0:\\n time.sleep(left_to_wait)\\n\\n lock.release()\\n\\n ret = func(*args, **kwargs)\\n last_time_called[0] = time.clock()\\n return ret\\n\\n return rate_limited_function\\n\\n return decorate\",\n \"def consumer(max_consumers, consumer_func, consumer_q, output_q):\\n consumers = []\\n for i in range(max_consumers):\\n th = threading.Thread(name=\\\"consumer\\\", target=consumer_func, args=(consumer_q, output_q))\\n th.daemon = True\\n consumers.append(th)\\n th.start()\\n\\n return tuple(consumers)\",\n \"def rate_limited(max_per_second):\\n lock = threading.Lock()\\n min_interval = 1.0 / max_per_second\\n\\n def decorate(func):\\n last_time_called = time.perf_counter()\\n\\n @wraps(func)\\n def rate_limited_function(*args, **kwargs):\\n lock.acquire()\\n nonlocal last_time_called\\n try:\\n elapsed = time.perf_counter() - last_time_called\\n left_to_wait = min_interval - elapsed\\n if left_to_wait > 0:\\n time.sleep(left_to_wait)\\n\\n return func(*args, **kwargs)\\n finally:\\n last_time_called = time.perf_counter()\\n lock.release()\\n\\n return rate_limited_function\\n\\n return decorate\",\n \"async def find_divisibles(inrange, div_by):\\r\\n\\r\\n print(\\\"finding nums in range {} divisible by {}\\\".format(inrange, div_by))\\r\\n located = []\\r\\n for i in range(inrange):\\r\\n if i % div_by == 0:\\r\\n located.append(i)\\r\\n if i % 50000 == 0:\\r\\n # come back to find_divisibale of particuluar task when sleep is over\\r\\n await asyncio.sleep(0.0001) # it tells thread to run other task\\r\\n print(\\\"Done w/ nums in range {} divisible by {}\\\".format(inrange, div_by))\\r\\n return located\",\n \"def __init__(self, maxsize=0, workers=10):\\n self.queue = JoinableQueue(maxsize=maxsize)\\n [spawn(self.worker) for x in range(workers)]\",\n \"def hardwareConcurrency(self):\\n return 1\",\n \"def restrict(self):\\n calls = []\\n while self[1:] not in calls:\\n calls.append(self[1:])\\n self.restrict_once()\",\n \"def acquire(self, blocking=True, shared=False):\",\n \"def putonqueue(self, nr, *args):\\n self.outqueues[10-nr].put_nowait(args)\\n self.tickqueue.put_nowait('go')\",\n \"def stress(self, queue, ct):\\n result = []\\n for i in range(ct//2): # populate with half\\n queue.enqueue(i)\\n\\n while not queue.is_empty(): # take away 2, add 1\\n i += 1\\n queue.enqueue(i)\\n\\n result.append(queue.dequeue()) # will eventually drain\\n if queue.is_empty():\\n return result\\n result.append(queue.dequeue())\\n return result\",\n \"def fetch_and_lock(self, worker_id, max_tasks, **kwargs):\\n\\n params = {**{'workerId': worker_id, 'maxTasks': max_tasks}, **kwargs}\\n return self.client.post(\\\"external-task/fetchAndLock\\\", params)\",\n \"def _threads(self, counter):\\r\\n\\r\\n t = self.context.threads\\r\\n if not isinstance(t, int):\\r\\n t = 3\\r\\n if t < counter:\\r\\n return t\\r\\n return counter\",\n \"def rate_limited(max_per_second):\\n\\n lock = threading.Lock()\\n min_interval = 1.0 / max_per_second\\n\\n def decorate(func):\\n last_time_called = time.perf_counter()\\n\\n @wraps(func)\\n def rate_limited_function(*args, **kwargs):\\n with lock:\\n nonlocal last_time_called\\n elapsed = time.perf_counter() - last_time_called\\n left_to_wait = min_interval - elapsed\\n if left_to_wait > 0:\\n time.sleep(left_to_wait)\\n last_time_called = time.perf_counter()\\n return func(*args, **kwargs)\\n\\n return rate_limited_function\\n\\n return decorate\",\n \"async def concurrently_execute(\\n func: Callable[[T], Any],\\n args: Iterable[T],\\n limit: int,\\n delay_cancellation: bool = False,\\n) -> None:\\n it = iter(args)\\n\\n async def _concurrently_execute_inner(value: T) -> None:\\n try:\\n while True:\\n await maybe_awaitable(func(value))\\n value = next(it)\\n except StopIteration:\\n pass\\n\\n # We use `itertools.islice` to handle the case where the number of args is\\n # less than the limit, avoiding needlessly spawning unnecessary background\\n # tasks.\\n if delay_cancellation:\\n await yieldable_gather_results_delaying_cancellation(\\n _concurrently_execute_inner,\\n (value for value in itertools.islice(it, limit)),\\n )\\n else:\\n await yieldable_gather_results(\\n _concurrently_execute_inner,\\n (value for value in itertools.islice(it, limit)),\\n )\",\n \"def worker(*args):\\n global count\\n # We're using the lock as a context here, so it gets released\\n # at the end of this block\\n with args[0]:\\n count += 1\\n print(count)\\n count += 4\\n print(count)\\n count -= 4\\n print(count)\",\n \"def test_unblock_immediatly_after_release():\\r\\n with throttle_client(b\\\"[semaphores]\\\\nA=1\\\") as client:\\r\\n one = client.new_peer(expires_in=timedelta(minutes=1))\\r\\n two = client.new_peer(expires_in=timedelta(minutes=1))\\r\\n # Acquire first lease\\r\\n client.acquire(one, \\\"A\\\")\\r\\n\\r\\n # Wait for `two` in a seperate thread so we can use this thread to release `one`\\r\\n def wait_for_two():\\r\\n client.acquire(two, \\\"A\\\", block_for=timedelta(seconds=15))\\r\\n\\r\\n t = Thread(target=wait_for_two)\\r\\n t.start()\\r\\n\\r\\n # Unblock `t`\\r\\n client.release(one)\\r\\n\\r\\n # Three seconds should be ample time for `t` to return\\r\\n t.join(3)\\r\\n # If `t` is alive, the join timed out, which should not be the case\\r\\n assert not t.is_alive()\",\n \"async def street_parallel2(street_name, semaphores, requests, delayQ, \\r\\n timeoutQ, advance2, quit_app, start=0, triggered=False):\\r\\n amber_duration = 2\\r\\n overlap1 = 2\\r\\n trigger_delay = 3\\r\\n loop = asyncio.get_running_loop()\\r\\n print(f'{street_name} has a RED light at {int(loop.time()-start)}!')\\r\\n while 1:\\r\\n async with semaphores[2]:\\r\\n # change to red\\r\\n if triggered:\\r\\n await requests[1].wait()\\r\\n requests[1].clear()\\r\\n requests[0].set()\\r\\n delayQ.put_nowait(trigger_delay)\\r\\n else:\\r\\n timeout = await timeoutQ.get()\\r\\n await asyncio.sleep(timeout)\\r\\n requests[0].set()\\r\\n delayQ.put_nowait(0)\\r\\n await advance2.wait()\\r\\n if quit_app.is_set():\\r\\n break\\r\\n else:\\r\\n advance2.clear()\\r\\n async with semaphores[0]:\\r\\n # wait for green (value=1)\\r\\n await asyncio.sleep(overlap1)\\r\\n print(f'{street_name} has a GREEN light at {int(loop.time()-start)}!')\\r\\n await requests[0].wait()\\r\\n requests[0].clear()\\r\\n request_delay = await delayQ.get()\\r\\n await asyncio.sleep(request_delay)\\r\\n print(f'{street_name} has an AMBER light at {int(loop.time()-start)}!')\\r\\n await asyncio.sleep(amber_duration)\\r\\n print(f'{street_name} has a RED light at {int(loop.time()-start)}!')\\r\\n await advance2.wait()\\r\\n if quit_app.is_set():\\r\\n break\\r\\n else:\\r\\n advance2.clear()\\r\\n return True\",\n \"def wait_until_got_number_of_callbacks(self, number):\\n slept = 0.0\\n time_step = 0.1\\n while not len(self.futures) == number and slept < TIMEOUT:\\n time.sleep(time_step)\\n slept += time_step\",\n \"def _wait_expiration(self, raiden, transfer, sleep=DEFAULT_EVENTS_POLL_TIMEOUT):\\n # pylint: disable=no-self-use\\n\\n expiration = transfer.lock.expiration + 1\\n\\n while True:\\n current_block = raiden.get_block_number()\\n\\n if current_block > expiration:\\n return\\n\\n gevent.sleep(sleep)\",\n \"def set_process_limits(): # pragma: no cover\\n # Set a new session id so that this process and all its children will be\\n # in a new process group, so we can kill them all later if we need to.\\n os.setsid()\\n\\n # No subprocesses.\\n resource.setrlimit(resource.RLIMIT_NPROC, (0, 0))\\n\\n # CPU seconds, not wall clock time.\\n cpu = LIMITS[\\\"CPU\\\"]\\n if cpu:\\n resource.setrlimit(resource.RLIMIT_CPU, (cpu, cpu))\\n\\n # Total process virtual memory.\\n vmem = LIMITS[\\\"VMEM\\\"]\\n if vmem:\\n resource.setrlimit(resource.RLIMIT_AS, (vmem, vmem))\\n\\n # Size of written files. Can be zero (nothing can be written).\\n fsize = LIMITS[\\\"FSIZE\\\"]\\n resource.setrlimit(resource.RLIMIT_FSIZE, (fsize, fsize))\",\n \"def task5(self):\\n\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6282102","0.6080879","0.60440874","0.59983575","0.59436536","0.5899353","0.5844908","0.5839281","0.5807662","0.573292","0.571161","0.566539","0.5640771","0.55995965","0.5567172","0.55225784","0.5494984","0.5481205","0.54737425","0.539674","0.53499925","0.5322443","0.5281023","0.52435887","0.52432996","0.52432996","0.5228655","0.52090526","0.52089137","0.5186201","0.5185332","0.5159914","0.51541233","0.5142499","0.512709","0.51201546","0.51112777","0.50842714","0.50802165","0.50443697","0.5036579","0.50210774","0.50169384","0.5005189","0.5002908","0.5001441","0.49957836","0.49909216","0.49825996","0.4965871","0.49384716","0.49384242","0.49355718","0.49341464","0.49180904","0.4909524","0.48787257","0.4866172","0.4864727","0.48609602","0.48468098","0.48448914","0.4840845","0.4834827","0.4830688","0.4829571","0.4824468","0.4824403","0.48180604","0.48160517","0.47992763","0.47955003","0.47890323","0.47861934","0.4785786","0.47809243","0.4776901","0.47613096","0.47598323","0.47467133","0.4745806","0.47384992","0.47215652","0.4715521","0.470729","0.47065726","0.47052866","0.46983227","0.4695036","0.46909004","0.46908733","0.46897942","0.4688776","0.46849567","0.46761867","0.46752143","0.46730667","0.46704188","0.46620283","0.46612674"],"string":"[\n \"0.6282102\",\n \"0.6080879\",\n \"0.60440874\",\n \"0.59983575\",\n \"0.59436536\",\n \"0.5899353\",\n \"0.5844908\",\n \"0.5839281\",\n \"0.5807662\",\n \"0.573292\",\n \"0.571161\",\n \"0.566539\",\n \"0.5640771\",\n \"0.55995965\",\n \"0.5567172\",\n \"0.55225784\",\n \"0.5494984\",\n \"0.5481205\",\n \"0.54737425\",\n \"0.539674\",\n \"0.53499925\",\n \"0.5322443\",\n \"0.5281023\",\n \"0.52435887\",\n \"0.52432996\",\n \"0.52432996\",\n \"0.5228655\",\n \"0.52090526\",\n \"0.52089137\",\n \"0.5186201\",\n \"0.5185332\",\n \"0.5159914\",\n \"0.51541233\",\n \"0.5142499\",\n \"0.512709\",\n \"0.51201546\",\n \"0.51112777\",\n \"0.50842714\",\n \"0.50802165\",\n \"0.50443697\",\n \"0.5036579\",\n \"0.50210774\",\n \"0.50169384\",\n \"0.5005189\",\n \"0.5002908\",\n \"0.5001441\",\n \"0.49957836\",\n \"0.49909216\",\n \"0.49825996\",\n \"0.4965871\",\n \"0.49384716\",\n \"0.49384242\",\n \"0.49355718\",\n \"0.49341464\",\n \"0.49180904\",\n \"0.4909524\",\n \"0.48787257\",\n \"0.4866172\",\n \"0.4864727\",\n \"0.48609602\",\n \"0.48468098\",\n \"0.48448914\",\n \"0.4840845\",\n \"0.4834827\",\n \"0.4830688\",\n \"0.4829571\",\n \"0.4824468\",\n \"0.4824403\",\n \"0.48180604\",\n \"0.48160517\",\n \"0.47992763\",\n \"0.47955003\",\n \"0.47890323\",\n \"0.47861934\",\n \"0.4785786\",\n \"0.47809243\",\n \"0.4776901\",\n \"0.47613096\",\n \"0.47598323\",\n \"0.47467133\",\n \"0.4745806\",\n \"0.47384992\",\n \"0.47215652\",\n \"0.4715521\",\n \"0.470729\",\n \"0.47065726\",\n \"0.47052866\",\n \"0.46983227\",\n \"0.4695036\",\n \"0.46909004\",\n \"0.46908733\",\n \"0.46897942\",\n \"0.4688776\",\n \"0.46849567\",\n \"0.46761867\",\n \"0.46752143\",\n \"0.46730667\",\n \"0.46704188\",\n \"0.46620283\",\n \"0.46612674\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6391296"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":87492,"cells":{"query":{"kind":"string","value":"Close queue clients connection"},"document":{"kind":"string","value":"def close_queue_clients(queue_client_list: List[QueueClient], loop: asyncio.AbstractEventLoop):\n client_close_tasks = []\n for client in queue_client_list:\n close_task = asyncio.ensure_future(client.close())\n client_close_tasks.append(close_task)\n loop.run_until_complete(gather_with_concurrency(1, client_close_tasks))"},"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 _close_clients(self):\n for client in self.clients:\n client.close()","def closeConnecetion(self):\n client.close()","def close(self):\n for conn in self._conns:\n conn.send((self.CLOSE,()))","def __close_connection_with_clients(self):\n for current_client in self.__connected_clients:\n self.__close_connection_with_client(current_client)","def close(self):\n self._router_socket.close()\n for client in self._active_clients.values():\n client.close()","def close(self):\n if self.client:\n self.client.connection.close()\n self.client = None","def close(self):\n _LOGGER.debug(\"Closing websocket connections\")\n if self._remote_task:\n self._remote_task.cancel()\n if self._control_task:\n self._control_task.cancel()\n if self._app_monitor_task:\n self._app_monitor_task.cancel()\n if self._managed_session:\n _LOGGER.debug(\"Closing managed ClientSession\")\n self._loop.create_task(self.session.close())","def __del__(self) -> None:\n if self.close_client_at_del:\n self.client.close()","def close_TCP_connections(self):\n if self.master_connection is not None:\n self.master_connection.end_connection()","def remove_connections(self):\n while self.qsize() > 0:\n try:\n cnx = self.get(block=True, timeout=self.queue_timeout)\n except queue.Empty:\n pass\n else:\n try:\n cnx.close_connection()\n except (RuntimeError, OSError, InterfaceError):\n pass\n finally:\n self.remove_connection(cnx)","def close(self):\n self._connection.close()","def close_connections(self):\n self._connection.close_connection()","def end(self):\n self.MAIN_CONNECTION.close()\n self.connection_with_client.close()","def shutdown(self):\n self.channel.close()\n self.conn.close()","def close(self):\n logging.debug(\"Closing down Talk Object with session '%s'\" % self.session_key)\n for client in self.tcp_clients:\n client.server.queue_message(\n \"TALK %s: END\" % self.session_key,\n client.sock\n )","def close_connection(self):\r\n self.running = False\r\n self.client_socket.close()","def close(self):\n self.context['socket'].close()","async def _close(self):\n for w in self.workers:\n w.cancel()\n\n await self.session.close()","def client_close(self, transport, reason):\n\n client = self.clients[transport]\n\n if client.ident.registered:\n client.send_as_user('QUIT', f':{reason}')\n for chan in client.joined_channels.values():\n chan.user_quit(client, reason)\n\n client._write(f'ERROR :Closing Link: {client.ident.hostname} ({reason})')\n\n del self.clients[transport]\n transport.close()\n log.info(f'{client} ## Closed connection ({reason})')","def close(self):\n self._state = ServerState.CLOSED\n self.sock.close()\n clients = list(self._clients.values())\n for conn in clients:\n conn.close()\n for thread in self._get_threads():\n thread.join()","def close(self):\n if self.pending_messages:\n print(\"?? closing client socket with unsent messages. message list follows. \")\n for message in self.pending_messages:\n print(\"*** \", message)\n self.socket_.shutdown(socket.SHUT_RDWR)\n self.socket_.close()","def close(self):\n self._client.close()\n self._transport.close()","def close(self): \n self.connection.close()","def close(self):\n for key in self.unique_connections.keys():\n self.unique_connections[key].close()\n\n self.connected = False","def close(self): \n\t\tself.connection = None","def close(self):\n self.running.value = False\n # Empty queues or processes won't join.\n while not self.pimap_data_queue.empty(): self.pimap_data_queue.get()\n while not self.received_address_queue.empty(): self.received_address_queue.get()\n for worker_process in self.sense_worker_processes:\n worker_process.join()\n for worker_process in self.pimap_worker_processes:\n worker_process.join()\n self.socket.close()","def close(self):\n self.shutdown()\n self._client.close()","def close(self):\r\n self.conn.close()\r\n gc.collect()","def on_close(self):\n self.clients.remove(self)","def close(self):\n if self._closed:\n return\n try:\n with self.lock:\n if not self._closed:\n self._closed = True\n for address in list(self.connections):\n self.remove(address)\n except TypeError as e:\n pass","def close(self):\n if not self.isqueue:\n return self.connection\n return None","def stop(self):\n for cli in self.kafka_clients.itervalues():\n cli.close()","def closeConnection(self):\n print(\"closing connection...\")\n self.s.close()\n quit()","def close(self):\n self.conn.close()\n self.destroy()","def close(queue_id):\n r = update_queue_state(queue_id, 'close')\n\n if r.status_code != 200:\n click.echo('Close queue failed, please ensure input is correct.')\n else:\n click.echo(r.text)","def killconnections(self):\n for conn in self._connections:\n try:conn.close()\n except:pass\n self._connections=[]","async def close(self) -> None:\n logger.debug(\n f\"['{self._instance_connection_string}']: Waiting for _current to be cancelled\"\n )\n self._current.cancel()\n logger.debug(\n f\"['{self._instance_connection_string}']: Waiting for _next to be cancelled\"\n )\n self._next.cancel()\n logger.debug(\n f\"['{self._instance_connection_string}']: Waiting for _client_session to close\"\n )\n await self._client_session.close()","async def close_connection(self):\n\t\t...","def close(self):\r\n # close the connection and the socket\r\n self.conn.close()\r\n self.theSocket.close()","def close(self):\n with self.connlock:\n self.conn.do_close()","def close(self):\r\n if self.connected:\r\n #self.onClose()\r\n self.con.close()\r\n self._close() # Reset the thread and connection, and stop all sounds playing through the output.\r\n else:\r\n raise UserError(errors['NotConnectedYet'])","def close(self):\n\t\tself._client.close_session()","async def close(self) -> None:\n\n if self._client:\n await self._client.__aexit__()","def __close_conn(self, target_host_socket):\n self.__client.close()\n target_host_socket.close()\n logging.info(\"Client <> Target closed\")","def Close(self):\n self._sock.close()","def close_db_connection(client):\n client.close()","async def close_connections(self) -> None:\n for db_client in self._db_client_map.values():\n await db_client.close()\n\n self.logger.info(\"Tortoise-ORM closed connections\")","def close_connection(self):\n\n self._connection.close()\n print(\"Closed connection....\")","def close(self):\n\n\t\t# close connection\n\t\tself.eyetribe.close()\n\t\tself.connected = False","def close(self):\n self.connection = None","def __del__(self):\n self.lock.acquire()\n try:\n pywikibot.debug(u\"Closing connection pool (%s connections)\"\n % len(self.connections),\n _logger)\n for key in self.connections:\n for connection in self.connections[key]:\n connection.close()\n except (AttributeError, TypeError):\n pass # this shows up when logger has been destroyed first\n finally:\n self.lock.release()","def close_connection(self):\n self.nodesocket.close()","def _close_connection(self):\n if self.connection:\n self.connection.destroy()\n self.connection = None","def ssh_close_connections(cls):\n for key in cls._ssh_connections.keys():\n print \"closing ssh connection %s\" % key\n connection = cls._ssh_connections[key]\n del cls._ssh_connections[key]\n connection.close()","def _close_subscription_websockets(self):\n try:\n # Guard against concurrent subscription creation\n self.subscription_lock.acquire()\n\n for id, subscription_websocket in list(self.subscription_websockets.items()):\n subscription_websocket['server'].close()\n del self.subscription_websockets[id]\n finally:\n self.subscription_lock.release()","def n_close(conns):\n\n\tfor conn in conns:\n\t\tconn.close()","def close_client(client):\r\n client.close()\r\n broadcast(bytes(\"%s ha abbandonato\" % clients[client], \"utf8\"))\r\n del clients[client]\r\n del roles[client]\r\n del score[client]\r\n del address[client]","def close(self):\n self.connect.close()","def on_close(self):\n for channel in self.channels.values():\n channel.close(flush=False)","def close(self):\n self.client.close()\n self.context.term()","def end(self):\n if self.conn:\n self.conn.close()\n self.conn = None\n #print(\"closed.\")","def close(self):\n self.conn.close()","def close(self):\n self.conn.close()","def close(self):\n self.conn.close()","def close_connection(self):\n\t\tself.session.close()","def _close(self): # used by model_tests\n Connection.__pool__.pop(self.id)\n self.connected = False","def closeConnection(self, ca):\n #log.debug(f\"{self.name}: closing connections for.\")\n if ca in self.connections:\n del self.connections[ca]\n if ca in self.server.ixes: # remoter still there\n self.server.ixes[ca].serviceSends() # send final bytes to socket\n self.server.removeIx(ca)","def __close_connection_with_client(self, current_client):\n self.__connected_clients.remove(current_client)\n current_client.close_connection()","def delete_queue(self):\n self.work_queue_client.delete_queue()","def close_ssh_client(self):\n self.ssh_client.close()","def _close_client(self):\n if self.client_socket and self.client_socket.socknum != adafruit_esp32spi_socket.NO_SOCKET_AVAIL:\n self.client_socket.close()\n self.client_socket = None","def cleanup_aerospike(args, client):\n client.close()","def close_connection(self):\n self.conn.close()","def close(self):\n self.control_conn.sendall('CLOSE'.encode())","def close_all_connections(self):\n self.close_TCP_connections()\n self.close_UDP_connection()","def close(self, **kwargs):\r\n del(self._client_write)\r\n del(self._client_read)\r\n self._client_write = None\r\n self._client_read = None","def __del__(self):\n self.close_connection()\n self.close_engine()","def close(self):\n self._paramiko_ssh_client.close()\n self._paramiko_ssh_client = None","def close(self):\n try:\n self.connection.Close()\n del self.connection\n except:\n pass","def _close(self):\n\n #to kill children loop over native children list\n #DO NOT create a custom process list\n #since terminated processes don't get correctly garbage collected\n for lw in multiprocessing.active_children():\n if type(lw) == loop_worker.LoopWorker:\n lw.kill()\n\n logging.info(\"Closing Agent\")\n closemsg = {'process': {\n 'name': multiprocessing.current_process().name, \n 'status': 'closed'}}\n \n self.msg_queue.put(dict(to=settings.IOTTLY_XMPP_SERVER_USER,msg='/json ' + json.dumps(closemsg)))\n self.msg_queue.put(None)\n if self.broker_process:\n self.broker_process.join()\n logging.info(\"Agent closed\")","async def close(self):\n self.debug(\"Closing connection\")\n self.real_local_key = self.local_key\n if self.heartbeater is not None:\n self.heartbeater.cancel()\n try:\n await self.heartbeater\n except asyncio.CancelledError:\n pass\n self.heartbeater = None\n if self.dispatcher is not None:\n self.dispatcher.abort()\n self.dispatcher = None\n if self.transport is not None:\n transport = self.transport\n self.transport = None\n transport.close()","async def close(self):\n waiters = []\n while self._hosts:\n host = self._hosts.popleft()\n waiters.append(host.close())\n await asyncio.gather(*waiters, loop=self._loop)\n self._closed = True","def close(self):\n self._conn.close()","def close(self):\r\n if not self.closed:\r\n for result in self._decrefAsync():\r\n pass","def close(self) -> None:\n _LOGGER.info('Shutting down connections to deCONZ.')\n if self.websocket:\n self.websocket.stop()","def shutdown(self):\n ts.client.transport.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close(self):\n self.connection.close()","def close_connection(self):\r\n if self.conn:\r\n self.conn.close()","def __exit__(self, *args):\n self.sock.close()","def close(self):\n\n self.conn.close()","def close(self):\n if self.socket is not None:\n self.socket.close()\n self.socket = None\n self.socket_lock = None\n\n # help blocking send thread close\n self.send_queue.put(None)\n self.send_queue = None\n getLogger(__name__).info(\"Connection closed.\")","def cleanup(self):\n self.sock.close()","def __del__(self):\n try:\n self.api.transport.session.close()\n except Exception as e:\n log.debug(f\"Failed to close VSO API connection with: {e}\")","def close_connection(self):\n self.session.close()"],"string":"[\n \"def _close_clients(self):\\n for client in self.clients:\\n client.close()\",\n \"def closeConnecetion(self):\\n client.close()\",\n \"def close(self):\\n for conn in self._conns:\\n conn.send((self.CLOSE,()))\",\n \"def __close_connection_with_clients(self):\\n for current_client in self.__connected_clients:\\n self.__close_connection_with_client(current_client)\",\n \"def close(self):\\n self._router_socket.close()\\n for client in self._active_clients.values():\\n client.close()\",\n \"def close(self):\\n if self.client:\\n self.client.connection.close()\\n self.client = None\",\n \"def close(self):\\n _LOGGER.debug(\\\"Closing websocket connections\\\")\\n if self._remote_task:\\n self._remote_task.cancel()\\n if self._control_task:\\n self._control_task.cancel()\\n if self._app_monitor_task:\\n self._app_monitor_task.cancel()\\n if self._managed_session:\\n _LOGGER.debug(\\\"Closing managed ClientSession\\\")\\n self._loop.create_task(self.session.close())\",\n \"def __del__(self) -> None:\\n if self.close_client_at_del:\\n self.client.close()\",\n \"def close_TCP_connections(self):\\n if self.master_connection is not None:\\n self.master_connection.end_connection()\",\n \"def remove_connections(self):\\n while self.qsize() > 0:\\n try:\\n cnx = self.get(block=True, timeout=self.queue_timeout)\\n except queue.Empty:\\n pass\\n else:\\n try:\\n cnx.close_connection()\\n except (RuntimeError, OSError, InterfaceError):\\n pass\\n finally:\\n self.remove_connection(cnx)\",\n \"def close(self):\\n self._connection.close()\",\n \"def close_connections(self):\\n self._connection.close_connection()\",\n \"def end(self):\\n self.MAIN_CONNECTION.close()\\n self.connection_with_client.close()\",\n \"def shutdown(self):\\n self.channel.close()\\n self.conn.close()\",\n \"def close(self):\\n logging.debug(\\\"Closing down Talk Object with session '%s'\\\" % self.session_key)\\n for client in self.tcp_clients:\\n client.server.queue_message(\\n \\\"TALK %s: END\\\" % self.session_key,\\n client.sock\\n )\",\n \"def close_connection(self):\\r\\n self.running = False\\r\\n self.client_socket.close()\",\n \"def close(self):\\n self.context['socket'].close()\",\n \"async def _close(self):\\n for w in self.workers:\\n w.cancel()\\n\\n await self.session.close()\",\n \"def client_close(self, transport, reason):\\n\\n client = self.clients[transport]\\n\\n if client.ident.registered:\\n client.send_as_user('QUIT', f':{reason}')\\n for chan in client.joined_channels.values():\\n chan.user_quit(client, reason)\\n\\n client._write(f'ERROR :Closing Link: {client.ident.hostname} ({reason})')\\n\\n del self.clients[transport]\\n transport.close()\\n log.info(f'{client} ## Closed connection ({reason})')\",\n \"def close(self):\\n self._state = ServerState.CLOSED\\n self.sock.close()\\n clients = list(self._clients.values())\\n for conn in clients:\\n conn.close()\\n for thread in self._get_threads():\\n thread.join()\",\n \"def close(self):\\n if self.pending_messages:\\n print(\\\"?? closing client socket with unsent messages. message list follows. \\\")\\n for message in self.pending_messages:\\n print(\\\"*** \\\", message)\\n self.socket_.shutdown(socket.SHUT_RDWR)\\n self.socket_.close()\",\n \"def close(self):\\n self._client.close()\\n self._transport.close()\",\n \"def close(self): \\n self.connection.close()\",\n \"def close(self):\\n for key in self.unique_connections.keys():\\n self.unique_connections[key].close()\\n\\n self.connected = False\",\n \"def close(self): \\n\\t\\tself.connection = None\",\n \"def close(self):\\n self.running.value = False\\n # Empty queues or processes won't join.\\n while not self.pimap_data_queue.empty(): self.pimap_data_queue.get()\\n while not self.received_address_queue.empty(): self.received_address_queue.get()\\n for worker_process in self.sense_worker_processes:\\n worker_process.join()\\n for worker_process in self.pimap_worker_processes:\\n worker_process.join()\\n self.socket.close()\",\n \"def close(self):\\n self.shutdown()\\n self._client.close()\",\n \"def close(self):\\r\\n self.conn.close()\\r\\n gc.collect()\",\n \"def on_close(self):\\n self.clients.remove(self)\",\n \"def close(self):\\n if self._closed:\\n return\\n try:\\n with self.lock:\\n if not self._closed:\\n self._closed = True\\n for address in list(self.connections):\\n self.remove(address)\\n except TypeError as e:\\n pass\",\n \"def close(self):\\n if not self.isqueue:\\n return self.connection\\n return None\",\n \"def stop(self):\\n for cli in self.kafka_clients.itervalues():\\n cli.close()\",\n \"def closeConnection(self):\\n print(\\\"closing connection...\\\")\\n self.s.close()\\n quit()\",\n \"def close(self):\\n self.conn.close()\\n self.destroy()\",\n \"def close(queue_id):\\n r = update_queue_state(queue_id, 'close')\\n\\n if r.status_code != 200:\\n click.echo('Close queue failed, please ensure input is correct.')\\n else:\\n click.echo(r.text)\",\n \"def killconnections(self):\\n for conn in self._connections:\\n try:conn.close()\\n except:pass\\n self._connections=[]\",\n \"async def close(self) -> None:\\n logger.debug(\\n f\\\"['{self._instance_connection_string}']: Waiting for _current to be cancelled\\\"\\n )\\n self._current.cancel()\\n logger.debug(\\n f\\\"['{self._instance_connection_string}']: Waiting for _next to be cancelled\\\"\\n )\\n self._next.cancel()\\n logger.debug(\\n f\\\"['{self._instance_connection_string}']: Waiting for _client_session to close\\\"\\n )\\n await self._client_session.close()\",\n \"async def close_connection(self):\\n\\t\\t...\",\n \"def close(self):\\r\\n # close the connection and the socket\\r\\n self.conn.close()\\r\\n self.theSocket.close()\",\n \"def close(self):\\n with self.connlock:\\n self.conn.do_close()\",\n \"def close(self):\\r\\n if self.connected:\\r\\n #self.onClose()\\r\\n self.con.close()\\r\\n self._close() # Reset the thread and connection, and stop all sounds playing through the output.\\r\\n else:\\r\\n raise UserError(errors['NotConnectedYet'])\",\n \"def close(self):\\n\\t\\tself._client.close_session()\",\n \"async def close(self) -> None:\\n\\n if self._client:\\n await self._client.__aexit__()\",\n \"def __close_conn(self, target_host_socket):\\n self.__client.close()\\n target_host_socket.close()\\n logging.info(\\\"Client <> Target closed\\\")\",\n \"def Close(self):\\n self._sock.close()\",\n \"def close_db_connection(client):\\n client.close()\",\n \"async def close_connections(self) -> None:\\n for db_client in self._db_client_map.values():\\n await db_client.close()\\n\\n self.logger.info(\\\"Tortoise-ORM closed connections\\\")\",\n \"def close_connection(self):\\n\\n self._connection.close()\\n print(\\\"Closed connection....\\\")\",\n \"def close(self):\\n\\n\\t\\t# close connection\\n\\t\\tself.eyetribe.close()\\n\\t\\tself.connected = False\",\n \"def close(self):\\n self.connection = None\",\n \"def __del__(self):\\n self.lock.acquire()\\n try:\\n pywikibot.debug(u\\\"Closing connection pool (%s connections)\\\"\\n % len(self.connections),\\n _logger)\\n for key in self.connections:\\n for connection in self.connections[key]:\\n connection.close()\\n except (AttributeError, TypeError):\\n pass # this shows up when logger has been destroyed first\\n finally:\\n self.lock.release()\",\n \"def close_connection(self):\\n self.nodesocket.close()\",\n \"def _close_connection(self):\\n if self.connection:\\n self.connection.destroy()\\n self.connection = None\",\n \"def ssh_close_connections(cls):\\n for key in cls._ssh_connections.keys():\\n print \\\"closing ssh connection %s\\\" % key\\n connection = cls._ssh_connections[key]\\n del cls._ssh_connections[key]\\n connection.close()\",\n \"def _close_subscription_websockets(self):\\n try:\\n # Guard against concurrent subscription creation\\n self.subscription_lock.acquire()\\n\\n for id, subscription_websocket in list(self.subscription_websockets.items()):\\n subscription_websocket['server'].close()\\n del self.subscription_websockets[id]\\n finally:\\n self.subscription_lock.release()\",\n \"def n_close(conns):\\n\\n\\tfor conn in conns:\\n\\t\\tconn.close()\",\n \"def close_client(client):\\r\\n client.close()\\r\\n broadcast(bytes(\\\"%s ha abbandonato\\\" % clients[client], \\\"utf8\\\"))\\r\\n del clients[client]\\r\\n del roles[client]\\r\\n del score[client]\\r\\n del address[client]\",\n \"def close(self):\\n self.connect.close()\",\n \"def on_close(self):\\n for channel in self.channels.values():\\n channel.close(flush=False)\",\n \"def close(self):\\n self.client.close()\\n self.context.term()\",\n \"def end(self):\\n if self.conn:\\n self.conn.close()\\n self.conn = None\\n #print(\\\"closed.\\\")\",\n \"def close(self):\\n self.conn.close()\",\n \"def close(self):\\n self.conn.close()\",\n \"def close(self):\\n self.conn.close()\",\n \"def close_connection(self):\\n\\t\\tself.session.close()\",\n \"def _close(self): # used by model_tests\\n Connection.__pool__.pop(self.id)\\n self.connected = False\",\n \"def closeConnection(self, ca):\\n #log.debug(f\\\"{self.name}: closing connections for.\\\")\\n if ca in self.connections:\\n del self.connections[ca]\\n if ca in self.server.ixes: # remoter still there\\n self.server.ixes[ca].serviceSends() # send final bytes to socket\\n self.server.removeIx(ca)\",\n \"def __close_connection_with_client(self, current_client):\\n self.__connected_clients.remove(current_client)\\n current_client.close_connection()\",\n \"def delete_queue(self):\\n self.work_queue_client.delete_queue()\",\n \"def close_ssh_client(self):\\n self.ssh_client.close()\",\n \"def _close_client(self):\\n if self.client_socket and self.client_socket.socknum != adafruit_esp32spi_socket.NO_SOCKET_AVAIL:\\n self.client_socket.close()\\n self.client_socket = None\",\n \"def cleanup_aerospike(args, client):\\n client.close()\",\n \"def close_connection(self):\\n self.conn.close()\",\n \"def close(self):\\n self.control_conn.sendall('CLOSE'.encode())\",\n \"def close_all_connections(self):\\n self.close_TCP_connections()\\n self.close_UDP_connection()\",\n \"def close(self, **kwargs):\\r\\n del(self._client_write)\\r\\n del(self._client_read)\\r\\n self._client_write = None\\r\\n self._client_read = None\",\n \"def __del__(self):\\n self.close_connection()\\n self.close_engine()\",\n \"def close(self):\\n self._paramiko_ssh_client.close()\\n self._paramiko_ssh_client = None\",\n \"def close(self):\\n try:\\n self.connection.Close()\\n del self.connection\\n except:\\n pass\",\n \"def _close(self):\\n\\n #to kill children loop over native children list\\n #DO NOT create a custom process list\\n #since terminated processes don't get correctly garbage collected\\n for lw in multiprocessing.active_children():\\n if type(lw) == loop_worker.LoopWorker:\\n lw.kill()\\n\\n logging.info(\\\"Closing Agent\\\")\\n closemsg = {'process': {\\n 'name': multiprocessing.current_process().name, \\n 'status': 'closed'}}\\n \\n self.msg_queue.put(dict(to=settings.IOTTLY_XMPP_SERVER_USER,msg='/json ' + json.dumps(closemsg)))\\n self.msg_queue.put(None)\\n if self.broker_process:\\n self.broker_process.join()\\n logging.info(\\\"Agent closed\\\")\",\n \"async def close(self):\\n self.debug(\\\"Closing connection\\\")\\n self.real_local_key = self.local_key\\n if self.heartbeater is not None:\\n self.heartbeater.cancel()\\n try:\\n await self.heartbeater\\n except asyncio.CancelledError:\\n pass\\n self.heartbeater = None\\n if self.dispatcher is not None:\\n self.dispatcher.abort()\\n self.dispatcher = None\\n if self.transport is not None:\\n transport = self.transport\\n self.transport = None\\n transport.close()\",\n \"async def close(self):\\n waiters = []\\n while self._hosts:\\n host = self._hosts.popleft()\\n waiters.append(host.close())\\n await asyncio.gather(*waiters, loop=self._loop)\\n self._closed = True\",\n \"def close(self):\\n self._conn.close()\",\n \"def close(self):\\r\\n if not self.closed:\\r\\n for result in self._decrefAsync():\\r\\n pass\",\n \"def close(self) -> None:\\n _LOGGER.info('Shutting down connections to deCONZ.')\\n if self.websocket:\\n self.websocket.stop()\",\n \"def shutdown(self):\\n ts.client.transport.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close(self):\\n self.connection.close()\",\n \"def close_connection(self):\\r\\n if self.conn:\\r\\n self.conn.close()\",\n \"def __exit__(self, *args):\\n self.sock.close()\",\n \"def close(self):\\n\\n self.conn.close()\",\n \"def close(self):\\n if self.socket is not None:\\n self.socket.close()\\n self.socket = None\\n self.socket_lock = None\\n\\n # help blocking send thread close\\n self.send_queue.put(None)\\n self.send_queue = None\\n getLogger(__name__).info(\\\"Connection closed.\\\")\",\n \"def cleanup(self):\\n self.sock.close()\",\n \"def __del__(self):\\n try:\\n self.api.transport.session.close()\\n except Exception as e:\\n log.debug(f\\\"Failed to close VSO API connection with: {e}\\\")\",\n \"def close_connection(self):\\n self.session.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.74559975\",\n \"0.74452204\",\n \"0.7294628\",\n \"0.7047551\",\n \"0.6944966\",\n \"0.677886\",\n \"0.6763271\",\n \"0.6743381\",\n \"0.67432\",\n \"0.67000616\",\n \"0.6695658\",\n \"0.6674143\",\n \"0.66615736\",\n \"0.66608673\",\n \"0.6640044\",\n \"0.6629691\",\n \"0.66181344\",\n \"0.6616973\",\n \"0.66131306\",\n \"0.6568658\",\n \"0.6536984\",\n \"0.6536721\",\n \"0.653427\",\n \"0.6531346\",\n \"0.6521563\",\n \"0.6518298\",\n \"0.6513694\",\n \"0.6511036\",\n \"0.6507591\",\n \"0.65067077\",\n \"0.6490649\",\n \"0.648855\",\n \"0.6485894\",\n \"0.6470112\",\n \"0.6449408\",\n \"0.64462435\",\n \"0.64197797\",\n \"0.64180225\",\n \"0.64105195\",\n \"0.64088196\",\n \"0.6399915\",\n \"0.63743204\",\n \"0.63717866\",\n \"0.63696444\",\n \"0.6359209\",\n \"0.63546115\",\n \"0.6353151\",\n \"0.6350987\",\n \"0.63488424\",\n \"0.6346763\",\n \"0.63365316\",\n \"0.63241285\",\n \"0.630963\",\n \"0.63084775\",\n \"0.6304198\",\n \"0.63032734\",\n \"0.62986845\",\n \"0.6280997\",\n \"0.62799686\",\n \"0.62790585\",\n \"0.6277215\",\n \"0.6274092\",\n \"0.6274092\",\n \"0.6274092\",\n \"0.6267048\",\n \"0.6261002\",\n \"0.62607634\",\n \"0.6248613\",\n \"0.6243887\",\n \"0.62418497\",\n \"0.6236616\",\n \"0.62362814\",\n \"0.6235064\",\n \"0.6232826\",\n \"0.6231493\",\n \"0.6228949\",\n \"0.6224698\",\n \"0.62186986\",\n \"0.6218062\",\n \"0.621755\",\n \"0.621286\",\n \"0.6202892\",\n \"0.6202785\",\n \"0.6199003\",\n \"0.6198273\",\n \"0.6193176\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.6192258\",\n \"0.61870956\",\n \"0.6168886\",\n \"0.61685044\",\n \"0.6163314\",\n \"0.6158961\",\n \"0.61580044\",\n \"0.61521053\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74610734"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":87493,"cells":{"query":{"kind":"string","value":"Main function, triggered by Azure Storage Queue, parsed queue content and try to download the databricks output metadata file to get each succefully processed file location. Then enqueue to ingest queue for ingestion to ADX on later Azure function. If the file is checkpoint compact file, the code will shrink the file size."},"document":{"kind":"string","value":"def main(msg: func.QueueMessage) -> None:\n code_start_time = time.time()\n logging.info('Python queue trigger function processed a queue item: %s',\n msg.get_body().decode('utf-8'))\n # modify the log level of azure sdk requests\n logging.getLogger('azure').setLevel(logging.WARNING)\n init_config_values()\n\n tc = TelemetryClient(APPINSIGHTS_INSTRUMENTATIONKEY)\n tc.context.application.ver = '1.0'\n tc.context.properties[\"PROCESS_PROGRAM\"] = PROCESS_PROGRAM_NAME\n tc.context.properties[\"PROCESS_START\"] = time.time()\n\n # 1. Get trigger file content (rename event)\n content_json = json.loads(msg.get_body().decode('utf-8'))\n\n logging.info(\"meta-data event content: {}\".format(msg.get_body().decode('utf-8')))\n file_url = content_json['data']['destinationUrl']\n logging.info(f\"file_url: {file_url}\")\n event_time = content_json['eventTime']\n\n # 2. Download metadata blob content\n logging.info(f\"{HEADER} Download blob file from {file_url}\")\n temp_blob_client = BlobClient.from_blob_url(blob_url=file_url, logging_enable=False)\n blob_path = temp_blob_client.blob_name\n container_name = temp_blob_client.container_name\n\n try:\n metadata_file_content = get_blob_content(container_name, blob_path)\n except Exception:\n logging.exception(f\"Failed to download blob from url {file_url}\")\n raise\n\n # 3. Parse split output file from the metadata\n queue_msg_list = generate_metadata_queue_messages(event_time, metadata_file_content)\n logging.info(\n f\"{HEADER} Generate metadata queue_messages from {file_url}, {len(queue_msg_list)} messages\")\n\n # 4. Loop to enqueue msg to ADX ingest queue\n queue_client_list = []\n for q_url in ADX_INGEST_QUEUE_URL_LIST:\n queue_client = get_queue_client(q_url)\n queue_client_list.append(queue_client)\n\n asyncio.set_event_loop(asyncio.new_event_loop())\n loop = asyncio.get_event_loop()\n tasks = gen_metadata_msg_enqueue_tasks(queue_msg_list, queue_client_list, tc)\n loop.run_until_complete(gather_with_concurrency(CONCURRENT_ENQUEUE_TASKS, tasks))\n close_queue_clients(queue_client_list, loop)\n loop.close()\n\n logging.info(f\"{HEADER} Done queuing up messages to Ingestion queue\")\n\n if file_url.endswith(\".compact\"): # reduce compact file size\n update_blob_content(container_name,\n blob_path,\n get_shrinked_checkpoint_content(\n metadata_file_content, MAX_COMPACT_FILE_RECORDS))\n logging.info(f\"{HEADER} Reduced checkpoint files {file_url}, max lines is {MAX_COMPACT_FILE_RECORDS}\")\n\n code_duration = time.time() - code_start_time\n tc.track_event(METADATA_HANDLE_EVENT_NAME,\n {'FILE_URL': file_url},\n {METADATA_HANDLE_EVENT_NAME + '_DURATION_SEC': code_duration})\n tc.flush()"},"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_metadata_queue_messages(event_time: str, metadata_file_content: str) -> List[str]:\n ingest_queue_msg_list = []\n current_part_num = 100000 #Max part number\n global MAX_COMPACT_FILE_RECORDS\n\n for line in reversed(metadata_file_content.splitlines()):\n logging.info(f\"{HEADER} Processing metadata line content: {line}\")\n if not is_json(line):\n logging.info(f\"{HEADER} Skip non JSON line content: {line}\")\n continue\n\n pnum = get_part_number(line)\n\n if pnum > current_part_num:\n break # Reached files in previous batch, stop parsing\n\n current_part_num = pnum\n\n split_output_file_json = json.loads(line)\n output_abfss_path = split_output_file_json[\"path\"]\n output_file_size = split_output_file_json[\"size\"]\n output_file_modification_time = split_output_file_json[\"modificationTime\"]\n\n try:\n https_url = convert_abfss_path_to_https(output_abfss_path)\n except Exception: # pylint: disable=bare-except\n logging.warning(f\"{HEADER} Skip invalid abfss path {output_abfss_path}\", exc_info=True)\n continue\n\n queue_msg = INGEST_QUEUE_MSG_TEMPLATE.format(blob_size=output_file_size,\n blob_url=https_url,\n event_time=event_time,\n modification_time=output_file_modification_time)\n minify_msg = json.dumps(json.loads(queue_msg))\n ingest_queue_msg_list.append(minify_msg)\n\n MAX_COMPACT_FILE_RECORDS = max(len(ingest_queue_msg_list), MAX_COMPACT_FILE_RECORDS)\n return ingest_queue_msg_list","def queueLoader(input_queue, blockm_df, config, start_time):\n continue_run = True\n try: \n # create the column names that will go in each numprov file\n column_names = [t for t in (''.join(tech) \\\n for tech in powerset(config['techlist']))]\n except:\n print('ERROR - STEP 2 (MASTER): FAILED CALLING POWERSET')\n print(traceback.format_exc())\n return False, None \n \n # build the data strings that will go into the queue\n if continue_run:\n # initialize the list that holds the paths to all of the temporary area \n # tables\n append_list = []\n try:\n temp_time = time.localtime()\n for i in range(len(config['speedList'])):\n numprov_file_path = config['temp_csvs_dir_path']\\\n + 'block_numprov/block_numprov_%s_%s.csv'\\\n % (config['speedList'][i], config['fbd_vintage'])\n numprov_zero_file_path = config['temp_csvs_dir_path']\\\n +'block_numprov/block_numprov_%s_with_zero_%s.csv'\\\n % (config['speedList'][i], config['fbd_vintage'])\n temp_area_table_file_path = config['output_dir_path']\\\n +'area_table_%s.csv' % (config['speedList'][i])\n workerSpeed = config['speedList'][i]\n fbd_df = config['temp_pickles'] + 'enhanced_fbd_df.pkl'\n d_speed, u_speed = config['down_speed'][i], config['up_speed'][i]\n \n # insert the information into the queue\n temp_tuple = (numprov_file_path, numprov_zero_file_path, \n temp_area_table_file_path, workerSpeed, d_speed, \n u_speed, column_names, fbd_df, blockm_df, start_time)\n input_queue.put(temp_tuple) \n append_list.append(temp_area_table_file_path)\n\n my_message = \"\"\"\n INFO - STEP 2 (MASTER): TASK 4 of 5 - COMPLETED LOADING THE QUEUE TO MAKE BLOCK\n NUMPROV FILES,\n \"\"\"\n my_message = ' '.join(my_message.split())\n print(nbmf.logMessage(my_message, temp_time, time.localtime(),\n time.mktime(time.localtime())-time.mktime(start_time)))\n return True, append_list\n\n except:\n my_message = \"\"\"\n ERROR - STEP 2 (MASTER): TASK 4 of 5 - FAILED LOADING THE QUEUE TO MAKE BLOCK\n NUMPROV FILES,\n \"\"\"\n my_message = ' '.join(my_message.split())\n my_message += '\\n' + traceback.format_exc()\n print(nbmf.logMessage(my_message, temp_time, time.localtime(),\n time.mktime(time.localtime())-time.mktime(start_time)))\n return False, None","def breakOutBlockData(input_queue, output_queue, message_queue, config, \n db_config, start_time):\n\n try:\n temp_time = time.localtime()\n continue_run, county_fips = getCounty_fips(config, start_time)\n\n if continue_run:\n continue_run = changeTogeom(db_config, config, start_time)\n\n if continue_run:\n continue_run = makeBlockTablePickle(config, db_config, start_time)\n\n if continue_run:\n for _ in range(config['number_servers']):\n message_queue.put('parse_blockdf')\n\n continue_run, county_counter = loadBlockQueue(input_queue, county_fips, \n config, start_time)\n\n if continue_run:\n continue_run = s0f.processWork(config, input_queue, output_queue, \n county_counter, start_time)\n\n if continue_run:\n continue_run = changeToGEOMETRY(config, db_config, start_time)\n \n if continue_run:\n my_message = \"\"\"\n INFO - STEP 0 (MASTER): TASK 6 OF 13 - COMPLETED CREATING COUNTY\n LEVEL GEOJSON BLOCK FILES\n \"\"\"\n my_message = ' '.join(my_message.split())\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time))) \n gc.collect() \n return True\n \n else:\n my_message = \"\"\"\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO CREATE COUNTY\n LEVEL GEOJSON BLOCK FILES\n \"\"\"\n my_message = ' '.join(my_message.split()) + '\\n' + traceback.format_exc()\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return False \n\n except:\n my_message = \"\"\"\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO CREATE COUNTY\n LEVEL GEOJSON BLOCK FILES\n \"\"\"\n my_message = ' '.join(my_message.split()) + '\\n' + traceback.format_exc()\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return False","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--process_queue\", action='store_true',\n dest=\"process_queue\",\n help=\"Process also schedd queue (Running/Idle/Pending jobs)\")\n parser.add_argument(\"--feed_es\", action='store_true',\n dest=\"feed_es\",\n help=\"Feed to Elasticsearch\")\n parser.add_argument(\"--feed_es_for_queues\", action='store_true',\n dest=\"feed_es_for_queues\",\n help=\"Feed queue data also to Elasticsearch\")\n parser.add_argument(\"--feed_amq\", action='store_true',\n dest=\"feed_amq\",\n help=\"Feed to CERN AMQ\")\n\n parser.add_argument(\"--schedd_filter\", default='',\n type=str, dest=\"schedd_filter\",\n help=(\"Comma separated list of schedd names to process \"\n \"[default is to process all]\"))\n parser.add_argument(\"--skip_history\", action='store_true',\n dest=\"skip_history\",\n help=\"Skip processing the history. (Only do queues.)\")\n parser.add_argument(\"--read_only\", action='store_true',\n dest=\"read_only\",\n help=\"Only read the info, don't submit it.\")\n parser.add_argument(\"--dry_run\", action='store_true',\n dest=\"dry_run\",\n help=(\"Don't even read info, just pretend to. (Still \"\n \"query the collector for the schedd's though.)\"))\n parser.add_argument(\"--keep_full_queue_data\", action='store_true',\n dest=\"keep_full_queue_data\",\n help=\"Drop all but some fields for running jobs.\")\n parser.add_argument(\"--amq_bunch_size\", default=5000,\n type=int, dest=\"amq_bunch_size\",\n help=(\"Send docs to AMQ in bunches of this number \"\n \"[default: %(default)d]\"))\n parser.add_argument(\"--es_bunch_size\", default=250,\n type=int, dest=\"es_bunch_size\",\n help=(\"Send docs to ES in bunches of this number \"\n \"[default: %(default)d]\"))\n parser.add_argument(\"--query_queue_batch_size\", default=50,\n type=int, dest=\"query_queue_batch_size\",\n help=(\"Send docs to listener in batches of this number \"\n \"[default: %(default)d]\"))\n parser.add_argument(\"--upload_pool_size\", default=8,\n type=int, dest=\"upload_pool_size\",\n help=(\"Number of parallel processes for uploading \"\n \"[default: %(default)d]\"))\n parser.add_argument(\"--query_pool_size\", default=8,\n type=int, dest=\"query_pool_size\",\n help=(\"Number of parallel processes for querying \"\n \"[default: %(default)d]\"))\n\n parser.add_argument(\"--es_hostname\", default='es-cms.cern.ch',\n type=str, dest=\"es_hostname\",\n help=\"Hostname of the elasticsearch instance to be used \"\n \"[default: %(default)s]\")\n parser.add_argument(\"--es_port\", default=9203,\n type=int, dest=\"es_port\",\n help=\"Port of the elasticsearch instance to be used \"\n \"[default: %(default)d]\")\n parser.add_argument(\"--es_index_template\", default='cms',\n type=str, dest=\"es_index_template\",\n help=(\"Trunk of index pattern. \"\n \"Needs to start with 'cms' \"\n \"[default: %(default)s]\"))\n parser.add_argument(\"--log_dir\", default='log/',\n type=str, dest=\"log_dir\",\n help=\"Directory for logging information [default: %(default)s]\")\n parser.add_argument(\"--log_level\", default='WARNING',\n type=str, dest=\"log_level\",\n help=\"Log level (CRITICAL/ERROR/WARNING/INFO/DEBUG) \"\n \"[default: %(default)s]\")\n parser.add_argument(\"--email_alerts\", default=[], action='append',\n dest=\"email_alerts\",\n help=\"Email addresses for alerts [default: none]\")\n\n args = parser.parse_args()\n set_up_logging(args)\n\n # --dry_run implies read_only\n args.read_only = args.read_only or args.dry_run\n\n main_driver(args)","def gen_metadata_msg_enqueue_tasks(queue_msg_list: List[str],\n queue_client_list: List[QueueClient],\n tc: TelemetryClient) -> None:\n\n tasks = []\n for idx, queue_msg in enumerate(queue_msg_list):\n output_obj = json.loads(queue_msg)\n\n queue_index = idx % len(queue_client_list)\n logging.debug(\n f\"{HEADER} Try to send message to ingest queue {queue_index}, queue_msg: {queue_msg}\")\n\n base64_message = base64.b64encode(queue_msg.encode('ascii')).decode('ascii')\n\n file_url = output_obj['data']['url']\n size = int(output_obj['data']['contentLength'])\n\n tc.track_event(METADATA_HANDLE_EVENT_NAME,\n {'FILE_URL': file_url},\n {METADATA_HANDLE_EVENT_NAME + '_SIZE': size,\n METADATA_HANDLE_EVENT_NAME + '_COUNT': 1})\n\n # round robin to enqueue message\n task = asyncio.ensure_future(send_queue_messages(\n queue_client_list[queue_index], base64_message, queue_msg))\n tasks.append(task)\n tc.flush()\n return tasks","def __init__(\n self,\n dag_id: str = DAG_ID,\n start_date: pendulum.DateTime = pendulum.datetime(2018, 5, 14),\n schedule_interval: str = \"@weekly\",\n dataset_id: str = \"crossref\",\n dataset_description: str = \"The Crossref Events dataset: https://www.eventdata.crossref.org/guide/\",\n queue: str = \"remote_queue\",\n merge_partition_field: str = \"id\",\n schema_folder: str = default_schema_folder(),\n batch_load: bool = True,\n airflow_vars: List = None,\n mailto: str = \"aniek.roelofs@curtin.edu.au\",\n max_threads: int = min(32, os.cpu_count() + 4),\n max_processes: int = os.cpu_count(),\n ):\n\n if airflow_vars is None:\n airflow_vars = [\n AirflowVars.DATA_PATH,\n AirflowVars.PROJECT_ID,\n AirflowVars.DATA_LOCATION,\n AirflowVars.DOWNLOAD_BUCKET,\n AirflowVars.TRANSFORM_BUCKET,\n ]\n super().__init__(\n dag_id,\n start_date,\n schedule_interval,\n dataset_id,\n merge_partition_field,\n schema_folder,\n dataset_description=dataset_description,\n queue=queue,\n batch_load=batch_load,\n airflow_vars=airflow_vars,\n load_bigquery_table_kwargs={\"ignore_unknown_values\": True},\n )\n self.mailto = mailto\n self.max_threads = max_threads\n self.max_processes = max_processes\n\n self.add_setup_task(self.check_dependencies)\n self.add_task_chain(\n [self.download, self.upload_downloaded, self.transform, self.upload_transformed, self.bq_load_partition]\n )\n self.add_task_chain([self.bq_delete_old, self.bq_append_new, self.cleanup], trigger_rule=\"none_failed\")","def run(known_args, pipeline_args):\n\n pipeline_args.extend([\n '--runner=DataflowRunner',\n '--project=wikidetox-viz',\n '--staging_location=gs://wikidetox-viz-dataflow/staging',\n '--temp_location=gs://wikidetox-viz-dataflow/tmp',\n '--job_name=yiqing-ingest-job-truncated-content-run-in-batch',\n '--num_workers=90',\n ])\n\n pipeline_options = PipelineOptions(pipeline_args)\n pipeline_options.view_as(SetupOptions).save_main_session = True\n if not(known_args.batchno == None):\n known_args.input = 'gs://wikidetox-viz-dataflow/input_lists/7z_file_list_batched_%d'%(known_args.batchno)\n known_args.table = 'wikidetox-viz:wikidetox_conversations.ingested_conversations_batch_%d'%(known_args.batchno) \n print('Running batch %d'%(known_args.batchno)) \n with beam.Pipeline(options=pipeline_options) as p:\n pcoll = (p | ReadFromText(known_args.input)\n | beam.ParDo(WriteDecompressedFile())\n | beam.io.Write(bigquery.BigQuerySink(known_args.table, schema=known_args.schema, validate = True)))","def makeLargeTracts(input_queue, output_queue, config, db_config):\n\n \n # capture the process name\n my_name = mp.current_process().name\n my_ip_address = socket.gethostbyname(socket.gethostname())\n\n while True:\n try:\n # get the next element out of the queue\n inputs = input_queue.get()\n try:\n if inputs[0] is None: break\n\n # extract the terms from the queue list\n numprov_path = inputs[0] \n blockm_df = inputs[1] \n out_tract_path = inputs[2] \n out_county_path = inputs[3] \n out_tract_df = inputs[4]\n out_county_df = inputs[5] \n start_time = inputs[6] \n worker_speed = inputs[7]\n config = inputs[8]\n geom = 'geoid%s' % config['census_vintage'][2:]\n\n continue_run, block_numprov = openNumprovFile(numprov_path, geom, \n my_name, my_ip_address, worker_speed, \n start_time, output_queue)\n\n if continue_run:\n continue_run, block_numprov = mergeWithDataFrame(\n block_numprov, blockm_df, geom, my_name, \n my_ip_address, worker_speed, start_time, \n output_queue) \n\n if continue_run:\n for geo in ['tract', 'county']:\n continue_run, out_df = findPerCapitaProviders(my_name, \n my_ip_address, geo, block_numprov, \n output_queue, start_time, config, \n worker_speed, eval('out_%s_df' % geo))\n \n if continue_run:\n continue_run = outputGeoData(out_df, \n eval('out_%s_path' % geo), my_name, \n my_ip_address, geo, worker_speed, \n start_time, output_queue)\n\n except:\n pass\n\n except:\n # nothing in the queue, wait and check again\n time.sleep(1)\n\n return True","def Flush(self, state):\n self.bigquery = bigquery.GetBigQueryClient()\n # BigQuery job ids must be alphanum plus dash and underscore.\n urn_str = rdfvalue.RDFURN(self.source_urn).RelativeName(\"aff4:/\").replace(\n \"/\", \"_\").replace(\":\", \"\").replace(\".\", \"-\")\n\n for tracker in self.temp_output_trackers.values():\n # Close out the gzip handle and pass the original file handle to the\n # bigquery client so it sees the gzip'd content.\n tracker.gzip_filehandle.write(b\"\\n\")\n tracker.gzip_filehandle.close()\n tracker.gzip_filehandle_parent.seek(0)\n\n # e.g. job_id: hunts_HFFE1D044_Results_ExportedFile_1446056474\n job_id = \"{0}_{1}_{2}\".format(\n urn_str, tracker.output_type,\n rdfvalue.RDFDatetime.Now().AsSecondsSinceEpoch())\n\n # If we have a job id stored, that means we failed last time. Re-use the\n # job id and append to the same file if it continues to fail. This avoids\n # writing many files on failure.\n if tracker.output_type in self.output_jobids:\n job_id = self.output_jobids[tracker.output_type]\n else:\n self.output_jobids[tracker.output_type] = job_id\n\n if (state.failure_count + self.failure_count >=\n config.CONFIG[\"BigQuery.max_upload_failures\"]):\n logging.error(\n \"Exceeded BigQuery.max_upload_failures for %s, giving up.\",\n self.source_urn)\n else:\n try:\n self.bigquery.InsertData(tracker.output_type,\n tracker.gzip_filehandle_parent,\n tracker.schema, job_id)\n self.failure_count = max(0, self.failure_count - 1)\n del self.output_jobids[tracker.output_type]\n except bigquery.BigQueryJobUploadError:\n self.failure_count += 1\n\n # Now that everything is in bigquery we can remove the output streams\n self.temp_output_trackers = {}","def main_process_function(project_id, config_file, retention, backup_type, expiration):\n print(\"Running bigquery dataset export for project:{}\".format(project_id))\n # Reading backup-parameters from json config\n with open(config_file) as f:\n master_config = json.load(f)\n backup_config = master_config[\"backup\"]\n\n location = backup_config[\"location\"]\n schema_path = backup_config[\"schema_uri\"]\n table_path = backup_config[\"table_uri\"]\n project_backup_config = backup_config[\"projects_dict\"][project_id]\n mapped_list = []\n\n # Get timestamp\n timestamp = datetime.now().strftime(\"%Y-%m-%d\")\n\n # Creating Big Query Client\n client = bigquery.Client(project=project_id)\n\n # Getting mapped relation between datasets and their tables\n if backup_type == \"all\":\n # Get all datasets\n datasets = list_all_datasets(client=client)\n # Map dataset->[tables]\n dataset_tables_map = get_datasets_tables_dict(\n client=client, project_id=project_id, datasets=datasets\n )\n mapped_list.append(dataset_tables_map)\n elif backup_type == \"config\":\n # Extract the backup pattern from config\n backup_pattern = project_backup_config[\"backup_pattern\"]\n for key, value in backup_pattern.items():\n dataset_tables_map = {}\n if value == \"all\":\n # Map dataset->[tables]\n dataset_tables_map = get_datasets_tables_dict(\n client=client, project_id=project_id, datasets=[key]\n )\n mapped_list.append(dataset_tables_map)\n else:\n # Map dataset->[tables]\n dataset_tables_map[key] = value\n mapped_list.append(dataset_tables_map)\n else:\n print(\n \"Please provide a valid backup_type option. Choose from ['all', 'config']\"\n )\n return None\n\n # Performing dataset export to gcs (data, schema)\n if mapped_list:\n for datasets_tables_dict in mapped_list:\n for bq_dataset_name in datasets_tables_dict.keys():\n print(\"Backup Operation on dataset: {}\".format(bq_dataset_name))\n for bq_table_name in datasets_tables_dict[bq_dataset_name]:\n print(\"Backing up table: {}\".format(bq_table_name))\n try:\n # Getting dataset and table objects\n dataset_ref = bigquery.DatasetReference(\n project_id, bq_dataset_name\n )\n table_ref = dataset_ref.table(bq_table_name)\n table_obj = client.get_table(table_ref)\n\n # Specifying extract-job parameters\n gcs_table_path = table_path.format(\n bucket_name=project_backup_config[\"bucket_name\"],\n retention=retention,\n dataset_name=bq_dataset_name,\n timestamp=timestamp,\n table_file_name=bq_table_name + \"-*.json\",\n )\n job_config = bigquery.ExtractJobConfig()\n job_config.compression = bigquery.Compression.GZIP\n job_config.destination_format = (\n bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON\n )\n\n # Exporting table-data to gcs\n extract_job = client.extract_table(\n table_ref,\n gcs_table_path,\n job_config=job_config,\n location=location,\n )\n extract_job.result()\n\n # Extracting table-schema\n table_schema = table_obj.schema\n table_schema = [\n {\n \"name\": item.name,\n \"mode\": item.mode,\n \"type\": item.field_type,\n }\n for item in table_schema\n ]\n json_schema = json.dumps(table_schema)\n\n # Defining schema-path\n gcs_schema_path = schema_path.format(\n bucket_name=project_backup_config[\"bucket_name\"],\n retention=retention,\n dataset_name=bq_dataset_name,\n timestamp=timestamp,\n schema_file_name=bq_table_name + \"-schema.json\",\n )\n\n # Writing table-schema to gcs\n sa_credentials = os.getenv(\"GOOGLE_APPLICATION_CREDENTIALS\")\n fs = gcsfs.GCSFileSystem(\n project=project_id, token=sa_credentials\n )\n with fs.open(\n gcs_schema_path,\n \"w\",\n metadata={\"Content-Type\": \"application/json\"},\n ) as f:\n f.write(json_schema)\n except Exception as error:\n print(\n \"Exception occurred for project {} at function {} inside export-loop: {}\".format(\n project_id, \"main_process_function\", error\n )\n )\n # Deleting backup data based on the backup_data_policy\n backup_data_policy = {\n \"daily\": 1,\n \"weekly\": 7,\n \"monthly\": 30,\n \"yearly\": 365,\n }\n if str(expiration).title() == \"True\":\n try:\n bucket_name = project_backup_config[\"bucket_name\"]\n storage_client = storage.Client(project_id)\n client_bucket = storage_client.get_bucket(bucket_name)\n delete_date = (\n datetime.now()\n - timedelta(days=backup_data_policy[retention])\n ).strftime(\"%Y-%m-%d\")\n delete_path = \"{retention}/{dataset_name}/{timestamp}\".format(\n retention=retention,\n dataset_name=bq_dataset_name,\n timestamp=delete_date,\n )\n for file in client_bucket.list_blobs(prefix=delete_path):\n file.delete()\n print(\"Deleted '{}'\".format(file.name))\n except Exception as error:\n print(\n \"Exception occurred at function {} inside expiration-loop: {}\".format(\n \"main_process_function\", error\n )\n )\n else:\n pass\n return None\n else:\n print(\"The mapping between datasets and their tables is empty.