \"0.6006301\",\n \"0.6005864\",\n \"0.59997535\",\n \"0.5997092\",\n \"0.5995834\",\n \"0.59919614\",\n \"0.59889936\",\n \"0.59850556\",\n \"0.5979546\",\n \"0.5979161\",\n \"0.5977076\",\n \"0.59769106\",\n \"0.59752184\",\n \"0.59337616\",\n \"0.5922739\",\n \"0.59219813\",\n \"0.5915432\",\n \"0.5913267\",\n \"0.59120286\",\n \"0.590383\",\n \"0.5896157\",\n \"0.5894813\",\n \"0.5888959\",\n \"0.5888887\",\n \"0.58881044\",\n \"0.58880395\",\n \"0.588536\",\n \"0.58826625\",\n \"0.587243\",\n \"0.5867339\",\n \"0.5862555\",\n \"0.5861763\",\n \"0.58578426\",\n \"0.5849514\",\n \"0.58492357\",\n \"0.5848579\",\n \"0.58481103\",\n \"0.58481103\",\n \"0.58481103\",\n \"0.58481103\",\n \"0.58481103\",\n \"0.58471817\",\n \"0.58458203\",\n \"0.581197\",\n \"0.5811311\",\n \"0.5809334\",\n \"0.5806933\",\n \"0.5805576\",\n \"0.58052397\",\n \"0.58023626\",\n \"0.58008116\",\n \"0.5799232\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6222603"},"document_rank":{"kind":"string","value":"29"}}},{"rowIdx":7198,"cells":{"query":{"kind":"string","value":"Returns the logged in user after removing the wanted user from his followers list"},"document":{"kind":"string","value":"def unfollow(self, user):\n if self.is_following(user):\n self.followed.remove(user)\n return self"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def unfollowUser(following):\n \n cur, user_id, con = initialise(3, True)\n cur.execute(\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\", (user_id, following))\n finish(con)","def unfollow(self, user):\n f = self.followed.filter_by(followed_id=user.id).first()\n if f:\n db.session.delete(f)","def unfollow_user(username):\n user_ID = before_request()\n user_ID = None\n if user_ID != None:\n user_ID = str(g.user['_id'])\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n mongo.db.users.update({'_id': g.user['_id']}, {\n '$pull': {'follows': whom_id}})\n flash('You are no longer following \"%s\"' % username)\n if redis_obj.get(user_ID):\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\n else:\n redis_obj.delete(session['user_id'])\n print \"Invalidating cache after Unfollow\"\n return redirect(url_for('user_timeline', username=username))","def user_unfollow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.remove(following)\n db.session.commit()\n return {'unfollowed': True}","def unfollow_user(username):\n if not g.user:\n abort(401)\n whom_id = get_user_id(username)\n if whom_id is None:\n abort(404)\n db = get_db()\n db.execute('delete from follower where who_id=? and whom_id=?',\n [session['user_id'], whom_id])\n db.commit()\n flash('You are no longer following \"%s\"' % username)\n return redirect(url_for('user_timeline', username=username))","def removeFollower(self,id):\n # DELETE /followers/$id\n pass","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followeeId not in self.users:\n self._create_user(followeeId)\n if followerId not in self.users:\n self._create_user(followerId)\n if followerId != followeeId and followeeId in self.users[followerId]:\n self.users[followerId].remove(followeeId)","def delete(self, request, *args, **kwargs):\n\n following_username = token_payload(request)\n\n followed_username = self.kwargs.get(self.look_url_kwarg)\n try:\n followed_user = User.objects.get(username=followed_username)\n followed_user_id = followed_user.id\n\n is_following = FollowUser.objects.filter(\n following_username=following_username)\n for user in is_following:\n if user.following_username == following_username and user.followed_username == followed_username:\n returned_user = FollowUser.objects.get(pk=user.id)\n returned_user.delete()\n\n followed_user_profile = Profile.objects.get(\n profile_user=followed_user_id)\n serializer_profile = self.serializer_classprofile(\n followed_user_profile)\n return Response({\n \"user\": following_username,\n \"unfollowing\": serializer_profile.data\n })\n return Response({\n \"message\": 'User has been unfollowed or You are unfollowing a user you were not orignally following'\n }, status=status.HTTP_400_BAD_REQUEST)\n except:\n return Response({\n \"message\": 'User does not exist'\n }, status=status.HTTP_400_BAD_REQUEST)","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\n self.followList[followerId].remove(followeeId)\n # print(self.followList)","def unfollowing_and_removing(self, user_id):\n if self.unfollowing(user_id):\n ind = [i for i, j in enumerate(self.monitored_users) if j.get('user', '') == user_id]\n if ind:\n self.monitored_users.remove(self.monitored_users[ind[0]])","def unfollow(request, usertostopfollow):\n stop_follow = Member.objects.get(user__username=usertostopfollow)\n user = Member.objects.get(user=request.user)\n user.following.remove(stop_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def unfollow(self, followerId: int, followeeId: int) -> None:\n following = self.user_followed[followerId]\n if followeeId in following:\n following.remove(followeeId)\n self.user_followed[followerId] = following","def remove_user(self, user_id):\n if user_id in self:\n user = self[user_id]\n del self[user_id]\n return user","def unfollow(self, followerId, followeeId):\n # 在user_pool 中查询这个用户 follower\n if self.user_pool[followerId]:\n # 如果在用户的关注列表中才删除\n if followeeId in self.user_pool[followerId].follows:\n self.user_pool[followerId].follows.remove(followeeId)","def unfollow(self, followerId: int, followeeId: int) -> None:\n self.users[followerId].discard(followeeId)","def remove(self, user_id):\n pass","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\n return\n if followeeId not in self.users[followerId].followees.keys():\n return\n self.users[followerId].followees.pop(followeeId)\n\n\n\n # Your Twitter object will be instantiated and called as such:\n # obj = Twitter()\n # obj.postTweet(userId,tweetId)\n # param_2 = obj.getNewsFeed(userId)\n # obj.follow(followerId,followeeId)\n # obj.unfollow(followerId,followeeId)","def delete(self, request, username):\n followed_user_exists = User.objects.filter(username=username).exists()\n if not followed_user_exists:\n return Response({'error': 'user not found'},\n status.HTTP_404_NOT_FOUND)\n followed_user = User.objects.get(username=username)\n user_exists = Follow.is_user_already_followed(\n followed_user_id=followed_user.id,\n user_id=request.user.id\n )\n if user_exists:\n instance = Follow.objects.filter(\n user=self.request.user.id, followed_user=followed_user.id\n )\n instance.delete()\n return Response({'message': 'user unfollowed'},\n status.HTTP_200_OK)\n return Response({'message': 'user not in followers'},\n status.HTTP_404_NOT_FOUND)","def unfollow_user(request, course_id, followed_user_id):\r\n user = cc.User.from_django_user(request.user)\r\n followed_user = cc.User.find(followed_user_id)\r\n user.unfollow(followed_user)\r\n return JsonResponse({})","def delete(self, request, username):\n\n # Retrieve the user from the user table if the user exists\n try:\n user_to_unfollow = User.objects.get(username=username)\n current_user = request.user\n # If a user is trying to unfollow themselves then stop the request\n if user_to_unfollow.profile.id == current_user.profile.id:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['CANNOT_UNFOLLOW_SELF']},\n status=status.HTTP_400_BAD_REQUEST\n )\n # Check if the user to be unfollowed\n # is in the current users following list\n try:\n profile_id = user_to_unfollow.profile.id\n user_being_followed = CustomFollows.objects.get(\n to_profile_id=profile_id,\n from_profile_id=current_user.profile.id\n )\n # If not tell the user the request can't happen\n # Because they don't follow the user\n except Exception as e:\n return Response(\n {\n \"errors\": FOLLOW_USER_MSGS['USER_UNFOLLOWED_ALREADY']\n },\n status=status.HTTP_400_BAD_REQUEST\n )\n # Otherwise unfollow the user as requested\n current_user.profile.follows.remove(user_to_unfollow.profile)\n # Get the following & followers username list\n # And the following & followers count for the current user\n user_following_data = get_user_following_data(current_user)\n return Response(\n {\n \"message\": FOLLOW_USER_MSGS['USER_UNFOLLOW_SUCCESSFUL'],\n \"following\": user_following_data[\"following\"],\n \"followers\": user_following_data[\"followers\"],\n \"followingCount\": user_following_data[\"followingCount\"],\n \"followersCount\": user_following_data[\"followersCount\"]\n },\n status=status.HTTP_200_OK\n )\n # End request if we cannot find the user we want to unfollow.\n except User.DoesNotExist:\n return Response(\n {\"errors\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\n status=status.HTTP_404_NOT_FOUND\n )","def delete_user():","def pop_user(self):\n return self._pop_user()","def test_remove_followers(self):\n pass","def test_un_following_existing_user(self):\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)","def delete_user(self):\n User.user_list.remove(self)","def delete_user(self):\n User.user_list.remove(self)","def delete_user(self):\n User.user_list.remove(self)","def unfriend(self, remove):\n remover_friends_list = self # person terminating the friendship \n \n # remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n\n #remove friend from removee friend list\n friends_list = FriendList.objects.get(user=removee)\n friend_list.remove_friend(self.user)","def unfollow_friend(username):\n\n if not g.user:\n print \"401\"\n abort(401)\n whom_id = get_user_id(username)\n print whom_id\n if whom_id is None:\n abort(404)\n unfollow_query(whom_id)\n flash('You are no longer following \"%s\"' % username)\n name = {'name of unfollowing user': username}\n ############### REDIS cache invalidate #####################\n R_SERVER.delete(user_timeline_key)\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)","def unfollow(self, followerId, followeeId):\n if followerId in self.follows:\n if followeeId in self.follows[followerId]:\n self.follows[followerId].remove(followeeId)","def remove_user(self):\n self.currentuser = None\n self.carlocked = False","def delete_user():\n #TODO user delete\n pass","def delete_follow_request(request):\n username = request.POST[\"user\"]\n requester = get_user_from_username(request.user, username)\n\n follow_request = get_object_or_404(\n models.UserFollowRequest, user_subject=requester, user_object=request.user\n )\n follow_request.raise_not_deletable(request.user)\n\n follow_request.delete()\n return redirect(f\"/user/{request.user.localname}\")","def unfriend(self, removee):\n remover_friends_list = self # person terminating the friendship\n # Remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n # Remove friend from removee's friend list\n friends_list = FriendList.objects.get(user=removee)\n friends_list.remove_friend(self.user)","def unfollow(self, followerId: int, followeeId: int) -> None:\n # Time Complexity: O(1)\n if followerId in self.followees and followeeId in self.followees[followerId]:\n self.followees[followerId].remove(followeeId)","def remove_user(self, u: \"Node\") -> None:\n\n if u in self.users_:\n self.users_[u] -= 1\n if self.users_[u] == 0:\n del self.users_[u]","def remove_friend():\n if request.method == 'POST':\n username = get_username()\n user_id = get_id_from_username(username)\n friend_to_remove = get_id_from_username(request.form['remove_user'])\n if not friend_to_remove or friend_to_remove==user_id:\n return redirect(url_for('message.converse'))\n remove_friend_db(user_id, friend_to_remove)\n return redirect(url_for('message.converse'))","def _onRemove(self, event):\n sel = self.userlist.GetSelection()\n if sel >= 0:\n c.removeUser(self.userlist.GetString(sel))\n self.userlist.Delete(sel)\n del self.users[sel]\n if len(self.users) >= 0:\n self.userlist.SetSelection(0)\n else:\n self.userlist.SetSelection(-1)","def _remove(users, room_name):\n global users_removed\n users_removed = []\n\n try:\n\n for word in users['message']['text'].split():\n\n if word == 'myself':\n user = users['message']['sender']['name']\n check_result = redis.srem(room_name, \"<\" + user + \">\")\n \n if check_result == 1:\n users_removed.append(\"<\" + user + \">\")\n else:\n users_removed.append('Not found ->> ' + \"<\" + user + \">\")\n\n check_continue = 1\n text = '```User removed: %s ```' % (','.join(users_removed))\n\n for _item in range(len(users['message']['text'].split())):\n\n _item = _item + 1\n\n try:\n _type = users['message']['annotations'][_item]['userMention']['user']['type']\n user = users['message']['annotations'][_item]['userMention']['user']['name']\n \n if _type == 'BOT':\n\n if check_continue == 1:\n continue\n else:\n text = 'Please add user with @'\n continue\n \n user = users['message']['annotations'][_item]['userMention']['user']['name']\n check_result = redis.srem(room_name, \"<\" + user + \">\")\n\n except:\n pass\n\n if check_result == 1:\n users_removed.append(\"<\" + user + \">\")\n else:\n users_removed.append(\"Not found ->> \" + \"<\" + user + \">\")\n text = \"```Removed users: %s ```\" % (','.join(list(set(users_removed))))\n return text\n except:\n\n text = 'Please add user with @'\n return text","def unfollow(self, followerId: int, followeeId: int) -> None:\n if followeeId in self.follow_map[followerId]:\n self.follow_map[followerId].remove(followeeId)","def user_unfollow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/destroy/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following[self.user_id].pop(user_id, None)\n return result[\"friendship_status\"][\"following\"] is False","def remove_user(users, curr_username, user_role, request_ip):\n #TODO: error checking\n log_connector.add_log('DELETE USER', \"Removed {} user(s)\".format(len(users)), curr_username, user_role, request_ip)\n user_connector.remove_user(users)","def test_unfollowing_existing_user_not_authenticated(self):\n self.client.credentials()\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def remove_user(self, username):\n\n row = self.c.execute(\"SELECT * FROM profiles WHERE name =?\",\n (username,))\n for i in row:\n user = i[1]\n print(user)\n if user == username:\n self.c.execute(\"SELECT id FROM profiles WHERE name=?\",\n (username,))\n i_d = self.c.fetchone()[0]\n self.c.execute(\"DELETE FROM events WHERE user_id=?\", (i_d,))\n self.c.execute(\"DELETE FROM profiles WHERE name=?\", (username,))\n self.conn.commit()\n return True\n else:\n print ('User not found.')","def unfollow(self, followerId, followeeId):\n if followerId in self.follow_map and followeeId in self.follow_map[followerId]:\n self.follow_map[followerId].remove(followeeId)","def remove_user(self, username): # remove only users from json file\n return self._user(username=username, remove=True)","def delete_user(self):\n\n User.user_list.remove(self)","def rm_favoriting_user_id(self, circuit_id, user_id):\n key = ':'.join(\n [CIRCUIT_FAV_USRS_1, \n str(circuit_id), \n CIRCUIT_FAV_USRS_2]\n )\n self.RS.srem(key, user_id)","def deleteUser(self, userList, index):\n\n if(self.adminAccess):\n ret = userList.pop(index)\n print(\"User has been deleted\")\n \n return userList","def unfollow(self, followerId, followeeId):\r\n self.follows[followerId].discard(followeeId)","def unfriend(self, removee):\n\t\tremover_friend_list = self # person terminating the friendship\n\n\t\t# Remove friend from friend request\n\t\tremover_friend_list.remove_friend(removee)\n\n\t\t# Remove friend from the removeee friend list\n\t\tfriends_list = FriendList.objects.get(user=removee)\n\t\tfriends_list.remove_friend(self.user)","def delete_by(self, user):\n if user.is_superuser or user is self.added_by:\n self.delete()","def remove(self, user):\r\n url = '{0}/{1}'.format(self.get_url(), user)\r\n\r\n return http.Request('DELETE', url), parsers.parse_empty","def del_user(self, username):\n pass","def delete_user(self) -> 'outputs.ActingUserResponse':\n return pulumi.get(self, \"delete_user\")","def delete_user():\n del globalopts.appdata[request.user]\n del globalopts.users[request.user]\n return \"\", 200","def remove(self, user):\n if user != self.head:\n user.group = None\n user.save()\n self.players.remove(user)","def delete_user(id):\n pass","def unfollow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 1:\n self.users_hat[following_index, user_index] = 0\n elif self.is_verbose():\n self.log(f\"User {following_index} was not following user {user_index}\")","def stop_following(follow_id):\n\n followed_user = User.query.get(follow_id)\n g.user.following.remove(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def remove_user(user):\n # user.confirmed = False\n # user = get_user_by_phone(phone_num)\n db.session.delete(user)\n db.session.commit()\n\n return user\n # DELETE FROM users WHERE user.phone_num == phone)","def del_user(request):\r\n mdict = request.matchdict\r\n\r\n # Submit a username.\r\n del_username = mdict.get('username', None)\r\n\r\n if del_username is None:\r\n LOG.error('No username to remove.')\r\n request.response.status_int = 400\r\n return _api_response(request, {\r\n 'error': 'Bad Request: No username to remove.',\r\n })\r\n\r\n u = UserMgr.get(username=del_username)\r\n\r\n if not u:\r\n LOG.error('Username not found.')\r\n request.response.status_int = 404\r\n return _api_response(request, {\r\n 'error': 'User not found.',\r\n })\r\n\r\n try:\r\n # First delete all the tag references for this user's bookmarks.\r\n res = DBSession.query(Bmark.bid).filter(Bmark.username == u.username)\r\n bids = [b[0] for b in res]\r\n\r\n qry = bmarks_tags.delete(bmarks_tags.c.bmark_id.in_(bids))\r\n qry.execute()\r\n\r\n # Delete all of the bmarks for this year.\r\n Bmark.query.filter(Bmark.username == u.username).delete()\r\n DBSession.delete(u)\r\n return _api_response(request, {\r\n 'success': True,\r\n 'message': 'Removed user: ' + del_username\r\n })\r\n except Exception, exc:\r\n # There might be cascade issues or something that causes us to fail in\r\n # removing.\r\n LOG.error(exc)\r\n request.response.status_int = 500\r\n return _api_response(request, {\r\n 'error': 'Bad Request: ' + str(exc)\r\n })","def remove(self, update, context):\n\n telegram_user = update.message.from_user\n if len(context.args) != 1:\n message = (\n \"To remove a subscriptions from your list please use /remove <entryname>. To see all your \"\n \"subscriptions along with their entry names use /list ! \"\n )\n update.message.reply_text(message)\n return\n\n entry = self.db.get_user_bookmark(telegram_id=telegram_user.id, alias=context.args[0])\n if entry:\n self.db.remove_user_bookmark(telegram_id=telegram_user.id, url=entry[0])\n message = \"I removed \" + context.args[0] + \" from your subscriptions!\"\n update.message.reply_text(message)\n else:\n message = (\n \"I can not find an entry with label \"\n + context.args[0]\n + \"in your subscriptions! Please check your subscriptions using /list and use the delete command \"\n \"again! \"\n )\n update.message.reply_text(message)","def _remove_user(self):\n name = False\n while not name: #While name not set\n name = input(\"Please enter the username of the user you would like to remove: \").lower()\n userID = self._get_user_id(name)\n if not userID:\n name = False\n command = \"remove_user {0}\\r\\n\".format(userID)\n return(command)","def stop_following(follow_id):\n\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n followed_user = User.query.get_or_404(follow_id)\n g.user.following.remove(followed_user)\n db.session.commit()\n\n return redirect(f\"/users/{g.user.id}/following\")","def fusion_api_remove_user(self, name=None, uri=None, api=None, headers=None):\n return self.user.delete(name, uri, api, headers)","def remove_user(self, email):\n try:\n self.users.pop(email)\n except KeyError:\n raise UserDoesNotExist","def onUserDeletion(event):\n request = getRequest()\n if not IProductLayer.providedBy(request):\n return\n\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n\n principal_id = event.principal\n principal_jid = xmpp_users.getUserJID(principal_id)\n\n pass_storage = getUtility(IXMPPPasswordStorage)\n pass_storage.remove(principal_id)\n\n d = users.deletePrincipal(client, principal_jid)\n return d","def removeUser(self, fullName):\n logger.debug(\"Func: removeUser\")\n\n # old Name removeUser\n currentDB = self._loadUsers()\n del currentDB[fullName]\n self._dumpJson(currentDB, self._pathsDict[\"usersFile\"])\n self._usersDict = currentDB\n return None, None","def onUserDeletion(event):\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n storage = getUtility(IPubSubStorage)\n\n principal_id = event.principal\n principal_jid = xmpp_users.getUserJID(principal_id)\n\n if principal_id in storage.leaf_nodes:\n storage.leaf_nodes.remove(principal_id)\n if principal_id in storage.publishers:\n del storage.publishers[principal_id]\n if principal_id in storage.node_items:\n del storage.node_items[principal_id]\n if principal_id in storage.collections['people']:\n storage.collections['people'].remove(principal_id)\n\n pass_storage = getUtility(IXMPPPasswordStorage)\n pass_storage.remove(principal_id)\n\n d = deletePrincipal(client, principal_jid)\n return d","def unfollow(self, followerId: 'int', followeeId: 'int') -> 'None':\n self.followees[followerId].discard(followeeId)","def remove_user(user_id):\n user = Users.query.get(user_id)\n if user_id in [0, 1]:\n return 'Removal of default User #%s (%s) is forbidden.' % (user_id, user.login), 'warning'\n db_session.delete(user)\n db_session.commit()\n return 'User #%s (%s) has been deleted.' % (user_id, user.login), 'success'","def user_disappears(self, user):\n pass","def delete_model(self, request, obj):\n From = User.objects.get(id=obj.From.id)\n To = User.objects.get(id=obj.To.id)\n From.following_numDe()\n To.followed_numDe()\n obj.delete()","def unfollow_profile(self):\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()","async def removeuser(self, ctx, user: discord.Member):\n\n if check_key(user.id):\n delete_key(user.id)\n await self.bot.say(\"{}, you are way out of this league.\".format(user.mention))\n else:\n await self.bot.say(\"That user does not exist in this league.\")","def unfollow_me(self):\n return self.follow_me('unfollow_topic')","def deleted_user(request):\n auth.logout(request)\n messages.success(request, \"Your profile has been deleted. Please contact us if you want to undo this.\")\n return redirect(reverse('index'))","def follow_user(cls, user, following):\r\n pass","def unfollow(self, followerId: int, followeeId: int) -> None:\n self.followees[followerId].discard(followeeId)","def remove_users(caller, role, *users):\r\n # can always remove self (at this layer)\r\n if not(len(users) == 1 and caller == users[0]):\r\n _check_caller_authority(caller, role)\r\n role.remove_users(*users)","def on_removeuser(self, username):\n self.users.remove(username)\n print ('%s left the room.' % username)","def remove_admin(self, project_id, user_id):\n current_user = request.environ.get('repoze.who.identity')['user']\n user = controller_globals._get_user_from_email(current_user.email)\n\n # make sure we're actually the project lead\n if not self._current_user_leads_review(project_id):\n return \"<font color='red'>tsk, tsk. you're not the project lead, %s.</font>\" % user.fullname\n\n leader_to_remove = Session.query(model.User).filter_by(id=user_id).one()\n review = self._get_review_from_id(project_id)\n review.leaders.remove(leader_to_remove)\n Session.add(review)\n Session.commit()\n\n redirect(url(controller=\"review\", action=\"admin\", project_id=project_id))","def unfollow(self, other):\n\t\tif self.follows(other):\n\t\t\tself.followed.remove(other)","def view_remove_user(self, user, username):\r\n user.realm._checker.removeUser(username)","def auto_unfollow_nonfollowers():\n\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs here that you want to keep following even if they don't\n # follow you back\n users_keep_following = set([])\n\n not_following_back = following - followers\n\n # make sure the \"already followed\" file exists\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n out_file.write(\"\")\n\n # update the \"already followed\" file with users who didn't follow back\n already_followed = set(not_following_back)\n af_list = []\n with open(ALREADY_FOLLOWED_FILE) as in_file:\n for line in in_file:\n af_list.append(int(line))\n\n already_followed.update(set(af_list))\n del af_list\n\n with open(ALREADY_FOLLOWED_FILE, \"w\") as out_file:\n for val in already_followed:\n out_file.write(str(val) + \"\\n\")\n\n for user_id in not_following_back:\n if user_id not in users_keep_following:\n t.friendships.destroy(user_id=user_id)\n print(\"unfollowed %d\" % (user_id))","def author_following(self):\n\t\tpass","def delete(self):\n\n user_id = get_jwt_identity()\n user = user_crud.get(user_id)\n if not user:\n abort(404, message=\"User not Found\")\n all_tokens = auth_crud.get_user_tokens(user_id)\n tokens = [token.to_dict() for token in all_tokens]\n for token in tokens:\n auth_crud.revoke_token(token['id'], user_id)\n user = user_crud.remove(user_id)\n\n return {'msg': 'User Removed'}","def test_unfollow_user_without_auth(self):\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response = self.client.delete(self.unfollow_url,\n data=self.followed_user)\n self.assertEqual(response.data['detail'],\n \"Authentication credentials were not provided.\")\n\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def test_unfollowing_non_existing_user(self):\n response = self.client.delete(\n reverse(\n 'follow',\n kwargs={'username': 'NotThere'}\n )\n )\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)","def user_playlist_unfollow(self, user, playlist_id, **kwargs):\n return self._delete(\n \"users/%s/playlists/%s/followers\" % (user, playlist_id), **kwargs\n )","def staff_remove(request):\n username = request.params['remove']\n user = models.User.get_by_username(username)\n user.staff = False\n return httpexceptions.HTTPSeeOther(\n location=request.route_url('admin_staff'))","def get_sentinel_user():\n return get_user_model().objects.get_or_create(username='deleted user').first()","def getFollowers():\n\n cur, user_id = initialise(3)\n cur.execute(\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\", [user_id])\n tempFollowers = cur.fetchall()\n followers = []\n for follower in tempFollowers:\n followers.append(follower[0])\n return followers","def reject_follow(made_request_id, approver_id):\n\n if not g.user.id == approver_id:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\"), 403\n\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\n g.user.from_users.remove(wanted_to_follow_user)\n db.session.commit()\n flash(f\"Follow request from {wanted_to_follow_user.username} rejected.\", \"success\")\n\n return redirect(f\"/users/{g.user.id}\")","def channel_removeowner(token, channel_id, u_id):\n # Check if token is valid and raise AccessError if not\n curr_id = database.get_current_user(token)\n # gets current channel data\n curr_channel = database.get_channel_data(channel_id)\n # gets the permissions of current user from database\n user_perms = database.get_permission_dict(curr_id)\n\n u_id_permission = database.get_permission_dict(u_id)\n if u_id_permission[\"permission_id\"] == 1:\n raise error.AccessError(description=\"user being removed is the owner of the slackr\")\n\n # checks if u_id is not an owner of the channel\n # also checks if current auth user is an owner of the channel\n is_u_owner = False\n is_curr_owner = False\n for owner_id in curr_channel[\"owner_ids\"]:\n if u_id == owner_id:\n is_u_owner = True\n if curr_id == owner_id:\n is_curr_owner = True\n if is_u_owner is False:\n raise error.InputError(description=\"user being removed is not an owner of the channel\")\n\n\n # if the auth user is owner of slackr, allows him to remove u_id as owner\n if user_perms[\"permission_id\"] == 1:\n # removes the user from channel_owner\n curr_channel[\"owner_ids\"].remove(u_id)\n # if the auth user is an owner of the channel, allow him to remove u_id as owner of channel\n elif is_curr_owner is True:\n # adds the user into channel_owner\n curr_channel[\"owner_ids\"].remove(u_id)\n # else the auth user is not an owner and thus cannot use addowner\n else:\n raise error.AccessError(description=\"\"\"Authorised user user is not an owner of the channel,\n or of the slackr\"\"\")","def unfriend(self, user_id, target_id):\n if self.database is None:\n raise Exception(\"No database.