\")\n return None","def createBlockNumprovFiles(input_queue, output_queue, message_queue, config, \n blockm_df, start_time):\n try: \n temp_time = time.localtime()\n\n for _ in range(config['number_servers']):\n message_queue.put('create_block_numprov')\n\n # load the input_queue\n continue_run, append_list = queueLoader(input_queue, blockm_df, config, \n start_time)\n\n # process the outputs from the workers\n if continue_run:\n continue_run = processWork(config, input_queue, output_queue, \n start_time)\n\n if continue_run:\n my_message = \"\"\"\n INFO - STEP 2 (MASTER): COMPLETED CREATING NUMPROV FILES FOR\n ALL SPEEDS\n \"\"\"\n my_message = ' '.join(my_message.split())\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return True, append_list\n else:\n my_message = \"\"\"\n ERROR - STEP 2 (MASTER): FAILED TO CREATE NUMPROV FILES FOR\n ALL SPEEDS\n \"\"\"\n my_message = ' '.join(my_message.split())\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return False, None\n\n except:\n my_message = \"\"\"\n ERROR - STEP 2 (MASTER): UNHANDLED FAILURE IN EXECUTING DISTRIBUTED \n BUILD OF NUMPROV FILES\n \"\"\"\n my_message = ' '.join(my_message.split())\n my_message += \"\\n\" + traceback.format_exc()\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return False, None","def preprocess_job(self):\r\n while not self._coordinator.should_stop():\r\n raw_entry = self._fread_queue.get()\r\n if raw_entry is None:\r\n return\r\n preprocessed_entry_dict = self.preprocess_entry(raw_entry)\r\n if preprocessed_entry_dict is not None:\r\n feed_dict = dict([(self._tensors_to_enqueue[label], value)\r\n for label, value in preprocessed_entry_dict.items()])\r\n try:\r\n self._tensorflow_session.run(self._enqueue_op, feed_dict=feed_dict)\r\n except (tf.errors.CancelledError, RuntimeError):\r\n break\r\n logger.debug('Exiting thread %s' % threading.current_thread().name)","def test_google_storage_small_batch_size(sdc_builder, sdc_executor, gcp):\n\n gcp_file_name = 'test_9_records.xls'\n\n pipeline_builder = sdc_builder.get_pipeline_builder()\n\n bucket_name = get_random_string(ascii_lowercase, 10)\n\n storage_client = gcp.storage_client\n\n google_cloud_storage = pipeline_builder.add_stage('Google Cloud Storage', type='origin')\n\n google_cloud_storage.set_attributes(bucket=bucket_name,\n common_prefix='gcs-test',\n prefix_pattern=gcp_file_name,\n data_format='EXCEL',\n max_batch_size_in_records=1,\n excel_header_option='WITH_HEADER')\n\n pipeline_finisher_executor = pipeline_builder.add_stage('Pipeline Finisher Executor')\n pipeline_finisher_executor.set_attributes(preconditions=['${record:eventType() == \\'no-more-data\\'}'],\n on_record_error='DISCARD')\n\n google_cloud_storage >= pipeline_finisher_executor\n\n wiretap = pipeline_builder.add_wiretap()\n google_cloud_storage >> wiretap.destination\n\n pipeline = pipeline_builder.build(title='Google Cloud Storage').configure_for_environment(gcp)\n sdc_executor.add_pipeline(pipeline)\n\n created_bucket = gcp.retry_429(storage_client.create_bucket)(bucket_name)\n try:\n blob = created_bucket.blob('gcs-test/' + gcp_file_name)\n blob.upload_from_filename('resources/gcp/' + gcp_file_name)\n\n # Start the pipeline and make sure the timeout is defined, since the original bug involved\n # an infinite loop reading the same file over and over again\n sdc_executor.start_pipeline(pipeline).wait_for_finished(timeout_sec=300)\n\n # Check that the file has been read through exactly once\n output_records = [record.field for record in wiretap.output_records]\n assert len(output_records) == 9\n # Check that no error records were generated\n error_records = [record.field for record in wiretap.error_records]\n assert len(error_records) == 0\n\n finally:\n logger.info('Deleting bucket %s ...', created_bucket.name)\n gcp.retry_429(created_bucket.delete)(force=True)","def create_ingest_task(storage, task_queue):\n for filename in storage.list_files(prefix='build/'):\n t = IngestTask(\n chunk_path=storage.get_path_to_file('build/'+filename),\n chunk_encoding='npz',\n layer_path=storage.layer_path,\n )\n task_queue.insert(t)","def process_queue(\n *,\n stac_bucket: str,\n cog_pds_meta_pds: Dict[str, str],\n queue: str,\n message_batch_size: int,\n sns_reconcile_target_arn: str,\n catalog_update_queue: str,\n catalog_update_table: str,\n corrupted_xml_queue: str,\n delete_processed_messages: bool = False,\n):\n\n buckets = {\n \"stac\": stac_bucket,\n }\n processed_messages = 0\n for msg in sqs_messages(queue):\n\n try:\n process_message(\n msg,\n {\n **buckets,\n **{\n \"cog\": msg[\"bucket\"],\n \"metadata\": cog_pds_meta_pds[msg[\"bucket\"]],\n },\n },\n sns_reconcile_target_arn,\n catalog_update_queue,\n catalog_update_table,\n )\n except ParseError:\n LOGGER.info(\"Corrupted XML for %s quicklook.\", msg[\"key\"].split(\"/\")[-1])\n get_client(\"sqs\").send_message(\n QueueUrl=corrupted_xml_queue, MessageBody=msg[\"key\"]\n )\n\n # Remove message from queue\n if delete_processed_messages:\n get_client(\"sqs\").delete_message(\n QueueUrl=queue, ReceiptHandle=msg[\"ReceiptHandle\"]\n )\n\n processed_messages += 1\n if processed_messages == message_batch_size:\n break","def process_quasar(folder, set_type, doc_size):\n print(\"def process_quasar(folder, set_type, doc_size) ...\")\n\n # create counter for enumeration of batch-files\n counter = 0\n\n # Question File and Path\n question_file = set_type + \"_questions.json\"\n question_file_path = Path(\"/\".join([folder, \"questions\", question_file]))\n\n # Contexts File and Path\n context_file = set_type + \"_contexts.json\"\n context_file_path = Path(\"/\".join([folder, \"contexts\", doc_size, context_file]))\n\n with open(question_file_path, \"r\") as qf, open(context_file_path, \"r\") as cf:\n question_id_list = list()\n data_dict = dict()\n batches_data = list()\n\n # Parse each line separate to avoid memory issues\n for line in qf:\n parsed_question = json.loads(line)\n question_id = parsed_question[\"uid\"]\n question_id_list.append(question_id)\n data_dict[question_id] = {\"answer\": parsed_question[\"answer\"]}\n data_dict[question_id].update({\"question\": parsed_question[\"question\"]})\n\n # in order to create batches with the size of 30 and to avoid Memory Errors\n if len(data_dict) == 30:\n contexts_counter = 0\n for line2 in cf:\n parsed_answer = json.loads(line2)\n # Answer ID should have a corresponding question ID\n answer_id = parsed_answer[\"uid\"]\n if answer_id in question_id_list:\n contexts_counter += 1\n # List of contexts with retrieval scores, contexts are sorted from highest to lowest score\n answer_contexts = parsed_answer[\"contexts\"]\n # remove scores of contexts\n cleaned_answer_contexts = [ls_elem[1] for ls_elem in answer_contexts]\n data_dict[answer_id].update({\"contexts\": cleaned_answer_contexts})\n if contexts_counter == 30:\n contexts_counter = 0\n break\n\n # add information where answer in context is\n answers_list, questions_list, contexts_list = add_end_idx(data_dict)\n\n # create the batch-encodings\n batches_data.append(create_encodings(answers_list, questions_list, contexts_list))\n data_dict.clear()\n question_id_list.clear()\n # if len(batches_data) % 1000 == 0:\n\n print(\"\\n length batches_data \" + str(len(batches_data)) + \" \" + str(counter))\n\n if len(batches_data) == 2000:\n counter += 1\n save_batch_files(\"/local/anasbori/bert_odqa/ODQA_Bert_Project/batch_output\", batches_data,\n counter)\n\n batches_data.clear()\n\n counter += 1\n save_batch_files(Path(\"/local/anasbori/bert_odqa/ODQA_Bert_Project/batch_output\"), batches_data, counter)","def main(context=None, event=None): # , s3_rawdata_path='RAW/FP_english_noweather.csv'):\n\n # get_logs()\n # df = get_predata('sample/FP_english_noweather.csv')\n df = get_predata()\n original_size = df.shape[0]\n print(original_size)\n\n df = clean_df(df, top=2)\n df = normQnA(df)\n try:\n df.reset_index(inplace=True)\n except:\n pass\n\n save_data(df)\n\n #print(\"Original size was {0}, output size is {1}\".format(original_size, df.shape))\n update_log(er=\"Original size was {0}, output size is {1}\".format(original_size, df.shape), upload=True )","def create_inference_tasks(task_queue, image_layer_path, convnet_path, \n mask_layer_path, output_layer_path, output_block_start, output_block_size, \n grid_size, patch_size, patch_overlap, cropping_margin_size,\n output_key='output', num_output_channels=3, \n image_mip=1, output_mip=1, mask_mip=3):\n for z in tqdm(range(grid_size[0]), desc='z loop'):\n for y in range(grid_size[1]):\n for x in range(grid_size[2]):\n output_offset = tuple(s+x*b for (s, x, b) in \n zip(output_block_start, (z, y, x), \n output_block_size))\n task = InferenceTask(\n image_layer_path=image_layer_path,\n convnet_path=convnet_path,\n mask_layer_path=mask_layer_path,\n output_layer_path=output_layer_path,\n output_offset=output_offset,\n output_shape=output_block_size,\n patch_size=patch_size, \n patch_overlap=patch_overlap,\n cropping_margin_size=cropping_margin_size,\n output_key=output_key,\n num_output_channels=num_output_channels,\n image_mip=image_mip,\n output_mip=output_mip,\n mask_mip=mask_mip\n )\n task_queue.insert(task)\n task_queue.wait('Uploading InferenceTasks')\n\n vol = CloudVolume(output_layer_path, mip=output_mip)\n vol.provenance.processing.append({\n 'method': {\n 'task': 'InferenceTask',\n 'image_layer_path': image_layer_path,\n 'convnet_path': convnet_path,\n 'mask_layer_path': mask_layer_path,\n 'output_layer_path': output_layer_path,\n 'output_offset': output_offset,\n 'output_shape': output_block_size,\n 'patch_size': patch_size,\n 'patch_overlap': patch_overlap,\n 'cropping_margin_size': cropping_margin_size,\n 'output_key': output_key,\n 'num_output_channels': num_output_channels,\n 'image_mip': image_mip,\n 'output_mip': output_mip,\n 'mask_mip': mask_mip,\n },\n 'by': OPERATOR_CONTACT,\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\n })\n vol.commit_provenance()","async def _parse_from_queue(queue):\n loop = asyncio.get_event_loop()\n file_name = TMP_FOLDER + f\"{os.getpid()}.csv\"\n with open(file_name, \"w+\", encoding=\"utf8\") as file:\n logging.info(f\"Write to {file_name}\")\n csv_writer = csv.writer(file)\n\n records = await queue.get()\n while records != \"DONE\":\n if records:\n for record in list(records):\n manifest_row = [\n record.get(\"did\"),\n \" \".join(\n [url.replace(\" \", \"%20\") for url in record.get(\"urls\")]\n ),\n \" \".join(\n [auth.replace(\" \", \"%20\") for auth in record.get(\"authz\")]\n ),\n \" \".join([a.replace(\" \", \"%20\") for a in record.get(\"acl\")]),\n record.get(\"hashes\", {}).get(\"md5\"),\n record.get(\"size\"),\n record.get(\"file_name\"),\n ]\n loop.run_in_executor(None, csv_writer.writerow, manifest_row)\n\n records = await queue.get()\n\n file.flush()","def process_queue(self):\n if not self.has_queue:\n log.debug('Thumbnail queue empty.')\n return\n\n queue = []\n with LockFile(self._queue_path):\n with open(self._queue_path) as fp:\n for line in fp:\n line = line.strip()\n if not line:\n continue\n if not os.path.exists(line):\n log.debug('File does not exist : %r', line)\n continue\n queue.append(line)\n # Clear queue file\n with atomic_writer(self._queue_path, 'wb') as fp:\n fp.write('')\n\n succeeded = True\n for i, img_path in enumerate(queue):\n log.debug('Generating thumbnail {}/{} ...', i + 1, len(queue))\n if not self.generate_thumbnail(img_path):\n succeeded = False\n\n return succeeded","def job_ingestion(self):\n\n # Map a file type to a DAO\n if self.file_type == SmsFileTypes.SAMPLE_LIST:\n self.file_dao = SmsSampleDao()\n elif self.file_type == SmsFileTypes.N0:\n self.file_dao = SmsN0Dao()\n else:\n self.file_dao = None\n\n if self.file_dao:\n # look up if any rows exist already for the file\n records = self.file_dao.get_from_filepath(self.file_path)\n\n if records:\n logging.warning(f'File already ingested: {self.file_path}')\n return\n\n data_to_ingest = self.read_data_from_cloud_manifest(self.file_path)\n\n self.validate_columns(data_to_ingest[\"fieldnames\"], self.file_dao)\n\n self.write_data_to_manifest_table(data_to_ingest[\"rows\"])","def main():\n #Connection to blob\n #blob_connection(\"../Resume\")\n #blob_download()\n \n \n logging.getLogger().setLevel(logging.INFO)\n\n # Extract data from upstream.\n observations = extract()\n\n # Spacy: Spacy NLP\n nlp = spacy.load('en')\n\n # Transform data to have appropriate fields\n observations, nlp = transform(observations, nlp)\n\n # Load data for downstream consumption\n load(observations, nlp)\n load_to_json(observations, nlp)\n \n #Send data to CosmosDB\n send_to_Cosmos()\n \n #Create Azure search Datasource, Index and indexer\n implement_Azure_search()\n \n \n# DB=CosmosConnection.DBManager(config_cosmos.COSMOSDB_HOST, config_cosmos.COSMOSDB_KEY)\n# new_db_id='db_resume'\n# new_col_id='col_resume'\n# DB.create_database(new_db_id)\n# DB.create_collection(new_db_id,new_col_id)\n# #if new_db_id not in DB.list_databases:\n# #DB.create_database(new_db_id)\n# #if new_col_id not in DB.list_collections(new_db_id):\n# #DB.create_collection(new_db_id,new_col_id)\n# DB.create_doc(new_db_id, new_col_id, load_to_json(observations, nlp))\n pass","def main():\n spark = create_spark_session()\n input_data = \"s3a://udacity-dend/\"\n output_data = \"s3a://dend-bucket-cpm/\"\n\n process_song_data(spark, input_data, output_data)\n process_log_data(spark, input_data, output_data)","def __init__(\n self,\n *,\n dag_id: str,\n cloud_workspace: CloudWorkspace,\n publisher_id: str,\n format_specification: str,\n bq_dataset_id: str = \"onix\",\n bq_table_name: str = \"onix\",\n bq_dataset_description: str = \"Thoth ONIX Feed\",\n bq_table_description: str = None,\n api_dataset_id: str = \"onix\",\n host_name: str = \"https://export.thoth.pub\",\n schema_folder: str = default_schema_folder(),\n observatory_api_conn_id: str = AirflowConns.OBSERVATORY_API,\n catchup: bool = False,\n start_date: pendulum.DateTime = pendulum.datetime(2022, 12, 1),\n schedule: str = \"@weekly\",\n ):\n super().__init__(\n dag_id,\n start_date=start_date,\n schedule=schedule,\n airflow_conns=[observatory_api_conn_id],\n catchup=catchup,\n tags=[\"oaebu\"],\n )\n\n self.dag_id = dag_id\n self.cloud_workspace = cloud_workspace\n self.publisher_id = publisher_id\n self.bq_dataset_id = bq_dataset_id\n self.bq_table_name = bq_table_name\n self.bq_dataset_description = bq_dataset_description\n self.bq_table_description = bq_table_description\n self.api_dataset_id = api_dataset_id\n self.host_name = host_name\n self.format_specification = format_specification\n self.schema_folder = schema_folder\n self.observatory_api_conn_id = observatory_api_conn_id\n\n check_workflow_inputs(self)\n\n self.add_setup_task(self.check_dependencies)\n self.add_task(self.download)\n self.add_task(self.upload_downloaded)\n self.add_task(self.transform)\n self.add_task(self.upload_transformed)\n self.add_task(self.bq_load)\n self.add_task(self.add_new_dataset_releases)\n self.add_task(self.cleanup)","def process_data():\n for message in get_messages_from_sqs():\n try:\n message_content = json.loads(message.body)\n input_file = urllib.unquote_plus(message_content\n ['Records'][0]['s3']['object']\n ['key']).encode('utf-8')\n s3.download_file(input_bucket_name, input_file, input_file)\n output_file = os.path.join(output_dir, os.path.splitext(input_file)[0]+'.csv')\n parse_patient_data(input_file, output_file)\n upload_data(output_file)\n cleanup_files(input_file, output_file)\n except:\n message.change_visibility(VisibilityTimeout=0)\n continue\n else:\n message.delete()","def reconstruct_segmentation_queue_to_file(self, data_queue, result_folder, result_suffix, save_raw=True):\n\n for data_out in iter(data_queue.get, None):\n\n results, paths= data_out\n\n if self.data_format == 'channels_first':\n results = np.moveaxis(results, 1, -1)\n\n if len(paths) == 1: # same-tile_augmentation\n\n for i in range(4):\n results[i, :, :, :] = np.rot90(results[i, :, :, :], -i)\n\n results_mean = np.mean(results, axis=0)\n predictions = np.expand_dims(np.argmax(results_mean,-1),0)\n\n elif len(paths) == len(results): # multi-tiles\n predictions = np.argmax(results, -1)\n\n else:\n raise Exception('Invalid result dimension!')\n\n for ind in range(len(predictions)):\n\n pred = predictions[ind]\n path = paths[ind]\n\n raw = cv2.cvtColor(cv2.imread(path),cv2.COLOR_BGR2RGB)\n\n tile_gray_coded = cv2.resize(self.gray_code(pred),(raw.shape[1],raw.shape[0]), interpolation=cv2.INTER_NEAREST)\n tile_color_coded = cv2.resize(self.color_code(pred), (raw.shape[1],raw.shape[0]), interpolation=cv2.INTER_NEAREST)\n\n result_path_base = result_folder + os.path.sep + os.path.splitext(os.path.basename(path))[0]\n\n rgb_result = cv2.cvtColor((tile_color_coded*0.2+raw*0.8).astype(np.uint8), cv2.COLOR_RGB2BGR)\n\n cv2.imwrite(result_path_base + '_result_rgb_' + result_suffix + self.ext, rgb_result)\n cv2.imwrite(result_path_base + '_result_mask_' + result_suffix + self.ext, tile_gray_coded)\n\n if save_raw:\n mdict = {}\n mdict['Raw'] = results\n\n sio.savemat(result_path_base + '_result_data_' + result_suffix + '.mat', mdict)","def test_stuff_in_queues_integrated(integrated_ff):\n search_res = ff_utils.search_metadata('search/?limit=all&type=File', key=integrated_ff['ff_key'])\n # just take the first handful\n for item in search_res[:8]:\n ff_utils.patch_metadata({}, obj_id=item['uuid'], key=integrated_ff['ff_key'])\n time.sleep(3) # let queues catch up\n stuff_in_queue = ff_utils.stuff_in_queues(integrated_ff['ff_env_index_namespace'], check_secondary=True)\n assert stuff_in_queue\n with pytest.raises(Exception) as exec_info:\n ff_utils.stuff_in_queues(None, check_secondary=True) # fail if no env specified\n assert 'Must provide a full fourfront environment name' in str(exec_info.value)","def main():\n spark = create_spark_session()\n logging.info('Spark Session created')\n input_data = \"s3a://udacity-dend/\"\n output_data = \"s3a://dend-emr-project\"\n #input_data = './data/'\n #output_data = '/Users/daniel/Desktop/output/'\n logging.info(f'Set input path to {input_data}')\n logging.info(f'Set output path to {output_data}')\n \n copy_raw_data(spark, input_data, output_data)\n\n s3_data = restore_data_from_s3(spark, output_data)\n \n sas_desc_string = load_sas_desc_file(input_data)\n \n process_fact_table(spark, s3_data, output_data, sas_desc_string)\n \n process_dim_tables(spark, s3_data, output_data, sas_desc_string)\n\n data_quality_check(spark, output_data)\n \n logging.info('ETL process successfully finished.')","def process_job(q):\n del log_msg[:]\n logger.info('Processing Job %s', q.id)\n\n datatype = q.datatype\n input_dir = q.input_dir\n output_dir = q.output_dir\n processor = q.processor\n if datatype.lower() == 'laz':\n block_name = proper_block_name(input_dir)\n elif datatype.lower() == 'ortho':\n block_name = proper_block_name_ortho(input_dir)\n if datatype.lower() == 'laz' or datatype.lower() == 'ortho':\n logger.info('Verifying las tiles in directory...')\n log_msg.append('Verifying las tiles in directory...\\n')\n has_error, remarks = verify_dir(input_dir, datatype.lower())\n\n if has_error:\n assign_status(q, error=True)\n log_msg.append('Error in verify_las/verify_raster!\\n {0} \\n'.format(remarks))\n else:\n logger.info('Renaming tiles...')\n\n logger.info('BLOCK NAME %s', block_name)\n log_msg.append('BLOCK NAME {0}\\n'.format(block_name))\n\n in_coverage, block_uid = find_in_coverage(block_name)\n\n #: Check first if folder or `block_name` is in `Cephgeo_LidarCoverageBlock`\n #: If not found, `output_dir` is not created and data is not processed\n if in_coverage:\n logger.info('Found in Lidar Coverage model %s %s',\n block_name, block_uid)\n log_msg.append('Found in Lidar Coverage model {0} {1}\\n'.format(\n block_name, block_uid))\n\n rename_tiles(input_dir, output_dir, processor,\n block_name, block_uid, q)\n logger.info('Status %s Status Timestamp %s',\n q.status, q.status_timestamp)\n log_msg.append('Status {0} Status Timestamp {1}\\n'.format(\n q.status, q.status_timestamp))\n\n else:\n has_error = True\n logger.error('ERROR NOT FOUND IN MODEL %s %s', block_name, block_uid)\n log_msg.append('ERROR NOT FOUND IN MODEL {0} {1}\\n'.format(block_name, block_uid))\n assign_status(q, error=True)\n # for DEM\n else:\n logger.info('Handler not implemented for type: %s',\n str(q.datatype))\n log_msg.append('Handler not implemented for type: {0}\\n'.format(\n str(q.datatype)))\n assign_status(q, error=True)\n\n paragraph = ''\n for par in log_msg:\n paragraph = paragraph + par\n\n #: Save log messages from renaming tiles to `Automation_AutomationJob.log`\n with PSQL_DB.atomic() as txn:\n new_q = (Automation_AutomationJob\n .update(data_processing_log=paragraph, status_timestamp=datetime.now())\n .where(Automation_AutomationJob.id == q.id))\n new_q.execute()","def load_raw_to_bq(event, context):\n\n import os\n\n\n print(f\"Processing .....\")\n\n file = event\n project = os.environ.get('ENV_PROJECT')\n dataset = os.environ.get('ENV_DATASET')\n bucket = file.get(\"bucket\")\n tableCsv = file.get(\"name\")\n tableDestList = tableCsv.split(\".\")\n tableDest = tableDestList[0]\n table_id = f'{project}.{dataset}.{tableDest}'\n\n from Configuration import Configuration\n\n Configuration(tableCsv,bucket,table_id)\n\n\n print(f\"End Process.\")","def test_runner_large_file(caplog, replay_rate):\n\n caplog.set_level(logging.INFO)\n\n path = 'test_data/test_data.parquet'\n time_column = 'requesttimestamp'\n start_date = datetime.datetime(2020, 7, 10, 0, 1, 0)\n end_date = datetime.datetime(2020, 7, 10, 0, 3, 0)\n replay_rate = replay_rate\n bootstrap_servers = 'kafka:9092'\n topic = 'test_stream_2'\n\n logging.debug(\"starting\")\n\n fileconnector = ParquetFileConnector(path=path, time_column=time_column, \n start_date=start_date, end_date=end_date)\n\n fileconnector.startup_checks()\n\n publisher = KafkaPublisher(\n bootstrap_servers=bootstrap_servers,\n topic=topic\n )\n\n runner = CentralRunner(db_connection=fileconnector, \n output_system=publisher, \n start_time=start_date, \n end_time=end_date,\n replay_rate=replay_rate )\n\n start = time.perf_counter()\n \n runner.run()\n\n #publisher.close()\n\n end = time.perf_counter()\n\n code_time = end - start\n\n period_duration = (end_date - datetime.timedelta(seconds=replay_rate) - start_date).total_seconds()\n\n assert abs(code_time - period_duration/replay_rate) < 1\n #assert int(code_time) == (end_date - start_date).total_seconds() * replay_rate","def __init__(\n self,\n *,\n dag_id: str,\n cloud_workspace: CloudWorkspace,\n bq_dataset_id: str = \"crossref_metadata\",\n bq_table_name: str = \"crossref_metadata\",\n api_dataset_id: str = \"crossref_metadata\",\n schema_folder: str = os.path.join(default_schema_folder(), \"crossref_metadata\"),\n dataset_description: str = \"The Crossref Metadata Plus dataset: https://www.crossref.org/services/metadata-retrieval/metadata-plus/\",\n table_description: str = \"The Crossref Metadata Plus dataset: https://www.crossref.org/services/metadata-retrieval/metadata-plus/\",\n crossref_metadata_conn_id: str = \"crossref_metadata\",\n observatory_api_conn_id: str = AirflowConns.OBSERVATORY_API,\n max_processes: int = os.cpu_count(),\n batch_size: int = 20,\n start_date: pendulum.DateTime = pendulum.datetime(2020, 6, 7),\n schedule: str = \"0 0 7 * *\",\n catchup: bool = True,\n queue: str = \"remote_queue\",\n max_active_runs: int = 1,\n ):\n\n super().__init__(\n dag_id=dag_id,\n start_date=start_date,\n schedule=schedule,\n catchup=catchup,\n airflow_conns=[observatory_api_conn_id, crossref_metadata_conn_id],\n tags=[Tag.academic_observatory],\n max_active_runs=max_active_runs,\n queue=queue,\n )\n self.cloud_workspace = cloud_workspace\n self.bq_dataset_id = bq_dataset_id\n self.bq_table_name = bq_table_name\n self.api_dataset_id = api_dataset_id\n self.schema_folder = schema_folder\n self.dataset_description = dataset_description\n self.table_description = table_description\n self.crossref_metadata_conn_id = crossref_metadata_conn_id\n self.observatory_api_conn_id = observatory_api_conn_id\n self.max_processes = max_processes\n self.batch_size = batch_size\n\n self.add_setup_task(self.check_dependencies)\n self.add_setup_task(self.check_release_exists)\n self.add_task(self.download)\n self.add_task(self.upload_downloaded)\n self.add_operator(\n WorkflowBashOperator(\n workflow=self,\n task_id=\"extract\",\n bash_command='tar -xv -I \"pigz -d\" -f {{ release.download_file_path }} -C {{ release.extract_folder }}',\n )\n )\n self.add_task(self.transform)\n self.add_task(self.upload_transformed)\n self.add_task(self.bq_load)\n self.add_task(self.add_new_dataset_releases)\n self.add_task(self.cleanup)","def handle_event(event, context):\n\n try:\n print(\"Received event: \" + json.dumps(event, indent=2))\n\n # grab resources section of event, get task execution ids\n task_execution_arns = event['resources']\n\n # now fetch the input filter info from each task_detail, fire off jobs\n new_files_to_process = []\n for task_execution_arn in task_execution_arns:\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/datasync.html#DataSync.Client.describe_task_execution\n response = datasync_client.describe_task_execution(TaskExecutionArn=task_execution_arn)\n print(\"Task execution details: \" + str(response))\n # this will be the location of the data in configured s3 bucket:\n # 'Includes': [\n # {\n # 'FilterType': 'SIMPLE_PATTERN',\n # 'Value': 'string'\n # },\n # ]\n if len(response['Includes']) > 0:\n file = response['Includes'][0]['Value']\n # files typically start with leading '/', strip that leading '/'\n print(\"Got filename:\" + file)\n if file.startswith('/', 0):\n new_files_to_process.append(file.lstrip('/'))\n else:\n new_files_to_process.append(file)\n else:\n print(\"Response didn't contain Includes files...\")\n\n if len(new_files_to_process) == 0:\n print('No files were parsed from input...exiting')\n return\n\n for new_file_to_process in new_files_to_process:\n state_machine_arn = os.environ['STATE_MACHINE_ARN']\n payload = {\"ObjectName\": new_file_to_process}\n json_payload = json.dumps(payload)\n print('Starting bcl2fastq with payload %s' % json_payload)\n #\n response = step_client.start_execution(stateMachineArn=state_machine_arn, input=json_payload)\n print(response)\n\n except Exception as e:\n print(e)\n print('Error handling event. %s' % e)\n raise e","def create_transfer_tasks(\n task_queue, src_layer_path, dest_layer_path, \n chunk_size=None, shape=Vec(2048, 2048, 64), \n fill_missing=False, translate=(0,0,0), \n bounds=None, mip=0, preserve_chunk_size=True\n ):\n shape = Vec(*shape)\n vol = CloudVolume(src_layer_path, mip=mip)\n translate = Vec(*translate) // vol.downsample_ratio\n \n if not chunk_size:\n chunk_size = vol.info['scales'][mip]['chunk_sizes'][0]\n chunk_size = Vec(*chunk_size)\n\n try:\n dvol = CloudVolume(dest_layer_path, mip=mip)\n except Exception: # no info file\n info = copy.deepcopy(vol.info)\n dvol = CloudVolume(dest_layer_path, info=info)\n dvol.commit_info()\n\n dvol.info['scales'] = dvol.info['scales'][:mip+1]\n dvol.info['scales'][mip]['chunk_sizes'] = [ chunk_size.tolist() ]\n dvol.commit_info()\n\n create_downsample_scales(dest_layer_path, \n mip=mip, ds_shape=shape, preserve_chunk_size=preserve_chunk_size)\n \n if bounds is None:\n bounds = vol.bounds.clone()\n else:\n bounds = vol.bbox_to_mip(bounds, mip=0, to_mip=mip)\n bounds = Bbox.clamp(bounds, vol.bounds)\n\n total = int(reduce(operator.mul, np.ceil(bounds.size3() / shape)))\n for startpt in tqdm(xyzrange( bounds.minpt, bounds.maxpt, shape ), desc=\"Inserting Transfer Tasks\", total=total):\n task = TransferTask(\n src_path=src_layer_path,\n dest_path=dest_layer_path,\n shape=shape.clone(),\n offset=startpt.clone(),\n fill_missing=fill_missing,\n translate=translate,\n mip=mip,\n )\n task_queue.insert(task)\n task_queue.wait('Uploading Transfer Tasks')\n\n job_details = {\n 'method': {\n 'task': 'TransferTask',\n 'src': src_layer_path,\n 'dest': dest_layer_path,\n 'shape': list(map(int, shape)),\n 'fill_missing': fill_missing,\n 'translate': list(map(int, translate)),\n 'bounds': [\n bounds.minpt.tolist(),\n bounds.maxpt.tolist()\n ],\n 'mip': mip,\n },\n 'by': OPERATOR_CONTACT,\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\n }\n\n dvol = CloudVolume(dest_layer_path)\n dvol.provenance.sources = [ src_layer_path ]\n dvol.provenance.processing.append(job_details) \n dvol.commit_provenance()\n\n if vol.path.protocol != 'boss':\n vol.provenance.processing.append(job_details)\n vol.commit_provenance()","def _pre_flight_checks(self, num_containers, virtual_cores, memory,\n files, queue):\n try:\n # check response from RM\n met = self.yarn_api.cluster_metrics()\n except YARNException:\n raise\n except requests.RequestException as e:\n if isinstance(e, requests.Timeout):\n m = 'Connection timeout'\n else:\n m = 'Connection error'\n raise YARNException(m + ' when talking to the '\n 'YARN REST server at {}. This can mean that '\n 'the server/port values are wrong, that you '\n 'are using the wrong protocol (http/https) or '\n 'that you need to route through a proxy.'\n ''.format(self.yarn_api.url))\n if met['activeNodes'] < 1:\n raise KnitException('No name-nodes active')\n # What if we simply don't have the full yarn-site.xml available?\n mmin = int(self.conf.get('yarn.scheduler.minimum-allocation-mb', 1024))\n # 300MB default allocation for AM in client.scala\n mem = (max(300, mmin) + num_containers * max(memory, mmin))\n if met['availableMB'] < mem:\n raise KnitException('Memory estimate for app (%iMB) exceeds cluster'\n ' capacity (%iMB)' % (mem, met['availableMB']))\n c = 1 + num_containers * virtual_cores\n if met['availableVirtualCores'] < c:\n raise KnitException('vCPU request for app (%i) exceeds cluster capa'\n 'city (%i)' % (c, met['availableVirtualCores']))\n nodes = self.yarn_api.nodes()\n if all((max(mmin, memory) > n['availMemoryMB']) and\n (virtual_cores > n['availableVirtualCores'])\n for n in nodes):\n # cannot test without multiple nodemanagers\n raise KnitException('No NodeManager can fit any single container')\n if self.hdfs:\n df = self.hdfs.df()\n cap = (df['capacity'] - df['used']) // 2**20\n fs = [self.JAR_FILE_PATH] + [f for f in files\n if not f.startswith('hdfs://')]\n need = sum(os.stat(f).st_size for f in fs) // 2**20\n # NB: if replication > 1 this might not be enough\n if cap < need:\n raise KnitException('HDFS space requirement (%iMB) exceeds'\n 'capacity (%iMB)' % (need, cap))","def main():\n spark = create_spark_session()\n input_data = \"s3a://udacity-dend\"\n output_data = \"s3a://vivek1bucket\"\n \n process_song_data(spark, input_data, output_data) \n process_log_data(spark, input_data, output_data)","def _submit_to_queue(self, script_file):","def runDataExtraction():\r\n config = CONFIG['steps']['DataExtraction']\r\n ci = config['inputs']\r\n co = config['outputs']\r\n columns = ci['columns']\r\n nrows = ci['nrows']\r\n input_bucket = ci['bucket']\r\n no_of_files = ci['no_of_files']\r\n\r\n output_bucket = co['bucket']\r\n csv_name_prefix = co['csv_name_prefix']\r\n\r\n minio_config = CONFIG['artifacts']['minio']\r\n minioClient = create_minio_client(minio_config[\"endpoint_url\"],\r\n access_key=minio_config[\"access_key\"],\r\n secret_key=minio_config[\"secret_key\"],\r\n secure=minio_config['secure'])\r\n\r\n boto_client = boto3.client(\"s3\",\r\n endpoint_url=minio_config[\"endpoint_url\"],\r\n aws_access_key_id=minio_config[\"access_key\"],\r\n aws_secret_access_key=minio_config[\"secret_key\"],\r\n region_name=minio_config[\"region_name\"])\r\n\r\n zip_files = get_files(input_bucket, boto_client, file_type='zip')\r\n\r\n no_of_files_to_process = no_of_files if no_of_files is not None else len(\r\n zip_files)\r\n for zip_file in tqdm(zip_files[:no_of_files_to_process], total=no_of_files_to_process):\r\n process_file(zip_file, input_bucket, output_bucket, minioClient, columns,\r\n nrows=nrows, output_csv_name_prefix=csv_name_prefix)","def run_etl(_):\n\n _ = storage.Client() # Storage Client\n \n # Extract\n bom_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'raw/bill_of_materials.csv')\n cb_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'raw/comp_boss.csv')\n pq_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'raw/price_quote.csv')\n \n bom = pd.read_csv(bom_name)\n cb = pd.read_csv(cb_name)\n pq = pd.read_csv(pq_name)\n\n # Transform\n cb[\"connection_type_id\"] = cb[\"connection_type_id\"].replace(to_replace=\"9999\", value=np.nan)\n cb[\"height_over_tube\"] = cb[\"height_over_tube\"].replace(to_replace=\"9999\", value=np.nan)\n cb['groove'] = cb.apply(lambda row: convert_to_intbool(row['groove']), axis=1)\n cb['unique_feature'] = cb.apply(lambda row: convert_to_intbool(row['unique_feature']), axis=1)\n cb['orientation'] = cb.apply(lambda row: convert_to_intbool(row['orientation']), axis=1)\n\n bom = pd.wide_to_long(\n df=bom,\n stubnames=[\"component_id\", \"quantity\"],\n i=\"tube_assembly_id\",\n j=\"id\",\n sep=\"_\"\n ).reset_index().sort_values([\"tube_assembly_id\", \"id\"], ignore_index=True)\\\n .drop(\"id\", axis=1).dropna(subset=[\"component_id\"])\n\n pq['bracket_pricing'] = pq.apply(lambda row: convert_to_intbool(row['bracket_pricing']), axis=1)\n\n # Load\n bom_output_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'refined/tube_assembly.csv')\n cb_output_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'refined/component.csv')\n pq_output_name = \"{0}{1}{2}\".format(\"gs://\", BUCKET_NAME, 'refined/price_quote.csv')\n\n bom.to_csv(bom_output_name, index=False)\n cb.to_csv(cb_output_name, index=False)\n pq.to_csv(pq_output_name, index=False)\n\n return f'ETL Ok. Check Data Studio.'","def handleDQMFileSaver(self):\n\n runIsComplete = getattr(self.jobBag, \"runIsComplete\", False)\n multiRun = getattr(self.jobBag, \"multiRun\", False)\n runLimits = getattr(self.jobBag, \"runLimits\", \"\")\n self.logger.info(\"DQMFileSaver set to multiRun: %s, runIsComplete: %s, runLimits: %s\",\n multiRun, runIsComplete, runLimits)\n\n procScript = \"cmssw_handle_dqm_filesaver.py\"\n\n cmd = \"%s --input_pkl %s --output_pkl %s\" % (\n procScript,\n os.path.join(self.stepSpace.location, self.configPickle),\n os.path.join(self.stepSpace.location, self.configPickle))\n\n if hasattr(self.step.data.application.configuration, \"pickledarguments\"):\n pklArgs = encodeUnicodeToBytes(self.step.data.application.configuration.pickledarguments)\n args = pickle.loads(pklArgs)\n datasetName = args.get('datasetName', None)\n if datasetName:\n cmd += \" --datasetName %s\" % (datasetName)\n if multiRun and runLimits:\n cmd += \" --multiRun --runLimits=%s\" % (runLimits)\n if runIsComplete:\n cmd += \" --runIsComplete\"\n self.scramRun(cmd)\n\n return","def run(argv=None):\n\n\n pubsubTopicName = \"projects/data-qe-da7e1252/topics/sk-firewall-json\"\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--input',\n dest='input',\n default='gs://dataflow-samples/shakespeare/kinglear.txt',\n help='Input file to process.')\n parser.add_argument('--output',\n dest='output',\n # CHANGE 1/5: The Google Cloud Storage path is required\n # for outputting the results.\n #default='gs://YOUR_OUTPUT_BUCKET/AND_OUTPUT_PREFIX',\n #default=\"/Users/skanabargi/python/stream/output\",\n default='gs://data-qe-da7e1252/tmp/sk_out',\n help='Output file to write results to.')\n known_args, pipeline_args = parser.parse_known_args(argv)\n\n pipeline_args.extend([\n # CHANGE 2/5: (OPTIONAL) Change this to DataflowRunner to\n # run your pipeline on the Google Cloud Dataflow Service.\n '--runner=DataflowRunner',\n # CHANGE 3/5: Your project ID is required in order to run your pipeline on\n # the Google Cloud Dataflow Service.\n '--project=data-qe-da7e1252',\n # CHANGE 4/5: Your Google Cloud Storage path is required for staging local\n # files.\n #'--staging_location=gs://YOUR_BUCKET_NAME/AND_STAGING_DIRECTORY',\n '--staging_location=gs://data-qe-da7e1252/tmp/stage/',\n # CHANGE 5/5: Your Google Cloud Storage path is required for temporary\n # files.\n #'--temp_location=gs://YOUR_BUCKET_NAME/AND_TEMP_DIRECTORY',\n '--temp_location=gs://data-qe-da7e1252/tmp/local',\n '--experiments=allow_non_updatable_job',\n '--job_name=sk-pubsub-to-gcs-5',\n ])\n\n # We use the save_main_session option because one or more DoFn's in this\n # workflow rely on global context (e.g., a module imported at module level).\n pipeline_options = PipelineOptions(pipeline_args)\n pipeline_options.view_as(SetupOptions).save_main_session = True\n pipeline_options.view_as(StandardOptions).streaming = True\n with beam.Pipeline(options=pipeline_options) as p:\n\n # Read the text file[pattern] into a PCollection.\n #lines = p | ReadFromText(known_args.input)\n lines = p | beam.io.ReadFromPubSub(topic=pubsubTopicName)\n\n\n\n # Count the occurrences of each word.\n output = ( lines | 'window' >> beam.WindowInto(window.FixedWindows(60)))\n\n # Write the output using a \"Write\" transform that has side effects.\n # pylint: disable=expression-not-assigned\n output | 'writeTOGcs' >> WriteToText(known_args.output)","def task(self, chunk_filename_queue, writer):\n self.init_structs()\n while True:\n filename = chunk_filename_queue.get()\n for item in self.single_file_gen(filename):\n writer.send_bytes(item)","def sub_trans_data_processing(intputFilePath, outputFilePath, dpiDuplicatesFilePath, dpiRejectionFilePath):\n spark = SparkSession.builder.appName(\"challenge1\").master(\"local\").getOrCreate()\n\n mobile_df = spark.read.format(\"csv\").option(\"header\", \"true\").load(intputFilePath)\n\n mobile_df_excluded = mobile_df.filter(is_int_col(mobile_df['SubscriberId']) == False)\n\n subscriber_mobile_df = mobile_df.filter(is_int_col(mobile_df['SubscriberId']))\n unique_subscriber_mobile_df = subscriber_mobile_df.dropDuplicates()\n\n duplicated_subscriber_mobile_rdd = subscriber_mobile_df.rdd.map(lambda line: (line, 1)).reduceByKey(lambda v1, v2: v1 + v2)\n duplicated_subscriber_mobile_rdd = duplicated_subscriber_mobile_rdd.filter(lambda line: line[1] != 1)\n duplicated_subscriber_mobile_rdd = duplicated_subscriber_mobile_rdd.map(lambda line: line[0])\n\n mobile_subscriber_summary_df = unique_subscriber_mobile_df.groupBy(\"SubscriberId\").agg(\n func.sum(\"TotalBytes\").alias(\"Total_Bytes\"),\n func.count(\"TotalBytes\").alias(\"Total_Count\"),\n func.current_date().alias(\"Insertion_Date\"),\n func.first('IMEI').alias('IMEI'),\n ((func.unix_timestamp(func.max(\"EndTime\")) - func.unix_timestamp(func.min(\"StartTime\"))) / 60).alias(\n 'Total_Time_In_Mins'))\n\n subscriber_services = unique_subscriber_mobile_df.rdd.map(lambda line: ((line[7], line[19]), 1)).reduceByKey(lambda v1, v2: v1 + v2)\n subscriber_services = subscriber_services.map(lambda line: (line[1], line[0])).sortByKey(False)\n subscriber_services = subscriber_services.map(lambda line: line[1])\n subscriber_services = subscriber_services.groupByKey().map(lambda line: (line[0], extract_result(line[1])[0], extract_result(line[1])[1]))\n subscriber_services = subscriber_services.toDF([\"SubscriberId\", \"MostUsedServiceName_1\", \"MostUsedServiceName_2\"])\n\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.join(subscriber_services, ['SubscriberId'])\n\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\"Event_Type\", func.lit(\"Mobile\"))\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\"MAC_Address\", func.lit(\"N/A\"))\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\"Access_Point \", func.lit(\"N/A\"))\n\n mobile_subscriber_summary_df.write.csv(outputFilePath)\n mobile_df_excluded.rdd.saveAsTextFile(dpiRejectionFilePath)\n duplicated_subscriber_mobile_rdd.saveAsTextFile(dpiDuplicatesFilePath)","def pre_analyze(analyze_id, part_number, workspace, use_cache):\n\n LOG.info('Pre Analyze Step')\n\n file_path = os.path.join(workspace, analyze_id)\n source_file_without_extension = 'source_' + str(part_number)\n source_file_with_extension = source_file_without_extension + '.zip'\n zip_file_path = os.path.join(file_path, source_file_with_extension)\n analyze_dir_path = os.path.join(file_path, source_file_without_extension)\n\n with zipfile.ZipFile(zip_file_path) as zip_file:\n try:\n zip_file.extractall(analyze_dir_path)\n except Exception:\n LOG.error('Failed to extract all files from the ZIP.')\n\n if use_cache:\n list_of_other_files = ['sources-root/compile_command.json',\n 'sources-root/cached_files']\n\n difference = set(zip_file.namelist()).difference(\n list_of_other_files)\n\n if difference:\n LOG.debug('Store files in Redis: \\n%s', difference)\n for file_name in zip_file.namelist():\n if file_name not in list_of_other_files:\n LOG.debug(os.path.join(analyze_dir_path, file_name))\n with open(os.path.join(analyze_dir_path, file_name), 'rb') as file:\n file_in_bytes = file.read()\n readable_hash = hashlib.md5(\n file_in_bytes).hexdigest()\n try:\n REDIS_DATABASE.set(\n readable_hash, file_in_bytes)\n LOG.debug(REDIS_DATABASE.get(\n readable_hash).decode('UTF-8'))\n except Exception:\n LOG.error(\n 'Failed to store file %s in Redis.', file_name)\n sys.exit(1)\n\n sources_root_path = os.path.join(analyze_dir_path, 'sources-root')\n\n compile_command_path = os.path.join(\n sources_root_path, 'compile_command.json')\n\n with open(compile_command_path) as compile_command_json:\n try:\n # Assume that there is only one compile command\n compile_command = json.load(compile_command_json)[0]\n except IndexError:\n LOG.error('The compile_command.json was empty.')\n except Exception:\n REDIS_DATABASE.hset(analyze_id, 'state',\n AnalyzeStatus.PREANALYZE_FAILED.name)\n LOG.error('Failed to read in compile command.')\n\n if use_cache:\n cached_files_path = os.path.join(\n sources_root_path, 'cached_files')\n\n with open(cached_files_path) as read_in_cached_files:\n try:\n cached_files = json.load(read_in_cached_files)\n except Exception:\n REDIS_DATABASE.hset(analyze_id, 'state',\n AnalyzeStatus.PREANALYZE_FAILED.name)\n LOG.error('Failed to read in skipped file list.')\n\n if cached_files:\n LOG.debug('Restore files from Redis: \\n%s', cached_files.items())\n\n for file_hash in cached_files:\n new_file_path = os.path.join(\n sources_root_path, cached_files[file_hash][1:])\n if not os.path.exists(os.path.dirname(new_file_path)):\n try:\n os.makedirs(os.path.dirname(new_file_path))\n except OSError:\n LOG.error('Failed to create directories.')\n\n with open(new_file_path, 'wb+') as file_to_write_out:\n file_to_write_out.write(REDIS_DATABASE.get(file_hash))\n\n compile_command['directory'] = os.path.join(\n sources_root_path, compile_command['directory'][1:])\n\n if 'command' in compile_command:\n command = compile_command['command']\n\n modified_command = []\n\n # Update existing paths in build command\n for command_part in command:\n modified_command.append(command_part)\n\n # Add missing paths in build command\n paths_of_dependencies = os.path.join(\n sources_root_path, 'paths_of_dependencies.json')\n\n with open(paths_of_dependencies) as paths:\n try:\n path_list = json.load(paths)\n except Exception:\n REDIS_DATABASE.hset(analyze_id, 'state',\n AnalyzeStatus.PREANALYZE_FAILED.name)\n LOG.error('Failed to read in paths of dependencies.')\n\n for path in path_list:\n if 'usr/include' not in path and 'usr/lib' not in path:\n modified_command.append('-I' + sources_root_path + path)\n\n compile_command['command'] = modified_command\n\n # This is needed because tu_collector accepts only 'command'\n # but CodeChecker only works with 'arguments'\n compile_command['arguments'] = compile_command['command']\n\n modified_compile_command = os.path.join(\n sources_root_path, 'modified_compile_command.json')\n\n with open(modified_compile_command, 'w') as modified_cdb:\n modified_cdb.write('[' + json.dumps(compile_command) + ']')\n\n return analyze_dir_path","def control(batch_info):\n \n sbatch, script, cps, mem = batch_info\n qsub_name = qsub_prep(sbatch, script, cps, mem)\n\n \n batch_id = os.popen(\"sbatch \" + qsub_name).read()\n batch_id = batch_id.strip().split()[-1]\n\n \n while True:\n output = os.popen(\"squeue -u koerstz\").read()\n \n if batch_id in output:\n time.sleep(5)\n continue \n \n else:\n break\n\n # create data frame:\n df = pd.read_pickle(sbatch + \".pkl\")\n \n return df","def loadBlockQueue(input_queue, county_fips, config, start_time):\n try:\n temp_time = time.localtime()\n county_counter = 0\n for c in county_fips:\n input_queue.put((c))\n county_counter += 1\n my_message = \"\"\"\n INFO - STEP 0 (MASTER): TASK 6 OF 13 - COMPLETED LOADING INPUT \n QUEUE WITH COUNTY DATA\n \"\"\"\n my_message = ' '.join(my_message.split())\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return True, county_counter\n\n except:\n my_message = \"\"\"\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO LOADING QUEUE WITH\n COUNTY DATA\n \"\"\"\n my_message = ' '.join(my_message.split()) + '\\n' + traceback.format_exc()\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \n time.mktime(time.localtime()) - time.mktime(start_time)))\n return False, None","def _worker(self, task, task_queue):\n with _thread_lock:\n task.process_number = self.counter\n self.counter += 1\n if task.output:\n if not task.has_error and task.processtime > 0:\n if not os.path.isfile(task.logfile):\n task.logfile = \"\"\n task_queue.put(task)\n return\n try:\n if not os.path.exists(task.folder):\n task.add_error('Could not find folder: {}'.format(task.folder))\n task.logfile = \"\"\n else:\n tmp_kwargs = dict(mode='a+', prefix=self.logfile_prefix,\n suffix='.log', dir=task.folder, delete=False)\n with NamedTemporaryFile(**tmp_kwargs) as logfile:\n logfile.write('########### MACRO #############\\n')\n logfile.write(\"\\n\".join(task.macro))\n logfile.write('\\n\\n######### OUTPUT LOG ##########')\n logfile.flush()\n task.logfile = logfile.name\n starttime = time.clock()\n exe_args = dict(macro=task.macro,\n logfile=logfile,\n anybodycon_path=self.anybodycon_path,\n timeout=self.timeout,\n keep_macrofile=self.keep_logfiles,\n env=self.env)\n retcode = _execute_anybodycon(**exe_args)\n endtime = time.clock()\n logfile.seek(0)\n if retcode == _KILLED_BY_ANYPYTOOLS:\n task.processtime = 0\n return\n task.processtime = endtime - starttime\n task.output = parse_anybodycon_output(\n logfile.read(),\n self.ignore_errors,\n self.warnings_to_include)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n task.add_error(str(exc_type) + '\\n' + str(fname) + '\\n' + str(exc_tb.tb_lineno))\n logger.debug(str(e))\n finally:\n if not self.keep_logfiles and not task.has_error:\n try:\n silentremove(logfile.name)\n task.logfile = \"\"\n except OSError as e:\n pass # Ignore if AnyBody has not released the log file.\n task_queue.put(task)","def test_google_storage_post_processing(sdc_builder, sdc_executor, gcp, action):\n pipeline_builder = sdc_builder.get_pipeline_builder()\n\n bucket_name = get_random_string(ascii_lowercase, 10)\n\n storage_client = gcp.storage_client\n\n google_cloud_storage = pipeline_builder.add_stage('Google Cloud Storage', type='origin')\n\n google_cloud_storage.set_attributes(bucket=bucket_name,\n common_prefix='gcs-test',\n prefix_pattern='**/*.txt',\n data_format='TEXT',\n post_processing_option=action)\n wiretap = pipeline_builder.add_wiretap()\n\n google_cloud_storage >> wiretap.destination\n\n pipeline = pipeline_builder.build().configure_for_environment(gcp)\n sdc_executor.add_pipeline(pipeline)\n\n created_bucket = gcp.retry_429(storage_client.create_bucket)(bucket_name)\n try:\n data = [get_random_string(ascii_letters, 100) for _ in range(10)]\n file_path = 'gcs-test/a/b/c/d/e/'\n file_name = 'sdc-test.txt'\n blob = created_bucket.blob(file_path + file_name)\n blob.upload_from_string('\\n'.join(data))\n\n logger.info('Starting GCS Origin pipeline and wait until the information is read ...')\n sdc_executor.start_pipeline(pipeline)\n sdc_executor.wait_for_pipeline_metric(pipeline, 'input_record_count', 10)\n sdc_executor.stop_pipeline(pipeline)\n\n rows_from_wiretap = [record.field['text'] for record in wiretap.output_records]\n\n # If post processing option is enabled, old file path should not exist\n assert not storage_client.get_bucket(bucket_name).blob(file_path + file_name).exists()\n\n # If ARCHIVE, default prefix is empty, so it gets moved to root of bucket\n if action == 'ARCHIVE':\n assert storage_client.get_bucket(bucket_name).blob(file_name).exists()\n\n assert len(data) == len(rows_from_wiretap)\n assert rows_from_wiretap == data\n finally:\n logger.info('Deleting bucket %s ...', created_bucket.name)\n gcp.retry_429(created_bucket.delete)(force=True)","def azure_blob(Config, file_name, dump_path, db): \n azureBlobConf = Config[\"ARCHIVE_AZURE\"] \n account_name = azureBlobConf.get('account_name')\n account_key = azureBlobConf.get('account_key')\n container_name = azureBlobConf.get('container_name') \n \n logging.debug('Initiating archive to Azure blob storage')\n logging.debug(\"Uploading the backup file - '{}'\".format(dump_path))\n \n result = AzureBlobStorage(\n account_name, \n account_key, \n container_name, \n file_name, \n dump_path).upload()\n\n if result == True:\n logging.debug(\"Successfully uploaded the backup '{}' to blob container '{}'\".format(file_name, container_name))\n else:\n logging.error('Upload Failed..!')\n logging.error('Error Message: {}'.format(result))\n notification_slack(Config, db=db, task='Archive', status='Failed')","def finish_stager_tasks(self):\n\n update_files = {}\n messages = []\n while not self.finished_queue.empty():\n file = self.finished_queue.get()\n update_files[file['content_id']] = {'status': ContentStatus.AVAILABLE,\n 'pfn_size': file['pfn_size'],\n 'pfn': file['pfn']}\n msg = {'event_type': 'FILE_AVAILABLE',\n 'payload': {'scope': file['scope'],\n 'name': file['name'],\n 'startEvent': file['min_id'],\n 'lastEvent': file['max_id'],\n 'pfn': file['pfn']},\n 'created_at': date_to_str(datetime.datetime.utcnow())}\n messages.append(msg)\n\n self.logger.info('Got %s staged outputs' % len(update_files))\n update_contents_by_id(update_files)\n\n if self.send_messaging:\n for msg in messages:\n self.messaging_queue.put(msg)","def queue (self):\n\n with self.__t.steps():\n import exception\n from lib import schema\n from lib import common\n from lib import process\n from lib import data\n from sqlalchemy import and_\n import json\n from collections import OrderedDict\n\n with schema.select(\"process_queue\", schema.table.process_queue.status==None) as select:\n for queued in select.limit(1000).all():\n blocked = False\n if len(queued.depend) > 0:\n for depend_id in queued.depend:\n depend = schema.select_one(\"series\", schema.table.series.id==depend_id)\n match_tags = json.dumps(OrderedDict(sorted(data.decode_tags(depend.tags).items())))\n if depend and schema.select_one(\"process_queue\", schema.table.process_queue.tags==match_tags):\n blocked = True\n break # queued dependencies\n if not blocked:\n queued.status = \"queued\"\n schema.save(queued)\n run.apply_async([queued.tags]) #queue process\n self.__t.ok()\n self.apply_async(queue=\"control\", countdown=30) #queue next","def perform_process(transformer: transformer_class.Transformer, check_md: dict) -> dict:\n # Process each CSV file into BETYdb\n start_timestamp = datetime.datetime.now()\n files_count = 0\n files_csv = 0\n lines_read = 0\n error_count = 0\n files_loaded = []\n for one_file in check_md['list_files']():\n files_count += 1\n if os.path.splitext(one_file)[1].lower() == '.csv':\n files_csv += 1\n\n # Make sure we can access the file\n if not os.path.exists(one_file):\n msg = \"Unable to access csv file '%s'\" % one_file\n logging.debug(msg)\n return {'code': -1000,\n 'error': msg}\n\n try:\n # Read in the lines from the file\n with open(one_file, 'r') as in_file:\n reader = csv.DictReader(in_file)\n files_loaded.append(one_file)\n for row in reader:\n centroid_lonlat = [row['lon'], row['lat']]\n time_fmt = row['dp_time']\n timestamp = row['timestamp']\n dp_metadata = {\n \"source\": row['source'],\n \"value\": row['value']\n }\n trait = row['trait']\n\n __internal__.create_datapoint_with_dependencies(transformer.args.clowder_url, transformer.args.clowder_key,\n trait, (centroid_lonlat[1], centroid_lonlat[0]), time_fmt,\n time_fmt, dp_metadata, timestamp)\n lines_read += 1\n\n except Exception:\n logging.exception(\"Error reading CSV file '%s'. Continuing processing\", os.path.basename(one_file))\n error_count += 1\n\n if files_csv <= 0:\n logging.info(\"No CSV files were found in the list of files to process\")\n if error_count > 0:\n logging.error(\"Errors were found during processing\")\n return {'code': -1001, 'error': \"Too many errors occurred during processing. Please correct and try again\"}\n\n return {\n 'code': 0,\n configuration.TRANSFORMER_NAME: {\n 'version': configuration.TRANSFORMER_VERSION,\n 'utc_timestamp': datetime.datetime.utcnow().isoformat(),\n 'processing_time': str(datetime.datetime.now() - start_timestamp),\n 'num_files_received': str(files_count),\n 'num_csv_files': str(files_csv),\n 'lines_loaded': str(lines_read),\n 'files_processed': str(files_loaded)\n }\n }","def main() -> None:\n jobStatus = list()\n adt = AuditManager(\n config.get(\"Audit\").get(\"database\"),\n config.get(\"Audit\").get(\"user\"),\n config.get(\"Audit\").get(\"password\"),\n config.get(\"Audit\").get(\"host\"),\n config.get(\"Audit\").get(\"port\"),\n )\n jobMeta = adt.getStepLogData()\n adt.closeConnection()\n with concurrent.futures.ThreadPoolExecutor(\n max_workers=config.get(\"spark\").get(\"parallelJobs\", 2)\n ) as executor:\n spark_jobs = {\n executor.submit(processFile, fileMeta): fileMeta for fileMeta in jobMeta\n }\n for status in concurrent.futures.as_completed(spark_jobs):\n fileStatus = status.result()\n jobStatus.append(fileStatus)\n logger.info(jobStatus)","def _drain_queue(self):\n while self.queue:\n self._export_batch()","def main():\n #Set up objects\n client = BigQueryClient()\n writer = FileWriter()\n\n #Send data from big query to a given file.\n # 500 is the limit of data points fetched.\n client.produce_json_data(writer, 500)","def push_results_to_file(file_name,queue_name, aux_q,queue_service):\n # verify the queues exist\n queue_service.create_queue(queue_name)\n queue_service.create_queue(aux_q)\n # open file for write\n f1=open('./{0}.csv'.format(file_name), 'w+')\n while queue_service.get_queue_metadata(queue_name).approximate_message_count > 0:\n messages = queue_service.get_messages(queue_name,1)\n if len(messages)>0 :\n for message in messages: \n line = '{0},{1},{2}'.format(message.id,message.insertion_time,message.content)\n queue_service.put_message(aux_q,line)\n f1.write(line)\n f1.write('\\n')\n queue_service.delete_message(queue_name, message.id, message.pop_receipt)\n f1.close()","def test_run(self):\n engine = Engine(self.config_file, self.api_token, 23)\n engine.msg_wait_iterations = 0\n\n # Put some stuff on the task queue\n self.setup_helper.add_volumetric_tasks(self.aws_creds[\"access_key\"],\n self.aws_creds['secret_key'],\n self.upload_queue_url, engine.backend)\n\n engine.join()\n engine.run()\n\n # Check for tile to exist\n s3 = boto3.resource('s3')\n ingest_bucket = s3.Bucket(self.ingest_bucket_name)\n\n with tempfile.NamedTemporaryFile() as test_file:\n with open(test_file.name, 'wb') as raw_data:\n ingest_bucket.download_fileobj(VOLUMETRIC_CUBOID_KEY, raw_data)\n with open(test_file.name, 'rb') as raw_data:\n # Using an empty CloudVolume dataset so all values should be 0.\n # dtype set in boss-v0.2-test.json under chunk_processor.params.info.data_type\n cuboid = self.s3_object_to_cuboid(raw_data.read(), 'uint8')\n unique_vals = np.unique(cuboid)\n assert 1 == len(unique_vals)\n assert 0 == unique_vals[0]","async def main():\n \n # workflow status\n global status\n\n # Mode says which objects must be archived: DB dump, source files or both.\n try:\n mode=sys.argv[1]\n except IndexError:\n mode = 'all'\n\n # queue of files to be archived\n files_to_upload = deque()\n \n logger.trace(\"Archiving ...\")\n # Tasks to archive files and database dump\n list_of_threads = get_list_of_threads(mode=mode)\n\n tar_names = await asyncio.gather(*list_of_threads)\n\n # Clear names list, removing None elements if exist\n tar_names = [name for name in tar_names if name]\n\n files_to_upload.extend(tar_names)\n logger.trace(\"Ok.\")\n\n logger.trace(\"Uploading ...\")\n\n # Connect to the ftp-server and upload the archived files.\n await upload_to_ftp_server(host=FTP.SERVER.value,\n port=FTP.PORT.value,\n login=FTP.LOGIN.value,\n password=FTP.PASSWORD.value,\n files=files_to_upload)\n\n # Remove archived and dump files on the server site.\n clear_garbage(mode=mode, files=tar_names)\n\n # Check the workflow status. If it's not empty, send an error email.\n if len(status) > 0 and ERROR_NOTIFICATION_BY_EMAIL:\n backup_email()","def runQueueEnqueue(self):\n raise NotImplementedError","def _start_input_file_worker(\n q_in: Queue, input_data_path: str, batch_size: int\n) -> None:\n input_data_file = open(input_data_path, \"r\")\n enum_idx = 0\n batch = []\n for line in input_data_file:\n unique_id = str(uuid.uuid1())\n batch.append((enum_idx, unique_id, line))\n # If the batch to send is the size then push to queue and rest batch\n if len(batch) == batch_size:\n q_in.put(batch)\n batch = []\n enum_idx += 1\n if batch:\n q_in.put(batch)","def _work(self, payload):\n pdf_path = payload['pdf_path']\n jpeg_prefixes = payload['jpeg_prefixes']\n\n webhook_url = payload['webhook_url']\n webhook_data = payload['webhook_data']\n\n page_start = payload['page_start']\n page_end = payload['page_end']\n\n self._log('Connecting to s3')\n connection, bucket = self._connect_to_s3(payload['s3'])\n\n # create batches of pages to convert\n for batch_first_page in range(page_start, page_end + 1, self.PAGES_PER_BATCH):\n batch_last_page = min(batch_first_page + self.PAGES_PER_BATCH - 1, page_end)\n batch_pages = range(batch_first_page, batch_last_page + 1)\n\n batch_jpeg_prefixes = jpeg_prefixes[batch_first_page - page_start:\n batch_last_page + 1 - page_start]\n self._convert_batch(bucket, pdf_path, batch_pages, batch_jpeg_prefixes,\n webhook_url, webhook_data)","def main(content):\n # with open(in_path) as f:\n redi = redis.StrictRedis(host=\"10.243.55.67\", port=6379)\n filename = content[0].split('/')[-1]\n file_content = content[1]\n #file_content=content\n # line = f.read()\n # hdfs_client = Client('http://10.243.55.67:50070/', root='/')\n # outpath = os.path.join(\"/bres\", middle_path)\n # l = []\n ret = {}\n ret[\"name\"] = \"\"\n ret[\"alias\"] = \"\"\n # index get id\n index = filename.split('__')[0]\n \n # index = in_path.split(split_char)[0].split('/')[-1]\n ret[\"vid\"] = index\n redis_result = redi.get(str(index)).decode(\"utf-8\").split(\"@@\")\n if len(redis_result) == 1:\n ret[\"name\"] = redis_result[-1]\n elif len(redis_result) == 2:\n ret[\"name\"] = redis_result[0]\n ret['alias'] = redis_result[1]\n try:\n obj = json.loads(file_content, encoding=\"utf8\")\n # url, content, status = obj[\"url\"], obj[\"content\"], obj[\"status\"]\n url, content = obj[\"url\"], obj[\"content\"]\n html_handler = html_parse(content, url)\n handler = BaikeGraph()\n # ret[\"url\"] = url\n content = html_handler.html_clean()\n # parse title tag box\n ret[\"iid\"] = html_handler.parse_itemId()\n #html_handler.create_rel(index,ret[\"iid\"])\n boxes = html_handler.parse_box_new()\n desc = html_handler.parse_desc_new()\n titles = html_handler.parse_title_new()\n tags = html_handler.parse_tag_new()\n dict_final = ChainMap(boxes, desc, ret, titles, tags)\n handler.create_baike_node(dict(dict_final))\n html_handler.parse_polysemantic()\n\n except Exception as e:\n print('exception in main @@@@@@@@@@@@@@@@@2222')\n print(e)\n pass\n return","def main():\n spark = create_spark_session()\n\n input_data = \"s3a://udacitydenanodegree2020/\"\n output_data = \"s3a://udacitydenanodegree2020/output/\"\n\n process_song_data(spark, input_data, output_data) \n process_log_data(spark, input_data, output_data)","def _process_bigquery():\n config_path = os.path.join(app.app.config['DATASET_CONFIG_DIR'],\n 'bigquery.json')\n table_names = []\n participant_id_column = ''\n sample_id_column = ''\n sample_file_columns = []\n if os.path.isfile(config_path):\n bigquery_config = _parse_json_file(config_path)\n table_names = bigquery_config['table_names']\n participant_id_column = bigquery_config['participant_id_column']\n sample_id_column = bigquery_config.get('sample_id_column', '')\n sample_file_columns = bigquery_config.get('sample_file_columns', {})\n table_names.sort()\n\n table_names_dict = OrderedDict()\n for full_table_name in table_names:\n splits = full_table_name.rsplit('.', 2)\n if len(splits) != 3:\n raise ValueError(\n 'Unknown format for table name %s. Expected BigQuery project_id.dataset_id.table_name'\n % full_table_name)\n project_id, dataset_id, table_name = splits\n client = bigquery.Client(project=project_id)\n dataset_ref = client.dataset(dataset_id, project=project_id)\n table_ref = dataset_ref.table(table_name)\n description = client.get_table(table_ref).description\n table_names_dict[full_table_name] = description\n\n # A dict from table name (project_id.dataset_id.table_name) to table description.