\")\n if user_id is None or len(user_id) == 0:\n raise Exception(\"Bad parameter.\")\n if target_id is None or len(target_id) == 0:\n raise Exception(\"Bad parameter.\")\n return self.database.delete_friend(user_id, target_id)","def _user_delete(sender, instance, using, **kwargs):\n Booking.objects.filter(requester=instance).update(\n requester=get_sentinel_user(instance.group)\n )","def remove_user(self, email):\n return run_transaction(\n self.sessionfactory,\n lambda session: remove_user_txn(session, email))","def unfollow(username):\n follow_form = FollowForm()\n unfollow_form = UnfollowForm()\n\n if unfollow_form.validate_on_submit():\n try:\n current_user.unfollow(username)\n flash('Unfollowed {}'.format(username))\n except ValueError as excep:\n flash(str(excep))\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)\n\n return render_template('user_history.html',\n username=username,\n follow_form=follow_form,\n unfollow_form=unfollow_form)"],"string":"[\n \"def unfollowUser(following):\\n \\n cur, user_id, con = initialise(3, True)\\n cur.execute(\\\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\\\", (user_id, following))\\n finish(con)\",\n \"def unfollow(self, user):\\n f = self.followed.filter_by(followed_id=user.id).first()\\n if f:\\n db.session.delete(f)\",\n \"def unfollow_user(username):\\n user_ID = before_request()\\n user_ID = None\\n if user_ID != None:\\n user_ID = str(g.user['_id'])\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n mongo.db.users.update({'_id': g.user['_id']}, {\\n '$pull': {'follows': whom_id}})\\n flash('You are no longer following \\\"%s\\\"' % username)\\n if redis_obj.get(user_ID):\\n return redirect(url_for('user_timeline', username=username, userId=pickle.loads(redis_obj.get(user_ID))))\\n else:\\n redis_obj.delete(session['user_id'])\\n print \\\"Invalidating cache after Unfollow\\\"\\n return redirect(url_for('user_timeline', username=username))\",\n \"def user_unfollow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.remove(following)\\n db.session.commit()\\n return {'unfollowed': True}\",\n \"def unfollow_user(username):\\n if not g.user:\\n abort(401)\\n whom_id = get_user_id(username)\\n if whom_id is None:\\n abort(404)\\n db = get_db()\\n db.execute('delete from follower where who_id=? and whom_id=?',\\n [session['user_id'], whom_id])\\n db.commit()\\n flash('You are no longer following \\\"%s\\\"' % username)\\n return redirect(url_for('user_timeline', username=username))\",\n \"def removeFollower(self,id):\\n # DELETE /followers/$id\\n pass\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followeeId not in self.users:\\n self._create_user(followeeId)\\n if followerId not in self.users:\\n self._create_user(followerId)\\n if followerId != followeeId and followeeId in self.users[followerId]:\\n self.users[followerId].remove(followeeId)\",\n \"def delete(self, request, *args, **kwargs):\\n\\n following_username = token_payload(request)\\n\\n followed_username = self.kwargs.get(self.look_url_kwarg)\\n try:\\n followed_user = User.objects.get(username=followed_username)\\n followed_user_id = followed_user.id\\n\\n is_following = FollowUser.objects.filter(\\n following_username=following_username)\\n for user in is_following:\\n if user.following_username == following_username and user.followed_username == followed_username:\\n returned_user = FollowUser.objects.get(pk=user.id)\\n returned_user.delete()\\n\\n followed_user_profile = Profile.objects.get(\\n profile_user=followed_user_id)\\n serializer_profile = self.serializer_classprofile(\\n followed_user_profile)\\n return Response({\\n \\\"user\\\": following_username,\\n \\\"unfollowing\\\": serializer_profile.data\\n })\\n return Response({\\n \\\"message\\\": 'User has been unfollowed or You are unfollowing a user you were not orignally following'\\n }, status=status.HTTP_400_BAD_REQUEST)\\n except:\\n return Response({\\n \\\"message\\\": 'User does not exist'\\n }, status=status.HTTP_400_BAD_REQUEST)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId != followeeId and followeeId in self.followList.get(followerId, []):\\n self.followList[followerId].remove(followeeId)\\n # print(self.followList)\",\n \"def unfollowing_and_removing(self, user_id):\\n if self.unfollowing(user_id):\\n ind = [i for i, j in enumerate(self.monitored_users) if j.get('user', '') == user_id]\\n if ind:\\n self.monitored_users.remove(self.monitored_users[ind[0]])\",\n \"def unfollow(request, usertostopfollow):\\n stop_follow = Member.objects.get(user__username=usertostopfollow)\\n user = Member.objects.get(user=request.user)\\n user.following.remove(stop_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n following = self.user_followed[followerId]\\n if followeeId in following:\\n following.remove(followeeId)\\n self.user_followed[followerId] = following\",\n \"def remove_user(self, user_id):\\n if user_id in self:\\n user = self[user_id]\\n del self[user_id]\\n return user\",\n \"def unfollow(self, followerId, followeeId):\\n # 在user_pool 中查询这个用户 follower\\n if self.user_pool[followerId]:\\n # 如果在用户的关注列表中才删除\\n if followeeId in self.user_pool[followerId].follows:\\n self.user_pool[followerId].follows.remove(followeeId)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n self.users[followerId].discard(followeeId)\",\n \"def remove(self, user_id):\\n pass\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followerId not in self.users.keys() or followeeId not in self.users.keys():\\n return\\n if followeeId not in self.users[followerId].followees.keys():\\n return\\n self.users[followerId].followees.pop(followeeId)\\n\\n\\n\\n # Your Twitter object will be instantiated and called as such:\\n # obj = Twitter()\\n # obj.postTweet(userId,tweetId)\\n # param_2 = obj.getNewsFeed(userId)\\n # obj.follow(followerId,followeeId)\\n # obj.unfollow(followerId,followeeId)\",\n \"def delete(self, request, username):\\n followed_user_exists = User.objects.filter(username=username).exists()\\n if not followed_user_exists:\\n return Response({'error': 'user not found'},\\n status.HTTP_404_NOT_FOUND)\\n followed_user = User.objects.get(username=username)\\n user_exists = Follow.is_user_already_followed(\\n followed_user_id=followed_user.id,\\n user_id=request.user.id\\n )\\n if user_exists:\\n instance = Follow.objects.filter(\\n user=self.request.user.id, followed_user=followed_user.id\\n )\\n instance.delete()\\n return Response({'message': 'user unfollowed'},\\n status.HTTP_200_OK)\\n return Response({'message': 'user not in followers'},\\n status.HTTP_404_NOT_FOUND)\",\n \"def unfollow_user(request, course_id, followed_user_id):\\r\\n user = cc.User.from_django_user(request.user)\\r\\n followed_user = cc.User.find(followed_user_id)\\r\\n user.unfollow(followed_user)\\r\\n return JsonResponse({})\",\n \"def delete(self, request, username):\\n\\n # Retrieve the user from the user table if the user exists\\n try:\\n user_to_unfollow = User.objects.get(username=username)\\n current_user = request.user\\n # If a user is trying to unfollow themselves then stop the request\\n if user_to_unfollow.profile.id == current_user.profile.id:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['CANNOT_UNFOLLOW_SELF']},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Check if the user to be unfollowed\\n # is in the current users following list\\n try:\\n profile_id = user_to_unfollow.profile.id\\n user_being_followed = CustomFollows.objects.get(\\n to_profile_id=profile_id,\\n from_profile_id=current_user.profile.id\\n )\\n # If not tell the user the request can't happen\\n # Because they don't follow the user\\n except Exception as e:\\n return Response(\\n {\\n \\\"errors\\\": FOLLOW_USER_MSGS['USER_UNFOLLOWED_ALREADY']\\n },\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n # Otherwise unfollow the user as requested\\n current_user.profile.follows.remove(user_to_unfollow.profile)\\n # Get the following & followers username list\\n # And the following & followers count for the current user\\n user_following_data = get_user_following_data(current_user)\\n return Response(\\n {\\n \\\"message\\\": FOLLOW_USER_MSGS['USER_UNFOLLOW_SUCCESSFUL'],\\n \\\"following\\\": user_following_data[\\\"following\\\"],\\n \\\"followers\\\": user_following_data[\\\"followers\\\"],\\n \\\"followingCount\\\": user_following_data[\\\"followingCount\\\"],\\n \\\"followersCount\\\": user_following_data[\\\"followersCount\\\"]\\n },\\n status=status.HTTP_200_OK\\n )\\n # End request if we cannot find the user we want to unfollow.\\n except User.DoesNotExist:\\n return Response(\\n {\\\"errors\\\": FOLLOW_USER_MSGS['USER_NOT_FOUND']},\\n status=status.HTTP_404_NOT_FOUND\\n )\",\n \"def delete_user():\",\n \"def pop_user(self):\\n return self._pop_user()\",\n \"def test_remove_followers(self):\\n pass\",\n \"def test_un_following_existing_user(self):\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def unfriend(self, remove):\\n remover_friends_list = self # person terminating the friendship \\n \\n # remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n\\n #remove friend from removee friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friend_list.remove_friend(self.user)\",\n \"def unfollow_friend(username):\\n\\n if not g.user:\\n print \\\"401\\\"\\n abort(401)\\n whom_id = get_user_id(username)\\n print whom_id\\n if whom_id is None:\\n abort(404)\\n unfollow_query(whom_id)\\n flash('You are no longer following \\\"%s\\\"' % username)\\n name = {'name of unfollowing user': username}\\n ############### REDIS cache invalidate #####################\\n R_SERVER.delete(user_timeline_key)\\n return jsonify(Username=name, Status_code=status.HTTP_200_OK)\",\n \"def unfollow(self, followerId, followeeId):\\n if followerId in self.follows:\\n if followeeId in self.follows[followerId]:\\n self.follows[followerId].remove(followeeId)\",\n \"def remove_user(self):\\n self.currentuser = None\\n self.carlocked = False\",\n \"def delete_user():\\n #TODO user delete\\n pass\",\n \"def delete_follow_request(request):\\n username = request.POST[\\\"user\\\"]\\n requester = get_user_from_username(request.user, username)\\n\\n follow_request = get_object_or_404(\\n models.UserFollowRequest, user_subject=requester, user_object=request.user\\n )\\n follow_request.raise_not_deletable(request.user)\\n\\n follow_request.delete()\\n return redirect(f\\\"/user/{request.user.localname}\\\")\",\n \"def unfriend(self, removee):\\n remover_friends_list = self # person terminating the friendship\\n # Remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n # Remove friend from removee's friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friends_list.remove_friend(self.user)\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n # Time Complexity: O(1)\\n if followerId in self.followees and followeeId in self.followees[followerId]:\\n self.followees[followerId].remove(followeeId)\",\n \"def remove_user(self, u: \\\"Node\\\") -> None:\\n\\n if u in self.users_:\\n self.users_[u] -= 1\\n if self.users_[u] == 0:\\n del self.users_[u]\",\n \"def remove_friend():\\n if request.method == 'POST':\\n username = get_username()\\n user_id = get_id_from_username(username)\\n friend_to_remove = get_id_from_username(request.form['remove_user'])\\n if not friend_to_remove or friend_to_remove==user_id:\\n return redirect(url_for('message.converse'))\\n remove_friend_db(user_id, friend_to_remove)\\n return redirect(url_for('message.converse'))\",\n \"def _onRemove(self, event):\\n sel = self.userlist.GetSelection()\\n if sel >= 0:\\n c.removeUser(self.userlist.GetString(sel))\\n self.userlist.Delete(sel)\\n del self.users[sel]\\n if len(self.users) >= 0:\\n self.userlist.SetSelection(0)\\n else:\\n self.userlist.SetSelection(-1)\",\n \"def _remove(users, room_name):\\n global users_removed\\n users_removed = []\\n\\n try:\\n\\n for word in users['message']['text'].split():\\n\\n if word == 'myself':\\n user = users['message']['sender']['name']\\n check_result = redis.srem(room_name, \\\"<\\\" + user + \\\">\\\")\\n \\n if check_result == 1:\\n users_removed.append(\\\"<\\\" + user + \\\">\\\")\\n else:\\n users_removed.append('Not found ->> ' + \\\"<\\\" + user + \\\">\\\")\\n\\n check_continue = 1\\n text = '```User removed: %s ```' % (','.join(users_removed))\\n\\n for _item in range(len(users['message']['text'].split())):\\n\\n _item = _item + 1\\n\\n try:\\n _type = users['message']['annotations'][_item]['userMention']['user']['type']\\n user = users['message']['annotations'][_item]['userMention']['user']['name']\\n \\n if _type == 'BOT':\\n\\n if check_continue == 1:\\n continue\\n else:\\n text = 'Please add user with @'\\n continue\\n \\n user = users['message']['annotations'][_item]['userMention']['user']['name']\\n check_result = redis.srem(room_name, \\\"<\\\" + user + \\\">\\\")\\n\\n except:\\n pass\\n\\n if check_result == 1:\\n users_removed.append(\\\"<\\\" + user + \\\">\\\")\\n else:\\n users_removed.append(\\\"Not found ->> \\\" + \\\"<\\\" + user + \\\">\\\")\\n text = \\\"```Removed users: %s ```\\\" % (','.join(list(set(users_removed))))\\n return text\\n except:\\n\\n text = 'Please add user with @'\\n return text\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n if followeeId in self.follow_map[followerId]:\\n self.follow_map[followerId].remove(followeeId)\",\n \"def user_unfollow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/destroy/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following[self.user_id].pop(user_id, None)\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is False\",\n \"def remove_user(users, curr_username, user_role, request_ip):\\n #TODO: error checking\\n log_connector.add_log('DELETE USER', \\\"Removed {} user(s)\\\".format(len(users)), curr_username, user_role, request_ip)\\n user_connector.remove_user(users)\",\n \"def test_unfollowing_existing_user_not_authenticated(self):\\n self.client.credentials()\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def remove_user(self, username):\\n\\n row = self.c.execute(\\\"SELECT * FROM profiles WHERE name =?\\\",\\n (username,))\\n for i in row:\\n user = i[1]\\n print(user)\\n if user == username:\\n self.c.execute(\\\"SELECT id FROM profiles WHERE name=?\\\",\\n (username,))\\n i_d = self.c.fetchone()[0]\\n self.c.execute(\\\"DELETE FROM events WHERE user_id=?\\\", (i_d,))\\n self.c.execute(\\\"DELETE FROM profiles WHERE name=?\\\", (username,))\\n self.conn.commit()\\n return True\\n else:\\n print ('User not found.')\",\n \"def unfollow(self, followerId, followeeId):\\n if followerId in self.follow_map and followeeId in self.follow_map[followerId]:\\n self.follow_map[followerId].remove(followeeId)\",\n \"def remove_user(self, username): # remove only users from json file\\n return self._user(username=username, remove=True)\",\n \"def delete_user(self):\\n\\n User.user_list.remove(self)\",\n \"def rm_favoriting_user_id(self, circuit_id, user_id):\\n key = ':'.join(\\n [CIRCUIT_FAV_USRS_1, \\n str(circuit_id), \\n CIRCUIT_FAV_USRS_2]\\n )\\n self.RS.srem(key, user_id)\",\n \"def deleteUser(self, userList, index):\\n\\n if(self.adminAccess):\\n ret = userList.pop(index)\\n print(\\\"User has been deleted\\\")\\n \\n return userList\",\n \"def unfollow(self, followerId, followeeId):\\r\\n self.follows[followerId].discard(followeeId)\",\n \"def unfriend(self, removee):\\n\\t\\tremover_friend_list = self # person terminating the friendship\\n\\n\\t\\t# Remove friend from friend request\\n\\t\\tremover_friend_list.remove_friend(removee)\\n\\n\\t\\t# Remove friend from the removeee friend list\\n\\t\\tfriends_list = FriendList.objects.get(user=removee)\\n\\t\\tfriends_list.remove_friend(self.user)\",\n \"def delete_by(self, user):\\n if user.is_superuser or user is self.added_by:\\n self.delete()\",\n \"def remove(self, user):\\r\\n url = '{0}/{1}'.format(self.get_url(), user)\\r\\n\\r\\n return http.Request('DELETE', url), parsers.parse_empty\",\n \"def del_user(self, username):\\n pass\",\n \"def delete_user(self) -> 'outputs.ActingUserResponse':\\n return pulumi.get(self, \\\"delete_user\\\")\",\n \"def delete_user():\\n del globalopts.appdata[request.user]\\n del globalopts.users[request.user]\\n return \\\"\\\", 200\",\n \"def remove(self, user):\\n if user != self.head:\\n user.group = None\\n user.save()\\n self.players.remove(user)\",\n \"def delete_user(id):\\n pass\",\n \"def unfollow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 1:\\n self.users_hat[following_index, user_index] = 0\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was not following user {user_index}\\\")\",\n \"def stop_following(follow_id):\\n\\n followed_user = User.query.get(follow_id)\\n g.user.following.remove(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def remove_user(user):\\n # user.confirmed = False\\n # user = get_user_by_phone(phone_num)\\n db.session.delete(user)\\n db.session.commit()\\n\\n return user\\n # DELETE FROM users WHERE user.phone_num == phone)\",\n \"def del_user(request):\\r\\n mdict = request.matchdict\\r\\n\\r\\n # Submit a username.\\r\\n del_username = mdict.get('username', None)\\r\\n\\r\\n if del_username is None:\\r\\n LOG.error('No username to remove.')\\r\\n request.response.status_int = 400\\r\\n return _api_response(request, {\\r\\n 'error': 'Bad Request: No username to remove.',\\r\\n })\\r\\n\\r\\n u = UserMgr.get(username=del_username)\\r\\n\\r\\n if not u:\\r\\n LOG.error('Username not found.')\\r\\n request.response.status_int = 404\\r\\n return _api_response(request, {\\r\\n 'error': 'User not found.',\\r\\n })\\r\\n\\r\\n try:\\r\\n # First delete all the tag references for this user's bookmarks.\\r\\n res = DBSession.query(Bmark.bid).filter(Bmark.username == u.username)\\r\\n bids = [b[0] for b in res]\\r\\n\\r\\n qry = bmarks_tags.delete(bmarks_tags.c.bmark_id.in_(bids))\\r\\n qry.execute()\\r\\n\\r\\n # Delete all of the bmarks for this year.\\r\\n Bmark.query.filter(Bmark.username == u.username).delete()\\r\\n DBSession.delete(u)\\r\\n return _api_response(request, {\\r\\n 'success': True,\\r\\n 'message': 'Removed user: ' + del_username\\r\\n })\\r\\n except Exception, exc:\\r\\n # There might be cascade issues or something that causes us to fail in\\r\\n # removing.\\r\\n LOG.error(exc)\\r\\n request.response.status_int = 500\\r\\n return _api_response(request, {\\r\\n 'error': 'Bad Request: ' + str(exc)\\r\\n })\",\n \"def remove(self, update, context):\\n\\n telegram_user = update.message.from_user\\n if len(context.args) != 1:\\n message = (\\n \\\"To remove a subscriptions from your list please use /remove <entryname>. To see all your \\\"\\n \\\"subscriptions along with their entry names use /list ! \\\"\\n )\\n update.message.reply_text(message)\\n return\\n\\n entry = self.db.get_user_bookmark(telegram_id=telegram_user.id, alias=context.args[0])\\n if entry:\\n self.db.remove_user_bookmark(telegram_id=telegram_user.id, url=entry[0])\\n message = \\\"I removed \\\" + context.args[0] + \\\" from your subscriptions!\\\"\\n update.message.reply_text(message)\\n else:\\n message = (\\n \\\"I can not find an entry with label \\\"\\n + context.args[0]\\n + \\\"in your subscriptions! Please check your subscriptions using /list and use the delete command \\\"\\n \\\"again! \\\"\\n )\\n update.message.reply_text(message)\",\n \"def _remove_user(self):\\n name = False\\n while not name: #While name not set\\n name = input(\\\"Please enter the username of the user you would like to remove: \\\").lower()\\n userID = self._get_user_id(name)\\n if not userID:\\n name = False\\n command = \\\"remove_user {0}\\\\r\\\\n\\\".format(userID)\\n return(command)\",\n \"def stop_following(follow_id):\\n\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n followed_user = User.query.get_or_404(follow_id)\\n g.user.following.remove(followed_user)\\n db.session.commit()\\n\\n return redirect(f\\\"/users/{g.user.id}/following\\\")\",\n \"def fusion_api_remove_user(self, name=None, uri=None, api=None, headers=None):\\n return self.user.delete(name, uri, api, headers)\",\n \"def remove_user(self, email):\\n try:\\n self.users.pop(email)\\n except KeyError:\\n raise UserDoesNotExist\",\n \"def onUserDeletion(event):\\n request = getRequest()\\n if not IProductLayer.providedBy(request):\\n return\\n\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n\\n principal_id = event.principal\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n pass_storage.remove(principal_id)\\n\\n d = users.deletePrincipal(client, principal_jid)\\n return d\",\n \"def removeUser(self, fullName):\\n logger.debug(\\\"Func: removeUser\\\")\\n\\n # old Name removeUser\\n currentDB = self._loadUsers()\\n del currentDB[fullName]\\n self._dumpJson(currentDB, self._pathsDict[\\\"usersFile\\\"])\\n self._usersDict = currentDB\\n return None, None\",\n \"def onUserDeletion(event):\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n storage = getUtility(IPubSubStorage)\\n\\n principal_id = event.principal\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n\\n if principal_id in storage.leaf_nodes:\\n storage.leaf_nodes.remove(principal_id)\\n if principal_id in storage.publishers:\\n del storage.publishers[principal_id]\\n if principal_id in storage.node_items:\\n del storage.node_items[principal_id]\\n if principal_id in storage.collections['people']:\\n storage.collections['people'].remove(principal_id)\\n\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n pass_storage.remove(principal_id)\\n\\n d = deletePrincipal(client, principal_jid)\\n return d\",\n \"def unfollow(self, followerId: 'int', followeeId: 'int') -> 'None':\\n self.followees[followerId].discard(followeeId)\",\n \"def remove_user(user_id):\\n user = Users.query.get(user_id)\\n if user_id in [0, 1]:\\n return 'Removal of default User #%s (%s) is forbidden.' % (user_id, user.login), 'warning'\\n db_session.delete(user)\\n db_session.commit()\\n return 'User #%s (%s) has been deleted.' % (user_id, user.login), 'success'\",\n \"def user_disappears(self, user):\\n pass\",\n \"def delete_model(self, request, obj):\\n From = User.objects.get(id=obj.From.id)\\n To = User.objects.get(id=obj.To.id)\\n From.following_numDe()\\n To.followed_numDe()\\n obj.delete()\",\n \"def unfollow_profile(self):\\n self.find_clickable_element(self.ISFOLLOWED_BTN).click()\",\n \"async def removeuser(self, ctx, user: discord.Member):\\n\\n if check_key(user.id):\\n delete_key(user.id)\\n await self.bot.say(\\\"{}, you are way out of this league.\\\".format(user.mention))\\n else:\\n await self.bot.say(\\\"That user does not exist in this league.\\\")\",\n \"def unfollow_me(self):\\n return self.follow_me('unfollow_topic')\",\n \"def deleted_user(request):\\n auth.logout(request)\\n messages.success(request, \\\"Your profile has been deleted. Please contact us if you want to undo this.\\\")\\n return redirect(reverse('index'))\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def unfollow(self, followerId: int, followeeId: int) -> None:\\n self.followees[followerId].discard(followeeId)\",\n \"def remove_users(caller, role, *users):\\r\\n # can always remove self (at this layer)\\r\\n if not(len(users) == 1 and caller == users[0]):\\r\\n _check_caller_authority(caller, role)\\r\\n role.remove_users(*users)\",\n \"def on_removeuser(self, username):\\n self.users.remove(username)\\n print ('%s left the room.' % username)\",\n \"def remove_admin(self, project_id, user_id):\\n current_user = request.environ.get('repoze.who.identity')['user']\\n user = controller_globals._get_user_from_email(current_user.email)\\n\\n # make sure we're actually the project lead\\n if not self._current_user_leads_review(project_id):\\n return \\\"<font color='red'>tsk, tsk. you're not the project lead, %s.</font>\\\" % user.fullname\\n\\n leader_to_remove = Session.query(model.User).filter_by(id=user_id).one()\\n review = self._get_review_from_id(project_id)\\n review.leaders.remove(leader_to_remove)\\n Session.add(review)\\n Session.commit()\\n\\n redirect(url(controller=\\\"review\\\", action=\\\"admin\\\", project_id=project_id))\",\n \"def unfollow(self, other):\\n\\t\\tif self.follows(other):\\n\\t\\t\\tself.followed.remove(other)\",\n \"def view_remove_user(self, user, username):\\r\\n user.realm._checker.removeUser(username)\",\n \"def auto_unfollow_nonfollowers():\\n\\n following = set(t.friends.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n followers = set(t.followers.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n # put user IDs here that you want to keep following even if they don't\\n # follow you back\\n users_keep_following = set([])\\n\\n not_following_back = following - followers\\n\\n # make sure the \\\"already followed\\\" file exists\\n if not os.path.isfile(ALREADY_FOLLOWED_FILE):\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n out_file.write(\\\"\\\")\\n\\n # update the \\\"already followed\\\" file with users who didn't follow back\\n already_followed = set(not_following_back)\\n af_list = []\\n with open(ALREADY_FOLLOWED_FILE) as in_file:\\n for line in in_file:\\n af_list.append(int(line))\\n\\n already_followed.update(set(af_list))\\n del af_list\\n\\n with open(ALREADY_FOLLOWED_FILE, \\\"w\\\") as out_file:\\n for val in already_followed:\\n out_file.write(str(val) + \\\"\\\\n\\\")\\n\\n for user_id in not_following_back:\\n if user_id not in users_keep_following:\\n t.friendships.destroy(user_id=user_id)\\n print(\\\"unfollowed %d\\\" % (user_id))\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def delete(self):\\n\\n user_id = get_jwt_identity()\\n user = user_crud.get(user_id)\\n if not user:\\n abort(404, message=\\\"User not Found\\\")\\n all_tokens = auth_crud.get_user_tokens(user_id)\\n tokens = [token.to_dict() for token in all_tokens]\\n for token in tokens:\\n auth_crud.revoke_token(token['id'], user_id)\\n user = user_crud.remove(user_id)\\n\\n return {'msg': 'User Removed'}\",\n \"def test_unfollow_user_without_auth(self):\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response = self.client.delete(self.unfollow_url,\\n data=self.followed_user)\\n self.assertEqual(response.data['detail'],\\n \\\"Authentication credentials were not provided.\\\")\\n\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_unfollowing_non_existing_user(self):\\n response = self.client.delete(\\n reverse(\\n 'follow',\\n kwargs={'username': 'NotThere'}\\n )\\n )\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\",\n \"def user_playlist_unfollow(self, user, playlist_id, **kwargs):\\n return self._delete(\\n \\\"users/%s/playlists/%s/followers\\\" % (user, playlist_id), **kwargs\\n )\",\n \"def staff_remove(request):\\n username = request.params['remove']\\n user = models.User.get_by_username(username)\\n user.staff = False\\n return httpexceptions.HTTPSeeOther(\\n location=request.route_url('admin_staff'))\",\n \"def get_sentinel_user():\\n return get_user_model().objects.get_or_create(username='deleted user').first()\",\n \"def getFollowers():\\n\\n cur, user_id = initialise(3)\\n cur.execute(\\\"SELECT following FROM followers WHERE user = (SELECT username FROM users WHERE id = ?)\\\", [user_id])\\n tempFollowers = cur.fetchall()\\n followers = []\\n for follower in tempFollowers:\\n followers.append(follower[0])\\n return followers\",\n \"def reject_follow(made_request_id, approver_id):\\n\\n if not g.user.id == approver_id:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\"), 403\\n\\n wanted_to_follow_user = User.query.get_or_404(made_request_id)\\n g.user.from_users.remove(wanted_to_follow_user)\\n db.session.commit()\\n flash(f\\\"Follow request from {wanted_to_follow_user.username} rejected.