\n app.app.config['TABLES'] = table_names_dict\n app.app.config['PARTICIPANT_ID_COLUMN'] = participant_id_column\n app.app.config['SAMPLE_ID_COLUMN'] = sample_id_column\n app.app.config['SAMPLE_FILE_COLUMNS'] = sample_file_columns","def main():\n # Initiate Spark Session\n spark = create_spark_session()\n \n # Data files\n # Root Data Path\n # Uncomment below line for AWS S3\n #input_data = \"s3a://udacity-dend\"\n # Uncomment below line for local files\n input_data = \"data\"\n\n # Warehouse\n # Root WH\n # Uncomment below line for AWS S3\n #output_data = \"s3a://jerryespn-project-out\"\n # Uncomment below line for local files\n output_data = \"spark-warehouse\"\n \n process_song_data(spark, input_data, output_data) \n process_log_data(spark, input_data, output_data)","def get_file (queue):\n\n # get event from queue\n event = queue.get(True)\n\n try:\n # get name of new file\n filename = str(event.src_path)\n filetype = 'new'\n\n except AttributeError as e:\n # instead of event, queue entry is a filename added in\n # [run_blackbox]\n filename = event\n filetype = 'pre-existing'\n\n\n log.info ('detected a {} file: {}'.format(filetype, filename))\n\n\n # only continue if a fits file\n if 'fits' not in filename:\n\n log.info ('{} is not a fits file; skipping it'.format(filename))\n filename = None\n\n else:\n\n # if filename is a temporary rsync copy (default\n # behaviour of rsync is to create a temporary file\n # starting with .[filename].[randomstr]; can be\n # changed with option \"--inplace\"), then let filename\n # refer to the eventual file created by rsync\n fn_head, fn_tail = os.path.split(filename)\n if fn_tail[0] == '.':\n filename = '{}/{}'.format(fn_head, '.'\n .join(fn_tail.split('.')[1:-1]))\n log.info ('changed filename from rsync temporary file {} to {}'\n .format(event.src_path, filename))\n\n # this while loop below replaces the old [copying]\n # function; it times out after wait_max is reached\n wait_max = 180\n t0 = time.time()\n nsleep = 0\n while time.time()-t0 < wait_max:\n\n try:\n # read the file\n data = read_hdulist(filename)\n\n except:\n\n process = False\n if nsleep==0:\n log.warning ('file {} has not completely arrived yet; '\n 'will keep trying to read it in for {}s'\n .format(filename, wait_max))\n\n # give file a bit of time to arrive before next read attempt\n time.sleep(5)\n nsleep += 1\n\n else:\n # if fits file was read fine, set process flag to True\n process = True\n log.info ('successfully read file {} within {:.1f}s'\n .format(filename, time.time()-t0))\n # and break out of while loop\n break\n\n\n if not process:\n log.info ('{}s limit for reading file reached, not processing {}'\n .format(wait_max, filename))\n filename = None\n\n\n return filename","def copy_blobs_in_gcp_storage(source_bucket_name):\n\n # extract file and convert in dataframe\n extracted_df = extract()\n\n # transform the df\n transformed_df = transform (extracted_df)\n\n # the function loads clean csv content as csv file in clean-zone-bucket\n load(transformed_df)\n\n\n return \"Function executed sucessfully!\"","def create_mask_affinity_map_tasks(task_queue, aff_input_layer_path, aff_output_layer_path, \n aff_mip, mask_layer_path, mask_mip, output_block_start, output_block_size, grid_size ):\n for z in tqdm(range(grid_size[0]), desc='z loop'):\n for y in range(grid_size[1]):\n for x in range(grid_size[2]):\n output_bounds = Bbox.from_slices(tuple(slice(s+x*b, s+x*b+b)\n for (s, x, b) in zip(output_block_start, (z, y, x), output_block_size)))\n task = MaskAffinitymapTask(\n aff_input_layer_path=aff_input_layer_path,\n aff_output_layer_path=aff_output_layer_path,\n aff_mip=aff_mip, \n mask_layer_path=mask_layer_path,\n mask_mip=mask_mip,\n output_bounds=output_bounds,\n )\n task_queue.insert(task)\n task_queue.wait()\n\n vol = CloudVolume(output_layer_path, mip=aff_mip)\n vol.provenance.processing.append({\n 'method': {\n 'task': 'InferenceTask',\n 'aff_input_layer_path': aff_input_layer_path,\n 'aff_output_layer_path': aff_output_layer_path,\n 'aff_mip': aff_mip,\n 'mask_layer_path': mask_layer_path,\n 'mask_mip': mask_mip,\n 'output_block_start': output_block_start,\n 'output_block_size': output_block_size, \n 'grid_size': grid_size,\n },\n 'by': OPERATOR_CONTACT,\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\n })\n vol.commit_provenance()","def _download_job_proecess(channel_id, file_info, report_queue, options):\n\n publisher_id = uuid.uuid4().hex\n download_dir = options.get('download_dir', '.')\n saving_file_name_type = options.get('file_name_type')\n skip_duplicate = options.get('skip_duplicate', False)\n\n file_id = file_info.get('file_id')\n file_meta = file_info.get('metadata', {})\n download_uri = file_info.get('download_uri')\n\n if saving_file_name_type == 'id':\n file_name = file_id\n else:\n file_name = file_meta.get('x-abeja-meta-filename') or file_id\n\n download_path = None\n is_downloading = True\n try:\n # download file content\n with generate_retry_session() as session:\n download_stream_res = session.get(\n download_uri, stream=True, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\n # update pre-signed url if expired\n if download_stream_res.status_code == 403:\n url = \"{}/channels/{}/{}\".format(ABEJA_API_URL,\n channel_id, file_id)\n with generate_user_session() as user_session:\n res = user_session.get(\n url, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\n res.raise_for_status()\n file_info = res.json()\n download_uri = file_info.get('download_uri')\n with generate_retry_session() as session:\n download_stream_res = session.get(\n download_uri, stream=True, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\n download_stream_res.raise_for_status()\n total_size = int(download_stream_res.headers.get('content-length', 0))\n\n initialize_options = {\n 'file_name': file_name,\n 'total': total_size,\n }\n report_queue.put(\n (INITIALIZE_REPORT, publisher_id, 0, initialize_options))\n download_path = _get_default_file_path(download_dir, file_name)\n result_options = {\n 'source': file_id,\n 'destination': download_path,\n }\n if os.path.exists(download_path):\n if skip_duplicate:\n report_queue.put(\n (SKIP_REPORT, publisher_id, 0, result_options))\n return\n else:\n # update destination path ot resolve conflict\n result_options['destination'] = _resolve_file_path(\n download_dir, file_name)\n is_downloading = True\n with open(download_path, 'wb') as f:\n for chunk in download_stream_res.iter_content(chunk_size=HTTP_READ_CHUNK_SIZE):\n # update tqdm progress bar with chunk data size\n report_queue.put(\n (PROGRESS_REPORT, publisher_id, len(chunk), None))\n f.write(chunk)\n\n report_queue.put((FINISH_REPORT, publisher_id, 0, result_options))\n except:\n if is_downloading and download_path is not None and os.path.exists(download_path):\n os.remove(download_path)\n options = {\n 'source': file_id,\n 'error': 'Failed to download {} of channel_id {}'.format(file_id, channel_id)\n }\n report_queue.put((RAISE_ERROR, publisher_id, 0, options))\n raise","def multiprocessing_job(self, job_function: Callable[['Task.Task'], 'Task.Task']=None) -> None:\n\n if job_function is None:\n raise ValueError(\"Job Function object not provided.\")\n while True:\n try:\n task = self.input_queue.get(timeout=1)\n if task:\n '''\n Based on how this should work, job_function must take\n in a Submission object as its sole parameter, and\n return a Submission object. To avoid major overhauls,\n it's best to keep the '*_job' functions mostly the same,\n but changing the function parametes and what it returns.\n '''\n if self.logger.getEffectiveLevel() == logging.DEBUG:\n self.logger.debug(f\"\\nTask Received: {task._to_string()}\\n\")\n else:\n self.logger.info(f\"\\nTask Received: {task.get_job_id()} containing file {task.get_file_name()}\\n\")\n try:\n # Push the name of the manager and its current jobs.\n if self.slack_queue:\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \"Pending\", 1]\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\n # Perform the job function on the task\n task = job_function(task)\n # Upon success, push information to the Slack Bot (if enabled)\n if self.slack_queue:\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \"Complete\", 2]\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\n # Log the task completion\n self.logger.info(f\"\\nTask Completed: {task.get_job_id()} containing file {task.get_file_name()}\\n\")\n # Place the task in the output queue\n self.output_queue.put(task)\n # Mark the task as finished via task_done\n self.input_queue.task_done()\n # Take a short breather and let the CPU focus on other stuff.\n sleep(1)\n except KeyboardInterrupt as k:\n if self.logger.getEffectiveLevel() == logging.DEBUG:\n self.logger.exception(msg=f\"Task {task.job_id} encountered an exception in job {job_function.__name__} due to SIGINT event.\\n\")\n else:\n self.logger.error(msg=f\"\\nTask {task.job_id} encountered an exception in job {job_function.__name__}\\nError Message: {k}\\n\")\n # If any errors that can be caught occur\n except BaseException as e:\n # Log the event with varying degrees of information\n if self.logger.getEffectiveLevel() == logging.DEBUG:\n self.logger.exception(msg=f\"Task {task.job_id} encountered an exception in job {job_function.__name__}\\nTask Information:\\n{task._to_string()}\")\n else:\n self.logger.error(msg=f\"\\nTask {task.job_id} encountered an exception in job {job_function.__name__}\\nError Message: {e}\\n\")\n # Inform the submitter via Slack Notification (not Slack Bot)\n # If the Slack Bot is enabled pass the error information to it\n if self.slack_queue:\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \"Error\", 2]\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\n # Place the task in the error queue\n self.error_queue.put(task)\n # Mark the task as finished via task_done\n self.input_queue.task_done()\n else:\n #Media was None signal (this kills the Process)\n self.input_queue.task_done()\n break\n except ValueError:\n break\n # If the worker does not receive anything it will throw this exception, which will kill the process.\n except queue.Empty:\n self.logger.debug(f\"\\nA process has enountered the Queue.Empty exception indicating there are no more things to process.\")\n break","def reaper(self):\n if not self.superuser_request:\n self.abort(402, 'uploads must be from an authorized drone')\n with tempfile.TemporaryDirectory(prefix='.tmp', dir=config.get_item('persistent', 'data_path')) as tempdir_path:\n try:\n file_store = files.FileStore(self.request, tempdir_path)\n except files.FileStoreException as e:\n self.abort(400, str(e))\n now = datetime.datetime.utcnow()\n fileinfo = dict(\n name=file_store.filename,\n created=now,\n modified=now,\n size=file_store.size,\n hash=file_store.hash,\n tags=file_store.tags,\n metadata=file_store.metadata\n )\n container = reaperutil.create_container_hierarchy(file_store.metadata)\n f = container.find(file_store.filename)\n target_path = os.path.join(config.get_item('persistent', 'data_path'), util.path_from_hash(fileinfo['hash']))\n if not f:\n file_store.move_file(target_path)\n container.add_file(fileinfo)\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\n elif not file_store.identical(util.path_from_hash(fileinfo['hash']), f['hash']):\n file_store.move_file(target_path)\n container.update_file(fileinfo)\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\n throughput = file_store.size / file_store.duration.total_seconds()\n log.info('Received %s [%s, %s/s] from %s' % (file_store.filename, util.hrsize(file_store.size), util.hrsize(throughput), self.request.client_addr))","def main():\n spark = create_spark_session()\n\n input_data = config['STORAGE']['INPUT_DATA']\n output_data = config['STORAGE']['OUTPUT_DATA']\n\n process_song_data(spark, input_data, output_data)\n process_log_data(spark, input_data, output_data)","def handleDQMFileSaver(self):\n if not hasattr(self.process, \"dqmSaver\"):\n return\n\n runIsComplete = getattr(self.jobBag, \"runIsComplete\", False)\n multiRun = getattr(self.jobBag, \"multiRun\", False)\n runLimits = getattr(self.jobBag, \"runLimits\", \"\")\n self.logger.info(\"DQMFileSaver set to multiRun: %s, runIsComplete: %s, runLimits: %s\",\n multiRun, runIsComplete, runLimits)\n\n self.process.dqmSaver.runIsComplete = cms.untracked.bool(runIsComplete)\n if multiRun and isCMSSWSupported(self.getCmsswVersion(), \"CMSSW_8_0_0\"):\n self.process.dqmSaver.forceRunNumber = cms.untracked.int32(999999)\n if hasattr(self.step.data.application.configuration, \"pickledarguments\"):\n args = pickle.loads(self.step.data.application.configuration.pickledarguments)\n datasetName = args.get('datasetName', None)\n if datasetName:\n if multiRun:\n # then change the dataset name in order to get a different root file name\n datasetName = datasetName.rsplit('/', 1)\n datasetName[0] += runLimits\n datasetName = \"/\".join(datasetName)\n self.process.dqmSaver.workflow = cms.untracked.string(datasetName)\n return","def main():\n\n\n\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\n\n # fetch and sort the .mnc and .tag files\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\n tag_files = [f for f in skulls_folder if 'tag' in f]\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n \n mnc_files.sort()\n tag_files.sort()\n mnc_names.sort()\n\n # Process and package ndarrays as tuples inside npy file\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\n \n print('\\n' * 5)\n\n # Push the npy files to GCP Cloud Storage\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)","def main():\n spark = create_spark_session()\n input_data = \"s3a://udacity-dend/\"\n output_data = \"data/analytics\"\n \n process_song_data(spark, input_data, output_data) \n process_log_data(spark, input_data, output_data)","def main():\n spark = create_spark_session()\n input_data = \"s3a://udacity-dend/\"\n output_data = \"s3a://udacity-data-lake/output/\"\n\n process_song_data(spark, input_data, output_data)\n process_log_data(spark, input_data, output_data)","async def main():\n\n usage = \"usage: prog [options] <intelligence_query/local_file_with_hashes>\"\n parser = argparse.ArgumentParser(\n usage=usage,\n description=(\n \"Allows you to download the top-n files returned by a given\"\n \"VirusTotal Intelligence search. Example: \"\n 'python %prog type:\"peexe\" positives:5+ -n 10 --apikey=<your api key>'\n ),\n )\n\n parser.add_argument(\n \"query\",\n type=str,\n nargs=\"+\",\n help=\"a VirusTotal Intelligence search query.\",\n )\n\n parser.add_argument(\n \"-n\",\n \"--numfiles\",\n dest=\"numfiles\",\n default=100,\n help=\"Number of files to download\",\n )\n\n parser.add_argument(\"--apikey\", required=True, help=\"Your VirusTotal API key\")\n\n parser.add_argument(\n \"-o\",\n \"--output-path\",\n required=False,\n help=\"The path where you want to put the files in\",\n )\n\n parser.add_argument(\n \"-w\",\n \"--workers\",\n dest=\"workers\",\n default=4,\n help=\"Concurrent workers for downloading files\",\n )\n\n args = parser.parse_args()\n\n if not args.query:\n parser.error(\"No search query provided\")\n\n if not args.apikey:\n parser.error(\"No API key provided\")\n\n search = \" \".join(args.query)\n search = search.strip().strip(\"'\")\n storage_path = args.output_path\n numfiles = int(args.numfiles)\n workers = int(args.workers)\n api_key = args.apikey\n handler = DownloadTopNFilesHandler(api_key, numfiles)\n\n logging.info(\"Starting VirusTotal Intelligence downloader\")\n logging.info(\"* VirusTotal Intelligence search: %s\", search)\n logging.info(\"* Number of files to download: %s\", numfiles)\n\n files_path = handler.create_download_folder(storage_path)\n enqueue_files_task = asyncio.create_task(handler.queue_file_hashes(search))\n\n download_tasks = []\n for _ in range(workers):\n download_tasks.append(\n asyncio.create_task(handler.download_files(files_path))\n )\n\n await asyncio.gather(enqueue_files_task)\n # Wait until all the files have been queued and downloaded, then cancel\n # download tasks that are idle\n await handler.queue.join()","def fastq_qc(demultiplex_result, out_dir, config):\n\n pigz_cores = int(config['fastqTrim']['pigz_cores'])\n cutadapt_cores = int(config['fastqTrim']['cutadapt_cores'])\n\n r1_adapter = config['fastqTrim']['r1_adapter']\n r2_adapter = config['fastqTrim']['r1_adapter']\n length_threshold = config['fastqTrim']['length_threshold']\n quality_threshold = config['fastqTrim']['quality_threshold']\n r1_left_cut = config['fastqTrim']['r1_left_cut']\n r1_right_cut = config['fastqTrim']['r1_right_cut']\n r2_left_cut = config['fastqTrim']['r2_left_cut']\n r2_right_cut = config['fastqTrim']['r2_right_cut']\n overlap = config['fastqTrim']['overlap']\n total_reads_threshold = int(config['fastqTrim']['total_reads_threshold'])\n\n results = []\n for (uid, index_name), sub_df in demultiplex_result.groupby(['uid', 'index_name']):\n sample_demultiplex_total = sub_df['Trimmed'].sum()\n if sample_demultiplex_total < total_reads_threshold:\n log.info(f'In uid {uid}: index {index_name} skipped '\n f'due to too less reads: {sample_demultiplex_total}')\n continue\n # process R1\n r1_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R1.fq.gz'\n r1_out = f'{out_dir}/{uid}_{index_name}_R1.trimed.fq.gz'\n r1_cmd = f'pigz -cd -p {pigz_cores} {r1_path_pattern} | ' \\\n f'cutadapt -j {cutadapt_cores} --report=minimal -O {overlap} ' \\\n f'-q {quality_threshold} -u {r1_left_cut} ' \\\n f'-u -{r1_right_cut} -m {length_threshold} ' \\\n f'-a {r1_adapter} -o {r1_out} -'\n r1_result = subprocess.run(r1_cmd, stdout=subprocess.PIPE,\n encoding='utf8', shell=True, check=True)\n\n # get R1 result stat\n lines = []\n for line in r1_result.stdout.split('\\n'):\n ll = line.split('\\t')\n if len(ll) > 1:\n lines.append(ll)\n s = pd.Series({name: number for name, number in zip(*lines)})\n s['uid'] = uid\n s['index_name'] = index_name\n s['read_type'] = 'R1'\n results.append(s)\n\n # process R2\n r2_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R2.fq.gz'\n r2_out = f'{out_dir}/{uid}_{index_name}_R2.trimed.fq.gz'\n r2_cmd = f'pigz -cd -p {pigz_cores} {r2_path_pattern} | ' \\\n f'cutadapt -j {cutadapt_cores} --report=minimal -O {overlap} ' \\\n f'-q {quality_threshold} -u {r2_left_cut} ' \\\n f'-u -{r2_right_cut} -m {length_threshold} ' \\\n f'-a {r2_adapter} -o {r2_out} -'\n r2_result = subprocess.run(r2_cmd, stdout=subprocess.PIPE,\n encoding='utf8', shell=True, check=True)\n # get R2 result stat\n lines = []\n for line in r2_result.stdout.split('\\n'):\n ll = line.split('\\t')\n if len(ll) > 1:\n lines.append(ll)\n s = pd.Series({name: number for name, number in zip(*lines)})\n s['uid'] = uid\n s['index_name'] = index_name\n s['read_type'] = 'R2'\n results.append(s)\n\n fastq_final_result = pd.DataFrame(results)\n if len(results) == 0:\n # all sample skipped\n return fastq_final_result\n fastq_final_result['out_reads_rate'] = \\\n fastq_final_result['out_reads'].astype(int) / fastq_final_result['in_reads'].astype(int)\n fastq_final_result['out_bp_rate'] = \\\n fastq_final_result['out_reads'].astype(int) / fastq_final_result['in_reads'].astype(int)\n\n # clean up\n for (uid, index_name), sub_df in demultiplex_result.groupby(['uid', 'index_name']):\n r_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R*.fq.gz'\n r_rm_cmd = f'rm -f {r_path_pattern}'\n subprocess.run(r_rm_cmd, shell=True)\n # remove unknown reads\n r_path_pattern = f'{out_dir}/{uid}_L*_unknown_R*.fq.gz'\n r_rm_cmd = f'rm -f {r_path_pattern}'\n subprocess.run(r_rm_cmd, shell=True)\n\n return fastq_final_result","def image_resize(job_object):\n try:\n job = json.loads(job_object.arg)\n base64_file = job['image']\n args = job['args'] if 'args' in job else {}\n del job['image']\n logging.info(job)\n \n def write_file(local_path,filename,file_b64):\n logging.debug(\"about to save to \" + \"%s/%s\" % (local_path,filename))\n if not os.path.exists(local_path): os.makedirs(local_path)\n image_file = base64.b64decode(file_b64)\n local_file = open(\"%s/%s\" % (local_path,filename), \"w\")\n local_file.write(image_file)\n local_file.close()\n \n def download_file(url,local_path,filename):\n print \"downloading \" + url\n f = urllib2.urlopen(urllib2.Request(url))\n print \"about to save to \" + \"%s/%s\" % (local_path,filename)\n if not os.path.exists(local_path): os.makedirs(local_path)\n # Open our local file for writing\n local_file = open(\"%s/%s\" % (local_path,filename), \"w\")\n local_file.write(f.read())\n local_file.close()\n \n local_path = '%s/upload/%s' % (options.asset_root,job['path'])\n local_path_wfile = '%s/%s%s' % (local_path,job['file'],job['extension'])\n filename = '%s%s' % (job['file'],job['extension'])\n #download_file(job['url'],local_path,filename)\n write_file(local_path,filename,base64_file)\n \n def resize_and_save(local_file,new_file,maxsize=None,maxh=None,maxw=None,crop=None):\n \"\"\"Resize the image and save\"\"\"\n logging.debug(\"maxw = %s, maxsize=%s, crop=%s\" % (maxw,maxsize,crop))\n img = Image.open(local_file)\n width,height = img.size\n width,height = float(width), float(height)\n ratio = float(1)\n if crop is not None:\n size = float(maxsize)\n if width <= height and width > size:\n ratio = size/width\n elif height < width and height > size:\n ratio = size/height\n else: \n ratio = 1 # too small\n elif maxsize:\n size = float(maxsize)\n if width >= height and width > size:\n ratio = size/width\n elif height > width and height > size:\n ratio = size/height\n else: \n ratio = 1 # too small\n elif maxh:\n size = maxh\n if height > size:\n ratio = size/height\n else:\n # too small\n ratio = 1\n elif maxw:\n size = maxw\n if width > size:\n ratio = size/width\n else:\n # too small\n ratio = 1\n else:\n raise Exception(\"must specify max width, OR max size\")\n \n print(\"old: ratio = %s: size(x,y) = %s,%s\" % (ratio,width,height))\n height = int(height*ratio)\n width = int(width*ratio)\n print(\"new ratio = %s: size(x,y) = %s,%s\" % (ratio,width,height))\n img = img.resize((width, height),Image.ANTIALIAS)\n if crop is not None:\n log.debug(\"in crop %s\" % crop)\n crop = int(crop)\n if width > crop:\n amt = int((int(width) - crop)/2)\n img = img.crop((amt,0,amt + crop, crop))\n elif height > crop:\n amt = int((int(height) - crop)/2)\n img = img.crop((0,amt,crop,amt+crop))\n \n log.debug(\"saving new file %s\" % new_file)\n if img.mode != \"RGB\":\n img = img.convert(\"RGB\")\n img.save(new_file)\n \n \n if os.path.exists(local_path_wfile):\n if args != {}:\n ext = args['extension'] if 'extension' in args else \"_t\"\n resize_and_save(local_path_wfile,\n '%s/%s%s.jpg' % (local_path,job['file'],ext),\n maxsize=args['maxsize'],\n crop=args['crop'])\n else:\n resize_and_save(local_path_wfile,'%s/%s_t.jpg' % (local_path,job['file']),maxsize=100)\n resize_and_save(local_path_wfile,'%s/%s_m.jpg' % (local_path,job['file']),maxw=317)\n resize_and_save(local_path_wfile,'%s/%s_l.jpg' % (local_path,job['file']),maxsize=800)\n keeptrying = False\n else:\n logging.error(\"haven't found file? %s\" % local_path_wfile)\n \n # delete original\n logging.debug(\"About to delete original %s\" % local_path_wfile)\n os.remove(local_path_wfile)\n \n except:\n traceback.print_exc()","def prepare_reader(self, unused_filename_queue):\n raise NotImplementedError()","def run(self):\r\n filesizes = {}\r\n # Build up dict with key as filesize and value is list of filenames.\r\n for path, dirs, files in walk( self._path ):\r\n for filename in files:\r\n filepath = joinpath( path, filename )\r\n filesize = stat( filepath ).st_size\r\n filesizes.setdefault( filesize, [] ).append( filepath )\r\n\r\n\r\n #Compare content hash of all files which have the same size\r\n #if two or more files have same hash and size they are added to the queue \r\n for files in [ flist for flist in filesizes.values() if len(flist)>1 ]:\r\n #run over all files in dir with the same size if there is more then one\r\n duplicates = {}\r\n for filepath in files:\r\n with open( filepath ) as openfile:\r\n filehash = md5( openfile.read() ).hexdigest()\r\n if filehash not in duplicates:\r\n duplicates.setdefault(filehash, []).append (filepath)\r\n else:\r\n duplicates[filehash].append(filepath)\r\n for duplicate in [ duplicate for duplicate in duplicates.values() if len(duplicate)>1 ]:\r\n self._queue.put(duplicate)\r\n self._finished_scan[0] = 1","def test_ClearOldFile(self):\n q = Queue(self.path, chunksize=10)\n for i in range(15):\n q.put('var1')\n\n for i in range(11):\n q.get()\n\n q = Queue(self.path, chunksize=10)\n self.assertEqual(q.qsize(), 15)\n\n for i in range(11):\n q.get()\n q.task_done()\n self.assertEqual(q.qsize(), 4)","def process_single_file( name, \n jobStrList, \n ccu, \n flagSilent=False ):\n\n startTime = time.time()\n\n ret = STAT_OK\n\n s = \"%s: %s -> %s. %s \" % ( name, jobStrList[0], jobStrList[1], MSG_DELIMITER )\n\n cprint(\"%s. \" % (jobStrList[0]), flagSilent)\n\n # Upload file.\n try:\n # Get the blob even it is not exist.\n bcu = ccu.get_blob_client(blob=jobStrList[1])\n \n # bIdx = barrierUpload.wait()\n # if ( 0 == bIdx ):\n # barrierUpload.reset()\n \n fp = open( jobStrList[0], \"rb\" )\n\n # print(\"%s: Before upload. \" % (name))\n bcu.upload_blob(fp, blob_type=\"BlockBlob\", overwrite=True)\n # print(\"%s: After upload. \" % (name))\n except OSError as ex:\n s = s + \"Exeption: %s. %s \" % ( str(ex), MSG_DELIMITER )\n ret = STAT_FAIL\n except Exception as ex:\n s = s + \"Exeption: %s. File closed. %s \" % ( str(ex), MSG_DELIMITER )\n ret = STAT_FAIL\n fp.close()\n else:\n fp.close()\n\n endTime = time.time()\n \n s = s + \"%ds for processing. %s \" % (endTime - startTime, MSG_DELIMITER )\n\n cprint(\"%s: %s\" % (name, s), flagSilent)\n\n return [ret, s]","def run_job(self):\n try:\n info(\"fetching testing invoice data from get_data_after_rescoring_flag_check function\")\n testing_data = self.get_data_after_rescoring_flag_check()\n self.common_util.dataframe_empty_check(testing_data)\n info(\"geting and processing data from customer history\")\n customer_history_data_query = self.query.get_customer_history_data_query()\n customer_history_data = self.database.get_data_from_database(customer_history_data_query)\n self.common_util.dataframe_empty_check(testing_data)\n self.common_util.dataframe_empty_check(customer_history_data)\n model_name = self.common_util.load_last_model_name('SC_LR')\n info(\"inferencing started\")\n predicted_data = self.common_util.get_selfcure_lr_prediction(testing_data, customer_history_data, 'SC_LR')\n info(\"storing prediction to the table\")\n self.database.save_prediction_to_table(predicted_data, model_name)\n info(\"Appling P flag for all predcitited invoice\")\n self.database.update_processed_flag_for_open_invoice(predicted_data)\n except Exception as err:\n error('Exception Occured while running inferencingJob job :' + str(err))\n raise Exception('Exception Occured while running inferencingJob job : {}'.format(str(err)))","def process_results(refresh_count, output_dir, ext_queue, result_queue,\n num_of_workers=8):\n workers_dict = {} # keep track of worker processes\n input_queue = Queue() # asynchronously feed workers task to do \n worker_output_queue = Queue() # output queue from workers\n ack_queue = Queue()\n bug_dict = {} # dict to keep track of how many duplicates of each bug, if\n # exists\n try:\n # separate the non-ads from the ads for ease of handchecking\n os.makedirs(output_dir)\n os.makedirs(os.path.join(output_dir, 'notad'))\n except OSError:\n # Directory is created, Okay to pass\n pass\n\n for i in range(num_of_workers):\n p = Process(target=curl_worker, args=(output_dir, input_queue,\\\n worker_output_queue, i, ack_queue))\n p.start()\n workers_dict[i] = p\n # uses a pool nodesurl' workers\n # curl_worker_pool = Pool(processes=8)\n # manager = Manager()\n # curl_result_queue = manager.Queue()\n \n dl_counter = 0 # keep track of how many bugs downloaded\n while True:\n try:\n found_bugs = json.loads(ext_queue.get(block=True, timeout=2))\n except Exception:\n LOG.debug('No more bugs found, break out of queue')\n break\n\n for entry in found_bugs:\n bug = parse_buginfo(entry)\n try:\n # matched an entry in the bugdict, incr count and continue\n bug_dict[bug] += 1\n continue\n except KeyError:\n bug_dict[bug] = 1 \n\n try:\n saved_location ='Visit%d_%s%d' % (refresh_count, bug.get_name(), dl_counter)\n dl_counter += 1\n save_to_path = os.path.join( output_dir, '%s' % saved_location)\n input_queue.put((saved_location, save_to_path, bug))\n except Exception as e:\n LOG.exception('%s' % e)\n\n for i in range(num_of_workers):\n # send stop signal\n input_queue.put((\"STOP\",))\n \n stopped = 0\n while stopped < len(workers_dict):\n ack = ack_queue.get()\n p = workers_dict[ack]\n p.join(timeout=1)\n if p.is_alive():\n p.terminate()\n LOG.debug('terminating process %d' % ack)\n stopped += 1\n \n while not worker_output_queue.empty():\n # receive results from the worker\n cbug = worker_output_queue.get()\n # ugly code here\n bugcount = bug_dict[cbug]\n del bug_dict[cbug]\n bug_dict[cbug] = bugcount\n\n with open( os.path.join(output_dir, 'bug_dict%d.pkl' % refresh_count), 'w') as fwtr:\n cPickle.dump(bug_dict, fwtr)\n result_queue.put(bug_dict)\n return","def teardown(self):\n #save the HDF output and upload it to the data service with all the meta data\n self.bqSession.update_mex( 'Returning results')\n log.debug('Storing HDF output')\n\n #constructing and storing HDF file\n mex_id = self.bqSession.mex.uri.split('/')[-1]\n dt = datetime.now().strftime('%Y%m%dT%H%M%S')\n final_output_file = \"ModuleExecutions/Dream3D/%s_%s_%s.h5\"%(self.options.OutputPrefix, dt, mex_id)\n\n #does not accept no name on the resource\n cl_model = etree.Element('resource', resource_type='table', name=final_output_file)\n\n #module identifier (a descriptor to be found by the Dream3D model)\n etree.SubElement(cl_model, 'tag', name='module_identifier', value='Dream3D')\n\n #hdf filename\n etree.SubElement(cl_model, 'tag', name='OutputFile', value=final_output_file)\n\n #pipeline param\n #etree.SubElement(cl_model, 'tag', name='RefFrameZDir', value=self.options.RefFrameZDir)\n\n #input hdf url\n #etree.SubElement(cl_model, 'tag', name='InputFile', type='link', value=self.options.InputFile)\n\n #input pipeline\n #etree.SubElement(cl_model, 'tag', name='pipeline_url', type='link', value=self.options.pipeline_url)\n\n #description\n etree.SubElement(cl_model, 'tag', name='description', value = 'HDF output file from Dream3D Module')\n\n #storing the HDF file in blobservice\n log.debug('before postblob') #!!!\n r = self.bqSession.postblob(self.output_file, xml = cl_model)\n r_xml = etree.fromstring(r)\n\n outputTag = etree.Element('tag', name ='outputs')\n etree.SubElement(outputTag, 'tag', name='output_hdf', type='table', value=r_xml[0].get('uri',''))\n\n self.bqSession.finish_mex(tags=[outputTag])","async def consume_mult(queue, fpath):\n while True:\n if not queue.qsize():\n await asyncio.sleep(0.001)\n\n else:\n qitem = queue.get_nowait()\n try:\n jlqitem = json.loads(qitem)\n\n print('got', jlqitem)\n with open(fpath, 'a') as f:\n f.write(f\"{json.dumps(jlqitem)}\\n\")\n except Exception as exc:\n print(exc.__repr__())\n print('nope', qitem)\n\n queue.task_done()\n await asyncio.sleep(0.001)","def single_inference_process_fn(inference_initializer, inference_mode_config, in_project_meta_json, request_queue,\n result_meta_queue, progress_queue, project):\n single_image_inference = inference_initializer()\n inference_mode = InferenceModeFactory.create(\n inference_mode_config, ProjectMeta.from_json(in_project_meta_json), single_image_inference)\n\n project_meta_sent = False\n req = ''\n while req is not None:\n req = request_queue.get()\n if req is not None:\n # Send the resulting project meta to the parent project to make sure we only write the meta JSON once.\n if not project_meta_sent:\n try:\n result_meta_queue.put(inference_mode.out_meta.to_json(), block=False)\n except queue.Full:\n pass\n project_meta_sent = True\n\n in_ann = Annotation.load_json_file(req.item_paths.ann_path, inference_mode.out_meta)\n ann = inference_mode.infer_annotate_image_file(req.item_paths.img_path, in_ann)\n out_dataset = project.datasets.get(req.ds_name)\n out_dataset.add_item_file(\n req.item_name, req.item_paths.img_path, ann=ann, _validate_item=False, _use_hardlink=True)\n progress_queue.put(1)","def test_4_empty_fastq(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, os.pardir, \"110221_empty_FC12345AAXX\"),\n os.path.join(data_dir, \"run_info-empty.yaml\")]\n subprocess.check_call(cl)","def process():\n print(OPTS)\n\n # Remove dataset files if they exist from before\n p = PATH.proc\n if p.exists():\n shutil.rmtree(p)\n p.mkdir()\n\n with multiprocessing.Pool(OPTS['cpus']) as pool:\n chunks = [0, 1] if OPTS['dev'] else range(100)\n partition_paths = pool.map(_process, chunks)\n\n fastparquet.writer.merge(partition_paths)","def setup(self, context):\n myindex = context.get_partition_index()\n self._files_to_consume = self._files[myindex::context.get_num_partitions()]\n self.logger.info(\"TextFileSpout files to consume %s\" % self._files_to_consume)\n self._lines_to_consume = self._get_next_lines()\n self._emit_count = 0","def _worker():\n try:\n logger.info('Looping...')\n temp_list = []\n for file in ['data_unfcc.csv','data_ebal.csv']:\n temp_list.append(os.path.isfile(file))\n if not all(temp_list):\n print('Starting from scratch...')\n download_source()\n create_database()\n create_index()\n\n time_mod = datetime.strptime(time.ctime(os.stat('data_ebal.csv').st_mtime),'%a %b %d %H:%M:%S %Y')\n time_now = datetime.now()\n\n if (time_now - time_mod).seconds > 3600:\n download_source()\n get_updated_records('unfcc')\n get_updated_records('ebal')\n create_index()\n except Exception as e:\n logger.warning('Main Loop error')","def main():\n #global comm, rank, args\n #comm = MPI.COMM_WORLD\n #rank = comm.Get_rank()\n rank = 0\n\n parser = argparse.ArgumentParser(description=\"Receive data and dispatch\" +\n \"analysis tasks to a mpi queue\")\n parser.add_argument('--config', type=str, help='Lists the configuration file',\n default='configs/config-middle.json')\n parser.add_argument(\"--transport_rx\", help=\"Specifies the name of the transport section that is used to configure the reader\",\n default=\"transport_rx\")\n parser.add_argument(\"--transport_tx\", help=\"Specifies the name of the transport section that is used to configure the writer\",\n default=\"transport_tx\")\n args = parser.parse_args()\n\n with open(args.config, \"r\") as df:\n cfg = json.load(df)\n timeout = 120\n\n # The middleman uses both a reader and a writer. Each is configured with using\n # their respective section of the config file. Therefore some keys are duplicated,\n # such as channel_range. Make sure that these items are the same in both sections\n\n with open(\"configs/logger.yaml\", \"r\") as f:\n log_cfg = yaml.safe_load(f.read())\n logging.config.dictConfig(log_cfg)\n logger = logging.getLogger('middleman')\n\n # Create ADIOS reader object\n reader = reader_gen(cfg[args.transport_rx], gen_channel_name(cfg[\"diagnostic\"]))\n reader.Open()\n stream_attrs = None\n stream_varname = gen_var_name(cfg)[rank]\n logger.info(f\"Main: Stream varname: {stream_varname}\")\n\n dq = queue.Queue()\n msg = None\n worker = threading.Thread(target=forward, args=(dq, cfg, args, timeout))\n worker.start()\n\n rx_list = []\n stream_data = None\n while True:\n stepStatus = reader.BeginStep()\n logger.info(f\"Main: stepStatus = {stepStatus}, currentStep = {reader.CurrentStep()}\")\n if stepStatus:\n # Read data\n stream_data = reader.Get(stream_varname, save=False)\n if stream_attrs is None:\n if reader.InquireAttribute(\"stream_attrs\"):\n stream_attrs = reader.get_attrs(\"stream_attrs\")\n\n stream_data = reader.Get(stream_varname, save=False)\n rx_list.append(reader.CurrentStep())\n\n # Generate message id and publish is\n msg = AdiosMessage(tstep_idx=reader.CurrentStep(), data=stream_data, attrs=stream_attrs)\n dq.put_nowait(msg)\n logger.info(f\"Main: Published message {msg}\")\n reader.EndStep()\n else:\n logger.info(f\"Main: Exiting: StepStatus={stepStatus}\")\n break\n\n # last_step = reader.CurrentStep()\n\n logger.info(\"Main: Exiting main loop\")\n worker.join()\n logger.info(\"Main: Workers have joined\")\n dq.join()\n logger.info(\"Main: Queue joined\")\n logger.info(\"Main: Finished\")","def process(self, element, bucket, ingestFrom, all_edits):\n if isinstance(element, basestring):\n chunk_name = element\n page_ids = None\n else:\n # If specified on selected pages.\n (chunk_name, data) = element\n if data['datadump'] == []:\n logging.info('USERLOG: chunk %s skipped.', chunk_name)\n return\n page_ids = data['selected_pages']\n logging.info('USERLOG: Running ingestion process on %s.', chunk_name)\n if ingestFrom == 'local':\n input_stream = chunk_name\n else:\n cmd = \"gsutil -m cp {path} {chunk}\".format(filepath=path.join('gs://', bucket, chunk_name), chunk=chunk_name)\n status = os.WEXITSTATUS(os.system(cmd))\n if status != 0:\n raise Exception(\"GSUTIL COPY Error, exited with status {}\".format(status))\n input_stream = chunk_name\n # Running ingestion on the xml file\n last_revision = None\n last_completed = time.time()\n i = 0\n for i, content in enumerate(parse_stream(bz2.BZ2File(chunk_name))):\n if (page_ids is not None) and (content['page_id'] not in page_ids):\n continue\n self.processed_revisions.inc()\n if content['text'] is None:\n content['text'] = \"\"\n if (content[\"comment\"] is not None and \"POV\" in content[\"comment\"]) or all_edits:\n yield json.dumps((last_revision, content))\n last_revision = content\n logging.info('INGESTION_LOG: CHUNK %(chunk): revision %(revid) ingested, time elapsed: %(time).',\n extra={'chunk':chunk_name, 'revid':content['rev_id'], 'time':time.time() - last_completed))\n last_completed = time.time()\n if ingestFrom != 'local': os.system(\"rm %s\" % chunk_name)\n logging.info('USERLOG: Ingestion on file %(chunk) complete! %(cnt) lines emitted',\n extra={'chunk':chunk_name, 'cnt':i})","def main():\n gcs_client = storage.Client()\n bucket_name: str = os.environ.get(\"GCS_BUCKET_NAME\")\n blob_name: str = os.environ.get(\"GCS_BLOB_NAME\")\n\n print(f\"Uploading BigEarth to bucket {bucket_name} and blob {blob_name}\")\n\n for url, blob_name in [\n (\"http://bigearth.net/downloads/BigEarthNet-v1.0.tar.gz\", blob_name),\n (\"http://bigearth.net/static/documents/patches_with_cloud_and_shadow.csv\",\n \"patches_with_cloud_and_shadow.csv\"),\n (\"http://bigearth.net/static/documents/patches_with_snow.csv\", \"patches_with_seasonal_snow.csv\"),\n ]:\n with GCSObjectStreamUploader(client=gcs_client, bucket_name=bucket_name, blob_name=blob_name) as gcs_uploader:\n with requests.get(url, stream=True) as response_stream:\n for chunk in response_stream.raw.stream(128*2000, decode_content=False):\n gcs_uploader.write(chunk)","def submit_command_to_queue(command, queue=None, max_jobs_in_queue=None, queue_file=None, queue_parameters={'max_mem':5000, 'queue':'short', 'logs_dir':'/tmp', 'modules':['Python/3.6.2']}, dummy_dir=\"/tmp\", script_name=None):\n #if max_jobs_in_queue is not None:\n # while number_of_jobs_in_queue() >= max_jobs_in_queue: time.sleep(5)\n\n if not os.path.exists(dummy_dir): os.makedirs(dummy_dir)\n if not script_name:\n script_name = \"submit_\"+hashlib.sha224(command).hexdigest()+\".sh\"\n script= os.path.join(dummy_dir, script_name)\n if queue_file is not None:\n # Use a queue file as header of the bash file\n fd=open(script,\"w\")\n with open(queue_file,\"r\") as queue_standard:\n data=queue_standard.read()\n fd.write(data)\n fd.write(\"%s\\n\\n\"%(command))\n fd.close()\n queue_standard.close()\n if queue is not None:\n os.system(\"sbatch -p %s %s\" % (queue,script))\n else:\n os.system(\"sbatch %s\"% (script))\n else:\n if queue_parameters is not None:\n # Write manually the bash file\n with open(script, \"w\") as fd:\n fd.write('#!/bin/bash\\n') # bash file header\n fd.write('#SBATCH --mem={}\\n'.format(queue_parameters['max_mem'])) # max memory\n fd.write('#SBATCH -p {}\\n'.format(queue_parameters['queue'])) # queue name\n fd.write('#SBATCH -o {}.out\\n'.format(os.path.join(queue_parameters['logs_dir'], script_name))) # standard output\n fd.write('#SBATCH -e {}.err\\n'.format(os.path.join(queue_parameters['logs_dir'], script_name))) # standard error\n for module in queue_parameters['modules']:\n fd.write('module load {}\\n'.format(module)) # modules to load\n fd.write('{}\\n'.format(command)) # command\n os.system(\"sbatch {}\".format(script)) # execute bash file\n else:\n # Execute directly the command without bash file\n if queue is not None:\n os.system(\"echo \\\"%s\\\" | sbatch -p %s\" % (command, queue))\n else:\n os.system(\"echo \\\"%s\\\" | sbatch\" % command)","def execute_augmentation(queue_images: Queue, progress: tqdm, output: str, factor: int) -> None:\n while not queue_images.empty():\n element = queue_images.get(block=False)\n augmentation(element, output, factor)\n progress.update(1)","def handle_delf_init(self, job):\n\n # Check which, if any, servers the file exists on\n self.put_job_in_all_queues(job)\n list_job_results = self.get_internal_results_from_all_servers()\n if len(list_job_results) == 0:\n # There were no servers active\n self.put_external_result(self.generate_failure_job(\"Unsuccessful, no servers running\"))\n return\n\n return_result = copy.deepcopy(list_job_results[0])\n return_result.result[\"file_exists\"] = False\n\n for result in list_job_results:\n if result.result[\"file_exists\"]:\n return_result.result[\"file_exists\"] = True\n\n return_result.result[\"outcome\"] = \"success\"\n return_result.processed_by = None\n\n self.put_external_result(return_result)","def get_fastq_info(output_dir, flowcell_id, storage_directory):\n filenames = os.listdir(output_dir)\n\n # Filter for gzipped fastq files\n filenames = filter(lambda x: \".fastq.gz\" in x, filenames)\n\n # Remove undetermined fastqs\n filenames = filter(lambda x: \"Undetermined\" not in x, filenames)\n\n # Check that the path actually has fastq files\n if not filenames:\n raise Exception(\"no fastq files in output directory {}\".format(output_dir))\n\n # Cell info keyed by dlp library id\n cell_info = {}\n\n # Fastq filenames and info keyed by fastq id, read end\n fastq_file_info = []\n\n for filename in filenames:\n match = re.match(\n r\"^(\\w+)-(\\w+)-R(\\d+)-C(\\d+)_S(\\d+)(_L(\\d+))?_R([12])_001.fastq.gz$\",\n filename,\n )\n\n if match is None:\n raise Exception(\n \"unrecognized fastq filename structure for {}\".format(filename)\n )\n\n filename_fields = match.groups()\n\n # primary_sample_id = filename_fields[0]\n library_id = filename_fields[1]\n row = int(filename_fields[2])\n column = int(filename_fields[3])\n lane_number = filename_fields[6]\n if lane_number is not None:\n lane_number = int(lane_number)\n read_end = int(filename_fields[7])\n\n if library_id not in cell_info:\n cell_info[library_id] = query_colossus_dlp_cell_info(library_id)\n\n index_sequence = cell_info[library_id][row, column][\"index_sequence\"]\n sample_id = cell_info[library_id][row, column][\"sample_id\"]\n\n fastq_path = os.path.join(output_dir, filename)\n\n if not fastq_path.startswith(storage_directory):\n raise Exception(\n \"file {} expected in directory {}\".format(fastq_path, storage_directory)\n )\n fastq_filename = fastq_path.replace(storage_directory, \"\")\n fastq_filename = filename.lstrip(\"/\")\n\n fastq_file_info.append(\n dict(\n dataset_type=\"FQ\",\n sample_id=sample_id,\n library_id=library_id,\n library_type=BRC_LIBRARY_TYPE,\n index_format=BRC_INDEX_FORMAT,\n sequence_lanes=[\n dict(\n flowcell_id=flowcell_id,\n lane_number=lane_number,\n sequencing_centre=BRC_SEQ_CENTRE,\n sequencing_instrument=BRC_INSTRUMENT,\n read_type=BRC_READ_TYPE,\n )\n ],\n size=os.path.getsize(fastq_path),\n created=pd.Timestamp(\n time.ctime(os.path.getmtime(fastq_path)), tz=\"Canada/Pacific\"\n ),\n file_type=\"FQ\",\n read_end=read_end,\n index_sequence=index_sequence,\n compression=\"GZIP\",\n filename=fastq_filename,\n )\n )\n\n return fastq_file_info","def __init__(self,\r\n tensorflow_session: tf.compat.v1.Session,\r\n data_format: str = 'NHWC',\r\n batch_size: int = 32,\r\n num_threads: int = max(4, multiprocessing.cpu_count()),\r\n min_after_dequeue: int = 1000,\r\n fread_queue_capacity: int = 0,\r\n preprocess_queue_capacity: int = 0,\r\n staging=False,\r\n shuffle=None,\r\n testing=False,\r\n ):\r\n assert tensorflow_session is not None and isinstance(tensorflow_session, tf.Session)\r\n assert isinstance(batch_size, int) and batch_size > 0\r\n if shuffle is None:\r\n shuffle = staging\r\n self.testing = testing\r\n if testing:\r\n assert not shuffle and not staging\r\n # if num_threads != 1:\r\n # logger.info('Forcing use of single thread for live testing.')\r\n # num_threads = 1\r\n self.staging = staging\r\n self.shuffle = shuffle\r\n self.data_format = data_format.upper()\r\n assert self.data_format == 'NHWC' or self.data_format == 'NCHW'\r\n self.batch_size = batch_size\r\n self.num_threads = num_threads\r\n self._tensorflow_session = tensorflow_session\r\n self._coordinator = tf.train.Coordinator()\r\n self.all_threads = []\r\n\r\n # Setup file read queue\r\n self._fread_queue_capacity = fread_queue_capacity\r\n if self._fread_queue_capacity == 0:\r\n self._fread_queue_capacity = (num_threads + 1) * batch_size\r\n self._fread_queue = queue.Queue(maxsize=self._fread_queue_capacity)\r\n\r\n with tf.variable_scope(''.join(c for c in self.short_name if c.isalnum())):\r\n # Setup preprocess queue\r\n labels, dtypes, shapes = self._determine_dtypes_and_shapes()\r\n self._preprocess_queue_capacity = (min_after_dequeue + (num_threads + 1) * batch_size\r\n if preprocess_queue_capacity == 0\r\n else preprocess_queue_capacity)\r\n if shuffle:\r\n self._preprocess_queue = tf.RandomShuffleQueue(\r\n capacity=self._preprocess_queue_capacity,\r\n min_after_dequeue=min_after_dequeue,\r\n dtypes=dtypes, shapes=shapes,\r\n )\r\n else:\r\n self._preprocess_queue = tf.FIFOQueue(\r\n capacity=self._preprocess_queue_capacity,\r\n dtypes=dtypes, shapes=shapes,\r\n )\r\n self._tensors_to_enqueue = OrderedDict([\r\n (label, tf.placeholder(dtype, shape=shape, name=label))\r\n for label, dtype, shape in zip(labels, dtypes, shapes)\r\n ])\r\n\r\n self._enqueue_op = \\\r\n self._preprocess_queue.enqueue(tuple(self._tensors_to_enqueue.values()))\r\n self._preprocess_queue_close_op = \\\r\n self._preprocess_queue.close(cancel_pending_enqueues=True)\r\n self._preprocess_queue_size_op = self._preprocess_queue.size()\r\n self._preprocess_queue_clear_op = \\\r\n self._preprocess_queue.dequeue_up_to(self._preprocess_queue.size())\r\n \r\n if not staging:\r\n output_tensors = self._preprocess_queue.dequeue_many(self.batch_size)\r\n if not isinstance(output_tensors, list):\r\n output_tensors = [output_tensors]\r\n self._output_tensors = dict([\r\n (label, tensor) for label, tensor in zip(labels, output_tensors)\r\n ])\r\n else:\r\n # Setup on-GPU staging area\r\n self._staging_area = tf.contrib.staging.StagingArea(\r\n dtypes=dtypes,\r\n shapes=[tuple([batch_size] + list(shape)) for shape in shapes],\r\n capacity=1, # This does not have to be high\r\n )\r\n self._staging_area_put_op = \\\r\n self._staging_area.put(self._preprocess_queue.dequeue_many(batch_size))\r\n self._staging_area_clear_op = self._staging_area.clear()\r\n\r\n self._output_tensors = dict([\r\n (label, tensor) for label, tensor in zip(labels, self._staging_area.get())\r\n ])\r\n \r\n logger.info('Initialized data source: \"%s\"' % self.short_name)"],"string":"[\n \"def generate_metadata_queue_messages(event_time: str, metadata_file_content: str) -> List[str]:\\n ingest_queue_msg_list = []\\n current_part_num = 100000 #Max part number\\n global MAX_COMPACT_FILE_RECORDS\\n\\n for line in reversed(metadata_file_content.splitlines()):\\n logging.info(f\\\"{HEADER} Processing metadata line content: {line}\\\")\\n if not is_json(line):\\n logging.info(f\\\"{HEADER} Skip non JSON line content: {line}\\\")\\n continue\\n\\n pnum = get_part_number(line)\\n\\n if pnum > current_part_num:\\n break # Reached files in previous batch, stop parsing\\n\\n current_part_num = pnum\\n\\n split_output_file_json = json.loads(line)\\n output_abfss_path = split_output_file_json[\\\"path\\\"]\\n output_file_size = split_output_file_json[\\\"size\\\"]\\n output_file_modification_time = split_output_file_json[\\\"modificationTime\\\"]\\n\\n try:\\n https_url = convert_abfss_path_to_https(output_abfss_path)\\n except Exception: # pylint: disable=bare-except\\n logging.warning(f\\\"{HEADER} Skip invalid abfss path {output_abfss_path}\\\", exc_info=True)\\n continue\\n\\n queue_msg = INGEST_QUEUE_MSG_TEMPLATE.format(blob_size=output_file_size,\\n blob_url=https_url,\\n event_time=event_time,\\n modification_time=output_file_modification_time)\\n minify_msg = json.dumps(json.loads(queue_msg))\\n ingest_queue_msg_list.append(minify_msg)\\n\\n MAX_COMPACT_FILE_RECORDS = max(len(ingest_queue_msg_list), MAX_COMPACT_FILE_RECORDS)\\n return ingest_queue_msg_list\",\n \"def queueLoader(input_queue, blockm_df, config, start_time):\\n continue_run = True\\n try: \\n # create the column names that will go in each numprov file\\n column_names = [t for t in (''.join(tech) \\\\\\n for tech in powerset(config['techlist']))]\\n except:\\n print('ERROR - STEP 2 (MASTER): FAILED CALLING POWERSET')\\n print(traceback.format_exc())\\n return False, None \\n \\n # build the data strings that will go into the queue\\n if continue_run:\\n # initialize the list that holds the paths to all of the temporary area \\n # tables\\n append_list = []\\n try:\\n temp_time = time.localtime()\\n for i in range(len(config['speedList'])):\\n numprov_file_path = config['temp_csvs_dir_path']\\\\\\n + 'block_numprov/block_numprov_%s_%s.csv'\\\\\\n % (config['speedList'][i], config['fbd_vintage'])\\n numprov_zero_file_path = config['temp_csvs_dir_path']\\\\\\n +'block_numprov/block_numprov_%s_with_zero_%s.csv'\\\\\\n % (config['speedList'][i], config['fbd_vintage'])\\n temp_area_table_file_path = config['output_dir_path']\\\\\\n +'area_table_%s.csv' % (config['speedList'][i])\\n workerSpeed = config['speedList'][i]\\n fbd_df = config['temp_pickles'] + 'enhanced_fbd_df.pkl'\\n d_speed, u_speed = config['down_speed'][i], config['up_speed'][i]\\n \\n # insert the information into the queue\\n temp_tuple = (numprov_file_path, numprov_zero_file_path, \\n temp_area_table_file_path, workerSpeed, d_speed, \\n u_speed, column_names, fbd_df, blockm_df, start_time)\\n input_queue.put(temp_tuple) \\n append_list.append(temp_area_table_file_path)\\n\\n my_message = \\\"\\\"\\\"\\n INFO - STEP 2 (MASTER): TASK 4 of 5 - COMPLETED LOADING THE QUEUE TO MAKE BLOCK\\n NUMPROV FILES,\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n print(nbmf.logMessage(my_message, temp_time, time.localtime(),\\n time.mktime(time.localtime())-time.mktime(start_time)))\\n return True, append_list\\n\\n except:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 2 (MASTER): TASK 4 of 5 - FAILED LOADING THE QUEUE TO MAKE BLOCK\\n NUMPROV FILES,\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n my_message += '\\\\n' + traceback.format_exc()\\n print(nbmf.logMessage(my_message, temp_time, time.localtime(),\\n time.mktime(time.localtime())-time.mktime(start_time)))\\n return False, None\",\n \"def breakOutBlockData(input_queue, output_queue, message_queue, config, \\n db_config, start_time):\\n\\n try:\\n temp_time = time.localtime()\\n continue_run, county_fips = getCounty_fips(config, start_time)\\n\\n if continue_run:\\n continue_run = changeTogeom(db_config, config, start_time)\\n\\n if continue_run:\\n continue_run = makeBlockTablePickle(config, db_config, start_time)\\n\\n if continue_run:\\n for _ in range(config['number_servers']):\\n message_queue.put('parse_blockdf')\\n\\n continue_run, county_counter = loadBlockQueue(input_queue, county_fips, \\n config, start_time)\\n\\n if continue_run:\\n continue_run = s0f.processWork(config, input_queue, output_queue, \\n county_counter, start_time)\\n\\n if continue_run:\\n continue_run = changeToGEOMETRY(config, db_config, start_time)\\n \\n if continue_run:\\n my_message = \\\"\\\"\\\"\\n INFO - STEP 0 (MASTER): TASK 6 OF 13 - COMPLETED CREATING COUNTY\\n LEVEL GEOJSON BLOCK FILES\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time))) \\n gc.collect() \\n return True\\n \\n else:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO CREATE COUNTY\\n LEVEL GEOJSON BLOCK FILES\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split()) + '\\\\n' + traceback.format_exc()\\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return False \\n\\n except:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO CREATE COUNTY\\n LEVEL GEOJSON BLOCK FILES\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split()) + '\\\\n' + traceback.format_exc()\\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return False\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--process_queue\\\", action='store_true',\\n dest=\\\"process_queue\\\",\\n help=\\\"Process also schedd queue (Running/Idle/Pending jobs)\\\")\\n parser.add_argument(\\\"--feed_es\\\", action='store_true',\\n dest=\\\"feed_es\\\",\\n help=\\\"Feed to Elasticsearch\\\")\\n parser.add_argument(\\\"--feed_es_for_queues\\\", action='store_true',\\n dest=\\\"feed_es_for_queues\\\",\\n help=\\\"Feed queue data also to Elasticsearch\\\")\\n parser.add_argument(\\\"--feed_amq\\\", action='store_true',\\n dest=\\\"feed_amq\\\",\\n help=\\\"Feed to CERN AMQ\\\")\\n\\n parser.add_argument(\\\"--schedd_filter\\\", default='',\\n type=str, dest=\\\"schedd_filter\\\",\\n help=(\\\"Comma separated list of schedd names to process \\\"\\n \\\"[default is to process all]\\\"))\\n parser.add_argument(\\\"--skip_history\\\", action='store_true',\\n dest=\\\"skip_history\\\",\\n help=\\\"Skip processing the history. (Only do queues.)\\\")\\n parser.add_argument(\\\"--read_only\\\", action='store_true',\\n dest=\\\"read_only\\\",\\n help=\\\"Only read the info, don't submit it.\\\")\\n parser.add_argument(\\\"--dry_run\\\", action='store_true',\\n dest=\\\"dry_run\\\",\\n help=(\\\"Don't even read info, just pretend to. (Still \\\"\\n \\\"query the collector for the schedd's though.)\\\"))\\n parser.add_argument(\\\"--keep_full_queue_data\\\", action='store_true',\\n dest=\\\"keep_full_queue_data\\\",\\n help=\\\"Drop all but some fields for running jobs.\\\")\\n parser.add_argument(\\\"--amq_bunch_size\\\", default=5000,\\n type=int, dest=\\\"amq_bunch_size\\\",\\n help=(\\\"Send docs to AMQ in bunches of this number \\\"\\n \\\"[default: %(default)d]\\\"))\\n parser.add_argument(\\\"--es_bunch_size\\\", default=250,\\n type=int, dest=\\\"es_bunch_size\\\",\\n help=(\\\"Send docs to ES in bunches of this number \\\"\\n \\\"[default: %(default)d]\\\"))\\n parser.add_argument(\\\"--query_queue_batch_size\\\", default=50,\\n type=int, dest=\\\"query_queue_batch_size\\\",\\n help=(\\\"Send docs to listener in batches of this number \\\"\\n \\\"[default: %(default)d]\\\"))\\n parser.add_argument(\\\"--upload_pool_size\\\", default=8,\\n type=int, dest=\\\"upload_pool_size\\\",\\n help=(\\\"Number of parallel processes for uploading \\\"\\n \\\"[default: %(default)d]\\\"))\\n parser.add_argument(\\\"--query_pool_size\\\", default=8,\\n type=int, dest=\\\"query_pool_size\\\",\\n help=(\\\"Number of parallel processes for querying \\\"\\n \\\"[default: %(default)d]\\\"))\\n\\n parser.add_argument(\\\"--es_hostname\\\", default='es-cms.cern.ch',\\n type=str, dest=\\\"es_hostname\\\",\\n help=\\\"Hostname of the elasticsearch instance to be used \\\"\\n \\\"[default: %(default)s]\\\")\\n parser.add_argument(\\\"--es_port\\\", default=9203,\\n type=int, dest=\\\"es_port\\\",\\n help=\\\"Port of the elasticsearch instance to be used \\\"\\n \\\"[default: %(default)d]\\\")\\n parser.add_argument(\\\"--es_index_template\\\", default='cms',\\n type=str, dest=\\\"es_index_template\\\",\\n help=(\\\"Trunk of index pattern. \\\"\\n \\\"Needs to start with 'cms' \\\"\\n \\\"[default: %(default)s]\\\"))\\n parser.add_argument(\\\"--log_dir\\\", default='log/',\\n type=str, dest=\\\"log_dir\\\",\\n help=\\\"Directory for logging information [default: %(default)s]\\\")\\n parser.add_argument(\\\"--log_level\\\", default='WARNING',\\n type=str, dest=\\\"log_level\\\",\\n help=\\\"Log level (CRITICAL/ERROR/WARNING/INFO/DEBUG) \\\"\\n \\\"[default: %(default)s]\\\")\\n parser.add_argument(\\\"--email_alerts\\\", default=[], action='append',\\n dest=\\\"email_alerts\\\",\\n help=\\\"Email addresses for alerts [default: none]\\\")\\n\\n args = parser.parse_args()\\n set_up_logging(args)\\n\\n # --dry_run implies read_only\\n args.read_only = args.read_only or args.dry_run\\n\\n main_driver(args)\",\n \"def gen_metadata_msg_enqueue_tasks(queue_msg_list: List[str],\\n queue_client_list: List[QueueClient],\\n tc: TelemetryClient) -> None:\\n\\n tasks = []\\n for idx, queue_msg in enumerate(queue_msg_list):\\n output_obj = json.loads(queue_msg)\\n\\n queue_index = idx % len(queue_client_list)\\n logging.debug(\\n f\\\"{HEADER} Try to send message to ingest queue {queue_index}, queue_msg: {queue_msg}\\\")\\n\\n base64_message = base64.b64encode(queue_msg.encode('ascii')).decode('ascii')\\n\\n file_url = output_obj['data']['url']\\n size = int(output_obj['data']['contentLength'])\\n\\n tc.track_event(METADATA_HANDLE_EVENT_NAME,\\n {'FILE_URL': file_url},\\n {METADATA_HANDLE_EVENT_NAME + '_SIZE': size,\\n METADATA_HANDLE_EVENT_NAME + '_COUNT': 1})\\n\\n # round robin to enqueue message\\n task = asyncio.ensure_future(send_queue_messages(\\n queue_client_list[queue_index], base64_message, queue_msg))\\n tasks.append(task)\\n tc.flush()\\n return tasks\",\n \"def __init__(\\n self,\\n dag_id: str = DAG_ID,\\n start_date: pendulum.DateTime = pendulum.datetime(2018, 5, 14),\\n schedule_interval: str = \\\"@weekly\\\",\\n dataset_id: str = \\\"crossref\\\",\\n dataset_description: str = \\\"The Crossref Events dataset: https://www.eventdata.crossref.org/guide/\\\",\\n queue: str = \\\"remote_queue\\\",\\n merge_partition_field: str = \\\"id\\\",\\n schema_folder: str = default_schema_folder(),\\n batch_load: bool = True,\\n airflow_vars: List = None,\\n mailto: str = \\\"aniek.roelofs@curtin.edu.au\\\",\\n max_threads: int = min(32, os.cpu_count() + 4),\\n max_processes: int = os.cpu_count(),\\n ):\\n\\n if airflow_vars is None:\\n airflow_vars = [\\n AirflowVars.DATA_PATH,\\n AirflowVars.PROJECT_ID,\\n AirflowVars.DATA_LOCATION,\\n AirflowVars.DOWNLOAD_BUCKET,\\n AirflowVars.TRANSFORM_BUCKET,\\n ]\\n super().