\\\", \\\"success\\\")\\n\\n return redirect(f\\\"/users/{g.user.id}\\\")\",\n \"def channel_removeowner(token, channel_id, u_id):\\n # Check if token is valid and raise AccessError if not\\n curr_id = database.get_current_user(token)\\n # gets current channel data\\n curr_channel = database.get_channel_data(channel_id)\\n # gets the permissions of current user from database\\n user_perms = database.get_permission_dict(curr_id)\\n\\n u_id_permission = database.get_permission_dict(u_id)\\n if u_id_permission[\\\"permission_id\\\"] == 1:\\n raise error.AccessError(description=\\\"user being removed is the owner of the slackr\\\")\\n\\n # checks if u_id is not an owner of the channel\\n # also checks if current auth user is an owner of the channel\\n is_u_owner = False\\n is_curr_owner = False\\n for owner_id in curr_channel[\\\"owner_ids\\\"]:\\n if u_id == owner_id:\\n is_u_owner = True\\n if curr_id == owner_id:\\n is_curr_owner = True\\n if is_u_owner is False:\\n raise error.InputError(description=\\\"user being removed is not an owner of the channel\\\")\\n\\n\\n # if the auth user is owner of slackr, allows him to remove u_id as owner\\n if user_perms[\\\"permission_id\\\"] == 1:\\n # removes the user from channel_owner\\n curr_channel[\\\"owner_ids\\\"].remove(u_id)\\n # if the auth user is an owner of the channel, allow him to remove u_id as owner of channel\\n elif is_curr_owner is True:\\n # adds the user into channel_owner\\n curr_channel[\\\"owner_ids\\\"].remove(u_id)\\n # else the auth user is not an owner and thus cannot use addowner\\n else:\\n raise error.AccessError(description=\\\"\\\"\\\"Authorised user user is not an owner of the channel,\\n or of the slackr\\\"\\\"\\\")\",\n \"def unfriend(self, user_id, target_id):\\n if self.database is None:\\n raise Exception(\\\"No database.\\\")\\n if user_id is None or len(user_id) == 0:\\n raise Exception(\\\"Bad parameter.\\\")\\n if target_id is None or len(target_id) == 0:\\n raise Exception(\\\"Bad parameter.\\\")\\n return self.database.delete_friend(user_id, target_id)\",\n \"def _user_delete(sender, instance, using, **kwargs):\\n Booking.objects.filter(requester=instance).update(\\n requester=get_sentinel_user(instance.group)\\n )\",\n \"def remove_user(self, email):\\n return run_transaction(\\n self.sessionfactory,\\n lambda session: remove_user_txn(session, email))\",\n \"def unfollow(username):\\n follow_form = FollowForm()\\n unfollow_form = UnfollowForm()\\n\\n if unfollow_form.validate_on_submit():\\n try:\\n current_user.unfollow(username)\\n flash('Unfollowed {}'.format(username))\\n except ValueError as excep:\\n flash(str(excep))\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\\n\\n return render_template('user_history.html',\\n username=username,\\n follow_form=follow_form,\\n unfollow_form=unfollow_form)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7169295","0.70720047","0.70225513","0.699361","0.6943185","0.682216","0.6751137","0.67458344","0.6741086","0.6738047","0.6737277","0.6707055","0.66615844","0.6618556","0.6605784","0.6594128","0.65536183","0.6531541","0.6502671","0.6478856","0.64270645","0.6386949","0.63730955","0.63557","0.635173","0.635173","0.635173","0.63329494","0.6327335","0.6309968","0.6284286","0.627003","0.62681854","0.6265805","0.6249155","0.62380797","0.62123746","0.6208475","0.6192651","0.6165077","0.61405295","0.61282945","0.61005497","0.6094172","0.60859257","0.6083459","0.60790837","0.60442585","0.60070646","0.6006908","0.6003245","0.5998903","0.5991501","0.5990848","0.59900767","0.5989706","0.5987541","0.59701294","0.5960098","0.59539247","0.5953452","0.59527725","0.59405565","0.5927349","0.5921064","0.59168595","0.5914433","0.5906906","0.5902731","0.5901519","0.5894958","0.5879183","0.5874065","0.5859014","0.58507174","0.5846768","0.5834417","0.583422","0.5822393","0.58210754","0.5820766","0.5820664","0.5815014","0.58138","0.58135366","0.5808244","0.5802004","0.57948285","0.5790052","0.578862","0.5768945","0.5764873","0.57522005","0.57428735","0.5737553","0.5730244","0.5726366","0.5717167","0.5716724","0.5716476"],"string":"[\n \"0.7169295\",\n \"0.70720047\",\n \"0.70225513\",\n \"0.699361\",\n \"0.6943185\",\n \"0.682216\",\n \"0.6751137\",\n \"0.67458344\",\n \"0.6741086\",\n \"0.6738047\",\n \"0.6737277\",\n \"0.6707055\",\n \"0.66615844\",\n \"0.6618556\",\n \"0.6605784\",\n \"0.6594128\",\n \"0.65536183\",\n \"0.6531541\",\n \"0.6502671\",\n \"0.6478856\",\n \"0.64270645\",\n \"0.6386949\",\n \"0.63730955\",\n \"0.63557\",\n \"0.635173\",\n \"0.635173\",\n \"0.635173\",\n \"0.63329494\",\n \"0.6327335\",\n \"0.6309968\",\n \"0.6284286\",\n \"0.627003\",\n \"0.62681854\",\n \"0.6265805\",\n \"0.6249155\",\n \"0.62380797\",\n \"0.62123746\",\n \"0.6208475\",\n \"0.6192651\",\n \"0.6165077\",\n \"0.61405295\",\n \"0.61282945\",\n \"0.61005497\",\n \"0.6094172\",\n \"0.60859257\",\n \"0.6083459\",\n \"0.60790837\",\n \"0.60442585\",\n \"0.60070646\",\n \"0.6006908\",\n \"0.6003245\",\n \"0.5998903\",\n \"0.5991501\",\n \"0.5990848\",\n \"0.59900767\",\n \"0.5989706\",\n \"0.5987541\",\n \"0.59701294\",\n \"0.5960098\",\n \"0.59539247\",\n \"0.5953452\",\n \"0.59527725\",\n \"0.59405565\",\n \"0.5927349\",\n \"0.5921064\",\n \"0.59168595\",\n \"0.5914433\",\n \"0.5906906\",\n \"0.5902731\",\n \"0.5901519\",\n \"0.5894958\",\n \"0.5879183\",\n \"0.5874065\",\n \"0.5859014\",\n \"0.58507174\",\n \"0.5846768\",\n \"0.5834417\",\n \"0.583422\",\n \"0.5822393\",\n \"0.58210754\",\n \"0.5820766\",\n \"0.5820664\",\n \"0.5815014\",\n \"0.58138\",\n \"0.58135366\",\n \"0.5808244\",\n \"0.5802004\",\n \"0.57948285\",\n \"0.5790052\",\n \"0.578862\",\n \"0.5768945\",\n \"0.5764873\",\n \"0.57522005\",\n \"0.57428735\",\n \"0.5737553\",\n \"0.5730244\",\n \"0.5726366\",\n \"0.5717167\",\n \"0.5716724\",\n \"0.5716476\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65360326"},"document_rank":{"kind":"string","value":"17"}}},{"rowIdx":7199,"cells":{"query":{"kind":"string","value":"Returns true if the current user is following the desired user"},"document":{"kind":"string","value":"def is_following(self, user):\n return self.followed.filter(followers.c.followed_id == user.id).count() > 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_user_following(self, user_id):\n return user_id in self.following","def is_following(self, you, them):\n if self.filter(from_user=you, to_user=them).count() > 0:\n return True\n return False","def is_following(self, user):\n return self.followed.filter_by(\n followed_id=user.id).first() is not None","def is_follower(self, you, them):\n if self.filter(from_user=them, to_user=you).count() > 0:\n return True\n return False","def is_followed_by(self, user):\n return self.followers.filter_by(\n follower_id=user.id).first() is not None","def is_authenticated_user_following(self, username=None,id=None):\n if not self.is_authenticated:\n raise PicplzError(\"is_authenticated_user_following requires an authenticated API instance\")\n \n return None","def is_following_by_username(self, id):\n return self.followed.filter(followers.c.followed_id == id).count() > 0","def user_follow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n if user_id in self._users_following.get(self.user_id, []):\n self.logger.debug(\"User %s already followed\", user_id)\n return False\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/create/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following.pop(self.user_id) # reset\n return result[\"friendship_status\"][\"following\"] is True","def belongs_to_user(self) -> bool:\n return flask.g.user is not None and flask.g.user.id == getattr(\n self, 'user_id', False\n )","def follows(self, other):\n\t\treturn self.followed.filter(followers.c.followed_by == other.id).count() > 0","def follow_user(cls, user, following):\r\n pass","def test_is_following(self):\n\n self.u1.following.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_following(self.u2))\n self.assertFalse(self.u2.is_following(self.u1))","def is_user_player(self, user):\n return self.user == user","async def is_following(self, TargetId: int):\n\n data = {\"targetUserIds\": [TargetId]}\n e = await self.request.request(url=f'https://friends.roblox.com/v1/user/following-exists',\n method='post',\n data=data)\n return e['followings'][0]['isFollowing']","def follow_user(cls, user, following):\n pass","def test_is_followed_by(self):\n\n self.u1.followers.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_followed_by(self.u2))\n self.assertFalse(self.u2.is_followed_by(self.u1))","def same_user_or_shiftleader(self, user):\n try:\n return (\n self.get_object().userid == user\n or user.is_superuser\n or user.userprofile.has_shift_leader_rights\n )\n except UserProfile.DoesNotExist:\n return False","def is_following(source_id, destination_id):\n if Forward.objects.filter(source_id=source_id,\n destination_id=destination_id):\n return True\n return False","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def author_following(self):\n\t\tpass","def follow(self, user_index, following_index):\n if user_index >= self.num_users or following_index >= self.num_users:\n raise ValueError(\n f\"Number of users is {self.num_users}, but indices \"\n f\"{user_index} and {following_index} were requested.\"\n )\n if self.users_hat[following_index, user_index] == 0:\n self.users_hat[following_index, user_index] = 1\n elif self.is_verbose():\n self.log(f\"User {following_index} was already following user {user_index}\")","def user_playlist_is_following(self, playlist_id, user_ids, **kwargs):\n return self._get(\n API.PLAYLIST_FOLLOWERS_CONTAINS.value.format( # pylint: disable=no-member\n playlist_id=playlist_id\n ),\n ids=\",\".join(user_ids),\n **kwargs,\n )","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def follow(self, user):\n if not self.is_following(user):\n self.followed.append(user)\n return self","def user_present(ctx: Context, channel: TextChannel) -> bool:\n for member in channel.members:\n if member.id == ctx.author.id:\n return True\n\n return False","def _has_following_ratio_of(self, user: _InstagramUser, ratio: float) -> bool:\n follower_count = len(self._user_follower_info(uid=user.uid))\n following_count = len(self._user_following_info(uid=user.uid))\n\n if follower_count == 0:\n return True\n\n return (following_count / follower_count) > ratio","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def user_follow():\n data = request.get_json(force=True)\n follower = User.query.get(data['follower'])\n following = User.query.get(data['following'])\n follower.followcheck.append(following)\n db.session.commit()\n return {'followed': True}","def checkIfUserIsCurrent(self,userId : str) -> bool:\n\n if userId == userId[0]:\n return True\n else:\n return False","def is_user_playing(self, user):\n return user in self.active_games","def followed_by(self):\r\n return relationships.FollowedBy(self)","def doesfollow(user):\n return jsonify({\n 'follows': isFollowed(g.user,user)\n })","def followed_by(self):\n return relationships.FollowedBy(self)","def is_owned_by(self, user):\n return user and user.id == self.user_id","def user_is_same(current_user_number, past_user_numbers):\n if current_user_number == past_user_numbers:\n return True\n else:\n return False","def ref_user_flag(self):\n try:\n ref = User.objects.get(\n associated_emails__email__iexact=self.reference_email,\n associated_emails__is_verified=True)\n return True\n except ObjectDoesNotExist:\n return False","def follow(self, user):\n if not self.is_following(user):\n f = Follow(follower=self, followed=user)\n db.session.add(f)","def test_following_existing_user(self):\n response = self.client.post(\n reverse(\n 'follow',\n kwargs={'username': self.followed['user'].get('username')}\n )\n )\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)","def is_user_change_required(self):\n return self.__running_user != self.__desired_user","def follow_user(self, user):\n self.nav_user(user)\n follow_button = self.driver.find_element_by_xpath(\n \"//button[contains(text(), 'Follow')]\")\n follow_button.click()\n time.sleep(1)\n self.driver.get(self.base_url)","def following(self):\n return self.data.get(\"following\")","def check_active(self, user):\r\n if not self.require_active:\r\n # Ignore & move on.\r\n return True\r\n\r\n return user.is_active","def is_me(self, m):\n return m.author == self.client.user","def test_func(self):\n answer = self.get_object()\n return True if self.request.user == answer.author or self.request.user.is_superuser else False","def is_user_already_followed(followed_user_id, user_id):\n result = Follow.objects.filter(followed_user=followed_user_id,\n user=user_id).exists()\n return result","def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])","def _is_follow_request(environ, result):\n r = Request(environ)\n if r.params.get(\"action\") == \"follow\":\n return True\n else:\n return False","def was_followed(sender, instance, created, **kwargs):\n\n sendr = User.objects.get(id=instance.user_id)\n followed = User.objects.get(id=instance.followed_user_id)\n if created:\n notify.send(sender=sendr, recipient=followed, verb='followed',\n description=\"{} followed you.\".format(sendr.username))","def is_visible_to(self, user):\n return True","def user_unfollow(self, user_id: int) -> bool:\n assert self.user_id, \"Login required\"\n user_id = int(user_id)\n data = self.with_action_data({\"user_id\": user_id})\n result = self.private_request(f\"friendships/destroy/{user_id}/\", data)\n if self.user_id in self._users_following:\n self._users_following[self.user_id].pop(user_id, None)\n return result[\"friendship_status\"][\"following\"] is False","def get_is_self(self, obj: Profile) -> bool:\n request: HttpRequest = self.context.get('request')\n if request:\n if request.user.is_authenticated:\n return obj == request.user.profile\n return False","def save_following(self, user, path=None):\n # Redis has an end_cursor if we've collected this data before\n end_cursor = self.redis.get(''.join(['gh:', user.login, ':following:endCursor']))\n if end_cursor:\n end_cursor = end_cursor.decode('utf-8')\n end_cursor = ''.join(['\"', end_cursor, '\"'])\n following = u.following(first=100, after=end_cursor)\n else:\n following = u.following(first=100)\n if not following:\n return False\n while True:\n if following['data']['user']['following']['edges']:\n index = ''.join(['gh_following-', self.timestamp])\n self._write_to_datastore(index=index,\n doc_type='GithubFollowing',\n document=following,\n login=user.login,\n path=path)\n has_next_page = following['data']['user']['following']['pageInfo']['hasNextPage']\n end_cursor = following['data']['user']['following']['pageInfo']['endCursor']\n if has_next_page:\n following = u.following(first=100, after=end_cursor)\n else:\n # Cache the end_cursor where we last collected data\n self.redis.set(''.join(['gh:', u.login, ':following:endCursor']), end_cursor)\n break\n else:\n break\n\n return True","def test_person_is_following(self):\n person = Person.objects.create(\n username='tom', email='tom@gmail.com', password='fake_password'\n )\n self.assertFalse(person.is_following(1))\n show = FollowedShows.objects.create(\n user=person,\n show_name='show1',\n show_id=1,\n air_days='monday, tuesday',\n air_time='10:00',\n summary='summary here',\n network='network here'\n )\n self.assertTrue(person.is_following(show.show_id))","def check_inflight_already_running(self, user: Identifier) -> bool:\n with self._lock:\n for flow in self.in_flight:\n if flow.requestor == user:\n return True\n return False","def test_user_already_followed(self):\n self.authorize_user(self.user)\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response2 = self.client.post(self.follow_url, format='json')\n self.assertEqual(response2.content,\n b'{\"detail\": {\"error\": \"user already followed\"}}')\n self.assertEqual(response2.status_code, status.HTTP_400_BAD_REQUEST)","def can_add_player(self, user):\n user_profile = user.get_profile()\n if user_profile.credits < self.entrance_fee:\n return False\n if self.is_user_playing(user):\n return False\n return True","def follow(self, other):\n\t\tif not self.follows(other):\n\t\t\tself.followed.append(other)","def test_func(self):\n if not self.request.user.is_authenticated:\n return False\n if self.request.user.is_staff:\n return True\n return self.get_user() == self.request.user","def follow_user(self:'InstaClient', user:str, nav_to_user:bool=True):\n # Check User Vadility\n profile = self.get_profile(user)\n if not profile:\n raise InvalidUserError(user)\n\n # Navigate to User Page\n self._nav_user(user, check_user=False)\n \n if self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.MESSAGE_USER_BTN))):\n # User already followed\n pass\n else:\n follow_button = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.FOLLOW_BTN)), url=ClientUrls.NAV_USER.format(user))\n self._press_button(follow_button)\n profile.requested_by_viewer = True\n return profile","def logged_in(self):\n return self.user is not None","def is_registered(self):\n if self.user == getpass.getuser():\n return True\n else:\n return False","def is_participant(self):\n if self.user is None:\n return False\n if unicode(self.user._id) in self.barcamp.event.participants:\n return True\n return False","def authorizes(self, user):\n return self.owner == user or self.workers.filter(pk=user.id).exists()","def accept_follow_request(request):\n username = request.POST[\"user\"]\n requester = get_user_from_username(request.user, username)\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester, user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n # Request already dealt with.\n return redirect(request.user.local_path)\n follow_request.accept()\n\n return redirect(request.user.local_path)","def can_update_user(cls, db_tuple, target, actor):\n return target.user_id == actor.user_id","def test_following(self):\n\n follow1 = Follows(\n user_being_followed_id = self.testuser2.id,\n user_following_id = self.testuser.id\n )\n follow2 = Follows(\n user_being_followed_id = self.testuser3.id,\n user_following_id = self.testuser.id\n )\n\n db.session.add_all((follow1, follow2))\n db.session.commit()\n\n with self.client as c:\n with c.session_transaction() as session:\n session[CURR_USER_KEY] = self.testuser.id\n \n response = c.get(f\"/users/{self.testuser.id}/following\")\n data = str(response.data)\n\n self.assertEqual(response.status_code, 200)\n self.assertIn(\"@bob\", data)\n self.assertIn(\"@carl\", data)\n self.assertNotIn(\"@alvin\", data)","def following_and_storing(self, user_obj):\n if self.following(user_obj['user']):\n self.monitored_users.append({'user': user_obj['user'], 'username': user_obj['username'],\n 'followDate': datetime.now().timestamp()})","def user_appears(self, user):\n pass","def check_user(self):\n try:\n if self.get_customer()[0][0] == self.dni:\n return True\n else:\n return False\n except:\n return False","def follow(self, follower, followee):\n pass","def test_func(self):\n member_to_finish = self.get_object()\n return self.request.user.rfid == member_to_finish.rfid","def test_user_follow_self(self):\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.authorize_user(self.user)\n response = self.client.post(self.follow_self_url, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)","def following_changed(sender, action, instance, *args, **kwargs):\n\n # m2mchanged.connect specified in apps.py\n\n following = instance.following.all()\n creator = instance.user\n\n if creator in following:\n raise ValidationError (\"can't like own post\")","def follows(self, name):\r\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\r\n\r\n return http.Request('GET', url), resource.parse_boolean","def test_func(self):\n post = self.get_object()\n if self.request.user == post.author:\n return True\n return False","def is_active_user(self):\n\n return self.is_active","def is_user(self, user='') -> int:\n try:\n if user in self.users:\n return(1)\n else:\n return(0)\n except Exception as error:\n print(f\"Error: self.is_user({user}) -> {error}\")","def author_is_me(message: discord.Message) -> bool:\n return message.author == config.bot.user","def liked_by(self, user):\n return Likes.objects.filter(recipe=self, chef=user).exists()","def test_get_list_of_following_users_with_auth(self):\n with self.settings(\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\n self.authorize_user(self.user)\n self.register_user(self.user1)\n self.client.post(self.follow_url, format='json')\n response = self.client.get(self.following_list_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def logged_in(self):\n return self.auth.get_user_by_session() is not None","def is_profile_complete(self):\n return bool(self.fullname and self.username and self.email)","def user_has_access(self, user):\n if self.visibility == self.PUBLIC:\n return True\n elif self.visibility == self.PRIVATE and self.created_by == user:\n return True\n elif self.visibility in (self.ORG_ONLY, self.ORG_ONLY_NO_EXTERNAL):\n if user.external and self.visibility == self.ORG_ONLY_NO_EXTERNAL:\n return False\n elif self.organization.memberships.filter(user=user).count() >= 1:\n return True\n return False","def has_access(self, user):\n if user.is_superuser:\n return True\n return self.user_objects(user).filter(id=self.id).exists()","def is_active(self):\n return self.status == ACTIVE_USER","def gatekeeper():\n\n if user.name in GATEKEEPERS:\n return True\n\n return False","def is_leader(self):\n return self.__is_leader","def early_return(bot:Bot, ctx:Context):\n return ctx.message.author.bot or ctx.message.author.id == bot.user.id","def can_be_viewed_by(self,user):\n return True","def show_following(user_id):\n\n\n user = User.query.get_or_404(user_id)\n return render_template('users/following.html', user=user)","def test_follow_manually_approved(self):\n activity = {\n \"@context\": \"https://www.w3.org/ns/activitystreams\",\n \"id\": \"https://example.com/users/rat/follows/123\",\n \"type\": \"Follow\",\n \"actor\": \"https://example.com/users/rat\",\n \"object\": \"https://example.com/user/mouse\",\n }\n\n self.local_user.manually_approves_followers = True\n self.local_user.save(\n broadcast=False, update_fields=[\"manually_approves_followers\"]\n )\n\n with patch(\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\"):\n views.inbox.activity_task(activity)\n\n # notification created\n notification = models.Notification.objects.get()\n self.assertEqual(notification.user, self.local_user)\n self.assertEqual(notification.notification_type, \"FOLLOW_REQUEST\")\n\n # the request should exist\n request = models.UserFollowRequest.objects.get()\n self.assertEqual(request.user_subject, self.remote_user)\n self.assertEqual(request.user_object, self.local_user)\n\n # the follow relationship should not exist\n follow = models.UserFollows.objects.all()\n self.assertEqual(list(follow), [])","def is_logged_in(self) -> bool:\n return self.id is not None and self.username is not None","def is_participant(self,user):\n if user.is_superuser:\n return True\n\n if user.groups.filter(name=self.participants_group_name).count() > 0:\n return True\n else:\n return False","def still_deciding(self):\n for player in self.players:\n if isinstance(player, user.User):\n if not player.has_played:\n return True\n return False","def is_eligible(self, myself):\n if self.author().screen_name == myself.screen_name:\n log_.debug(\"Not replying to my own tweets\")\n return False\n if self.is_retweet():\n log_.debug(\"Not processing pure retweets\")\n return False\n return True","def follows(self):\r\n return relationships.Follows(self)"],"string":"[\n \"def is_user_following(self, user_id):\\n return user_id in self.following\",\n \"def is_following(self, you, them):\\n if self.filter(from_user=you, to_user=them).count() > 0:\\n return True\\n return False\",\n \"def is_following(self, user):\\n return self.followed.filter_by(\\n followed_id=user.id).first() is not None\",\n \"def is_follower(self, you, them):\\n if self.filter(from_user=them, to_user=you).count() > 0:\\n return True\\n return False\",\n \"def is_followed_by(self, user):\\n return self.followers.filter_by(\\n follower_id=user.id).first() is not None\",\n \"def is_authenticated_user_following(self, username=None,id=None):\\n if not self.is_authenticated:\\n raise PicplzError(\\\"is_authenticated_user_following requires an authenticated API instance\\\")\\n \\n return None\",\n \"def is_following_by_username(self, id):\\n return self.followed.filter(followers.c.followed_id == id).count() > 0\",\n \"def user_follow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n if user_id in self._users_following.get(self.user_id, []):\\n self.logger.debug(\\\"User %s already followed\\\", user_id)\\n return False\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/create/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following.pop(self.user_id) # reset\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is True\",\n \"def belongs_to_user(self) -> bool:\\n return flask.g.user is not None and flask.g.user.id == getattr(\\n self, 'user_id', False\\n )\",\n \"def follows(self, other):\\n\\t\\treturn self.followed.filter(followers.c.followed_by == other.id).count() > 0\",\n \"def follow_user(cls, user, following):\\r\\n pass\",\n \"def test_is_following(self):\\n\\n self.u1.following.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_following(self.u2))\\n self.assertFalse(self.u2.is_following(self.u1))\",\n \"def is_user_player(self, user):\\n return self.user == user\",\n \"async def is_following(self, TargetId: int):\\n\\n data = {\\\"targetUserIds\\\": [TargetId]}\\n e = await self.request.request(url=f'https://friends.roblox.com/v1/user/following-exists',\\n method='post',\\n data=data)\\n return e['followings'][0]['isFollowing']\",\n \"def follow_user(cls, user, following):\\n pass\",\n \"def test_is_followed_by(self):\\n\\n self.u1.followers.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_followed_by(self.u2))\\n self.assertFalse(self.u2.is_followed_by(self.u1))\",\n \"def same_user_or_shiftleader(self, user):\\n try:\\n return (\\n self.get_object().userid == user\\n or user.is_superuser\\n or user.userprofile.has_shift_leader_rights\\n )\\n except UserProfile.DoesNotExist:\\n return False\",\n \"def is_following(source_id, destination_id):\\n if Forward.objects.filter(source_id=source_id,\\n destination_id=destination_id):\\n return True\\n return False\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def author_following(self):\\n\\t\\tpass\",\n \"def follow(self, user_index, following_index):\\n if user_index >= self.num_users or following_index >= self.num_users:\\n raise ValueError(\\n f\\\"Number of users is {self.num_users}, but indices \\\"\\n f\\\"{user_index} and {following_index} were requested.\\\"\\n )\\n if self.users_hat[following_index, user_index] == 0:\\n self.users_hat[following_index, user_index] = 1\\n elif self.is_verbose():\\n self.log(f\\\"User {following_index} was already following user {user_index}\\\")\",\n \"def user_playlist_is_following(self, playlist_id, user_ids, **kwargs):\\n return self._get(\\n API.PLAYLIST_FOLLOWERS_CONTAINS.value.format( # pylint: disable=no-member\\n playlist_id=playlist_id\\n ),\\n ids=\\\",\\\".join(user_ids),\\n **kwargs,\\n )\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n self.followed.append(user)\\n return self\",\n \"def user_present(ctx: Context, channel: TextChannel) -> bool:\\n for member in channel.members:\\n if member.id == ctx.author.id:\\n return True\\n\\n return False\",\n \"def _has_following_ratio_of(self, user: _InstagramUser, ratio: float) -> bool:\\n follower_count = len(self._user_follower_info(uid=user.uid))\\n following_count = len(self._user_following_info(uid=user.uid))\\n\\n if follower_count == 0:\\n return True\\n\\n return (following_count / follower_count) > ratio\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def user_follow():\\n data = request.get_json(force=True)\\n follower = User.query.get(data['follower'])\\n following = User.query.get(data['following'])\\n follower.followcheck.append(following)\\n db.session.commit()\\n return {'followed': True}\",\n \"def checkIfUserIsCurrent(self,userId : str) -> bool:\\n\\n if userId == userId[0]:\\n return True\\n else:\\n return False\",\n \"def is_user_playing(self, user):\\n return user in self.active_games\",\n \"def followed_by(self):\\r\\n return relationships.FollowedBy(self)\",\n \"def doesfollow(user):\\n return jsonify({\\n 'follows': isFollowed(g.user,user)\\n })\",\n \"def followed_by(self):\\n return relationships.FollowedBy(self)\",\n \"def is_owned_by(self, user):\\n return user and user.id == self.user_id\",\n \"def user_is_same(current_user_number, past_user_numbers):\\n if current_user_number == past_user_numbers:\\n return True\\n else:\\n return False\",\n \"def ref_user_flag(self):\\n try:\\n ref = User.objects.get(\\n associated_emails__email__iexact=self.reference_email,\\n associated_emails__is_verified=True)\\n return True\\n except ObjectDoesNotExist:\\n return False\",\n \"def follow(self, user):\\n if not self.is_following(user):\\n f = Follow(follower=self, followed=user)\\n db.session.add(f)\",\n \"def test_following_existing_user(self):\\n response = self.client.post(\\n reverse(\\n 'follow',\\n kwargs={'username': self.followed['user'].get('username')}\\n )\\n )\\n\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\",\n \"def is_user_change_required(self):\\n return self.__running_user != self.__desired_user\",\n \"def follow_user(self, user):\\n self.nav_user(user)\\n follow_button = self.driver.find_element_by_xpath(\\n \\\"//button[contains(text(), 'Follow')]\\\")\\n follow_button.click()\\n time.sleep(1)\\n self.driver.get(self.base_url)\",\n \"def following(self):\\n return self.data.get(\\\"following\\\")\",\n \"def check_active(self, user):\\r\\n if not self.require_active:\\r\\n # Ignore & move on.\\r\\n return True\\r\\n\\r\\n return user.is_active\",\n \"def is_me(self, m):\\n return m.author == self.client.user\",\n \"def test_func(self):\\n answer = self.get_object()\\n return True if self.request.user == answer.author or self.request.user.is_superuser else False\",\n \"def is_user_already_followed(followed_user_id, user_id):\\n result = Follow.objects.filter(followed_user=followed_user_id,\\n user=user_id).exists()\\n return result\",\n \"def follow(request, usertofollow):\\n to_follow = Member.objects.get(user__username=usertofollow)\\n user = Member.objects.get(user=request.user)\\n user.following.add(to_follow)\\n user.save()\\n return redirect(request.META['HTTP_REFERER'])\",\n \"def _is_follow_request(environ, result):\\n r = Request(environ)\\n if r.params.get(\\\"action\\\") == \\\"follow\\\":\\n return True\\n else:\\n return False\",\n \"def was_followed(sender, instance, created, **kwargs):\\n\\n sendr = User.objects.get(id=instance.user_id)\\n followed = User.objects.get(id=instance.followed_user_id)\\n if created:\\n notify.send(sender=sendr, recipient=followed, verb='followed',\\n description=\\\"{} followed you.\\\".format(sendr.username))\",\n \"def is_visible_to(self, user):\\n return True\",\n \"def user_unfollow(self, user_id: int) -> bool:\\n assert self.user_id, \\\"Login required\\\"\\n user_id = int(user_id)\\n data = self.with_action_data({\\\"user_id\\\": user_id})\\n result = self.private_request(f\\\"friendships/destroy/{user_id}/\\\", data)\\n if self.user_id in self._users_following:\\n self._users_following[self.user_id].pop(user_id, None)\\n return result[\\\"friendship_status\\\"][\\\"following\\\"] is False\",\n \"def get_is_self(self, obj: Profile) -> bool:\\n request: HttpRequest = self.context.get('request')\\n if request:\\n if request.user.is_authenticated:\\n return obj == request.user.profile\\n return False\",\n \"def save_following(self, user, path=None):\\n # Redis has an end_cursor if we've collected this data before\\n end_cursor = self.redis.get(''.join(['gh:', user.login, ':following:endCursor']))\\n if end_cursor:\\n end_cursor = end_cursor.decode('utf-8')\\n end_cursor = ''.join(['\\\"', end_cursor, '\\\"'])\\n following = u.following(first=100, after=end_cursor)\\n else:\\n following = u.following(first=100)\\n if not following:\\n return False\\n while True:\\n if following['data']['user']['following']['edges']:\\n index = ''.join(['gh_following-', self.timestamp])\\n self._write_to_datastore(index=index,\\n doc_type='GithubFollowing',\\n document=following,\\n login=user.login,\\n path=path)\\n has_next_page = following['data']['user']['following']['pageInfo']['hasNextPage']\\n end_cursor = following['data']['user']['following']['pageInfo']['endCursor']\\n if has_next_page:\\n following = u.following(first=100, after=end_cursor)\\n else:\\n # Cache the end_cursor where we last collected data\\n self.redis.set(''.join(['gh:', u.login, ':following:endCursor']), end_cursor)\\n break\\n else:\\n break\\n\\n return True\",\n \"def test_person_is_following(self):\\n person = Person.objects.create(\\n username='tom', email='tom@gmail.com', password='fake_password'\\n )\\n self.assertFalse(person.is_following(1))\\n show = FollowedShows.objects.create(\\n user=person,\\n show_name='show1',\\n show_id=1,\\n air_days='monday, tuesday',\\n air_time='10:00',\\n summary='summary here',\\n network='network here'\\n )\\n self.assertTrue(person.is_following(show.show_id))\",\n \"def check_inflight_already_running(self, user: Identifier) -> bool:\\n with self._lock:\\n for flow in self.in_flight:\\n if flow.requestor == user:\\n return True\\n return False\",\n \"def test_user_already_followed(self):\\n self.authorize_user(self.user)\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response2 = self.client.post(self.follow_url, format='json')\\n self.assertEqual(response2.content,\\n b'{\\\"detail\\\": {\\\"error\\\": \\\"user already followed\\\"}}')\\n self.assertEqual(response2.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def can_add_player(self, user):\\n user_profile = user.get_profile()\\n if user_profile.credits < self.entrance_fee:\\n return False\\n if self.is_user_playing(user):\\n return False\\n return True\",\n \"def follow(self, other):\\n\\t\\tif not self.follows(other):\\n\\t\\t\\tself.followed.append(other)\",\n \"def test_func(self):\\n if not self.request.user.is_authenticated:\\n return False\\n if self.request.user.is_staff:\\n return True\\n return self.get_user() == self.request.user\",\n \"def follow_user(self:'InstaClient', user:str, nav_to_user:bool=True):\\n # Check User Vadility\\n profile = self.get_profile(user)\\n if not profile:\\n raise InvalidUserError(user)\\n\\n # Navigate to User Page\\n self._nav_user(user, check_user=False)\\n \\n if self._check_existence(EC.presence_of_element_located((By.XPATH, Paths.MESSAGE_USER_BTN))):\\n # User already followed\\n pass\\n else:\\n follow_button = self._find_element(EC.presence_of_element_located((By.XPATH, Paths.FOLLOW_BTN)), url=ClientUrls.NAV_USER.format(user))\\n self._press_button(follow_button)\\n profile.requested_by_viewer = True\\n return profile\",\n \"def logged_in(self):\\n return self.user is not None\",\n \"def is_registered(self):\\n if self.user == getpass.getuser():\\n return True\\n else:\\n return False\",\n \"def is_participant(self):\\n if self.user is None:\\n return False\\n if unicode(self.user._id) in self.barcamp.event.participants:\\n return True\\n return False\",\n \"def authorizes(self, user):\\n return self.owner == user or self.workers.filter(pk=user.id).exists()\",\n \"def accept_follow_request(request):\\n username = request.POST[\\\"user\\\"]\\n requester = get_user_from_username(request.user, username)\\n\\n try:\\n follow_request = models.UserFollowRequest.objects.get(\\n user_subject=requester, user_object=request.user\\n )\\n except models.UserFollowRequest.DoesNotExist:\\n # Request already dealt with.\\n return redirect(request.user.local_path)\\n follow_request.accept()\\n\\n return redirect(request.user.local_path)\",\n \"def can_update_user(cls, db_tuple, target, actor):\\n return target.user_id == actor.user_id\",\n \"def test_following(self):\\n\\n follow1 = Follows(\\n user_being_followed_id = self.testuser2.id,\\n user_following_id = self.testuser.id\\n )\\n follow2 = Follows(\\n user_being_followed_id = self.testuser3.id,\\n user_following_id = self.testuser.id\\n )\\n\\n db.session.add_all((follow1, follow2))\\n db.session.commit()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session[CURR_USER_KEY] = self.testuser.id\\n \\n response = c.get(f\\\"/users/{self.testuser.id}/following\\\")\\n data = str(response.data)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertIn(\\\"@bob\\\", data)\\n self.assertIn(\\\"@carl\\\", data)\\n self.assertNotIn(\\\"@alvin\\\", data)\",\n \"def following_and_storing(self, user_obj):\\n if self.following(user_obj['user']):\\n self.monitored_users.append({'user': user_obj['user'], 'username': user_obj['username'],\\n 'followDate': datetime.now().timestamp()})\",\n \"def user_appears(self, user):\\n pass\",\n \"def check_user(self):\\n try:\\n if self.get_customer()[0][0] == self.dni:\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def follow(self, follower, followee):\\n pass\",\n \"def test_func(self):\\n member_to_finish = self.get_object()\\n return self.request.user.rfid == member_to_finish.rfid\",\n \"def test_user_follow_self(self):\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.authorize_user(self.user)\\n response = self.client.post(self.follow_self_url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"def following_changed(sender, action, instance, *args, **kwargs):\\n\\n # m2mchanged.connect specified in apps.py\\n\\n following = instance.following.all()\\n creator = instance.user\\n\\n if creator in following:\\n raise ValidationError (\\\"can't like own post\\\")\",\n \"def follows(self, name):\\r\\n url = '{0}/{1}/{2}'.format(self.get_url(), 'following', name)\\r\\n\\r\\n return http.Request('GET', url), resource.parse_boolean\",\n \"def test_func(self):\\n post = self.get_object()\\n if self.request.user == post.author:\\n return True\\n return False\",\n \"def is_active_user(self):\\n\\n return self.is_active\",\n \"def is_user(self, user='') -> int:\\n try:\\n if user in self.users:\\n return(1)\\n else:\\n return(0)\\n except Exception as error:\\n print(f\\\"Error: self.is_user({user}) -> {error}\\\")\",\n \"def author_is_me(message: discord.Message) -> bool:\\n return message.author == config.bot.user\",\n \"def liked_by(self, user):\\n return Likes.objects.filter(recipe=self, chef=user).exists()\",\n \"def test_get_list_of_following_users_with_auth(self):\\n with self.settings(\\n EMAIL_BACKEND='django.core.mail.backends.locmem.EmailBackend'):\\n self.authorize_user(self.user)\\n self.register_user(self.user1)\\n self.client.post(self.follow_url, format='json')\\n response = self.client.get(self.following_list_url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def is_profile_complete(self):\\n return bool(self.fullname and self.username and self.email)\",\n \"def user_has_access(self, user):\\n if self.visibility == self.PUBLIC:\\n return True\\n elif self.visibility == self.PRIVATE and self.created_by == user:\\n return True\\n elif self.visibility in (self.ORG_ONLY, self.ORG_ONLY_NO_EXTERNAL):\\n if user.external and self.visibility == self.ORG_ONLY_NO_EXTERNAL:\\n return False\\n elif self.organization.memberships.filter(user=user).count() >= 1:\\n return True\\n return False\",\n \"def has_access(self, user):\\n if user.is_superuser:\\n return True\\n return self.user_objects(user).filter(id=self.id).exists()\",\n \"def is_active(self):\\n return self.status == ACTIVE_USER\",\n \"def gatekeeper():\\n\\n if user.name in GATEKEEPERS:\\n return True\\n\\n return False\",\n \"def is_leader(self):\\n return self.__is_leader\",\n \"def early_return(bot:Bot, ctx:Context):\\n return ctx.message.author.bot or ctx.message.author.id == bot.user.id\",\n \"def can_be_viewed_by(self,user):\\n return True\",\n \"def show_following(user_id):\\n\\n\\n user = User.query.get_or_404(user_id)\\n return render_template('users/following.html', user=user)\",\n \"def test_follow_manually_approved(self):\\n activity = {\\n \\\"@context\\\": \\\"https://www.w3.org/ns/activitystreams\\\",\\n \\\"id\\\": \\\"https://example.com/users/rat/follows/123\\\",\\n \\\"type\\\": \\\"Follow\\\",\\n \\\"actor\\\": \\\"https://example.com/users/rat\\\",\\n \\\"object\\\": \\\"https://example.com/user/mouse\\\",\\n }\\n\\n self.local_user.manually_approves_followers = True\\n self.local_user.save(\\n broadcast=False, update_fields=[\\\"manually_approves_followers\\\"]\\n )\\n\\n with patch(\\\"bookwyrm.models.activitypub_mixin.broadcast_task.apply_async\\\"):\\n views.inbox.activity_task(activity)\\n\\n # notification created\\n notification = models.Notification.objects.get()\\n self.assertEqual(notification.user, self.local_user)\\n self.assertEqual(notification.notification_type, \\\"FOLLOW_REQUEST\\\")\\n\\n # the request should exist\\n request = models.UserFollowRequest.objects.get()\\n self.assertEqual(request.user_subject, self.remote_user)\\n self.assertEqual(request.user_object, self.local_user)\\n\\n # the follow relationship should not exist\\n follow = models.UserFollows.objects.all()\\n self.assertEqual(list(follow), [])\",\n \"def is_logged_in(self) -> bool:\\n return self.id is not None and self.username is not None\",\n \"def is_participant(self,user):\\n if user.is_superuser:\\n return True\\n\\n if user.groups.filter(name=self.participants_group_name).count() > 0:\\n return True\\n else:\\n return False\",\n \"def still_deciding(self):\\n for player in self.players:\\n if isinstance(player, user.User):\\n if not player.has_played:\\n return True\\n return False\",\n \"def is_eligible(self, myself):\\n if self.author().screen_name == myself.screen_name:\\n log_.debug(\\\"Not replying to my own tweets\\\")\\n return False\\n if self.is_retweet():\\n log_.debug(\\\"Not processing pure retweets\\\")\\n return False\\n return True\",\n \"def follows(self):\\r\\n return relationships.Follows(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7629527","0.7531042","0.7460013","0.73141354","0.72661006","0.70735085","0.70591176","0.7049096","0.6914572","0.69122463","0.68232995","0.6788982","0.67763567","0.6769098","0.6714947","0.65794","0.6538119","0.652213","0.6521558","0.64769983","0.64418167","0.64249146","0.63472104","0.63472104","0.6337133","0.63186246","0.6311464","0.63054794","0.62878215","0.6269004","0.6267999","0.6235971","0.6171771","0.61068225","0.60893387","0.6071489","0.6061535","0.6047455","0.60352004","0.60232085","0.599043","0.5983538","0.59715176","0.5964473","0.59626156","0.595811","0.59545606","0.5939112","0.5935984","0.5934003","0.5921513","0.5916213","0.59051514","0.5867161","0.58584785","0.5838697","0.582179","0.58127296","0.578747","0.57797843","0.57761806","0.5770214","0.5755082","0.5749518","0.5748161","0.573074","0.57289755","0.57272846","0.5726887","0.5716321","0.57141","0.5707292","0.57056195","0.57047826","0.56861633","0.5684884","0.56845057","0.5681332","0.56799924","0.56756353","0.5672672","0.5672672","0.5672672","0.56680423","0.5663431","0.5659798","0.5656082","0.56489426","0.56360656","0.5634647","0.5631955","0.5615115","0.5610024","0.5607456","0.5595033","0.559239","0.5585263","0.5585249","0.55837804"],"string":"[\n \"0.7629527\",\n \"0.7531042\",\n \"0.7460013\",\n \"0.73141354\",\n \"0.72661006\",\n \"0.70735085\",\n \"0.70591176\",\n \"0.7049096\",\n \"0.6914572\",\n \"0.69122463\",\n \"0.68232995\",\n \"0.6788982\",\n \"0.67763567\",\n \"0.6769098\",\n \"0.6714947\",\n \"0.65794\",\n \"0.6538119\",\n \"0.652213\",\n \"0.6521558\",\n \"0.64769983\",\n \"0.64418167\",\n \"0.64249146\",\n \"0.63472104\",\n \"0.63472104\",\n \"0.6337133\",\n \"0.63186246\",\n \"0.6311464\",\n \"0.63054794\",\n \"0.62878215\",\n \"0.6269004\",\n \"0.6267999\",\n \"0.6235971\",\n \"0.6171771\",\n \"0.61068225\",\n \"0.60893387\",\n \"0.6071489\",\n \"0.6061535\",\n \"0.6047455\",\n \"0.60352004\",\n \"0.60232085\",\n \"0.599043\",\n \"0.5983538\",\n \"0.59715176\",\n \"0.5964473\",\n \"0.59626156\",\n \"0.595811\",\n \"0.59545606\",\n \"0.5939112\",\n \"0.5935984\",\n \"0.5934003\",\n \"0.5921513\",\n \"0.5916213\",\n \"0.59051514\",\n \"0.5867161\",\n \"0.58584785\",\n \"0.5838697\",\n \"0.582179\",\n \"0.58127296\",\n \"0.578747\",\n \"0.57797843\",\n \"0.57761806\",\n \"0.5770214\",\n \"0.5755082\",\n \"0.5749518\",\n \"0.5748161\",\n \"0.573074\",\n \"0.57289755\",\n \"0.57272846\",\n \"0.5726887\",\n \"0.5716321\",\n \"0.57141\",\n \"0.5707292\",\n \"0.57056195\",\n \"0.57047826\",\n \"0.56861633\",\n \"0.5684884\",\n \"0.56845057\",\n \"0.5681332\",\n \"0.56799924\",\n \"0.56756353\",\n \"0.5672672\",\n \"0.5672672\",\n \"0.5672672\",\n \"0.56680423\",\n \"0.5663431\",\n \"0.5659798\",\n \"0.5656082\",\n \"0.56489426\",\n \"0.56360656\",\n \"0.5634647\",\n \"0.5631955\",\n \"0.5615115\",\n \"0.5610024\",\n \"0.5607456\",\n \"0.5595033\",\n \"0.559239\",\n \"0.5585263\",\n \"0.5585249\",\n \"0.55837804\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7424364"},"document_rank":{"kind":"string","value":"4"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":71,"numItemsPerPage":100,"numTotalItems":95772,"offset":7100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDAwMDkxNywic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzQwMDQ1MTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.Jssi1elZuXVIA1ACTuuQvKXgsV-D4dVycZbtIGoXx8aghjD3lfjKxVKEYAysTRGaPv6-94cw3Qg88MIpDqYYBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
The selector function accepts a component instance and returns the appropriate key to index plot_classes dictionary. | def __init__(self, selector, plot_classes, allow_mismatch=False):
self.selector = selector
self.plot_classes = OrderedDict(plot_classes)
interface = self._define_interface(self.plot_classes.values(), allow_mismatch)
self.style_opts, self.plot_options = interface | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def node_selector(self) -> Dict[str, str]:\n return self._node_selector",
"def lookup_class_idx(self,label):\r\n \r\n return self.class_labels[label]",
"def getSelector(self, node):\n self.checkModelOpen()\n calcEngine = CalcEngine.factory(self.client_session)\n return... | [
"0.54593",
"0.53855276",
"0.5289531",
"0.52059805",
"0.51838773",
"0.5170124",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"0.51380694",
"... | 0.4618312 | 68 |
Get the state of the Plot for a given frame number. | def __getitem__(self, frame):
if not self.dynamic == 'open' and isinstance(frame, int) and frame > len(self):
self.warning("Showing last frame available: %d" % len(self))
if not self.drawn: self.handles['fig'] = self.initialize_plot()
if not self.dynamic == 'open' and not isinstance(frame, tuple):
frame = self.keys[frame]
self.update_frame(frame)
return self.state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getFrame(self, num):\n\n return self.data[num]",
"def get_plot_state(self_or_cls, obj, renderer=None, **kwargs):\n if not isinstance(obj, Plot):\n obj = self_or_cls.get_plot(obj=obj, renderer=renderer, **kwargs)\n return obj.state",
"def get_frame(self, frame):\n retu... | [
"0.6576043",
"0.64040893",
"0.6168167",
"0.61134017",
"0.6076815",
"0.6055128",
"0.5951637",
"0.5882845",
"0.58641803",
"0.5848422",
"0.5799237",
"0.5756058",
"0.5718137",
"0.5702686",
"0.56998396",
"0.56985414",
"0.5595627",
"0.5593545",
"0.5543105",
"0.5521952",
"0.5484779"... | 0.6989357 | 0 |
Required on each MPLPlot type to get the data corresponding just to the current frame out from the object. | def _get_frame(self, key):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n return [self.axes]",
"def plot_data(self):",
"def _get_raw_data(self, idx=0):\n # Get the time step then make a data frame\n raise NotImplementedError('Code me up!')\n #data = self._data[???]\n return data",
"def __data_frame(self):\n return sel... | [
"0.6572209",
"0.6476473",
"0.64533323",
"0.6261248",
"0.6070728",
"0.59735954",
"0.5929763",
"0.5889976",
"0.5844909",
"0.5839488",
"0.5793765",
"0.5782455",
"0.57820135",
"0.5781594",
"0.5757953",
"0.5725428",
"0.5717131",
"0.5696034",
"0.5693247",
"0.56396365",
"0.56344974"... | 0.0 | -1 |
Matches a specification against the current Plot. | def matches(self, spec):
if callable(spec) and not isinstance(spec, type): return spec(self)
elif isinstance(spec, type): return isinstance(self, spec)
else:
raise ValueError("Matching specs have to be either a type or a callable.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_match():\n fig, ax = plt.subplots()\n ax.plot([0, 1], [0, 2])\n slide = _get_empty_slide()\n text = ax.set_title(\"TITLE TEXT\")\n shape = figpptx.send(fig, slide=slide, match=text)\n assert get_typename(shape) == \"Shape\"\n shapes = _get_shapes(slide, individual=True)\n assert le... | [
"0.5498152",
"0.54748654",
"0.54442275",
"0.51404667",
"0.5081998",
"0.5065666",
"0.50126404",
"0.50075436",
"0.4972846",
"0.49207228",
"0.49176952",
"0.4876025",
"0.48672763",
"0.48624754",
"0.48509756",
"0.48303744",
"0.48123005",
"0.4783596",
"0.47810036",
"0.47768372",
"0... | 0.51842225 | 3 |
Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class. | def traverse(self, fn=None, specs=None, full_breadth=True):
accumulator = []
matches = specs is None
if not matches:
for spec in specs:
matches = self.matches(spec)
if matches: break
if matches:
accumulator.append(fn(self) if fn else self)
# Assumes composite objects are iterables
if hasattr(self, 'subplots') and self.subplots:
for el in self.subplots.values():
accumulator += el.traverse(fn, specs, full_breadth)
if not full_breadth: break
return accumulator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_available_figures(self):\n return sorted((method[5:], func) \\\n for method, func in self.__class__.__dict__.iteritems() \\\n if method.startswith(\"plot_\") and callable(func))",
"def get_plots(self):\n return list(self.plots.values())",
"def _sp... | [
"0.52858",
"0.52142483",
"0.51448363",
"0.50744987",
"0.50589556",
"0.48802635",
"0.48739997",
"0.48073155",
"0.4798928",
"0.4749356",
"0.47428975",
"0.47420156",
"0.47197765",
"0.46932372",
"0.46918872",
"0.4680866",
"0.46731734",
"0.46569225",
"0.4638593",
"0.46093962",
"0.... | 0.56958634 | 0 |
Returns the formatted dimension group strings for a particular frame. | def _frame_title(self, key, group_size=2, separator='\n'):
if self.dynamic == 'open' and self.current_key:
key = self.current_key
if self.layout_dimensions is not None:
dimensions, key = zip(*self.layout_dimensions.items())
elif not self.dynamic and (not self.uniform or len(self) == 1) or self.subplot:
return ''
else:
key = key if isinstance(key, tuple) else (key,)
dimensions = self.dimensions
dimension_labels = [dim.pprint_value_string(k) for dim, k in
zip(dimensions, key)]
groups = [', '.join(dimension_labels[i*group_size:(i+1)*group_size])
for i in range(len(dimension_labels))]
return util.safe_unicode(separator.join(g for g in groups if g)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_grp_string(self):\n\n grp = self.get_grp()\n\n if grp == -1:\n\n return \"\"\n\n return \"grp \" + str(grp)",
"def format_to_string(groups: List[List]) -> str:\n\tgroups_str = \"\"\n\tcount = 1\n\tfor group in groups:\n\t\tgroups_str += f\"Group {count}: {group} \\n\"\n\t\... | [
"0.64216906",
"0.63535005",
"0.6112604",
"0.59159833",
"0.58164614",
"0.5747988",
"0.5726756",
"0.57234955",
"0.56927526",
"0.56833076",
"0.565327",
"0.5643954",
"0.5607467",
"0.55895174",
"0.55715007",
"0.5488538",
"0.5466888",
"0.5411597",
"0.5394599",
"0.53788805",
"0.5364... | 0.5254912 | 33 |
Given an object, a specific key and the normalization options this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned. | def compute_ranges(self, obj, key, ranges):
all_table = all(isinstance(el, Table) for el in obj.traverse(lambda x: x, [Element]))
if obj is None or not self.normalize or all_table:
return OrderedDict()
# Get inherited ranges
ranges = self.ranges if ranges is None else dict(ranges)
# Get element identifiers from current object and resolve
# with selected normalization options
norm_opts = self._get_norm_opts(obj)
# Traverse displayed object if normalization applies
# at this level, and ranges for the group have not
# been supplied from a composite plot
return_fn = lambda x: x if isinstance(x, Element) else None
for group, (axiswise, framewise) in norm_opts.items():
elements = []
# Skip if ranges are cached or already computed by a
# higher-level container object.
framewise = framewise or self.dynamic
if group in ranges and (not framewise or ranges is not self.ranges):
continue
elif not framewise: # Traverse to get all elements
elements = obj.traverse(return_fn, [group])
elif key is not None: # Traverse to get elements for each frame
frame = self._get_frame(key)
elements = [] if frame is None else frame.traverse(return_fn, [group])
if not axiswise or ((not framewise or len(elements) == 1)
and isinstance(obj, HoloMap)): # Compute new ranges
self._compute_group_range(group, elements, ranges)
self.ranges.update(ranges)
return ranges | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_norm_opts(self, obj):\n norm_opts = {}\n\n # Get all elements' type.group.label specs and ids\n type_val_fn = lambda x: (x.id, (type(x).__name__, util.group_sanitizer(x.group, escape=False),\n util.label_sanitizer(x.label, escape=False))) \\\n ... | [
"0.6445774",
"0.48843622",
"0.4852263",
"0.48259893",
"0.4770748",
"0.47455063",
"0.4726611",
"0.4720005",
"0.4644569",
"0.46267",
"0.45563284",
"0.4543702",
"0.454177",
"0.45273983",
"0.4494524",
"0.4494449",
"0.44733185",
"0.44697282",
"0.44692782",
"0.44428465",
"0.4435969... | 0.689226 | 0 |
Gets the normalization options for a LabelledData object by traversing the object for to find elements and their ids. The id is then used to select the appropriate OptionsTree, accumulating the normalization options into a dictionary. Returns a dictionary of normalization options for each element in the tree. | def _get_norm_opts(self, obj):
norm_opts = {}
# Get all elements' type.group.label specs and ids
type_val_fn = lambda x: (x.id, (type(x).__name__, util.group_sanitizer(x.group, escape=False),
util.label_sanitizer(x.label, escape=False))) \
if isinstance(x, Element) else None
element_specs = {(idspec[0], idspec[1]) for idspec in obj.traverse(type_val_fn)
if idspec is not None}
# Group elements specs by ID and override normalization
# options sequentially
key_fn = lambda x: -1 if x[0] is None else x[0]
id_groups = groupby(sorted(element_specs, key=key_fn), key_fn)
for gid, element_spec_group in id_groups:
gid = None if gid == -1 else gid
group_specs = [el for _, el in element_spec_group]
backend = self.renderer.backend
optstree = Store.custom_options(
backend=backend).get(gid, Store.options(backend=backend))
# Get the normalization options for the current id
# and match against customizable elements
for opts in optstree:
path = tuple(opts.path.split('.')[1:])
applies = any(path == spec[:i] for spec in group_specs
for i in range(1, 4))
if applies and 'norm' in opts.groups:
nopts = opts['norm'].options
if 'axiswise' in nopts or 'framewise' in nopts:
norm_opts.update({path: (nopts.get('axiswise', False),
nopts.get('framewise', False))})
element_specs = [spec for _, spec in element_specs]
norm_opts.update({spec: (False, False) for spec in element_specs
if not any(spec[:i] in norm_opts.keys() for i in range(1, 4))})
return norm_opts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dictize(self):\n dict = {}\n for node in self.sort():\n logger.debug(\"Dictize: id %s has name %s\" % (node._id, node.name))\n x = node._kwargs()\n dict[node._id]={\"klass\":node.__class__.__name__, \n \"kwargs\": x,\n ... | [
"0.4882127",
"0.48138362",
"0.46735653",
"0.46150512",
"0.45790586",
"0.44817707",
"0.44758487",
"0.4465512",
"0.442252",
"0.44222006",
"0.43991864",
"0.43809223",
"0.4366402",
"0.43599775",
"0.4356347",
"0.43552524",
"0.43511787",
"0.42940468",
"0.42261603",
"0.42139208",
"0... | 0.6927882 | 0 |
Traverses the supplied object getting all options in opts for the specified opt_type and specs. Also takes into account the plotting class defaults for plot options. If a keyfn is supplied the returned options will be grouped by the returned keys. | def _traverse_options(cls, obj, opt_type, opts, specs=None, keyfn=None, defaults=True):
def lookup(x):
"""
Looks up options for object, including plot defaults,
keyfn determines returned key otherwise None key is used.