__init__(\\n dag_id,\\n start_date,\\n schedule_interval,\\n dataset_id,\\n merge_partition_field,\\n schema_folder,\\n dataset_description=dataset_description,\\n queue=queue,\\n batch_load=batch_load,\\n airflow_vars=airflow_vars,\\n load_bigquery_table_kwargs={\\\"ignore_unknown_values\\\": True},\\n )\\n self.mailto = mailto\\n self.max_threads = max_threads\\n self.max_processes = max_processes\\n\\n self.add_setup_task(self.check_dependencies)\\n self.add_task_chain(\\n [self.download, self.upload_downloaded, self.transform, self.upload_transformed, self.bq_load_partition]\\n )\\n self.add_task_chain([self.bq_delete_old, self.bq_append_new, self.cleanup], trigger_rule=\\\"none_failed\\\")\",\n \"def run(known_args, pipeline_args):\\n\\n pipeline_args.extend([\\n '--runner=DataflowRunner',\\n '--project=wikidetox-viz',\\n '--staging_location=gs://wikidetox-viz-dataflow/staging',\\n '--temp_location=gs://wikidetox-viz-dataflow/tmp',\\n '--job_name=yiqing-ingest-job-truncated-content-run-in-batch',\\n '--num_workers=90',\\n ])\\n\\n pipeline_options = PipelineOptions(pipeline_args)\\n pipeline_options.view_as(SetupOptions).save_main_session = True\\n if not(known_args.batchno == None):\\n known_args.input = 'gs://wikidetox-viz-dataflow/input_lists/7z_file_list_batched_%d'%(known_args.batchno)\\n known_args.table = 'wikidetox-viz:wikidetox_conversations.ingested_conversations_batch_%d'%(known_args.batchno) \\n print('Running batch %d'%(known_args.batchno)) \\n with beam.Pipeline(options=pipeline_options) as p:\\n pcoll = (p | ReadFromText(known_args.input)\\n | beam.ParDo(WriteDecompressedFile())\\n | beam.io.Write(bigquery.BigQuerySink(known_args.table, schema=known_args.schema, validate = True)))\",\n \"def makeLargeTracts(input_queue, output_queue, config, db_config):\\n\\n \\n # capture the process name\\n my_name = mp.current_process().name\\n my_ip_address = socket.gethostbyname(socket.gethostname())\\n\\n while True:\\n try:\\n # get the next element out of the queue\\n inputs = input_queue.get()\\n try:\\n if inputs[0] is None: break\\n\\n # extract the terms from the queue list\\n numprov_path = inputs[0] \\n blockm_df = inputs[1] \\n out_tract_path = inputs[2] \\n out_county_path = inputs[3] \\n out_tract_df = inputs[4]\\n out_county_df = inputs[5] \\n start_time = inputs[6] \\n worker_speed = inputs[7]\\n config = inputs[8]\\n geom = 'geoid%s' % config['census_vintage'][2:]\\n\\n continue_run, block_numprov = openNumprovFile(numprov_path, geom, \\n my_name, my_ip_address, worker_speed, \\n start_time, output_queue)\\n\\n if continue_run:\\n continue_run, block_numprov = mergeWithDataFrame(\\n block_numprov, blockm_df, geom, my_name, \\n my_ip_address, worker_speed, start_time, \\n output_queue) \\n\\n if continue_run:\\n for geo in ['tract', 'county']:\\n continue_run, out_df = findPerCapitaProviders(my_name, \\n my_ip_address, geo, block_numprov, \\n output_queue, start_time, config, \\n worker_speed, eval('out_%s_df' % geo))\\n \\n if continue_run:\\n continue_run = outputGeoData(out_df, \\n eval('out_%s_path' % geo), my_name, \\n my_ip_address, geo, worker_speed, \\n start_time, output_queue)\\n\\n except:\\n pass\\n\\n except:\\n # nothing in the queue, wait and check again\\n time.sleep(1)\\n\\n return True\",\n \"def Flush(self, state):\\n self.bigquery = bigquery.GetBigQueryClient()\\n # BigQuery job ids must be alphanum plus dash and underscore.\\n urn_str = rdfvalue.RDFURN(self.source_urn).RelativeName(\\\"aff4:/\\\").replace(\\n \\\"/\\\", \\\"_\\\").replace(\\\":\\\", \\\"\\\").replace(\\\".\\\", \\\"-\\\")\\n\\n for tracker in self.temp_output_trackers.values():\\n # Close out the gzip handle and pass the original file handle to the\\n # bigquery client so it sees the gzip'd content.\\n tracker.gzip_filehandle.write(b\\\"\\\\n\\\")\\n tracker.gzip_filehandle.close()\\n tracker.gzip_filehandle_parent.seek(0)\\n\\n # e.g. job_id: hunts_HFFE1D044_Results_ExportedFile_1446056474\\n job_id = \\\"{0}_{1}_{2}\\\".format(\\n urn_str, tracker.output_type,\\n rdfvalue.RDFDatetime.Now().AsSecondsSinceEpoch())\\n\\n # If we have a job id stored, that means we failed last time. Re-use the\\n # job id and append to the same file if it continues to fail. This avoids\\n # writing many files on failure.\\n if tracker.output_type in self.output_jobids:\\n job_id = self.output_jobids[tracker.output_type]\\n else:\\n self.output_jobids[tracker.output_type] = job_id\\n\\n if (state.failure_count + self.failure_count >=\\n config.CONFIG[\\\"BigQuery.max_upload_failures\\\"]):\\n logging.error(\\n \\\"Exceeded BigQuery.max_upload_failures for %s, giving up.\\\",\\n self.source_urn)\\n else:\\n try:\\n self.bigquery.InsertData(tracker.output_type,\\n tracker.gzip_filehandle_parent,\\n tracker.schema, job_id)\\n self.failure_count = max(0, self.failure_count - 1)\\n del self.output_jobids[tracker.output_type]\\n except bigquery.BigQueryJobUploadError:\\n self.failure_count += 1\\n\\n # Now that everything is in bigquery we can remove the output streams\\n self.temp_output_trackers = {}\",\n \"def main_process_function(project_id, config_file, retention, backup_type, expiration):\\n print(\\\"Running bigquery dataset export for project:{}\\\".format(project_id))\\n # Reading backup-parameters from json config\\n with open(config_file) as f:\\n master_config = json.load(f)\\n backup_config = master_config[\\\"backup\\\"]\\n\\n location = backup_config[\\\"location\\\"]\\n schema_path = backup_config[\\\"schema_uri\\\"]\\n table_path = backup_config[\\\"table_uri\\\"]\\n project_backup_config = backup_config[\\\"projects_dict\\\"][project_id]\\n mapped_list = []\\n\\n # Get timestamp\\n timestamp = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n\\n # Creating Big Query Client\\n client = bigquery.Client(project=project_id)\\n\\n # Getting mapped relation between datasets and their tables\\n if backup_type == \\\"all\\\":\\n # Get all datasets\\n datasets = list_all_datasets(client=client)\\n # Map dataset->[tables]\\n dataset_tables_map = get_datasets_tables_dict(\\n client=client, project_id=project_id, datasets=datasets\\n )\\n mapped_list.append(dataset_tables_map)\\n elif backup_type == \\\"config\\\":\\n # Extract the backup pattern from config\\n backup_pattern = project_backup_config[\\\"backup_pattern\\\"]\\n for key, value in backup_pattern.items():\\n dataset_tables_map = {}\\n if value == \\\"all\\\":\\n # Map dataset->[tables]\\n dataset_tables_map = get_datasets_tables_dict(\\n client=client, project_id=project_id, datasets=[key]\\n )\\n mapped_list.append(dataset_tables_map)\\n else:\\n # Map dataset->[tables]\\n dataset_tables_map[key] = value\\n mapped_list.append(dataset_tables_map)\\n else:\\n print(\\n \\\"Please provide a valid backup_type option. Choose from ['all', 'config']\\\"\\n )\\n return None\\n\\n # Performing dataset export to gcs (data, schema)\\n if mapped_list:\\n for datasets_tables_dict in mapped_list:\\n for bq_dataset_name in datasets_tables_dict.keys():\\n print(\\\"Backup Operation on dataset: {}\\\".format(bq_dataset_name))\\n for bq_table_name in datasets_tables_dict[bq_dataset_name]:\\n print(\\\"Backing up table: {}\\\".format(bq_table_name))\\n try:\\n # Getting dataset and table objects\\n dataset_ref = bigquery.DatasetReference(\\n project_id, bq_dataset_name\\n )\\n table_ref = dataset_ref.table(bq_table_name)\\n table_obj = client.get_table(table_ref)\\n\\n # Specifying extract-job parameters\\n gcs_table_path = table_path.format(\\n bucket_name=project_backup_config[\\\"bucket_name\\\"],\\n retention=retention,\\n dataset_name=bq_dataset_name,\\n timestamp=timestamp,\\n table_file_name=bq_table_name + \\\"-*.json\\\",\\n )\\n job_config = bigquery.ExtractJobConfig()\\n job_config.compression = bigquery.Compression.GZIP\\n job_config.destination_format = (\\n bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON\\n )\\n\\n # Exporting table-data to gcs\\n extract_job = client.extract_table(\\n table_ref,\\n gcs_table_path,\\n job_config=job_config,\\n location=location,\\n )\\n extract_job.result()\\n\\n # Extracting table-schema\\n table_schema = table_obj.schema\\n table_schema = [\\n {\\n \\\"name\\\": item.name,\\n \\\"mode\\\": item.mode,\\n \\\"type\\\": item.field_type,\\n }\\n for item in table_schema\\n ]\\n json_schema = json.dumps(table_schema)\\n\\n # Defining schema-path\\n gcs_schema_path = schema_path.format(\\n bucket_name=project_backup_config[\\\"bucket_name\\\"],\\n retention=retention,\\n dataset_name=bq_dataset_name,\\n timestamp=timestamp,\\n schema_file_name=bq_table_name + \\\"-schema.json\\\",\\n )\\n\\n # Writing table-schema to gcs\\n sa_credentials = os.getenv(\\\"GOOGLE_APPLICATION_CREDENTIALS\\\")\\n fs = gcsfs.GCSFileSystem(\\n project=project_id, token=sa_credentials\\n )\\n with fs.open(\\n gcs_schema_path,\\n \\\"w\\\",\\n metadata={\\\"Content-Type\\\": \\\"application/json\\\"},\\n ) as f:\\n f.write(json_schema)\\n except Exception as error:\\n print(\\n \\\"Exception occurred for project {} at function {} inside export-loop: {}\\\".format(\\n project_id, \\\"main_process_function\\\", error\\n )\\n )\\n # Deleting backup data based on the backup_data_policy\\n backup_data_policy = {\\n \\\"daily\\\": 1,\\n \\\"weekly\\\": 7,\\n \\\"monthly\\\": 30,\\n \\\"yearly\\\": 365,\\n }\\n if str(expiration).title() == \\\"True\\\":\\n try:\\n bucket_name = project_backup_config[\\\"bucket_name\\\"]\\n storage_client = storage.Client(project_id)\\n client_bucket = storage_client.get_bucket(bucket_name)\\n delete_date = (\\n datetime.now()\\n - timedelta(days=backup_data_policy[retention])\\n ).strftime(\\\"%Y-%m-%d\\\")\\n delete_path = \\\"{retention}/{dataset_name}/{timestamp}\\\".format(\\n retention=retention,\\n dataset_name=bq_dataset_name,\\n timestamp=delete_date,\\n )\\n for file in client_bucket.list_blobs(prefix=delete_path):\\n file.delete()\\n print(\\\"Deleted '{}'\\\".format(file.name))\\n except Exception as error:\\n print(\\n \\\"Exception occurred at function {} inside expiration-loop: {}\\\".format(\\n \\\"main_process_function\\\", error\\n )\\n )\\n else:\\n pass\\n return None\\n else:\\n print(\\\"The mapping between datasets and their tables is empty.\\\")\\n return None\",\n \"def createBlockNumprovFiles(input_queue, output_queue, message_queue, config, \\n blockm_df, start_time):\\n try: \\n temp_time = time.localtime()\\n\\n for _ in range(config['number_servers']):\\n message_queue.put('create_block_numprov')\\n\\n # load the input_queue\\n continue_run, append_list = queueLoader(input_queue, blockm_df, config, \\n start_time)\\n\\n # process the outputs from the workers\\n if continue_run:\\n continue_run = processWork(config, input_queue, output_queue, \\n start_time)\\n\\n if continue_run:\\n my_message = \\\"\\\"\\\"\\n INFO - STEP 2 (MASTER): COMPLETED CREATING NUMPROV FILES FOR\\n ALL SPEEDS\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return True, append_list\\n else:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 2 (MASTER): FAILED TO CREATE NUMPROV FILES FOR\\n ALL SPEEDS\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return False, None\\n\\n except:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 2 (MASTER): UNHANDLED FAILURE IN EXECUTING DISTRIBUTED \\n BUILD OF NUMPROV FILES\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n my_message += \\\"\\\\n\\\" + traceback.format_exc()\\n print(nbmf.logMessage(my_message.strip(), temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return False, None\",\n \"def preprocess_job(self):\\r\\n while not self._coordinator.should_stop():\\r\\n raw_entry = self._fread_queue.get()\\r\\n if raw_entry is None:\\r\\n return\\r\\n preprocessed_entry_dict = self.preprocess_entry(raw_entry)\\r\\n if preprocessed_entry_dict is not None:\\r\\n feed_dict = dict([(self._tensors_to_enqueue[label], value)\\r\\n for label, value in preprocessed_entry_dict.items()])\\r\\n try:\\r\\n self._tensorflow_session.run(self._enqueue_op, feed_dict=feed_dict)\\r\\n except (tf.errors.CancelledError, RuntimeError):\\r\\n break\\r\\n logger.debug('Exiting thread %s' % threading.current_thread().name)\",\n \"def test_google_storage_small_batch_size(sdc_builder, sdc_executor, gcp):\\n\\n gcp_file_name = 'test_9_records.xls'\\n\\n pipeline_builder = sdc_builder.get_pipeline_builder()\\n\\n bucket_name = get_random_string(ascii_lowercase, 10)\\n\\n storage_client = gcp.storage_client\\n\\n google_cloud_storage = pipeline_builder.add_stage('Google Cloud Storage', type='origin')\\n\\n google_cloud_storage.set_attributes(bucket=bucket_name,\\n common_prefix='gcs-test',\\n prefix_pattern=gcp_file_name,\\n data_format='EXCEL',\\n max_batch_size_in_records=1,\\n excel_header_option='WITH_HEADER')\\n\\n pipeline_finisher_executor = pipeline_builder.add_stage('Pipeline Finisher Executor')\\n pipeline_finisher_executor.set_attributes(preconditions=['${record:eventType() == \\\\'no-more-data\\\\'}'],\\n on_record_error='DISCARD')\\n\\n google_cloud_storage >= pipeline_finisher_executor\\n\\n wiretap = pipeline_builder.add_wiretap()\\n google_cloud_storage >> wiretap.destination\\n\\n pipeline = pipeline_builder.build(title='Google Cloud Storage').configure_for_environment(gcp)\\n sdc_executor.add_pipeline(pipeline)\\n\\n created_bucket = gcp.retry_429(storage_client.create_bucket)(bucket_name)\\n try:\\n blob = created_bucket.blob('gcs-test/' + gcp_file_name)\\n blob.upload_from_filename('resources/gcp/' + gcp_file_name)\\n\\n # Start the pipeline and make sure the timeout is defined, since the original bug involved\\n # an infinite loop reading the same file over and over again\\n sdc_executor.start_pipeline(pipeline).wait_for_finished(timeout_sec=300)\\n\\n # Check that the file has been read through exactly once\\n output_records = [record.field for record in wiretap.output_records]\\n assert len(output_records) == 9\\n # Check that no error records were generated\\n error_records = [record.field for record in wiretap.error_records]\\n assert len(error_records) == 0\\n\\n finally:\\n logger.info('Deleting bucket %s ...', created_bucket.name)\\n gcp.retry_429(created_bucket.delete)(force=True)\",\n \"def create_ingest_task(storage, task_queue):\\n for filename in storage.list_files(prefix='build/'):\\n t = IngestTask(\\n chunk_path=storage.get_path_to_file('build/'+filename),\\n chunk_encoding='npz',\\n layer_path=storage.layer_path,\\n )\\n task_queue.insert(t)\",\n \"def process_queue(\\n *,\\n stac_bucket: str,\\n cog_pds_meta_pds: Dict[str, str],\\n queue: str,\\n message_batch_size: int,\\n sns_reconcile_target_arn: str,\\n catalog_update_queue: str,\\n catalog_update_table: str,\\n corrupted_xml_queue: str,\\n delete_processed_messages: bool = False,\\n):\\n\\n buckets = {\\n \\\"stac\\\": stac_bucket,\\n }\\n processed_messages = 0\\n for msg in sqs_messages(queue):\\n\\n try:\\n process_message(\\n msg,\\n {\\n **buckets,\\n **{\\n \\\"cog\\\": msg[\\\"bucket\\\"],\\n \\\"metadata\\\": cog_pds_meta_pds[msg[\\\"bucket\\\"]],\\n },\\n },\\n sns_reconcile_target_arn,\\n catalog_update_queue,\\n catalog_update_table,\\n )\\n except ParseError:\\n LOGGER.info(\\\"Corrupted XML for %s quicklook.\\\", msg[\\\"key\\\"].split(\\\"/\\\")[-1])\\n get_client(\\\"sqs\\\").send_message(\\n QueueUrl=corrupted_xml_queue, MessageBody=msg[\\\"key\\\"]\\n )\\n\\n # Remove message from queue\\n if delete_processed_messages:\\n get_client(\\\"sqs\\\").delete_message(\\n QueueUrl=queue, ReceiptHandle=msg[\\\"ReceiptHandle\\\"]\\n )\\n\\n processed_messages += 1\\n if processed_messages == message_batch_size:\\n break\",\n \"def process_quasar(folder, set_type, doc_size):\\n print(\\\"def process_quasar(folder, set_type, doc_size) ...\\\")\\n\\n # create counter for enumeration of batch-files\\n counter = 0\\n\\n # Question File and Path\\n question_file = set_type + \\\"_questions.json\\\"\\n question_file_path = Path(\\\"/\\\".join([folder, \\\"questions\\\", question_file]))\\n\\n # Contexts File and Path\\n context_file = set_type + \\\"_contexts.json\\\"\\n context_file_path = Path(\\\"/\\\".join([folder, \\\"contexts\\\", doc_size, context_file]))\\n\\n with open(question_file_path, \\\"r\\\") as qf, open(context_file_path, \\\"r\\\") as cf:\\n question_id_list = list()\\n data_dict = dict()\\n batches_data = list()\\n\\n # Parse each line separate to avoid memory issues\\n for line in qf:\\n parsed_question = json.loads(line)\\n question_id = parsed_question[\\\"uid\\\"]\\n question_id_list.append(question_id)\\n data_dict[question_id] = {\\\"answer\\\": parsed_question[\\\"answer\\\"]}\\n data_dict[question_id].update({\\\"question\\\": parsed_question[\\\"question\\\"]})\\n\\n # in order to create batches with the size of 30 and to avoid Memory Errors\\n if len(data_dict) == 30:\\n contexts_counter = 0\\n for line2 in cf:\\n parsed_answer = json.loads(line2)\\n # Answer ID should have a corresponding question ID\\n answer_id = parsed_answer[\\\"uid\\\"]\\n if answer_id in question_id_list:\\n contexts_counter += 1\\n # List of contexts with retrieval scores, contexts are sorted from highest to lowest score\\n answer_contexts = parsed_answer[\\\"contexts\\\"]\\n # remove scores of contexts\\n cleaned_answer_contexts = [ls_elem[1] for ls_elem in answer_contexts]\\n data_dict[answer_id].update({\\\"contexts\\\": cleaned_answer_contexts})\\n if contexts_counter == 30:\\n contexts_counter = 0\\n break\\n\\n # add information where answer in context is\\n answers_list, questions_list, contexts_list = add_end_idx(data_dict)\\n\\n # create the batch-encodings\\n batches_data.append(create_encodings(answers_list, questions_list, contexts_list))\\n data_dict.clear()\\n question_id_list.clear()\\n # if len(batches_data) % 1000 == 0:\\n\\n print(\\\"\\\\n length batches_data \\\" + str(len(batches_data)) + \\\" \\\" + str(counter))\\n\\n if len(batches_data) == 2000:\\n counter += 1\\n save_batch_files(\\\"/local/anasbori/bert_odqa/ODQA_Bert_Project/batch_output\\\", batches_data,\\n counter)\\n\\n batches_data.clear()\\n\\n counter += 1\\n save_batch_files(Path(\\\"/local/anasbori/bert_odqa/ODQA_Bert_Project/batch_output\\\"), batches_data, counter)\",\n \"def main(context=None, event=None): # , s3_rawdata_path='RAW/FP_english_noweather.csv'):\\n\\n # get_logs()\\n # df = get_predata('sample/FP_english_noweather.csv')\\n df = get_predata()\\n original_size = df.shape[0]\\n print(original_size)\\n\\n df = clean_df(df, top=2)\\n df = normQnA(df)\\n try:\\n df.reset_index(inplace=True)\\n except:\\n pass\\n\\n save_data(df)\\n\\n #print(\\\"Original size was {0}, output size is {1}\\\".format(original_size, df.shape))\\n update_log(er=\\\"Original size was {0}, output size is {1}\\\".format(original_size, df.shape), upload=True )\",\n \"def create_inference_tasks(task_queue, image_layer_path, convnet_path, \\n mask_layer_path, output_layer_path, output_block_start, output_block_size, \\n grid_size, patch_size, patch_overlap, cropping_margin_size,\\n output_key='output', num_output_channels=3, \\n image_mip=1, output_mip=1, mask_mip=3):\\n for z in tqdm(range(grid_size[0]), desc='z loop'):\\n for y in range(grid_size[1]):\\n for x in range(grid_size[2]):\\n output_offset = tuple(s+x*b for (s, x, b) in \\n zip(output_block_start, (z, y, x), \\n output_block_size))\\n task = InferenceTask(\\n image_layer_path=image_layer_path,\\n convnet_path=convnet_path,\\n mask_layer_path=mask_layer_path,\\n output_layer_path=output_layer_path,\\n output_offset=output_offset,\\n output_shape=output_block_size,\\n patch_size=patch_size, \\n patch_overlap=patch_overlap,\\n cropping_margin_size=cropping_margin_size,\\n output_key=output_key,\\n num_output_channels=num_output_channels,\\n image_mip=image_mip,\\n output_mip=output_mip,\\n mask_mip=mask_mip\\n )\\n task_queue.insert(task)\\n task_queue.wait('Uploading InferenceTasks')\\n\\n vol = CloudVolume(output_layer_path, mip=output_mip)\\n vol.provenance.processing.append({\\n 'method': {\\n 'task': 'InferenceTask',\\n 'image_layer_path': image_layer_path,\\n 'convnet_path': convnet_path,\\n 'mask_layer_path': mask_layer_path,\\n 'output_layer_path': output_layer_path,\\n 'output_offset': output_offset,\\n 'output_shape': output_block_size,\\n 'patch_size': patch_size,\\n 'patch_overlap': patch_overlap,\\n 'cropping_margin_size': cropping_margin_size,\\n 'output_key': output_key,\\n 'num_output_channels': num_output_channels,\\n 'image_mip': image_mip,\\n 'output_mip': output_mip,\\n 'mask_mip': mask_mip,\\n },\\n 'by': OPERATOR_CONTACT,\\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\\n })\\n vol.commit_provenance()\",\n \"async def _parse_from_queue(queue):\\n loop = asyncio.get_event_loop()\\n file_name = TMP_FOLDER + f\\\"{os.getpid()}.csv\\\"\\n with open(file_name, \\\"w+\\\", encoding=\\\"utf8\\\") as file:\\n logging.info(f\\\"Write to {file_name}\\\")\\n csv_writer = csv.writer(file)\\n\\n records = await queue.get()\\n while records != \\\"DONE\\\":\\n if records:\\n for record in list(records):\\n manifest_row = [\\n record.get(\\\"did\\\"),\\n \\\" \\\".join(\\n [url.replace(\\\" \\\", \\\"%20\\\") for url in record.get(\\\"urls\\\")]\\n ),\\n \\\" \\\".join(\\n [auth.replace(\\\" \\\", \\\"%20\\\") for auth in record.get(\\\"authz\\\")]\\n ),\\n \\\" \\\".join([a.replace(\\\" \\\", \\\"%20\\\") for a in record.get(\\\"acl\\\")]),\\n record.get(\\\"hashes\\\", {}).get(\\\"md5\\\"),\\n record.get(\\\"size\\\"),\\n record.get(\\\"file_name\\\"),\\n ]\\n loop.run_in_executor(None, csv_writer.writerow, manifest_row)\\n\\n records = await queue.get()\\n\\n file.flush()\",\n \"def process_queue(self):\\n if not self.has_queue:\\n log.debug('Thumbnail queue empty.')\\n return\\n\\n queue = []\\n with LockFile(self._queue_path):\\n with open(self._queue_path) as fp:\\n for line in fp:\\n line = line.strip()\\n if not line:\\n continue\\n if not os.path.exists(line):\\n log.debug('File does not exist : %r', line)\\n continue\\n queue.append(line)\\n # Clear queue file\\n with atomic_writer(self._queue_path, 'wb') as fp:\\n fp.write('')\\n\\n succeeded = True\\n for i, img_path in enumerate(queue):\\n log.debug('Generating thumbnail {}/{} ...', i + 1, len(queue))\\n if not self.generate_thumbnail(img_path):\\n succeeded = False\\n\\n return succeeded\",\n \"def job_ingestion(self):\\n\\n # Map a file type to a DAO\\n if self.file_type == SmsFileTypes.SAMPLE_LIST:\\n self.file_dao = SmsSampleDao()\\n elif self.file_type == SmsFileTypes.N0:\\n self.file_dao = SmsN0Dao()\\n else:\\n self.file_dao = None\\n\\n if self.file_dao:\\n # look up if any rows exist already for the file\\n records = self.file_dao.get_from_filepath(self.file_path)\\n\\n if records:\\n logging.warning(f'File already ingested: {self.file_path}')\\n return\\n\\n data_to_ingest = self.read_data_from_cloud_manifest(self.file_path)\\n\\n self.validate_columns(data_to_ingest[\\\"fieldnames\\\"], self.file_dao)\\n\\n self.write_data_to_manifest_table(data_to_ingest[\\\"rows\\\"])\",\n \"def main():\\n #Connection to blob\\n #blob_connection(\\\"../Resume\\\")\\n #blob_download()\\n \\n \\n logging.getLogger().setLevel(logging.INFO)\\n\\n # Extract data from upstream.\\n observations = extract()\\n\\n # Spacy: Spacy NLP\\n nlp = spacy.load('en')\\n\\n # Transform data to have appropriate fields\\n observations, nlp = transform(observations, nlp)\\n\\n # Load data for downstream consumption\\n load(observations, nlp)\\n load_to_json(observations, nlp)\\n \\n #Send data to CosmosDB\\n send_to_Cosmos()\\n \\n #Create Azure search Datasource, Index and indexer\\n implement_Azure_search()\\n \\n \\n# DB=CosmosConnection.DBManager(config_cosmos.COSMOSDB_HOST, config_cosmos.COSMOSDB_KEY)\\n# new_db_id='db_resume'\\n# new_col_id='col_resume'\\n# DB.create_database(new_db_id)\\n# DB.create_collection(new_db_id,new_col_id)\\n# #if new_db_id not in DB.list_databases:\\n# #DB.create_database(new_db_id)\\n# #if new_col_id not in DB.list_collections(new_db_id):\\n# #DB.create_collection(new_db_id,new_col_id)\\n# DB.create_doc(new_db_id, new_col_id, load_to_json(observations, nlp))\\n pass\",\n \"def main():\\n spark = create_spark_session()\\n input_data = \\\"s3a://udacity-dend/\\\"\\n output_data = \\\"s3a://dend-bucket-cpm/\\\"\\n\\n process_song_data(spark, input_data, output_data)\\n process_log_data(spark, input_data, output_data)\",\n \"def __init__(\\n self,\\n *,\\n dag_id: str,\\n cloud_workspace: CloudWorkspace,\\n publisher_id: str,\\n format_specification: str,\\n bq_dataset_id: str = \\\"onix\\\",\\n bq_table_name: str = \\\"onix\\\",\\n bq_dataset_description: str = \\\"Thoth ONIX Feed\\\",\\n bq_table_description: str = None,\\n api_dataset_id: str = \\\"onix\\\",\\n host_name: str = \\\"https://export.thoth.pub\\\",\\n schema_folder: str = default_schema_folder(),\\n observatory_api_conn_id: str = AirflowConns.OBSERVATORY_API,\\n catchup: bool = False,\\n start_date: pendulum.DateTime = pendulum.datetime(2022, 12, 1),\\n schedule: str = \\\"@weekly\\\",\\n ):\\n super().__init__(\\n dag_id,\\n start_date=start_date,\\n schedule=schedule,\\n airflow_conns=[observatory_api_conn_id],\\n catchup=catchup,\\n tags=[\\\"oaebu\\\"],\\n )\\n\\n self.dag_id = dag_id\\n self.cloud_workspace = cloud_workspace\\n self.publisher_id = publisher_id\\n self.bq_dataset_id = bq_dataset_id\\n self.bq_table_name = bq_table_name\\n self.bq_dataset_description = bq_dataset_description\\n self.bq_table_description = bq_table_description\\n self.api_dataset_id = api_dataset_id\\n self.host_name = host_name\\n self.format_specification = format_specification\\n self.schema_folder = schema_folder\\n self.observatory_api_conn_id = observatory_api_conn_id\\n\\n check_workflow_inputs(self)\\n\\n self.add_setup_task(self.check_dependencies)\\n self.add_task(self.download)\\n self.add_task(self.upload_downloaded)\\n self.add_task(self.transform)\\n self.add_task(self.upload_transformed)\\n self.add_task(self.bq_load)\\n self.add_task(self.add_new_dataset_releases)\\n self.add_task(self.cleanup)\",\n \"def process_data():\\n for message in get_messages_from_sqs():\\n try:\\n message_content = json.loads(message.body)\\n input_file = urllib.unquote_plus(message_content\\n ['Records'][0]['s3']['object']\\n ['key']).encode('utf-8')\\n s3.download_file(input_bucket_name, input_file, input_file)\\n output_file = os.path.join(output_dir, os.path.splitext(input_file)[0]+'.csv')\\n parse_patient_data(input_file, output_file)\\n upload_data(output_file)\\n cleanup_files(input_file, output_file)\\n except:\\n message.change_visibility(VisibilityTimeout=0)\\n continue\\n else:\\n message.delete()\",\n \"def reconstruct_segmentation_queue_to_file(self, data_queue, result_folder, result_suffix, save_raw=True):\\n\\n for data_out in iter(data_queue.get, None):\\n\\n results, paths= data_out\\n\\n if self.data_format == 'channels_first':\\n results = np.moveaxis(results, 1, -1)\\n\\n if len(paths) == 1: # same-tile_augmentation\\n\\n for i in range(4):\\n results[i, :, :, :] = np.rot90(results[i, :, :, :], -i)\\n\\n results_mean = np.mean(results, axis=0)\\n predictions = np.expand_dims(np.argmax(results_mean,-1),0)\\n\\n elif len(paths) == len(results): # multi-tiles\\n predictions = np.argmax(results, -1)\\n\\n else:\\n raise Exception('Invalid result dimension!')\\n\\n for ind in range(len(predictions)):\\n\\n pred = predictions[ind]\\n path = paths[ind]\\n\\n raw = cv2.cvtColor(cv2.imread(path),cv2.COLOR_BGR2RGB)\\n\\n tile_gray_coded = cv2.resize(self.gray_code(pred),(raw.shape[1],raw.shape[0]), interpolation=cv2.INTER_NEAREST)\\n tile_color_coded = cv2.resize(self.color_code(pred), (raw.shape[1],raw.shape[0]), interpolation=cv2.INTER_NEAREST)\\n\\n result_path_base = result_folder + os.path.sep + os.path.splitext(os.path.basename(path))[0]\\n\\n rgb_result = cv2.cvtColor((tile_color_coded*0.2+raw*0.8).astype(np.uint8), cv2.COLOR_RGB2BGR)\\n\\n cv2.imwrite(result_path_base + '_result_rgb_' + result_suffix + self.ext, rgb_result)\\n cv2.imwrite(result_path_base + '_result_mask_' + result_suffix + self.ext, tile_gray_coded)\\n\\n if save_raw:\\n mdict = {}\\n mdict['Raw'] = results\\n\\n sio.savemat(result_path_base + '_result_data_' + result_suffix + '.mat', mdict)\",\n \"def test_stuff_in_queues_integrated(integrated_ff):\\n search_res = ff_utils.search_metadata('search/?limit=all&type=File', key=integrated_ff['ff_key'])\\n # just take the first handful\\n for item in search_res[:8]:\\n ff_utils.patch_metadata({}, obj_id=item['uuid'], key=integrated_ff['ff_key'])\\n time.sleep(3) # let queues catch up\\n stuff_in_queue = ff_utils.stuff_in_queues(integrated_ff['ff_env_index_namespace'], check_secondary=True)\\n assert stuff_in_queue\\n with pytest.raises(Exception) as exec_info:\\n ff_utils.stuff_in_queues(None, check_secondary=True) # fail if no env specified\\n assert 'Must provide a full fourfront environment name' in str(exec_info.value)\",\n \"def main():\\n spark = create_spark_session()\\n logging.info('Spark Session created')\\n input_data = \\\"s3a://udacity-dend/\\\"\\n output_data = \\\"s3a://dend-emr-project\\\"\\n #input_data = './data/'\\n #output_data = '/Users/daniel/Desktop/output/'\\n logging.info(f'Set input path to {input_data}')\\n logging.info(f'Set output path to {output_data}')\\n \\n copy_raw_data(spark, input_data, output_data)\\n\\n s3_data = restore_data_from_s3(spark, output_data)\\n \\n sas_desc_string = load_sas_desc_file(input_data)\\n \\n process_fact_table(spark, s3_data, output_data, sas_desc_string)\\n \\n process_dim_tables(spark, s3_data, output_data, sas_desc_string)\\n\\n data_quality_check(spark, output_data)\\n \\n logging.info('ETL process successfully finished.')\",\n \"def process_job(q):\\n del log_msg[:]\\n logger.info('Processing Job %s', q.id)\\n\\n datatype = q.datatype\\n input_dir = q.input_dir\\n output_dir = q.output_dir\\n processor = q.processor\\n if datatype.lower() == 'laz':\\n block_name = proper_block_name(input_dir)\\n elif datatype.lower() == 'ortho':\\n block_name = proper_block_name_ortho(input_dir)\\n if datatype.lower() == 'laz' or datatype.lower() == 'ortho':\\n logger.info('Verifying las tiles in directory...')\\n log_msg.append('Verifying las tiles in directory...\\\\n')\\n has_error, remarks = verify_dir(input_dir, datatype.lower())\\n\\n if has_error:\\n assign_status(q, error=True)\\n log_msg.append('Error in verify_las/verify_raster!\\\\n {0} \\\\n'.format(remarks))\\n else:\\n logger.info('Renaming tiles...')\\n\\n logger.info('BLOCK NAME %s', block_name)\\n log_msg.append('BLOCK NAME {0}\\\\n'.format(block_name))\\n\\n in_coverage, block_uid = find_in_coverage(block_name)\\n\\n #: Check first if folder or `block_name` is in `Cephgeo_LidarCoverageBlock`\\n #: If not found, `output_dir` is not created and data is not processed\\n if in_coverage:\\n logger.info('Found in Lidar Coverage model %s %s',\\n block_name, block_uid)\\n log_msg.append('Found in Lidar Coverage model {0} {1}\\\\n'.format(\\n block_name, block_uid))\\n\\n rename_tiles(input_dir, output_dir, processor,\\n block_name, block_uid, q)\\n logger.info('Status %s Status Timestamp %s',\\n q.status, q.status_timestamp)\\n log_msg.append('Status {0} Status Timestamp {1}\\\\n'.format(\\n q.status, q.status_timestamp))\\n\\n else:\\n has_error = True\\n logger.error('ERROR NOT FOUND IN MODEL %s %s', block_name, block_uid)\\n log_msg.append('ERROR NOT FOUND IN MODEL {0} {1}\\\\n'.format(block_name, block_uid))\\n assign_status(q, error=True)\\n # for DEM\\n else:\\n logger.info('Handler not implemented for type: %s',\\n str(q.datatype))\\n log_msg.append('Handler not implemented for type: {0}\\\\n'.format(\\n str(q.datatype)))\\n assign_status(q, error=True)\\n\\n paragraph = ''\\n for par in log_msg:\\n paragraph = paragraph + par\\n\\n #: Save log messages from renaming tiles to `Automation_AutomationJob.log`\\n with PSQL_DB.atomic() as txn:\\n new_q = (Automation_AutomationJob\\n .update(data_processing_log=paragraph, status_timestamp=datetime.now())\\n .where(Automation_AutomationJob.id == q.id))\\n new_q.execute()\",\n \"def load_raw_to_bq(event, context):\\n\\n import os\\n\\n\\n print(f\\\"Processing .....\\\")\\n\\n file = event\\n project = os.environ.get('ENV_PROJECT')\\n dataset = os.environ.get('ENV_DATASET')\\n bucket = file.get(\\\"bucket\\\")\\n tableCsv = file.get(\\\"name\\\")\\n tableDestList = tableCsv.split(\\\".\\\")\\n tableDest = tableDestList[0]\\n table_id = f'{project}.{dataset}.{tableDest}'\\n\\n from Configuration import Configuration\\n\\n Configuration(tableCsv,bucket,table_id)\\n\\n\\n print(f\\\"End Process.\\\")\",\n \"def test_runner_large_file(caplog, replay_rate):\\n\\n caplog.set_level(logging.INFO)\\n\\n path = 'test_data/test_data.parquet'\\n time_column = 'requesttimestamp'\\n start_date = datetime.datetime(2020, 7, 10, 0, 1, 0)\\n end_date = datetime.datetime(2020, 7, 10, 0, 3, 0)\\n replay_rate = replay_rate\\n bootstrap_servers = 'kafka:9092'\\n topic = 'test_stream_2'\\n\\n logging.debug(\\\"starting\\\")\\n\\n fileconnector = ParquetFileConnector(path=path, time_column=time_column, \\n start_date=start_date, end_date=end_date)\\n\\n fileconnector.startup_checks()\\n\\n publisher = KafkaPublisher(\\n bootstrap_servers=bootstrap_servers,\\n topic=topic\\n )\\n\\n runner = CentralRunner(db_connection=fileconnector, \\n output_system=publisher, \\n start_time=start_date, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n start = time.perf_counter()\\n \\n runner.run()\\n\\n #publisher.close()\\n\\n end = time.perf_counter()\\n\\n code_time = end - start\\n\\n period_duration = (end_date - datetime.timedelta(seconds=replay_rate) - start_date).total_seconds()\\n\\n assert abs(code_time - period_duration/replay_rate) < 1\\n #assert int(code_time) == (end_date - start_date).total_seconds() * replay_rate\",\n \"def __init__(\\n self,\\n *,\\n dag_id: str,\\n cloud_workspace: CloudWorkspace,\\n bq_dataset_id: str = \\\"crossref_metadata\\\",\\n bq_table_name: str = \\\"crossref_metadata\\\",\\n api_dataset_id: str = \\\"crossref_metadata\\\",\\n schema_folder: str = os.path.join(default_schema_folder(), \\\"crossref_metadata\\\"),\\n dataset_description: str = \\\"The Crossref Metadata Plus dataset: https://www.crossref.org/services/metadata-retrieval/metadata-plus/\\\",\\n table_description: str = \\\"The Crossref Metadata Plus dataset: https://www.crossref.org/services/metadata-retrieval/metadata-plus/\\\",\\n crossref_metadata_conn_id: str = \\\"crossref_metadata\\\",\\n observatory_api_conn_id: str = AirflowConns.OBSERVATORY_API,\\n max_processes: int = os.cpu_count(),\\n batch_size: int = 20,\\n start_date: pendulum.DateTime = pendulum.datetime(2020, 6, 7),\\n schedule: str = \\\"0 0 7 * *\\\",\\n catchup: bool = True,\\n queue: str = \\\"remote_queue\\\",\\n max_active_runs: int = 1,\\n ):\\n\\n super().__init__(\\n dag_id=dag_id,\\n start_date=start_date,\\n schedule=schedule,\\n catchup=catchup,\\n airflow_conns=[observatory_api_conn_id, crossref_metadata_conn_id],\\n tags=[Tag.academic_observatory],\\n max_active_runs=max_active_runs,\\n queue=queue,\\n )\\n self.cloud_workspace = cloud_workspace\\n self.bq_dataset_id = bq_dataset_id\\n self.bq_table_name = bq_table_name\\n self.api_dataset_id = api_dataset_id\\n self.schema_folder = schema_folder\\n self.dataset_description = dataset_description\\n self.table_description = table_description\\n self.crossref_metadata_conn_id = crossref_metadata_conn_id\\n self.observatory_api_conn_id = observatory_api_conn_id\\n self.max_processes = max_processes\\n self.batch_size = batch_size\\n\\n self.add_setup_task(self.check_dependencies)\\n self.add_setup_task(self.check_release_exists)\\n self.add_task(self.download)\\n self.add_task(self.upload_downloaded)\\n self.add_operator(\\n WorkflowBashOperator(\\n workflow=self,\\n task_id=\\\"extract\\\",\\n bash_command='tar -xv -I \\\"pigz -d\\\" -f {{ release.download_file_path }} -C {{ release.extract_folder }}',\\n )\\n )\\n self.add_task(self.transform)\\n self.add_task(self.upload_transformed)\\n self.add_task(self.bq_load)\\n self.add_task(self.add_new_dataset_releases)\\n self.add_task(self.cleanup)\",\n \"def handle_event(event, context):\\n\\n try:\\n print(\\\"Received event: \\\" + json.dumps(event, indent=2))\\n\\n # grab resources section of event, get task execution ids\\n task_execution_arns = event['resources']\\n\\n # now fetch the input filter info from each task_detail, fire off jobs\\n new_files_to_process = []\\n for task_execution_arn in task_execution_arns:\\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/datasync.html#DataSync.Client.describe_task_execution\\n response = datasync_client.describe_task_execution(TaskExecutionArn=task_execution_arn)\\n print(\\\"Task execution details: \\\" + str(response))\\n # this will be the location of the data in configured s3 bucket:\\n # 'Includes': [\\n # {\\n # 'FilterType': 'SIMPLE_PATTERN',\\n # 'Value': 'string'\\n # },\\n # ]\\n if len(response['Includes']) > 0:\\n file = response['Includes'][0]['Value']\\n # files typically start with leading '/', strip that leading '/'\\n print(\\\"Got filename:\\\" + file)\\n if file.startswith('/', 0):\\n new_files_to_process.append(file.lstrip('/'))\\n else:\\n new_files_to_process.append(file)\\n else:\\n print(\\\"Response didn't contain Includes files...\\\")\\n\\n if len(new_files_to_process) == 0:\\n print('No files were parsed from input...exiting')\\n return\\n\\n for new_file_to_process in new_files_to_process:\\n state_machine_arn = os.environ['STATE_MACHINE_ARN']\\n payload = {\\\"ObjectName\\\": new_file_to_process}\\n json_payload = json.dumps(payload)\\n print('Starting bcl2fastq with payload %s' % json_payload)\\n #\\n response = step_client.start_execution(stateMachineArn=state_machine_arn, input=json_payload)\\n print(response)\\n\\n except Exception as e:\\n print(e)\\n print('Error handling event. %s' % e)\\n raise e\",\n \"def create_transfer_tasks(\\n task_queue, src_layer_path, dest_layer_path, \\n chunk_size=None, shape=Vec(2048, 2048, 64), \\n fill_missing=False, translate=(0,0,0), \\n bounds=None, mip=0, preserve_chunk_size=True\\n ):\\n shape = Vec(*shape)\\n vol = CloudVolume(src_layer_path, mip=mip)\\n translate = Vec(*translate) // vol.downsample_ratio\\n \\n if not chunk_size:\\n chunk_size = vol.info['scales'][mip]['chunk_sizes'][0]\\n chunk_size = Vec(*chunk_size)\\n\\n try:\\n dvol = CloudVolume(dest_layer_path, mip=mip)\\n except Exception: # no info file\\n info = copy.deepcopy(vol.info)\\n dvol = CloudVolume(dest_layer_path, info=info)\\n dvol.commit_info()\\n\\n dvol.info['scales'] = dvol.info['scales'][:mip+1]\\n dvol.info['scales'][mip]['chunk_sizes'] = [ chunk_size.tolist() ]\\n dvol.commit_info()\\n\\n create_downsample_scales(dest_layer_path, \\n mip=mip, ds_shape=shape, preserve_chunk_size=preserve_chunk_size)\\n \\n if bounds is None:\\n bounds = vol.bounds.clone()\\n else:\\n bounds = vol.bbox_to_mip(bounds, mip=0, to_mip=mip)\\n bounds = Bbox.clamp(bounds, vol.bounds)\\n\\n total = int(reduce(operator.mul, np.ceil(bounds.size3() / shape)))\\n for startpt in tqdm(xyzrange( bounds.minpt, bounds.maxpt, shape ), desc=\\\"Inserting Transfer Tasks\\\", total=total):\\n task = TransferTask(\\n src_path=src_layer_path,\\n dest_path=dest_layer_path,\\n shape=shape.clone(),\\n offset=startpt.clone(),\\n fill_missing=fill_missing,\\n translate=translate,\\n mip=mip,\\n )\\n task_queue.insert(task)\\n task_queue.wait('Uploading Transfer Tasks')\\n\\n job_details = {\\n 'method': {\\n 'task': 'TransferTask',\\n 'src': src_layer_path,\\n 'dest': dest_layer_path,\\n 'shape': list(map(int, shape)),\\n 'fill_missing': fill_missing,\\n 'translate': list(map(int, translate)),\\n 'bounds': [\\n bounds.minpt.tolist(),\\n bounds.maxpt.tolist()\\n ],\\n 'mip': mip,\\n },\\n 'by': OPERATOR_CONTACT,\\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\\n }\\n\\n dvol = CloudVolume(dest_layer_path)\\n dvol.provenance.sources = [ src_layer_path ]\\n dvol.provenance.processing.append(job_details) \\n dvol.commit_provenance()\\n\\n if vol.path.protocol != 'boss':\\n vol.provenance.processing.append(job_details)\\n vol.commit_provenance()\",\n \"def _pre_flight_checks(self, num_containers, virtual_cores, memory,\\n files, queue):\\n try:\\n # check response from RM\\n met = self.yarn_api.cluster_metrics()\\n except YARNException:\\n raise\\n except requests.RequestException as e:\\n if isinstance(e, requests.Timeout):\\n m = 'Connection timeout'\\n else:\\n m = 'Connection error'\\n raise YARNException(m + ' when talking to the '\\n 'YARN REST server at {}. This can mean that '\\n 'the server/port values are wrong, that you '\\n 'are using the wrong protocol (http/https) or '\\n 'that you need to route through a proxy.'\\n ''.format(self.yarn_api.url))\\n if met['activeNodes'] < 1:\\n raise KnitException('No name-nodes active')\\n # What if we simply don't have the full yarn-site.xml available?\\n mmin = int(self.conf.get('yarn.scheduler.minimum-allocation-mb', 1024))\\n # 300MB default allocation for AM in client.scala\\n mem = (max(300, mmin) + num_containers * max(memory, mmin))\\n if met['availableMB'] < mem:\\n raise KnitException('Memory estimate for app (%iMB) exceeds cluster'\\n ' capacity (%iMB)' % (mem, met['availableMB']))\\n c = 1 + num_containers * virtual_cores\\n if met['availableVirtualCores'] < c:\\n raise KnitException('vCPU request for app (%i) exceeds cluster capa'\\n 'city (%i)' % (c, met['availableVirtualCores']))\\n nodes = self.yarn_api.nodes()\\n if all((max(mmin, memory) > n['availMemoryMB']) and\\n (virtual_cores > n['availableVirtualCores'])\\n for n in nodes):\\n # cannot test without multiple nodemanagers\\n raise KnitException('No NodeManager can fit any single container')\\n if self.hdfs:\\n df = self.hdfs.df()\\n cap = (df['capacity'] - df['used']) // 2**20\\n fs = [self.JAR_FILE_PATH] + [f for f in files\\n if not f.startswith('hdfs://')]\\n need = sum(os.stat(f).st_size for f in fs) // 2**20\\n # NB: if replication > 1 this might not be enough\\n if cap < need:\\n raise KnitException('HDFS space requirement (%iMB) exceeds'\\n 'capacity (%iMB)' % (need, cap))\",\n \"def main():\\n spark = create_spark_session()\\n input_data = \\\"s3a://udacity-dend\\\"\\n output_data = \\\"s3a://vivek1bucket\\\"\\n \\n process_song_data(spark, input_data, output_data) \\n process_log_data(spark, input_data, output_data)\",\n \"def _submit_to_queue(self, script_file):\",\n \"def runDataExtraction():\\r\\n config = CONFIG['steps']['DataExtraction']\\r\\n ci = config['inputs']\\r\\n co = config['outputs']\\r\\n columns = ci['columns']\\r\\n nrows = ci['nrows']\\r\\n input_bucket = ci['bucket']\\r\\n no_of_files = ci['no_of_files']\\r\\n\\r\\n output_bucket = co['bucket']\\r\\n csv_name_prefix = co['csv_name_prefix']\\r\\n\\r\\n minio_config = CONFIG['artifacts']['minio']\\r\\n minioClient = create_minio_client(minio_config[\\\"endpoint_url\\\"],\\r\\n access_key=minio_config[\\\"access_key\\\"],\\r\\n secret_key=minio_config[\\\"secret_key\\\"],\\r\\n secure=minio_config['secure'])\\r\\n\\r\\n boto_client = boto3.client(\\\"s3\\\",\\r\\n endpoint_url=minio_config[\\\"endpoint_url\\\"],\\r\\n aws_access_key_id=minio_config[\\\"access_key\\\"],\\r\\n aws_secret_access_key=minio_config[\\\"secret_key\\\"],\\r\\n region_name=minio_config[\\\"region_name\\\"])\\r\\n\\r\\n zip_files = get_files(input_bucket, boto_client, file_type='zip')\\r\\n\\r\\n no_of_files_to_process = no_of_files if no_of_files is not None else len(\\r\\n zip_files)\\r\\n for zip_file in tqdm(zip_files[:no_of_files_to_process], total=no_of_files_to_process):\\r\\n process_file(zip_file, input_bucket, output_bucket, minioClient, columns,\\r\\n nrows=nrows, output_csv_name_prefix=csv_name_prefix)\",\n \"def run_etl(_):\\n\\n _ = storage.Client() # Storage Client\\n \\n # Extract\\n bom_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'raw/bill_of_materials.csv')\\n cb_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'raw/comp_boss.csv')\\n pq_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'raw/price_quote.csv')\\n \\n bom = pd.read_csv(bom_name)\\n cb = pd.read_csv(cb_name)\\n pq = pd.read_csv(pq_name)\\n\\n # Transform\\n cb[\\\"connection_type_id\\\"] = cb[\\\"connection_type_id\\\"].replace(to_replace=\\\"9999\\\", value=np.nan)\\n cb[\\\"height_over_tube\\\"] = cb[\\\"height_over_tube\\\"].replace(to_replace=\\\"9999\\\", value=np.nan)\\n cb['groove'] = cb.apply(lambda row: convert_to_intbool(row['groove']), axis=1)\\n cb['unique_feature'] = cb.apply(lambda row: convert_to_intbool(row['unique_feature']), axis=1)\\n cb['orientation'] = cb.apply(lambda row: convert_to_intbool(row['orientation']), axis=1)\\n\\n bom = pd.wide_to_long(\\n df=bom,\\n stubnames=[\\\"component_id\\\", \\\"quantity\\\"],\\n i=\\\"tube_assembly_id\\\",\\n j=\\\"id\\\",\\n sep=\\\"_\\\"\\n ).reset_index().sort_values([\\\"tube_assembly_id\\\", \\\"id\\\"], ignore_index=True)\\\\\\n .drop(\\\"id\\\", axis=1).dropna(subset=[\\\"component_id\\\"])\\n\\n pq['bracket_pricing'] = pq.apply(lambda row: convert_to_intbool(row['bracket_pricing']), axis=1)\\n\\n # Load\\n bom_output_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'refined/tube_assembly.csv')\\n cb_output_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'refined/component.csv')\\n pq_output_name = \\\"{0}{1}{2}\\\".format(\\\"gs://\\\", BUCKET_NAME, 'refined/price_quote.csv')\\n\\n bom.to_csv(bom_output_name, index=False)\\n cb.to_csv(cb_output_name, index=False)\\n pq.to_csv(pq_output_name, index=False)\\n\\n return f'ETL Ok. Check Data Studio.'\",\n \"def handleDQMFileSaver(self):\\n\\n runIsComplete = getattr(self.jobBag, \\\"runIsComplete\\\", False)\\n multiRun = getattr(self.jobBag, \\\"multiRun\\\", False)\\n runLimits = getattr(self.jobBag, \\\"runLimits\\\", \\\"\\\")\\n self.logger.info(\\\"DQMFileSaver set to multiRun: %s, runIsComplete: %s, runLimits: %s\\\",\\n multiRun, runIsComplete, runLimits)\\n\\n procScript = \\\"cmssw_handle_dqm_filesaver.py\\\"\\n\\n cmd = \\\"%s --input_pkl %s --output_pkl %s\\\" % (\\n procScript,\\n os.path.join(self.stepSpace.location, self.configPickle),\\n os.path.join(self.stepSpace.location, self.configPickle))\\n\\n if hasattr(self.step.data.application.configuration, \\\"pickledarguments\\\"):\\n pklArgs = encodeUnicodeToBytes(self.step.data.application.configuration.pickledarguments)\\n args = pickle.loads(pklArgs)\\n datasetName = args.get('datasetName', None)\\n if datasetName:\\n cmd += \\\" --datasetName %s\\\" % (datasetName)\\n if multiRun and runLimits:\\n cmd += \\\" --multiRun --runLimits=%s\\\" % (runLimits)\\n if runIsComplete:\\n cmd += \\\" --runIsComplete\\\"\\n self.scramRun(cmd)\\n\\n return\",\n \"def run(argv=None):\\n\\n\\n pubsubTopicName = \\\"projects/data-qe-da7e1252/topics/sk-firewall-json\\\"\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--input',\\n dest='input',\\n default='gs://dataflow-samples/shakespeare/kinglear.txt',\\n help='Input file to process.')\\n parser.add_argument('--output',\\n dest='output',\\n # CHANGE 1/5: The Google Cloud Storage path is required\\n # for outputting the results.\\n #default='gs://YOUR_OUTPUT_BUCKET/AND_OUTPUT_PREFIX',\\n #default=\\\"/Users/skanabargi/python/stream/output\\\",\\n default='gs://data-qe-da7e1252/tmp/sk_out',\\n help='Output file to write results to.')\\n known_args, pipeline_args = parser.parse_known_args(argv)\\n\\n pipeline_args.extend([\\n # CHANGE 2/5: (OPTIONAL) Change this to DataflowRunner to\\n # run your pipeline on the Google Cloud Dataflow Service.\\n '--runner=DataflowRunner',\\n # CHANGE 3/5: Your project ID is required in order to run your pipeline on\\n # the Google Cloud Dataflow Service.\\n '--project=data-qe-da7e1252',\\n # CHANGE 4/5: Your Google Cloud Storage path is required for staging local\\n # files.\\n #'--staging_location=gs://YOUR_BUCKET_NAME/AND_STAGING_DIRECTORY',\\n '--staging_location=gs://data-qe-da7e1252/tmp/stage/',\\n # CHANGE 5/5: Your Google Cloud Storage path is required for temporary\\n # files.\\n #'--temp_location=gs://YOUR_BUCKET_NAME/AND_TEMP_DIRECTORY',\\n '--temp_location=gs://data-qe-da7e1252/tmp/local',\\n '--experiments=allow_non_updatable_job',\\n '--job_name=sk-pubsub-to-gcs-5',\\n ])\\n\\n # We use the save_main_session option because one or more DoFn's in this\\n # workflow rely on global context (e.g., a module imported at module level).\\n pipeline_options = PipelineOptions(pipeline_args)\\n pipeline_options.view_as(SetupOptions).save_main_session = True\\n pipeline_options.view_as(StandardOptions).streaming = True\\n with beam.Pipeline(options=pipeline_options) as p:\\n\\n # Read the text file[pattern] into a PCollection.\\n #lines = p | ReadFromText(known_args.input)\\n lines = p | beam.io.ReadFromPubSub(topic=pubsubTopicName)\\n\\n\\n\\n # Count the occurrences of each word.\\n output = ( lines | 'window' >> beam.WindowInto(window.FixedWindows(60)))\\n\\n # Write the output using a \\\"Write\\\" transform that has side effects.\\n # pylint: disable=expression-not-assigned\\n output | 'writeTOGcs' >> WriteToText(known_args.output)\",\n \"def task(self, chunk_filename_queue, writer):\\n self.init_structs()\\n while True:\\n filename = chunk_filename_queue.get()\\n for item in self.single_file_gen(filename):\\n writer.send_bytes(item)\",\n \"def sub_trans_data_processing(intputFilePath, outputFilePath, dpiDuplicatesFilePath, dpiRejectionFilePath):\\n spark = SparkSession.builder.appName(\\\"challenge1\\\").master(\\\"local\\\").getOrCreate()\\n\\n mobile_df = spark.read.format(\\\"csv\\\").option(\\\"header\\\", \\\"true\\\").load(intputFilePath)\\n\\n mobile_df_excluded = mobile_df.filter(is_int_col(mobile_df['SubscriberId']) == False)\\n\\n subscriber_mobile_df = mobile_df.filter(is_int_col(mobile_df['SubscriberId']))\\n unique_subscriber_mobile_df = subscriber_mobile_df.dropDuplicates()\\n\\n duplicated_subscriber_mobile_rdd = subscriber_mobile_df.rdd.map(lambda line: (line, 1)).reduceByKey(lambda v1, v2: v1 + v2)\\n duplicated_subscriber_mobile_rdd = duplicated_subscriber_mobile_rdd.filter(lambda line: line[1] != 1)\\n duplicated_subscriber_mobile_rdd = duplicated_subscriber_mobile_rdd.map(lambda line: line[0])\\n\\n mobile_subscriber_summary_df = unique_subscriber_mobile_df.groupBy(\\\"SubscriberId\\\").agg(\\n func.sum(\\\"TotalBytes\\\").alias(\\\"Total_Bytes\\\"),\\n func.count(\\\"TotalBytes\\\").alias(\\\"Total_Count\\\"),\\n func.current_date().alias(\\\"Insertion_Date\\\"),\\n func.first('IMEI').alias('IMEI'),\\n ((func.unix_timestamp(func.max(\\\"EndTime\\\")) - func.unix_timestamp(func.min(\\\"StartTime\\\"))) / 60).alias(\\n 'Total_Time_In_Mins'))\\n\\n subscriber_services = unique_subscriber_mobile_df.rdd.map(lambda line: ((line[7], line[19]), 1)).reduceByKey(lambda v1, v2: v1 + v2)\\n subscriber_services = subscriber_services.map(lambda line: (line[1], line[0])).sortByKey(False)\\n subscriber_services = subscriber_services.map(lambda line: line[1])\\n subscriber_services = subscriber_services.groupByKey().map(lambda line: (line[0], extract_result(line[1])[0], extract_result(line[1])[1]))\\n subscriber_services = subscriber_services.toDF([\\\"SubscriberId\\\", \\\"MostUsedServiceName_1\\\", \\\"MostUsedServiceName_2\\\"])\\n\\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.join(subscriber_services, ['SubscriberId'])\\n\\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\\\"Event_Type\\\", func.lit(\\\"Mobile\\\"))\\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\\\"MAC_Address\\\", func.lit(\\\"N/A\\\"))\\n mobile_subscriber_summary_df = mobile_subscriber_summary_df.withColumn(\\\"Access_Point \\\", func.lit(\\\"N/A\\\"))\\n\\n mobile_subscriber_summary_df.write.csv(outputFilePath)\\n mobile_df_excluded.rdd.saveAsTextFile(dpiRejectionFilePath)\\n duplicated_subscriber_mobile_rdd.saveAsTextFile(dpiDuplicatesFilePath)\",\n \"def pre_analyze(analyze_id, part_number, workspace, use_cache):\\n\\n LOG.info('Pre Analyze Step')\\n\\n file_path = os.path.join(workspace, analyze_id)\\n source_file_without_extension = 'source_' + str(part_number)\\n source_file_with_extension = source_file_without_extension + '.zip'\\n zip_file_path = os.path.join(file_path, source_file_with_extension)\\n analyze_dir_path = os.path.join(file_path, source_file_without_extension)\\n\\n with zipfile.ZipFile(zip_file_path) as zip_file:\\n try:\\n zip_file.extractall(analyze_dir_path)\\n except Exception:\\n LOG.error('Failed to extract all files from the ZIP.')\\n\\n if use_cache:\\n list_of_other_files = ['sources-root/compile_command.json',\\n 'sources-root/cached_files']\\n\\n difference = set(zip_file.namelist()).difference(\\n list_of_other_files)\\n\\n if difference:\\n LOG.debug('Store files in Redis: \\\\n%s', difference)\\n for file_name in zip_file.namelist():\\n if file_name not in list_of_other_files:\\n LOG.debug(os.path.join(analyze_dir_path, file_name))\\n with open(os.path.join(analyze_dir_path, file_name), 'rb') as file:\\n file_in_bytes = file.read()\\n readable_hash = hashlib.md5(\\n file_in_bytes).hexdigest()\\n try:\\n REDIS_DATABASE.set(\\n readable_hash, file_in_bytes)\\n LOG.debug(REDIS_DATABASE.get(\\n readable_hash).decode('UTF-8'))\\n except Exception:\\n LOG.error(\\n 'Failed to store file %s in Redis.', file_name)\\n sys.exit(1)\\n\\n sources_root_path = os.path.join(analyze_dir_path, 'sources-root')\\n\\n compile_command_path = os.path.join(\\n sources_root_path, 'compile_command.json')\\n\\n with open(compile_command_path) as compile_command_json:\\n try:\\n # Assume that there is only one compile command\\n compile_command = json.load(compile_command_json)[0]\\n except IndexError:\\n LOG.error('The compile_command.json was empty.')\\n except Exception:\\n REDIS_DATABASE.hset(analyze_id, 'state',\\n AnalyzeStatus.PREANALYZE_FAILED.name)\\n LOG.error('Failed to read in compile command.')\\n\\n if use_cache:\\n cached_files_path = os.path.join(\\n sources_root_path, 'cached_files')\\n\\n with open(cached_files_path) as read_in_cached_files:\\n try:\\n cached_files = json.load(read_in_cached_files)\\n except Exception:\\n REDIS_DATABASE.hset(analyze_id, 'state',\\n AnalyzeStatus.PREANALYZE_FAILED.name)\\n LOG.error('Failed to read in skipped file list.')\\n\\n if cached_files:\\n LOG.debug('Restore files from Redis: \\\\n%s', cached_files.items())\\n\\n for file_hash in cached_files:\\n new_file_path = os.path.join(\\n sources_root_path, cached_files[file_hash][1:])\\n if not os.path.exists(os.path.dirname(new_file_path)):\\n try:\\n os.makedirs(os.path.dirname(new_file_path))\\n except OSError:\\n LOG.error('Failed to create directories.')\\n\\n with open(new_file_path, 'wb+') as file_to_write_out:\\n file_to_write_out.write(REDIS_DATABASE.get(file_hash))\\n\\n compile_command['directory'] = os.path.join(\\n sources_root_path, compile_command['directory'][1:])\\n\\n if 'command' in compile_command:\\n command = compile_command['command']\\n\\n modified_command = []\\n\\n # Update existing paths in build command\\n for command_part in command:\\n modified_command.append(command_part)\\n\\n # Add missing paths in build command\\n paths_of_dependencies = os.path.join(\\n sources_root_path, 'paths_of_dependencies.json')\\n\\n with open(paths_of_dependencies) as paths:\\n try:\\n path_list = json.load(paths)\\n except Exception:\\n REDIS_DATABASE.hset(analyze_id, 'state',\\n AnalyzeStatus.PREANALYZE_FAILED.name)\\n LOG.error('Failed to read in paths of dependencies.')\\n\\n for path in path_list:\\n if 'usr/include' not in path and 'usr/lib' not in path:\\n modified_command.append('-I' + sources_root_path + path)\\n\\n compile_command['command'] = modified_command\\n\\n # This is needed because tu_collector accepts only 'command'\\n # but CodeChecker only works with 'arguments'\\n compile_command['arguments'] = compile_command['command']\\n\\n modified_compile_command = os.path.join(\\n sources_root_path, 'modified_compile_command.json')\\n\\n with open(modified_compile_command, 'w') as modified_cdb:\\n modified_cdb.write('[' + json.dumps(compile_command) + ']')\\n\\n return analyze_dir_path\",\n \"def control(batch_info):\\n \\n sbatch, script, cps, mem = batch_info\\n qsub_name = qsub_prep(sbatch, script, cps, mem)\\n\\n \\n batch_id = os.popen(\\\"sbatch \\\" + qsub_name).read()\\n batch_id = batch_id.strip().split()[-1]\\n\\n \\n while True:\\n output = os.popen(\\\"squeue -u koerstz\\\").read()\\n \\n if batch_id in output:\\n time.sleep(5)\\n continue \\n \\n else:\\n break\\n\\n # create data frame:\\n df = pd.read_pickle(sbatch + \\\".pkl\\\")\\n \\n return df\",\n \"def loadBlockQueue(input_queue, county_fips, config, start_time):\\n try:\\n temp_time = time.localtime()\\n county_counter = 0\\n for c in county_fips:\\n input_queue.put((c))\\n county_counter += 1\\n my_message = \\\"\\\"\\\"\\n INFO - STEP 0 (MASTER): TASK 6 OF 13 - COMPLETED LOADING INPUT \\n QUEUE WITH COUNTY DATA\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split())\\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return True, county_counter\\n\\n except:\\n my_message = \\\"\\\"\\\"\\n ERROR - STEP 0 (MASTER): TASK 6 OF 13 - FAILED TO LOADING QUEUE WITH\\n COUNTY DATA\\n \\\"\\\"\\\"\\n my_message = ' '.join(my_message.split()) + '\\\\n' + traceback.format_exc()\\n print(nbmf.logMessage(my_message,temp_time, time.localtime(), \\n time.mktime(time.localtime()) - time.mktime(start_time)))\\n return False, None\",\n \"def _worker(self, task, task_queue):\\n with _thread_lock:\\n task.process_number = self.counter\\n self.counter += 1\\n if task.output:\\n if not task.has_error and task.processtime > 0:\\n if not os.path.isfile(task.logfile):\\n task.logfile = \\\"\\\"\\n task_queue.put(task)\\n return\\n try:\\n if not os.path.exists(task.folder):\\n task.add_error('Could not find folder: {}'.format(task.folder))\\n task.logfile = \\\"\\\"\\n else:\\n tmp_kwargs = dict(mode='a+', prefix=self.logfile_prefix,\\n suffix='.log', dir=task.folder, delete=False)\\n with NamedTemporaryFile(**tmp_kwargs) as logfile:\\n logfile.write('########### MACRO #############\\\\n')\\n logfile.write(\\\"\\\\n\\\".join(task.macro))\\n logfile.write('\\\\n\\\\n######### OUTPUT LOG ##########')\\n logfile.flush()\\n task.logfile = logfile.name\\n starttime = time.clock()\\n exe_args = dict(macro=task.macro,\\n logfile=logfile,\\n anybodycon_path=self.anybodycon_path,\\n timeout=self.timeout,\\n keep_macrofile=self.keep_logfiles,\\n env=self.env)\\n retcode = _execute_anybodycon(**exe_args)\\n endtime = time.clock()\\n logfile.seek(0)\\n if retcode == _KILLED_BY_ANYPYTOOLS:\\n task.processtime = 0\\n return\\n task.processtime = endtime - starttime\\n task.output = parse_anybodycon_output(\\n logfile.read(),\\n self.ignore_errors,\\n self.warnings_to_include)\\n except Exception as e:\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\\n task.add_error(str(exc_type) + '\\\\n' + str(fname) + '\\\\n' + str(exc_tb.tb_lineno))\\n logger.debug(str(e))\\n finally:\\n if not self.keep_logfiles and not task.has_error:\\n try:\\n silentremove(logfile.name)\\n task.logfile = \\\"\\\"\\n except OSError as e:\\n pass # Ignore if AnyBody has not released the log file.\\n task_queue.put(task)\",\n \"def test_google_storage_post_processing(sdc_builder, sdc_executor, gcp, action):\\n pipeline_builder = sdc_builder.get_pipeline_builder()\\n\\n bucket_name = get_random_string(ascii_lowercase, 10)\\n\\n storage_client = gcp.storage_client\\n\\n google_cloud_storage = pipeline_builder.add_stage('Google Cloud Storage', type='origin')\\n\\n google_cloud_storage.set_attributes(bucket=bucket_name,\\n common_prefix='gcs-test',\\n prefix_pattern='**/*.txt',\\n data_format='TEXT',\\n post_processing_option=action)\\n wiretap = pipeline_builder.add_wiretap()\\n\\n google_cloud_storage >> wiretap.destination\\n\\n pipeline = pipeline_builder.build().configure_for_environment(gcp)\\n sdc_executor.add_pipeline(pipeline)\\n\\n created_bucket = gcp.retry_429(storage_client.create_bucket)(bucket_name)\\n try:\\n data = [get_random_string(ascii_letters, 100) for _ in range(10)]\\n file_path = 'gcs-test/a/b/c/d/e/'\\n file_name = 'sdc-test.txt'\\n blob = created_bucket.blob(file_path + file_name)\\n blob.upload_from_string('\\\\n'.join(data))\\n\\n logger.info('Starting GCS Origin pipeline and wait until the information is read ...')\\n sdc_executor.start_pipeline(pipeline)\\n sdc_executor.wait_for_pipeline_metric(pipeline, 'input_record_count', 10)\\n sdc_executor.stop_pipeline(pipeline)\\n\\n rows_from_wiretap = [record.field['text'] for record in wiretap.output_records]\\n\\n # If post processing option is enabled, old file path should not exist\\n assert not storage_client.get_bucket(bucket_name).blob(file_path + file_name).exists()\\n\\n # If ARCHIVE, default prefix is empty, so it gets moved to root of bucket\\n if action == 'ARCHIVE':\\n assert storage_client.get_bucket(bucket_name).blob(file_name).exists()\\n\\n assert len(data) == len(rows_from_wiretap)\\n assert rows_from_wiretap == data\\n finally:\\n logger.info('Deleting bucket %s ...', created_bucket.name)\\n gcp.retry_429(created_bucket.delete)(force=True)\",\n \"def azure_blob(Config, file_name, dump_path, db): \\n azureBlobConf = Config[\\\"ARCHIVE_AZURE\\\"] \\n account_name = azureBlobConf.get('account_name')\\n account_key = azureBlobConf.get('account_key')\\n container_name = azureBlobConf.get('container_name') \\n \\n logging.debug('Initiating archive to Azure blob storage')\\n logging.debug(\\\"Uploading the backup file - '{}'\\\".format(dump_path))\\n \\n result = AzureBlobStorage(\\n account_name, \\n account_key, \\n container_name, \\n file_name, \\n dump_path).upload()\\n\\n if result == True:\\n logging.debug(\\\"Successfully uploaded the backup '{}' to blob container '{}'\\\".format(file_name, container_name))\\n else:\\n logging.error('Upload Failed..!')