"""
options = cls.lookup_options(x, opt_type)
selected = {o: options.options[o]
for o in opts if o in options.options}
if opt_type == 'plot' and defaults:
plot = Store.registry[cls.backend].get(type(x))
selected['defaults'] = {o: getattr(plot, o) for o in opts
if o not in selected and hasattr(plot, o)}
key = keyfn(x) if keyfn else None
return (key, selected)
# Traverse object and accumulate options by key
traversed = obj.traverse(lookup, specs)
options = defaultdict(lambda: defaultdict(list))
default_opts = defaultdict(lambda: defaultdict(list))
for key, opts in traversed:
defaults = opts.pop('defaults', {})
for opt, v in opts.items():
options[key][opt].append(v)
for opt, v in defaults.items():
default_opts[key][opt].append(v)
# Merge defaults into dictionary if not explicitly specified
for key, opts in default_opts.items():
for opt, v in opts.items():
if opt not in options[key]:
options[key][opt] = v
return options if keyfn else options[None] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _all_opt_infos(self):\n for info in self._opts.values():\n yield info, None\n for group in self._groups.values():\n for info in group._opts.values():\n yield info, group",
"def get_plot_kwargs(cfg, option, key=None):\n plot_kwargs = cfg.get(option, {}).ge... | [
"0.5892671",
"0.5606828",
"0.56063414",
"0.550141",
"0.54399914",
"0.5378535",
"0.5304472",
"0.52757764",
"0.52567726",
"0.52553415",
"0.52336997",
"0.5221113",
"0.5217911",
"0.51816285",
"0.5152726",
"0.51514137",
"0.5142801",
"0.51401424",
"0.5134124",
"0.5131874",
"0.51075... | 0.86038965 | 0 |
Looks up options for object, including plot defaults, keyfn determines returned key otherwise None key is used. | def lookup(x):
options = cls.lookup_options(x, opt_type)
selected = {o: options.options[o]
for o in opts if o in options.options}
if opt_type == 'plot' and defaults:
plot = Store.registry[cls.backend].get(type(x))
selected['defaults'] = {o: getattr(plot, o) for o in opts
if o not in selected and hasattr(plot, o)}
key = keyfn(x) if keyfn else None
return (key, selected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _traverse_options(cls, obj, opt_type, opts, specs=None, keyfn=None, defaults=True):\n def lookup(x):\n \"\"\"\n Looks up options for object, including plot defaults,\n keyfn determines returned key otherwise None key is used.\n \"\"\"\n options = cl... | [
"0.6476717",
"0.6211503",
"0.57079715",
"0.5571008",
"0.5534396",
"0.547191",
"0.54293185",
"0.5385394",
"0.53531367",
"0.53213716",
"0.5306876",
"0.5284712",
"0.5159889",
"0.51395476",
"0.5116941",
"0.51114714",
"0.50779635",
"0.507736",
"0.5073758",
"0.5022548",
"0.5020879"... | 0.6997502 | 0 |
Uses traversal to find the appropriate projection for a nested object. Respects projections set on Overlays before considering Element based settings, before finally looking up the default projection on the plot type. If more than one nonNone projection type is found an exception is raised. | def _get_projection(cls, obj):
isoverlay = lambda x: isinstance(x, CompositeOverlay)
opts = cls._traverse_options(obj, 'plot', ['projection'],
[CompositeOverlay, Element],
keyfn=isoverlay)
from_overlay = not all(p is None for p in opts[True]['projection'])
projections = opts[from_overlay]['projection']
custom_projs = [p for p in projections if p is not None]
if len(set(custom_projs)) > 1:
raise Exception("An axis may only be assigned one projection type")
return custom_projs[0] if custom_projs else None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def projectionManip(*args, fitBBox: bool=True, projType: int=0, switchType: bool=True, q=True,\n query=True, **kwargs)->Union[None, Any]:\n pass",
"def create_projection_from_projector_element(\n self, element, weight, type, projector_id\n ):\n projection = {\n \... | [
"0.61008054",
"0.57861453",
"0.541124",
"0.5349921",
"0.5343465",
"0.5273637",
"0.5211582",
"0.51546365",
"0.5075276",
"0.504999",
"0.49605206",
"0.49293154",
"0.48868015",
"0.48221928",
"0.48099476",
"0.4738513",
"0.47346017",
"0.47128424",
"0.47019666",
"0.46675426",
"0.459... | 0.6758612 | 0 |
Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm. | def refresh(self, **kwargs):
traverse_setter(self, '_force', True)
key = self.current_key if self.current_key else self.keys[0]
stream_params = stream_parameters(self.streams)
key = tuple(None if d in stream_params else k
for d, k in zip(self.dimensions, key))
stream_key = util.wrap_tuple_streams(key, self.dimensions, self.streams)
self.update(stream_key)
if self.comm is not None:
self.push() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_plot():\n pass",
"def plot_refresh():\n figure.canvas.draw()",
"def _UpdatePlot( self ):\n self._BusyDoOp( self._UpdatePlotImpl )",
"def update_plot(self,ax):\n self.replot(ax)",
"def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data... | [
"0.7288777",
"0.6962232",
"0.6900487",
"0.6737718",
"0.6697221",
"0.6682248",
"0.6682248",
"0.6682248",
"0.6682248",
"0.6682248",
"0.66806316",
"0.66681284",
"0.6639615",
"0.6597625",
"0.6559798",
"0.6359195",
"0.63513744",
"0.63109744",
"0.6272057",
"0.6253011",
"0.62247986"... | 0.0 | -1 |
Pushes updated plot data via the Comm. | def push(self):
if self.comm is None:
raise Exception('Renderer does not have a comm.')
diff = self.renderer.diff(self)
self.comm.send(diff) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_plot():\n pass",
"def on_new_data(self):\n\n if self.connected:\n tab_open = self.tab_open()\n\n # Update plot data\n for i, series in enumerate(self.measurements_list):\n if i == tab_open:\n self.plotted_data[i].setData(... | [
"0.6883734",
"0.68772954",
"0.6770715",
"0.6723177",
"0.6713831",
"0.66170454",
"0.6524996",
"0.6524996",
"0.6524996",
"0.6524996",
"0.6524996",
"0.65054625",
"0.6487483",
"0.6401084",
"0.6326303",
"0.6279867",
"0.622038",
"0.60897267",
"0.60824525",
"0.6045853",
"0.6021864",... | 0.63146627 | 15 |
Initializes comm and attaches streams. | def init_comm(self, obj):
comm = None
if self.dynamic or self.renderer.widget_mode == 'live':
comm = self.renderer.comms[self.renderer.mode][0](self)
attach_streams(self, obj)
return comm | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n # Open stata as pipe; make a queue for non-blocking. Start the thread.\n self.proc = sp.Popen(['stata-mp'], stdin=sp.PIPE, stdout=sp.PIPE, bufsize=1)\n\n self.qu = Queue()\n\n self.thread = Thread(target = self.enqueue_output, args = (self.proc.stdout,\n ... | [
"0.62361443",
"0.6131124",
"0.6047162",
"0.60436773",
"0.59891397",
"0.598821",
"0.5979925",
"0.59722435",
"0.59679276",
"0.59299994",
"0.5921529",
"0.5889616",
"0.588344",
"0.5872382",
"0.58591175",
"0.58533263",
"0.5845079",
"0.5841991",
"0.582825",
"0.57430446",
"0.5731562... | 0.5991638 | 4 |
Returns the total number of available frames. | def __len__(self):
return len(self.keys) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_total_frames(self) -> int:\n return self.num_frames",
"def size(self):\n if self.frames is None:\n return 0\n return self.frames.size",
"def frames(self):\n frame_count = 0\n if self.is_video() or self.is_audio():\n if self.__dict__['nb_frames']:... | [
"0.8600426",
"0.77835464",
"0.7621566",
"0.75558245",
"0.7531837",
"0.7526261",
"0.7391324",
"0.7189588",
"0.71559983",
"0.70775414",
"0.70692587",
"0.7036695",
"0.7004031",
"0.69709444",
"0.6951622",
"0.688492",
"0.68131065",
"0.6812733",
"0.6789151",
"0.66947013",
"0.664887... | 0.0 | -1 |
Computes the zorder of element in the NdOverlay taking into account possible batching of elements. | def get_zorder(self, overlay, key, el):
spec = util.get_overlay_spec(overlay, key, el)
try:
return self.ordering.index(spec)
except ValueError:
self.ordering = sorted(self.ordering+[spec])
return self.ordering.index(spec) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getz_index(self):\n return self._getz_index",
"def optimise_z(z, *args):\n x, y, elements, coordinates = args\n window_com = np.array([x, y, z])\n return pore_diameter(elements, coordinates, com=window_com)[0]",
"def z(self):\r\n return self.position.z",
"def _set_planar_pixel_order(im... | [
"0.57028675",
"0.55984366",
"0.5478423",
"0.54625237",
"0.5441317",
"0.537248",
"0.5289451",
"0.52060914",
"0.5195802",
"0.51892036",
"0.51678",
"0.5121228",
"0.51193726",
"0.50830746",
"0.507015",
"0.5063056",
"0.5060691",
"0.5056381",
"0.5056132",
"0.50311744",
"0.5008574",... | 0.72016543 | 0 |
Gets the extents for the axes from the current View. The globally computed ranges can optionally override the extents. | def get_extents(self, view, ranges):
ndims = len(view.dimensions())
num = 6 if self.projection == '3d' else 4
if self.apply_ranges:
if ranges:
dims = view.dimensions()
x0, x1 = ranges[dims[0].name]
if ndims > 1:
y0, y1 = ranges[dims[1].name]
else:
y0, y1 = (np.NaN, np.NaN)
if self.projection == '3d':
if len(dims) > 2:
z0, z1 = ranges[dims[2].name]
else:
z0, z1 = np.NaN, np.NaN
else:
x0, x1 = view.range(0)
y0, y1 = view.range(1) if ndims > 1 else (np.NaN, np.NaN)
if self.projection == '3d':
z0, z1 = view.range(2)
if self.projection == '3d':
range_extents = (x0, y0, z0, x1, y1, z1)
else:
range_extents = (x0, y0, x1, y1)
else:
range_extents = (np.NaN,) * num
if self.apply_extents:
norm_opts = self.lookup_options(view, 'norm').options
if norm_opts.get('framewise', False) or self.dynamic:
extents = view.extents
else:
extent_list = self.hmap.traverse(lambda x: x.extents, [Element])
extents = util.max_extents(extent_list, self.projection == '3d')
else:
extents = (np.NaN,) * num
return tuple(l1 if l2 is None or not np.isfinite(l2) else
l2 for l1, l2 in zip(range_extents, extents)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extents(self):\n self._updateExtents()\n return self.mExtents",
"def extents(self):\n x0, y0, width, height = self._rect_bbox\n xmin, xmax = sorted([x0, x0 + width])\n ymin, ymax = sorted([y0, y0 + height])\n return xmin, xmax, ymin, ymax",
"def extents(self):\n\n ... | [
"0.72161704",
"0.6875994",
"0.6859144",
"0.67906785",
"0.65129864",
"0.63847166",
"0.63576",
"0.6300293",
"0.6264915",
"0.6224861",
"0.61757976",
"0.61531943",
"0.61249256",
"0.61061025",
"0.60827875",
"0.6021081",
"0.5992878",
"0.599001",
"0.59846133",
"0.5962699",
"0.596207... | 0.7218878 | 0 |
Set the plot(s) to the given frame number. Operates by manipulating the matplotlib objects held in the self._handles dictionary. If n is greater than the number of available frames, update using the last available frame. | def update_frame(self, key, ranges=None): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __plot_n__(self, refresh=False, *args):\n # If plot is not requested, return:\n if not self.plotneVar.get() or not self.plotniVar.get():\n return\n\n # Check for a closed window:\n if 'n' in self.plots.keys() and not matplotlib.pyplot.fignum_exists(self.plots['n'].number)... | [
"0.62682223",
"0.576072",
"0.5558097",
"0.55435854",
"0.5533847",
"0.55313236",
"0.54860616",
"0.54809326",
"0.547151",
"0.5446553",
"0.54084885",
"0.5352229",
"0.53280175",
"0.53238046",
"0.5293158",
"0.52775246",
"0.5230508",
"0.52274555",
"0.5225835",
"0.5224569",
"0.52202... | 0.0 | -1 |
Given a HoloMap compute the appropriate (mapwise or framewise) ranges in order to apply the Compositor collapse operations in display mode (data collapse should already have happened). | def _apply_compositor(self, holomap, ranges=None, keys=None, dimensions=None):
# Compute framewise normalization
defaultdim = holomap.ndims == 1 and holomap.kdims[0].name != 'Frame'
if keys and ranges and dimensions and not defaultdim:
dim_inds = [dimensions.index(d) for d in holomap.kdims]
sliced_keys = [tuple(k[i] for i in dim_inds) for k in keys]
frame_ranges = OrderedDict([(slckey, self.compute_ranges(holomap, key, ranges[key]))
for key, slckey in zip(keys, sliced_keys) if slckey in holomap.data.keys()])
else:
mapwise_ranges = self.compute_ranges(holomap, None, None)
frame_ranges = OrderedDict([(key, self.compute_ranges(holomap, key, mapwise_ranges))
for key in holomap.keys()])
ranges = frame_ranges.values()
return Compositor.collapse(holomap, (ranges, frame_ranges.keys()), mode='display') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _modify_map_size(self, merged_map):\n pos_x_white, pos_y_white = np.where(merged_map == 255)\n pos_x_black, pos_y_black = np.where(merged_map == 0)\n\n pos_x_M = np.amax(np.hstack((pos_x_black, pos_x_white)))\n pos_x_m = np.amin(np.hstack((pos_x_black, pos_x_white)))\n pos_y_... | [
"0.53061557",
"0.5123476",
"0.50569475",
"0.5041872",
"0.4931829",
"0.49258262",
"0.4896154",
"0.4868902",
"0.4862584",
"0.48051956",
"0.48001003",
"0.47694784",
"0.47593305",
"0.47555342",
"0.4747587",
"0.47454724",
"0.47438157",
"0.47292355",
"0.47042343",
"0.46995413",
"0.... | 0.77395713 | 0 |
Creates a clone of the Layout with the nthframe for each Element. | def _get_frame(self, key):
layout_frame = self.layout.clone(shared_data=False)
keyisint = isinstance(key, int)
if not isinstance(key, tuple): key = (key,)
nthkey_fn = lambda x: zip(tuple(x.name for x in x.kdims),
list(x.data.keys())[min([key[0], len(x)-1])])
if key == self.current_key:
return self.current_frame
else:
self.current_key = key
for path, item in self.layout.items():
if self.dynamic == 'open':
if keyisint:
counts = item.traverse(lambda x: x.counter, (DynamicMap,))
if key[0] >= counts[0]:
item.traverse(lambda x: next(x), (DynamicMap,))
dim_keys = item.traverse(nthkey_fn, (DynamicMap,))[0]
else:
dim_keys = zip([d.name for d in self.dimensions
if d in item.dimensions('key')], key)
self.current_key = tuple(k[1] for k in dim_keys)
elif item.traverse(lambda x: x, [DynamicMap]):
with dimensionless_cache(item, not self._force or not self.drawn):
key, frame = util.get_dynamic_item(item, self.dimensions, key)
layout_frame[path] = frame
continue
elif self.uniform:
dim_keys = zip([d.name for d in self.dimensions
if d in item.dimensions('key')], key)
else:
dim_keys = item.traverse(nthkey_fn, (HoloMap,))[0]
if dim_keys:
obj = item.select((HoloMap,), **dict(dim_keys))
if isinstance(obj, HoloMap) and len(obj) == 0:
continue
else:
layout_frame[path] = obj
else:
layout_frame[path] = item
traverse_setter(self, '_force', False)
self.current_frame = layout_frame
return layout_frame | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_layout(self):\n\n for h in range(0, self.num_layout_heads):\n self.set_random_layout(h)\n self.set_sliding_window_layout(h)\n self.set_global_layout_itc(h)\n\n self.check_and_propagate_first_head_layout()\n return self.layout",
"def clone(self):\n ... | [
"0.64056253",
"0.64034027",
"0.6147005",
"0.6052775",
"0.5999924",
"0.59923536",
"0.58694065",
"0.5848677",
"0.5804697",
"0.58001226",
"0.5778056",
"0.5773629",
"0.5630633",
"0.55947256",
"0.5549548",
"0.54448736",
"0.54406804",
"0.54355043",
"0.5418124",
"0.5387226",
"0.5353... | 0.0 | -1 |
Playing with spatial convs after transpose convolutions. Recent testing suggests this is the best setup so far. Revisit idea of interleaving spatial convolutions between transpose layers to achieve more cleanly defined shapes. | def decoder_setup_1():
decoder = RetinaDecoder(
# pre-pooling
{'op': 'avg', 'kernel': (1, 2, 2), 'causal': True},
# grouped temporal conv stacks:
[
{
'in': 15, 'out': [45, 45, 15], 'kernel': (2, 1, 1),
'stride': 1, 'groups': 15, 'acivation': nn.ReLU,
'pool': {'op': 'avg', 'kernel': (2, 2, 2), 'causal': True}
}
],
# spatial conv layers: {in, out, kernel, stride}
[
# {'in': 15, 'out': 64, 'kernel': (1, 3, 3), 'stride': 1}
],
# for each ConvRNN cell:
[
],
# temporal convolution stack(s)
[
{
'in': 15, 'out': [128, 256, 128], 'kernel': (2, 3, 3),
'stride': 1, 'groups': 1, 'acivation': nn.ReLU
}
],
# ConvTranspose layers: {in, out, kernel, stride}
[
{'in': 128, 'out': 64, 'kernel': (3, 3, 3), 'stride': (2, 2, 2)},
{'in': 64, 'out': 16, 'kernel': (3, 3, 3), 'stride': (1, 2, 2)},
],
# post conv layers
[
{'in': 16, 'out': 8, 'kernel': (1, 3, 3), 'stride': 1},
{'in': 8, 'out': 1, 'kernel': (1, 1, 1), 'stride': 1}
],
)
return decoder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_transposed_conv2d_model(self):\n tf.compat.v1.reset_default_graph()\n\n _ = transposed_conv2d_model()\n\n conn_graph = ConnectedGraph(tf.compat.v1.get_default_graph(), ['input_1'], ['conv2d_transpose/BiasAdd'])\n self.assertEqual(conn_graph.get_all_ops()['conv2d_transpose/conv2... | [
"0.6420241",
"0.63742214",
"0.630493",
"0.62457377",
"0.6166526",
"0.61490655",
"0.60816073",
"0.6013133",
"0.59594655",
"0.59204423",
"0.5848845",
"0.5842317",
"0.58345294",
"0.5829648",
"0.58118945",
"0.5805117",
"0.57954824",
"0.5793485",
"0.5791456",
"0.57831615",
"0.5781... | 0.0 | -1 |
This setup was the first big success, solid base config to work from. Note the lack of causal pooling, I hadn't built that module yet. | def decoder_setup_2():
decoder = RetinaDecoder(
# pre-pooling
{'op': 'avg', 'kernel': (1, 2, 2), 'causal': False},
# grouped temporal conv stacks:
[
{
'in': 15, 'out': [45, 45, 15], 'kernel': (2, 1, 1),
'stride': 1, 'groups': 15, 'acivation': nn.ReLU,
'pool': {'op': 'avg', 'kernel': (2, 2, 2), 'causal': False}
}
],
# spatial conv layers: {in, out, kernel, stride}
[
],
# for each ConvRNN cell:
[
],
# temporal convolution stack(s)
[
{
'in': 15, 'out': [128, 256, 128], 'kernel': (2, 3, 3),
'stride': 1, 'groups': 1, 'acivation': nn.ReLU
}
],
# ConvTranspose layers: {in, out, kernel, stride}
[
{'in': 128, 'out': 64, 'kernel': (3, 3, 3), 'stride': (2, 2, 2)},
{'in': 64, 'out': 1, 'kernel': (3, 3, 3), 'stride': (1, 2, 2)},
],
# post conv layers
[
],
)
return decoder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_configs():",
"def config():",
"def config():",
"def _setup_pipeline_cfg(self):",
"def init_config(self):\n pass",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def _configure(self):\n pass",
"def get_base_config(self):\n... | [
"0.6253901",
"0.62495774",
"0.62495774",
"0.61576325",
"0.6040787",
"0.60355586",
"0.60355586",
"0.60355586",
"0.60355586",
"0.59917283",
"0.5979285",
"0.59686154",
"0.593352",
"0.59138405",
"0.5896729",
"0.58624494",
"0.58605725",
"0.58148235",
"0.5778351",
"0.57762766",
"0.... | 0.0 | -1 |
This is the same as setup_2, which was the first breakthrough network, except here the pooling operations have been set to causal mode. On colab, 2x 20 epochs with lr=1e1 and batch_sz=8 has produced strong strong decoding results. | def decoder_setup_5():
decoder = RetinaDecoder(
# pre-pooling
{'op': 'avg', 'kernel': (1, 2, 2), 'causal': True},
# grouped temporal conv stacks:
[
{
'in': 15, 'out': [45, 45, 15], 'kernel': (2, 1, 1),
'stride': 1, 'groups': 15, 'acivation': nn.ReLU,
'pool': {'op': 'avg', 'kernel': (2, 2, 2), 'causal': True}
}
],
# spatial conv layers: {in, out, kernel, stride}
[
# {'in': 15, 'out': 64, 'kernel': (1, 3, 3), 'stride': 1}
],
# for each ConvRNN cell:
[
],
# temporal convolution stack(s)
[
{
'in': 15, 'out': [128, 256, 128], 'kernel': (2, 3, 3),
'stride': 1, 'groups': 1, 'acivation': nn.ReLU
}
],
# ConvTranspose layers: {in, out, kernel, stride}
[
{'in': 128, 'out': 64, 'kernel': (3, 3, 3), 'stride': (2, 2, 2)},
{'in': 64, 'out': 1, 'kernel': (3, 3, 3), 'stride': (1, 2, 2)},
],
# post conv layers
[
],
)
return decoder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main_train(lr, bs, cuda_id, not_distrib, fp16, loss_scale):\r\n torch.backends.cudnn.benchmark = True\r\n if fp16: assert torch.backends.cudnn.enabled, \"missing cudnn\"\r\n stats = (np.array([ 0.4914 , 0.48216, 0.44653]), np.array([ 0.24703, 0.24349, 0.26159]))\r\n sz=32\r\n PATH = Path(\".... | [
"0.67401975",
"0.6634952",
"0.657736",
"0.649362",
"0.6486684",
"0.64751244",
"0.64618695",
"0.64333904",
"0.64330065",
"0.6408339",
"0.64069897",
"0.6405594",
"0.6391012",
"0.63891834",
"0.638887",
"0.6384175",
"0.6357176",
"0.6355764",
"0.63509816",
"0.6346752",
"0.6345361"... | 0.0 | -1 |
Create a onehot encoding of x of size k. | def one_hot(x, k, dtype=np.float32):
return np.array(x[:, None] == np.arange(k), dtype) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _one_hot(z, K):\n z_one_hot = np.zeros((z.size, K))\n z_one_hot[np.arange(z.size), z] = 1\n return z_one_hot",
"def one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)",
"def _one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype... | [
"0.804429",
"0.801011",
"0.78977334",
"0.7889785",
"0.7800311",
"0.7714966",
"0.75706875",
"0.75658363",
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"0.73576915",
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"0.7206042",
"0.72045",
"0.7190287",
"0.7164403",
"0.71621656",
"... | 0.7974455 | 2 |
Returns a Bokeh glyph object. | def _init_glyph(self, plot, mapping, properties):
level = properties.pop('level', 'underlay')
renderer = plot.add_tile(mapping['tile_source'], level=level)
renderer.alpha = properties.get('alpha', 1)
return renderer, renderer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createReferenceGlyph(self):\n return _libsbml.GeneralGlyph_createReferenceGlyph(self)",
"def createTextGlyph(self):\n return _libsbml.Layout_createTextGlyph(self)",
"def _init_glyph(self, plot, mapping, properties):\n properties.pop('source')\n properties.pop('legend')\n ... | [
"0.64251554",
"0.6417896",
"0.6385368",
"0.61207485",
"0.6027903",
"0.5853716",
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"0.5454618",
"0.5413018",
"0.5380582",
"0.5379364",
"0.5375702",
"0.5330651",
... | 0.5845592 | 6 |
This Method is used to display the snake on the screen | def draw_snake(self, dis, snake_Part, snake_Body):
for x in snake_Body:
if self.left:
direction = pygame.transform.rotate(x[2], x[3])
dis.blit(direction, (x[0], x[1]))
elif self.right:
direction = pygame.transform.rotate(x[2], x[3])
dis.blit(direction, (x[0], x[1]))
elif self.up:
direction = pygame.transform.rotate(x[2], x[3])
dis.blit(direction, (x[0], x[1]))
elif self.down:
direction = pygame.transform.rotate(x[2], x[3])
dis.blit(direction, (x[0], x[1]))
if self.left:
x1 = self.x + self.x_change - 10
y1 = self.y + self.y_change - 10
hdir = pygame.transform.rotate(Head, -90)
dis.blit(hdir, (x1,y1))
elif self.right:
x1 = self.x + self.x_change - 10
y1 = self.y + self.y_change - 10
hdir = pygame.transform.rotate(Head, 90)
dis.blit(hdir, (x1,y1))
elif self.up:
x1 = self.x + self.x_change - 10
y1 = self.y + self.y_change - 10
hdir = pygame.transform.rotate(Head, 180)
dis.blit(hdir, (x1,y1))
elif self.down:
x1 = self.x + self.x_change - 10
y1 = self.y + self.y_change - 10
hdir = pygame.transform.rotate(Head, 0)
dis.blit(hdir, (x1,y1))
pygame.display.update | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _draw_snake(self):\n if self._snake is not None:\n for pixel in self._snake.body:\n self._sensehat.set_pixel(pixel.x, pixel.y, self._snake.color)",
"def snakePrint():\n for snake in snake_pos: \n pg.draw.rect(game_disp, white, snake)",
"def draw(self):\n ... | [
"0.79485714",
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"0.75067466",
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"0.69155216",
"0.68954194",
"0.6849644",
"0.6842827",
"0.6826483",
"0.67571... | 0.70201904 | 11 |
This Function Displays the Food | def fooddraw(foodx, foody):
dis.blit(Food, (foodx, foody))
pygame.display.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_food(self):\n for dish in self.food:\n print(dish.get_name())",
"def food_eaten(self):\r\n # get values from GUI\r\n\r\n foodList = \"\"\r\n foodCost=0\r\n if self.is_eggs.get():\r\n foodList += \"eggs $2.00\\n\"\r\n foodCost... | [
"0.77116036",
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"0.62275267",
"0.6203672",
"0.61670315",
"0.6158984",
"0.60892135",
"0.60... | 0.5860002 | 39 |
This Function Shows a message on the screen | def message(msg ,font_size, color , pos):
font_style = pygame.font.SysFont( "Times New Roman" , font_size)
mesg = font_style.render(msg, True, color)
dis.blit(mesg, pos)
pygame.display.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_message():",
"def showMessage(self):",
"def showMessage(self, message):\r\n print message",
"def display_message(self, message):\n\t\tself.render('message.html', {'message': message})",
"def display(self,message):\r\n \r\n print(message)",
"def showme(message):\n print... | [
"0.8671978",
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"0.75889695",
"0.75355613",
"0.7512984",
"0.7485261",
"0.74392... | 0.70739853 | 42 |
This Function is used to control the Movement of the Snake | def Movement():
keys = pygame.key.get_pressed()
if keys[pygame.K_LEFT] and not snake.ang==90:
snake.x_change = -snake.vel
snake.y_change = 0
snake.left = True
snake.right = False
snake.up = False
snake.down = False
snake.ang = -90
elif keys[pygame.K_RIGHT] and not snake.ang==-90:
snake.x_change = snake.vel
snake.y_change = 0
snake.left = False
snake.right = True
snake.up = False
snake.down = False
snake.ang = 90
elif keys[pygame.K_UP] and not snake.ang==0:
snake.x_change = 0
snake.y_change = -snake.vel
snake.left = False
snake.right = False
snake.up = True
snake.down = False
snake.ang = 180
elif keys[pygame.K_DOWN] and not snake.ang==180:
snake.x_change = 0
snake.y_change = snake.vel
snake.left = False
snake.right = False
snake.up = False
snake.down = True
snake.ang = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self):\r\n piece = []\r\n if self.direction == \"UP\":\r\n piece = [self.body[0][0], self.body[0][1] - self.POS_CHANGE] # create piece at new coordinates\r\n elif self.direction == \"DOWN\":\r\n piece = [self.body[0][0], self.body[0][1] + self.POS_CHANGE]\r\n ... | [
"0.7399674",
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"0.6397963",
"0.6363882",
"0.6341559",
"0.63338673",
"0.6333867... | 0.78153515 | 0 |
This Function Shows the current Score of the Player | def scoredraw(score):
msg = "SCORE : " + str(score - 4)
font_style = pygame.font.SysFont( "Microsoft Sans Serif" , 30)
mesg = font_style.render(msg, True, (255,255,255))
dis.blit(mesg, (10,10))
pygame.display.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_score(self, win, player, computer):\n font = pygame.font.SysFont('comicsans', 70)\n if player < 10 and computer < 10:\n pygame.draw.rect(win, black, (150, 30, 75, 50))\n pygame.draw.rect(win, black, (295, 30, 75, 50))\n text1 = font.render(str(player), 1, ... | [
"0.7532108",
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"0.73177207",
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"0.714652",
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"0.70110327",
"0.6995284",
"0.6994245",
"0.6992214",
"0.6988295",
"0.6986813"... | 0.0 | -1 |
This Function draws The Background of the Game | def redraw(dis):
dis.blit(Bg,(0,0)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_bg(self):\n self.screen.fill(self.bg)",
"def mainmenu_background():\n gameDisplay.fill((40, 0, 40))",
"def mainmenu_background():\n gameDisplay.fill((40, 0, 40))",
"def drawBackground(self,screen):\n pygame.draw.rect(screen,(240,240,240),(self.basepos[0],self.base... | [
"0.8316609",
"0.82724404",
"0.82724404",
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"0.80437565",
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"0.76865375",
"0.75081706",
"0.7474712",
"0.74424314",
"0.7403836",
"0.738532... | 0.7090386 | 34 |
This Function Calculates distance between Food and Snake | def Distance(foodx,foody):
di = ((snake.x - foodx)**2) + ((snake.y - foody)**2)
d = int(math.sqrt(di))
return d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def measure_distance(self):\n # set Trigger to HIGH\n GPIO.output(self.GPIO_TRIGGER, True)\n\n # set Trigger after 0.01ms to LOW\n time.sleep(0.00001)\n GPIO.output(self.GPIO_TRIGGER, False)\n\n start_time = time.time()\n stop_time = time.time()\n\n # save St... | [
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"0.60385036",
"0.6023612",
"0.60140026",
"0.5991973",
"0.59842... | 0.8156581 | 0 |
This Function Shows the Start screen at the Start of the Game | def StartScreen():
dis.blit(Sl, (dis_width/2 - 200, 0))
message("Snake" , 90, (23, 252, 3), (dis_width/2 - 100, 400))
message("Press Enter To Start", 50, (143,250,3), (dis_width/2 - 190, 500))
pygame.display.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gamemode_startscreen(self) -> None:\n self.__draw_startscreen()",
"def displayStartScreen(self):\n self.model.buttons.draw(self.screen)\n Title=myfont.render(\"THE WORLD ENDS WITH COINS\", 1, random.choice(all_color))\n self.screen.blit(Title, (550, 300))\n pygame.display.u... | [
"0.8430267",
"0.8425534",
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"0.7912492",
"0.7737424",
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"0.70596254",
"0.70382",
"0.70099735",
"0.69944596",
"0.69934815... | 0.7316806 | 9 |
This function Show an End Screen If Player Losses the Game | def EndScreen(score):
message("Game Over", 100, (255, 16, 16), (dis_width/2 - 210 , dis_height/2 -100) )
message(" Press Enter to Try Again and Esc to Exit", 30 , (50,255,100), (dis_width/2 - 250, dis_height/2 + 50))
last_score = "Your Score is : " +str(score - 4)
message(last_score, 50, (255,255,255), (dis_width/2 - 170, dis_height/2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_end_game(self):\n game_view = self.get_view.get_game_view\n character = self.model.get_character\n\n if character.alive:\n game_view.game_win()\n else:\n game_view.game_over()\n\n game_view.update_display()",
"def end_game(self):\n pygam... | [
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"0.72027653",
"0.71485794",
"0.714262",
"0.7102258",
"0.70943487... | 0.70578074 | 22 |
If the user presses the close button in the Title Bar This Function Closes the Game | def Quit():
for event in pygame.event.get():
if event.type == pygame.QUIT:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def OnClose(self, event):\r\n pos.app.main.Exit()",
"def end_game():\n pygame.quit()\n exit()",
"def __window_close(self):\n pass",
"def onBtnCloseClicked(self):\n self.close()",
"def exit_game(root):\n root.destroy()",
"def end_pygame(self):\n pygame.quit()",
"def ... | [
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"0.7045907",
"0.7045907",
"0.7045907"... | 0.7133421 | 12 |
Main_Game function which includes all the functions and commands to run the game Returns None. | def main_game():
#All the initial perimeters required to Run the Game
Game_quit = False
Loss = False
foodx = round(random.randrange(50, dis_width - 50))
foody = round(random.randrange(50, dis_height - 50))
snake_Body = []
score = 4
redraw(dis)
while not Game_quit:
keys = pygame.key.get_pressed()
Game_quit = Quit()
if not snake.Start:
StartScreen()
if keys[pygame.K_RETURN]:
snake.Start = True
if Loss:
EndScreen(score)
if keys[pygame.K_RETURN]:
main_game()
elif keys[pygame.K_ESCAPE]:
Game_quit = True
snake.x = dis_width/2
snake.y = dis_height/2
#Playing Game......
while snake.Start and not Loss and not Game_quit :
Clock.tick(20)
Game_quit = Quit()
Movement()
snake.x += snake.x_change
snake.y += snake.y_change
if snake.x < 0 or snake.x > dis_width - snake.width or snake.y < 0 or snake.y > dis_height - snake.height:
Loss = True
#For Drawing Snake Body having different x and y with each part of snake
snake_Part = []
snake_Part.append(snake.x)
snake_Part.append(snake.y)
snake_Part.append(Mid)
snake_Part.append(snake.ang)
snake_Body.append(snake_Part)
if len(snake_Body) > score:
del snake_Body[0]
#For Drawing the Head of the snake
snake_x = snake.x
snake_y = snake.y
snake_x += snake.x_change
snake_y += snake.y_change
headposx = snake_x
headposy = snake_y
#Collision Detection between head and body
for track in snake_Body[:-1]:
if track[0] == headposx and track[1] == headposy:
Loss = True
snake.draw_snake(dis, snake_Part, snake_Body)
fooddraw(foodx, foody)
d = Distance(foodx,foody)
#Collision Detection between Snake and the Food
if d < 40:
foodx = round(random.randrange(50, dis_width - 50))
foody = round(random.randrange(50, dis_height - 50))
score += 1
scoredraw(score)
redraw(dis) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\r\n gameclass = data.game.GameClass()\r\n gameclass.main_loop()",
"def main():\n\n name, game = select_game(vgc.KNOWN_GAMES)\n print('---- Launching: %s -----'%name)\n game.game.main()\n sys.exit(0)",
"def main():\n g = Game(800, 600)\n g.start()",
"def main():\n if \"c... | [
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"0.7592117",
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"0.7072199",
"0.70497036",
"0.69795376",
"0.69768864",
"0.6971949",
"0.697... | 0.0 | -1 |
Inserts the (priority, content) element and percolates it up in the binary heap. | def push(self, item: tuple):
self.__heap.append(item)
self.__sift_up(self.__len__() - 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert(self, data):\n # add data to list'end\n self.heap_list.append(data)\n # adjust max-heap from bottom to top\n self.sift_up(len(self.heap_list)-1)",
"def insert(self, element):\n if self.size >= self.maxsize : \n return\n self.size+= 1\n self.H... | [
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"0.6604025",
"0.6573611",
... | 0.6376806 | 35 |
Pops the item wih the largest priority and repairs the heap. | def pop(self) -> tuple:
item = self.__heap.popleft()
if len(self) > 1:
self.__heap.appendleft(self.__heap.pop())
self.__sift_down(0)
return item | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def push_pop(self, item, priority):\n if self.size() < 1:\n raise ValueError('Priority queue is empty and has no front item')\n else:\n # TODO: Replace and return min item from heap, if any\n ...",
"def delete_max(self):\n retval = self.heap_list[1]\n ... | [
"0.77817386",
"0.76223326",
"0.7581046",
"0.7518329",
"0.74608326",
"0.73750526",
"0.72169954",
"0.7203851",
"0.7195688",
"0.7184285",
"0.7160784",
"0.7153954",
"0.71533555",
"0.70930916",
"0.70832413",
"0.7074128",
"0.7068215",
"0.70491946",
"0.70461",
"0.7043287",
"0.703058... | 0.70076555 | 23 |
Updates the priority of an item. | def update(self, idx: int, new_priority: T.Union[int, float]):
old_priority, item = self.__heap[idx]
self.__heap[idx] = (new_priority, item)
if new_priority < old_priority:
self.__sift_up(idx)
else:
self.__sift_down(idx) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPriority(self, p):\n self.priority = p",
"def increase_priority(self):\n if self._priority > 0:\n self._priority -= 1",
"def schedule_update_priority(self, func, pri, *args, **kwargs):\n self.unschedule(func)\n new_item = _Item(func, pri, args, kwargs)\n for i,s... | [
"0.7033521",
"0.70131207",
"0.6888573",
"0.68740356",
"0.6721127",
"0.6596157",
"0.657843",
"0.64021325",
"0.6299",
"0.6285044",
"0.6251421",
"0.624495",
"0.6241001",
"0.62359715",
"0.6228096",
"0.6220238",
"0.6217791",
"0.6217791",
"0.6208242",
"0.62000865",
"0.61397344",
... | 0.66484094 | 5 |
Percolates up the item if the first element in the item is smaller than the first element in the parent. | def __sift_up(self, i: int):
while i > 0:
parent = (i - 1) // 2
if self.__heap[i][0] < self.__heap[parent][0]:
tmp = self.__heap[parent]
self.__heap[parent] = self.__heap[i]
self.__heap[i] = tmp
i = parent | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def percolate_up(self, index):\n if self.min is True:\n parent = self._parent(index)\n if index > 0 and self._data[index] < self._data[parent]:\n self._swap(index, parent)\n self.percolate_up(parent)\n if self.min is False:\n parent = sel... | [
"0.73008174",
"0.6977387",
"0.69455796",
"0.6931688",
"0.6897218",
"0.68804336",
"0.6834447",
"0.6793737",
"0.67846876",
"0.67604595",
"0.672829",
"0.6725234",
"0.6719083",
"0.66191155",
"0.65959144",
"0.6558018",
"0.65543073",
"0.6469654",
"0.64037776",
"0.63678527",
"0.6363... | 0.69485795 | 2 |
Percolates down the item if the first element in the item is larger than the first element in the child. | def __sift_down(self, i: int):
while (2 * i + 1) <= self.__len__() - 1:
child_idx = self.__get_smallest_child(i)
if self.__heap[i][0] > self.__heap[child_idx][0]:
tmp = self.__heap[i]
self.__heap[i] = self.__heap[child_idx]
self.__heap[child_idx] = tmp
i = child_idx | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perc_down(self, i): #\r\n while (i * 2) <= self.size:\r\n mc = self.max_child(i) ## find max child\r\n if self.items[i] < self.items[mc]:\r\n tmp = self.items[i]\r\n self.items[i] = self.items[mc]\r\n self.items[mc] = tmp\r\n ... | [
"0.7993369",
"0.7516703",
"0.7435907",
"0.7400076",
"0.7334863",
"0.7306124",
"0.719238",
"0.71682036",
"0.710217",
"0.70360464",
"0.68641955",
"0.6791557",
"0.6775774",
"0.66466707",
"0.65461844",
"0.653898",
"0.64824",
"0.64718825",
"0.64513063",
"0.6429633",
"0.6399211",
... | 0.71848524 | 7 |
Push item to its leaf in the tree and update sums. | def push(self, value):
idx = self.__capacity - 1 + self.__size
self.__tree[idx] = value
self.__update(idx)
self.__size += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, item):\r\n self.root = self.recurse_add(self.root, item)",
"def push(self, item): # 05:27 Lecture Week 2 \"Stacks\" (16:24)\n oldfirst = self.first # Save a link to the list\n self.first = self._Node(item, oldfirst) # first points to most recent Node\n self.N += 1",
"def add(t... | [
"0.73173857",
"0.66109264",
"0.6454965",
"0.6436707",
"0.6278545",
"0.6239465",
"0.6177632",
"0.6154286",
"0.6113809",
"0.6101891",
"0.6088831",
"0.60733277",
"0.60294855",
"0.59839755",
"0.59787667",
"0.597623",
"0.59745806",
"0.5972004",
"0.596576",
"0.5932941",
"0.5916967"... | 0.6273207 | 5 |
Updates the value of a leaf node and all the sums above it. Idx expected in the [0, capacity] range. | def update(self, idx, value):
idx = self.__capacity - 1 + idx
self.__tree[idx] = value
self.__update(idx) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(\n self, index: Union[int, np.ndarray], value: Union[float, np.ndarray]\n ):\n\n tree_index = self.capacity + index\n self._tree[tree_index] = value\n\n # Propagate up the tree.\n parent = tree_index // 2\n while np.any(parent > 0):\n left = self._... | [
"0.74209356",
"0.6767374",
"0.6753369",
"0.6640915",
"0.62751794",
"0.6238642",
"0.61967045",
"0.61868465",
"0.618199",
"0.6152516",
"0.6152516",
"0.6152516",
"0.6084542",
"0.5986752",
"0.5953413",
"0.5944968",
"0.58795923",
"0.581223",
"0.5790687",
"0.57693315",
"0.57603663"... | 0.74235 | 0 |
Returns the leaf in a given interval. | def get(self, subtree_sum):
idx = 0
while True:
# if idx is a leaf node return the idx and the value
if idx >= self.__capacity - 1:
return (idx - self.__capacity + 1, self.__tree[idx])
# else continue down
left = 2 * idx + 1
right = 2 * idx + 2
left_sum = self.__tree[left]
if left_sum >= subtree_sum:
idx = left
else:
idx = right
subtree_sum -= left_sum | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interval(self):\n return Interval(self._ll_tree.get_left(), self._ll_tree.get_right())",
"def get_leaves(self):\n raise NotImplementedError()",
"def get_leaf(self, leaf_index):\n return self.__leaves_db.get(encode_int(leaf_index))",
"def insert(self, interval):\n if self.root ... | [
"0.6567261",
"0.6479266",
"0.64509547",
"0.64048004",
"0.6272358",
"0.59726596",
"0.58964354",
"0.58870226",
"0.5835878",
"0.58030605",
"0.578581",
"0.5713284",
"0.5709979",
"0.5675937",
"0.56747264",
"0.56653345",
"0.5579153",
"0.5558996",
"0.54881215",
"0.5477241",
"0.54582... | 0.0 | -1 |
Return the sum of all elements in the tree. | def get_sum(self):
return self.__tree[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sum(self) -> int:\n return self.root.sum",
"def sum_tree(t):\n \"*** YOUR CODE HERE ***\"\n if is_leaf(t):\n return entry(t)\n total = entry(t)\n for subtree in subtrees(t):\n total += sum_tree(subtree)\n return total",
"def sum_of_tree(root_elem):\r\n\tif root_elem is N... | [
"0.8006212",
"0.7781466",
"0.77158576",
"0.7709356",
"0.7609589",
"0.71323264",
"0.7114631",
"0.70373267",
"0.7006873",
"0.7004098",
"0.68861806",
"0.68853563",
"0.68853563",
"0.68846256",
"0.688014",
"0.6859513",
"0.68298334",
"0.6807194",
"0.68041164",
"0.6771166",
"0.67473... | 0.755793 | 5 |
Receives the idx of a leaf node and updates the sums on all the nodes above it based on its current value. | def __update(self, idx):
parent = (idx - 1) // 2
while parent >= 0:
left, right = 2 * parent + 1, 2 * parent + 2
self.__tree[parent] = self.__tree[left] + self.__tree[right]
parent = (parent - 1) // 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(\n self, index: Union[int, np.ndarray], value: Union[float, np.ndarray]\n ):\n\n tree_index = self.capacity + index\n self._tree[tree_index] = value\n\n # Propagate up the tree.\n parent = tree_index // 2\n while np.any(parent > 0):\n left = self._... | [
"0.70218706",
"0.6763772",
"0.65720445",
"0.6464258",
"0.634624",
"0.62440336",
"0.62352777",
"0.6203399",
"0.6152715",
"0.61511225",
"0.61511225",
"0.61511225",
"0.61307853",
"0.6130343",
"0.6128592",
"0.61171615",
"0.6116346",
"0.60709554",
"0.60432565",
"0.60112834",
"0.59... | 0.75952435 | 0 |
Given an arbitrary number of functions we create a pipeline where the output is piped between functions. you can also specify a tuple of arguments that should be passed to functions in the pipeline. The first arg is always the output of the previous function. | def PipeLine(*funcs, **kwargs):
def wrapper(*data):
if len(funcs) == 1:
combinedArgs = data + kwargs.get(funcs[-1].__name__, tuple())
return funcs[-1](combinedArgs)
else:
combinedArgs = kwargs.get(funcs[-1].__name__, tuple())
if combinedArgs != ():
del kwargs[funcs[-1].__name__]
return funcs[-1](PipeLine(*funcs[:-1], **kwargs)(*data), *combinedArgs)
return wrapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pipe(*functions):\n\n return reduce(compose, functions, identity)",
"def make_pipeline(steps):\n def compose2(f, g):\n return lambda x: g(f(x))\n return functools.reduce(compose2, steps)",
"def tee_pipe(*funcs: Tuple[Callable[[GT], GS], ...]) -> Callable[[GT], GT]:\n\n piped = compose(*f... | [
"0.8024059",
"0.7620117",
"0.74363697",
"0.7264736",
"0.71780145",
"0.6964314",
"0.68261856",
"0.67939293",
"0.6666394",
"0.6643853",
"0.66330147",
"0.66295445",
"0.6628641",
"0.6607316",
"0.6598455",
"0.6594299",
"0.6561477",
"0.6514276",
"0.6458189",
"0.64448345",
"0.644368... | 0.74768734 | 2 |
Given an arbitrary number of functions we create a pipeline where the output is piped between functions. You can also specify a tuple of arguments that should be passed to the functions in the pipeline. The first argument is always the output of the previous function. This version uses the reduce builtin instead of using recursion. | def ReducePipeline(*funcs, **kwargs):
def accum(val, func):
funcArgs = kwargs.get(func.__name__, tuple())
if hasattr(val, "__call__"):
return func(val(), *funcArgs)
else:
return func(val, *funcArgs)
def wrapper(*data):
newFuncs = (partial(funcs[0], *data),) + funcs[1:]
return reduce(accum, newFuncs)
return wrapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pipe(*functions):\n\n return reduce(compose, functions, identity)",
"def make_pipeline(steps):\n def compose2(f, g):\n return lambda x: g(f(x))\n return functools.reduce(compose2, steps)",
"def compose(*fns):\n return functools.reduce(lambda f,g: lambda x: f(g(x)), fns)",
"def compose(... | [
"0.85493785",
"0.79058933",
"0.7589019",
"0.75349104",
"0.7516276",
"0.7404753",
"0.7371548",
"0.7317679",
"0.7314664",
"0.7298046",
"0.7296629",
"0.7294338",
"0.7245193",
"0.719107",
"0.71188146",
"0.7108474",
"0.710505",
"0.7088272",
"0.69122714",
"0.6900706",
"0.68673223",... | 0.8107064 | 1 |
Calculates new observation vector for GridWorld. | def calculate_next_vector(vector, displacement, max_length):
# recover vector to goal
alpha = vector[0] * pi
d = tan(alpha)
col = max_length * vector[1] / sqrt(d ** 2 + 1)
row = d * col
if abs(alpha) > pi / 2:
col = -col
new_row = row - displacement[0]
new_col = col - displacement[1]
# get vector back again
new_vec = np.zeros(2)
new_vec[0] = atan(new_row / (new_col + 1e-12))
if new_col < 0 < new_row:
new_vec[0] += pi
elif (new_col < 0) and (new_row <= 0):
new_vec[0] -= pi
new_vec[0] /= pi
new_vec[1] = sqrt(new_col ** 2 + new_row ** 2) / max_length
return new_vec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_loc(self) -> None:\n self.state[:, :, Boids.Attr.LOC] += self.state[:, :, Boids.Attr.VEL]\n # wrap-around the simulated environment\n self.state[:, :, Boids.Attr.LOC] %= np.expand_dims(self.env_bounds, axis=1)",
"def compute_observation(self):\n robotPos, robotOrn = p.getB... | [
"0.6230598",
"0.6210779",
"0.6134541",
"0.6021939",
"0.5974683",
"0.5839655",
"0.58316547",
"0.58069307",
"0.57648754",
"0.56969017",
"0.56762373",
"0.56254745",
"0.5595447",
"0.5594612",
"0.55772495",
"0.55599743",
"0.5549346",
"0.5541184",
"0.5500111",
"0.54753816",
"0.5457... | 0.0 | -1 |
Check move possibility using observation window | def is_move_possible(obs_window, displacement):
pos_row = obs_window.shape[0] // 2
pos_col = obs_window.shape[1] // 2
new_pos_row = pos_row + displacement[0]
new_pos_col = pos_col + displacement[1]
is_traversable = obs_window[new_pos_row, new_pos_col] == 0
is_shortcut = obs_window[new_pos_row, pos_col] or obs_window[pos_row, new_pos_col]
return is_traversable and (not is_shortcut) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ismoving(self):\n return self.dp.state()==PyTango.DevState.MOVING",
"def move_window():\n\tif SLIDING_WINDOW:\n\t\t# get the chosen predicates\n\t\tpred = Predicate.objects.filter(pk__in=[p+1 for p in toggles.CHOSEN_PREDS])\n\n\t\t# handle window properties\n\t\tfor p in pred:\n\t\t\tp.move_window()"... | [
"0.6529597",
"0.6286072",
"0.6259327",
"0.6203149",
"0.6197683",
"0.6197294",
"0.6170365",
"0.601616",
"0.5998505",
"0.5931079",
"0.5923095",
"0.5921639",
"0.5878888",
"0.5877249",
"0.58698124",
"0.585277",
"0.5812267",
"0.5797952",
"0.5775176",
"0.57483536",
"0.5737459",
"... | 0.7049266 | 0 |
Returns the CPModule from within the loaded Python module m an imported module returns the CPModule class | def find_cpmodule(m):
for v, val in list(m.__dict__.items()):
if isinstance(val, type) and issubclass(val, cellprofiler_core.module.Module):
return val
raise ValueError(
"Could not find cellprofiler_core.module.Module class in %s" % m.__file__
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _module(self):\n if self._module_cache is None:\n self._module_cache = load_module(self._name, self._path)\n return self._module_cache",
"def base_module(self) -> nn.Module:\n return getattr(__import__(\"src.modules\", fromlist=[\"\"]), self.name)",
"def get_module(self):\n ... | [
"0.6648748",
"0.64653724",
"0.64607435",
"0.6405533",
"0.63600814",
"0.6347188",
"0.63392264",
"0.63091576",
"0.625608",
"0.6237327",
"0.61681557",
"0.6154078",
"0.6154078",
"0.6154078",
"0.6154078",
"0.6154078",
"0.6122566",
"0.6057319",
"0.6050425",
"0.6026159",
"0.6026159"... | 0.7351052 | 0 |
Encrypts data based on what the TPLink smartplug is expecting | def __encrypt(string: str) -> str:
key = 171
result = b"\0\0\0" + chr(len(string)).encode('latin-1')
for i in string.encode('latin-1'):
a = key ^ i
key = a
result += chr(a).encode('latin-1')
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encrypt_data(self, params):\n raise NotImplementedError",
"def encrypt(data, key):\n data = six.ensure_binary(data)\n data = privy.hide(secret=data, password=key)\n data = six.ensure_text(data)\n return data",
"def Encrypt(self, data):\n\n if len(data) % 16 != 0:\n data... | [
"0.7355738",
"0.72868085",
"0.7221197",
"0.719316",
"0.71014744",
"0.70030105",
"0.69509864",
"0.69267815",
"0.68219936",
"0.6818995",
"0.6817378",
"0.67560524",
"0.67371756",
"0.6733585",
"0.6675217",
"0.6667538",
"0.654713",
"0.65417606",
"0.65303856",
"0.65274465",
"0.6526... | 0.0 | -1 |
Decrypts data based on what the TPLink smartplug is using | def __decrypt(string: str) -> str:
key = 171
result = ""
i: int
for i in string:
a = key ^ i
key = i
result += chr(a)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt(self, data):",
"def decrypt_data(self, encrypted_data):\n raise NotImplementedError",
"def decrypt_data ( aes_key, data ) :\n decoded_data = decode_data( data )\n salt = decoded_data[ 0 : Crypto.Cipher.AES.block_size ]\n encrypted_data = decoded_data[ Crypto.Cipher.AES.block_size : ]\n... | [
"0.8398597",
"0.7666521",
"0.76633376",
"0.7455099",
"0.74344987",
"0.73562866",
"0.73562866",
"0.73559135",
"0.7330574",
"0.7323575",
"0.7264788",
"0.7229528",
"0.7145882",
"0.71009696",
"0.6988444",
"0.69679904",
"0.6960979",
"0.6948455",
"0.6828047",
"0.6825578",
"0.682328... | 0.0 | -1 |
Performs any TPLink smartplug supported command. See the commands dictionary | def perform_command(self, cmd: str) -> dict:
sock_tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock_tcp.connect((self.ip, self.port))
sock_tcp.send(self.__encrypt(cmd))
data = sock_tcp.recv(2048)
sock_tcp.close()
data = self.__decrypt(data[4:])
if not data:
raise ConnectionError("Error: Could not communicate to the smart plug")
return json.loads(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def commands():",
"def commands():\n pass",
"def commands():\n pass",
"def commands():\n pass",
"def commands():\n pass",
"def silkscreen_commands(self, commands):\n self.pcb_layers[\"silkscreen\"].commands = commands",
"def execCommand(self, command, opts):\n if command == \"atta... | [
"0.62071913",
"0.6122376",
"0.6122376",
"0.6122376",
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"0.6035483",
"0.59634626",
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"0.5654879",
"0.56148833",
"0.55979323",
"0.55908215",
"0.5581... | 0.0 | -1 |
Turns the TPLink smartplug on or off depending on the state | def set_state(self, is_on: bool) -> None:
json_data = self.perform_command(self.commands["on"] if is_on else self.commands["off"])
if json_data["system"]["set_relay_state"]["err_code"] != 0:
raise Exception("Error: Error from the smartplug: " + json.dumps(json_data)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def turn_on(self, **kwargs):\n self.smartplug.turn_on()",
"def turn_on(self, **kwargs):\n self._state = True\n if(self._device['type'] == '_DT-PLUG' or self._device['type'] == '_THIMR'):\n self._send_cmd(self._device,'cmd=ctrl&devices={[' + self._device[\"sid\"] + ']}&op={\"cmd\":... | [
"0.7354772",
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"0.5903088",
"0.5894138",
"0.58599454",
"0.583519",
... | 0.58260435 | 21 |
default values of parameters from Ciresan et al. | def rotate_scale(X, min_angle=-15, max_angle=15, min_scale=0.85, max_scale=1.15, random_state=None, n_jobs=1):
if random_state is None:
rng = np.random
else:
rng = np.random.RandomState(random_state)
angles = rng.uniform(min_angle, max_angle, size=X.shape[0])
scales = rng.uniform(min_scale, max_scale, size=X.shape[0])
X_rot = Parallel(n_jobs=n_jobs)(delayed(rotate_scale_one)(X[i], angles[i], scales[i]) for i in range(X.shape[0]))
return np.array(X_rot, dtype='float32') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_default_parameters(self):\n super().set_default_parameters()",
"def default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n para... | [
"0.77844316",
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"0.69936126",
"0.698711",
"0.69810283",
"0.6930297",
"0.69... | 0.0 | -1 |
default values of parameters from Ciresan et al. | def elastic_transform(X, min_alpha=36, max_alpha=38, min_sigma=5, max_sigma=6, random_state=None, n_jobs=1):
if random_state is None:
rng = np.random
else:
rng = np.random.RandomState(random_state)
alphas = rng.uniform(min_alpha, max_alpha, size=X.shape[0])
sigmas = rng.uniform(min_sigma, max_sigma, size=X.shape[0])
X_elas = Parallel(n_jobs=n_jobs)(delayed(elastic_transform_one)(X[i], alphas[i], sigmas[i]) for i in range(X.shape[0]))
return np.array(X_elas, dtype='float32') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_default_parameters(self):\n super().set_default_parameters()",
"def default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n para... | [
"0.77844316",
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"0.7146505",
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"0.7112786",
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"0.70080286",
"0.70061696",
"0.69936126",
"0.698711",
"0.69810283",
"0.6930297",
"0.69... | 0.0 | -1 |
Elastic deformation of images as described in [Simard2003]_. .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003. | def elastic_transform_one(image, alpha, sigma, random_state=None):
if random_state is None:
random_state = np.random.randint(0, 999999)
rng = np.random.RandomState(random_state)
shape = image.shape
dx = gaussian_filter((rng.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((rng.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1))
return map_coordinates(image, indices, order=1).reshape(shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_random_elastic_deformation(dummy_input):\n # Test the 2D image: H, W, C\n image, label = dummy_input(image_size=(512, 512, 3),\n label_size=(512, 512, 1))\n transform = RandomElasticDeformation()\n _image, _label = transform(image, label, elastic_deformation_order... | [
"0.68137693",
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"0.55281556",
"0.5469116",
"0.545669... | 0.5255144 | 43 |
CPU mnist test for TF Training Instance Type c5.4xlarge Given above parameters, registers a task with family named after this test, runs the task, and waits for the task to be stopped before doing teardown operations of instance and cluster. | def test_ecs_tensorflow_training_mnist_cpu(cpu_only, ecs_container_instance, tensorflow_training, training_cmd,
ecs_cluster_name):
instance_id, cluster_arn = ecs_container_instance
ecs_utils.ecs_training_test_executor(ecs_cluster_name, cluster_arn, training_cmd, tensorflow_training, instance_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mnist():\n env = os.environ.copy()\n if not \"CUDA_VISIBLE_DEVICES\" in env:\n env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n subprocess.run(\n \"edflow -b template_tfe/config.yaml -t --max_batcher_per_epoch --num_epochs 1\",\n shell=True,\n check=True,\n env=env,\n ... | [
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"0.57621115",
"0.5742489",
"0.5... | 0.6672013 | 1 |
GPU mnist test for TF Training Instance Type p3.2xlarge Given above parameters, registers a task with family named after this test, runs the task, and waits for the task to be stopped before doing teardown operations of instance and cluster. | def test_ecs_tensorflow_training_mnist_gpu(gpu_only, ecs_container_instance, tensorflow_training, training_cmd,
ecs_cluster_name):
instance_id, cluster_arn = ecs_container_instance
num_gpus = ec2_utils.get_instance_num_gpus(instance_id)
ecs_utils.ecs_training_test_executor(ecs_cluster_name, cluster_arn, training_cmd, tensorflow_training, instance_id,
num_gpus=num_gpus) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mnist():\n env = os.environ.copy()\n if not \"CUDA_VISIBLE_DEVICES\" in env:\n env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n subprocess.run(\n \"edflow -b template_tfe/config.yaml -t --max_batcher_per_epoch --num_epochs 1\",\n shell=True,\n check=True,\n env=env,\n ... | [
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"0.57152057",
"0.5713483",
"0.5712434",
"0.569594",
"0.5687781... | 0.6573886 | 1 |
GPU Faster RCNN test for TF Training Instance Type g3.8xlarge Given above parameters, registers a task with family named after this test, runs the task, and waits for the task to be stopped before doing teardown operations of instance and cluster. | def test_ecs_tensorflow_training_fasterrcnn_gpu(gpu_only, ecs_container_instance, tensorflow_training, training_cmd,
ecs_cluster_name):
instance_id, cluster_arn = ecs_container_instance
num_gpus = ec2_utils.get_instance_num_gpus(instance_id)
ecs_utils.ecs_training_test_executor(ecs_cluster_name, cluster_arn, training_cmd, tensorflow_training, instance_id,
num_gpus=num_gpus) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_testing(gpu=0):\n # expected environment variables\n os.environ[\"BERT_BASE_DIR\"] = \"pretrained/cased_L-12_H-768_A-12\"\n os.environ[\"DATA_DIR\"] = \"dataset\"\n os.environ[\"OUTPUT_DIR\"] = \"output\"\n assert os.environ.get(\"BERT_BASE_DIR\") is not None\n assert os.environ.get(\"DATA... | [
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"0.5790498",
"0.5788643",
"0.57... | 0.6722839 | 0 |
Takes a results list and puts it in a pandas dataframe together with other relevant variables (runs, generations, and language class) | def language_stats_to_dataframe(results, n_runs, n_gens, possible_form_lengths):
if len(possible_form_lengths) == 1:
n_language_classes = 4
else:
n_language_classes = 7 #TODO: or should this be 6 (i.e. collapsing the two different reduplication strategies?)