\\n logging.error('Error Message: {}'.format(result))\\n notification_slack(Config, db=db, task='Archive', status='Failed')\",\n \"def finish_stager_tasks(self):\\n\\n update_files = {}\\n messages = []\\n while not self.finished_queue.empty():\\n file = self.finished_queue.get()\\n update_files[file['content_id']] = {'status': ContentStatus.AVAILABLE,\\n 'pfn_size': file['pfn_size'],\\n 'pfn': file['pfn']}\\n msg = {'event_type': 'FILE_AVAILABLE',\\n 'payload': {'scope': file['scope'],\\n 'name': file['name'],\\n 'startEvent': file['min_id'],\\n 'lastEvent': file['max_id'],\\n 'pfn': file['pfn']},\\n 'created_at': date_to_str(datetime.datetime.utcnow())}\\n messages.append(msg)\\n\\n self.logger.info('Got %s staged outputs' % len(update_files))\\n update_contents_by_id(update_files)\\n\\n if self.send_messaging:\\n for msg in messages:\\n self.messaging_queue.put(msg)\",\n \"def queue (self):\\n\\n with self.__t.steps():\\n import exception\\n from lib import schema\\n from lib import common\\n from lib import process\\n from lib import data\\n from sqlalchemy import and_\\n import json\\n from collections import OrderedDict\\n\\n with schema.select(\\\"process_queue\\\", schema.table.process_queue.status==None) as select:\\n for queued in select.limit(1000).all():\\n blocked = False\\n if len(queued.depend) > 0:\\n for depend_id in queued.depend:\\n depend = schema.select_one(\\\"series\\\", schema.table.series.id==depend_id)\\n match_tags = json.dumps(OrderedDict(sorted(data.decode_tags(depend.tags).items())))\\n if depend and schema.select_one(\\\"process_queue\\\", schema.table.process_queue.tags==match_tags):\\n blocked = True\\n break # queued dependencies\\n if not blocked:\\n queued.status = \\\"queued\\\"\\n schema.save(queued)\\n run.apply_async([queued.tags]) #queue process\\n self.__t.ok()\\n self.apply_async(queue=\\\"control\\\", countdown=30) #queue next\",\n \"def perform_process(transformer: transformer_class.Transformer, check_md: dict) -> dict:\\n # Process each CSV file into BETYdb\\n start_timestamp = datetime.datetime.now()\\n files_count = 0\\n files_csv = 0\\n lines_read = 0\\n error_count = 0\\n files_loaded = []\\n for one_file in check_md['list_files']():\\n files_count += 1\\n if os.path.splitext(one_file)[1].lower() == '.csv':\\n files_csv += 1\\n\\n # Make sure we can access the file\\n if not os.path.exists(one_file):\\n msg = \\\"Unable to access csv file '%s'\\\" % one_file\\n logging.debug(msg)\\n return {'code': -1000,\\n 'error': msg}\\n\\n try:\\n # Read in the lines from the file\\n with open(one_file, 'r') as in_file:\\n reader = csv.DictReader(in_file)\\n files_loaded.append(one_file)\\n for row in reader:\\n centroid_lonlat = [row['lon'], row['lat']]\\n time_fmt = row['dp_time']\\n timestamp = row['timestamp']\\n dp_metadata = {\\n \\\"source\\\": row['source'],\\n \\\"value\\\": row['value']\\n }\\n trait = row['trait']\\n\\n __internal__.create_datapoint_with_dependencies(transformer.args.clowder_url, transformer.args.clowder_key,\\n trait, (centroid_lonlat[1], centroid_lonlat[0]), time_fmt,\\n time_fmt, dp_metadata, timestamp)\\n lines_read += 1\\n\\n except Exception:\\n logging.exception(\\\"Error reading CSV file '%s'. Continuing processing\\\", os.path.basename(one_file))\\n error_count += 1\\n\\n if files_csv <= 0:\\n logging.info(\\\"No CSV files were found in the list of files to process\\\")\\n if error_count > 0:\\n logging.error(\\\"Errors were found during processing\\\")\\n return {'code': -1001, 'error': \\\"Too many errors occurred during processing. Please correct and try again\\\"}\\n\\n return {\\n 'code': 0,\\n configuration.TRANSFORMER_NAME: {\\n 'version': configuration.TRANSFORMER_VERSION,\\n 'utc_timestamp': datetime.datetime.utcnow().isoformat(),\\n 'processing_time': str(datetime.datetime.now() - start_timestamp),\\n 'num_files_received': str(files_count),\\n 'num_csv_files': str(files_csv),\\n 'lines_loaded': str(lines_read),\\n 'files_processed': str(files_loaded)\\n }\\n }\",\n \"def main() -> None:\\n jobStatus = list()\\n adt = AuditManager(\\n config.get(\\\"Audit\\\").get(\\\"database\\\"),\\n config.get(\\\"Audit\\\").get(\\\"user\\\"),\\n config.get(\\\"Audit\\\").get(\\\"password\\\"),\\n config.get(\\\"Audit\\\").get(\\\"host\\\"),\\n config.get(\\\"Audit\\\").get(\\\"port\\\"),\\n )\\n jobMeta = adt.getStepLogData()\\n adt.closeConnection()\\n with concurrent.futures.ThreadPoolExecutor(\\n max_workers=config.get(\\\"spark\\\").get(\\\"parallelJobs\\\", 2)\\n ) as executor:\\n spark_jobs = {\\n executor.submit(processFile, fileMeta): fileMeta for fileMeta in jobMeta\\n }\\n for status in concurrent.futures.as_completed(spark_jobs):\\n fileStatus = status.result()\\n jobStatus.append(fileStatus)\\n logger.info(jobStatus)\",\n \"def _drain_queue(self):\\n while self.queue:\\n self._export_batch()\",\n \"def main():\\n #Set up objects\\n client = BigQueryClient()\\n writer = FileWriter()\\n\\n #Send data from big query to a given file.\\n # 500 is the limit of data points fetched.\\n client.produce_json_data(writer, 500)\",\n \"def push_results_to_file(file_name,queue_name, aux_q,queue_service):\\n # verify the queues exist\\n queue_service.create_queue(queue_name)\\n queue_service.create_queue(aux_q)\\n # open file for write\\n f1=open('./{0}.csv'.format(file_name), 'w+')\\n while queue_service.get_queue_metadata(queue_name).approximate_message_count > 0:\\n messages = queue_service.get_messages(queue_name,1)\\n if len(messages)>0 :\\n for message in messages: \\n line = '{0},{1},{2}'.format(message.id,message.insertion_time,message.content)\\n queue_service.put_message(aux_q,line)\\n f1.write(line)\\n f1.write('\\\\n')\\n queue_service.delete_message(queue_name, message.id, message.pop_receipt)\\n f1.close()\",\n \"def test_run(self):\\n engine = Engine(self.config_file, self.api_token, 23)\\n engine.msg_wait_iterations = 0\\n\\n # Put some stuff on the task queue\\n self.setup_helper.add_volumetric_tasks(self.aws_creds[\\\"access_key\\\"],\\n self.aws_creds['secret_key'],\\n self.upload_queue_url, engine.backend)\\n\\n engine.join()\\n engine.run()\\n\\n # Check for tile to exist\\n s3 = boto3.resource('s3')\\n ingest_bucket = s3.Bucket(self.ingest_bucket_name)\\n\\n with tempfile.NamedTemporaryFile() as test_file:\\n with open(test_file.name, 'wb') as raw_data:\\n ingest_bucket.download_fileobj(VOLUMETRIC_CUBOID_KEY, raw_data)\\n with open(test_file.name, 'rb') as raw_data:\\n # Using an empty CloudVolume dataset so all values should be 0.\\n # dtype set in boss-v0.2-test.json under chunk_processor.params.info.data_type\\n cuboid = self.s3_object_to_cuboid(raw_data.read(), 'uint8')\\n unique_vals = np.unique(cuboid)\\n assert 1 == len(unique_vals)\\n assert 0 == unique_vals[0]\",\n \"async def main():\\n \\n # workflow status\\n global status\\n\\n # Mode says which objects must be archived: DB dump, source files or both.\\n try:\\n mode=sys.argv[1]\\n except IndexError:\\n mode = 'all'\\n\\n # queue of files to be archived\\n files_to_upload = deque()\\n \\n logger.trace(\\\"Archiving ...\\\")\\n # Tasks to archive files and database dump\\n list_of_threads = get_list_of_threads(mode=mode)\\n\\n tar_names = await asyncio.gather(*list_of_threads)\\n\\n # Clear names list, removing None elements if exist\\n tar_names = [name for name in tar_names if name]\\n\\n files_to_upload.extend(tar_names)\\n logger.trace(\\\"Ok.\\\")\\n\\n logger.trace(\\\"Uploading ...\\\")\\n\\n # Connect to the ftp-server and upload the archived files.\\n await upload_to_ftp_server(host=FTP.SERVER.value,\\n port=FTP.PORT.value,\\n login=FTP.LOGIN.value,\\n password=FTP.PASSWORD.value,\\n files=files_to_upload)\\n\\n # Remove archived and dump files on the server site.\\n clear_garbage(mode=mode, files=tar_names)\\n\\n # Check the workflow status. If it's not empty, send an error email.\\n if len(status) > 0 and ERROR_NOTIFICATION_BY_EMAIL:\\n backup_email()\",\n \"def runQueueEnqueue(self):\\n raise NotImplementedError\",\n \"def _start_input_file_worker(\\n q_in: Queue, input_data_path: str, batch_size: int\\n) -> None:\\n input_data_file = open(input_data_path, \\\"r\\\")\\n enum_idx = 0\\n batch = []\\n for line in input_data_file:\\n unique_id = str(uuid.uuid1())\\n batch.append((enum_idx, unique_id, line))\\n # If the batch to send is the size then push to queue and rest batch\\n if len(batch) == batch_size:\\n q_in.put(batch)\\n batch = []\\n enum_idx += 1\\n if batch:\\n q_in.put(batch)\",\n \"def _work(self, payload):\\n pdf_path = payload['pdf_path']\\n jpeg_prefixes = payload['jpeg_prefixes']\\n\\n webhook_url = payload['webhook_url']\\n webhook_data = payload['webhook_data']\\n\\n page_start = payload['page_start']\\n page_end = payload['page_end']\\n\\n self._log('Connecting to s3')\\n connection, bucket = self._connect_to_s3(payload['s3'])\\n\\n # create batches of pages to convert\\n for batch_first_page in range(page_start, page_end + 1, self.PAGES_PER_BATCH):\\n batch_last_page = min(batch_first_page + self.PAGES_PER_BATCH - 1, page_end)\\n batch_pages = range(batch_first_page, batch_last_page + 1)\\n\\n batch_jpeg_prefixes = jpeg_prefixes[batch_first_page - page_start:\\n batch_last_page + 1 - page_start]\\n self._convert_batch(bucket, pdf_path, batch_pages, batch_jpeg_prefixes,\\n webhook_url, webhook_data)\",\n \"def main(content):\\n # with open(in_path) as f:\\n redi = redis.StrictRedis(host=\\\"10.243.55.67\\\", port=6379)\\n filename = content[0].split('/')[-1]\\n file_content = content[1]\\n #file_content=content\\n # line = f.read()\\n # hdfs_client = Client('http://10.243.55.67:50070/', root='/')\\n # outpath = os.path.join(\\\"/bres\\\", middle_path)\\n # l = []\\n ret = {}\\n ret[\\\"name\\\"] = \\\"\\\"\\n ret[\\\"alias\\\"] = \\\"\\\"\\n # index get id\\n index = filename.split('__')[0]\\n \\n # index = in_path.split(split_char)[0].split('/')[-1]\\n ret[\\\"vid\\\"] = index\\n redis_result = redi.get(str(index)).decode(\\\"utf-8\\\").split(\\\"@@\\\")\\n if len(redis_result) == 1:\\n ret[\\\"name\\\"] = redis_result[-1]\\n elif len(redis_result) == 2:\\n ret[\\\"name\\\"] = redis_result[0]\\n ret['alias'] = redis_result[1]\\n try:\\n obj = json.loads(file_content, encoding=\\\"utf8\\\")\\n # url, content, status = obj[\\\"url\\\"], obj[\\\"content\\\"], obj[\\\"status\\\"]\\n url, content = obj[\\\"url\\\"], obj[\\\"content\\\"]\\n html_handler = html_parse(content, url)\\n handler = BaikeGraph()\\n # ret[\\\"url\\\"] = url\\n content = html_handler.html_clean()\\n # parse title tag box\\n ret[\\\"iid\\\"] = html_handler.parse_itemId()\\n #html_handler.create_rel(index,ret[\\\"iid\\\"])\\n boxes = html_handler.parse_box_new()\\n desc = html_handler.parse_desc_new()\\n titles = html_handler.parse_title_new()\\n tags = html_handler.parse_tag_new()\\n dict_final = ChainMap(boxes, desc, ret, titles, tags)\\n handler.create_baike_node(dict(dict_final))\\n html_handler.parse_polysemantic()\\n\\n except Exception as e:\\n print('exception in main @@@@@@@@@@@@@@@@@2222')\\n print(e)\\n pass\\n return\",\n \"def main():\\n spark = create_spark_session()\\n\\n input_data = \\\"s3a://udacitydenanodegree2020/\\\"\\n output_data = \\\"s3a://udacitydenanodegree2020/output/\\\"\\n\\n process_song_data(spark, input_data, output_data) \\n process_log_data(spark, input_data, output_data)\",\n \"def _process_bigquery():\\n config_path = os.path.join(app.app.config['DATASET_CONFIG_DIR'],\\n 'bigquery.json')\\n table_names = []\\n participant_id_column = ''\\n sample_id_column = ''\\n sample_file_columns = []\\n if os.path.isfile(config_path):\\n bigquery_config = _parse_json_file(config_path)\\n table_names = bigquery_config['table_names']\\n participant_id_column = bigquery_config['participant_id_column']\\n sample_id_column = bigquery_config.get('sample_id_column', '')\\n sample_file_columns = bigquery_config.get('sample_file_columns', {})\\n table_names.sort()\\n\\n table_names_dict = OrderedDict()\\n for full_table_name in table_names:\\n splits = full_table_name.rsplit('.', 2)\\n if len(splits) != 3:\\n raise ValueError(\\n 'Unknown format for table name %s. Expected BigQuery project_id.dataset_id.table_name'\\n % full_table_name)\\n project_id, dataset_id, table_name = splits\\n client = bigquery.Client(project=project_id)\\n dataset_ref = client.dataset(dataset_id, project=project_id)\\n table_ref = dataset_ref.table(table_name)\\n description = client.get_table(table_ref).description\\n table_names_dict[full_table_name] = description\\n\\n # A dict from table name (project_id.dataset_id.table_name) to table description.\\n app.app.config['TABLES'] = table_names_dict\\n app.app.config['PARTICIPANT_ID_COLUMN'] = participant_id_column\\n app.app.config['SAMPLE_ID_COLUMN'] = sample_id_column\\n app.app.config['SAMPLE_FILE_COLUMNS'] = sample_file_columns\",\n \"def main():\\n # Initiate Spark Session\\n spark = create_spark_session()\\n \\n # Data files\\n # Root Data Path\\n # Uncomment below line for AWS S3\\n #input_data = \\\"s3a://udacity-dend\\\"\\n # Uncomment below line for local files\\n input_data = \\\"data\\\"\\n\\n # Warehouse\\n # Root WH\\n # Uncomment below line for AWS S3\\n #output_data = \\\"s3a://jerryespn-project-out\\\"\\n # Uncomment below line for local files\\n output_data = \\\"spark-warehouse\\\"\\n \\n process_song_data(spark, input_data, output_data) \\n process_log_data(spark, input_data, output_data)\",\n \"def get_file (queue):\\n\\n # get event from queue\\n event = queue.get(True)\\n\\n try:\\n # get name of new file\\n filename = str(event.src_path)\\n filetype = 'new'\\n\\n except AttributeError as e:\\n # instead of event, queue entry is a filename added in\\n # [run_blackbox]\\n filename = event\\n filetype = 'pre-existing'\\n\\n\\n log.info ('detected a {} file: {}'.format(filetype, filename))\\n\\n\\n # only continue if a fits file\\n if 'fits' not in filename:\\n\\n log.info ('{} is not a fits file; skipping it'.format(filename))\\n filename = None\\n\\n else:\\n\\n # if filename is a temporary rsync copy (default\\n # behaviour of rsync is to create a temporary file\\n # starting with .[filename].[randomstr]; can be\\n # changed with option \\\"--inplace\\\"), then let filename\\n # refer to the eventual file created by rsync\\n fn_head, fn_tail = os.path.split(filename)\\n if fn_tail[0] == '.':\\n filename = '{}/{}'.format(fn_head, '.'\\n .join(fn_tail.split('.')[1:-1]))\\n log.info ('changed filename from rsync temporary file {} to {}'\\n .format(event.src_path, filename))\\n\\n # this while loop below replaces the old [copying]\\n # function; it times out after wait_max is reached\\n wait_max = 180\\n t0 = time.time()\\n nsleep = 0\\n while time.time()-t0 < wait_max:\\n\\n try:\\n # read the file\\n data = read_hdulist(filename)\\n\\n except:\\n\\n process = False\\n if nsleep==0:\\n log.warning ('file {} has not completely arrived yet; '\\n 'will keep trying to read it in for {}s'\\n .format(filename, wait_max))\\n\\n # give file a bit of time to arrive before next read attempt\\n time.sleep(5)\\n nsleep += 1\\n\\n else:\\n # if fits file was read fine, set process flag to True\\n process = True\\n log.info ('successfully read file {} within {:.1f}s'\\n .format(filename, time.time()-t0))\\n # and break out of while loop\\n break\\n\\n\\n if not process:\\n log.info ('{}s limit for reading file reached, not processing {}'\\n .format(wait_max, filename))\\n filename = None\\n\\n\\n return filename\",\n \"def copy_blobs_in_gcp_storage(source_bucket_name):\\n\\n # extract file and convert in dataframe\\n extracted_df = extract()\\n\\n # transform the df\\n transformed_df = transform (extracted_df)\\n\\n # the function loads clean csv content as csv file in clean-zone-bucket\\n load(transformed_df)\\n\\n\\n return \\\"Function executed sucessfully!\\\"\",\n \"def create_mask_affinity_map_tasks(task_queue, aff_input_layer_path, aff_output_layer_path, \\n aff_mip, mask_layer_path, mask_mip, output_block_start, output_block_size, grid_size ):\\n for z in tqdm(range(grid_size[0]), desc='z loop'):\\n for y in range(grid_size[1]):\\n for x in range(grid_size[2]):\\n output_bounds = Bbox.from_slices(tuple(slice(s+x*b, s+x*b+b)\\n for (s, x, b) in zip(output_block_start, (z, y, x), output_block_size)))\\n task = MaskAffinitymapTask(\\n aff_input_layer_path=aff_input_layer_path,\\n aff_output_layer_path=aff_output_layer_path,\\n aff_mip=aff_mip, \\n mask_layer_path=mask_layer_path,\\n mask_mip=mask_mip,\\n output_bounds=output_bounds,\\n )\\n task_queue.insert(task)\\n task_queue.wait()\\n\\n vol = CloudVolume(output_layer_path, mip=aff_mip)\\n vol.provenance.processing.append({\\n 'method': {\\n 'task': 'InferenceTask',\\n 'aff_input_layer_path': aff_input_layer_path,\\n 'aff_output_layer_path': aff_output_layer_path,\\n 'aff_mip': aff_mip,\\n 'mask_layer_path': mask_layer_path,\\n 'mask_mip': mask_mip,\\n 'output_block_start': output_block_start,\\n 'output_block_size': output_block_size, \\n 'grid_size': grid_size,\\n },\\n 'by': OPERATOR_CONTACT,\\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\\n })\\n vol.commit_provenance()\",\n \"def _download_job_proecess(channel_id, file_info, report_queue, options):\\n\\n publisher_id = uuid.uuid4().hex\\n download_dir = options.get('download_dir', '.')\\n saving_file_name_type = options.get('file_name_type')\\n skip_duplicate = options.get('skip_duplicate', False)\\n\\n file_id = file_info.get('file_id')\\n file_meta = file_info.get('metadata', {})\\n download_uri = file_info.get('download_uri')\\n\\n if saving_file_name_type == 'id':\\n file_name = file_id\\n else:\\n file_name = file_meta.get('x-abeja-meta-filename') or file_id\\n\\n download_path = None\\n is_downloading = True\\n try:\\n # download file content\\n with generate_retry_session() as session:\\n download_stream_res = session.get(\\n download_uri, stream=True, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\\n # update pre-signed url if expired\\n if download_stream_res.status_code == 403:\\n url = \\\"{}/channels/{}/{}\\\".format(ABEJA_API_URL,\\n channel_id, file_id)\\n with generate_user_session() as user_session:\\n res = user_session.get(\\n url, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\\n res.raise_for_status()\\n file_info = res.json()\\n download_uri = file_info.get('download_uri')\\n with generate_retry_session() as session:\\n download_stream_res = session.get(\\n download_uri, stream=True, timeout=PLATFORM_REQUEST_TIMEOUT_SECONDS)\\n download_stream_res.raise_for_status()\\n total_size = int(download_stream_res.headers.get('content-length', 0))\\n\\n initialize_options = {\\n 'file_name': file_name,\\n 'total': total_size,\\n }\\n report_queue.put(\\n (INITIALIZE_REPORT, publisher_id, 0, initialize_options))\\n download_path = _get_default_file_path(download_dir, file_name)\\n result_options = {\\n 'source': file_id,\\n 'destination': download_path,\\n }\\n if os.path.exists(download_path):\\n if skip_duplicate:\\n report_queue.put(\\n (SKIP_REPORT, publisher_id, 0, result_options))\\n return\\n else:\\n # update destination path ot resolve conflict\\n result_options['destination'] = _resolve_file_path(\\n download_dir, file_name)\\n is_downloading = True\\n with open(download_path, 'wb') as f:\\n for chunk in download_stream_res.iter_content(chunk_size=HTTP_READ_CHUNK_SIZE):\\n # update tqdm progress bar with chunk data size\\n report_queue.put(\\n (PROGRESS_REPORT, publisher_id, len(chunk), None))\\n f.write(chunk)\\n\\n report_queue.put((FINISH_REPORT, publisher_id, 0, result_options))\\n except:\\n if is_downloading and download_path is not None and os.path.exists(download_path):\\n os.remove(download_path)\\n options = {\\n 'source': file_id,\\n 'error': 'Failed to download {} of channel_id {}'.format(file_id, channel_id)\\n }\\n report_queue.put((RAISE_ERROR, publisher_id, 0, options))\\n raise\",\n \"def multiprocessing_job(self, job_function: Callable[['Task.Task'], 'Task.Task']=None) -> None:\\n\\n if job_function is None:\\n raise ValueError(\\\"Job Function object not provided.\\\")\\n while True:\\n try:\\n task = self.input_queue.get(timeout=1)\\n if task:\\n '''\\n Based on how this should work, job_function must take\\n in a Submission object as its sole parameter, and\\n return a Submission object. To avoid major overhauls,\\n it's best to keep the '*_job' functions mostly the same,\\n but changing the function parametes and what it returns.\\n '''\\n if self.logger.getEffectiveLevel() == logging.DEBUG:\\n self.logger.debug(f\\\"\\\\nTask Received: {task._to_string()}\\\\n\\\")\\n else:\\n self.logger.info(f\\\"\\\\nTask Received: {task.get_job_id()} containing file {task.get_file_name()}\\\\n\\\")\\n try:\\n # Push the name of the manager and its current jobs.\\n if self.slack_queue:\\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \\\"Pending\\\", 1]\\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\\n # Perform the job function on the task\\n task = job_function(task)\\n # Upon success, push information to the Slack Bot (if enabled)\\n if self.slack_queue:\\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \\\"Complete\\\", 2]\\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\\n # Log the task completion\\n self.logger.info(f\\\"\\\\nTask Completed: {task.get_job_id()} containing file {task.get_file_name()}\\\\n\\\")\\n # Place the task in the output queue\\n self.output_queue.put(task)\\n # Mark the task as finished via task_done\\n self.input_queue.task_done()\\n # Take a short breather and let the CPU focus on other stuff.\\n sleep(1)\\n except KeyboardInterrupt as k:\\n if self.logger.getEffectiveLevel() == logging.DEBUG:\\n self.logger.exception(msg=f\\\"Task {task.job_id} encountered an exception in job {job_function.__name__} due to SIGINT event.\\\\n\\\")\\n else:\\n self.logger.error(msg=f\\\"\\\\nTask {task.job_id} encountered an exception in job {job_function.__name__}\\\\nError Message: {k}\\\\n\\\")\\n # If any errors that can be caught occur\\n except BaseException as e:\\n # Log the event with varying degrees of information\\n if self.logger.getEffectiveLevel() == logging.DEBUG:\\n self.logger.exception(msg=f\\\"Task {task.job_id} encountered an exception in job {job_function.__name__}\\\\nTask Information:\\\\n{task._to_string()}\\\")\\n else:\\n self.logger.error(msg=f\\\"\\\\nTask {task.job_id} encountered an exception in job {job_function.__name__}\\\\nError Message: {e}\\\\n\\\")\\n # Inform the submitter via Slack Notification (not Slack Bot)\\n # If the Slack Bot is enabled pass the error information to it\\n if self.slack_queue:\\n slack_information = [self.name, task.fetch_info('user_initials'), task.get_file_name(), \\\"Error\\\", 2]\\n self.push_information_to_slack_bot(self.slack_queue, slack_information)\\n # Place the task in the error queue\\n self.error_queue.put(task)\\n # Mark the task as finished via task_done\\n self.input_queue.task_done()\\n else:\\n #Media was None signal (this kills the Process)\\n self.input_queue.task_done()\\n break\\n except ValueError:\\n break\\n # If the worker does not receive anything it will throw this exception, which will kill the process.\\n except queue.Empty:\\n self.logger.debug(f\\\"\\\\nA process has enountered the Queue.Empty exception indicating there are no more things to process.\\\")\\n break\",\n \"def reaper(self):\\n if not self.superuser_request:\\n self.abort(402, 'uploads must be from an authorized drone')\\n with tempfile.TemporaryDirectory(prefix='.tmp', dir=config.get_item('persistent', 'data_path')) as tempdir_path:\\n try:\\n file_store = files.FileStore(self.request, tempdir_path)\\n except files.FileStoreException as e:\\n self.abort(400, str(e))\\n now = datetime.datetime.utcnow()\\n fileinfo = dict(\\n name=file_store.filename,\\n created=now,\\n modified=now,\\n size=file_store.size,\\n hash=file_store.hash,\\n tags=file_store.tags,\\n metadata=file_store.metadata\\n )\\n container = reaperutil.create_container_hierarchy(file_store.metadata)\\n f = container.find(file_store.filename)\\n target_path = os.path.join(config.get_item('persistent', 'data_path'), util.path_from_hash(fileinfo['hash']))\\n if not f:\\n file_store.move_file(target_path)\\n container.add_file(fileinfo)\\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\\n elif not file_store.identical(util.path_from_hash(fileinfo['hash']), f['hash']):\\n file_store.move_file(target_path)\\n container.update_file(fileinfo)\\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\\n throughput = file_store.size / file_store.duration.total_seconds()\\n log.info('Received %s [%s, %s/s] from %s' % (file_store.filename, util.hrsize(file_store.size), util.hrsize(throughput), self.request.client_addr))\",\n \"def main():\\n spark = create_spark_session()\\n\\n input_data = config['STORAGE']['INPUT_DATA']\\n output_data = config['STORAGE']['OUTPUT_DATA']\\n\\n process_song_data(spark, input_data, output_data)\\n process_log_data(spark, input_data, output_data)\",\n \"def handleDQMFileSaver(self):\\n if not hasattr(self.process, \\\"dqmSaver\\\"):\\n return\\n\\n runIsComplete = getattr(self.jobBag, \\\"runIsComplete\\\", False)\\n multiRun = getattr(self.jobBag, \\\"multiRun\\\", False)\\n runLimits = getattr(self.jobBag, \\\"runLimits\\\", \\\"\\\")\\n self.logger.info(\\\"DQMFileSaver set to multiRun: %s, runIsComplete: %s, runLimits: %s\\\",\\n multiRun, runIsComplete, runLimits)\\n\\n self.process.dqmSaver.runIsComplete = cms.untracked.bool(runIsComplete)\\n if multiRun and isCMSSWSupported(self.getCmsswVersion(), \\\"CMSSW_8_0_0\\\"):\\n self.process.dqmSaver.forceRunNumber = cms.untracked.int32(999999)\\n if hasattr(self.step.data.application.configuration, \\\"pickledarguments\\\"):\\n args = pickle.loads(self.step.data.application.configuration.pickledarguments)\\n datasetName = args.get('datasetName', None)\\n if datasetName:\\n if multiRun:\\n # then change the dataset name in order to get a different root file name\\n datasetName = datasetName.rsplit('/', 1)\\n datasetName[0] += runLimits\\n datasetName = \\\"/\\\".join(datasetName)\\n self.process.dqmSaver.workflow = cms.untracked.string(datasetName)\\n return\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def main():\\n spark = create_spark_session()\\n input_data = \\\"s3a://udacity-dend/\\\"\\n output_data = \\\"data/analytics\\\"\\n \\n process_song_data(spark, input_data, output_data) \\n process_log_data(spark, input_data, output_data)\",\n \"def main():\\n spark = create_spark_session()\\n input_data = \\\"s3a://udacity-dend/\\\"\\n output_data = \\\"s3a://udacity-data-lake/output/\\\"\\n\\n process_song_data(spark, input_data, output_data)\\n process_log_data(spark, input_data, output_data)\",\n \"async def main():\\n\\n usage = \\\"usage: prog [options] <intelligence_query/local_file_with_hashes>\\\"\\n parser = argparse.ArgumentParser(\\n usage=usage,\\n description=(\\n \\\"Allows you to download the top-n files returned by a given\\\"\\n \\\"VirusTotal Intelligence search. Example: \\\"\\n 'python %prog type:\\\"peexe\\\" positives:5+ -n 10 --apikey=<your api key>'\\n ),\\n )\\n\\n parser.add_argument(\\n \\\"query\\\",\\n type=str,\\n nargs=\\\"+\\\",\\n help=\\\"a VirusTotal Intelligence search query.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--numfiles\\\",\\n dest=\\\"numfiles\\\",\\n default=100,\\n help=\\\"Number of files to download\\\",\\n )\\n\\n parser.add_argument(\\\"--apikey\\\", required=True, help=\\\"Your VirusTotal API key\\\")\\n\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--output-path\\\",\\n required=False,\\n help=\\\"The path where you want to put the files in\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-w\\\",\\n \\\"--workers\\\",\\n dest=\\\"workers\\\",\\n default=4,\\n help=\\\"Concurrent workers for downloading files\\\",\\n )\\n\\n args = parser.parse_args()\\n\\n if not args.query:\\n parser.error(\\\"No search query provided\\\")\\n\\n if not args.apikey:\\n parser.error(\\\"No API key provided\\\")\\n\\n search = \\\" \\\".join(args.query)\\n search = search.strip().strip(\\\"'\\\")\\n storage_path = args.output_path\\n numfiles = int(args.numfiles)\\n workers = int(args.workers)\\n api_key = args.apikey\\n handler = DownloadTopNFilesHandler(api_key, numfiles)\\n\\n logging.info(\\\"Starting VirusTotal Intelligence downloader\\\")\\n logging.info(\\\"* VirusTotal Intelligence search: %s\\\", search)\\n logging.info(\\\"* Number of files to download: %s\\\", numfiles)\\n\\n files_path = handler.create_download_folder(storage_path)\\n enqueue_files_task = asyncio.create_task(handler.queue_file_hashes(search))\\n\\n download_tasks = []\\n for _ in range(workers):\\n download_tasks.append(\\n asyncio.create_task(handler.download_files(files_path))\\n )\\n\\n await asyncio.gather(enqueue_files_task)\\n # Wait until all the files have been queued and downloaded, then cancel\\n # download tasks that are idle\\n await handler.queue.join()\",\n \"def fastq_qc(demultiplex_result, out_dir, config):\\n\\n pigz_cores = int(config['fastqTrim']['pigz_cores'])\\n cutadapt_cores = int(config['fastqTrim']['cutadapt_cores'])\\n\\n r1_adapter = config['fastqTrim']['r1_adapter']\\n r2_adapter = config['fastqTrim']['r1_adapter']\\n length_threshold = config['fastqTrim']['length_threshold']\\n quality_threshold = config['fastqTrim']['quality_threshold']\\n r1_left_cut = config['fastqTrim']['r1_left_cut']\\n r1_right_cut = config['fastqTrim']['r1_right_cut']\\n r2_left_cut = config['fastqTrim']['r2_left_cut']\\n r2_right_cut = config['fastqTrim']['r2_right_cut']\\n overlap = config['fastqTrim']['overlap']\\n total_reads_threshold = int(config['fastqTrim']['total_reads_threshold'])\\n\\n results = []\\n for (uid, index_name), sub_df in demultiplex_result.groupby(['uid', 'index_name']):\\n sample_demultiplex_total = sub_df['Trimmed'].sum()\\n if sample_demultiplex_total < total_reads_threshold:\\n log.info(f'In uid {uid}: index {index_name} skipped '\\n f'due to too less reads: {sample_demultiplex_total}')\\n continue\\n # process R1\\n r1_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R1.fq.gz'\\n r1_out = f'{out_dir}/{uid}_{index_name}_R1.trimed.fq.gz'\\n r1_cmd = f'pigz -cd -p {pigz_cores} {r1_path_pattern} | ' \\\\\\n f'cutadapt -j {cutadapt_cores} --report=minimal -O {overlap} ' \\\\\\n f'-q {quality_threshold} -u {r1_left_cut} ' \\\\\\n f'-u -{r1_right_cut} -m {length_threshold} ' \\\\\\n f'-a {r1_adapter} -o {r1_out} -'\\n r1_result = subprocess.run(r1_cmd, stdout=subprocess.PIPE,\\n encoding='utf8', shell=True, check=True)\\n\\n # get R1 result stat\\n lines = []\\n for line in r1_result.stdout.split('\\\\n'):\\n ll = line.split('\\\\t')\\n if len(ll) > 1:\\n lines.append(ll)\\n s = pd.Series({name: number for name, number in zip(*lines)})\\n s['uid'] = uid\\n s['index_name'] = index_name\\n s['read_type'] = 'R1'\\n results.append(s)\\n\\n # process R2\\n r2_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R2.fq.gz'\\n r2_out = f'{out_dir}/{uid}_{index_name}_R2.trimed.fq.gz'\\n r2_cmd = f'pigz -cd -p {pigz_cores} {r2_path_pattern} | ' \\\\\\n f'cutadapt -j {cutadapt_cores} --report=minimal -O {overlap} ' \\\\\\n f'-q {quality_threshold} -u {r2_left_cut} ' \\\\\\n f'-u -{r2_right_cut} -m {length_threshold} ' \\\\\\n f'-a {r2_adapter} -o {r2_out} -'\\n r2_result = subprocess.run(r2_cmd, stdout=subprocess.PIPE,\\n encoding='utf8', shell=True, check=True)\\n # get R2 result stat\\n lines = []\\n for line in r2_result.stdout.split('\\\\n'):\\n ll = line.split('\\\\t')\\n if len(ll) > 1:\\n lines.append(ll)\\n s = pd.Series({name: number for name, number in zip(*lines)})\\n s['uid'] = uid\\n s['index_name'] = index_name\\n s['read_type'] = 'R2'\\n results.append(s)\\n\\n fastq_final_result = pd.DataFrame(results)\\n if len(results) == 0:\\n # all sample skipped\\n return fastq_final_result\\n fastq_final_result['out_reads_rate'] = \\\\\\n fastq_final_result['out_reads'].astype(int) / fastq_final_result['in_reads'].astype(int)\\n fastq_final_result['out_bp_rate'] = \\\\\\n fastq_final_result['out_reads'].astype(int) / fastq_final_result['in_reads'].astype(int)\\n\\n # clean up\\n for (uid, index_name), sub_df in demultiplex_result.groupby(['uid', 'index_name']):\\n r_path_pattern = f'{out_dir}/{uid}_L*_{index_name}_R*.fq.gz'\\n r_rm_cmd = f'rm -f {r_path_pattern}'\\n subprocess.run(r_rm_cmd, shell=True)\\n # remove unknown reads\\n r_path_pattern = f'{out_dir}/{uid}_L*_unknown_R*.fq.gz'\\n r_rm_cmd = f'rm -f {r_path_pattern}'\\n subprocess.run(r_rm_cmd, shell=True)\\n\\n return fastq_final_result\",\n \"def image_resize(job_object):\\n try:\\n job = json.loads(job_object.arg)\\n base64_file = job['image']\\n args = job['args'] if 'args' in job else {}\\n del job['image']\\n logging.info(job)\\n \\n def write_file(local_path,filename,file_b64):\\n logging.debug(\\\"about to save to \\\" + \\\"%s/%s\\\" % (local_path,filename))\\n if not os.path.exists(local_path): os.makedirs(local_path)\\n image_file = base64.b64decode(file_b64)\\n local_file = open(\\\"%s/%s\\\" % (local_path,filename), \\\"w\\\")\\n local_file.write(image_file)\\n local_file.close()\\n \\n def download_file(url,local_path,filename):\\n print \\\"downloading \\\" + url\\n f = urllib2.urlopen(urllib2.Request(url))\\n print \\\"about to save to \\\" + \\\"%s/%s\\\" % (local_path,filename)\\n if not os.path.exists(local_path): os.makedirs(local_path)\\n # Open our local file for writing\\n local_file = open(\\\"%s/%s\\\" % (local_path,filename), \\\"w\\\")\\n local_file.write(f.read())\\n local_file.close()\\n \\n local_path = '%s/upload/%s' % (options.asset_root,job['path'])\\n local_path_wfile = '%s/%s%s' % (local_path,job['file'],job['extension'])\\n filename = '%s%s' % (job['file'],job['extension'])\\n #download_file(job['url'],local_path,filename)\\n write_file(local_path,filename,base64_file)\\n \\n def resize_and_save(local_file,new_file,maxsize=None,maxh=None,maxw=None,crop=None):\\n \\\"\\\"\\\"Resize the image and save\\\"\\\"\\\"\\n logging.debug(\\\"maxw = %s, maxsize=%s, crop=%s\\\" % (maxw,maxsize,crop))\\n img = Image.open(local_file)\\n width,height = img.size\\n width,height = float(width), float(height)\\n ratio = float(1)\\n if crop is not None:\\n size = float(maxsize)\\n if width <= height and width > size:\\n ratio = size/width\\n elif height < width and height > size:\\n ratio = size/height\\n else: \\n ratio = 1 # too small\\n elif maxsize:\\n size = float(maxsize)\\n if width >= height and width > size:\\n ratio = size/width\\n elif height > width and height > size:\\n ratio = size/height\\n else: \\n ratio = 1 # too small\\n elif maxh:\\n size = maxh\\n if height > size:\\n ratio = size/height\\n else:\\n # too small\\n ratio = 1\\n elif maxw:\\n size = maxw\\n if width > size:\\n ratio = size/width\\n else:\\n # too small\\n ratio = 1\\n else:\\n raise Exception(\\\"must specify max width, OR max size\\\")\\n \\n print(\\\"old: ratio = %s: size(x,y) = %s,%s\\\" % (ratio,width,height))\\n height = int(height*ratio)\\n width = int(width*ratio)\\n print(\\\"new ratio = %s: size(x,y) = %s,%s\\\" % (ratio,width,height))\\n img = img.resize((width, height),Image.ANTIALIAS)\\n if crop is not None:\\n log.debug(\\\"in crop %s\\\" % crop)\\n crop = int(crop)\\n if width > crop:\\n amt = int((int(width) - crop)/2)\\n img = img.crop((amt,0,amt + crop, crop))\\n elif height > crop:\\n amt = int((int(height) - crop)/2)\\n img = img.crop((0,amt,crop,amt+crop))\\n \\n log.debug(\\\"saving new file %s\\\" % new_file)\\n if img.mode != \\\"RGB\\\":\\n img = img.convert(\\\"RGB\\\")\\n img.save(new_file)\\n \\n \\n if os.path.exists(local_path_wfile):\\n if args != {}:\\n ext = args['extension'] if 'extension' in args else \\\"_t\\\"\\n resize_and_save(local_path_wfile,\\n '%s/%s%s.jpg' % (local_path,job['file'],ext),\\n maxsize=args['maxsize'],\\n crop=args['crop'])\\n else:\\n resize_and_save(local_path_wfile,'%s/%s_t.jpg' % (local_path,job['file']),maxsize=100)\\n resize_and_save(local_path_wfile,'%s/%s_m.jpg' % (local_path,job['file']),maxw=317)\\n resize_and_save(local_path_wfile,'%s/%s_l.jpg' % (local_path,job['file']),maxsize=800)\\n keeptrying = False\\n else:\\n logging.error(\\\"haven't found file? %s\\\" % local_path_wfile)\\n \\n # delete original\\n logging.debug(\\\"About to delete original %s\\\" % local_path_wfile)\\n os.remove(local_path_wfile)\\n \\n except:\\n traceback.print_exc()\",\n \"def prepare_reader(self, unused_filename_queue):\\n raise NotImplementedError()\",\n \"def run(self):\\r\\n filesizes = {}\\r\\n # Build up dict with key as filesize and value is list of filenames.\\r\\n for path, dirs, files in walk( self._path ):\\r\\n for filename in files:\\r\\n filepath = joinpath( path, filename )\\r\\n filesize = stat( filepath ).st_size\\r\\n filesizes.setdefault( filesize, [] ).append( filepath )\\r\\n\\r\\n\\r\\n #Compare content hash of all files which have the same size\\r\\n #if two or more files have same hash and size they are added to the queue \\r\\n for files in [ flist for flist in filesizes.values() if len(flist)>1 ]:\\r\\n #run over all files in dir with the same size if there is more then one\\r\\n duplicates = {}\\r\\n for filepath in files:\\r\\n with open( filepath ) as openfile:\\r\\n filehash = md5( openfile.read() ).hexdigest()\\r\\n if filehash not in duplicates:\\r\\n duplicates.setdefault(filehash, []).append (filepath)\\r\\n else:\\r\\n duplicates[filehash].append(filepath)\\r\\n for duplicate in [ duplicate for duplicate in duplicates.values() if len(duplicate)>1 ]:\\r\\n self._queue.put(duplicate)\\r\\n self._finished_scan[0] = 1\",\n \"def test_ClearOldFile(self):\\n q = Queue(self.path, chunksize=10)\\n for i in range(15):\\n q.put('var1')\\n\\n for i in range(11):\\n q.get()\\n\\n q = Queue(self.path, chunksize=10)\\n self.assertEqual(q.qsize(), 15)\\n\\n for i in range(11):\\n q.get()\\n q.task_done()\\n self.assertEqual(q.qsize(), 4)\",\n \"def process_single_file( name, \\n jobStrList, \\n ccu, \\n flagSilent=False ):\\n\\n startTime = time.time()\\n\\n ret = STAT_OK\\n\\n s = \\\"%s: %s -> %s. %s \\\" % ( name, jobStrList[0], jobStrList[1], MSG_DELIMITER )\\n\\n cprint(\\\"%s. \\\" % (jobStrList[0]), flagSilent)\\n\\n # Upload file.\\n try:\\n # Get the blob even it is not exist.\\n bcu = ccu.get_blob_client(blob=jobStrList[1])\\n \\n # bIdx = barrierUpload.wait()\\n # if ( 0 == bIdx ):\\n # barrierUpload.reset()\\n \\n fp = open( jobStrList[0], \\\"rb\\\" )\\n\\n # print(\\\"%s: Before upload. \\\" % (name))\\n bcu.upload_blob(fp, blob_type=\\\"BlockBlob\\\", overwrite=True)\\n # print(\\\"%s: After upload. \\\" % (name))\\n except OSError as ex:\\n s = s + \\\"Exeption: %s. %s \\\" % ( str(ex), MSG_DELIMITER )\\n ret = STAT_FAIL\\n except Exception as ex:\\n s = s + \\\"Exeption: %s. File closed. %s \\\" % ( str(ex), MSG_DELIMITER )\\n ret = STAT_FAIL\\n fp.close()\\n else:\\n fp.close()\\n\\n endTime = time.time()\\n \\n s = s + \\\"%ds for processing. %s \\\" % (endTime - startTime, MSG_DELIMITER )\\n\\n cprint(\\\"%s: %s\\\" % (name, s), flagSilent)\\n\\n return [ret, s]\",\n \"def run_job(self):\\n try:\\n info(\\\"fetching testing invoice data from get_data_after_rescoring_flag_check function\\\")\\n testing_data = self.get_data_after_rescoring_flag_check()\\n self.common_util.dataframe_empty_check(testing_data)\\n info(\\\"geting and processing data from customer history\\\")\\n customer_history_data_query = self.query.get_customer_history_data_query()\\n customer_history_data = self.database.get_data_from_database(customer_history_data_query)\\n self.common_util.dataframe_empty_check(testing_data)\\n self.common_util.dataframe_empty_check(customer_history_data)\\n model_name = self.common_util.load_last_model_name('SC_LR')\\n info(\\\"inferencing started\\\")\\n predicted_data = self.common_util.get_selfcure_lr_prediction(testing_data, customer_history_data, 'SC_LR')\\n info(\\\"storing prediction to the table\\\")\\n self.database.save_prediction_to_table(predicted_data, model_name)\\n info(\\\"Appling P flag for all predcitited invoice\\\")\\n self.database.update_processed_flag_for_open_invoice(predicted_data)\\n except Exception as err:\\n error('Exception Occured while running inferencingJob job :' + str(err))\\n raise Exception('Exception Occured while running inferencingJob job : {}'.format(str(err)))\",\n \"def process_results(refresh_count, output_dir, ext_queue, result_queue,\\n num_of_workers=8):\\n workers_dict = {} # keep track of worker processes\\n input_queue = Queue() # asynchronously feed workers task to do \\n worker_output_queue = Queue() # output queue from workers\\n ack_queue = Queue()\\n bug_dict = {} # dict to keep track of how many duplicates of each bug, if\\n # exists\\n try:\\n # separate the non-ads from the ads for ease of handchecking\\n os.makedirs(output_dir)\\n os.makedirs(os.path.join(output_dir, 'notad'))\\n except OSError:\\n # Directory is created, Okay to pass\\n pass\\n\\n for i in range(num_of_workers):\\n p = Process(target=curl_worker, args=(output_dir, input_queue,\\\\\\n worker_output_queue, i, ack_queue))\\n p.start()\\n workers_dict[i] = p\\n # uses a pool nodesurl' workers\\n # curl_worker_pool = Pool(processes=8)\\n # manager = Manager()\\n # curl_result_queue = manager.Queue()\\n \\n dl_counter = 0 # keep track of how many bugs downloaded\\n while True:\\n try:\\n found_bugs = json.loads(ext_queue.get(block=True, timeout=2))\\n except Exception:\\n LOG.debug('No more bugs found, break out of queue')\\n break\\n\\n for entry in found_bugs:\\n bug = parse_buginfo(entry)\\n try:\\n # matched an entry in the bugdict, incr count and continue\\n bug_dict[bug] += 1\\n continue\\n except KeyError:\\n bug_dict[bug] = 1 \\n\\n try:\\n saved_location ='Visit%d_%s%d' % (refresh_count, bug.get_name(), dl_counter)\\n dl_counter += 1\\n save_to_path = os.path.join( output_dir, '%s' % saved_location)\\n input_queue.put((saved_location, save_to_path, bug))\\n except Exception as e:\\n LOG.exception('%s' % e)\\n\\n for i in range(num_of_workers):\\n # send stop signal\\n input_queue.put((\\\"STOP\\\",))\\n \\n stopped = 0\\n while stopped < len(workers_dict):\\n ack = ack_queue.get()\\n p = workers_dict[ack]\\n p.join(timeout=1)\\n if p.is_alive():\\n p.terminate()\\n LOG.debug('terminating process %d' % ack)\\n stopped += 1\\n \\n while not worker_output_queue.empty():\\n # receive results from the worker\\n cbug = worker_output_queue.get()\\n # ugly code here\\n bugcount = bug_dict[cbug]\\n del bug_dict[cbug]\\n bug_dict[cbug] = bugcount\\n\\n with open( os.path.join(output_dir, 'bug_dict%d.pkl' % refresh_count), 'w') as fwtr:\\n cPickle.dump(bug_dict, fwtr)\\n result_queue.put(bug_dict)\\n return\",\n \"def teardown(self):\\n #save the HDF output and upload it to the data service with all the meta data\\n self.bqSession.update_mex( 'Returning results')\\n log.debug('Storing HDF output')\\n\\n #constructing and storing HDF file\\n mex_id = self.bqSession.mex.uri.split('/')[-1]\\n dt = datetime.now().strftime('%Y%m%dT%H%M%S')\\n final_output_file = \\\"ModuleExecutions/Dream3D/%s_%s_%s.h5\\\"%(self.options.OutputPrefix, dt, mex_id)\\n\\n #does not accept no name on the resource\\n cl_model = etree.Element('resource', resource_type='table', name=final_output_file)\\n\\n #module identifier (a descriptor to be found by the Dream3D model)\\n etree.SubElement(cl_model, 'tag', name='module_identifier', value='Dream3D')\\n\\n #hdf filename\\n etree.SubElement(cl_model, 'tag', name='OutputFile', value=final_output_file)\\n\\n #pipeline param\\n #etree.SubElement(cl_model, 'tag', name='RefFrameZDir', value=self.options.RefFrameZDir)\\n\\n #input hdf url\\n #etree.SubElement(cl_model, 'tag', name='InputFile', type='link', value=self.options.InputFile)\\n\\n #input pipeline\\n #etree.SubElement(cl_model, 'tag', name='pipeline_url', type='link', value=self.options.pipeline_url)\\n\\n #description\\n etree.SubElement(cl_model, 'tag', name='description', value = 'HDF output file from Dream3D Module')\\n\\n #storing the HDF file in blobservice\\n log.debug('before postblob') #!!!\\n r = self.bqSession.postblob(self.output_file, xml = cl_model)\\n r_xml = etree.fromstring(r)\\n\\n outputTag = etree.Element('tag', name ='outputs')\\n etree.SubElement(outputTag, 'tag', name='output_hdf', type='table', value=r_xml[0].get('uri',''))\\n\\n self.bqSession.finish_mex(tags=[outputTag])\",\n \"async def consume_mult(queue, fpath):\\n while True:\\n if not queue.qsize():\\n await asyncio.sleep(0.001)\\n\\n else:\\n qitem = queue.get_nowait()\\n try:\\n jlqitem = json.loads(qitem)\\n\\n print('got', jlqitem)\\n with open(fpath, 'a') as f:\\n f.write(f\\\"{json.dumps(jlqitem)}\\\\n\\\")\\n except Exception as exc:\\n print(exc.__repr__())\\n print('nope', qitem)\\n\\n queue.task_done()\\n await asyncio.sleep(0.001)\",\n \"def single_inference_process_fn(inference_initializer, inference_mode_config, in_project_meta_json, request_queue,\\n result_meta_queue, progress_queue, project):\\n single_image_inference = inference_initializer()\\n inference_mode = InferenceModeFactory.create(\\n inference_mode_config, ProjectMeta.from_json(in_project_meta_json), single_image_inference)\\n\\n project_meta_sent = False\\n req = ''\\n while req is not None:\\n req = request_queue.get()\\n if req is not None:\\n # Send the resulting project meta to the parent project to make sure we only write the meta JSON once.\\n if not project_meta_sent:\\n try:\\n result_meta_queue.put(inference_mode.out_meta.to_json(), block=False)\\n except queue.Full:\\n pass\\n project_meta_sent = True\\n\\n in_ann = Annotation.load_json_file(req.item_paths.ann_path, inference_mode.out_meta)\\n ann = inference_mode.infer_annotate_image_file(req.item_paths.img_path, in_ann)\\n out_dataset = project.datasets.get(req.ds_name)\\n out_dataset.add_item_file(\\n req.item_name, req.item_paths.img_path, ann=ann, _validate_item=False, _use_hardlink=True)\\n progress_queue.put(1)\",\n \"def test_4_empty_fastq(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, os.pardir, \\\"110221_empty_FC12345AAXX\\\"),\\n os.path.join(data_dir, \\\"run_info-empty.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def process():\\n print(OPTS)\\n\\n # Remove dataset files if they exist from before\\n p = PATH.proc\\n if p.exists():\\n shutil.rmtree(p)\\n p.mkdir()\\n\\n with multiprocessing.Pool(OPTS['cpus']) as pool:\\n chunks = [0, 1] if OPTS['dev'] else range(100)\\n partition_paths = pool.map(_process, chunks)\\n\\n fastparquet.writer.merge(partition_paths)\",\n \"def setup(self, context):\\n myindex = context.get_partition_index()\\n self._files_to_consume = self._files[myindex::context.get_num_partitions()]\\n self.logger.info(\\\"TextFileSpout files to consume %s\\\" % self._files_to_consume)\\n self._lines_to_consume = self._get_next_lines()\\n self._emit_count = 0\",\n \"def _worker():\\n try:\\n logger.info('Looping...')\\n temp_list = []\\n for file in ['data_unfcc.csv','data_ebal.csv']:\\n temp_list.append(os.path.isfile(file))\\n if not all(temp_list):\\n print('Starting from scratch...')\\n download_source()\\n create_database()\\n create_index()\\n\\n time_mod = datetime.strptime(time.ctime(os.stat('data_ebal.csv').st_mtime),'%a %b %d %H:%M:%S %Y')\\n time_now = datetime.now()\\n\\n if (time_now - time_mod).seconds > 3600:\\n download_source()\\n get_updated_records('unfcc')\\n get_updated_records('ebal')\\n create_index()\\n except Exception as e:\\n logger.warning('Main Loop error')\",\n \"def main():\\n #global comm, rank, args\\n #comm = MPI.COMM_WORLD\\n #rank = comm.Get_rank()\\n rank = 0\\n\\n parser = argparse.ArgumentParser(description=\\\"Receive data and dispatch\\\" +\\n \\\"analysis tasks to a mpi queue\\\")\\n parser.add_argument('--config', type=str, help='Lists the configuration file',\\n default='configs/config-middle.json')\\n parser.add_argument(\\\"--transport_rx\\\", help=\\\"Specifies the name of the transport section that is used to configure the reader\\\",\\n default=\\\"transport_rx\\\")\\n parser.add_argument(\\\"--transport_tx\\\", help=\\\"Specifies the name of the transport section that is used to configure the writer\\\",\\n default=\\\"transport_tx\\\")\\n args = parser.parse_args()\\n\\n with open(args.config, \\\"r\\\") as df:\\n cfg = json.load(df)\\n timeout = 120\\n\\n # The middleman uses both a reader and a writer. Each is configured with using\\n # their respective section of the config file. Therefore some keys are duplicated,\\n # such as channel_range. Make sure that these items are the same in both sections\\n\\n with open(\\\"configs/logger.yaml\\\", \\\"r\\\") as f:\\n log_cfg = yaml.safe_load(f.read())\\n logging.config.dictConfig(log_cfg)\\n logger = logging.getLogger('middleman')\\n\\n # Create ADIOS reader object\\n reader = reader_gen(cfg[args.transport_rx], gen_channel_name(cfg[\\\"diagnostic\\\"]))\\n reader.Open()\\n stream_attrs = None\\n stream_varname = gen_var_name(cfg)[rank]\\n logger.info(f\\\"Main: Stream varname: {stream_varname}\\\")\\n\\n dq = queue.Queue()\\n msg = None\\n worker = threading.Thread(target=forward, args=(dq, cfg, args, timeout))\\n worker.start()\\n\\n rx_list = []\\n stream_data = None\\n while True:\\n stepStatus = reader.BeginStep()\\n logger.info(f\\\"Main: stepStatus = {stepStatus}, currentStep = {reader.CurrentStep()}\\\")\\n if stepStatus:\\n # Read data\\n stream_data = reader.Get(stream_varname, save=False)\\n if stream_attrs is None:\\n if reader.InquireAttribute(\\\"stream_attrs\\\"):\\n stream_attrs = reader.get_attrs(\\\"stream_attrs\\\")\\n\\n stream_data = reader.Get(stream_varname, save=False)\\n rx_list.append(reader.CurrentStep())\\n\\n # Generate message id and publish is\\n msg = AdiosMessage(tstep_idx=reader.CurrentStep(), data=stream_data, attrs=stream_attrs)\\n dq.put_nowait(msg)\\n logger.info(f\\\"Main: Published message {msg}\\\")\\n reader.EndStep()\\n else:\\n logger.info(f\\\"Main: Exiting: StepStatus={stepStatus}\\\")\\n break\\n\\n # last_step = reader.CurrentStep()\\n\\n logger.info(\\\"Main: Exiting main loop\\\")\\n worker.join()\\n logger.info(\\\"Main: Workers have joined\\\")\\n dq.join()\\n logger.info(\\\"Main: Queue joined\\\")\\n logger.info(\\\"Main: Finished\\\")\",\n \"def process(self, element, bucket, ingestFrom, all_edits):\\n if isinstance(element, basestring):\\n chunk_name = element\\n page_ids = None\\n else:\\n # If specified on selected pages.\\n (chunk_name, data) = element\\n if data['datadump'] == []:\\n logging.info('USERLOG: chunk %s skipped.', chunk_name)\\n return\\n page_ids = data['selected_pages']\\n logging.info('USERLOG: Running ingestion process on %s.', chunk_name)\\n if ingestFrom == 'local':\\n input_stream = chunk_name\\n else:\\n cmd = \\\"gsutil -m cp {path} {chunk}\\\".format(filepath=path.join('gs://', bucket, chunk_name), chunk=chunk_name)\\n status = os.WEXITSTATUS(os.system(cmd))\\n if status != 0:\\n raise Exception(\\\"GSUTIL COPY Error, exited with status {}\\\".format(status))\\n input_stream = chunk_name\\n # Running ingestion on the xml file\\n last_revision = None\\n last_completed = time.time()\\n i = 0\\n for i, content in enumerate(parse_stream(bz2.BZ2File(chunk_name))):\\n if (page_ids is not None) and (content['page_id'] not in page_ids):\\n continue\\n self.processed_revisions.inc()\\n if content['text'] is None:\\n content['text'] = \\\"\\\"\\n if (content[\\\"comment\\\"] is not None and \\\"POV\\\" in content[\\\"comment\\\"]) or all_edits:\\n yield json.dumps((last_revision, content))\\n last_revision = content\\n logging.info('INGESTION_LOG: CHUNK %(chunk): revision %(revid) ingested, time elapsed: %(time).',\\n extra={'chunk':chunk_name, 'revid':content['rev_id'], 'time':time.time() - last_completed))\\n last_completed = time.time()\\n if ingestFrom != 'local': os.system(\\\"rm %s\\\" % chunk_name)\\n logging.info('USERLOG: Ingestion on file %(chunk) complete! %(cnt) lines emitted',\\n extra={'chunk':chunk_name, 'cnt':i})\",\n \"def main():\\n gcs_client = storage.Client()\\n bucket_name: str = os.environ.get(\\\"GCS_BUCKET_NAME\\\")\\n blob_name: str = os.environ.get(\\\"GCS_BLOB_NAME\\\")\\n\\n print(f\\\"Uploading BigEarth to bucket {bucket_name} and blob {blob_name}\\\")\\n\\n for url, blob_name in [\\n (\\\"http://bigearth.net/downloads/BigEarthNet-v1.0.tar.gz\\\", blob_name),\\n (\\\"http://bigearth.net/static/documents/patches_with_cloud_and_shadow.csv\\\",\\n \\\"patches_with_cloud_and_shadow.csv\\\"),\\n (\\\"http://bigearth.net/static/documents/patches_with_snow.csv\\\", \\\"patches_with_seasonal_snow.csv\\\"),\\n ]:\\n with GCSObjectStreamUploader(client=gcs_client, bucket_name=bucket_name, blob_name=blob_name) as gcs_uploader:\\n with requests.get(url, stream=True) as response_stream:\\n for chunk in response_stream.raw.stream(128*2000, decode_content=False):\\n gcs_uploader.write(chunk)\",\n \"def submit_command_to_queue(command, queue=None, max_jobs_in_queue=None, queue_file=None, queue_parameters={'max_mem':5000, 'queue':'short', 'logs_dir':'/tmp', 'modules':['Python/3.6.2']}, dummy_dir=\\\"/tmp\\\", script_name=None):\\n #if max_jobs_in_queue is not None:\\n # while number_of_jobs_in_queue() >= max_jobs_in_queue: time.sleep(5)\\n\\n if not os.path.exists(dummy_dir): os.makedirs(dummy_dir)\\n if not script_name:\\n script_name = \\\"submit_\\\"+hashlib.sha224(command).hexdigest()+\\\".sh\\\"\\n script= os.path.join(dummy_dir, script_name)\\n if queue_file is not None:\\n # Use a queue file as header of the bash file\\n fd=open(script,\\\"w\\\")\\n with open(queue_file,\\\"r\\\") as queue_standard:\\n data=queue_standard.read()\\n fd.write(data)\\n fd.write(\\\"%s\\\\n\\\\n\\\"%(command))\\n fd.close()\\n queue_standard.close()\\n if queue is not None:\\n os.system(\\\"sbatch -p %s %s\\\" % (queue,script))\\n else:\\n os.system(\\\"sbatch %s\\\"% (script))\\n else:\\n if queue_parameters is not None:\\n # Write manually the bash file\\n with open(script, \\\"w\\\") as fd:\\n fd.write('#!/bin/bash\\\\n') # bash file header\\n fd.write('#SBATCH --mem={}\\\\n'.format(queue_parameters['max_mem'])) # max memory\\n fd.write('#SBATCH -p {}\\\\n'.format(queue_parameters['queue'])) # queue name\\n fd.write('#SBATCH -o {}.out\\\\n'.format(os.path.join(queue_parameters['logs_dir'], script_name))) # standard output\\n fd.write('#SBATCH -e {}.err\\\\n'.format(os.path.join(queue_parameters['logs_dir'], script_name))) # standard error\\n for module in queue_parameters['modules']:\\n fd.write('module load {}\\\\n'.format(module)) # modules to load\\n fd.write('{}\\\\n'.format(command)) # command\\n os.system(\\\"sbatch {}\\\".format(script)) # execute bash file\\n else:\\n # Execute directly the command without bash file\\n if queue is not None:\\n os.system(\\\"echo \\\\\\\"%s\\\\\\\" | sbatch -p %s\\\" % (command, queue))\\n else:\\n os.system(\\\"echo \\\\\\\"%s\\\\\\\" | sbatch\\\" % command)\",\n \"def execute_augmentation(queue_images: Queue, progress: tqdm, output: str, factor: int) -> None:\\n while not queue_images.empty():\\n element = queue_images.get(block=False)\\n augmentation(element, output, factor)\\n progress.update(1)\",\n \"def handle_delf_init(self, job):\\n\\n # Check which, if any, servers the file exists on\\n self.put_job_in_all_queues(job)\\n list_job_results = self.get_internal_results_from_all_servers()\\n if len(list_job_results) == 0:\\n # There were no servers active\\n self.put_external_result(self.generate_failure_job(\\\"Unsuccessful, no servers running\\\"))\\n return\\n\\n return_result = copy.deepcopy(list_job_results[0])\\n return_result.result[\\\"file_exists\\\"] = False\\n\\n for result in list_job_results:\\n if result.result[\\\"file_exists\\\"]:\\n return_result.result[\\\"file_exists\\\"] = True\\n\\n return_result.result[\\\"outcome\\\"] = \\\"success\\\"\\n return_result.processed_by = None\\n\\n self.put_external_result(return_result)\",\n \"def get_fastq_info(output_dir, flowcell_id, storage_directory):\\n filenames = os.listdir(output_dir)\\n\\n # Filter for gzipped fastq files\\n filenames = filter(lambda x: \\\".fastq.gz\\\" in x, filenames)\\n\\n # Remove undetermined fastqs\\n filenames = filter(lambda x: \\\"Undetermined\\\" not in x, filenames)\\n\\n # Check that the path actually has fastq files\\n if not filenames:\\n raise Exception(\\\"no fastq files in output directory {}\\\".format(output_dir))\\n\\n # Cell info keyed by dlp library id\\n cell_info = {}\\n\\n # Fastq filenames and info keyed by fastq id, read end\\n fastq_file_info = []\\n\\n for filename in filenames:\\n match = re.match(\\n r\\\"^(\\\\w+)-(\\\\w+)-R(\\\\d+)-C(\\\\d+)_S(\\\\d+)(_L(\\\\d+))?_R([12])_001.fastq.gz$\\\",\\n filename,\\n )\\n\\n if match is None:\\n raise Exception(\\n \\\"unrecognized fastq filename structure for {}\\\".format(filename)\\n )\\n\\n filename_fields = match.groups()\\n\\n # primary_sample_id = filename_fields[0]\\n library_id = filename_fields[1]\\n row = int(filename_fields[2])\\n column = int(filename_fields[3])\\n lane_number = filename_fields[6]\\n if lane_number is not None:\\n lane_number = int(lane_number)\\n read_end = int(filename_fields[7])\\n\\n if library_id not in cell_info:\\n cell_info[library_id] = query_colossus_dlp_cell_info(library_id)\\n\\n index_sequence = cell_info[library_id][row, column][\\\"index_sequence\\\"]\\n sample_id = cell_info[library_id][row, column][\\\"sample_id\\\"]\\n\\n fastq_path = os.path.join(output_dir, filename)\\n\\n if not fastq_path.startswith(storage_directory):\\n raise Exception(\\n \\\"file {} expected in directory {}\\\".format(fastq_path, storage_directory)\\n )\\n fastq_filename = fastq_path.replace(storage_directory, \\\"\\\")\\n fastq_filename = filename.lstrip(\\\"/\\\")\\n\\n fastq_file_info.append(\\n dict(\\n dataset_type=\\\"FQ\\\",\\n sample_id=sample_id,\\n library_id=library_id,\\n library_type=BRC_LIBRARY_TYPE,\\n index_format=BRC_INDEX_FORMAT,\\n sequence_lanes=[\\n dict(\\n flowcell_id=flowcell_id,\\n lane_number=lane_number,\\n sequencing_centre=BRC_SEQ_CENTRE,\\n sequencing_instrument=BRC_INSTRUMENT,\\n read_type=BRC_READ_TYPE,\\n )\\n ],\\n size=os.path.getsize(fastq_path),\\n created=pd.Timestamp(\\n time.ctime(os.path.getmtime(fastq_path)), tz=\\\"Canada/Pacific\\\"\\n ),\\n file_type=\\\"FQ\\\",\\n read_end=read_end,\\n index_sequence=index_sequence,\\n compression=\\\"GZIP\\\",\\n filename=fastq_filename,\\n )\\n )\\n\\n return fastq_file_info\",\n \"def __init__(self,\\r\\n tensorflow_session: tf.compat.v1.Session,\\r\\n data_format: str = 'NHWC',\\r\\n batch_size: int = 32,\\r\\n num_threads: int = max(4, multiprocessing.cpu_count()),\\r\\n min_after_dequeue: int = 1000,\\r\\n fread_queue_capacity: int = 0,\\r\\n preprocess_queue_capacity: int = 0,\\r\\n staging=False,\\r\\n shuffle=None,\\r\\n testing=False,\\r\\n ):\\r\\n assert tensorflow_session is not None and isinstance(tensorflow_session, tf.Session)\\r\\n assert isinstance(batch_size, int) and batch_size > 0\\r\\n if shuffle is None:\\r\\n shuffle = staging\\r\\n self.testing = testing\\r\\n if testing:\\r\\n assert not shuffle and not staging\\r\\n # if num_threads != 1:\\r\\n # logger.info('Forcing use of single thread for live testing.')