column_proportion = np.array(results)
if n_language_classes == 4 and column_proportion.shape[2] > n_language_classes:
column_proportion_compositional_summed = np.zeros((n_runs, n_gens, n_language_classes))
for r in range(len(column_proportion_compositional_summed)):
for g in range(len(column_proportion_compositional_summed[0])):
column_proportion_compositional_summed[r][g] = np.array([column_proportion[r][g][0], column_proportion[r][g][1], column_proportion[r][g][2]+column_proportion[r][g][3], column_proportion[r][g][4]])
column_proportion = column_proportion_compositional_summed.flatten()
else:
column_proportion = column_proportion.flatten()
column_runs = []
for i in range(n_runs):
for j in range(n_gens):
for k in range(n_language_classes):
column_runs.append(i)
column_runs = np.array(column_runs)
column_generation = []
for i in range(n_runs):
for j in range(n_gens):
for k in range(n_language_classes):
column_generation.append(j)
column_generation = np.array(column_generation)
column_type = []
for i in range(n_runs):
for j in range(n_gens):
if len(possible_form_lengths) == 1:
column_type.append('degenerate')
column_type.append('holistic')
column_type.append('compositional')
column_type.append('other')
else:
column_type.append('degenerate')
column_type.append('holistic')
column_type.append('holistic_diversify_signal')
column_type.append('compositional')
column_type.append('compositional_reduplicate_segments')
column_type.append('compositional_reduplicate_whole_signal')
column_type.append('other')
data = {'run': column_runs,
'generation': column_generation,
'proportion': column_proportion,
'class': column_type}
lang_class_prop_over_gen_df = pd.DataFrame(data)
return lang_class_prop_over_gen_df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataframe(result):\n # List of elements in the search result\n names = []\n snippet = []\n url = []\n \n # Append search results to list\n for j,item in enumerate(result):\n for i,element in enumerate(result[j]['items']):\n names.append(result[j]['items'][i]['title... | [
"0.72283614",
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"0.6016165",
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"0.59397256",
"... | 0.73413634 | 0 |
Takes a pandas dataframe of results and turns it back into a simple results array, which only contains the populations' posterior probability distributions over generations. | def dataframe_to_language_stats(dataframe, n_runs, n_batches, n_gens, possible_form_lengths):
if len(possible_form_lengths) == 1:
n_language_classes = 4
else:
n_language_classes = 7 #TODO: or should this be 6 (i.e. collapsing the two different reduplication strategies?)
proportion_column = np.array(dataframe['proportion'])
proportion_column_as_results = proportion_column.reshape((n_runs*n_batches, n_gens, n_language_classes))
return proportion_column_as_results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pandas(self):\n names,prior,posterior = [],[],[]\n for iname,name in enumerate(self.posterior_parameter.row_names):\n names.append(name)\n posterior.append(np.sqrt(float(\n self.posterior_parameter[iname, iname]. x)))\n iprior = self.parcov.row_name... | [
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"0.53533596",
"0.53266215",
"0.5307653",
"0.5297774",
"0.5293929"... | 0.51801986 | 34 |
Takes a pandas dataframe which contains the proportions of language classes over generations and plots timecourses | def plot_timecourse_language_types(lang_class_prop_over_gen_df, title, file_path, file_name):
sns.set_style("darkgrid")
sns.set_context("talk")
fig, ax = plt.subplots()
if len(possible_form_lengths) == 1:
palette = sns.color_palette(["black", "red", "green", "grey"])
else:
palette = sns.color_palette(["black",
sns.color_palette("colorblind")[3],
sns.color_palette("colorblind")[1],
sns.color_palette("colorblind")[2],
sns.color_palette("colorblind")[9],
sns.color_palette("colorblind")[0],
sns.color_palette("colorblind")[7]])
sns.lineplot(x="generation", y="proportion", hue="class", data=lang_class_prop_over_gen_df, palette=palette)
# sns.lineplot(x="generation", y="proportion", hue="class", data=lang_class_prop_over_gen_df, palette=palette, ci=95, err_style="bars")
plt.tick_params(axis='both', which='major', labelsize=18)
plt.tick_params(axis='both', which='minor', labelsize=18)
plt.ylim(-0.05, 1.05)
plt.title(title, fontsize=22)
plt.xlabel('Generation', fontsize=20)
plt.ylabel('Mean proportion', fontsize=20)
handles, labels = ax.get_legend_handles_labels()
labels = ['D', 'H', 'H+Div.', 'C', 'C+Red.-part', 'C+Red.-whole', 'O']
# ax.legend(handles=handles[1:], labels=labels[1:])
ax.legend(handles=handles, labels=labels)
plt.tight_layout()
plt.savefig(file_path + "Timecourse_plot_lang_types_" + file_name + ".png")
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_word_class_pr_genre(df):\n df['nouns'] = df['nouns'] * 100\n df['verbs'] = df['verbs'] * 100\n df['adverbs'] = df['adverbs'] * 100\n # plotting nouns\n plotting_helper_method('nouns', 'genre', df)\n plt.title('Amount of nouns pr song pr. genre')\n plt.xlabel(\"Amount of nouns in each ... | [
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"0.53972",
"0.5382327",
"0.5376701",
"0.5373505",
"0.5348653",
"... | 0.66838074 | 0 |
Takes a pandas dataframe which contains the proportions of language classes over generations and generates a barplot (excluding the burnin period) | def plot_barplot_language_types(lang_class_prop_over_gen_df, title, file_path, file_name, n_runs, n_batches, n_gens, gen_start, lang_class_baselines_all, lang_class_baselines_fully_expressive, possible_form_lengths):
sns.set_style("darkgrid")
sns.set_context("talk")
if len(possible_form_lengths) == 1:
n_language_classes = 4
else:
n_language_classes = 7 #TODO: or should this be 6 (i.e. collapsing the two different reduplication strategies?)
proportion_column_as_results = dataframe_to_language_stats(lang_class_prop_over_gen_df, n_runs, n_batches, n_gens, possible_form_lengths)
proportion_column_from_start_gen = proportion_column_as_results[:, gen_start:]
proportion_column_from_start_gen = proportion_column_from_start_gen.flatten()
runs_column_from_start_gen = []
for i in range(n_runs*n_batches):
for j in range(gen_start, n_gens):
for k in range(n_language_classes):
runs_column_from_start_gen.append(i)
runs_column_from_start_gen = np.array(runs_column_from_start_gen)
generation_column_from_start_gen = []
for i in range(n_runs*n_batches):
for j in range(gen_start, n_gens):
for k in range(n_language_classes):
generation_column_from_start_gen.append(j)
generation_column_from_start_gen = np.array(generation_column_from_start_gen)
class_column_from_start_gen = []
for i in range(n_runs*n_batches):
for j in range(gen_start, n_gens):
if n_language_classes == 4:
class_column_from_start_gen.append('degenerate')
class_column_from_start_gen.append('holistic')
class_column_from_start_gen.append('compositional')
class_column_from_start_gen.append('other')
elif n_language_classes == 7:
class_column_from_start_gen.append('D')
class_column_from_start_gen.append('H')
class_column_from_start_gen.append('H+Div.')
class_column_from_start_gen.append('C')
class_column_from_start_gen.append('C+Red.-part')
class_column_from_start_gen.append('C+Red.-whole')
class_column_from_start_gen.append('O')
new_data_dict = {'run': runs_column_from_start_gen,
'generation': generation_column_from_start_gen,
'proportion': proportion_column_from_start_gen,
'class': class_column_from_start_gen}
lang_class_prop_over_gen_df_from_starting_gen = pd.DataFrame(new_data_dict)
if len(possible_form_lengths) == 1:
palette = sns.color_palette(["black", "red", "green", "grey"])
else:
palette = sns.color_palette(["black",
sns.color_palette("colorblind")[3],
sns.color_palette("colorblind")[1],
sns.color_palette("colorblind")[2],
sns.color_palette("colorblind")[9],
sns.color_palette("colorblind")[0],
sns.color_palette("colorblind")[7]])
sns.barplot(x="class", y="proportion", data=lang_class_prop_over_gen_df_from_starting_gen, palette=palette)
# plt.axhline(y=lang_class_baselines_all[0], xmin=0.0, xmax=0.25, color='k', linestyle='--', linewidth=2)
# plt.axhline(y=lang_class_baselines_all[1], xmin=0.25, xmax=0.5, color='k', linestyle='--', linewidth=2)
# plt.axhline(y=lang_class_baselines_all[2], xmin=0.5, xmax=0.75, color='k', linestyle='--', linewidth=2)
# plt.axhline(y=lang_class_baselines_all[3], xmin=0.75, xmax=1.0, color='k', linestyle='--', linewidth=2)
#
# if title == 'Mutual Understanding Only' or title == 'Minimal Effort & Mutual Understanding':
# plt.axhline(y=lang_class_baselines_fully_expressive[0], xmin=0.25, xmax=0.5, color='0.6', linestyle='--', linewidth=2)
# plt.axhline(y=lang_class_baselines_fully_expressive[1], xmin=0.5, xmax=0.75, color='0.6', linestyle='--', linewidth=2)
plt.tick_params(axis='both', which='major', labelsize=18)
plt.tick_params(axis='both', which='minor', labelsize=18)
plt.ylim(-0.05, 1.05)
plt.title(title, fontsize=22)
# plt.xlabel('Language class')
plt.xlabel('', fontsize=20)
plt.ylabel('Mean proportion', fontsize=20)
plt.tight_layout()
if holistic_without_partial_meaning is True:
plt.savefig(file_path + "Barplot_lang_types_" + file_name + "_burn_in_" + str(gen_start) + ".png")
else:
plt.savefig(file_path + "Barplot_lang_types_" + file_name + "_burn_in_" + str(gen_start) + "_NEW.png")
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bar_plot(df_NP):\n cnt = Counter()\n for tax_list in df_NP.taxonomy:\n for tax in list(tax_list):\n if tax != 'no':\n cnt[tax] += 1\n plt.bar(cnt.keys(),cnt.values())\n plt.xlabel('taxonomic provenance')\n plt.ylabel('number of molecules')\n plt.title('number ... | [
"0.6668762",
"0.6598685",
"0.6580668",
"0.65461254",
"0.6532494",
"0.6513617",
"0.6504175",
"0.6385042",
"0.63797927",
"0.63521934",
"0.6340164",
"0.63299674",
"0.619069",
"0.61589485",
"0.6137865",
"0.6120342",
"0.607238",
"0.6047123",
"0.60318536",
"0.59974545",
"0.5966363"... | 0.6864728 | 0 |
Takes a pandas dataframe which contains the proportions of language classes over generations and plots timecourses | def plot_timecourse_repair_counts(repair_counts_over_gen_df, title, file_path, file_name):
sns.set_style("darkgrid")
sns.set_context("talk")
fig, ax = plt.subplots()
palette = sns.color_palette("colorblind")
sns.lineplot(x="generation", y="independent_repair_proportion", data=repair_counts_over_gen_df, palette=palette)
# sns.lineplot(x="generation", y="proportion", hue="class", data=lang_class_prop_over_gen_df, palette=palette, ci=95, err_style="bars")
plt.tick_params(axis='both', which='major', labelsize=18)
plt.tick_params(axis='both', which='minor', labelsize=18)
plt.ylim(-0.05, 1.05)
plt.title(title, fontsize=22)
plt.xlabel('Generation', fontsize=20)
plt.ylabel('Mean proportion', fontsize=20)
# handles, labels = ax.get_legend_handles_labels()
#
# labels = ['D', 'H', 'H+Div.', 'C', 'C+Red.-part', 'C+Red.-whole', 'O']
# # ax.legend(handles=handles[1:], labels=labels[1:])
# ax.legend(handles=handles, labels=labels)
plt.tight_layout()
plt.savefig(file_path + "Timecourse_plot_repairs_" + file_name + ".png")
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_timecourse_language_types(lang_class_prop_over_gen_df, title, file_path, file_name):\n sns.set_style(\"darkgrid\")\n sns.set_context(\"talk\")\n\n fig, ax = plt.subplots()\n\n if len(possible_form_lengths) == 1:\n palette = sns.color_palette([\"black\", \"red\", \"green\", \"grey\"])\n ... | [
"0.66838074",
"0.6448972",
"0.6392558",
"0.6383187",
"0.6325968",
"0.58468354",
"0.5693776",
"0.5645798",
"0.5629048",
"0.5611402",
"0.55704165",
"0.5545969",
"0.54707754",
"0.5421868",
"0.541172",
"0.5408939",
"0.53972",
"0.5382327",
"0.5376701",
"0.5373505",
"0.5348653",
... | 0.5622483 | 9 |
Initialize a new HTTP client event router object uri is a URI for this event router. A new URI derived from this is created for the HTTP client event relay. host is the IP address of host name to which the HTTP connection is made. port is the TCP port number to which the HTTP connection is made. | def __init__(self, uri=None, host='', port=8082, simplex=False):
super(EventRouterHTTPC, self).__init__(uri)
relayuri = self.getUri()+"/HTTPC"
self._relay = EventRelayHTTPC(self, relayuri, host, port, simplex)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, router, uri=None, host='', port=8082, simplex=False):\n super(EventRelayHTTPC, self).__init__(uri)\n self._router = router\n self._queue = Queue()\n self._event = threading.Event()\n self._closing = False\n self._queueEvent = threading.Event()\n ... | [
"0.74562943",
"0.6506829",
"0.63234913",
"0.63234913",
"0.63085514",
"0.621488",
"0.60746145",
"0.6005634",
"0.59841245",
"0.5979701",
"0.5968453",
"0.5921957",
"0.5919456",
"0.58944875",
"0.58941567",
"0.5850365",
"0.58042604",
"0.5793125",
"0.5780519",
"0.5780519",
"0.57790... | 0.7386842 | 1 |
Function called to close down event router. | def close(self):
self._relay.close()
super(EventRouterHTTPC, self).close()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def closeEvent(self, event):\n\n sys.exit()",
"def on_closing_event(self):\n self.exit_event(None)",
"def closeEvent(self, event):\n sys.exit(0)",
"def close(self) -> None:\n self.relay(\"close\")()",
"def closeEvent(self, event):\n self.exit()\n event.accept()",
... | [
"0.71471226",
"0.71452016",
"0.70188725",
"0.7006168",
"0.6977439",
"0.6927479",
"0.68837065",
"0.6871763",
"0.68616617",
"0.68096364",
"0.6793978",
"0.6784437",
"0.67712516",
"0.6754609",
"0.6701007",
"0.6697934",
"0.6681519",
"0.6672537",
"0.6667044",
"0.66509724",
"0.66509... | 0.73405606 | 0 |
Initialize a new HTTP client event passing object An HTTP client is associated with an existing event router, and sends all messages received from that router to the HTTP connection, and forwards all messages received from the HTTP connection to the router. Interaction with the indicated EventRouter object takes place primarily through the 'receive' methods of this class and the supplied router. Because messages received from HTTP are sent onwards using the normal forwarding mechanisms, this class must perform loopdetection to stop events being bounced back to the HTTP connection. | def __init__(self, router, uri=None, host='', port=8082, simplex=False):
super(EventRelayHTTPC, self).__init__(uri)
self._router = router
self._queue = Queue()
self._event = threading.Event()
self._closing = False
self._queueEvent = threading.Event()
self._simplex = simplex
# Have 'router' send all subscriptions events to this object
router.routeEventFrom(None, None, self)
router.doSubscribeRequest(self, -1, None, None)
# Create HTTP "connection", and start thread to respond to new events from it.
self._httpcon = httplib.HTTPConnection(host=host, port=port)
self._thread = threading.Thread(name=uri, target=self.processEvent)
self._thread.start()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, uri=None, host='', port=8082, simplex=False):\n super(EventRouterHTTPC, self).__init__(uri)\n relayuri = self.getUri()+\"/HTTPC\"\n self._relay = EventRelayHTTPC(self, relayuri, host, port, simplex)\n return",
"def __init__(self, router):\n self._router = ro... | [
"0.61509657",
"0.5864122",
"0.57680523",
"0.5765937",
"0.5636957",
"0.542871",
"0.53911084",
"0.5345458",
"0.52738434",
"0.52246845",
"0.517456",
"0.51684",
"0.51025426",
"0.5091683",
"0.50771654",
"0.5053803",
"0.50480044",
"0.50263023",
"0.501474",
"0.49918574",
"0.49689016... | 0.74102676 | 0 |
This function receives messages from the associated router and queues them for transmission on the HTTP interface. | def receive(self, fromrouter, envelope):
event = envelope.unWrap(self.getUri())
if event:
Trace("%s receive %s from %s"%(self.getUri(),event,fromrouter), "EventLib.EventRelayHTTPC")
return self.queueItem(["forward",envelope])
return makeDeferred(StatusVal.OK) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recv_messages(self):\n while True:\n b = unwrap_read(self.sock.recv(4096))\n msgs = self.parser.feed(b)\n if msgs:\n for msg in msgs:\n self.router.incoming(msg)\n return",
"def __process_requests(self):\n\t\tfor receive... | [
"0.69763803",
"0.63376343",
"0.6313037",
"0.62614304",
"0.62203604",
"0.6201189",
"0.6163866",
"0.61292046",
"0.6127975",
"0.60782707",
"0.60757583",
"0.6066614",
"0.6057568",
"0.6052637",
"0.6049549",
"0.6036926",
"0.60339874",
"0.60330033",
"0.60038686",
"0.5997388",
"0.597... | 0.55585676 | 92 |
Shut down the event router thread | def close(self):
Trace("%s close"%(self.getUri()), "EventLib.EventRelayHTTPC")
self._httpcon.close()
self._closing = True
self._event.set()
self._queueEvent.set()
self._queue.put(["closedown",[]])
self._thread.join()
Trace("%s closed"%(self.getUri()), "EventLib.EventRelayHTTPC")
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shutdown(self):\n self.socket_thread.stop()",
"def shutdown(self):\n if not self.stop_event.is_set():\n self.stop_event.set()\n\n if self.pusher_thread:\n self.pusher_thread.join()",
"def __del__(self):\n AppHelper.stopEventLoop()",
"def __del__(self)... | [
"0.73733056",
"0.7205434",
"0.7066241",
"0.7066241",
"0.68949795",
"0.6849437",
"0.6786754",
"0.6766629",
"0.67512125",
"0.6745822",
"0.6738774",
"0.67175186",
"0.6715772",
"0.6702667",
"0.669924",
"0.66910434",
"0.6680487",
"0.66720706",
"0.66561806",
"0.66561806",
"0.664059... | 0.0 | -1 |
Add item to the queue, and return a deferred object that fires when an item is removed (or the queue is empty). | def queueItem(self, item):
Trace("%s queueItem (%s)"%(self.getUri(),item), "EventLib.EventRelayHTTPC")
if not self._closing:
self._queue.put(item)
self._queueEvent.set()
return makeQueueDeferred(StatusVal.OK, self._queue, self._event)
return makeDeferred(StatusVal.OK) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, item):\n completeDeferred = defer.Deferred()\n self.queue.append((item, completeDeferred))",
"def add(self, item: T) -> None:\n self._queue.append(item)\n if not self.is_empty():\n self._queue.sort(reverse=True)",
"def enqueue(self, item):\n self.queu... | [
"0.809805",
"0.66659725",
"0.6658853",
"0.6658853",
"0.649351",
"0.6432891",
"0.6412939",
"0.6350026",
"0.6251586",
"0.623737",
"0.6233821",
"0.6162664",
"0.6149751",
"0.6137725",
"0.6091136",
"0.60882497",
"0.60764444",
"0.6054781",
"0.5985582",
"0.5985582",
"0.598518",
"0... | 0.6929844 | 1 |
Wait for an item to be queued, then return it. | def getQueuedItem(self):
Trace("%s getQueuedItem ..."%(self.getUri()), context="EventLib.EventRelayHTTPC")
item = self._queue.get()
Trace("%s getQueuedItem (%s)"%(self.getUri(),item), context="EventLib.EventRelayHTTPC")
self._event.set()
return item | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except queue.Empty:\n return None\n return item",
"def get_item_from_queue(Q, timeout=0.01):\n try:\n item = Q.get(True, 0.01)\n except Queue.Empty:\n return None\n return item",
"def wor... | [
"0.69198966",
"0.69057333",
"0.6884813",
"0.6864645",
"0.68376034",
"0.6821141",
"0.6660425",
"0.6638609",
"0.65500504",
"0.6460967",
"0.6446401",
"0.63761413",
"0.63736504",
"0.6362188",
"0.6324351",
"0.62841207",
"0.6243727",
"0.61811554",
"0.6120136",
"0.61119175",
"0.6085... | 0.734153 | 0 |
This function is the HTTP client worker thread. | def processEvent(self):
# Note: break out of event dispatch loop when closedown event is received
# and closing flag is set. This is to prevent DoS attack by faked closedown
# event type, and to ensure that prior events received are all processed.
delay_on_error_min = 0.125 # Back off retry interval on error..
delay_on_error_max = 20.0 # ..
delay_on_error = delay_on_error_min # ..
while True:
if delay_on_error < delay_on_error_max:
delay_on_error *= 2
try:
# PLEASE NOTE: In the event that the HTTPC is run as duplex, not simplex
# then the post methods will be delayed if nothing is sent down to the client
# from the server. This timeout is controlled by QUEUE_WAIT_TIMEOUT in EventRouterHTTPS.py
if self._simplex == True:
self._queueEvent.wait()
self._queueEvent.clear()
if not self._queue.empty():
Trace("%s queue.get ..."%(self.getUri()), "EventLib.EventRelayHTTPC")
###msgbody = self._queue.get()
###Trace("%s get msgbody: %s"%(self.getUri(),msgbody), "EventLib.EventRelayHTTPC")
###self._event.set()
msgbody = self.getQueuedItem()
[typ,env] = msgbody
if typ == "closedown":
if self._closing: break
else:
# process request as an HTTP POST request
data = makeEnvelopeData(env)
headers = { "Content-type": "text/plain",
"Accept": "text/plain",
"Content-length": str(len(data)) }
self._httpcon.request("POST", "/request_path_ignored", data, headers)
response = self._httpcon.getresponse()
delay_on_error = delay_on_error_min
elif self._simplex == False:
# Nothing in queue:
# issue a GET for incoming events
_log.info("%s HTTP get ..."%(self.getUri()))
headers = { "Accept": "text/plain" }
self._httpcon.request("GET", "/request_path_ignored", None, headers)
response = self._httpcon.getresponse()
if response.status == 200:
delay_on_error = delay_on_error_min
msgbody = response.read()
Trace("%s get msgbody: %s"%(self.getUri(),msgbody), "EventLib.EventRelayHTTPC")
# Parse message and act accordingly
msgdata = parseMessageData(msgbody)
Trace("%s get msgdata: %s"%(self.getUri(),str(msgdata)), "EventLib.EventRelayHTTPC")
if msgdata == None:
#TODO: Log "Request body malformed"
pass
elif msgdata[0] == "forward":
# msgdata = ["forward", [['R1', 'R2', 'R3'], 'ev:typ', 'ev:src', 'payload']]
event = makeEvent(evtype=msgdata[1][1],source=msgdata[1][2],payload=msgdata[1][3])
env = constructEnvelope(msgdata[1][0], event)
self.forward(event, env)
elif msgdata[0] == "idle":
# Idle response gives client a chance to send if anything is queued
pass
else:
#TODO: handle closedown message?
Warn( "%s Request body unrecognized option: %s"%(self.getUri(),msgdata[0]), "EventRelayHTTPC")
pass
elif response.status == 503:
Trace( "%s processEvent error response: %u, %s"%(self.getUri(),response.status,response.reason), "EventLib.EventRelayHTTPC")
# Remote end closed down
break
else:
# TODO: (log error response)
Warn( "%s processEvent error response: %u, %s"%(self.getUri(),response.status,response.reason), "EventLib.EventRelayHTTPC")
time.sleep(delay_on_error)
except httplib.BadStatusLine, e:
# This can happen at closedown
Info( "%s processEvent bad response: %s"%(self.getUri(), str(e)), "EventLib.EventRelayHTTPC")
time.sleep(delay_on_error)
except httplib.CannotSendRequest, e:
# This can happen at closedown
Info( "%s Cannot send request: %s"%(self.getUri(), str(e)), "EventLib.EventRelayHTTPC")
time.sleep(delay_on_error)
except httplib.ResponseNotReady, e:
# This can happen at startup and sometimes other times:
# maybe multiple requests on a single HTTP connection object?
Info( "%s Response not ready: (%s)"%(self.getUri(), str(e)), "EventLib.EventRelayHTTPC")
time.sleep(delay_on_error)
except socket.error, e:
Warn( "%s Socket error: %s"%(self.getUri(), str(e)), "EventLib.EventRelayHTTPC")
time.sleep(delay_on_error)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __manage_client_thread(self, conn_socket: socket.socket, address: tuple):\n print(\"[THREAD] new thread started for client\")\n connected = True\n\n while connected:\n request_header = HttpServer.get_request_header(conn_socket)\n split_request_header = request_header.... | [
"0.6994863",
"0.68837047",
"0.6724171",
"0.65650207",
"0.6561778",
"0.64669704",
"0.63919336",
"0.63876474",
"0.63811946",
"0.63524044",
"0.6348768",
"0.63193417",
"0.6318059",
"0.62665766",
"0.6266232",
"0.6265155",
"0.6248112",
"0.6233416",
"0.6225023",
"0.61834395",
"0.618... | 0.0 | -1 |
Performs a single iteration of the algorithm. This includes collecting the data, updating the parameters, and adding the metrics of interest to the logger. Does not update the `curr_iter` attribute. | def step(self, snapshot_mode: str, meta_info: dict = None):
# Save snapshot to save the correct iteration count
self.save_snapshot()
if self.curr_checkpoint == -1:
self.train_teachers(snapshot_mode, None)
self.reached_checkpoint() # setting counter to 0
if self.curr_checkpoint == 0:
# Sample observations
ros, rets, all_lengths = self.sample()
# Log current progress
self.logger.add_value("max return", np.max(rets), 4)
self.logger.add_value("median return", np.median(rets), 4)
self.logger.add_value("avg return", np.mean(rets), 4)
self.logger.add_value("min return", np.min(rets), 4)
self.logger.add_value("std return", np.std(rets), 4)
self.logger.add_value("std var", self.expl_strat.std.item(), 4)
self.logger.add_value("avg rollout len", np.mean(all_lengths), 4)
self.logger.add_value("num total samples", np.sum(all_lengths))
# Save snapshot data
self.make_snapshot(snapshot_mode, np.mean(rets), meta_info)
# Update policy and value function
self.update(rollouts=ros) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iterate(self, data):\n \n # Append data to self.data\n self.data = np.append(self.data, data)\n \n for i, d in enumerate(data):\n update = self.current*self.likelihood(d)\n self.current = self._normalize(update)\n self.posterior = np.concatena... | [
"0.67205477",
"0.6347677",
"0.634362",
"0.61698365",
"0.61429197",
"0.6141512",
"0.60966563",
"0.599339",
"0.5989898",
"0.59228843",
"0.59223664",
"0.5900693",
"0.5892551",
"0.58662176",
"0.58467567",
"0.584372",
"0.582183",
"0.58217317",
"0.58217317",
"0.5800747",
"0.5751619... | 0.0 | -1 |
Samples observations from several samplers. | def sample(self) -> Tuple[List[List[StepSequence]], np.array, np.array]:
ros = []
rets = []
all_lengths = []
for sampler in self.samplers:
samples = sampler.sample()
ros.append(samples)
rets.extend([sample.undiscounted_return() for sample in samples])
all_lengths.extend([sample.length for sample in samples])
return ros, np.array(rets), np.array(all_lengths) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def samples(self):\n pass",
"def load_samplers(self):\n for sampler in self.gltf.samplers:\n # Use a sane default sampler if the sampler data is empty\n # Samplers can simply just be json data: \"{}\"\n if sampler.minFilter is sampler.magFilter is None:\n ... | [
"0.67290366",
"0.66936",
"0.66508645",
"0.6528074",
"0.6460033",
"0.6458007",
"0.63837016",
"0.6380185",
"0.6372922",
"0.63533103",
"0.63098216",
"0.6204721",
"0.6148462",
"0.6148462",
"0.6144332",
"0.61341447",
"0.6125732",
"0.61021185",
"0.60895056",
"0.6007772",
"0.5969845... | 0.0 | -1 |
Update the policy's (and value functions') parameters based on the collected rollout data. | def update(self, *args: Any, **kwargs: Any):
obss = []
losses = []
for t in range(self.num_teachers):
concat_ros = StepSequence.concat(kwargs["rollouts"][t])
concat_ros.torch(data_type=to.get_default_dtype())
obss.append(concat_ros.get_data_values("observations")[: self.min_steps])
# Train student
for epoch in range(self.num_epochs):
self.optimizer.zero_grad()
loss = 0
for t_idx, teacher in enumerate(self.teacher_policies):
s_dist = self.expl_strat.action_dist_at(self.policy(obss[t_idx]))
s_act = s_dist.sample()
t_dist = self.teacher_expl_strats[t_idx].action_dist_at(teacher(obss[t_idx]))
l = self.teacher_weights[t_idx] * self.criterion(t_dist.log_prob(s_act), s_dist.log_prob(s_act))
loss += l
losses.append([t_idx, l.item()])
print(f"Epoch {epoch} Loss: {loss.item()}")
loss.backward()
self.optimizer.step() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def policy_update_fn(self, data: Dict[str, Any], result: Dict[str, Any]) -> None:",
"def update_policy(self, *args, **kwargs):\r\n pass",
"def update_policy(self):\n pass",
"def update_Policy(self,inputpolicy):\n \n policyob = self.SD_Map.retrieve_ob(inputpolicy)\n policyob... | [
"0.73782724",
"0.7067951",
"0.66169596",
"0.63102925",
"0.60266966",
"0.5975819",
"0.595031",
"0.5920189",
"0.5888775",
"0.58857965",
"0.5820289",
"0.5794157",
"0.57780397",
"0.5770066",
"0.5761156",
"0.57414526",
"0.5721823",
"0.5698898",
"0.56790006",
"0.56590545",
"0.56160... | 0.5152639 | 71 |
Creates random environments of the given type. | def set_random_envs(self):
self.randomizer.randomize(num_samples=self.num_teachers)
params = self.randomizer.get_params(fmt="dict", dtype="numpy")
for e in range(self.num_teachers):
self.teacher_envs.append(deepcopy(self.env_real))
print({key: value[e] for key, value in params.items()})
self.teacher_envs[e].domain_param = {key: value[e] for key, value in params.items()} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_environment(seed, task_horizon):\n # Load the gym environment.\n environment = CartPoleEnv()\n environment = gym_wrappers.TimeLimit(environment, task_horizon)\n environment.seed(seed)\n environment = wrappers.GymWrapper(environment)\n environment = wrappers.SinglePrecisionWrapper(environ... | [
"0.639185",
"0.61963034",
"0.6018439",
"0.5951477",
"0.58627504",
"0.5845492",
"0.57870317",
"0.57843524",
"0.5763606",
"0.56360656",
"0.56098664",
"0.5605386",
"0.55746096",
"0.55565745",
"0.55424",
"0.5505138",
"0.5489955",
"0.5485594",
"0.5475609",
"0.54569376",
"0.5443105... | 0.5628647 | 10 |
Recursively load all teachers that can be found in the current experiment's directory. | def load_teachers(self):
# Get the experiment's directory to load from
ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)
self.load_teacher_experiment(ex_dir)
if len(self.teacher_policies) < self.num_teachers:
print(
f"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!"
)
self.load_teachers() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_teacher_experiment(self, exp: Experiment):\n _, _, extra = load_experiment(exp)\n self.unpack_teachers(extra)",
"def preload_all_problems(self):\n for _, _, filenames in os.walk(self.problemDir):\n for filename in filenames:\n if filename[-3:] == \".py\" an... | [
"0.66310847",
"0.59174895",
"0.5706464",
"0.56006503",
"0.5449008",
"0.5424291",
"0.54034406",
"0.53829217",
"0.5337041",
"0.53273314",
"0.53095233",
"0.5307464",
"0.52964854",
"0.5285679",
"0.52551943",
"0.5226202",
"0.5178625",
"0.5178625",
"0.51691693",
"0.5146963",
"0.514... | 0.7492254 | 0 |
Load teachers from PDDRTeachers experiment. | def load_teacher_experiment(self, exp: Experiment):
_, _, extra = load_experiment(exp)
self.unpack_teachers(extra) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_teachers(self):\n # Get the experiment's directory to load from\n ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)\n self.load_teacher_experiment(ex_dir)\n if len(self.teacher_policies) < self.num_teachers:\n print(\n ... | [
"0.7358244",
"0.5859265",
"0.5856855",
"0.5826998",
"0.5742936",
"0.552974",
"0.5405307",
"0.5320931",
"0.5187403",
"0.5179637",
"0.5173516",
"0.5170551",
"0.51504564",
"0.51137877",
"0.50919735",
"0.5080042",
"0.5077611",
"0.50590116",
"0.5048102",
"0.5040496",
"0.50286186",... | 0.7384021 | 0 |
Unpack teachers from PDDRTeachers experiment. | def unpack_teachers(self, extra: dict):
self.teacher_policies.extend(extra["teacher_policies"])
self.teacher_envs.extend(extra["teacher_envs"])
self.teacher_expl_strats.extend(extra["teacher_expl_strats"])
self.teacher_critics.extend(extra["teacher_critics"])
self.teacher_ex_dirs.extend(extra["teacher_ex_dirs"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_teacher_experiment(self, exp: Experiment):\n _, _, extra = load_experiment(exp)\n self.unpack_teachers(extra)",
"def prune_teachers(self):\n self.teacher_policies = self.teacher_policies[: self.num_teachers]\n self.teacher_envs = self.teacher_envs[: self.num_teachers]\n ... | [
"0.6385784",
"0.5618853",
"0.54759747",
"0.54556435",
"0.5198481",
"0.5048664",
"0.49990323",
"0.4985974",
"0.49433678",
"0.4874634",
"0.47989118",
"0.47896883",
"0.4787353",
"0.47753018",
"0.4730215",
"0.46576267",
"0.46548298",
"0.46495625",
"0.4646888",
"0.46451658",
"0.46... | 0.67790115 | 0 |
Prune teachers to only use the first num_teachers of them. | def prune_teachers(self):
self.teacher_policies = self.teacher_policies[: self.num_teachers]
self.teacher_envs = self.teacher_envs[: self.num_teachers]
self.teacher_expl_strats = self.teacher_expl_strats[: self.num_teachers]
self.teacher_critics = self.teacher_critics[: self.num_teachers]
self.teacher_ex_dirs = self.teacher_ex_dirs[: self.num_teachers] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanOrphanedLearners(self):\n\n # Before deleting Learners, ensure that if any Learners that are about to be\n # deleted point to a Team as their action, then that Team's count of\n # referincing Learners is decremented.\n for learner in self.learner_pop:\n if learner.ge... | [
"0.5696694",
"0.545549",
"0.54449904",
"0.5432693",
"0.52488464",
"0.5203805",
"0.5048298",
"0.50102764",
"0.493123",
"0.49195728",
"0.48889333",
"0.48790887",
"0.4860135",
"0.48572624",
"0.48336893",
"0.48332903",
"0.4776946",
"0.47586012",
"0.47427273",
"0.47353852",
"0.473... | 0.7730154 | 0 |
Mirror a point over the diagonal of the map | def __mirror(self, x, y):
return (self.width - x - 1, self.height - y - 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mirror_point_point(point, mirror):\n return add_vectors(mirror, subtract_vectors(mirror, point))",
"def mirror(self, p1=(0, 1), p2=(0, 0)):\n self.position = _reflect_points(self.position, p1, p2)\n return self",
"def op_mirror():\n mir = np.array([[1, 0, 0, 0],\n [0,... | [
"0.71411884",
"0.640484",
"0.6257936",
"0.59739065",
"0.59739065",
"0.58550274",
"0.5843202",
"0.5798668",
"0.5773249",
"0.57277185",
"0.57091093",
"0.57091093",
"0.5707049",
"0.56990564",
"0.5662024",
"0.5583394",
"0.55673605",
"0.55462587",
"0.5533044",
"0.54987687",
"0.547... | 0.6138612 | 3 |
Compute several gaussians and build the payout map | def __generate_payouts(self, num_hills, hill_size):
hills = []
for i in range(num_hills):
cx = random.randint(0, self.width - 1)
cy = random.randint(0, self.height - 1)
sx = random.random() * hill_size + 1
sy = random.random() * hill_size + 1
theta = random.random() * math.pi
hills.append(Gaussian2D((cx, cy), (sx, sy), theta))
# Add a mirror image one too to make the map fair
hills.append(Gaussian2D(self.__mirror(cx, cy), (sx, sy), theta + math.pi))
# Sum all the hills
money_payout_rates = [[0.0] * self.height for x in range(self.width)]
for y in range(self.height):
for x in range(self.width):
money_payout_rates[x][y] = sum([h.value((x,y)) for h in hills])
# Normalize the rates from 0->1
max_payout = max([max(row) for row in money_payout_rates])
min_payout = min([min(row) for row in money_payout_rates])
for y in range(self.height):
for x in range(self.width):
offset = money_payout_rates[x][y] - min_payout
money_payout_rates[x][y] = offset / (max_payout - min_payout)
money_payout_rates[x][y] = int(1000 * money_payout_rates[x][y]) / 1000.0
return money_payout_rates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\r\n template = np.zeros((outputSize,outputSize))\r\n ppd=float(outputSize)/float(angularSize) # pixels per deg\r\n \r\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-... | [
"0.5963468",
"0.59558034",
"0.5918713",
"0.589341",
"0.5847284",
"0.58300716",
"0.5703463",
"0.5690351",
"0.5650076",
"0.5646748",
"0.56256956",
"0.55975646",
"0.5596329",
"0.5573423",
"0.55663466",
"0.55646265",
"0.5511411",
"0.55077195",
"0.5489869",
"0.54598117",
"0.545031... | 0.0 | -1 |
Keep trying random points until it's mirror is far enough away | def __generate_spawn_points(self):
while True:
p1x = random.randint(0, self.width - 1)
p1y = random.randint(0, self.height - 1)
p2x, p2y = self.__mirror(p1x, p1y)
d_sq = (p1x - p2x)**2 + (p1y - p2y)**2
if d_sq >= (self.width / 2)**2:
break
return (p1x, p1y), (p2x, p2y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random(self):\n adj = self.adjacent()\n self.switch(random.choice([pos for pos in adj if self.in_grid(pos) and pos != self.prev]))",
"def bc19_4_radius(randomize=False):\n moves = [\n Point(0, 1),\n Point(1, 0),\n Point(0, -1),\n Point(-1, 0),\n Point(1, 1)... | [
"0.65802413",
"0.6314837",
"0.62275654",
"0.61400115",
"0.61373764",
"0.61373764",
"0.6013303",
"0.6008652",
"0.59597766",
"0.59432083",
"0.5940083",
"0.5926595",
"0.59224373",
"0.5892852",
"0.58895075",
"0.5842744",
"0.58392626",
"0.5830656",
"0.58206266",
"0.58205765",
"0.5... | 0.62696826 | 2 |
return orthanc object of study | def get(self):
return orthanc.study(self.orthanc_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def orthanc_studies(self):\n return [orthanc.study(x.orthanc_id) for x in self.studies]",
"def salome_study_init(theStudyId=0):\n\n global salome_study_initial\n global myStudyManager, myStudyId, myStudy, myStudyName\n global orb, lcc, naming_service, cm\n\n if salome_study_initial:\n s... | [
"0.6370708",
"0.5323299",
"0.51920646",
"0.5153776",
"0.5107432",
"0.5070821",
"0.5055284",
"0.49911162",
"0.49496976",
"0.49446896",
"0.49231794",
"0.49001023",
"0.4827991",
"0.48052597",
"0.4804953",
"0.47864586",
"0.4774238",
"0.47692737",
"0.4762107",
"0.4743235",
"0.4733... | 0.7259197 | 0 |
return orthanc objects of studies | def orthanc_studies(self):
return [orthanc.study(x.orthanc_id) for x in self.studies] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n return orthanc.study(self.orthanc_id)",
"def studies(self):\n return self._study_queryset",
"def DumpStudies():\n for name in myStudyManager.GetOpenStudies():\n s=myStudyManager.GetStudyByName(name)\n print \"study:\",name, s._get_StudyId()\n DumpStudy(s)",
"def searc... | [
"0.6256025",
"0.5748354",
"0.5600851",
"0.5587314",
"0.5289076",
"0.52684903",
"0.52684903",
"0.5218291",
"0.50912505",
"0.509042",
"0.50746655",
"0.50711817",
"0.50640666",
"0.5047204",
"0.49905527",
"0.49653897",
"0.49444157",
"0.49329725",
"0.49328998",
"0.4929676",
"0.492... | 0.7746891 | 0 |
Returns all numbers below N, that is a multiple of M. | def findMultiples(M, N):
numbers = []
for i in range(N):
if(i + 1 == N):
break
if(((i + 1) % M) == 0):
numbers.append(i+1)
return numbers | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sixn(m):\n if m <= 2:\n return ()\n if m > 2:\n yield 2\n if m > 3:\n yield 3\n for n in count(1):\n x = 6 * n - 1\n y = x + 2\n if x < m:\n yield x\n else:\n break\n if y < m:\n yield y\n else:\n ... | [
"0.67994684",
"0.67914283",
"0.6521023",
"0.64013743",
"0.632049",
"0.62614083",
"0.6100065",
"0.6055581",
"0.6036513",
"0.6017747",
"0.60122454",
"0.60112625",
"0.59835833",
"0.59610224",
"0.59551555",
"0.59433913",
"0.5900685",
"0.587324",
"0.5860821",
"0.5820013",
"0.58147... | 0.7943605 | 0 |
Check if object is exactly an instance of specified class | def is_same_class(obj, a_class):
return type(obj) == a_class | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.isKindOfClass(self))\n else:\n return False",
"def is_same_class(obj, a_class):\n return isinstance(obj, a_class)",
"def is_same_class(obj, a_class):\n if type(obj)... | [
"0.7938733",
"0.7921352",
"0.7915792",
"0.79037",
"0.79037",
"0.7898302",
"0.7889888",
"0.7864915",
"0.7833402",
"0.78156614",
"0.778558",
"0.77762145",
"0.77750516",
"0.77750516",
"0.77750516",
"0.77750516",
"0.77750516",
"0.77750516",
"0.777345",
"0.7769582",
"0.7744331",
... | 0.7816911 | 10 |
setup any state tied to the execution of the given method in a class. setup_method is invoked for every test method of a class. | def setup_method(self, objectCreation): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_method(self, method):\n pass",
"def setup_method(self, method):\n pass",
"def _decorate_setup(self, setup_method):\n @wraps(setup_method)\n def setup_method_wrapper(*args, **kwargs):\n \"\"\"setup method wrapper.\n\n * Locks the required resources for... | [
"0.75784695",
"0.75784695",
"0.74024475",
"0.7322949",
"0.72970635",
"0.72218513",
"0.70287365",
"0.6968257",
"0.6819937",
"0.67959106",
"0.6794511",
"0.6696449",
"0.66864085",
"0.6648191",
"0.6600183",
"0.659615",
"0.6559962",
"0.65412855",
"0.65281886",
"0.6489964",
"0.6485... | 0.61271554 | 48 |
teardown any state that was previously setup with a setup_method call. | def teardown_method(self, objectCreation): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def teardown_method(self, method) -> None:",
"def teardown_method(self, method):\n pass",
"def teardown_method(self, method):\n pass",
"def teardown_method(self, test_method):\n self.wo_obj = None\n self.config_data = None",
"def teardown_method(self):",
"def teardown_method(s... | [
"0.79968005",
"0.78846675",
"0.78846675",
"0.7871643",
"0.7792453",
"0.7720631",
"0.7659778",
"0.7625319",
"0.757913",
"0.757913",
"0.75738186",
"0.75738186",
"0.7567817",
"0.7567817",
"0.7567817",
"0.7565642",
"0.7565642",
"0.7565642",
"0.7450825",
"0.7437996",
"0.7437996",
... | 0.71103287 | 40 |
Stop the simulator thread. | def stop(self):
self._stop_event.set()
super().stop() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self._Thread__stop()",
"def stop(self):\n\n self.stop_thread = True",
"def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)",
"def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)",
"def stop(self) -> None:\n # set the stop event, then ca... | [
"0.81179816",
"0.796208",
"0.7771914",
"0.7771914",
"0.76687425",
"0.76618785",
"0.76455426",
"0.76039106",
"0.75676537",
"0.75364685",
"0.7525308",
"0.7504455",
"0.7481357",
"0.7431703",
"0.7421729",
"0.7421202",
"0.7421202",
"0.7421202",
"0.7421202",
"0.7421202",
"0.7421202... | 0.0 | -1 |
Returns true if the logged in user is active | def is_active(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_active(self):\n return self.status == ACTIVE_USER",
"def is_active(self):\n return self.user.is_active",
"def is_active_user(self):\n\n return self.is_active",
"def check_active(self, user):\r\n if not self.require_active:\r\n # Ignore & move on.\r\n r... | [
"0.88505435",
"0.8798741",
"0.8700004",
"0.854557",
"0.78350693",
"0.7738377",
"0.7705481",
"0.76957625",
"0.76723784",
"0.7669489",
"0.7668266",
"0.76530737",
"0.7633366",
"0.76033133",
"0.75975317",
"0.7560072",
"0.7550248",
"0.7520325",
"0.75200886",
"0.75200886",
"0.75200... | 0.775682 | 21 |
Returns false if the user is not logged in | def is_anonymous(self):
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logged_in(self):\n return self.auth.get_user_by_session() is not None",
"def isLoggedIn(self):\n session = self.getSession()\n if session is not None:\n return True\n return False",
"def is_logged_in(self) -> bool:\n return self.id is not None and self.username is ... | [
"0.85514784",
"0.8487751",
"0.8463887",
"0.84311163",
"0.83854413",
"0.83422846",
"0.8227351",
"0.81986076",
"0.8189232",
"0.81029665",
"0.80995315",
"0.8037724",
"0.79774845",
"0.7907793",
"0.78765494",
"0.7856657",
"0.7833945",
"0.7820103",
"0.7789442",
"0.77781737",
"0.771... | 0.0 | -1 |
Returns true if the user is logged in | def is_authenticated(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logged_in():\n\n if current_user.is_authenticated:\n return True\n\n return False",
"def is_logged_in():\n return 'user' in session",
"def logged_in(self):\n return self.auth.get_user_by_session() is not None",
"def isLoggedIn(self):\n session = self.getSession()\n if ses... | [
"0.88950294",
"0.8790608",
"0.8753903",
"0.8581064",
"0.8567044",
"0.84687513",
"0.84203804",
"0.8413966",
"0.8383255",
"0.8209496",
"0.82089865",
"0.8092678",
"0.80382335",
"0.8021618",
"0.79841244",
"0.7928396",
"0.7890733",
"0.78188694",
"0.77794355",
"0.7776155",
"0.77729... | 0.7648227 | 30 |
Returns true if the logged in user is an admin | def is_admin(self):
if self.type == 1:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_admin(user):\n return user.is_authenticated and user.id == app.config.get('ADMIN')",
"def is_admin_user(self):\n if \"is_admin\" in self._properties and self.is_admin == 'YES':\n return True\n return False",
"def isAdmin():\n\tif 'username' in session and session['usernam... | [
"0.88923126",
"0.8880638",
"0.8843605",
"0.87530965",
"0.87412965",
"0.86163646",
"0.8612412",
"0.85848016",
"0.84613305",
"0.8420776",
"0.84073126",
"0.840208",
"0.8374834",
"0.83690375",
"0.835545",
"0.8350487",
"0.8346385",
"0.8346304",
"0.83000356",
"0.8297389",
"0.828466... | 0.7866901 | 36 |
Returns the id of the user | def get_id(self):
return self.id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user_id(self):\n return self.id_user",
"def get_id(self):\n return self.user_id",
"def get_id(self) -> int:\n return self.user_id",
"def id(self) -> int:\n return self.user.id",
"def user_id(self):\n return self.status.user[\"id\"]",
"def get_user_id(self):\n ... | [
"0.87912023",
"0.8627861",
"0.8567077",
"0.8435428",
"0.8355682",
"0.82900876",
"0.8260854",
"0.8231441",
"0.8231441",
"0.82138103",
"0.82138103",
"0.82138103",
"0.82138103",
"0.82138103",
"0.82041234",
"0.8196352",
"0.8126279",
"0.8126279",
"0.8086236",
"0.8086236",
"0.80807... | 0.0 | -1 |
Returns the logged in user after adding the wanted user to his followers list | def follow(self, user):
if not self.is_following(user):
self.followed.append(user)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow_user(cls, user, following):\r\n pass",
"def follow(request, usertofollow):\n to_follow = Member.objects.get(user__username=usertofollow)\n user = Member.objects.get(user=request.user)\n user.following.add(to_follow)\n user.save()\n return redirect(request.META['HTTP_REFERER'])",
... | [
"0.7044998",
"0.69360614",
"0.6906444",
"0.6870493",
"0.67471176",
"0.66945994",
"0.66936237",
"0.668267",
"0.6638689",
"0.65982777",
"0.6567574",
"0.65394175",
"0.6528088",
"0.6518445",
"0.64401454",
"0.6416909",
"0.6398086",
"0.6381833",
"0.63553584",
"0.63553584",
"0.63504... | 0.6222603 | 29 |
Returns the logged in user after removing the wanted user from his followers list | def unfollow(self, user):
if self.is_following(user):
self.followed.remove(user)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unfollowUser(following):\n \n cur, user_id, con = initialise(3, True)\n cur.execute(\"DELETE FROM followers WHERE user = (SELECT username FROM users WHERE id = ?) AND following = ?\", (user_id, following))\n finish(con)",
"def unfollow(self, user):\n f = self.followed.filter_by(followed_id... | [
"0.7169295",
"0.70720047",
"0.70225513",
"0.699361",
"0.6943185",
"0.682216",
"0.6751137",
"0.67458344",
"0.6741086",
"0.6738047",
"0.6737277",
"0.6707055",
"0.66615844",
"0.6618556",
"0.6605784",
"0.6594128",
"0.65536183",
"0.6531541",
"0.6502671",
"0.6478856",
"0.64270645",... | 0.65360326 | 17 |
Returns true if the current user is following the desired user | def is_following(self, user):
return self.followed.filter(followers.c.followed_id == user.id).count() > 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_user_following(self, user_id):\n return user_id in self.following",
"def is_following(self, you, them):\n if self.filter(from_user=you, to_user=them).count() > 0:\n return True\n return False",
"def is_following(self, user):\n return self.followed.filter_by(\n ... | [
"0.7629527",
"0.7531042",
"0.7460013",
"0.73141354",
"0.72661006",
"0.70735085",
"0.70591176",
"0.7049096",
"0.6914572",
"0.69122463",
"0.68232995",
"0.6788982",
"0.67763567",
"0.6769098",
"0.6714947",
"0.65794",
"0.6538119",
"0.652213",
"0.6521558",
"0.64769983",
"0.64418167... | 0.7424364 | 4 |
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