\\r\\n # num_threads = 1\\r\\n self.staging = staging\\r\\n self.shuffle = shuffle\\r\\n self.data_format = data_format.upper()\\r\\n assert self.data_format == 'NHWC' or self.data_format == 'NCHW'\\r\\n self.batch_size = batch_size\\r\\n self.num_threads = num_threads\\r\\n self._tensorflow_session = tensorflow_session\\r\\n self._coordinator = tf.train.Coordinator()\\r\\n self.all_threads = []\\r\\n\\r\\n # Setup file read queue\\r\\n self._fread_queue_capacity = fread_queue_capacity\\r\\n if self._fread_queue_capacity == 0:\\r\\n self._fread_queue_capacity = (num_threads + 1) * batch_size\\r\\n self._fread_queue = queue.Queue(maxsize=self._fread_queue_capacity)\\r\\n\\r\\n with tf.variable_scope(''.join(c for c in self.short_name if c.isalnum())):\\r\\n # Setup preprocess queue\\r\\n labels, dtypes, shapes = self._determine_dtypes_and_shapes()\\r\\n self._preprocess_queue_capacity = (min_after_dequeue + (num_threads + 1) * batch_size\\r\\n if preprocess_queue_capacity == 0\\r\\n else preprocess_queue_capacity)\\r\\n if shuffle:\\r\\n self._preprocess_queue = tf.RandomShuffleQueue(\\r\\n capacity=self._preprocess_queue_capacity,\\r\\n min_after_dequeue=min_after_dequeue,\\r\\n dtypes=dtypes, shapes=shapes,\\r\\n )\\r\\n else:\\r\\n self._preprocess_queue = tf.FIFOQueue(\\r\\n capacity=self._preprocess_queue_capacity,\\r\\n dtypes=dtypes, shapes=shapes,\\r\\n )\\r\\n self._tensors_to_enqueue = OrderedDict([\\r\\n (label, tf.placeholder(dtype, shape=shape, name=label))\\r\\n for label, dtype, shape in zip(labels, dtypes, shapes)\\r\\n ])\\r\\n\\r\\n self._enqueue_op = \\\\\\r\\n self._preprocess_queue.enqueue(tuple(self._tensors_to_enqueue.values()))\\r\\n self._preprocess_queue_close_op = \\\\\\r\\n self._preprocess_queue.close(cancel_pending_enqueues=True)\\r\\n self._preprocess_queue_size_op = self._preprocess_queue.size()\\r\\n self._preprocess_queue_clear_op = \\\\\\r\\n self._preprocess_queue.dequeue_up_to(self._preprocess_queue.size())\\r\\n \\r\\n if not staging:\\r\\n output_tensors = self._preprocess_queue.dequeue_many(self.batch_size)\\r\\n if not isinstance(output_tensors, list):\\r\\n output_tensors = [output_tensors]\\r\\n self._output_tensors = dict([\\r\\n (label, tensor) for label, tensor in zip(labels, output_tensors)\\r\\n ])\\r\\n else:\\r\\n # Setup on-GPU staging area\\r\\n self._staging_area = tf.contrib.staging.StagingArea(\\r\\n dtypes=dtypes,\\r\\n shapes=[tuple([batch_size] + list(shape)) for shape in shapes],\\r\\n capacity=1, # This does not have to be high\\r\\n )\\r\\n self._staging_area_put_op = \\\\\\r\\n self._staging_area.put(self._preprocess_queue.dequeue_many(batch_size))\\r\\n self._staging_area_clear_op = self._staging_area.clear()\\r\\n\\r\\n self._output_tensors = dict([\\r\\n (label, tensor) for label, tensor in zip(labels, self._staging_area.get())\\r\\n ])\\r\\n \\r\\n logger.info('Initialized data source: \\\"%s\\\"' % self.short_name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6682282","0.6162708","0.6032964","0.5799686","0.5781316","0.5746755","0.5729667","0.57098687","0.5676677","0.5572735","0.5548825","0.5534193","0.55331063","0.55202967","0.55051416","0.5490857","0.5485531","0.54404837","0.54099286","0.5386266","0.5369767","0.5345825","0.5321007","0.52951044","0.52929926","0.52680176","0.52426827","0.52296793","0.5228706","0.5203877","0.51964134","0.5194348","0.5185853","0.5184494","0.5176932","0.51715994","0.5170464","0.5165811","0.5160286","0.5157153","0.5155736","0.5150366","0.5148906","0.5138685","0.5133218","0.5131411","0.5128319","0.5128103","0.51247245","0.51199377","0.5117868","0.5113418","0.51127803","0.51091474","0.51039785","0.50860435","0.50781876","0.50466454","0.50456285","0.5041008","0.50396836","0.5039023","0.5032424","0.50101537","0.5006649","0.49841237","0.49775824","0.49744874","0.49731395","0.4965241","0.49633583","0.49617532","0.49594957","0.4949249","0.49490473","0.4946146","0.49322277","0.4925238","0.4924107","0.49174136","0.49173972","0.49172857","0.49134636","0.49088588","0.49023467","0.48957458","0.4893393","0.48911914","0.48892328","0.48886034","0.48872828","0.48845485","0.48842314","0.48836794","0.48728612","0.48669127","0.48663038","0.48652825","0.48574042","0.48480338"],"string":"[\n \"0.6682282\",\n \"0.6162708\",\n \"0.6032964\",\n \"0.5799686\",\n \"0.5781316\",\n \"0.5746755\",\n \"0.5729667\",\n \"0.57098687\",\n \"0.5676677\",\n \"0.5572735\",\n \"0.5548825\",\n \"0.5534193\",\n \"0.55331063\",\n \"0.55202967\",\n \"0.55051416\",\n \"0.5490857\",\n \"0.5485531\",\n \"0.54404837\",\n \"0.54099286\",\n \"0.5386266\",\n \"0.5369767\",\n \"0.5345825\",\n \"0.5321007\",\n \"0.52951044\",\n \"0.52929926\",\n \"0.52680176\",\n \"0.52426827\",\n \"0.52296793\",\n \"0.5228706\",\n \"0.5203877\",\n \"0.51964134\",\n \"0.5194348\",\n \"0.5185853\",\n \"0.5184494\",\n \"0.5176932\",\n \"0.51715994\",\n \"0.5170464\",\n \"0.5165811\",\n \"0.5160286\",\n \"0.5157153\",\n \"0.5155736\",\n \"0.5150366\",\n \"0.5148906\",\n \"0.5138685\",\n \"0.5133218\",\n \"0.5131411\",\n \"0.5128319\",\n \"0.5128103\",\n \"0.51247245\",\n \"0.51199377\",\n \"0.5117868\",\n \"0.5113418\",\n \"0.51127803\",\n \"0.51091474\",\n \"0.51039785\",\n \"0.50860435\",\n \"0.50781876\",\n \"0.50466454\",\n \"0.50456285\",\n \"0.5041008\",\n \"0.50396836\",\n \"0.5039023\",\n \"0.5032424\",\n \"0.50101537\",\n \"0.5006649\",\n \"0.49841237\",\n \"0.49775824\",\n \"0.49744874\",\n \"0.49731395\",\n \"0.4965241\",\n \"0.49633583\",\n \"0.49617532\",\n \"0.49594957\",\n \"0.4949249\",\n \"0.49490473\",\n \"0.4946146\",\n \"0.49322277\",\n \"0.4925238\",\n \"0.4924107\",\n \"0.49174136\",\n \"0.49173972\",\n \"0.49172857\",\n \"0.49134636\",\n \"0.49088588\",\n \"0.49023467\",\n \"0.48957458\",\n \"0.4893393\",\n \"0.48911914\",\n \"0.48892328\",\n \"0.48886034\",\n \"0.48872828\",\n \"0.48845485\",\n \"0.48842314\",\n \"0.48836794\",\n \"0.48728612\",\n \"0.48669127\",\n \"0.48663038\",\n \"0.48652825\",\n \"0.48574042\",\n \"0.48480338\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7072427"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":87494,"cells":{"query":{"kind":"string","value":"Parse Text file and yield 1 line at a time."},"document":{"kind":"string","value":"def parse_text_file(file_path):\n with open(file_path, 'r') as f:\n for line in f:\n line = line.rstrip()\n if not line:\n continue\n yield line"},"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 txt_line_iterator(path):\n with tf.io.gfile.GFile(path) as f:\n for line in f:\n yield line.strip()","def parse(self, f):\n \n for line in f:\n self.parse_line(line)","def _gen_txt_data(self, f):\n\t\treader = iter(f)\n\n\t\tfor line_num, line in enumerate(reader):\n\t\t\tif line_num == 0 and self.has_header:\n\t\t\t\tcontinue\n\n\t\t\tdatum = line.rstrip('\\r\\n')\n\n\t\t\tyield datum, line_num+1","def iterSimpleText(f):\n\titer = _iterSimpleTextNoContinuation(f)\n\ttry:\n\t\twhile True:\n\t\t\tlineNumber, curLine = iter.next()\n\n\t\t\twhile curLine.endswith(\"\\\\\"):\n\t\t\t\ttry:\n\t\t\t\t\tlineNumber, newStuff = iter.next()\n\t\t\t\texcept StopIteration:\n\t\t\t\t\traise SourceParseError(\"File ends with a backslash\",\n\t\t\t\t\t\tlocation=\"line %d\"%lineNumber)\n\t\t\t\tcurLine = curLine[:-1]+newStuff\n\n\t\t\tyield lineNumber, curLine\n\texcept StopIteration: # all done, leave loop\n\t\tpass","def read_lines(fp: TextIO) -> Iterator[str]:\n while line := fp.readline().strip():\n yield line","def read_lines(filename):\n with file(filename) as f:\n for line in f:\n _line = line.strip()\n if _line:\n yield _line","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(file_path) as f:\n for line in f:\n pairs = line.split()\n words, tags = zip(*(pair.split(\"###\") for pair in pairs))\n yield self.text_to_instance([Token(word) for word in words], tags)","def file_iterator(file_path):\n with open(file_path, 'r') as f:\n\n for line in f:\n\n #each line is an example\n\n #each example is made of the function name and then a sequence of triplets\n #the triplets are (node, path, node)\n\n example_name, example_body = parse_line(line)\n\n #max length set while preprocessing, make sure none longer\n\n example_length = len(example_body)\n\n assert example_length <= MAX_LENGTH\n \n #need to pad all to maximum length\n\n example_body += [['<pad>', '<pad>', '<pad>']]*(MAX_LENGTH - example_length)\n \n assert len(example_body) == MAX_LENGTH\n\n yield example_name, example_body, example_length","def file_reading_iterator_raw(filename, options='r'):\n # actual loop\n with open(filename, options) as f:\n while True:\n line = f.readline()\n if not line:\n break\n # return line\n yield line","def parse(self, filename):\n infile = file(filename)\n for line in infile:\n self.parseLine(line)","def job_reader(path) -> Generator[ParsedActionType, None, None]:\n try:\n with open(path, \"r\") as f:\n parser = Parser()\n for line in f:\n result = parser.process_line(line)\n if result is not None:\n yield result\n except IOError as err:\n print(\"Error opening/reading from file '{0}': {1}\"\n .format(err.filename, err.strerror))","def read_file(file_name):\n with open(file_name, 'r') as fh:\n for line in fh:\n yield line.strip()","def get_line(trj_file):\n\twith open(trj_file) as file:\n\t\tfor i in file:\n\t\t\tyield i","def __iter__(self):\n \n for line in self.f:\n log_line = self.line_parser(line)\n yield log_line","def read_lines(file_path: str) -> Generator[str, None, None]:\n with open(file_path) as f:\n for line in f:\n yield line.rstrip()","def parse_lines(filename):\n line_counter = 0\n with open(filename, 'r') as rf:\n for line_txt in rf:\n try:\n d = json.loads(line_txt)\n tup = (\n d['attributed_to'],\n int(d['date_time'][8:10]),\n d.get('used_first_time_today', False),\n d.get('first_utm_source', 'unknown') \n )\n except:\n print('Error parsing line_txt:', line_txt)\n line_counter += 1\n if line_counter % 10 ** 6 == 0:\n print('read %dM lines' % (line_counter // 10 ** 6))\n yield tup # yield: https://stackoverflow.com/a/231855","def parse_plain_text_export(text_file):\n\n text_file.seek(0)\n for line in text_file.readlines():\n urls = re.findall(URL_REGEX, line) if line.strip() else ()\n for url in urls:\n yield {\n 'url': url,\n 'timestamp': str(datetime.now().timestamp()),\n 'title': None,\n 'tags': '',\n 'sources': [text_file.name],\n }","def ParseSeqFile(FilePath):\n SeqFile = rSeqFile(FilePath)\n TidyFile = TidyLines(SeqFile)\n \n result = []\n\n for line in TidyFile:\n t = ( ProcessLine(line) )\n result.append(t)\n return(result)","def fileStreamer(self, filePath):\n\t\tfor line in open(filePath):\n\t\t\tyield line","def readfile(path: str):\n with open(path) as file:\n for line in file.readlines():\n yield line.strip()","def file_generator(fhandle):\n for line in fhandle:\n yield line.strip()","def get_line(lines):\n for line in lines:\n yield line","def get_line(lines):\n for line in lines:\n yield line","def _lines(filename):\n \n handle = gzip.open(filename, 'rt') if _gz(filename) else open(filename)\n for line in handle:\n if not line.startswith('#'):\n yield line.strip().split('\\t')","def _read_text_linewise(p, mode):\n if mode == 'celeb':\n for line in open(p, \"r\"):\n yield \" <eotweet> \".join(json.loads(line)[\"text\"][:MAX_TWEETS_PER_USER])\n elif mode == 'follow':\n count = 0\n for line in open(p, \"r\", encoding=\"utf8\"):\n if count == TRAIN_COUNT + TEST_COUNT:\n break\n count += 1\n yield \" <eofollower> \".join([\" <eotweet> \".join(follower[:MAX_TWEETS_PER_USER])\n for follower in json.loads(line)[\"text\"][:MAX_FOLLOWERS_PER_CELEBRITY]])","def read_input():\n f = open(INPUT_FILE, 'r')\n \n number_of_lines = int(f.readline())\n for i in range(number_of_lines):\n yield [i+1, f.readline().split()]\n f.close()","def read_on(reader, f):\n while True:\n try:\n line = reader(f)\n except StopIteration:\n break\n\n if line is not None:\n yield line","def _actual_lines(file_handle):\n for line in file_handle:\n yield line","def FastaIterator(fh):\n def readTotitle(fh):\n \"\"\"returns a tuple ([lines before the next title line], next tile line)\n \"\"\"\n preLines = []\n while True:\n l = fh.readline().strip()\n if l.startswith('>'):\n return (preLines,l)\n elif l == '':\n return preLines,None\n else:\n preLines.append(l)\n\n\n if type(fh) in StringTypes:\n fh = file(fh)\n\n preLines,nextTitleLine =readTotitle(fh)\n\n while nextTitleLine != None:\n title = nextTitleLine[1:].rstrip()\n preLines,nextTitleLine=readTotitle(fh)\n yield (title,''.join(map(lambda x: x.rstrip(),preLines)))","def read_iter_from_file(path_to_file_read):\n with open(path_to_file_read, \"r\") as fichero:\n line = fichero.readline().strip()\n while line:\n yield line\n line = fichero.readline().strip()","def read_file(path: str) -> Iterator[Problem]:\n with open(path) as f:\n txt = f.read()\n\n for encoded_problem in txt.split('\\n\\n'):\n yield parse_alpha_encoding(encoded_problem)","def _header_line_generator(file_name):\n with FileSystems.open(file_name) as f:\n record = None\n while True:\n record = f.readline().decode('utf-8')\n while record and not record.strip(): # Skip empty lines.\n record = f.readline().decode('utf-8')\n if record and record.startswith('#'):\n yield record\n else:\n break\n yield record","def file_reading_iterator(filename, comment_char=\"#\", options='r'):\n # actual loop\n with open(filename, options) as f:\n while True:\n line = f.readline()\n if not line:\n break\n # get rid of comment_char\n line = line.partition(comment_char)[0]\n # get rid of white spaces\n line = line.rstrip()\n # return line\n yield line","def parse_lines(lines):\n for line in lines:\n yield Record(line)","def format_text(text: TTextType) -> typing.Iterator[TViewLine]:\n for line in text.splitlines():\n yield [(\"text\", line)]","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(cached_path(file_path), \"r\", encoding=\"utf-8\") as data_file:\n for lines in read_conll_file(data_file):\n fields = [line.rstrip().split(self._delimiter) for line in lines]\n # unzipping trick returns tuples, but our Fields need lists\n fields = [list(field) for field in zip(*fields)]\n tokens, tags = fields\n yield self.text_to_instance(tokens, tags)","def _iterate_lines(cls, text) -> typing.Generator[str, None, None]:\n for line in text.split('\\n'):\n yield line, line.lstrip().startswith(cls._CHECKBOX)","def read_file(file):\n try:\n with open(file) as file:\n for each_line in file:\n yield each_line\n except:\n print(\"File read failed\")\n raise","def _read_text_linewise(p, mode):\n if mode == 'celeb':\n for line in open(p, \"r\"):\n yield \" <eotweet> \".join(json.loads(line)[\"text\"][:MAX_TWEETS_PER_USER])\n elif mode == 'follow':\n for line in open(p, \"r\"):\n yield \" <eofollower> \".join([\" <eotweet> \".join(follower[:MAX_TWEETS_PER_USER])\n for follower in json.loads(line)[\"text\"][:MAX_FOLLOWERS_PER_CELEBRITY]])","def read_plaintext(textfilep, mapper=None):\n with open(os.path.expanduser(textfilep), 'r') as textin:\n if mapper is not None:\n for line in textin:\n yield mapper(line)\n else:\n for line in textin:\n yield line","def parse(handle):\n while True:\n record = __read(handle)\n if not record:\n break\n yield record","def iterfile(f):\n for line in f:\n fields = line.replace(\"\\n\",\"\").split(\"\\t\")\n yield float(fields[0]), fields[-1].lower()","def data_lines(filename):\n with open(filename, mode=\"rt\", encoding=\"utf-8\") as file:\n for line in file:\n line = line.partition(\"#\")[0].strip()\n if not line:\n continue\n\n entries = [entry.strip() for entry in line.split(\";\")]\n yield entries","def transform(file, tail=False, offset=False, stop=None):\n yield None\n line = \"\"\n pos = 0\n stop_keyword = '{\"message_keyword\":\"%s\"' % str(Message.STOP)\n stop_keyword_len = len(stop_keyword)\n\n while True:\n data = file.readline()\n\n if type(data) is bytes:\n data = data.decode(\"utf-8\")\n\n line += data\n\n if line.endswith(\"\\n\"):\n if stop and line[:stop_keyword_len] == stop_keyword:\n stop.set()\n\n if offset:\n yield (line, pos)\n pos += len(line.encode(\"utf-8\"))\n else:\n yield line\n\n line = \"\"\n\n if data == \"\":\n if not tail:\n break\n time.sleep(0.15)","def file_generator(file_object):\n while True:\n data = file_object.readline()\n if not data:\n break\n yield data.split(config.CSV_DELIMITER)","def _read_txt(file_path):\n translation_pairs = []\n with file_path.open() as f:\n for line in f:\n translation_pairs.append(\n evaluation.TranslationPair(source=None, translation=line.strip())\n )\n return translation_pairs","def tail(self, file):\n line=''\n while self.running:\n tmp = file.readline()\n if tmp:\n line += tmp\n if line.endswith(\"\\n\"):\n yield line\n line = ''\n\n else:\n time.sleep(0.5)","def read_input(input_file):\n \n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n \n if (i % 10000 == 0):\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def read_input(input_file):\n\n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n\n if i % 10000 == 0:\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def __iter__(self):\r\n example = []\r\n for line in open(self.fullpath):\r\n if line != '\\n':\r\n example.append(line.rstrip()) # remove newline\r\n else:\r\n yield example\r\n example = []","def word_runner(self):\n with open(self.filename) as doc:\n text = doc.readlines()\n for line in text:\n for word in line.split():\n yield word","def next(self):\n result = None\n while result is None:\n if self._filehandle is None:\n if self.compressed:\n self._filehandle = gzip.GzipFile(self._filename, \"r\")\n else:\n self._filehandle = open(self._filename, \"r\")\n line = self._filehandle.next()\n line = line.rstrip()\n self._totallines += 1\n result = self.decodeline(line)\n return result","def tokenizer(text):\n for entry in text.split('$$$$\\n'):\n if entry.rstrip():\n lines_stream = deque(entry.split('\\n'))\n else:\n continue\n\n # yield from _molfile(stream=lines_stream)\n for token in _molfile(stream=lines_stream):\n yield token\n\n if len(lines_stream):\n # yield from _sdfile(stream=lines_stream)\n for token in _sdfile(stream=lines_stream):\n yield token\n\n yield EndOfFile()","def simple_text_reader(text_file):\n with open(text_file, 'rt') as file:\n data = file.read()\n return data","def multipleFileReadLines(filePaths): \n \n buffers = [] \n filePositions = [] \n \n for filePath in filePaths: \n lines, filePosition= readMultipleFileLinesAndPositions(filePath) \n buffers.append(lines) \n filePositions.append(filePosition) \n \n linesRemaining = True \n \n while linesRemaining: \n currentLines = [] \n for i,fileBuffer in enumerate(buffers): \n currentLines.append(fileBuffer[0].strip()) \n \n del fileBuffer[0] \n \n if ( not(fileBuffer) and linesRemaining): \n lines, filePosition = readMultipleFileLinesAndPositions(filePaths[i],filePositions[i]) \n buffers[i] = lines \n filePositions[i] = filePosition \n linesRemaining = bool(lines) \n \n yield currentLines","def simple_corpus_iterator(corpus_file):\r\n l = corpus_file.readline()\r\n while l:\r\n line = l.strip()\r\n if line: # Nonempty line\r\n yield line\r\n else: # Empty line\r\n yield None \r\n l = corpus_file.readline()","def open_file_generator(filepath):\n with open(filepath, 'r') as infile:\n for line in infile:\n if line in ('\\n', '\\r\\n'):\n continue\n yield line","def read_next_code_chunk(self) -> List[str]:\n with open(self._filepath) as f:\n for line in f:\n yield [line.strip()]","def process_file(filename):\n print \"Reading and Parsing File: {}\".format(filename)\n parsed_entries = file_parser(filename)\n print \"Starting to Process Entries\"\n chunked_entires = chunk_entries(parsed_entries)\n return [process_entries(entry) for entry in chunked_entires]","def tokenize(fp):\n for line in fp:\n line = line.strip()\n if line[0] == '#':\n continue\n for tok in line.split():\n yield tok","def lex(lines):\n for line in lines:\n yield line.rstrip()","def reader(self):\n @contextmanager\n def generator(data):\n \"\"\"\n Args:\n data (str): could be a filename or the text to tokenize.\n Returns:\n a context manager that can be used in a `with` contruct,\n yielding each line of the tokenized `data`.\n \"\"\"\n if not os.path.exists(data):\n yield self.format(self.predict(data))\n else:\n with open(data) as f:\n yield self.format(self.predict(f.read()))\n return generator","def parseLowLevel(self):\n file_iter = iter(self.file_object)\n while True:\n line = next(file_iter)\n self.line_number += 1\n\n ## skip comments\n if line.startswith(\"#\"):\n continue\n\n ## semi-colon multi-line strings\n if line.startswith(\";\"):\n lmerge = [line[1:]]\n while True:\n line = next(file_iter)\n self.line_number += 1\n if line.startswith(\";\"):\n break\n lmerge.append(line)\n\n lmerge[-1] = lmerge[-1].rstrip()\n yield (DATA_VALUE, \"\".join(lmerge))\n continue\n\n ## split line into tokens\n for match in _token_regexp.finditer(line):\n label1, label2, string, token = match.groups()\n if label1 is not None and label2 is not None:\n yield DATA_LABEL, (label1, label2)\n elif string is not None:\n yield DATA_VALUE, string\n elif token is not None:\n token_parts = token.split('_')\n if len(token_parts) == 1 \\\n or token_parts[0].lower() not in (\"data\", \"loop\",\n \"global\", \"save\",\n \"stop\"):\n yield DATA_VALUE, token\n else:\n yield KEYWORD, (token_parts[0].lower(),\n '_'.join(token_parts[1:]))","def readfast(self, name=\"\", *args, **kwargs):\n\n assert _os.path.isfile(self.__str__()) == True\n\n with open(self.__str__(), *args, **kwargs) as file_handler:\n for line in file_handler:\n yield line","def sents(infile):\n with io.open(infile, 'r', encoding='utf8') as fin:\n for line in fin:\n yield line.strip()","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(file_path) as f:\n text_classification_reader = csv.reader(f, delimiter=\"\\t\")\n for line in text_classification_reader:\n yield (self.text_to_instance(line))","def get_file_data(file_name):\n try:\n with open(file_name) as file:\n for line in file:\n if line_is_valid(line):\n yield line.strip().split(\";\")\n except IOError as error:\n print(error)","def read_file(file_name: str):\n with open(file_name) as fread:\n for line in fread:\n yield line","def parse_file():\n\tfile_lines = []\n\n\t## For each line in the file, if it's not empty, store it\n\tfor line in fileinput.input():\n\t\tif len(line) > 1:\n\t\t\tfile_lines.append(line.strip())\n\t\n\trun_algorithms(file_lines)","def parse_file(self):\n with open(self.file_name, 'r', errors='ignore') as log_file:\n for line in log_file:\n self.process_line(line)","def _get_entries(path): \n with open(path, \"r\", encoding=\"utf-8\") as file:\n entry = \"\"\n for line in file: \n if not line == \"\\n\":\n entry += line\n else:\n yield entry.strip()\n entry = \"\"","def parse_text(filehandle: TextIO) -> Iterator[Fasta]:\n\n # Check that the file looks like UniProt text format\n first_line = next(filehandle)\n if not first_line.startswith(\"ID\"):\n raise TextParserError(\n \"Unexpected file format: first line of UniProt text file should start with 'ID'\"\n )\n filehandle.seek(0)\n\n fasta = Fasta(sequence=\"\")\n for line in filehandle:\n key = line[:2] # This is more efficient than using line.startswith\n if key == \"ID\":\n tokens = line.split()\n fasta.entry_name = tokens[1]\n fasta.reviewed = True if tokens[2] == \"Reviewed;\" else False\n elif key == \"AC\":\n if fasta.accession is None:\n accessions = line[5:].rstrip(\";\\n\").split(\"; \")\n fasta.accession = accessions[0]\n elif key == \"DT\":\n if \"sequence version\" in line:\n tokens = line[5:].strip(\".\\n\").split()\n fasta.version = int(tokens[3])\n elif key == \"DE\":\n if \"RecName\" in line:\n fasta.name = _extract_name(line)\n # Get the first SubName if no RecName found\n elif fasta.name is None and line[5:12] == \"SubName\":\n fasta.name = _extract_name(line)\n elif line[5:10] == \"Flags\" and \"Fragment\" in line:\n fasta.fragment = True\n elif key == \"GN\":\n if line[5:10] == \"Name=\":\n tokens = line[10:].split(\";\")\n # Remove evidence tags, if present\n gene_tokens = tokens[0].split(\" {\")\n fasta.gene = gene_tokens[0]\n elif key == \"OS\":\n # TODO: check for multiline species name (excluding brackets)\n if fasta.species is None:\n species_line = line[5:].strip().split(\" (\")\n fasta.species = species_line[0].strip(\".\")\n elif key == \"OX\":\n if \"NCBI_TaxID\" in line:\n tokens = line[5:].strip(\";\\n\").split(\"; \")\n # Remove evidence tag if present\n taxid_tokens = tokens[0][11:].split(\" {\")\n fasta.taxid = taxid_tokens[0]\n elif key == \"PE\":\n fasta.evidence = int(line[5])\n elif key == \" \":\n sequence_line = line.strip().replace(\" \", \"\")\n fasta.sequence += sequence_line\n elif key == \"//\":\n yield fasta\n fasta = Fasta(sequence=\"\")","def parse_file(self, path):\r\n return self._parse(antlr3.ANTLRFileStream(path))","def readText(self, filename, firstLine = 0, lastLine = None):\n \n assert filename.endswith('.txt')\n file = open(filename, 'r')\n self.samples = []\n\n li = 0\n while li < firstLine:\n if not file.readline():\n return\n li += 1\n\n while lastLine == None or li < lastLine:\n line = file.readline()\n if not line:\n return\n li += 1\n line = line.strip()\n if line:\n columns = line.split('|')\n if columns[1] == 'client-fps':\n self.samples.append(Sample(line, columns))","def _chunks(filename, start):\n with open(filename, 'r') as f:\n buffer = []\n for line in f:\n if line.startswith(start):\n if buffer:\n yield buffer\n buffer = []\n else:\n buffer.append(line.strip())","def parse_text(self):\n text = self.get_data()\n line1 = text[0]\n index_list = [0]\n start_index = 3\n for i in range(1, len(text)):\n\n if line1.startswith('*'):\n index_list, start_index = self.star_parser(index_list, line1)\n elif line1.startswith('.'):\n start_index = self.dot_parser(start_index, line1, text, i)\n else:\n print \"\".rjust(start_index) + line1\n line1 = text[i]\n # Parse the last line\n if text[-1].startswith('*'):\n self.star_parser(index_list, text[-1])\n elif text[-1].startswith('.'):\n print '-'.rjust(start_index) + text[-1].lstrip('.')\n else:\n print \"\".rjust(start_index) + text[-1]","def __iter__(self):\r\n with open(self.source, 'rb') as fin:\r\n for line in fin:\r\n yield to_unicode(line)","def read_file(path, tok=False):\n with open_file(path) as f:\n for line in f.readlines():\n words = split_sentence(line.strip(), tok)\n yield words","def rows(self):\n with self.input().open('r') as fobj:\n for line in fobj:\n yield line.strip('\\n').split('\\t')","def first_word_of_each_line(filepath):\n with open(filepath, 'r') as my_file:\n for line in my_file:\n line = line.strip()\n words = line.split()\n word = words[0]\n yield word","def __iter__(self):\n try:\n # Assume it is a file-like object and try treating it as such\n # Things that don't have seek will trigger an exception\n self.source.seek(0)\n for line in itertools.islice(self.source, self.limit):\n line = utils.to_unicode(line).split()\n i = 0\n while i < len(line):\n yield line[i: i + self.max_sentence_length]\n i += self.max_sentence_length\n except AttributeError:\n # If it didn't work like a file, use it as a string filename\n with utils.smart_open(self.source) as fin:\n for line in itertools.islice(fin, self.limit):\n line = utils.to_unicode(line).split()\n i = 0\n while i < len(line):\n yield [self.sos_token] + line[i: i + self.max_sentence_length - 2] + [self.eos_token]\n i += self.max_sentence_length","def next(self):\n\t\t# keep looping until we reach our batch size\n\t\twhile True:\n\t\t\tret = self.get_batch()\n\t\t\tself.index += self.batch_size\n\t\t\tif self.index >= len(self.texts) - self.batch_size:\n\t\t\t\tself.index = 0\n\t\t\tyield ret","def open_text_file(filepath):\n sentences = []\n sentencemanager = nmea.NMEASentenceManager()\n for line in open_file_generator(filepath):\n sentencemanager.process_sentence(line)\n sentences.append(line)\n return sentencemanager, sentences","def read_lines(files):\n for file in files:\n for line in file.readlines():\n try:\n line = line.decode('utf-8')\n except UnicodeDecodeError:\n line = line.decode('latin-1')\n yield line.strip()","def __iter__(self):\n # pylint: disable=no-member\n with builtins.open(self.path,\n mode=self.mode,\n buffering=self.buffering,\n encoding=self.encoding,\n errors=self.errors,\n newline=self.newline) as file_content:\n for line in file_content:\n yield line","def extract_data_from_file(self, path):\n try:\n with open(path, 'r') as file:\n for row in file:\n row = row.replace('\\n','')\n row = row.split(',')\n yield row\n except EOFError as err:\n exit(err)","def read_large_file(file_object):\n\n # Loop until the end of file\n while True:\n\n # Read line from the file\n data = file_object.readline()\n\n # Break if this is the end of the file\n if not data:\n break\n\n # Yield the line of data\n yield data","def _parse_fastq(f):\n header = ''\n seq = ''\n skip = False\n for line in f:\n if skip:\n skip = False\n continue\n line = line.strip()\n if line == '':\n continue\n if line[0] == '@':\n header = line.replace('@', '')\n elif line[0] == '+':\n yield header, seq\n skip = True\n else:\n seq = line.upper()","def read_line(f):\n\n accumulate = \"\"\n noncomment_line_seen = False\n comments = []\n\n for line in f:\n line = line.strip()\n if line.endswith(\"\\\\\"):\n accumulate += line[:-1]\n continue\n\n elif accumulate:\n line = accumulate + line\n accumulate = \"\"\n\n if not line or line[0] == \"#\":\n comments.append(line)\n continue\n\n if not noncomment_line_seen:\n noncomment_line_seen = True\n yield None, \"\", comments\n comments = []\n\n if line.startswith(\"$(\"):\n cp = line.index(\")\")\n macro = line[0:cp+1]\n actstr = line[cp + 1:]\n else:\n macro = \"\"\n actstr = line\n\n if actstr[0] == \"<\" and actstr[-1] == \">\":\n yield None, macro + actstr, comments\n comments = []\n macro = \"\"\n continue\n\n try:\n act = pkg.actions.fromstr(actstr)\n except (pkg.actions.MalformedActionError,\n pkg.actions.UnknownActionError,\n pkg.actions.InvalidActionError), e:\n # cannot convert; treat as special macro\n yield None, actstr, comments\n continue\n yield act, macro, comments\n\n comments = []","def parse_text_into_separate_test_cases(text): \n for test_case in text.split('\\n\\n'):\n yield test_case\n #for test_case in TEST_CASE_PATTERN.finditer(text):\n #yield test_case.group(0)","def __iter__(self):\n with open(self.inputfile) as fin:\n for line in fin:\n arr = line.strip().split()\n self.path = arr[0]\n self.y = int(arr[1])\n if len(arr) > 2:\n self.feats = map(float, arr[2:])\n yield self.path, self.y, self.feats\n else:\n yield self.path, self.y","def next_line(self, context, line):","def read_large_file(file_object):\n\n # Loop indefinitely until the end of the file\n while True:\n\n # Read a line from the file: data\n data = file_object.readline()\n\n # Break if this is the end of the file\n if not data:\n break\n\n # Yield the line of data\n yield data","def solve(input_file: typing.IO) -> typing.Generator[any, None, None]:\n data = [parse_line(line.strip()) for line in input_file if line.strip()]\n next_passwd = increment_until_valid(data[0])\n yield next_passwd\n yield increment_until_valid(next_passwd)","def load_line_by_line(file_path: str) -> Dataset[SingleTextInstance]:\n with open(file_path, \"r\") as f:\n return Dataset([SingleTextInstance(text=line.rstrip()) for line in f])","def parseFile(self, filename):\n\n f = open(filename, \"r\")\n s = f.read()\n f.close()\n\n logging.log(10, 'parsing filename %s: %d lines' % (filename, len(s)))\n\n self.parseString(s)","def _iter_from_disk(self):\n self.f.seek(0, 0) # relative to start\n for line in self.f:\n yield line\n self.f.seek(0, 2) # relative to end","def get_next_hundered_lines(file):\n count = 0\n result = []\n while count < 100:\n count += 1\n next_line = file.readline()\n if next_line != \"\":\n result.append(next_line)\n else:\n break\n return result","def _process(self, file: bytes) -> List[Tuple[str]]:\n decoded_text = file.decode('utf-8')\n # Replace end of line tokens\n if self.eol is not None and not self.split_by_sentence:\n decoded_text = decoded_text.replace('\\n', self.eol)\n\n # Split by sentence or unroll\n if self.split_by_sentence:\n nltk.download('punkt', quiet=True)\n text = [(sent.strip(),) for sent in nltk.tokenize.sent_tokenize(decoded_text)]\n else:\n text = [(decoded_text,)]\n\n return text","def get_plain_text_reader(encoding: str = \"utf-8\"):\n def reader(files: List[str]) -> Iterable[List[str]]:\n for path in files:\n\n if path.endswith(\".gz\"):\n with gzip.open(path, 'r') as f_data:\n for line in f_data:\n yield str(line, 'utf-8').strip().split(\" \")\n else:\n with open(path, encoding=encoding) as f_data:\n for line in f_data:\n yield line.strip().split(\" \")\n\n return reader"],"string":"[\n \"def txt_line_iterator(path):\\n with tf.io.gfile.GFile(path) as f:\\n for line in f:\\n yield line.strip()\",\n \"def parse(self, f):\\n \\n for line in f:\\n self.parse_line(line)\",\n \"def _gen_txt_data(self, f):\\n\\t\\treader = iter(f)\\n\\n\\t\\tfor line_num, line in enumerate(reader):\\n\\t\\t\\tif line_num == 0 and self.has_header:\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tdatum = line.rstrip('\\\\r\\\\n')\\n\\n\\t\\t\\tyield datum, line_num+1\",\n \"def iterSimpleText(f):\\n\\titer = _iterSimpleTextNoContinuation(f)\\n\\ttry:\\n\\t\\twhile True:\\n\\t\\t\\tlineNumber, curLine = iter.next()\\n\\n\\t\\t\\twhile curLine.endswith(\\\"\\\\\\\\\\\"):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tlineNumber, newStuff = iter.next()\\n\\t\\t\\t\\texcept StopIteration:\\n\\t\\t\\t\\t\\traise SourceParseError(\\\"File ends with a backslash\\\",\\n\\t\\t\\t\\t\\t\\tlocation=\\\"line %d\\\"%lineNumber)\\n\\t\\t\\t\\tcurLine = curLine[:-1]+newStuff\\n\\n\\t\\t\\tyield lineNumber, curLine\\n\\texcept StopIteration: # all done, leave loop\\n\\t\\tpass\",\n \"def read_lines(fp: TextIO) -> Iterator[str]:\\n while line := fp.readline().strip():\\n yield line\",\n \"def read_lines(filename):\\n with file(filename) as f:\\n for line in f:\\n _line = line.strip()\\n if _line:\\n yield _line\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(file_path) as f:\\n for line in f:\\n pairs = line.split()\\n words, tags = zip(*(pair.split(\\\"###\\\") for pair in pairs))\\n yield self.text_to_instance([Token(word) for word in words], tags)\",\n \"def file_iterator(file_path):\\n with open(file_path, 'r') as f:\\n\\n for line in f:\\n\\n #each line is an example\\n\\n #each example is made of the function name and then a sequence of triplets\\n #the triplets are (node, path, node)\\n\\n example_name, example_body = parse_line(line)\\n\\n #max length set while preprocessing, make sure none longer\\n\\n example_length = len(example_body)\\n\\n assert example_length <= MAX_LENGTH\\n \\n #need to pad all to maximum length\\n\\n example_body += [['<pad>', '<pad>', '<pad>']]*(MAX_LENGTH - example_length)\\n \\n assert len(example_body) == MAX_LENGTH\\n\\n yield example_name, example_body, example_length\",\n \"def file_reading_iterator_raw(filename, options='r'):\\n # actual loop\\n with open(filename, options) as f:\\n while True:\\n line = f.readline()\\n if not line:\\n break\\n # return line\\n yield line\",\n \"def parse(self, filename):\\n infile = file(filename)\\n for line in infile:\\n self.parseLine(line)\",\n \"def job_reader(path) -> Generator[ParsedActionType, None, None]:\\n try:\\n with open(path, \\\"r\\\") as f:\\n parser = Parser()\\n for line in f:\\n result = parser.process_line(line)\\n if result is not None:\\n yield result\\n except IOError as err:\\n print(\\\"Error opening/reading from file '{0}': {1}\\\"\\n .format(err.filename, err.strerror))\",\n \"def read_file(file_name):\\n with open(file_name, 'r') as fh:\\n for line in fh:\\n yield line.strip()\",\n \"def get_line(trj_file):\\n\\twith open(trj_file) as file:\\n\\t\\tfor i in file:\\n\\t\\t\\tyield i\",\n \"def __iter__(self):\\n \\n for line in self.f:\\n log_line = self.line_parser(line)\\n yield log_line\",\n \"def read_lines(file_path: str) -> Generator[str, None, None]:\\n with open(file_path) as f:\\n for line in f:\\n yield line.rstrip()\",\n \"def parse_lines(filename):\\n line_counter = 0\\n with open(filename, 'r') as rf:\\n for line_txt in rf:\\n try:\\n d = json.loads(line_txt)\\n tup = (\\n d['attributed_to'],\\n int(d['date_time'][8:10]),\\n d.get('used_first_time_today', False),\\n d.get('first_utm_source', 'unknown') \\n )\\n except:\\n print('Error parsing line_txt:', line_txt)\\n line_counter += 1\\n if line_counter % 10 ** 6 == 0:\\n print('read %dM lines' % (line_counter // 10 ** 6))\\n yield tup # yield: https://stackoverflow.com/a/231855\",\n \"def parse_plain_text_export(text_file):\\n\\n text_file.seek(0)\\n for line in text_file.readlines():\\n urls = re.findall(URL_REGEX, line) if line.strip() else ()\\n for url in urls:\\n yield {\\n 'url': url,\\n 'timestamp': str(datetime.now().timestamp()),\\n 'title': None,\\n 'tags': '',\\n 'sources': [text_file.name],\\n }\",\n \"def ParseSeqFile(FilePath):\\n SeqFile = rSeqFile(FilePath)\\n TidyFile = TidyLines(SeqFile)\\n \\n result = []\\n\\n for line in TidyFile:\\n t = ( ProcessLine(line) )\\n result.append(t)\\n return(result)\",\n \"def fileStreamer(self, filePath):\\n\\t\\tfor line in open(filePath):\\n\\t\\t\\tyield line\",\n \"def readfile(path: str):\\n with open(path) as file:\\n for line in file.readlines():\\n yield line.strip()\",\n \"def file_generator(fhandle):\\n for line in fhandle:\\n yield line.strip()\",\n \"def get_line(lines):\\n for line in lines:\\n yield line\",\n \"def get_line(lines):\\n for line in lines:\\n yield line\",\n \"def _lines(filename):\\n \\n handle = gzip.open(filename, 'rt') if _gz(filename) else open(filename)\\n for line in handle:\\n if not line.startswith('#'):\\n yield line.strip().split('\\\\t')\",\n \"def _read_text_linewise(p, mode):\\n if mode == 'celeb':\\n for line in open(p, \\\"r\\\"):\\n yield \\\" <eotweet> \\\".join(json.loads(line)[\\\"text\\\"][:MAX_TWEETS_PER_USER])\\n elif mode == 'follow':\\n count = 0\\n for line in open(p, \\\"r\\\", encoding=\\\"utf8\\\"):\\n if count == TRAIN_COUNT + TEST_COUNT:\\n break\\n count += 1\\n yield \\\" <eofollower> \\\".join([\\\" <eotweet> \\\".join(follower[:MAX_TWEETS_PER_USER])\\n for follower in json.loads(line)[\\\"text\\\"][:MAX_FOLLOWERS_PER_CELEBRITY]])\",\n \"def read_input():\\n f = open(INPUT_FILE, 'r')\\n \\n number_of_lines = int(f.readline())\\n for i in range(number_of_lines):\\n yield [i+1, f.readline().split()]\\n f.close()\",\n \"def read_on(reader, f):\\n while True:\\n try:\\n line = reader(f)\\n except StopIteration:\\n break\\n\\n if line is not None:\\n yield line\",\n \"def _actual_lines(file_handle):\\n for line in file_handle:\\n yield line\",\n \"def FastaIterator(fh):\\n def readTotitle(fh):\\n \\\"\\\"\\\"returns a tuple ([lines before the next title line], next tile line)\\n \\\"\\\"\\\"\\n preLines = []\\n while True:\\n l = fh.readline().strip()\\n if l.startswith('>'):\\n return (preLines,l)\\n elif l == '':\\n return preLines,None\\n else:\\n preLines.append(l)\\n\\n\\n if type(fh) in StringTypes:\\n fh = file(fh)\\n\\n preLines,nextTitleLine =readTotitle(fh)\\n\\n while nextTitleLine != None:\\n title = nextTitleLine[1:].rstrip()\\n preLines,nextTitleLine=readTotitle(fh)\\n yield (title,''.join(map(lambda x: x.rstrip(),preLines)))\",\n \"def read_iter_from_file(path_to_file_read):\\n with open(path_to_file_read, \\\"r\\\") as fichero:\\n line = fichero.readline().strip()\\n while line:\\n yield line\\n line = fichero.readline().strip()\",\n \"def read_file(path: str) -> Iterator[Problem]:\\n with open(path) as f:\\n txt = f.read()\\n\\n for encoded_problem in txt.split('\\\\n\\\\n'):\\n yield parse_alpha_encoding(encoded_problem)\",\n \"def _header_line_generator(file_name):\\n with FileSystems.open(file_name) as f:\\n record = None\\n while True:\\n record = f.readline().decode('utf-8')\\n while record and not record.strip(): # Skip empty lines.\\n record = f.readline().decode('utf-8')\\n if record and record.startswith('#'):\\n yield record\\n else:\\n break\\n yield record\",\n \"def file_reading_iterator(filename, comment_char=\\\"#\\\", options='r'):\\n # actual loop\\n with open(filename, options) as f:\\n while True:\\n line = f.readline()\\n if not line:\\n break\\n # get rid of comment_char\\n line = line.partition(comment_char)[0]\\n # get rid of white spaces\\n line = line.rstrip()\\n # return line\\n yield line\",\n \"def parse_lines(lines):\\n for line in lines:\\n yield Record(line)\",\n \"def format_text(text: TTextType) -> typing.Iterator[TViewLine]:\\n for line in text.splitlines():\\n yield [(\\\"text\\\", line)]\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(cached_path(file_path), \\\"r\\\", encoding=\\\"utf-8\\\") as data_file:\\n for lines in read_conll_file(data_file):\\n fields = [line.rstrip().split(self._delimiter) for line in lines]\\n # unzipping trick returns tuples, but our Fields need lists\\n fields = [list(field) for field in zip(*fields)]\\n tokens, tags = fields\\n yield self.text_to_instance(tokens, tags)\",\n \"def _iterate_lines(cls, text) -> typing.Generator[str, None, None]:\\n for line in text.split('\\\\n'):\\n yield line, line.lstrip().startswith(cls._CHECKBOX)\",\n \"def read_file(file):\\n try:\\n with open(file) as file:\\n for each_line in file:\\n yield each_line\\n except:\\n print(\\\"File read failed\\\")\\n raise\",\n \"def _read_text_linewise(p, mode):\\n if mode == 'celeb':\\n for line in open(p, \\\"r\\\"):\\n yield \\\" <eotweet> \\\".join(json.loads(line)[\\\"text\\\"][:MAX_TWEETS_PER_USER])\\n elif mode == 'follow':\\n for line in open(p, \\\"r\\\"):\\n yield \\\" <eofollower> \\\".join([\\\" <eotweet> \\\".join(follower[:MAX_TWEETS_PER_USER])\\n for follower in json.loads(line)[\\\"text\\\"][:MAX_FOLLOWERS_PER_CELEBRITY]])\",\n \"def read_plaintext(textfilep, mapper=None):\\n with open(os.path.expanduser(textfilep), 'r') as textin:\\n if mapper is not None:\\n for line in textin:\\n yield mapper(line)\\n else:\\n for line in textin:\\n yield line\",\n \"def parse(handle):\\n while True:\\n record = __read(handle)\\n if not record:\\n break\\n yield record\",\n \"def iterfile(f):\\n for line in f:\\n fields = line.replace(\\\"\\\\n\\\",\\\"\\\").split(\\\"\\\\t\\\")\\n yield float(fields[0]), fields[-1].lower()\",\n \"def data_lines(filename):\\n with open(filename, mode=\\\"rt\\\", encoding=\\\"utf-8\\\") as file:\\n for line in file:\\n line = line.partition(\\\"#\\\")[0].strip()\\n if not line:\\n continue\\n\\n entries = [entry.strip() for entry in line.split(\\\";\\\")]\\n yield entries\",\n \"def transform(file, tail=False, offset=False, stop=None):\\n yield None\\n line = \\\"\\\"\\n pos = 0\\n stop_keyword = '{\\\"message_keyword\\\":\\\"%s\\\"' % str(Message.STOP)\\n stop_keyword_len = len(stop_keyword)\\n\\n while True:\\n data = file.readline()\\n\\n if type(data) is bytes:\\n data = data.decode(\\\"utf-8\\\")\\n\\n line += data\\n\\n if line.endswith(\\\"\\\\n\\\"):\\n if stop and line[:stop_keyword_len] == stop_keyword:\\n stop.set()\\n\\n if offset:\\n yield (line, pos)\\n pos += len(line.encode(\\\"utf-8\\\"))\\n else:\\n yield line\\n\\n line = \\\"\\\"\\n\\n if data == \\\"\\\":\\n if not tail:\\n break\\n time.sleep(0.15)\",\n \"def file_generator(file_object):\\n while True:\\n data = file_object.readline()\\n if not data:\\n break\\n yield data.split(config.CSV_DELIMITER)\",\n \"def _read_txt(file_path):\\n translation_pairs = []\\n with file_path.open() as f:\\n for line in f:\\n translation_pairs.append(\\n evaluation.TranslationPair(source=None, translation=line.strip())\\n )\\n return translation_pairs\",\n \"def tail(self, file):\\n line=''\\n while self.running:\\n tmp = file.readline()\\n if tmp:\\n line += tmp\\n if line.endswith(\\\"\\\\n\\\"):\\n yield line\\n line = ''\\n\\n else:\\n time.sleep(0.5)\",\n \"def read_input(input_file):\\n \\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n \\n if (i % 10000 == 0):\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def read_input(input_file):\\n\\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n\\n if i % 10000 == 0:\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def __iter__(self):\\r\\n example = []\\r\\n for line in open(self.fullpath):\\r\\n if line != '\\\\n':\\r\\n example.append(line.rstrip()) # remove newline\\r\\n else:\\r\\n yield example\\r\\n example = []\",\n \"def word_runner(self):\\n with open(self.filename) as doc:\\n text = doc.readlines()\\n for line in text:\\n for word in line.split():\\n yield word\",\n \"def next(self):\\n result = None\\n while result is None:\\n if self._filehandle is None:\\n if self.compressed:\\n self._filehandle = gzip.GzipFile(self._filename, \\\"r\\\")\\n else:\\n self._filehandle = open(self._filename, \\\"r\\\")\\n line = self._filehandle.next()\\n line = line.rstrip()\\n self._totallines += 1\\n result = self.decodeline(line)\\n return result\",\n \"def tokenizer(text):\\n for entry in text.split('$$$$\\\\n'):\\n if entry.rstrip():\\n lines_stream = deque(entry.split('\\\\n'))\\n else:\\n continue\\n\\n # yield from _molfile(stream=lines_stream)\\n for token in _molfile(stream=lines_stream):\\n yield token\\n\\n if len(lines_stream):\\n # yield from _sdfile(stream=lines_stream)\\n for token in _sdfile(stream=lines_stream):\\n yield token\\n\\n yield EndOfFile()\",\n \"def simple_text_reader(text_file):\\n with open(text_file, 'rt') as file:\\n data = file.read()\\n return data\",\n \"def multipleFileReadLines(filePaths): \\n \\n buffers = [] \\n filePositions = [] \\n \\n for filePath in filePaths: \\n lines, filePosition= readMultipleFileLinesAndPositions(filePath) \\n buffers.append(lines) \\n filePositions.append(filePosition) \\n \\n linesRemaining = True \\n \\n while linesRemaining: \\n currentLines = [] \\n for i,fileBuffer in enumerate(buffers): \\n currentLines.append(fileBuffer[0].strip()) \\n \\n del fileBuffer[0] \\n \\n if ( not(fileBuffer) and linesRemaining): \\n lines, filePosition = readMultipleFileLinesAndPositions(filePaths[i],filePositions[i]) \\n buffers[i] = lines \\n filePositions[i] = filePosition \\n linesRemaining = bool(lines) \\n \\n yield currentLines\",\n \"def simple_corpus_iterator(corpus_file):\\r\\n l = corpus_file.readline()\\r\\n while l:\\r\\n line = l.strip()\\r\\n if line: # Nonempty line\\r\\n yield line\\r\\n else: # Empty line\\r\\n yield None \\r\\n l = corpus_file.readline()\",\n \"def open_file_generator(filepath):\\n with open(filepath, 'r') as infile:\\n for line in infile:\\n if line in ('\\\\n', '\\\\r\\\\n'):\\n continue\\n yield line\",\n \"def read_next_code_chunk(self) -> List[str]:\\n with open(self._filepath) as f:\\n for line in f:\\n yield [line.strip()]\",\n \"def process_file(filename):\\n print \\\"Reading and Parsing File: {}\\\".format(filename)\\n parsed_entries = file_parser(filename)\\n print \\\"Starting to Process Entries\\\"\\n chunked_entires = chunk_entries(parsed_entries)\\n return [process_entries(entry) for entry in chunked_entires]\",\n \"def tokenize(fp):\\n for line in fp:\\n line = line.strip()\\n if line[0] == '#':\\n continue\\n for tok in line.split():\\n yield tok\",\n \"def lex(lines):\\n for line in lines:\\n yield line.rstrip()\",\n \"def reader(self):\\n @contextmanager\\n def generator(data):\\n \\\"\\\"\\\"\\n Args:\\n data (str): could be a filename or the text to tokenize.\\n Returns:\\n a context manager that can be used in a `with` contruct,\\n yielding each line of the tokenized `data`.\\n \\\"\\\"\\\"\\n if not os.path.exists(data):\\n yield self.format(self.predict(data))\\n else:\\n with open(data) as f:\\n yield self.format(self.predict(f.read()))\\n return generator\",\n \"def parseLowLevel(self):\\n file_iter = iter(self.file_object)\\n while True:\\n line = next(file_iter)\\n self.line_number += 1\\n\\n ## skip comments\\n if line.startswith(\\\"#\\\"):\\n continue\\n\\n ## semi-colon multi-line strings\\n if line.startswith(\\\";\\\"):\\n lmerge = [line[1:]]\\n while True:\\n line = next(file_iter)\\n self.line_number += 1\\n if line.startswith(\\\";\\\"):\\n break\\n lmerge.append(line)\\n\\n lmerge[-1] = lmerge[-1].rstrip()\\n yield (DATA_VALUE, \\\"\\\".join(lmerge))\\n continue\\n\\n ## split line into tokens\\n for match in _token_regexp.finditer(line):\\n label1, label2, string, token = match.groups()\\n if label1 is not None and label2 is not None:\\n yield DATA_LABEL, (label1, label2)\\n elif string is not None:\\n yield DATA_VALUE, string\\n elif token is not None:\\n token_parts = token.split('_')\\n if len(token_parts) == 1 \\\\\\n or token_parts[0].lower() not in (\\\"data\\\", \\\"loop\\\",\\n \\\"global\\\", \\\"save\\\",\\n \\\"stop\\\"):\\n yield DATA_VALUE, token\\n else:\\n yield KEYWORD, (token_parts[0].lower(),\\n '_'.join(token_parts[1:]))\",\n \"def readfast(self, name=\\\"\\\", *args, **kwargs):\\n\\n assert _os.path.isfile(self.__str__()) == True\\n\\n with open(self.__str__(), *args, **kwargs) as file_handler:\\n for line in file_handler:\\n yield line\",\n \"def sents(infile):\\n with io.open(infile, 'r', encoding='utf8') as fin:\\n for line in fin:\\n yield line.strip()\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(file_path) as f:\\n text_classification_reader = csv.reader(f, delimiter=\\\"\\\\t\\\")\\n for line in text_classification_reader:\\n yield (self.text_to_instance(line))\",\n \"def get_file_data(file_name):\\n try:\\n with open(file_name) as file:\\n for line in file:\\n if line_is_valid(line):\\n yield line.strip().split(\\\";\\\")\\n except IOError as error:\\n print(error)\",\n \"def read_file(file_name: str):\\n with open(file_name) as fread:\\n for line in fread:\\n yield line\",\n \"def parse_file():\\n\\tfile_lines = []\\n\\n\\t## For each line in the file, if it's not empty, store it\\n\\tfor line in fileinput.input():\\n\\t\\tif len(line) > 1:\\n\\t\\t\\tfile_lines.append(line.strip())\\n\\t\\n\\trun_algorithms(file_lines)\",\n \"def parse_file(self):\\n with open(self.file_name, 'r', errors='ignore') as log_file:\\n for line in log_file:\\n self.process_line(line)\",\n \"def _get_entries(path): \\n with open(path, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n entry = \\\"\\\"\\n for line in file: \\n if not line == \\\"\\\\n\\\":\\n entry += line\\n else:\\n yield entry.strip()\\n entry = \\\"\\\"\",\n \"def parse_text(filehandle: TextIO) -> Iterator[Fasta]:\\n\\n # Check that the file looks like UniProt text format\\n first_line = next(filehandle)\\n if not first_line.startswith(\\\"ID\\\"):\\n raise TextParserError(\\n \\\"Unexpected file format: first line of UniProt text file should start with 'ID'\\\"\\n )\\n filehandle.seek(0)\\n\\n fasta = Fasta(sequence=\\\"\\\")\\n for line in filehandle:\\n key = line[:2] # This is more efficient than using line.startswith\\n if key == \\\"ID\\\":\\n tokens = line.split()\\n fasta.entry_name = tokens[1]\\n fasta.reviewed = True if tokens[2] == \\\"Reviewed;\\\" else False\\n elif key == \\\"AC\\\":\\n if fasta.accession is None:\\n accessions = line[5:].rstrip(\\\";\\\\n\\\").split(\\\"; \\\")\\n fasta.accession = accessions[0]\\n elif key == \\\"DT\\\":\\n if \\\"sequence version\\\" in line:\\n tokens = line[5:].strip(\\\".\\\\n\\\").split()\\n fasta.version = int(tokens[3])\\n elif key == \\\"DE\\\":\\n if \\\"RecName\\\" in line:\\n fasta.name = _extract_name(line)\\n # Get the first SubName if no RecName found\\n elif fasta.name is None and line[5:12] == \\\"SubName\\\":\\n fasta.name = _extract_name(line)\\n elif line[5:10] == \\\"Flags\\\" and \\\"Fragment\\\" in line:\\n fasta.fragment = True\\n elif key == \\\"GN\\\":\\n if line[5:10] == \\\"Name=\\\":\\n tokens = line[10:].split(\\\";\\\")\\n # Remove evidence tags, if present\\n gene_tokens = tokens[0].split(\\\" {\\\")\\n fasta.gene = gene_tokens[0]\\n elif key == \\\"OS\\\":\\n # TODO: check for multiline species name (excluding brackets)\\n if fasta.species is None:\\n species_line = line[5:].strip().split(\\\" (\\\")\\n fasta.species = species_line[0].strip(\\\".\\\")\\n elif key == \\\"OX\\\":\\n if \\\"NCBI_TaxID\\\" in line:\\n tokens = line[5:].strip(\\\";\\\\n\\\").split(\\\"; \\\")\\n # Remove evidence tag if present\\n taxid_tokens = tokens[0][11:].split(\\\" {\\\")\\n fasta.taxid = taxid_tokens[0]\\n elif key == \\\"PE\\\":\\n fasta.evidence = int(line[5])\\n elif key == \\\" \\\":\\n sequence_line = line.strip().replace(\\\" \\\", \\\"\\\")\\n fasta.sequence += sequence_line\\n elif key == \\\"//\\\":\\n yield fasta\\n fasta = Fasta(sequence=\\\"\\\")\",\n \"def parse_file(self, path):\\r\\n return self._parse(antlr3.ANTLRFileStream(path))\",\n \"def readText(self, filename, firstLine = 0, lastLine = None):\\n \\n assert filename.endswith('.txt')\\n file = open(filename, 'r')\\n self.samples = []\\n\\n li = 0\\n while li < firstLine:\\n if not file.readline():\\n return\\n li += 1\\n\\n while lastLine == None or li < lastLine:\\n line = file.readline()\\n if not line:\\n return\\n li += 1\\n line = line.strip()\\n if line:\\n columns = line.split('|')\\n if columns[1] == 'client-fps':\\n self.samples.append(Sample(line, columns))\",\n \"def _chunks(filename, start):\\n with open(filename, 'r') as f:\\n buffer = []\\n for line in f:\\n if line.startswith(start):\\n if buffer:\\n yield buffer\\n buffer = []\\n else:\\n buffer.append(line.strip())\",\n \"def parse_text(self):\\n text = self.get_data()\\n line1 = text[0]\\n index_list = [0]\\n start_index = 3\\n for i in range(1, len(text)):\\n\\n if line1.startswith('*'):\\n index_list, start_index = self.star_parser(index_list, line1)\\n elif line1.startswith('.'):\\n start_index = self.dot_parser(start_index, line1, text, i)\\n else:\\n print \\\"\\\".rjust(start_index) + line1\\n line1 = text[i]\\n # Parse the last line\\n if text[-1].startswith('*'):\\n self.star_parser(index_list, text[-1])\\n elif text[-1].startswith('.'):\\n print '-'.rjust(start_index) + text[-1].lstrip('.')\\n else:\\n print \\\"\\\".rjust(start_index) + text[-1]\",\n \"def __iter__(self):\\r\\n with open(self.source, 'rb') as fin:\\r\\n for line in fin:\\r\\n yield to_unicode(line)\",\n \"def read_file(path, tok=False):\\n with open_file(path) as f:\\n for line in f.readlines():\\n words = split_sentence(line.strip(), tok)\\n yield words\",\n \"def rows(self):\\n with self.input().open('r') as fobj:\\n for line in fobj:\\n yield line.strip('\\\\n').split('\\\\t')\",\n \"def first_word_of_each_line(filepath):\\n with open(filepath, 'r') as my_file:\\n for line in my_file:\\n line = line.strip()\\n words = line.split()\\n word = words[0]\\n yield word\",\n \"def __iter__(self):\\n try:\\n # Assume it is a file-like object and try treating it as such\\n # Things that don't have seek will trigger an exception\\n self.source.seek(0)\\n for line in itertools.islice(self.source, self.limit):\\n line = utils.to_unicode(line).split()\\n i = 0\\n while i < len(line):\\n yield line[i: i + self.max_sentence_length]\\n i += self.max_sentence_length\\n except AttributeError:\\n # If it didn't work like a file, use it as a string filename\\n with utils.smart_open(self.source) as fin:\\n for line in itertools.islice(fin, self.limit):\\n line = utils.to_unicode(line).split()\\n i = 0\\n while i < len(line):\\n yield [self.sos_token] + line[i: i + self.max_sentence_length - 2] + [self.eos_token]\\n i += self.max_sentence_length\",\n \"def next(self):\\n\\t\\t# keep looping until we reach our batch size\\n\\t\\twhile True:\\n\\t\\t\\tret = self.get_batch()\\n\\t\\t\\tself.index += self.batch_size\\n\\t\\t\\tif self.index >= len(self.texts) - self.batch_size:\\n\\t\\t\\t\\tself.index = 0\\n\\t\\t\\tyield ret\",\n \"def open_text_file(filepath):\\n sentences = []\\n sentencemanager = nmea.NMEASentenceManager()\\n for line in open_file_generator(filepath):\\n sentencemanager.process_sentence(line)\\n sentences.append(line)\\n return sentencemanager, sentences\",\n \"def read_lines(files):\\n for file in files:\\n for line in file.readlines():\\n try:\\n line = line.decode('utf-8')\\n except UnicodeDecodeError:\\n line = line.decode('latin-1')\\n yield line.strip()\",\n \"def __iter__(self):\\n # pylint: disable=no-member\\n with builtins.open(self.path,\\n mode=self.mode,\\n buffering=self.buffering,\\n encoding=self.encoding,\\n errors=self.errors,\\n newline=self.newline) as file_content:\\n for line in file_content:\\n yield line\",\n \"def extract_data_from_file(self, path):\\n try:\\n with open(path, 'r') as file:\\n for row in file:\\n row = row.replace('\\\\n','')\\n row = row.split(',')\\n yield row\\n except EOFError as err:\\n exit(err)\",\n \"def read_large_file(file_object):\\n\\n # Loop until the end of file\\n while True:\\n\\n # Read line from the file\\n data = file_object.readline()\\n\\n # Break if this is the end of the file\\n if not data:\\n break\\n\\n # Yield the line of data\\n yield data\",\n \"def _parse_fastq(f):\\n header = ''\\n seq = ''\\n skip = False\\n for line in f:\\n if skip:\\n skip = False\\n continue\\n line = line.strip()\\n if line == '':\\n continue\\n if line[0] == '@':\\n header = line.replace('@', '')\\n elif line[0] == '+':\\n yield header, seq\\n skip = True\\n else:\\n seq = line.upper()\",\n \"def read_line(f):\\n\\n accumulate = \\\"\\\"\\n noncomment_line_seen = False\\n comments = []\\n\\n for line in f:\\n line = line.strip()\\n if line.endswith(\\\"\\\\\\\\\\\"):\\n accumulate += line[:-1]\\n continue\\n\\n elif accumulate:\\n line = accumulate + line\\n accumulate = \\\"\\\"\\n\\n if not line or line[0] == \\\"#\\\":\\n comments.append(line)\\n continue\\n\\n if not noncomment_line_seen:\\n noncomment_line_seen = True\\n yield None, \\\"\\\", comments\\n comments = []\\n\\n if line.startswith(\\\"$(\\\"):\\n cp = line.index(\\\")\\\")\\n macro = line[0:cp+1]\\n actstr = line[cp + 1:]\\n else:\\n macro = \\\"\\\"\\n actstr = line\\n\\n if actstr[0] == \\\"<\\\" and actstr[-1] == \\\">\\\":\\n yield None, macro + actstr, comments\\n comments = []\\n macro = \\\"\\\"\\n continue\\n\\n try:\\n act = pkg.actions.fromstr(actstr)\\n except (pkg.actions.MalformedActionError,\\n pkg.actions.UnknownActionError,\\n pkg.actions.InvalidActionError), e:\\n # cannot convert; treat as special macro\\n yield None, actstr, comments\\n continue\\n yield act, macro, comments\\n\\n comments = []\",\n \"def parse_text_into_separate_test_cases(text): \\n for test_case in text.split('\\\\n\\\\n'):\\n yield test_case\\n #for test_case in TEST_CASE_PATTERN.finditer(text):\\n #yield test_case.group(0)\",\n \"def __iter__(self):\\n with open(self.inputfile) as fin:\\n for line in fin:\\n arr = line.strip().split()\\n self.path = arr[0]\\n self.y = int(arr[1])\\n if len(arr) > 2:\\n self.feats = map(float, arr[2:])\\n yield self.path, self.y, self.feats\\n else:\\n yield self.path, self.y\",\n \"def next_line(self, context, line):\",\n \"def read_large_file(file_object):\\n\\n # Loop indefinitely until the end of the file\\n while True:\\n\\n # Read a line from the file: data\\n data = file_object.readline()\\n\\n # Break if this is the end of the file\\n if not data:\\n break\\n\\n # Yield the line of data\\n yield data\",\n \"def solve(input_file: typing.IO) -> typing.Generator[any, None, None]:\\n data = [parse_line(line.strip()) for line in input_file if line.strip()]\\n next_passwd = increment_until_valid(data[0])\\n yield next_passwd\\n yield increment_until_valid(next_passwd)\",\n \"def load_line_by_line(file_path: str) -> Dataset[SingleTextInstance]:\\n with open(file_path, \\\"r\\\") as f:\\n return Dataset([SingleTextInstance(text=line.rstrip()) for line in f])\",\n \"def parseFile(self, filename):\\n\\n f = open(filename, \\\"r\\\")\\n s = f.read()\\n f.close()\\n\\n logging.log(10, 'parsing filename %s: %d lines' % (filename, len(s)))\\n\\n self.parseString(s)\",\n \"def _iter_from_disk(self):\\n self.f.seek(0, 0) # relative to start\\n for line in self.f:\\n yield line\\n self.f.seek(0, 2) # relative to end\",\n \"def get_next_hundered_lines(file):\\n count = 0\\n result = []\\n while count < 100:\\n count += 1\\n next_line = file.readline()\\n if next_line != \\\"\\\":\\n result.append(next_line)\\n else:\\n break\\n return result\",\n \"def _process(self, file: bytes) -> List[Tuple[str]]:\\n decoded_text = file.decode('utf-8')\\n # Replace end of line tokens\\n if self.eol is not None and not self.split_by_sentence:\\n decoded_text = decoded_text.replace('\\\\n', self.eol)\\n\\n # Split by sentence or unroll\\n if self.split_by_sentence:\\n nltk.download('punkt', quiet=True)\\n text = [(sent.strip(),) for sent in nltk.tokenize.sent_tokenize(decoded_text)]\\n else:\\n text = [(decoded_text,)]\\n\\n return text\",\n \"def get_plain_text_reader(encoding: str = \\\"utf-8\\\"):\\n def reader(files: List[str]) -> Iterable[List[str]]:\\n for path in files:\\n\\n if path.endswith(\\\".gz\\\"):\\n with gzip.open(path, 'r') as f_data:\\n for line in f_data:\\n yield str(line, 'utf-8').strip().split(\\\" \\\")\\n else:\\n with open(path, encoding=encoding) as f_data:\\n for line in f_data:\\n yield line.strip().split(\\\" \\\")\\n\\n return reader\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7017607","0.66851646","0.6601368","0.6538029","0.64616096","0.64355737","0.64092755","0.6401797","0.63687634","0.6289389","0.6269863","0.62646693","0.6264008","0.6262496","0.6234503","0.62180173","0.6187032","0.6181076","0.6180821","0.6168177","0.61633337","0.61516374","0.61516374","0.61265206","0.6121118","0.61205596","0.6098991","0.6075425","0.6046515","0.60427064","0.6037737","0.6029099","0.60169286","0.6004563","0.6003077","0.6000253","0.5991929","0.5980226","0.5952079","0.59409785","0.59259707","0.59226924","0.5899554","0.58914554","0.58892345","0.58872795","0.58867425","0.58867306","0.58582354","0.5840201","0.58390063","0.58379203","0.58378345","0.5837637","0.58322024","0.58320683","0.5830495","0.582278","0.58159214","0.58041996","0.5802273","0.58016807","0.57984567","0.57944655","0.57914925","0.57905364","0.5779001","0.57765","0.5776495","0.577225","0.5765666","0.57578427","0.57363635","0.5731226","0.57222795","0.57204264","0.570168","0.5694949","0.5693777","0.5693606","0.5691714","0.56850773","0.56820214","0.5675899","0.56724346","0.5647717","0.5647206","0.5642723","0.5640096","0.56396836","0.5628894","0.5628334","0.5628274","0.561709","0.55973625","0.55876195","0.5583529","0.5560743","0.5560374","0.5560327"],"string":"[\n \"0.7017607\",\n \"0.66851646\",\n \"0.6601368\",\n \"0.6538029\",\n \"0.64616096\",\n \"0.64355737\",\n \"0.64092755\",\n \"0.6401797\",\n \"0.63687634\",\n \"0.6289389\",\n \"0.6269863\",\n \"0.62646693\",\n \"0.6264008\",\n \"0.6262496\",\n \"0.6234503\",\n \"0.62180173\",\n \"0.6187032\",\n \"0.6181076\",\n \"0.6180821\",\n \"0.6168177\",\n \"0.61633337\",\n \"0.61516374\",\n \"0.61516374\",\n \"0.61265206\",\n \"0.6121118\",\n \"0.61205596\",\n \"0.6098991\",\n \"0.6075425\",\n \"0.6046515\",\n \"0.60427064\",\n \"0.6037737\",\n \"0.6029099\",\n \"0.60169286\",\n \"0.6004563\",\n \"0.6003077\",\n \"0.6000253\",\n \"0.5991929\",\n \"0.5980226\",\n \"0.5952079\",\n \"0.59409785\",\n \"0.59259707\",\n \"0.59226924\",\n \"0.5899554\",\n \"0.58914554\",\n \"0.58892345\",\n \"0.58872795\",\n \"0.58867425\",\n \"0.58867306\",\n \"0.58582354\",\n \"0.5840201\",\n \"0.58390063\",\n \"0.58379203\",\n \"0.58378345\",\n \"0.5837637\",\n \"0.58322024\",\n \"0.58320683\",\n \"0.5830495\",\n \"0.582278\",\n \"0.58159214\",\n \"0.58041996\",\n \"0.5802273\",\n \"0.58016807\",\n \"0.57984567\",\n \"0.57944655\",\n \"0.57914925\",\n \"0.57905364\",\n \"0.5779001\",\n \"0.57765\",\n \"0.5776495\",\n \"0.577225\",\n \"0.5765666\",\n \"0.57578427\",\n \"0.57363635\",\n \"0.5731226\",\n \"0.57222795\",\n \"0.57204264\",\n \"0.570168\",\n \"0.5694949\",\n \"0.5693777\",\n \"0.5693606\",\n \"0.5691714\",\n \"0.56850773\",\n \"0.56820214\",\n \"0.5675899\",\n \"0.56724346\",\n \"0.5647717\",\n \"0.5647206\",\n \"0.5642723\",\n \"0.5640096\",\n \"0.56396836\",\n \"0.5628894\",\n \"0.5628334\",\n \"0.5628274\",\n \"0.561709\",\n \"0.55973625\",\n \"0.55876195\",\n \"0.5583529\",\n \"0.5560743\",\n \"0.5560374\",\n \"0.5560327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75814044"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":87495,"cells":{"query":{"kind":"string","value":"Import a data set, as its origin data points, in shape of"},"document":{"kind":"string","value":"def fit(self, data):\n self.data = data\n self._not_included = data\n if self._expected_area == -1:\n self._expected_area = int(len(data) ** 0.5)\n pass\n self._x_extends = [data[0]['x'], data[0]['x']]\n self._y_extends = [data[0]['y'], data[0]['y']]\n self._value_extends = [data[0]['value'], data[0]['value']]\n print(\"Importing data...\")\n for idx in range(0, len(data)):\n self._x_extends = [min(self._x_extends[0], data[idx]['x']),\n max(self._x_extends[1], data[idx]['x'])]\n self._y_extends = [min(self._y_extends[0], data[idx]['y']),\n max(self._y_extends[1], data[idx]['y'])]\n self._value_extends = [min(self._value_extends[0], data[idx]['value']),\n max(self._value_extends[1], data[idx]['value'])]\n pass\n self._max_distance = ((self._x_extends[1] - self._x_extends[0]) ** 2\n + (self._y_extends[1] - self._y_extends[0]) ** 2) ** 0.5\n self._max_distance = 646.5583419822547\n if self._base_threshold == -1:\n self._base_threshold = 0.2\n pass\n print(self)\n self._index = {}\n print(\"Start clustering...\")\n self._cluster()\n count = 0\n best_clustering = self.leaves\n best_dis = self._remember[0]['result']\n source = self._remember[0]['threshold']\n while self._remember[-1]['result'] != self._expected_area:\n left = 0\n right = 0.15 # 1\n target_index = -1\n target_result = -1\n for idx in range(len(self._remember)):\n dis = self._expected_area - self._remember[idx]['result']\n if dis > 0 and best_dis > 0:\n continue\n if dis < 0 and best_dis < 0:\n continue\n if target_result == -1:\n target_index = idx\n target_result = abs(dis)\n pass\n elif abs(dis) <= target_result:\n target_index = idx\n target_result = abs(dis)\n # count = 0\n pass\n pass\n if best_dis > 0:\n # not enough, wanna lower\n right = source\n if target_index != -1:\n left = self._remember[target_index]['threshold']\n pass\n pass\n else:\n left = source\n if target_index != -1:\n right = self._remember[target_index]['threshold']\n pass\n pass\n if self._expected_area - self._remember[-1]['result'] > 0:\n self._base_threshold = self._base_threshold * (1 - self._alpha) + left * self._alpha\n pass\n else:\n self._base_threshold = self._base_threshold * (1 - self._alpha) + right * self._alpha\n pass\n self._cluster()\n if abs(self._expected_area - self._remember[-1]['result']) < abs(best_dis):\n best_clustering = self.leaves\n best_dis = self._expected_area - self._remember[-1]['result']\n source = self._remember[-1]['threshold']\n count = 0\n pass\n elif len(self._remember) >= 2 and \\\n abs(self._expected_area - self._remember[-1]['result']) \\\n >= abs(self._expected_area - self._remember[-2]['result']) and \\\n abs(self._expected_area - self._remember[-2]['result']) < self._expected_area ** 0.5:\n count += 1\n if count >= 5:\n break\n pass\n self.leaves = best_clustering\n result = {}\n for i in range(len(self.leaves)):\n node = self.leaves[i]\n self._index[i] = [n['id'] for n in node]\n for point in node:\n result[point['id']] = i\n pass\n pass\n for d in self.data:\n d['leaf_id'] = result[d['id']]\n pass\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 import_dataset(fpath):\r\n data = read_csv(fpath)\r\n print(data.head())\r\n print(data.shape)\r\n return data","def read_data_set():\n # shapes of datasets -- [] means expanded form:\n # - X: J\n # - net.R: J [x J x 1]\n # - F_DIST: J x J x num_features\n # - F_DIST_w1: J x J x num_features\n # - w['except_first'][-1]: (last weights) J x num_features [x 1]\n # - w['except_first'][1:-1]: (second to last weights) J x J x num_features\n # - first weights **were** also J x J x num_features\n # - w['first_for_r']: J x 1 x num_features\n\n read_X()\n read_weights(read_FDIST())","def load_data():\n x = np.genfromtxt(X_FILE, usecols=(0, 1))\n y = np.genfromtxt(Y_FILE, usecols=(0))\n\n return x, y","def import_data(path_dataset):\n\n X=np.array(pd.read_hdf(path_dataset))\n\n print('Data set shape:',np.shape(X))\n print('#####################################')\n \n return X","def import_data(self):\n\n # Import ordered names of origins\n origins_file = os.path.join(self.data_directory,'origins.txt')\n self.origins = np.loadtxt(origins_file,dtype=str,ndmin=1)\n\n # Import ordered names of destinations\n destinations_file = os.path.join(self.data_directory,'destinations.txt')\n self.destinations = np.loadtxt(destinations_file,dtype=str,ndmin=1)\n\n # Import origin supply\n originsupply_file = os.path.join(self.data_directory,'origin_supply.txt')\n self.origin_supply = np.loadtxt(originsupply_file,ndmin=1).astype('float64')\n\n # In case origin supply is not a list\n if not isinstance(self.origin_supply,(np.ndarray, np.generic)):\n self.origin_supply = np.array([self.origin_supply])\n\n # Import destination demand\n destinationdemand_file = os.path.join(self.data_directory,'destination_demand.txt')\n self.destination_demand = np.loadtxt(destinationdemand_file,ndmin=1).astype('float64')\n\n # In case destination demand is not a list\n if not isinstance(self.destination_demand,(np.ndarray, np.generic)):\n self.destination_demand = np.array([self.destination_demand])\n\n # Import origin locations\n originlocations_file = os.path.join(self.data_directory,'origin_locations.txt')\n self.origin_locations = np.loadtxt(originlocations_file,ndmin=1)\n\n # Import destination locations\n destinationlocations_file = os.path.join(self.data_directory,'destination_locations.txt')\n self.destination_locations = np.loadtxt(destinationlocations_file,ndmin=1)\n\n # Import initial and final destination sizes\n initialdestinationsizes_file = os.path.join(self.data_directory,'initial_destination_sizes.txt')\n self.initial_destination_sizes = np.loadtxt(initialdestinationsizes_file,ndmin=1)\n\n # In case destination sizes are not a list\n if not isinstance(self.initial_destination_sizes,(np.ndarray, np.generic)):\n self.initial_destination_sizes = np.array([self.initial_destination_sizes])\n\n # Import N,M\n self.N = self.origin_supply.shape[0]\n self.M = self.initial_destination_sizes.shape[0]\n\n # Import cost matrix\n costmatrix_file = os.path.join(self.data_directory,'cost_matrix.txt')\n self.cost_matrix = np.loadtxt(costmatrix_file).astype('float64')\n\n # Reshape cost matrix if necessary\n if self.N == 1:\n self.cost_matrix = np.reshape(self.cost_matrix[:,np.newaxis],(self.N,self.M))\n if self.M == 1:\n self.cost_matrix = np.reshape(self.cost_matrix[np.newaxis,:],(self.N,self.M))\n\n # Compute total initial and final destination sizes\n self.total_initial_sizes = np.sum(self.initial_destination_sizes)\n\n # Compute naive total cost\n self.total_cost = 0\n for i in range(self.N):\n for j in range(self.M):\n self.total_cost += self.cost_matrix[i,j]*(self.origin_supply[i]/self.N)","def load_data_set(filename):\n\n input_file = open(filename)\n\n num_features = len(input_file.readline().split('\\t')) - 1\n input_file.seek(0)\n data_mat = []\n label_mat = []\n\n for line in input_file.readlines():\n line_arr = []\n curr_line = line.strip().split('\\t')\n for i in range(num_features):\n line_arr.append(float(curr_line[i]))\n data_mat.append(line_arr)\n label_mat.append(float(curr_line[-1]))\n\n return data_mat, label_mat","def import_scatterdata(self):\n\n datalist = [name for name in os.listdir(self.dir_line.text()) if\n os.path.isfile(os.path.join(self.dir_line.text(), name)) and name.endswith('.h5')]\n\n data_os = os.path.join(self.dir_line.text(), random.choice(datalist))\n with h5py.File(data_os, 'r') as f:\n labelset = f['r_vs_thresh'].attrs['best'][0].decode()\n labels = pd.array(f['labels/' + labelset][()]).astype(int)\n columns = f['data'].attrs['datacolumns'] - 1\n columns = columns.tolist()\n dataset = pd.DataFrame(f['data'][()]).iloc[:, columns]\n dataset = dataset.divide(1000)\n dataset['labels'] = labels\n\n return dataset\n\n # TODO: implement a way to rotate over all datafiles","def _load_original_dataset(data_directory, setname):\n\n Sign = collections.namedtuple(\"Sign\", [\"visibility\", \"type\", \"name\"])\n data_directory = pathlib.Path(data_directory)\n filename = data_directory / setname / \"annotations.txt\"\n with tf.io.gfile.GFile(filename) as f:\n files, annotations = zip(*(l.strip().split(\":\", 1) for l in f))\n\n all_signs = []\n for annotation in annotations:\n signs = []\n for sign in annotation.split(\";\"):\n if sign == [\"\"] or not sign: continue\n parts = [s.strip() for s in sign.split(\",\")]\n if parts[0] == \"MISC_SIGNS\": continue\n signs.append(Sign(parts[0], parts[5], parts[6]))\n all_signs.append(signs)\n\n filepaths = (data_directory / setname / f for f in files)\n return zip(filepaths, all_signs)","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 loadData(fileName):\n with open(fileName, 'r') as openedFile:\n dataList = []\n for line in openedFile.readlines():\n line = line.rstrip('\\n').split('\\t')\n pt = Point(line[0], line[1], line[2])\n dataList.append(pt)\n return dataList","def load():\n filepath = dirname(abspath(__file__))\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\",\",\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\n names = list(data.dtype.names)\n endog = array(data[names[0]], dtype=float)\n endog_name = names[0]\n exog = column_stack(data[i] for i in names[1:]).astype(float)\n exog_name = names[1:]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def load_raw_data(self, input_files):\n\n log.debug(f\"Loading dataset {input_files}\") \n print(f\"Loading dataset\")\n\n # Load stroke information from XML files\n for file in input_files:\n new_strokeset = strokeset.StrokeSet(file)\n self.strokesets.append(new_strokeset)\n self.stroke_matrix.append(new_strokeset.as_delta_array())\n self.stroke_ascii.append(new_strokeset.get_text())\n\n done_msg = \"Finished parsing dataset. Imported {} lines\".format(len(self.get_strokesets()))\n print (done_msg)\n log.info(done_msg)","def load_dataset(filepath):\n \n X = list()\n x = list()\n\n Y = list()\n y = list()\n \n for line in open(filepath):\n # blank lines separate sequences\n if len(line) <= 1:\n X.append(x)\n Y.append(y)\n\n x = list()\n y = list()\n else:\n a, b = line.strip().split('\\t')\n x.append(a)\n y.append(b)\n \n return X, Y","def load():\n filepath = dirname(abspath(__file__))\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\n data = recfromtxt(open(filepath + '/spector.csv',\"rb\"), delimiter=\" \",\n names=True, dtype=float, usecols=(1,2,3,4))\n names = list(data.dtype.names)\n endog = array(data[names[3]], dtype=float)\n endog_name = names[3]\n exog = column_stack(data[i] for i in names[:3]).astype(float)\n exog_name = names[:3]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def load_data(train_file, test_file):\n\n data = np.asarray(pd.read_csv(train_file, header=0))\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\n\n x_tra = data[:, :-1]\n y_tra = data[:, -1]\n\n return x_tra, y_tra, data_ts","def data_import(filepath, scale):\n dataset_5 = np.load(filepath)\n\n row, col = dataset_5.shape\n if scale == 1:\n return dataset_5\n else:\n return np.reshape(np.average(np.reshape(dataset_5, (int(row * col / scale), scale)),\n axis=1), (row, int(col / scale)))","def loadtrainData():\n train_x = []\n train_y = []\n fileIn = open(PATH + 'traindata_Subtask4.txt')\n for line in fileIn.readlines():\n lineArr = line.strip().split()\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\n train_y.append(int(lineArr[-1]))\n return np.mat(train_x), np.mat(train_y).transpose()","def _load_data(data, n_prev = 100):\n\n docX, docY = [], []\n for i in range(len(data)-n_prev):\n docX.append(data.iloc[i:i+n_prev].as_matrix())\n docY.append(data.iloc[i+n_prev].as_matrix())\n alsX = np.array(docX)\n alsY = np.array(docY)\n\n return alsX, alsY","def import_data(path, num_examples):\n data = np.empty((num_examples, 5), dtype=\"float128\")\n y = np.empty((num_examples, 1), dtype=\"float128\")\n\n with open(path, 'r') as f:\n i = 0\n for line in f:\n example = []\n terms = line.strip().split(',')\n for j in range(len(terms)):\n if j == 4:\n y[i] = 2 * float(terms[j]) - 1\n else:\n example.append(float(terms[j]))\n data[i, 1:] = example\n data[i, 0] = 1\n i += 1\n\n data = normalize(np.asmatrix(data), axis=0)\n return [data, np.asmatrix(y)]","def importAllDatasets(directory):\n head_index = findIndex(temp_list, \"Gaze\")\n point_index = findIndex(temp_list, \"Point\")\n grab_index = findIndex(temp_list, \"Grab\")\n pos_index = findIndex(temp_list, \"Position\")\n\n head_data = pd.read_csv(temp_list[head_index]) if head_index != None else None\n point_data = pd.read_csv(temp_list[point_index]) if point_index != None else None\n grab_data = pd.read_csv(temp_list[grab_index]) if grab_index != None else None\n pos_data = pd.read_csv(temp_list[pos_index]) if pos_index != None else None\n\n\n return head_data, point_data, grab_data, pos_data","def _load_data(data, n_prev=max_length):\n docX, docY = [], []\n for i in range(len(data) - n_prev):\n docX.append(data.iloc[i:i + n_prev].as_matrix())\n docY.append(data.iloc[i + n_prev].as_matrix())\n alsX = np.array(docX)\n alsY = np.array(docY)\n\n return alsX, alsY","def load_dataset(\n self,\n ):\n with xr.open_dataset(self._filepath) as fdata:\n out = fdata.assign_coords({\n 'nCells': np.arange(fdata.dims['nCells']),\n })\n if self.time is not None:\n out = out.assign_coords({\n 'Time': self.time,\n })\n if 'nVertLevels' in fdata.dims:\n out = out.assign_coords({\n 'nVertLevels': np.arange(fdata.dims['nVertLevels']),\n })\n if 'nVertLevelsP1' in fdata.dims:\n out = out.assign_coords({\n 'nVertLevelsP1': np.arange(fdata.dims['nVertLevelsP1']),\n })\n if 'nEdges' in fdata.dims:\n out = out.assign_coords({\n 'nEdges': np.arange(fdata.dims['nEdges']),\n })\n if 'nVertices' in fdata.dims:\n out = out.assign_coords({\n 'nVertices': np.arange(fdata.dims['nVertices']),\n })\n if 'nVertLevelsLES' in fdata.dims:\n out = out.assign_coords({\n 'nVertLevelsLES': np.arange(fdata.dims['nVertLevelsLES']),\n })\n return out","def import_data(seed: object = 42) -> object:\n\n # Read input data\n df = pd.read_csv(\"x_train_gr_smpl.csv\").astype(int)\n\n # label data-frame rows based on sample data\n for x in range(10):\n index = ~pd.read_csv(\"y_train_smpl_%s.csv\" % x, squeeze=True).astype(bool) # reversed flags (~)\n df.loc[index, 'label'] = str(x)\n\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\n output_data_ordered = df.iloc[:, 2304].to_numpy()\n\n # Randomise instance order (forcing the same result each time)\n np.random.seed(seed)\n permutation = np.random.permutation(df.shape[0])\n\n # Create base input and output arrays\n input_data = input_data_ordered[permutation]\n output_data = output_data_ordered[permutation]\n\n return input_data, output_data, df, input_data_ordered, output_data_ordered","def load_own_data():\n data = Data()\n features = data.read_selected_features()\n dataframe = data.get_dataframe_with(features)\n return normalize(dataframe.values)","def loadData(self, file):\n self.data = batchImport(file, self.ps)","def loadDataFromFile(self, filename, axis=0):\n # Load\n file = \"/Users/Septien/Documents/Tesis/code/data/\" + filename + \".data\"\n with open(file) as dataFile:\n for line in dataFile:\n row = line.split(\",\")\n self.numAxes = len(row)\n d = row[axis]\n if (d != '?'):\n self.data.append(float(d))\n # Sort for further computation\n self.data.sort()\n # Get the range\n self.xAxisRange = [self.data[0], self.data[-1]]","def import_data(fndata):\n with open(fndata, 'rb') as f:\n # split lines\n lsdata = [line.split(',') for line in f.read().splitlines()]\n # map to float\n lsdata = [map(float, row) for row in lsdata]\n\n # use numpy array\n arrdata = np.array(lsdata)\n\n return arrdata","def read_from(self, filename):\n self.x, self.y = np.loadtxt(filename, unpack=True, usecols=(0, 1))","def extract_data(data_file):\n\n data_points = set()\n points_to_coordinates = {}\n x_coor_dict = {}\n # need this since we need to iterate through the first point indices in a fixed order\n first_point_indices = []\n\n with open(data_file) as input_file:\n # consume the first line, it only tells us how many points there are\n input_file.readline()\n point_number = 0\n current_x_coor = inf\n # point that's used as the indexing point for a list of points with the same x coordinates\n first_point_index = 1\n\n for line in input_file:\n point_number += 1\n line = line.strip('\\n')\n split_line = line.split(\" \")\n print(split_line)\n x_coor = float(split_line[1])\n # if we're seeing this x coordinate for the first time\n if current_x_coor != x_coor:\n first_point_index = point_number\n first_point_indices.append(first_point_index)\n x_coor_dict[first_point_index] = []\n current_x_coor = x_coor\n x_coor_dict[first_point_index].append(point_number)\n new_point = Point(x_coor, float(split_line[2]))\n points_to_coordinates[point_number] = new_point\n data_points.add(point_number)\n\n return data_points, points_to_coordinates, x_coor_dict, first_point_indices","def load_data(): \n\tdf = pandas.read_csv('data/iris.data', header=None)\n\ty = df.iloc[0:df.shape[0], 4].values\n\ty = np.where(y == 'Iris-setosa', 0, y)\n\ty = np.where(y == 'Iris-versicolor', 1, y)\n\ty = np.where(y == 'Iris-virginica', 2, y)\n\tx = df.iloc[0:df.shape[0], 0:4].values\n\tx = tuple(x)\n\ty = tuple(y)\n\ttraining_inputs = x[0:40] + x[50:90] + x[100:140]\n\ttraining_results = y[0:40] + y[50:90] + y[100:140]\n\ttraining_data = (training_inputs, training_results)\n\ttest_inputs = x[40:50] + x[90:100] + x[140:150]\n\ttest_results = y[40:50] + y[90:1000] + y[140:150]\n\ttest_data = (test_inputs, test_results)\n\treturn (training_data, test_data)","def load_dataset(filename):\n return [(\n lambda point: {\n 'coordinate': tuple(map(float, point[:-1])),\n 'label': int(point[-1])})\n (string.strip().rstrip().split(','))\n for string in open(filename, 'r').read()\n .strip().rstrip().split('\\n')]","def load_data(dataset_str, isnormalize):\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\n objects = []\n for i in range(len(names)):\n with open(\"data/ind.{}.{}\".format(dataset_str, names[i]), 'rb') as f:\n if sys.version_info > (3, 0):\n objects.append(pkl.load(f, encoding='latin1'))\n else:\n objects.append(pkl.load(f))\n\n x, y, tx, ty, allx, ally, graph = tuple(objects)\n test_idx_reorder = parse_index_file(\"data/ind.{}.test.index\".format(dataset_str))\n test_idx_range = np.sort(test_idx_reorder)\n\n if dataset_str == 'citeseer':\n # Fix citeseer dataset (there are some isolated nodes in the graph)\n # Find isolated nodes, add them as zero-vecs into the right position\n test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\n tx_extended[test_idx_range-min(test_idx_range), :] = tx\n tx = tx_extended\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\n ty_extended[test_idx_range-min(test_idx_range), :] = ty\n ty = ty_extended\n\n features = sp.vstack((allx, tx)).tolil()\n features[test_idx_reorder, :] = features[test_idx_range, :]\n adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))\n\t# This normalization is used for cora but not citeseer\n if isnormalize == 1:\n adj = adj+ sp.eye(adj.shape[0])\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n adj = adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)\n # end of normalization.\n labels = np.vstack((ally, ty))\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\n\n idx_test = test_idx_range.tolist()\n idx_train = range(len(y))\n idx_val = range(len(y), len(y)+500)\n\n train_mask = sample_mask(idx_train, labels.shape[0])\n val_mask = sample_mask(idx_val, labels.shape[0])\n test_mask = sample_mask(idx_test, labels.shape[0])\n\n y_train = np.zeros(labels.shape)\n y_val = np.zeros(labels.shape)\n y_test = np.zeros(labels.shape)\n y_train[train_mask, :] = labels[train_mask, :]\n y_val[val_mask, :] = labels[val_mask, :]\n y_test[test_mask, :] = labels[test_mask, :]\n \n\t\n return adj, normalize(features).toarray(), y_train, y_val, y_test, train_mask, val_mask, test_mask, labels","def _load_data(data, n_prev=3):\n docX, docY = [], []\n for i in range(len(data)-n_prev):\n docX.append(data.data.iloc[i:i+n_prev])\n docY.append(data.response.iloc[i+n_prev]) \n\n alsX = np.array(docX)\n alsY = np.array(docY)\n return np.matrix(alsX), np.matrix(alsY).T","def load_data(filename):\n data = []\n with open('data/' + filename) as raw_data:\n for line in raw_data.readlines():\n data.append(float(line.strip('\\n')))\n return data\n # data = np.mat(np.genfromtxt('data/' + filename)).T\n # return data","def read_data(path):\n data_set = []\n y = -1\n with open(path, \"r\") as file:\n for line in file:\n y = y+1\n data_set.append([])\n currentline = line.split(\",\")\n for x in currentline:\n data_set[y].append(float(x.rstrip()))\n return data_set","def load_pointing(name):\r\n pointing_data = np.loadtxt(os.path.join(data_dir, '{}.csv'.format(name)), delimiter=',')[:, 1:3]\r\n return pointing_data","def load_datasets(self):\n if self.processed_extension == '.npz':\n logger.info(f'Loading sets from npz:')\n \n logger.info(f'train: {self.train_path}')\n self.train_data = sparse.load_npz(self.train_path)\n\n logger.info(f'val: {self.val_path}')\n self.val_data = sparse.load_npz(self.val_path)\n\n logger.info(f'test: {self.test_path}')\n self.test_data = sparse.load_npz(self.test_path)\n \n # Split x and y\n self.train_data = [sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,-1])]\n \n self.val_data = [sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,-1])]\n \n self.test_data = [sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,-1])]\n \n elif self.processed_extension == '.csv':\n logger.info(f'Loading sets from csv:')\n \n logger.info(f'train: {self.train_path}')\n self.train_data = pd.read_csv(self.train_path)\n train_cols = self.train_data.columns\n self.train_data = [self.train_data[train_cols.difference(['TARGET'])],\n self.train_data['TARGET']]\n \n logger.info(f'val: {self.val_path}')\n self.val_data = pd.read_csv(self.val_path)\n self.val_data = [self.val_data[train_cols.difference(['TARGET'])],\n self.val_data['TARGET']]\n \n logger.info(f'test: {self.test_path}')\n self.test_data = pd.read_csv(self.test_path)\n self.test_data = [self.test_data[train_cols.difference(['TARGET'])],\n self.test_data['TARGET']]\n else:\n raise AttributeError(f'Wrong extension: {self.processed_extension}')\n self.n_train = self.train_data[0].shape[0]\n self.n_val = self.val_data[0].shape[0]\n self.n_test = self.test_data[0].shape[0]\n self.input_size = self.train_data[0].shape[1]\n self.n_examples = self.n_train + self.n_val + self.n_test\n \n logger.info(f'Set sizes:')\n logger.info(f'train: {self.n_train}')\n logger.info(f'val: {self.n_val}')\n logger.info(f'test: {self.n_test}')","def collect_datset(self):\n response = requests.get(self.url)\n lines = response.text.splitlines()\n data = []\n for item in lines:\n item = item.split(\",\")\n data.append(item)\n data.pop(0) # to remove labels from list\n dataset = np.matrix(data)\n return dataset","def load_file(filename):\n f_data = []\n # open the data-set file\n file = open(filename, \"r\")\n for line in file:\n row = line.strip() # a row in the file\n f_data.append(row) # append it to the 2D array\n\n return f_data","def load_data(directory: str, dataset_str: str):\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\n objects = []\n for i in range(len(names)):\n with open(\"{}/ind.{}.{}\".format(directory, dataset_str, names[i]), 'rb') as f:\n if sys.version_info > (3, 0):\n objects.append(pkl.load(f, encoding='latin1'))\n else:\n objects.append(pkl.load(f))\n\n x, y, tx, ty, allx, ally, graph = tuple(objects)\n test_idx_reorder = parse_index_file(\"{}/ind.{}.test.index\".format(directory, dataset_str))\n test_idx_range = np.sort(test_idx_reorder)\n\n if dataset_str == 'citeseer':\n # Fix citeseer dataset (there are some isolated nodes in the graph)\n # Find isolated nodes, add them as zero-vecs into the right position\n test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\n tx_extended[test_idx_range-min(test_idx_range), :] = tx\n tx = tx_extended\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\n ty_extended[test_idx_range-min(test_idx_range), :] = ty\n ty = ty_extended\n\n features = sp.vstack((allx, tx)).tolil()\n features[test_idx_reorder, :] = features[test_idx_range, :]\n\n labels = np.vstack((ally, ty))\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\n\n idx_test = test_idx_range.tolist()\n idx_train = range(len(y))\n idx_val = range(len(y), len(y)+500)\n\n train_mask = sample_mask(idx_train, labels.shape[0])\n val_mask = sample_mask(idx_val, labels.shape[0])\n test_mask = sample_mask(idx_test, labels.shape[0])\n\n y_train = np.zeros(labels.shape)\n y_val = np.zeros(labels.shape)\n y_test = np.zeros(labels.shape)\n y_train[train_mask, :] = labels[train_mask, :]\n y_val[val_mask, :] = labels[val_mask, :]\n y_test[test_mask, :] = labels[test_mask, :]\n\n return graph, features, y_train, y_val, y_test, train_mask, val_mask, test_mask","def load_data():\n\n print('Loading and Visualizing Data ...')\n\n file_name = path.join(getcwd(), 'ex3', 'src', 'data', 'ex3data1')\n data = scipy.io.loadmat(file_name)\n\n # training data stored in arrays X, y\n # y should be a row vector of labels\n return data['X'], data['y'].T[0]","def load_data(self, t):\n self.ensure_datafiles()\n datafile = os.path.join(self.data_dir, ALL_DATA[t])\n data = sio.loadmat(datafile)\n return data['X'].transpose(3, 0, 1, 2), data['y']","def load_data(start=0., stop=100., t_step=1,\n ins=['6 MO', '1 YR', '3 YR', '5 YR'], outs=['10 YR'], verbose=1):\n if 'DATA_PATH' not in os.environ:\n raise Exception(\"Cannot find DATA_PATH variable in the environment. \"\n \"DATA_PATH should be the folder that contains \"\n \"`quandl/` directory where the data is cached. \"\n \"Please export DATA_PATH before using `load_data`.\")\n\n # Create a directory for Quandl data if it does not exist\n quandl_data_path = os.path.join(os.environ['DATA_PATH'], 'quandl')\n if not os.path.isdir(quandl_data_path):\n os.makedirs(quandl_data_path)\n\n if verbose:\n sys.stdout.write('Loading data...')\n sys.stdout.flush()\n\n # Load the data\n dataset_path = os.path.join(quandl_data_path, 'USTYCR.pkl')\n if not os.path.exists(dataset_path):\n USTYCR = Quandl.get(\"USTREASURY/YIELD\")\n USTYCR.to_pickle(dataset_path)\n else:\n USTYCR = pd.read_pickle(dataset_path)\n\n X = USTYCR[ins].as_matrix()\n Y = USTYCR[outs].as_matrix()\n\n start = int((start/100.) * len(X))\n stop = int((stop/100.) * len(X))\n\n # Select the data points\n X = X[start:stop:t_step,:]\n Y = Y[start:stop:t_step,:]\n\n if verbose:\n sys.stdout.write('Done.\\n')\n print('# of loaded points: %d' % len(X))\n\n return X, Y","def load_testset(filename):\n pickle_name = filename + \".pickle\"\n try:\n print(\"trying to load \" + filename + \" from pickle\")\n dataset = pickle.load(open(pickle_name, \"rb\"))\n except:\n with open(filename, 'r') as csv_file:\n print(\"no pickle exists. parsing file \" + filename)\n dataset = [DataPoint(item[0:], \" \")\n for item\n in csv.reader(csv_file, delimiter=',')]\n pickle.dump(dataset, open(pickle_name, \"wb\"))\n print(\"loaded \" + filename)\n return dataset","def data_array(self) -> xr.Dataset:\n\n xr_data = xr.open_mfdataset(self.path_to_files,\n chunks=self.chunks,\n parallel=True)\n\n if not all(x in list(xr_data.coords) for x in self.DIMS):\n xr_data = xr_data.rename({\n 'latitude': 'lat',\n 'longitude': 'lon',\n })\n\n if self.subset_dict is not None:\n print(f'Cutting data using {self.subset_dict}')\n xr_data = self.cut(xr_data)\n\n if self.season is not None:\n xr_data = xr_data.where(xr_data.time.dt.season == self.season,\n drop=True)\n\n if self.rescale_longitude is True:\n xr_data = xr_data.assign_coords(lon=(((xr_data.lon + 180) % 360) -\n 180)).sortby('lon')\n\n return xr_data","def _load_data(self):\n\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\n self.task_num\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\n\n read_data_x = pd.read_csv(path_data_x)\n read_data_y = pd.read_csv(path_data_y)\n read_data_meta = pd.read_csv(path_meta)\n\n # Drop columns if it only contains 1 unique element\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\n\n num_col = pd.read_csv(path_numerical_columns)\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\n col_idx = np.arange(read_data_x.shape[-1])\n non_num_col = np.setdiff1d(col_idx, num_col)\n\n # new_data_x = np.array(read_data_x).astype(np.float32)\n new_data_x = np.array(read_data_x)\n new_data_y = np.array(read_data_y).astype(np.float32)\n new_data_meta = np.array(read_data_meta).astype(np.float32)\n\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\n\n\n # Winsorize dataset\n len_feat = new_data_x.shape[-1]\n idx_list = list(num_col)\n for i in range(len_feat):\n if i in idx_list:\n cur_data = new_data_x[:, i]\n cur_data = np.array(cur_data)\n lower_p = np.percentile(cur_data, 5)\n higher_p = np.percentile(cur_data, 95)\n cur_data[cur_data < lower_p] = lower_p\n cur_data[cur_data > higher_p] = higher_p\n new_data_x[:, i] = cur_data\n\n # Make sure target data is one-hot encoded\n if new_data_y.shape[-1] == 1:\n num_class = len(np.unique(new_data_y))\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\n new_data_y = new_data_y.astype('float32')\n self.orig_column_names = read_data_x.columns\n self.data_x = new_data_x # N x F\n self.data_y = new_data_y # N x C\n self.numerical_idx = num_col # list of idx\n self.non_num_idx = non_num_col # None\n\n # Calculate adjacency matrix\n self.meta_inf = new_data_meta.astype('float32') # N x 3\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def load_or_generate_data(self) -> None:\n\n # Training set defined as a 5 x 5 square:\n xg1 = np.linspace(-5, 10, 5)\n xg2 = np.linspace(0, 15, 5)\n x = np.zeros((xg1.size * xg2.size, 2))\n for i, x1 in enumerate(xg1):\n for j, x2 in enumerate(xg2):\n x[i + xg1.size * j, :] = [x1, x2]\n\n y = self.branin(x)[:, None]\n self.x, self.y = x, y","def _importFile(self, sourceFile):\n header = 1 if self.headerline else 0\n\n data = np.genfromtxt(\n sourceFile\n , delimiter=','\n , skip_header=header\n , names=[\n 'x'\n , 'accelX'\n , 'accelY'\n , 'accelZ'\n , 'gyroX'\n , 'gyroY'\n , 'gyroZ'\n ]\n )\n return data","def read_dataset(file, interner=None, point_factory=ListOpsDataPoint):\n data_set = []\n r = range(0, 10)\n\n if (interner == None):\n func = RespectfulNumbering(respect=r)\n interner = interners.Interner(not_present_function=func)\n\n\n reader = csv.reader(file, delimiter='\\t')\n for row in reader:\n #print(row)\n if len(row) > 1:\n try:\n data_set.append(point_factory(row[0], row[1], interner))\n except ValueError:\n print(\"Warning, could not read line: %s\"%row, file=sys.stderr)\n\n return data_set, interner","def open_xy(data):\n twotheta, intensity = [], []\n with open(data) as f:\n for line in f:\n row = line.split()\n twotheta.append(row[0])\n intensity.append(row[1])\n xyarray = list(zip(twotheta, intensity))\n xyarray = np.asarray(xyarray)\n xyarray = xyarray.astype(np.float)\n return xyarray","def import_data(textfile, uncertainty):\n\tf = open(textfile, 'r')\n\tdata = f.readlines()\n\tx = []\n\ty = []\n\tfor line in data:\n\t\tcoords = line.strip()\n\t\tcoords = coords.split(', ')\n\t\tx.append(coords[0])\n\t\ty.append(coords[1]) \n\t\n\tx = [float(i) for i in x]\n\tx = np.array(x).reshape((10,1))\n\ty = [float(i) for i in y]\n\ty = np.array(y).reshape((10,1))\n\tdata = {'x': x, 'y': y, 'var': uncertainty}\n\n\treturn data","def test_univariate(self):\n data = load_dataset(data_format=\"numpy\", standardize=True)\n assert data.shape[0] > 131, \"Imported time series collection has the wrong shape\"\n assert data.shape[-1] == 1000, \"Imported time series collection has the wrong shape\"","def get_train_data():\n # train set\n train = pd.read_csv(\"train.csv\")\n\n return train","def load_dataset_train():\n df_train = load_csv_file(\"31_train.csv\")\n df_train_target = load_csv_file(\"31_target_train.csv\")\n\n return df_train.values, df_train_target.values","def load_data_table(data_url):\r\n data_file = urllib2.urlopen(data_url)\r\n data = data_file.read()\r\n data_lines = data.split('\\n')\r\n print \"Loaded\", len(data_lines), \"data points\"\r\n data_tokens = [line.split(',') for line in data_lines]\r\n return [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])] \r\n for tokens in data_tokens]","def load_data_table(data_url):\n\tdata_file = urllib2.urlopen(data_url)\n\tdata = data_file.read()\n\tdata_lines = data.split('\\n')\n\tprint \"Loaded\", len(data_lines), \"data points\"\n\tdata_tokens = [line.split(',') for line in data_lines]\n\treturn [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])]\n\t\t\tfor tokens in data_tokens]","def load_data(loc='../data/SICK/'):\n trainA, trainB, testA, testB = [],[],[],[]\n trainS, testS = [],[]\n\n with open(loc + 'SICK_train.txt', 'rb') as f:\n for line in f:\n text = line.strip().split('\\t')\n trainA.append(text[1])\n trainB.append(text[2])\n trainS.append(text[3])\n with open(loc + 'SICK_test_annotated.txt', 'rb') as f:\n for line in f:\n text = line.strip().split('\\t')\n testA.append(text[1])\n testB.append(text[2])\n testS.append(text[3])\n\n trainS = [float(s) for s in trainS[1:]]\n testS = [float(s) for s in testS[1:]]\n\n return [trainA[1:], trainB[1:]], [testA[1:], testB[1:]], [trainS, testS]","def _load_data(self):\n\n from sklearn.datasets import fetch_openml\n mnist = fetch_openml('mnist_784', cache=True)\n # data_x = np.array(final_data_df)\n feat_data = np.array(mnist.data).astype('float32')\n target_data = mnist.target.astype('int64')\n shuffling_index = np.arange(feat_data.shape[0])\n np.random.shuffle(shuffling_index)\n feat_data = feat_data[shuffling_index]\n target_data = target_data[shuffling_index]\n\n cur_data_list = []\n cur_target_list = []\n for i in range(10):\n cur_mask = target_data == i\n cur_data_list.append(feat_data[cur_mask][:500])\n cur_target_list.append(target_data[cur_mask][:500])\n feat_data = np.concatenate(cur_data_list)\n target_data = np.concatenate(cur_target_list)\n\n self.data_x = feat_data\n self.data_y = self.to_one_hot_encoding(target_data)\n self.numerical_idx = np.arange(784)\n self.non_num_idx = None\n\n # Calculate adjacency matrix\n self.meta_inf = self.data_x.astype('float32')\n\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n path = os.getcwd() # reads the current path\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\n x_train, y_train = shuffle(x_train, y_train)\n\n return x_train, y_train, x_test","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n\n # split the data into 10% validation-set and 90% training set\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\n return raw_train, raw_valid, y_train, y_valid","def load_dataset(data_dir, img_size):\n global input_set\n global test_set\n\n imgs = []\n img_files = os.listdir(data_dir)\n for img in img_files:\n # try:\n tmp = scipy.misc.imread(data_dir + \"/\" + img)\n x, y, z = tmp.shape # shape : width * length * chanel\n coords_x = int(x / img_size) # 坐标\n coords_y = int(y / img_size) #\n coords = [(q, r) for q in range(coords_x) for r in range(coords_y)] # 列表 x * y\n for coord in coords:\n imgs.append((data_dir + \"/\" + img, coord)) # 为列表添加文件目录\n # except BaseException:\n # print(\"oops\")\n test_size = min(10, int(len(imgs) * 0.2))\n random.shuffle(imgs)\n test_set = imgs[:test_size]\n train_set_X = imgs[test_size:][:200]\n train_set = imgs[test_size:][200:400]\n return","def loader(filename,sep=',',rowskip=[], colskip=[], axis=1,names=1,fromstring=0):\n\n #manages excpetions to the csv file incase of missing data\n if (type(filename)==str) and (fromstring==1):\n iterable=filename.strip('\\n').split('\\n')\n content=np.array([i for i in csv.reader(iterable,delimiter=sep)])\n elif type(filename)==np.ndarray:\n content=filename\n else:\n content=np.array([i for i in\\\n csv.reader(open(filename,'r'),delimiter=sep)])\n #content=np.genfromtxt(filename,delimiter=sep,dtype=str)\n\n if rowskip:\n #rowskip.sort(reverse=True)\n content=np.delete(content,rowskip,0)\n #for i in rowskip: content.pop(i)\n\n if colskip:\n #colskip.sort(reverse=True)\n content=np.delete(content,colskip,1)\n #for i in colskip: content.pop(i)\n\n if axis==0: # if the file oriented column-wise\n #content=list(map(list,zip(*content)))\n content=content.T\n\n\n\n if names is 0:\n variables=np.arange(content.shape[1]).tolist()\n offset=0\n else:\n variables=content[0].tolist()\n offset=1\n\n try:\n content=np.array([conv_col(col) for col in\n content[offset:].T],dtype='object')\n arity=np.array([np.unique(i).size for i in content])\n return dataset(variables,content.T,arity)\n except ValueError: \n print( 'Data could not be loaded, failed converting to float.')\n return content","def load_data():\n data = []\n with euler.Resource('triangle.txt') as datafile:\n for line in datafile.readlines():\n data.insert(0, map(int, line.strip().split()))\n return data","def load(name):\n data = np.loadtxt(name)\n X, y = data[:, :-1], data[:, -1].astype(np.int)\n return X, y","def load_data_table(data_url):\n data_file = urllib2.urlopen(data_url)\n data = data_file.read()\n data = data.decode()\n data_lines = data.split('\\n')\n print(\"Loaded\", len(data_lines), \"data points\")\n data_tokens = [line.split(',') for line in data_lines]\n return [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])]\n for tokens in data_tokens]","def readdata(filename):\n\tdt = np.dtype([('date','int'),('val','<f8')])\n\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\n\treturn data","def loadxy(filename=None):\n\n import numpy as np\n from StringIO import StringIO\n import Tkinter\n from tkFileDialog import askopenfilename\n\n if filename is not None:\n print \"Opening %s\\n\" %(filename)\n else:\n root = Tkinter.Tk()\n root.withdraw()\n filename=askopenfilename(parent=root,title='Open File',filetypes=[('xy files','*.xy')])\n root.destroy()\n root.mainloop()\n\n if filename is not None:\n f = open(filename)\n d = np.genfromtxt(f, delimiter='\\t', unpack=True,\n skip_header=4, skip_footer=1)\n s = argsort(d)\n data = d[:,s[0]]\n return data","def load_data():\n data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", header=None)\n\n # utiliza somente as duas primeiras classes\n data = data[:100]\n # transforma as classes em 0 e 1\n data[4] = np.where(data.iloc[:, -1] == 'Iris-setosa', 0, 1)\n data = np.asmatrix(data, dtype='float64')\n return data","def load_data(self) -> None:","def load_data(self):\n\t\ti = 0\n\n\t\tpaths = glob.glob(self.file_path+'/rollout_*')\n\t\tself.rollouts = []\n\n\n\t\tfor path in paths:\n\t\t\tdata_point = np.load(path,encoding='latin1')\n\t\t\tself.rollouts.append(data_point)\n\n\t\treturn paths","def dataset_to_list_points(dir_dataset):\n points = list()\n with open(dir_dataset, 'rt') as reader:\n for point in reader:\n points.append(np.asarray(map(float, point.split(\"::\"))))\n return points","def data_loader(edges,features,y):\n\n\n edge_index = torch.tensor(edges, dtype=torch.long)\n edge_index = edge_index.t().contiguous()\n x = torch.tensor(features.todense(), dtype=torch.float)\n\n y = torch.tensor(y)\n\n data = Data(x=x, edge_index=edge_index, y = y)\n\n return data","def load(self, source):\n try:\n inputdata = self.__inputmanager.read(source)\n self.__suitables = self.__inputmanager.map(inputdata)\n self.__data = inputdata\n except ValueError as e:\n print (\"Failed to load the dataset: %s\" % e)\n raise\n\n self.__modules = self.import_suitable_visualizations(self.__suitables)\n self.__has_datefields = self.__inputmanager.has_date_points()\n # Converting the datakeys into strings.\n self.__datakeys = [str(i) for i in list(self.__data[0].keys())]\n return self.__suitables","def _load_training_and_test_sets(normalize):\n class_labels = []\n test_labels = []\n norm = None\n if normalize == True:\n norm = loading.get_normalize_vector()\n\n for i in range(0, 10):\n [training, test] = loading.load_number_set(i, 0.7, norm_vector=norm)\n labels = [str(i)] * training.shape[0]\n tlabels = [str(i)] * test.shape[0]\n if i == 0:\n train_points = training\n test_points = test\n else:\n train_points = np.concatenate((train_points, training), axis = 0)\n test_points = np.concatenate((test_points, test), axis = 0)\n class_labels.extend(labels)\n test_labels.extend(tlabels)\n\n return train_points, test_points, class_labels, test_labels","def load_data(self, filename='', limit=0.75, delim=','):\n with open(filename) as data:\n reader = csv.reader(data, delimiter=delim)\n f = list(reader)\n for x in range(len(f)):\n for y in range(len(f[0]) - 1):\n f[x][y] = float(f[x][y]) # convert elements of each array to type float except the last one\n\n lim = limit * (len(f)) # calculate where the training data and test data are divided\n lim = int(lim) # convert limit for indexing purposes\n results = (f[:lim], f[lim:]) # append training data and test data to tuple\n\n # for x in range(len(f)):\n # print(f[x])\n\n del f # delete f array which was temporary\n\n return results # return value","def import_data(inp, picks=None, array_keys={'X': 'X', 'y': 'y'}):\n if isinstance(inp, (mne.epochs.EpochsFIF, mne.epochs.BaseEpochs)):\n print('processing epochs')\n # if isinstance(picks, dict):\n # picks = mne.pick_types(inp.info, include=picks)\n inp.load_data()\n data = inp.get_data()\n events = inp.events[:, 2]\n if isinstance(picks, dict):\n print(\"Converting picks\")\n picks = mne.pick_types(inp.info, **picks)\n\n elif isinstance(inp, tuple) and len(inp) == 2:\n print('importing from tuple')\n data, events = inp\n\n elif isinstance(inp, str):\n # TODO: ADD CASE FOR RAW FILE\n fname = inp\n if fname[-3:] == 'fif':\n epochs = mne.epochs.read_epochs(fname, preload=True,\n verbose='CRITICAL')\n print(np.unique(epochs.events[:, 2]))\n events = epochs.events[:, 2]\n epochs.crop(tmin=-1., tmax=1.)\n data = epochs.get_data()\n if isinstance(picks, dict):\n print(\"Converting picks\")\n picks = mne.pick_types(epochs.info, **picks)\n\n else:\n if fname[-3:] == 'mat':\n datafile = sio.loadmat(fname)\n\n if fname[-3:] == 'npz':\n print('Importing from npz')\n datafile = np.load(fname)\n\n data = datafile[array_keys['X']]\n events = datafile[array_keys['y']]\n print('Extracting target variables from {}'\n .format(array_keys['y']))\n else:\n print(\"Dataset not found\")\n return None, None\n\n data = data.astype(np.float32)\n\n # Make sure that X is 3d here\n while data.ndim < 3:\n # (x, ) -> (1, 1, x)\n # (x, y) -> (1, x, y)\n data = np.expand_dims(data, 0)\n\n if isinstance(picks, (np.ndarray, list, tuple)):\n picks = np.asarray(picks)\n if np.any(data.shape[1] <= picks):\n raise ValueError(\"Invalid picks {} for n_channels {} \".format(\n max(len(picks), max(picks)), data.shape[1]))\n data = data[:, picks, :]\n\n return data, events","def load_data(filepath):\n data = import_csv(filepath, has_headers=False)\n x_data = data[:, 0:3]\n y_data = None\n if data.shape[1]>3:\n y_data = data[:, 3:]\n n_data = data.shape[0]\n\n return n_data, np.float64(x_data), np.float64(y_data)","def load_data(self):\n raw_data = np.genfromtxt(self.data_file, delimiter=',')\n self.n_clusters = int(raw_data[-1][-1] + 1)\n self.n_points = len(raw_data) // self.n_clusters\n \n # group data according to label\n data = [raw_data[raw_data[:,-1] == i][:,:-1] \\\n for i in range(self.n_clusters)]\n\n # take only a subset of the data\n if self.split:\n assert 0 <= self.split <= 1, \"Split must be in [0, 1)\"\n\n # update dataset info and print to stdout\n self.n_points = int(self.split * len(data[0]))\n subsampled = self.__len__() - int(self.ood is not None) * self.n_points\n print(f\"INFO: Subsampled {subsampled}/{len(raw_data)} points\")\n \n return [cluster[:self.n_points] for cluster in data]\n return data","def load_data(flatten=True):\n if flatten:\n reshape = _flatten\n else:\n reshape = _square\n\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = reshape(x_train)\n x_test = reshape(x_test)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n y_train = to_categorical(y_train, 10)\n y_test = to_categorical(y_test, 10)\n return x_train, y_train, x_test, y_test","def load_data(data_dir, atlas, scale):\n\n data_dir = Path(data_dir)\n data = pd.read_csv(data_dir / atlas / f'{scale}.csv', index_col=0)\n\n # drop medial stuff\n todrop = np.array(putils.DROP)[np.isin(putils.DROP, data.index)]\n if len(todrop) > 0:\n data = data.drop(todrop, axis=0)\n\n # get indices of diff hemispheres\n idx_lh = [n for n, f in enumerate(data.index) if 'lh_' in f]\n idx_rh = [n for n, f in enumerate(data.index) if 'rh_' in f]\n\n # get data array\n labels = np.asarray(data.columns)\n data = np.asarray(data).squeeze()\n\n return data[idx_lh], data[idx_rh], labels","def load_data(self, filename):\r\n #sqlcontext = SQLContext(self.sc)\r\n #df = sqlcontext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load(filename)\r\n #df = sc.textFile(r\"C:\\Users\\mohan\\Downloads\\patches.csv\").map(lambda line: line.split(\",\"))\r\n #print (df.count())\r\n df = self.sc.textFile(filename).map(lambda line: line.split(\",\"))\r\n l = df.map(lambda w: [int(float(c)) for c in w]).zipWithIndex()\r\n return l\r\n raise NotImplementedError","def load_data():\n with open('../data/dataset.txt', 'r') as data_file:\n return data_file.read().split('\\n')","def read_1D_comsol_data(self):\n x=[]\n y=[]\n with open(self.file, 'r') as rf:\n reader = csv.reader(rf, delimiter=',')\n for row in reader:\n x.append(row[0])\n y.append(row[1])\n x = np.asarray((x),dtype=float)\n y = np.asarray((y),dtype=float)\n return x,y","def read(self, data_set, location=None):\n self._open_file(data_set, location)\n\n for i, array_id in enumerate(\n data_set._h5_base_group['Data Arrays'].keys()):\n # Decoding string is needed because of h5py/issues/379\n name = array_id # will be overwritten if not in file\n dat_arr = data_set._h5_base_group['Data Arrays'][array_id]\n\n # write ensures these attributes always exist\n name = dat_arr.attrs['name'].decode()\n label = dat_arr.attrs['label'].decode()\n units = dat_arr.attrs['units'].decode()\n is_setpoint = str_to_bool(dat_arr.attrs['is_setpoint'].decode())\n # if not is_setpoint:\n set_arrays = dat_arr.attrs['set_arrays']\n set_arrays = [s.decode() for s in set_arrays]\n # else:\n # set_arrays = ()\n vals = dat_arr.value[:, 0]\n if 'shape' in dat_arr.attrs.keys():\n vals = vals.reshape(dat_arr.attrs['shape'])\n if array_id not in data_set.arrays.keys(): # create new array\n d_array = DataArray(\n name=name, array_id=array_id, label=label, parameter=None,\n units=units,\n is_setpoint=is_setpoint, set_arrays=(),\n preset_data=vals)\n data_set.add_array(d_array)\n else: # update existing array with extracted values\n d_array = data_set.arrays[array_id]\n d_array.name = name\n d_array.label = label\n d_array.units = units\n d_array.is_setpoint = is_setpoint\n d_array.ndarray = vals\n d_array.shape = dat_arr.attrs['shape']\n # needed because I cannot add set_arrays at this point\n data_set.arrays[array_id]._sa_array_ids = set_arrays\n\n # Add copy/ref of setarrays (not array id only)\n # Note, this is not pretty but a result of how the dataset works\n for array_id, d_array in data_set.arrays.items():\n for sa_id in d_array._sa_array_ids:\n d_array.set_arrays += (data_set.arrays[sa_id], )\n data_set = self.read_metadata(data_set)\n return data_set","def _load_data(self):\n\n path_data_x = \\\n '/workspace/base-ml/data/tadpole/adni_one_baseline_feature_data' \\\n '.csv'\n path_data_y = \\\n '/workspace/base-ml/data/tadpole/adni_one_baseline_label_data' \\\n '.csv'\n path_meta = '/workspace/base-ml/data/tadpole' \\\n '/adni_one_baseline_meta_data' \\\n '.csv'\n read_data_x = pd.read_csv(path_data_x)\n read_data_y = pd.read_csv(path_data_y) # 0 NL, 1, MCI, 2 Dementia\n read_data_meta = pd.read_csv(path_meta)[['AGE', 'PTGENDER', 'APOE4']]\n\n # Replace gender to numeric\n read_data_meta.PTGENDER = read_data_meta.PTGENDER.replace('Male', 0)\n read_data_meta.PTGENDER = read_data_meta.PTGENDER.replace('Female', 1)\n\n new_data_x = np.array(read_data_x).astype(np.float32)\n new_data_y = np.array(read_data_y).astype(np.float32)\n new_data_meta = np.array(read_data_meta).astype(np.float32)\n\n # Concat meta-information with feature vector input\n concat_meta = pd.DataFrame(new_data_meta)\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(0, 'zero')\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(1, 'one')\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(2, 'two')\n concat_meta = concat_meta.to_numpy()\n new_data_x = np.concatenate([concat_meta, new_data_x], 1)\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\n\n self.orig_column_names = ['Age', 'Gender', 'APOE4'] + \\\n list(read_data_x.columns)\n self.data_x = new_data_x\n self.data_y = self.to_one_hot_encoding(new_data_y)\n self.numerical_idx = np.arange(new_data_x.shape[-1])\n self.numerical_idx = np.delete(self.numerical_idx, [2]) # Remove APOE column idx\n self.non_num_idx = np.array([2])\n self.all_non_numerical_idx = None\n\n # self.numerical_idx = np.arange(self.data_x.shape[-1])\n # self.non_num_idx = None\n # self.all_non_numerical_idx = None\n\n # Calculate adjacency matrix\n self.meta_inf = new_data_meta.astype('float32')\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def loadData ( self ) :\n df = pd.read_json ( self.dataset )\n df = df[pd.notnull ( df[2] )]\n df[1] = df[1].apply ( self.clean_text )\n\n self.X = df[1]\n self.y = df[2]","def load_data(filename):\n assert os.path.exists(filename)==True\n dat = scipy.io.loadmat(filename)\n inputs = dat['inputs']\n #print len(inputs)\n targets = dat['targets']\n #print len(targets)\n assert len(inputs)==len(targets)\n\n global alldata\n global indim \n global outdim\n\n indim = len(inputs[0])\n outdim = 1\n #print indim\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\n alldata.setField('input',inputs)\n alldata.setField('target',targets)\n\n assert len(alldata['input'])==len(alldata['target'])\n print type(alldata)","def prepare_dataset(self, stock):\n dataset = pd.DataFrame(yf.download(stock))\n dataset_train = dataset[0:len(dataset)-30] # Training dataset will be the whole dataset minus the test dataset\n training_set = dataset_train.iloc[:, 1:2].values # Use the open values only\n\n # Feature Scaling\n sc = MinMaxScaler(feature_range=(0, 1)) # Scales all values so that they are in the range (0, 1)\n training_set_scaled = sc.fit_transform(training_set)\n\n # Create an input data structure with a timestep\n x_train = []\n y_train = []\n for i in range(self.timestep, len(dataset)-30):\n x_train.append(training_set_scaled[i - self.timestep:i, 0])\n y_train.append(training_set_scaled[i, 0])\n x_train, y_train = np.array(x_train), np.array(y_train)\n\n # Reshaping\n x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n\n return x_train, y_train","def input_shape(file_name):\n data = pd.read_csv(file_name,header=None)\n x_data = data.iloc[:,0]\n y_data = data.iloc[:,1]\n x = np.array(x_data).T\n y = np.array(y_data).T\n return x,y","def load_coords(self, attrs):\n ts = time.time()\n logger.info('Rechunking coordinates')\n meta_data = self._dst_h5['meta'][...]\n coords = np.dstack((meta_data['latitude'], meta_data['longitude']))[0]\n attrs['dtype'] = coords.dtype\n\n if isinstance(attrs['chunks'], int):\n attrs['chunks'] = (attrs['chunks'], 2)\n\n ds = self.init_dset('coordinates', coords.shape, attrs)\n ds[...] = coords\n logger.info('- coordinates transfered')\n tt = (time.time() - ts) / 60\n logger.debug('\\t- {:.2f} minutes'.format(tt))","def __loadPreProcessedData(self):\n le = joblib.load(self.le_filename)\n X = np.loadtxt(self.X_filename, delimiter=',').astype(int)\n raw_y = np.loadtxt(self.y_filename, delimiter=',').astype(int)\n y = le.inverse_transform(raw_y)\n ##Initialize atrtribute for this class\n self.le, self.X, self.y = le, X, y","def data_input(self):\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))\n if not os.path.isfile('{0}/{1}.csv'.format(path, self.data_file)):\n print 'Error: Dataset file is not exist.'\n exit()\n # Uplead Dataset.csv file.\n f = open('{0}/{1}.csv'.format(path, self.data_file), 'r')\n print 'Now uploading dataset File.....'\n f = list(f)\n # The Dataset contains heading, number of lines - heading\n self.number_of_VOCs = sum(1 for row in f)-1\n # Count number of columns, last column's value is empty, that is why -1.\n self.number_of_columns = len(f[0].split(',')) -1\n self.first_m_z = int(f[0].split(',')[3]) # find the first m/z value.\n self.last_m_z = int(f[0].split(',')[-2]) # find the last m/z value.\n print 'dataset includes ', self.number_of_VOCs, 'VOCs in all samples '\n print ('dataset includes ', self.number_of_columns, ' Columns, ',\n 'm/z values start from ', self.first_m_z,\n 'and end ', self.last_m_z)\n # Create a matrix with a shape of (number_of_VOCs X number_of_columns) filled with zeros.\n self.dataset = np.zeros((self.number_of_VOCs,\n self.number_of_columns))\n for line in range(1, len(f)):\n if int(float(f[line].strip().split(',')[0])) not in self.loaded_samples:\n self.loaded_samples.append(int(float(f[line].strip().split(',')[0])))\n for column in range(self.number_of_columns):\n self.dataset[line-1][column] = int(float(f[line].strip().split(',')[column]))","def load_velodyne_points(filename):\n points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)\n points[:, 3] = 1.0 # homogeneous\n return points","def read_data(self,filename):\n self.x = [] #Input values\n self.t = [] #Target values\n\n with open(filename, \"r\") as infile:\n lines = infile.readlines()\n self.n = len(lines)\n for line in lines:\n words = line.split()\n self.x.append(float(words[0]))\n self.t.append(float(words[1]))\n\n self.x = np.array(self.x)\n self.t = np.array(self.t)\n self.create_design_matrix()","def _importDataFromText(self, filename, with_axis=None):\n self.set_data_writable()\n try: \n _data = numpy.loadtxt(filename)\n except ValueError:\n _data = numpy.loadtxt(filename, dtype=complex)\n \n self.data = self._extract_data_with_axis(_data, with_axis)\n self.set_data_protected()","def _load_data(self):\n data_x, data_y = make_classification(n_samples=5000, n_features=20,\n n_informative=10,\n n_redundant=0, n_repeated=0,\n n_classes=2,\n n_clusters_per_class=4,\n weights=None, flip_y=0.01,\n class_sep=1.0, hypercube=True,\n shift=0.0, scale=1.0,\n shuffle=True,\n random_state=self.args.rand_seed)\n\n self.orig_column_names = np.arange(data_x.shape[-1])\n self.data_x = data_x\n self.data_y = self.to_one_hot_encoding(data_y)\n self.numerical_idx = np.arange(data_x.shape[-1])\n self.non_num_idx = None\n self.all_non_numerical_idx = None\n\n # Calculate adjacency matrix\n self.meta_inf = data_x[:, :1].astype('float32')\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def loaddevData():\n dev_x = []\n dev_y = []\n fileIn = open(PATH + 'devdata_Subtask4.txt')\n for line in fileIn.readlines():\n lineArr = line.strip().split()\n dev_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\n dev_y.append(int(lineArr[-1]))\n return np.mat(dev_x), np.mat(dev_y).transpose()","def importData(filename):\r\n data = pd.read_csv(filename)\r\n return data","def make_dataset_temperatures(filename, points):\r\n\r\n ds = Dataset(filename)\r\n\r\n lats, lons, cities, countries = get_lat_lon()\r\n\r\n # total lat,lon pairs: 1366\r\n print('The number of rows is ' + str(len(lats)*points))\r\n lon = ds.variables['longitude']\r\n lat = ds.variables['latitude']\r\n time = ds.variables['date_number']\r\n\r\n lon_array = lon[:]\r\n lat_array = lat[:]\r\n time_array = time[:]\r\n\r\n temperature = ds.variables['temperature']\r\n\r\n dates = []\r\n for time in time_array[:]:\r\n year = int(time)\r\n rem = time - year\r\n base = datetime(year, 1, 1)\r\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\r\n\r\n # second approach\r\n # for t in time_array[:]:\r\n # dates.append(num2date(t, units=time.units))\r\n\r\n dateResult = []\r\n temperatureResult = []\r\n latitudeResult = []\r\n longitudeResult = []\r\n cityResult = []\r\n countryResult = []\r\n\r\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\r\n\r\n # We want to find data for latitude, longitude\r\n # We first need to find the indexes\r\n i = np.abs(lon_array - longitude).argmin()\r\n j = np.abs(lat_array - latitude).argmin()\r\n\r\n for d in dates:\r\n dateResult.append(d)\r\n\r\n resultTemperature = temperature[:, j, i]\r\n for t in resultTemperature:\r\n temperatureResult.append(t)\r\n\r\n resultLatitues = np.full(\r\n shape=points,\r\n fill_value=latitude,\r\n dtype=np.float\r\n )\r\n for l in resultLatitues:\r\n latitudeResult.append(l)\r\n\r\n resultLongitudes = np.full(\r\n shape=points,\r\n fill_value=longitude,\r\n dtype=np.float\r\n )\r\n for l in resultLongitudes:\r\n longitudeResult.append(l)\r\n\r\n resultCities = np.full(\r\n shape=points,\r\n fill_value=city\r\n )\r\n for c in resultCities:\r\n cityResult.append(c)\r\n\r\n resultCountries = np.full(\r\n shape=points,\r\n fill_value=country\r\n )\r\n for c in resultCountries:\r\n countryResult.append(c)\r\n\r\n print('iteration no:' + str(i))\r\n\r\n df = pd.DataFrame()\r\n df['date'] = dateResult\r\n df['temperature'] = temperatureResult\r\n df['latitude'] = latitudeResult\r\n df['longitude'] = longitudeResult\r\n df['city'] = cityResult\r\n df['country'] = countryResult\r\n\r\n df.to_csv(r'C:\\Users\\stoja\\Desktop\\Temperatures.csv', index=False)\r\n return df","def seed_other_dataset(name: str, chunk_size: int, start=None, end=None):\n objects = []\n for chunk in pd.read_csv(name, chunksize=chunk_size, header=1):\n chunk_as_mat = chunk.to_numpy()\n chunk_start = datetime.datetime.strptime(str(chunk_as_mat[0][0]), \"%Y%m%d\")\n chunk_end = datetime.datetime.strptime(str(chunk_as_mat[-1][0]), \"%Y%m%d\")\n if start is not None and start > chunk_end:\n continue\n if end is not None and end < chunk_start:\n break\n # print(chunk.to_numpy())\n objects += insert_into_sql(chunk.to_numpy())\n return objects","def import_data(self):\n\n self.worksheet = (\n xlrd.open_workbook(filename=self.source).sheet_by_index(0)\n )\n # Import conversion data from worksheet and store as scipy arrays\n self.T_exp = np.array(\n self.worksheet.col_values(0, start_rowx=4, end_rowx=None)\n ) + 273.15\n self.HCout_raw = np.array(\n self.worksheet.col_values(4, start_rowx=4, end_rowx=None)\n )\n self.HCin_raw = np.array(\n self.worksheet.col_values(8, start_rowx=4, end_rowx=None)\n )\n self.eta_exp = (\n (self.HCin_raw - self.HCout_raw) / self.HCin_raw\n )\n self.T_model = np.linspace(\n self.T_exp[0] - 50, self.T_exp[-1] + 50, 25\n )\n self.T_array = self.T_model"],"string":"[\n \"def import_dataset(fpath):\\r\\n data = read_csv(fpath)\\r\\n print(data.head())\\r\\n print(data.shape)\\r\\n return data\",\n \"def read_data_set():\\n # shapes of datasets -- [] means expanded form:\\n # - X: J\\n # - net.R: J [x J x 1]\\n # - F_DIST: J x J x num_features\\n # - F_DIST_w1: J x J x num_features\\n # - w['except_first'][-1]: (last weights) J x num_features [x 1]\\n # - w['except_first'][1:-1]: (second to last weights) J x J x num_features\\n # - first weights **were** also J x J x num_features\\n # - w['first_for_r']: J x 1 x num_features\\n\\n read_X()\\n read_weights(read_FDIST())\",\n \"def load_data():\\n x = np.genfromtxt(X_FILE, usecols=(0, 1))\\n y = np.genfromtxt(Y_FILE, usecols=(0))\\n\\n return x, y\",\n \"def import_data(path_dataset):\\n\\n X=np.array(pd.read_hdf(path_dataset))\\n\\n print('Data set shape:',np.shape(X))\\n print('#####################################')\\n \\n return X\",\n \"def import_data(self):\\n\\n # Import ordered names of origins\\n origins_file = os.path.join(self.data_directory,'origins.txt')\\n self.origins = np.loadtxt(origins_file,dtype=str,ndmin=1)\\n\\n # Import ordered names of destinations\\n destinations_file = os.path.join(self.data_directory,'destinations.txt')\\n self.destinations = np.loadtxt(destinations_file,dtype=str,ndmin=1)\\n\\n # Import origin supply\\n originsupply_file = os.path.join(self.data_directory,'origin_supply.txt')\\n self.origin_supply = np.loadtxt(originsupply_file,ndmin=1).astype('float64')\\n\\n # In case origin supply is not a list\\n if not isinstance(self.origin_supply,(np.ndarray, np.generic)):\\n self.origin_supply = np.array([self.origin_supply])\\n\\n # Import destination demand\\n destinationdemand_file = os.path.join(self.data_directory,'destination_demand.txt')\\n self.destination_demand = np.loadtxt(destinationdemand_file,ndmin=1).astype('float64')\\n\\n # In case destination demand is not a list\\n if not isinstance(self.destination_demand,(np.ndarray, np.generic)):\\n self.destination_demand = np.array([self.destination_demand])\\n\\n # Import origin locations\\n originlocations_file = os.path.join(self.data_directory,'origin_locations.txt')\\n self.origin_locations = np.loadtxt(originlocations_file,ndmin=1)\\n\\n # Import destination locations\\n destinationlocations_file = os.path.join(self.data_directory,'destination_locations.txt')\\n self.destination_locations = np.loadtxt(destinationlocations_file,ndmin=1)\\n\\n # Import initial and final destination sizes\\n initialdestinationsizes_file = os.path.join(self.data_directory,'initial_destination_sizes.txt')\\n self.initial_destination_sizes = np.loadtxt(initialdestinationsizes_file,ndmin=1)\\n\\n # In case destination sizes are not a list\\n if not isinstance(self.initial_destination_sizes,(np.ndarray, np.generic)):\\n self.initial_destination_sizes = np.array([self.initial_destination_sizes])\\n\\n # Import N,M\\n self.N = self.origin_supply.shape[0]\\n self.M = self.initial_destination_sizes.shape[0]\\n\\n # Import cost matrix\\n costmatrix_file = os.path.join(self.data_directory,'cost_matrix.txt')\\n self.cost_matrix = np.loadtxt(costmatrix_file).astype('float64')\\n\\n # Reshape cost matrix if necessary\\n if self.N == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[:,np.newaxis],(self.N,self.M))\\n if self.M == 1:\\n self.cost_matrix = np.reshape(self.cost_matrix[np.newaxis,:],(self.N,self.M))\\n\\n # Compute total initial and final destination sizes\\n self.total_initial_sizes = np.sum(self.initial_destination_sizes)\\n\\n # Compute naive total cost\\n self.total_cost = 0\\n for i in range(self.N):\\n for j in range(self.M):\\n self.total_cost += self.cost_matrix[i,j]*(self.origin_supply[i]/self.N)\",\n \"def load_data_set(filename):\\n\\n input_file = open(filename)\\n\\n num_features = len(input_file.readline().split('\\\\t')) - 1\\n input_file.seek(0)\\n data_mat = []\\n label_mat = []\\n\\n for line in input_file.readlines():\\n line_arr = []\\n curr_line = line.strip().split('\\\\t')\\n for i in range(num_features):\\n line_arr.append(float(curr_line[i]))\\n data_mat.append(line_arr)\\n label_mat.append(float(curr_line[-1]))\\n\\n return data_mat, label_mat\",\n \"def import_scatterdata(self):\\n\\n datalist = [name for name in os.listdir(self.dir_line.text()) if\\n os.path.isfile(os.path.join(self.dir_line.text(), name)) and name.endswith('.h5')]\\n\\n data_os = os.path.join(self.dir_line.text(), random.choice(datalist))\\n with h5py.File(data_os, 'r') as f:\\n labelset = f['r_vs_thresh'].attrs['best'][0].decode()\\n labels = pd.array(f['labels/' + labelset][()]).astype(int)\\n columns = f['data'].attrs['datacolumns'] - 1\\n columns = columns.tolist()\\n dataset = pd.DataFrame(f['data'][()]).iloc[:, columns]\\n dataset = dataset.divide(1000)\\n dataset['labels'] = labels\\n\\n return dataset\\n\\n # TODO: implement a way to rotate over all datafiles\",\n \"def _load_original_dataset(data_directory, setname):\\n\\n Sign = collections.namedtuple(\\\"Sign\\\", [\\\"visibility\\\", \\\"type\\\", \\\"name\\\"])\\n data_directory = pathlib.Path(data_directory)\\n filename = data_directory / setname / \\\"annotations.txt\\\"\\n with tf.io.gfile.GFile(filename) as f:\\n files, annotations = zip(*(l.strip().split(\\\":\\\", 1) for l in f))\\n\\n all_signs = []\\n for annotation in annotations:\\n signs = []\\n for sign in annotation.split(\\\";\\\"):\\n if sign == [\\\"\\\"] or not sign: continue\\n parts = [s.strip() for s in sign.split(\\\",\\\")]\\n if parts[0] == \\\"MISC_SIGNS\\\": continue\\n signs.append(Sign(parts[0], parts[5], parts[6]))\\n all_signs.append(signs)\\n\\n filepaths = (data_directory / setname / f for f in files)\\n return zip(filepaths, all_signs)\",\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 loadData(fileName):\\n with open(fileName, 'r') as openedFile:\\n dataList = []\\n for line in openedFile.readlines():\\n line = line.rstrip('\\\\n').split('\\\\t')\\n pt = Point(line[0], line[1], line[2])\\n dataList.append(pt)\\n return dataList\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\\\",\\\",\\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\\n names = list(data.dtype.names)\\n endog = array(data[names[0]], dtype=float)\\n endog_name = names[0]\\n exog = column_stack(data[i] for i in names[1:]).astype(float)\\n exog_name = names[1:]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def load_raw_data(self, input_files):\\n\\n log.debug(f\\\"Loading dataset {input_files}\\\") \\n print(f\\\"Loading dataset\\\")\\n\\n # Load stroke information from XML files\\n for file in input_files:\\n new_strokeset = strokeset.StrokeSet(file)\\n self.strokesets.append(new_strokeset)\\n self.stroke_matrix.append(new_strokeset.as_delta_array())\\n self.stroke_ascii.append(new_strokeset.get_text())\\n\\n done_msg = \\\"Finished parsing dataset. Imported {} lines\\\".format(len(self.get_strokesets()))\\n print (done_msg)\\n log.info(done_msg)\",\n \"def load_dataset(filepath):\\n \\n X = list()\\n x = list()\\n\\n Y = list()\\n y = list()\\n \\n for line in open(filepath):\\n # blank lines separate sequences\\n if len(line) <= 1:\\n X.append(x)\\n Y.append(y)\\n\\n x = list()\\n y = list()\\n else:\\n a, b = line.strip().split('\\\\t')\\n x.append(a)\\n y.append(b)\\n \\n return X, Y\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\\n data = recfromtxt(open(filepath + '/spector.csv',\\\"rb\\\"), delimiter=\\\" \\\",\\n names=True, dtype=float, usecols=(1,2,3,4))\\n names = list(data.dtype.names)\\n endog = array(data[names[3]], dtype=float)\\n endog_name = names[3]\\n exog = column_stack(data[i] for i in names[:3]).astype(float)\\n exog_name = names[:3]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def load_data(train_file, test_file):\\n\\n data = np.asarray(pd.read_csv(train_file, header=0))\\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\\n\\n x_tra = data[:, :-1]\\n y_tra = data[:, -1]\\n\\n return x_tra, y_tra, data_ts\",\n \"def data_import(filepath, scale):\\n dataset_5 = np.load(filepath)\\n\\n row, col = dataset_5.shape\\n if scale == 1:\\n return dataset_5\\n else:\\n return np.reshape(np.average(np.reshape(dataset_5, (int(row * col / scale), scale)),\\n axis=1), (row, int(col / scale)))\",\n \"def loadtrainData():\\n train_x = []\\n train_y = []\\n fileIn = open(PATH + 'traindata_Subtask4.txt')\\n for line in fileIn.readlines():\\n lineArr = line.strip().split()\\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\\n train_y.append(int(lineArr[-1]))\\n return np.mat(train_x), np.mat(train_y).transpose()\",\n \"def _load_data(data, n_prev = 100):\\n\\n docX, docY = [], []\\n for i in range(len(data)-n_prev):\\n docX.append(data.iloc[i:i+n_prev].as_matrix())\\n docY.append(data.iloc[i+n_prev].as_matrix())\\n alsX = np.array(docX)\\n alsY = np.array(docY)\\n\\n return alsX, alsY\",\n \"def import_data(path, num_examples):\\n data = np.empty((num_examples, 5), dtype=\\\"float128\\\")\\n y = np.empty((num_examples, 1), dtype=\\\"float128\\\")\\n\\n with open(path, 'r') as f:\\n i = 0\\n for line in f:\\n example = []\\n terms = line.strip().split(',')\\n for j in range(len(terms)):\\n if j == 4:\\n y[i] = 2 * float(terms[j]) - 1\\n else:\\n example.append(float(terms[j]))\\n data[i, 1:] = example\\n data[i, 0] = 1\\n i += 1\\n\\n data = normalize(np.asmatrix(data), axis=0)\\n return [data, np.asmatrix(y)]\",\n \"def importAllDatasets(directory):\\n head_index = findIndex(temp_list, \\\"Gaze\\\")\\n point_index = findIndex(temp_list, \\\"Point\\\")\\n grab_index = findIndex(temp_list, \\\"Grab\\\")\\n pos_index = findIndex(temp_list, \\\"Position\\\")\\n\\n head_data = pd.read_csv(temp_list[head_index]) if head_index != None else None\\n point_data = pd.read_csv(temp_list[point_index]) if point_index != None else None\\n grab_data = pd.read_csv(temp_list[grab_index]) if grab_index != None else None\\n pos_data = pd.read_csv(temp_list[pos_index]) if pos_index != None else None\\n\\n\\n return head_data, point_data, grab_data, pos_data\",\n \"def _load_data(data, n_prev=max_length):\\n docX, docY = [], []\\n for i in range(len(data) - n_prev):\\n docX.append(data.iloc[i:i + n_prev].as_matrix())\\n docY.append(data.iloc[i + n_prev].as_matrix())\\n alsX = np.array(docX)\\n alsY = np.array(docY)\\n\\n return alsX, alsY\",\n \"def load_dataset(\\n self,\\n ):\\n with xr.open_dataset(self._filepath) as fdata:\\n out = fdata.assign_coords({\\n 'nCells': np.arange(fdata.dims['nCells']),\\n })\\n if self.time is not None:\\n out = out.assign_coords({\\n 'Time': self.time,\\n })\\n if 'nVertLevels' in fdata.dims:\\n out = out.assign_coords({\\n 'nVertLevels': np.arange(fdata.dims['nVertLevels']),\\n })\\n if 'nVertLevelsP1' in fdata.dims:\\n out = out.assign_coords({\\n 'nVertLevelsP1': np.arange(fdata.dims['nVertLevelsP1']),\\n })\\n if 'nEdges' in fdata.dims:\\n out = out.assign_coords({\\n 'nEdges': np.arange(fdata.dims['nEdges']),\\n })\\n if 'nVertices' in fdata.dims:\\n out = out.assign_coords({\\n 'nVertices': np.arange(fdata.dims['nVertices']),\\n })\\n if 'nVertLevelsLES' in fdata.dims:\\n out = out.assign_coords({\\n 'nVertLevelsLES': np.arange(fdata.dims['nVertLevelsLES']),\\n })\\n return out\",\n \"def import_data(seed: object = 42) -> object:\\n\\n # Read input data\\n df = pd.read_csv(\\\"x_train_gr_smpl.csv\\\").astype(int)\\n\\n # label data-frame rows based on sample data\\n for x in range(10):\\n index = ~pd.read_csv(\\\"y_train_smpl_%s.csv\\\" % x, squeeze=True).astype(bool) # reversed flags (~)\\n df.loc[index, 'label'] = str(x)\\n\\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\\n output_data_ordered = df.iloc[:, 2304].to_numpy()\\n\\n # Randomise instance order (forcing the same result each time)\\n np.random.seed(seed)\\n permutation = np.random.permutation(df.shape[0])\\n\\n # Create base input and output arrays\\n input_data = input_data_ordered[permutation]\\n output_data = output_data_ordered[permutation]\\n\\n return input_data, output_data, df, input_data_ordered, output_data_ordered\",\n \"def load_own_data():\\n data = Data()\\n features = data.read_selected_features()\\n dataframe = data.get_dataframe_with(features)\\n return normalize(dataframe.values)\",\n \"def loadData(self, file):\\n self.data = batchImport(file, self.ps)\",\n \"def loadDataFromFile(self, filename, axis=0):\\n # Load\\n file = \\\"/Users/Septien/Documents/Tesis/code/data/\\\" + filename + \\\".data\\\"\\n with open(file) as dataFile:\\n for line in dataFile:\\n row = line.split(\\\",\\\")\\n self.numAxes = len(row)\\n d = row[axis]\\n if (d != '?'):\\n self.data.append(float(d))\\n # Sort for further computation\\n self.data.sort()\\n # Get the range\\n self.xAxisRange = [self.data[0], self.data[-1]]\",\n \"def import_data(fndata):\\n with open(fndata, 'rb') as f:\\n # split lines\\n lsdata = [line.split(',') for line in f.read().splitlines()]\\n # map to float\\n lsdata = [map(float, row) for row in lsdata]\\n\\n # use numpy array\\n arrdata = np.array(lsdata)\\n\\n return arrdata\",\n \"def read_from(self, filename):\\n self.x, self.y = np.loadtxt(filename, unpack=True, usecols=(0, 1))\",\n \"def extract_data(data_file):\\n\\n data_points = set()\\n points_to_coordinates = {}\\n x_coor_dict = {}\\n # need this since we need to iterate through the first point indices in a fixed order\\n first_point_indices = []\\n\\n with open(data_file) as input_file:\\n # consume the first line, it only tells us how many points there are\\n input_file.readline()\\n point_number = 0\\n current_x_coor = inf\\n # point that's used as the indexing point for a list of points with the same x coordinates\\n first_point_index = 1\\n\\n for line in input_file:\\n point_number += 1\\n line = line.strip('\\\\n')\\n split_line = line.split(\\\" \\\")\\n print(split_line)\\n x_coor = float(split_line[1])\\n # if we're seeing this x coordinate for the first time\\n if current_x_coor != x_coor:\\n first_point_index = point_number\\n first_point_indices.append(first_point_index)\\n x_coor_dict[first_point_index] = []\\n current_x_coor = x_coor\\n x_coor_dict[first_point_index].append(point_number)\\n new_point = Point(x_coor, float(split_line[2]))\\n points_to_coordinates[point_number] = new_point\\n data_points.add(point_number)\\n\\n return data_points, points_to_coordinates, x_coor_dict, first_point_indices\",\n \"def load_data(): \\n\\tdf = pandas.read_csv('data/iris.data', header=None)\\n\\ty = df.iloc[0:df.shape[0], 4].values\\n\\ty = np.where(y == 'Iris-setosa', 0, y)\\n\\ty = np.where(y == 'Iris-versicolor', 1, y)\\n\\ty = np.where(y == 'Iris-virginica', 2, y)\\n\\tx = df.iloc[0:df.shape[0], 0:4].values\\n\\tx = tuple(x)\\n\\ty = tuple(y)\\n\\ttraining_inputs = x[0:40] + x[50:90] + x[100:140]\\n\\ttraining_results = y[0:40] + y[50:90] + y[100:140]\\n\\ttraining_data = (training_inputs, training_results)\\n\\ttest_inputs = x[40:50] + x[90:100] + x[140:150]\\n\\ttest_results = y[40:50] + y[90:1000] + y[140:150]\\n\\ttest_data = (test_inputs, test_results)\\n\\treturn (training_data, test_data)\",\n \"def load_dataset(filename):\\n return [(\\n lambda point: {\\n 'coordinate': tuple(map(float, point[:-1])),\\n 'label': int(point[-1])})\\n (string.strip().rstrip().split(','))\\n for string in open(filename, 'r').read()\\n .strip().rstrip().split('\\\\n')]\",\n \"def load_data(dataset_str, isnormalize):\\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\\n objects = []\\n for i in range(len(names)):\\n with open(\\\"data/ind.{}.{}\\\".format(dataset_str, names[i]), 'rb') as f:\\n if sys.version_info > (3, 0):\\n objects.append(pkl.load(f, encoding='latin1'))\\n else:\\n objects.append(pkl.load(f))\\n\\n x, y, tx, ty, allx, ally, graph = tuple(objects)\\n test_idx_reorder = parse_index_file(\\\"data/ind.{}.test.index\\\".format(dataset_str))\\n test_idx_range = np.sort(test_idx_reorder)\\n\\n if dataset_str == 'citeseer':\\n # Fix citeseer dataset (there are some isolated nodes in the graph)\\n # Find isolated nodes, add them as zero-vecs into the right position\\n test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\\n tx_extended[test_idx_range-min(test_idx_range), :] = tx\\n tx = tx_extended\\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\\n ty_extended[test_idx_range-min(test_idx_range), :] = ty\\n ty = ty_extended\\n\\n features = sp.vstack((allx, tx)).tolil()\\n features[test_idx_reorder, :] = features[test_idx_range, :]\\n adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))\\n\\t# This normalization is used for cora but not citeseer\\n if isnormalize == 1:\\n adj = adj+ sp.eye(adj.shape[0])\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n adj = adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)\\n # end of normalization.\\n labels = np.vstack((ally, ty))\\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\\n\\n idx_test = test_idx_range.tolist()\\n idx_train = range(len(y))\\n idx_val = range(len(y), len(y)+500)\\n\\n train_mask = sample_mask(idx_train, labels.shape[0])\\n val_mask = sample_mask(idx_val, labels.shape[0])\\n test_mask = sample_mask(idx_test, labels.shape[0])\\n\\n y_train = np.zeros(labels.shape)\\n y_val = np.zeros(labels.shape)\\n y_test = np.zeros(labels.shape)\\n y_train[train_mask, :] = labels[train_mask, :]\\n y_val[val_mask, :] = labels[val_mask, :]\\n y_test[test_mask, :] = labels[test_mask, :]\\n \\n\\t\\n return adj, normalize(features).toarray(), y_train, y_val, y_test, train_mask, val_mask, test_mask, labels\",\n \"def _load_data(data, n_prev=3):\\n docX, docY = [], []\\n for i in range(len(data)-n_prev):\\n docX.append(data.data.iloc[i:i+n_prev])\\n docY.append(data.response.iloc[i+n_prev]) \\n\\n alsX = np.array(docX)\\n alsY = np.array(docY)\\n return np.matrix(alsX), np.matrix(alsY).T\",\n \"def load_data(filename):\\n data = []\\n with open('data/' + filename) as raw_data:\\n for line in raw_data.readlines():\\n data.append(float(line.strip('\\\\n')))\\n return data\\n # data = np.mat(np.genfromtxt('data/' + filename)).T\\n # return data\",\n \"def read_data(path):\\n data_set = []\\n y = -1\\n with open(path, \\\"r\\\") as file:\\n for line in file:\\n y = y+1\\n data_set.append([])\\n currentline = line.split(\\\",\\\")\\n for x in currentline:\\n data_set[y].append(float(x.rstrip()))\\n return data_set\",\n \"def load_pointing(name):\\r\\n pointing_data = np.loadtxt(os.path.join(data_dir, '{}.csv'.format(name)), delimiter=',')[:, 1:3]\\r\\n return pointing_data\",\n \"def load_datasets(self):\\n if self.processed_extension == '.npz':\\n logger.info(f'Loading sets from npz:')\\n \\n logger.info(f'train: {self.train_path}')\\n self.train_data = sparse.load_npz(self.train_path)\\n\\n logger.info(f'val: {self.val_path}')\\n self.val_data = sparse.load_npz(self.val_path)\\n\\n logger.info(f'test: {self.test_path}')\\n self.test_data = sparse.load_npz(self.test_path)\\n \\n # Split x and y\\n self.train_data = [sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,-1])]\\n \\n self.val_data = [sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,-1])]\\n \\n self.test_data = [sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,-1])]\\n \\n elif self.processed_extension == '.csv':\\n logger.info(f'Loading sets from csv:')\\n \\n logger.info(f'train: {self.train_path}')\\n self.train_data = pd.read_csv(self.train_path)\\n train_cols = self.train_data.columns\\n self.train_data = [self.train_data[train_cols.difference(['TARGET'])],\\n self.train_data['TARGET']]\\n \\n logger.info(f'val: {self.val_path}')\\n self.val_data = pd.read_csv(self.val_path)\\n self.val_data = [self.val_data[train_cols.difference(['TARGET'])],\\n self.val_data['TARGET']]\\n \\n logger.info(f'test: {self.test_path}')\\n self.test_data = pd.read_csv(self.test_path)\\n self.test_data = [self.test_data[train_cols.difference(['TARGET'])],\\n self.test_data['TARGET']]\\n else:\\n raise AttributeError(f'Wrong extension: {self.processed_extension}')\\n self.n_train = self.train_data[0].shape[0]\\n self.n_val = self.val_data[0].shape[0]\\n self.n_test = self.test_data[0].shape[0]\\n self.input_size = self.train_data[0].shape[1]\\n self.n_examples = self.n_train + self.n_val + self.n_test\\n \\n logger.info(f'Set sizes:')\\n logger.info(f'train: {self.n_train}')\\n logger.info(f'val: {self.n_val}')\\n logger.info(f'test: {self.n_test}')\",\n \"def collect_datset(self):\\n response = requests.get(self.url)\\n lines = response.text.splitlines()\\n data = []\\n for item in lines:\\n item = item.split(\\\",\\\")\\n data.append(item)\\n data.pop(0) # to remove labels from list\\n dataset = np.matrix(data)\\n return dataset\",\n \"def load_file(filename):\\n f_data = []\\n # open the data-set file\\n file = open(filename, \\\"r\\\")\\n for line in file:\\n row = line.strip() # a row in the file\\n f_data.append(row) # append it to the 2D array\\n\\n return f_data\",\n \"def load_data(directory: str, dataset_str: str):\\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\\n objects = []\\n for i in range(len(names)):\\n with open(\\\"{}/ind.{}.{}\\\".format(directory, dataset_str, names[i]), 'rb') as f:\\n if sys.version_info > (3, 0):\\n objects.append(pkl.load(f, encoding='latin1'))\\n else:\\n objects.append(pkl.load(f))\\n\\n x, y, tx, ty, allx, ally, graph = tuple(objects)\\n test_idx_reorder = parse_index_file(\\\"{}/ind.{}.test.index\\\".format(directory, dataset_str))\\n test_idx_range = np.sort(test_idx_reorder)\\n\\n if dataset_str == 'citeseer':\\n # Fix citeseer dataset (there are some isolated nodes in the graph)\\n # Find isolated nodes, add them as zero-vecs into the right position\\n test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\\n tx_extended[test_idx_range-min(test_idx_range), :] = tx\\n tx = tx_extended\\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\\n ty_extended[test_idx_range-min(test_idx_range), :] = ty\\n ty = ty_extended\\n\\n features = sp.vstack((allx, tx)).tolil()\\n features[test_idx_reorder, :] = features[test_idx_range, :]\\n\\n labels = np.vstack((ally, ty))\\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\\n\\n idx_test = test_idx_range.tolist()\\n idx_train = range(len(y))\\n idx_val = range(len(y), len(y)+500)\\n\\n train_mask = sample_mask(idx_train, labels.shape[0])\\n val_mask = sample_mask(idx_val, labels.shape[0])\\n test_mask = sample_mask(idx_test, labels.shape[0])\\n\\n y_train = np.zeros(labels.shape)\\n y_val = np.zeros(labels.shape)\\n y_test = np.zeros(labels.shape)\\n y_train[train_mask, :] = labels[train_mask, :]\\n y_val[val_mask, :] = labels[val_mask, :]\\n y_test[test_mask, :] = labels[test_mask, :]\\n\\n return graph, features, y_train, y_val, y_test, train_mask, val_mask, test_mask\",\n \"def load_data():\\n\\n print('Loading and Visualizing Data ...')\\n\\n file_name = path.join(getcwd(), 'ex3', 'src', 'data', 'ex3data1')\\n data = scipy.io.loadmat(file_name)\\n\\n # training data stored in arrays X, y\\n # y should be a row vector of labels\\n return data['X'], data['y'].T[0]\",\n \"def load_data(self, t):\\n self.ensure_datafiles()\\n datafile = os.path.join(self.data_dir, ALL_DATA[t])\\n data = sio.loadmat(datafile)\\n return data['X'].transpose(3, 0, 1, 2), data['y']\",\n \"def load_data(start=0., stop=100., t_step=1,\\n ins=['6 MO', '1 YR', '3 YR', '5 YR'], outs=['10 YR'], verbose=1):\\n if 'DATA_PATH' not in os.environ:\\n raise Exception(\\\"Cannot find DATA_PATH variable in the environment. \\\"\\n \\\"DATA_PATH should be the folder that contains \\\"\\n \\\"`quandl/` directory where the data is cached. \\\"\\n \\\"Please export DATA_PATH before using `load_data`.\\\")\\n\\n # Create a directory for Quandl data if it does not exist\\n quandl_data_path = os.path.join(os.environ['DATA_PATH'], 'quandl')\\n if not os.path.isdir(quandl_data_path):\\n os.makedirs(quandl_data_path)\\n\\n if verbose:\\n sys.stdout.write('Loading data...')\\n sys.stdout.flush()\\n\\n # Load the data\\n dataset_path = os.path.join(quandl_data_path, 'USTYCR.pkl')\\n if not os.path.exists(dataset_path):\\n USTYCR = Quandl.get(\\\"USTREASURY/YIELD\\\")\\n USTYCR.to_pickle(dataset_path)\\n else:\\n USTYCR = pd.read_pickle(dataset_path)\\n\\n X = USTYCR[ins].as_matrix()\\n Y = USTYCR[outs].as_matrix()\\n\\n start = int((start/100.) * len(X))\\n stop = int((stop/100.) * len(X))\\n\\n # Select the data points\\n X = X[start:stop:t_step,:]\\n Y = Y[start:stop:t_step,:]\\n\\n if verbose:\\n sys.stdout.write('Done.\\\\n')\\n print('# of loaded points: %d' % len(X))\\n\\n return X, Y\",\n \"def load_testset(filename):\\n pickle_name = filename + \\\".pickle\\\"\\n try:\\n print(\\\"trying to load \\\" + filename + \\\" from pickle\\\")\\n dataset = pickle.load(open(pickle_name, \\\"rb\\\"))\\n except:\\n with open(filename, 'r') as csv_file:\\n print(\\\"no pickle exists. parsing file \\\" + filename)\\n dataset = [DataPoint(item[0:], \\\" \\\")\\n for item\\n in csv.reader(csv_file, delimiter=',')]\\n pickle.dump(dataset, open(pickle_name, \\\"wb\\\"))\\n print(\\\"loaded \\\" + filename)\\n return dataset\",\n \"def data_array(self) -> xr.Dataset:\\n\\n xr_data = xr.open_mfdataset(self.path_to_files,\\n chunks=self.chunks,\\n parallel=True)\\n\\n if not all(x in list(xr_data.coords) for x in self.DIMS):\\n xr_data = xr_data.rename({\\n 'latitude': 'lat',\\n 'longitude': 'lon',\\n })\\n\\n if self.subset_dict is not None:\\n print(f'Cutting data using {self.subset_dict}')\\n xr_data = self.cut(xr_data)\\n\\n if self.season is not None:\\n xr_data = xr_data.where(xr_data.time.dt.season == self.season,\\n drop=True)\\n\\n if self.rescale_longitude is True:\\n xr_data = xr_data.assign_coords(lon=(((xr_data.lon + 180) % 360) -\\n 180)).sortby('lon')\\n\\n return xr_data\",\n \"def _load_data(self):\\n\\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\\\\n self.task_num\\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\\n\\n read_data_x = pd.read_csv(path_data_x)\\n read_data_y = pd.read_csv(path_data_y)\\n read_data_meta = pd.read_csv(path_meta)\\n\\n # Drop columns if it only contains 1 unique element\\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\\n\\n num_col = pd.read_csv(path_numerical_columns)\\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\\n col_idx = np.arange(read_data_x.shape[-1])\\n non_num_col = np.setdiff1d(col_idx, num_col)\\n\\n # new_data_x = np.array(read_data_x).astype(np.float32)\\n new_data_x = np.array(read_data_x)\\n new_data_y = np.array(read_data_y).astype(np.float32)\\n new_data_meta = np.array(read_data_meta).astype(np.float32)\\n\\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\\n\\n\\n # Winsorize dataset\\n len_feat = new_data_x.shape[-1]\\n idx_list = list(num_col)\\n for i in range(len_feat):\\n if i in idx_list:\\n cur_data = new_data_x[:, i]\\n cur_data = np.array(cur_data)\\n lower_p = np.percentile(cur_data, 5)\\n higher_p = np.percentile(cur_data, 95)\\n cur_data[cur_data < lower_p] = lower_p\\n cur_data[cur_data > higher_p] = higher_p\\n new_data_x[:, i] = cur_data\\n\\n # Make sure target data is one-hot encoded\\n if new_data_y.shape[-1] == 1:\\n num_class = len(np.unique(new_data_y))\\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\\n new_data_y = new_data_y.astype('float32')\\n self.orig_column_names = read_data_x.columns\\n self.data_x = new_data_x # N x F\\n self.data_y = new_data_y # N x C\\n self.numerical_idx = num_col # list of idx\\n self.non_num_idx = non_num_col # None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = new_data_meta.astype('float32') # N x 3\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def load_or_generate_data(self) -> None:\\n\\n # Training set defined as a 5 x 5 square:\\n xg1 = np.linspace(-5, 10, 5)\\n xg2 = np.linspace(0, 15, 5)\\n x = np.zeros((xg1.size * xg2.size, 2))\\n for i, x1 in enumerate(xg1):\\n for j, x2 in enumerate(xg2):\\n x[i + xg1.size * j, :] = [x1, x2]\\n\\n y = self.branin(x)[:, None]\\n self.x, self.y = x, y\",\n \"def _importFile(self, sourceFile):\\n header = 1 if self.headerline else 0\\n\\n data = np.genfromtxt(\\n sourceFile\\n , delimiter=','\\n , skip_header=header\\n , names=[\\n 'x'\\n , 'accelX'\\n , 'accelY'\\n , 'accelZ'\\n , 'gyroX'\\n , 'gyroY'\\n , 'gyroZ'\\n ]\\n )\\n return data\",\n \"def read_dataset(file, interner=None, point_factory=ListOpsDataPoint):\\n data_set = []\\n r = range(0, 10)\\n\\n if (interner == None):\\n func = RespectfulNumbering(respect=r)\\n interner = interners.Interner(not_present_function=func)\\n\\n\\n reader = csv.reader(file, delimiter='\\\\t')\\n for row in reader:\\n #print(row)\\n if len(row) > 1:\\n try:\\n data_set.append(point_factory(row[0], row[1], interner))\\n except ValueError:\\n print(\\\"Warning, could not read line: %s\\\"%row, file=sys.stderr)\\n\\n return data_set, interner\",\n \"def open_xy(data):\\n twotheta, intensity = [], []\\n with open(data) as f:\\n for line in f:\\n row = line.split()\\n twotheta.append(row[0])\\n intensity.append(row[1])\\n xyarray = list(zip(twotheta, intensity))\\n xyarray = np.asarray(xyarray)\\n xyarray = xyarray.astype(np.float)\\n return xyarray\",\n \"def import_data(textfile, uncertainty):\\n\\tf = open(textfile, 'r')\\n\\tdata = f.readlines()\\n\\tx = []\\n\\ty = []\\n\\tfor line in data:\\n\\t\\tcoords = line.strip()\\n\\t\\tcoords = coords.split(', ')\\n\\t\\tx.append(coords[0])\\n\\t\\ty.append(coords[1]) \\n\\t\\n\\tx = [float(i) for i in x]\\n\\tx = np.array(x).reshape((10,1))\\n\\ty = [float(i) for i in y]\\n\\ty = np.array(y).reshape((10,1))\\n\\tdata = {'x': x, 'y': y, 'var': uncertainty}\\n\\n\\treturn data\",\n \"def test_univariate(self):\\n data = load_dataset(data_format=\\\"numpy\\\", standardize=True)\\n assert data.shape[0] > 131, \\\"Imported time series collection has the wrong shape\\\"\\n assert data.shape[-1] == 1000, \\\"Imported time series collection has the wrong shape\\\"\",\n \"def get_train_data():\\n # train set\\n train = pd.read_csv(\\\"train.csv\\\")\\n\\n return train\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def load_data_table(data_url):\\r\\n data_file = urllib2.urlopen(data_url)\\r\\n data = data_file.read()\\r\\n data_lines = data.split('\\\\n')\\r\\n print \\\"Loaded\\\", len(data_lines), \\\"data points\\\"\\r\\n data_tokens = [line.split(',') for line in data_lines]\\r\\n return [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])] \\r\\n for tokens in data_tokens]\",\n \"def load_data_table(data_url):\\n\\tdata_file = urllib2.urlopen(data_url)\\n\\tdata = data_file.read()\\n\\tdata_lines = data.split('\\\\n')\\n\\tprint \\\"Loaded\\\", len(data_lines), \\\"data points\\\"\\n\\tdata_tokens = [line.split(',') for line in data_lines]\\n\\treturn [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])]\\n\\t\\t\\tfor tokens in data_tokens]\",\n \"def load_data(loc='../data/SICK/'):\\n trainA, trainB, testA, testB = [],[],[],[]\\n trainS, testS = [],[]\\n\\n with open(loc + 'SICK_train.txt', 'rb') as f:\\n for line in f:\\n text = line.strip().split('\\\\t')\\n trainA.append(text[1])\\n trainB.append(text[2])\\n trainS.append(text[3])\\n with open(loc + 'SICK_test_annotated.txt', 'rb') as f:\\n for line in f:\\n text = line.strip().split('\\\\t')\\n testA.append(text[1])\\n testB.append(text[2])\\n testS.append(text[3])\\n\\n trainS = [float(s) for s in trainS[1:]]\\n testS = [float(s) for s in testS[1:]]\\n\\n return [trainA[1:], trainB[1:]], [testA[1:], testB[1:]], [trainS, testS]\",\n \"def _load_data(self):\\n\\n from sklearn.datasets import fetch_openml\\n mnist = fetch_openml('mnist_784', cache=True)\\n # data_x = np.array(final_data_df)\\n feat_data = np.array(mnist.data).astype('float32')\\n target_data = mnist.target.astype('int64')\\n shuffling_index = np.arange(feat_data.shape[0])\\n np.random.shuffle(shuffling_index)\\n feat_data = feat_data[shuffling_index]\\n target_data = target_data[shuffling_index]\\n\\n cur_data_list = []\\n cur_target_list = []\\n for i in range(10):\\n cur_mask = target_data == i\\n cur_data_list.append(feat_data[cur_mask][:500])\\n cur_target_list.append(target_data[cur_mask][:500])\\n feat_data = np.concatenate(cur_data_list)\\n target_data = np.concatenate(cur_target_list)\\n\\n self.data_x = feat_data\\n self.data_y = self.to_one_hot_encoding(target_data)\\n self.numerical_idx = np.arange(784)\\n self.non_num_idx = None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = self.data_x.astype('float32')\\n\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n path = os.getcwd() # reads the current path\\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\\n x_train, y_train = shuffle(x_train, y_train)\\n\\n return x_train, y_train, x_test\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n\\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n\\n # split the data into 10% validation-set and 90% training set\\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\\n return raw_train, raw_valid, y_train, y_valid\",\n \"def load_dataset(data_dir, img_size):\\n global input_set\\n global test_set\\n\\n imgs = []\\n img_files = os.listdir(data_dir)\\n for img in img_files:\\n # try:\\n tmp = scipy.misc.imread(data_dir + \\\"/\\\" + img)\\n x, y, z = tmp.shape # shape : width * length * chanel\\n coords_x = int(x / img_size) # 坐标\\n coords_y = int(y / img_size) #\\n coords = [(q, r) for q in range(coords_x) for r in range(coords_y)] # 列表 x * y\\n for coord in coords:\\n imgs.append((data_dir + \\\"/\\\" + img, coord)) # 为列表添加文件目录\\n # except BaseException:\\n # print(\\\"oops\\\")\\n test_size = min(10, int(len(imgs) * 0.2))\\n random.shuffle(imgs)\\n test_set = imgs[:test_size]\\n train_set_X = imgs[test_size:][:200]\\n train_set = imgs[test_size:][200:400]\\n return\",\n \"def loader(filename,sep=',',rowskip=[], colskip=[], axis=1,names=1,fromstring=0):\\n\\n #manages excpetions to the csv file incase of missing data\\n if (type(filename)==str) and (fromstring==1):\\n iterable=filename.strip('\\\\n').split('\\\\n')\\n content=np.array([i for i in csv.reader(iterable,delimiter=sep)])\\n elif type(filename)==np.ndarray:\\n content=filename\\n else:\\n content=np.array([i for i in\\\\\\n csv.reader(open(filename,'r'),delimiter=sep)])\\n #content=np.genfromtxt(filename,delimiter=sep,dtype=str)\\n\\n if rowskip:\\n #rowskip.sort(reverse=True)\\n content=np.delete(content,rowskip,0)\\n #for i in rowskip: content.pop(i)\\n\\n if colskip:\\n #colskip.sort(reverse=True)\\n content=np.delete(content,colskip,1)\\n #for i in colskip: content.pop(i)\\n\\n if axis==0: # if the file oriented column-wise\\n #content=list(map(list,zip(*content)))\\n content=content.T\\n\\n\\n\\n if names is 0:\\n variables=np.arange(content.shape[1]).tolist()\\n offset=0\\n else:\\n variables=content[0].tolist()\\n offset=1\\n\\n try:\\n content=np.array([conv_col(col) for col in\\n content[offset:].T],dtype='object')\\n arity=np.array([np.unique(i).size for i in content])\\n return dataset(variables,content.T,arity)\\n except ValueError: \\n print( 'Data could not be loaded, failed converting to float.')\\n return content\",\n \"def load_data():\\n data = []\\n with euler.Resource('triangle.txt') as datafile:\\n for line in datafile.readlines():\\n data.insert(0, map(int, line.strip().split()))\\n return data\",\n \"def load(name):\\n data = np.loadtxt(name)\\n X, y = data[:, :-1], data[:, -1].astype(np.int)\\n return X, y\",\n \"def load_data_table(data_url):\\n data_file = urllib2.urlopen(data_url)\\n data = data_file.read()\\n data = data.decode()\\n data_lines = data.split('\\\\n')\\n print(\\\"Loaded\\\", len(data_lines), \\\"data points\\\")\\n data_tokens = [line.split(',') for line in data_lines]\\n return [[tokens[0], float(tokens[1]), float(tokens[2]), int(tokens[3]), float(tokens[4])]\\n for tokens in data_tokens]\",\n \"def readdata(filename):\\n\\tdt = np.dtype([('date','int'),('val','<f8')])\\n\\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\\n\\treturn data\",\n \"def loadxy(filename=None):\\n\\n import numpy as np\\n from StringIO import StringIO\\n import Tkinter\\n from tkFileDialog import askopenfilename\\n\\n if filename is not None:\\n print \\\"Opening %s\\\\n\\\" %(filename)\\n else:\\n root = Tkinter.Tk()\\n root.withdraw()\\n filename=askopenfilename(parent=root,title='Open File',filetypes=[('xy files','*.xy')])\\n root.destroy()\\n root.mainloop()\\n\\n if filename is not None:\\n f = open(filename)\\n d = np.genfromtxt(f, delimiter='\\\\t', unpack=True,\\n skip_header=4, skip_footer=1)\\n s = argsort(d)\\n data = d[:,s[0]]\\n return data\",\n \"def load_data():\\n data = pd.read_csv(\\\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\\\", header=None)\\n\\n # utiliza somente as duas primeiras classes\\n data = data[:100]\\n # transforma as classes em 0 e 1\\n data[4] = np.where(data.iloc[:, -1] == 'Iris-setosa', 0, 1)\\n data = np.asmatrix(data, dtype='float64')\\n return data\",\n \"def load_data(self) -> None:\",\n \"def load_data(self):\\n\\t\\ti = 0\\n\\n\\t\\tpaths = glob.glob(self.file_path+'/rollout_*')\\n\\t\\tself.rollouts = []\\n\\n\\n\\t\\tfor path in paths:\\n\\t\\t\\tdata_point = np.load(path,encoding='latin1')\\n\\t\\t\\tself.rollouts.append(data_point)\\n\\n\\t\\treturn paths\",\n \"def dataset_to_list_points(dir_dataset):\\n points = list()\\n with open(dir_dataset, 'rt') as reader:\\n for point in reader:\\n points.append(np.asarray(map(float, point.split(\\\"::\\\"))))\\n return points\",\n \"def data_loader(edges,features,y):\\n\\n\\n edge_index = torch.tensor(edges, dtype=torch.long)\\n edge_index = edge_index.t().contiguous()\\n x = torch.tensor(features.todense(), dtype=torch.float)\\n\\n y = torch.tensor(y)\\n\\n data = Data(x=x, edge_index=edge_index, y = y)\\n\\n return data\",\n \"def load(self, source):\\n try:\\n inputdata = self.__inputmanager.read(source)\\n self.__suitables = self.__inputmanager.map(inputdata)\\n self.__data = inputdata\\n except ValueError as e:\\n print (\\\"Failed to load the dataset: %s\\\" % e)\\n raise\\n\\n self.__modules = self.import_suitable_visualizations(self.__suitables)\\n self.__has_datefields = self.__inputmanager.has_date_points()\\n # Converting the datakeys into strings.\\n self.__datakeys = [str(i) for i in list(self.__data[0].keys())]\\n return self.__suitables\",\n \"def _load_training_and_test_sets(normalize):\\n class_labels = []\\n test_labels = []\\n norm = None\\n if normalize == True:\\n norm = loading.get_normalize_vector()\\n\\n for i in range(0, 10):\\n [training, test] = loading.load_number_set(i, 0.7, norm_vector=norm)\\n labels = [str(i)] * training.shape[0]\\n tlabels = [str(i)] * test.shape[0]\\n if i == 0:\\n train_points = training\\n test_points = test\\n else:\\n train_points = np.concatenate((train_points, training), axis = 0)\\n test_points = np.concatenate((test_points, test), axis = 0)\\n class_labels.extend(labels)\\n test_labels.extend(tlabels)\\n\\n return train_points, test_points, class_labels, test_labels\",\n \"def load_data(self, filename='', limit=0.75, delim=','):\\n with open(filename) as data:\\n reader = csv.reader(data, delimiter=delim)\\n f = list(reader)\\n for x in range(len(f)):\\n for y in range(len(f[0]) - 1):\\n f[x][y] = float(f[x][y]) # convert elements of each array to type float except the last one\\n\\n lim = limit * (len(f)) # calculate where the training data and test data are divided\\n lim = int(lim) # convert limit for indexing purposes\\n results = (f[:lim], f[lim:]) # append training data and test data to tuple\\n\\n # for x in range(len(f)):\\n # print(f[x])\\n\\n del f # delete f array which was temporary\\n\\n return results # return value\",\n \"def import_data(inp, picks=None, array_keys={'X': 'X', 'y': 'y'}):\\n if isinstance(inp, (mne.epochs.EpochsFIF, mne.epochs.BaseEpochs)):\\n print('processing epochs')\\n # if isinstance(picks, dict):\\n # picks = mne.pick_types(inp.info, include=picks)\\n inp.load_data()\\n data = inp.get_data()\\n events = inp.events[:, 2]\\n if isinstance(picks, dict):\\n print(\\\"Converting picks\\\")\\n picks = mne.pick_types(inp.info, **picks)\\n\\n elif isinstance(inp, tuple) and len(inp) == 2:\\n print('importing from tuple')\\n data, events = inp\\n\\n elif isinstance(inp, str):\\n # TODO: ADD CASE FOR RAW FILE\\n fname = inp\\n if fname[-3:] == 'fif':\\n epochs = mne.epochs.read_epochs(fname, preload=True,\\n verbose='CRITICAL')\\n print(np.unique(epochs.events[:, 2]))\\n events = epochs.events[:, 2]\\n epochs.crop(tmin=-1., tmax=1.)\\n data = epochs.get_data()\\n if isinstance(picks, dict):\\n print(\\\"Converting picks\\\")\\n picks = mne.pick_types(epochs.info, **picks)\\n\\n else:\\n if fname[-3:] == 'mat':\\n datafile = sio.loadmat(fname)\\n\\n if fname[-3:] == 'npz':\\n print('Importing from npz')\\n datafile = np.load(fname)\\n\\n data = datafile[array_keys['X']]\\n events = datafile[array_keys['y']]\\n print('Extracting target variables from {}'\\n .format(array_keys['y']))\\n else:\\n print(\\\"Dataset not found\\\")\\n return None, None\\n\\n data = data.astype(np.float32)\\n\\n # Make sure that X is 3d here\\n while data.ndim < 3:\\n # (x, ) -> (1, 1, x)\\n # (x, y) -> (1, x, y)\\n data = np.expand_dims(data, 0)\\n\\n if isinstance(picks, (np.ndarray, list, tuple)):\\n picks = np.asarray(picks)\\n if np.any(data.shape[1] <= picks):\\n raise ValueError(\\\"Invalid picks {} for n_channels {} \\\".format(\\n max(len(picks), max(picks)), data.shape[1]))\\n data = data[:, picks, :]\\n\\n return data, events\",\n \"def load_data(filepath):\\n data = import_csv(filepath, has_headers=False)\\n x_data = data[:, 0:3]\\n y_data = None\\n if data.shape[1]>3:\\n y_data = data[:, 3:]\\n n_data = data.shape[0]\\n\\n return n_data, np.float64(x_data), np.float64(y_data)\",\n \"def load_data(self):\\n raw_data = np.genfromtxt(self.data_file, delimiter=',')\\n self.n_clusters = int(raw_data[-1][-1] + 1)\\n self.n_points = len(raw_data) // self.n_clusters\\n \\n # group data according to label\\n data = [raw_data[raw_data[:,-1] == i][:,:-1] \\\\\\n for i in range(self.n_clusters)]\\n\\n # take only a subset of the data\\n if self.split:\\n assert 0 <= self.split <= 1, \\\"Split must be in [0, 1)\\\"\\n\\n # update dataset info and print to stdout\\n self.n_points = int(self.split * len(data[0]))\\n subsampled = self.__len__() - int(self.ood is not None) * self.n_points\\n print(f\\\"INFO: Subsampled {subsampled}/{len(raw_data)} points\\\")\\n \\n return [cluster[:self.n_points] for cluster in data]\\n return data\",\n \"def load_data(flatten=True):\\n if flatten:\\n reshape = _flatten\\n else:\\n reshape = _square\\n\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n x_train = reshape(x_train)\\n x_test = reshape(x_test)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n y_train = to_categorical(y_train, 10)\\n y_test = to_categorical(y_test, 10)\\n return x_train, y_train, x_test, y_test\",\n \"def load_data(data_dir, atlas, scale):\\n\\n data_dir = Path(data_dir)\\n data = pd.read_csv(data_dir / atlas / f'{scale}.csv', index_col=0)\\n\\n # drop medial stuff\\n todrop = np.array(putils.DROP)[np.isin(putils.DROP, data.index)]\\n if len(todrop) > 0:\\n data = data.drop(todrop, axis=0)\\n\\n # get indices of diff hemispheres\\n idx_lh = [n for n, f in enumerate(data.index) if 'lh_' in f]\\n idx_rh = [n for n, f in enumerate(data.index) if 'rh_' in f]\\n\\n # get data array\\n labels = np.asarray(data.columns)\\n data = np.asarray(data).squeeze()\\n\\n return data[idx_lh], data[idx_rh], labels\",\n \"def load_data(self, filename):\\r\\n #sqlcontext = SQLContext(self.sc)\\r\\n #df = sqlcontext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load(filename)\\r\\n #df = sc.textFile(r\\\"C:\\\\Users\\\\mohan\\\\Downloads\\\\patches.csv\\\").map(lambda line: line.split(\\\",\\\"))\\r\\n #print (df.count())\\r\\n df = self.sc.textFile(filename).map(lambda line: line.split(\\\",\\\"))\\r\\n l = df.map(lambda w: [int(float(c)) for c in w]).zipWithIndex()\\r\\n return l\\r\\n raise NotImplementedError\",\n \"def load_data():\\n with open('../data/dataset.txt', 'r') as data_file:\\n return data_file.read().split('\\\\n')\",\n \"def read_1D_comsol_data(self):\\n x=[]\\n y=[]\\n with open(self.file, 'r') as rf:\\n reader = csv.reader(rf, delimiter=',')\\n for row in reader:\\n x.append(row[0])\\n y.append(row[1])\\n x = np.asarray((x),dtype=float)\\n y = np.asarray((y),dtype=float)\\n return x,y\",\n \"def read(self, data_set, location=None):\\n self._open_file(data_set, location)\\n\\n for i, array_id in enumerate(\\n data_set._h5_base_group['Data Arrays'].keys()):\\n # Decoding string is needed because of h5py/issues/379\\n name = array_id # will be overwritten if not in file\\n dat_arr = data_set._h5_base_group['Data Arrays'][array_id]\\n\\n # write ensures these attributes always exist\\n name = dat_arr.attrs['name'].decode()\\n label = dat_arr.attrs['label'].decode()\\n units = dat_arr.attrs['units'].decode()\\n is_setpoint = str_to_bool(dat_arr.attrs['is_setpoint'].decode())\\n # if not is_setpoint:\\n set_arrays = dat_arr.attrs['set_arrays']\\n set_arrays = [s.decode() for s in set_arrays]\\n # else:\\n # set_arrays = ()\\n vals = dat_arr.value[:, 0]\\n if 'shape' in dat_arr.attrs.keys():\\n vals = vals.reshape(dat_arr.attrs['shape'])\\n if array_id not in data_set.arrays.keys(): # create new array\\n d_array = DataArray(\\n name=name, array_id=array_id, label=label, parameter=None,\\n units=units,\\n is_setpoint=is_setpoint, set_arrays=(),\\n preset_data=vals)\\n data_set.add_array(d_array)\\n else: # update existing array with extracted values\\n d_array = data_set.arrays[array_id]\\n d_array.name = name\\n d_array.label = label\\n d_array.units = units\\n d_array.is_setpoint = is_setpoint\\n d_array.ndarray = vals\\n d_array.shape = dat_arr.attrs['shape']\\n # needed because I cannot add set_arrays at this point\\n data_set.arrays[array_id]._sa_array_ids = set_arrays\\n\\n # Add copy/ref of setarrays (not array id only)\\n # Note, this is not pretty but a result of how the dataset works\\n for array_id, d_array in data_set.arrays.items():\\n for sa_id in d_array._sa_array_ids:\\n d_array.set_arrays += (data_set.arrays[sa_id], )\\n data_set = self.read_metadata(data_set)\\n return data_set\",\n \"def _load_data(self):\\n\\n path_data_x = \\\\\\n '/workspace/base-ml/data/tadpole/adni_one_baseline_feature_data' \\\\\\n '.csv'\\n path_data_y = \\\\\\n '/workspace/base-ml/data/tadpole/adni_one_baseline_label_data' \\\\\\n '.csv'\\n path_meta = '/workspace/base-ml/data/tadpole' \\\\\\n '/adni_one_baseline_meta_data' \\\\\\n '.csv'\\n read_data_x = pd.read_csv(path_data_x)\\n read_data_y = pd.read_csv(path_data_y) # 0 NL, 1, MCI, 2 Dementia\\n read_data_meta = pd.read_csv(path_meta)[['AGE', 'PTGENDER', 'APOE4']]\\n\\n # Replace gender to numeric\\n read_data_meta.PTGENDER = read_data_meta.PTGENDER.replace('Male', 0)\\n read_data_meta.PTGENDER = read_data_meta.PTGENDER.replace('Female', 1)\\n\\n new_data_x = np.array(read_data_x).astype(np.float32)\\n new_data_y = np.array(read_data_y).astype(np.float32)\\n new_data_meta = np.array(read_data_meta).astype(np.float32)\\n\\n # Concat meta-information with feature vector input\\n concat_meta = pd.DataFrame(new_data_meta)\\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(0, 'zero')\\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(1, 'one')\\n concat_meta.iloc[:, 2] = concat_meta.iloc[:, 2].replace(2, 'two')\\n concat_meta = concat_meta.to_numpy()\\n new_data_x = np.concatenate([concat_meta, new_data_x], 1)\\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\\n\\n self.orig_column_names = ['Age', 'Gender', 'APOE4'] + \\\\\\n list(read_data_x.columns)\\n self.data_x = new_data_x\\n self.data_y = self.to_one_hot_encoding(new_data_y)\\n self.numerical_idx = np.arange(new_data_x.shape[-1])\\n self.numerical_idx = np.delete(self.numerical_idx, [2]) # Remove APOE column idx\\n self.non_num_idx = np.array([2])\\n self.all_non_numerical_idx = None\\n\\n # self.numerical_idx = np.arange(self.data_x.shape[-1])\\n # self.non_num_idx = None\\n # self.all_non_numerical_idx = None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = new_data_meta.astype('float32')\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def loadData ( self ) :\\n df = pd.read_json ( self.dataset )\\n df = df[pd.notnull ( df[2] )]\\n df[1] = df[1].apply ( self.clean_text )\\n\\n self.X = df[1]\\n self.y = df[2]\",\n \"def load_data(filename):\\n assert os.path.exists(filename)==True\\n dat = scipy.io.loadmat(filename)\\n inputs = dat['inputs']\\n #print len(inputs)\\n targets = dat['targets']\\n #print len(targets)\\n assert len(inputs)==len(targets)\\n\\n global alldata\\n global indim \\n global outdim\\n\\n indim = len(inputs[0])\\n outdim = 1\\n #print indim\\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\\n alldata.setField('input',inputs)\\n alldata.setField('target',targets)\\n\\n assert len(alldata['input'])==len(alldata['target'])\\n print type(alldata)\",\n \"def prepare_dataset(self, stock):\\n dataset = pd.DataFrame(yf.download(stock))\\n dataset_train = dataset[0:len(dataset)-30] # Training dataset will be the whole dataset minus the test dataset\\n training_set = dataset_train.iloc[:, 1:2].values # Use the open values only\\n\\n # Feature Scaling\\n sc = MinMaxScaler(feature_range=(0, 1)) # Scales all values so that they are in the range (0, 1)\\n training_set_scaled = sc.fit_transform(training_set)\\n\\n # Create an input data structure with a timestep\\n x_train = []\\n y_train = []\\n for i in range(self.timestep, len(dataset)-30):\\n x_train.append(training_set_scaled[i - self.timestep:i, 0])\\n y_train.append(training_set_scaled[i, 0])\\n x_train, y_train = np.array(x_train), np.array(y_train)\\n\\n # Reshaping\\n x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\\n\\n return x_train, y_train\",\n \"def input_shape(file_name):\\n data = pd.read_csv(file_name,header=None)\\n x_data = data.iloc[:,0]\\n y_data = data.iloc[:,1]\\n x = np.array(x_data).T\\n y = np.array(y_data).T\\n return x,y\",\n \"def load_coords(self, attrs):\\n ts = time.time()\\n logger.info('Rechunking coordinates')\\n meta_data = self._dst_h5['meta'][...]\\n coords = np.dstack((meta_data['latitude'], meta_data['longitude']))[0]\\n attrs['dtype'] = coords.dtype\\n\\n if isinstance(attrs['chunks'], int):\\n attrs['chunks'] = (attrs['chunks'], 2)\\n\\n ds = self.init_dset('coordinates', coords.shape, attrs)\\n ds[...] = coords\\n logger.info('- coordinates transfered')\\n tt = (time.time() - ts) / 60\\n logger.debug('\\\\t- {:.2f} minutes'.format(tt))\",\n \"def __loadPreProcessedData(self):\\n le = joblib.load(self.le_filename)\\n X = np.loadtxt(self.X_filename, delimiter=',').astype(int)\\n raw_y = np.loadtxt(self.y_filename, delimiter=',').astype(int)\\n y = le.inverse_transform(raw_y)\\n ##Initialize atrtribute for this class\\n self.le, self.X, self.y = le, X, y\",\n \"def data_input(self):\\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))\\n if not os.path.isfile('{0}/{1}.csv'.format(path, self.data_file)):\\n print 'Error: Dataset file is not exist.'\\n exit()\\n # Uplead Dataset.csv file.\\n f = open('{0}/{1}.csv'.format(path, self.data_file), 'r')\\n print 'Now uploading dataset File.....'\\n f = list(f)\\n # The Dataset contains heading, number of lines - heading\\n self.number_of_VOCs = sum(1 for row in f)-1\\n # Count number of columns, last column's value is empty, that is why -1.\\n self.number_of_columns = len(f[0].split(',')) -1\\n self.first_m_z = int(f[0].split(',')[3]) # find the first m/z value.\\n self.last_m_z = int(f[0].split(',')[-2]) # find the last m/z value.\\n print 'dataset includes ', self.number_of_VOCs, 'VOCs in all samples '\\n print ('dataset includes ', self.number_of_columns, ' Columns, ',\\n 'm/z values start from ', self.first_m_z,\\n 'and end ', self.last_m_z)\\n # Create a matrix with a shape of (number_of_VOCs X number_of_columns) filled with zeros.\\n self.dataset = np.zeros((self.number_of_VOCs,\\n self.number_of_columns))\\n for line in range(1, len(f)):\\n if int(float(f[line].strip().split(',')[0])) not in self.loaded_samples:\\n self.loaded_samples.append(int(float(f[line].strip().split(',')[0])))\\n for column in range(self.number_of_columns):\\n self.dataset[line-1][column] = int(float(f[line].strip().split(',')[column]))\",\n \"def load_velodyne_points(filename):\\n points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)\\n points[:, 3] = 1.0 # homogeneous\\n return points\",\n \"def read_data(self,filename):\\n self.x = [] #Input values\\n self.t = [] #Target values\\n\\n with open(filename, \\\"r\\\") as infile:\\n lines = infile.readlines()\\n self.n = len(lines)\\n for line in lines:\\n words = line.split()\\n self.x.append(float(words[0]))\\n self.t.append(float(words[1]))\\n\\n self.x = np.array(self.x)\\n self.t = np.array(self.t)\\n self.create_design_matrix()\",\n \"def _importDataFromText(self, filename, with_axis=None):\\n self.set_data_writable()\\n try: \\n _data = numpy.loadtxt(filename)\\n except ValueError:\\n _data = numpy.loadtxt(filename, dtype=complex)\\n \\n self.data = self._extract_data_with_axis(_data, with_axis)\\n self.set_data_protected()\",\n \"def _load_data(self):\\n data_x, data_y = make_classification(n_samples=5000, n_features=20,\\n n_informative=10,\\n n_redundant=0, n_repeated=0,\\n n_classes=2,\\n n_clusters_per_class=4,\\n weights=None, flip_y=0.01,\\n class_sep=1.0, hypercube=True,\\n shift=0.0, scale=1.0,\\n shuffle=True,\\n random_state=self.args.rand_seed)\\n\\n self.orig_column_names = np.arange(data_x.shape[-1])\\n self.data_x = data_x\\n self.data_y = self.to_one_hot_encoding(data_y)\\n self.numerical_idx = np.arange(data_x.shape[-1])\\n self.non_num_idx = None\\n self.all_non_numerical_idx = None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = data_x[:, :1].astype('float32')\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def loaddevData():\\n dev_x = []\\n dev_y = []\\n fileIn = open(PATH + 'devdata_Subtask4.txt')\\n for line in fileIn.readlines():\\n lineArr = line.strip().split()\\n dev_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\\n dev_y.append(int(lineArr[-1]))\\n return np.mat(dev_x), np.mat(dev_y).transpose()\",\n \"def importData(filename):\\r\\n data = pd.read_csv(filename)\\r\\n return data\",\n \"def make_dataset_temperatures(filename, points):\\r\\n\\r\\n ds = Dataset(filename)\\r\\n\\r\\n lats, lons, cities, countries = get_lat_lon()\\r\\n\\r\\n # total lat,lon pairs: 1366\\r\\n print('The number of rows is ' + str(len(lats)*points))\\r\\n lon = ds.variables['longitude']\\r\\n lat = ds.variables['latitude']\\r\\n time = ds.variables['date_number']\\r\\n\\r\\n lon_array = lon[:]\\r\\n lat_array = lat[:]\\r\\n time_array = time[:]\\r\\n\\r\\n temperature = ds.variables['temperature']\\r\\n\\r\\n dates = []\\r\\n for time in time_array[:]:\\r\\n year = int(time)\\r\\n rem = time - year\\r\\n base = datetime(year, 1, 1)\\r\\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\\r\\n\\r\\n # second approach\\r\\n # for t in time_array[:]:\\r\\n # dates.append(num2date(t, units=time.units))\\r\\n\\r\\n dateResult = []\\r\\n temperatureResult = []\\r\\n latitudeResult = []\\r\\n longitudeResult = []\\r\\n cityResult = []\\r\\n countryResult = []\\r\\n\\r\\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\\r\\n\\r\\n # We want to find data for latitude, longitude\\r\\n # We first need to find the indexes\\r\\n i = np.abs(lon_array - longitude).argmin()\\r\\n j = np.abs(lat_array - latitude).argmin()\\r\\n\\r\\n for d in dates:\\r\\n dateResult.append(d)\\r\\n\\r\\n resultTemperature = temperature[:, j, i]\\r\\n for t in resultTemperature:\\r\\n temperatureResult.append(t)\\r\\n\\r\\n resultLatitues = np.full(\\r\\n shape=points,\\r\\n fill_value=latitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLatitues:\\r\\n latitudeResult.append(l)\\r\\n\\r\\n resultLongitudes = np.full(\\r\\n shape=points,\\r\\n fill_value=longitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLongitudes:\\r\\n longitudeResult.append(l)\\r\\n\\r\\n resultCities = np.full(\\r\\n shape=points,\\r\\n fill_value=city\\r\\n )\\r\\n for c in resultCities:\\r\\n cityResult.append(c)\\r\\n\\r\\n resultCountries = np.full(\\r\\n shape=points,\\r\\n fill_value=country\\r\\n )\\r\\n for c in resultCountries:\\r\\n countryResult.append(c)\\r\\n\\r\\n print('iteration no:' + str(i))\\r\\n\\r\\n df = pd.DataFrame()\\r\\n df['date'] = dateResult\\r\\n df['temperature'] = temperatureResult\\r\\n df['latitude'] = latitudeResult\\r\\n df['longitude'] = longitudeResult\\r\\n df['city'] = cityResult\\r\\n df['country'] = countryResult\\r\\n\\r\\n df.to_csv(r'C:\\\\Users\\\\stoja\\\\Desktop\\\\Temperatures.csv', index=False)\\r\\n return df\",\n \"def seed_other_dataset(name: str, chunk_size: int, start=None, end=None):\\n objects = []\\n for chunk in pd.read_csv(name, chunksize=chunk_size, header=1):\\n chunk_as_mat = chunk.to_numpy()\\n chunk_start = datetime.datetime.strptime(str(chunk_as_mat[0][0]), \\\"%Y%m%d\\\")\\n chunk_end = datetime.datetime.strptime(str(chunk_as_mat[-1][0]), \\\"%Y%m%d\\\")\\n if start is not None and start > chunk_end:\\n continue\\n if end is not None and end < chunk_start:\\n break\\n # print(chunk.to_numpy())\\n objects += insert_into_sql(chunk.to_numpy())\\n return objects\",\n \"def import_data(self):\\n\\n self.worksheet = (\\n xlrd.open_workbook(filename=self.source).sheet_by_index(0)\\n )\\n # Import conversion data from worksheet and store as scipy arrays\\n self.T_exp = np.array(\\n self.worksheet.col_values(0, start_rowx=4, end_rowx=None)\\n ) + 273.15\\n self.HCout_raw = np.array(\\n self.worksheet.col_values(4, start_rowx=4, end_rowx=None)\\n )\\n self.HCin_raw = np.array(\\n self.worksheet.col_values(8, start_rowx=4, end_rowx=None)\\n )\\n self.eta_exp = (\\n (self.HCin_raw - self.HCout_raw) / self.HCin_raw\\n )\\n self.T_model = np.linspace(\\n self.T_exp[0] - 50, self.T_exp[-1] + 50, 25\\n )\\n self.T_array = self.T_model\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6362504","0.6320649","0.62760663","0.6209483","0.60866785","0.6017939","0.5989352","0.59728557","0.59344363","0.59291804","0.5890433","0.5882488","0.58824617","0.58749497","0.5872966","0.58425945","0.58192444","0.5801761","0.5795395","0.57905436","0.57881874","0.5740074","0.57183653","0.5709869","0.570467","0.56818444","0.56817347","0.5675889","0.5648147","0.564813","0.56373334","0.5635061","0.56316006","0.562118","0.5620338","0.5619123","0.5615294","0.5576291","0.557019","0.55621445","0.5561124","0.55571306","0.5551038","0.55480784","0.55461365","0.5539995","0.553565","0.5531477","0.55289406","0.5528852","0.55262387","0.55121785","0.55067366","0.5503506","0.5503245","0.5496891","0.54876983","0.54864067","0.54842454","0.54784006","0.5474935","0.5465247","0.5457976","0.5448704","0.54438806","0.5439928","0.5439622","0.54390305","0.54066885","0.5401583","0.53969514","0.5388825","0.53883016","0.53807616","0.5380189","0.537872","0.5377075","0.53768504","0.5373261","0.53705126","0.5362613","0.5362308","0.53612405","0.53605497","0.53586257","0.53530085","0.5348643","0.5348085","0.5343134","0.53426147","0.5341655","0.5337383","0.5332186","0.5331924","0.53305256","0.53220314","0.531501","0.5312797","0.530992","0.5297923","0.52954817"],"string":"[\n \"0.6362504\",\n \"0.6320649\",\n \"0.62760663\",\n \"0.6209483\",\n \"0.60866785\",\n \"0.6017939\",\n \"0.5989352\",\n \"0.59728557\",\n \"0.59344363\",\n \"0.59291804\",\n \"0.5890433\",\n \"0.5882488\",\n \"0.58824617\",\n \"0.58749497\",\n \"0.5872966\",\n \"0.58425945\",\n \"0.58192444\",\n \"0.5801761\",\n \"0.5795395\",\n \"0.57905436\",\n \"0.57881874\",\n \"0.5740074\",\n \"0.57183653\",\n \"0.5709869\",\n \"0.570467\",\n \"0.56818444\",\n \"0.56817347\",\n \"0.5675889\",\n \"0.5648147\",\n \"0.564813\",\n \"0.56373334\",\n \"0.5635061\",\n \"0.56316006\",\n \"0.562118\",\n \"0.5620338\",\n \"0.5619123\",\n \"0.5615294\",\n \"0.5576291\",\n \"0.557019\",\n \"0.55621445\",\n \"0.5561124\",\n \"0.55571306\",\n \"0.5551038\",\n \"0.55480784\",\n \"0.55461365\",\n \"0.5539995\",\n \"0.553565\",\n \"0.5531477\",\n \"0.55289406\",\n \"0.5528852\",\n \"0.55262387\",\n \"0.55121785\",\n \"0.55067366\",\n \"0.5503506\",\n \"0.5503245\",\n \"0.5496891\",\n \"0.54876983\",\n \"0.54864067\",\n \"0.54842454\",\n \"0.54784006\",\n \"0.5474935\",\n \"0.5465247\",\n \"0.5457976\",\n \"0.5448704\",\n \"0.54438806\",\n \"0.5439928\",\n \"0.5439622\",\n \"0.54390305\",\n \"0.54066885\",\n \"0.5401583\",\n \"0.53969514\",\n \"0.5388825\",\n \"0.53883016\",\n \"0.53807616\",\n \"0.5380189\",\n \"0.537872\",\n \"0.5377075\",\n \"0.53768504\",\n \"0.5373261\",\n \"0.53705126\",\n \"0.5362613\",\n \"0.5362308\",\n \"0.53612405\",\n \"0.53605497\",\n \"0.53586257\",\n \"0.53530085\",\n \"0.5348643\",\n \"0.5348085\",\n \"0.5343134\",\n \"0.53426147\",\n \"0.5341655\",\n \"0.5337383\",\n \"0.5332186\",\n \"0.5331924\",\n \"0.53305256\",\n \"0.53220314\",\n \"0.531501\",\n \"0.5312797\",\n \"0.530992\",\n \"0.5297923\",\n \"0.52954817\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":87496,"cells":{"query":{"kind":"string","value":"Gather all points in some autogrowing areas"},"document":{"kind":"string","value":"def _cluster(self):\n self._not_included = self.data\n self.leaves = []\n flag = int(rand() * len(self.data))\n flag = self._generate(flag)\n while len(self._not_included) > 0:\n flag = self._generate(flag)\n if flag == -1:\n break\n pass\n self._remember.append({\n 'threshold': self._base_threshold,\n 'result': len(self.leaves)\n })\n print(len(self._remember), {\n 'threshold': self._base_threshold,\n 'result': len(self.leaves)\n })\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 grid_points(self):\n for i in range(self.rows):\n for j in range(self.cols):\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\n if i == 0:\n print_gps(max_lat,max_lon,\"grid\")\n if j == 0:\n print_gps(max_lat,min_lon,\"grid\")\n if j == 0:\n print_gps(min_lat,min_lon,\"grid\")\n print_gps(min_lat,max_lon,\"grid\")","def _gather_points(self):\n # This is just a stub for now. We should really find the lines only\n # inside the screen range here.\n\n x = self.index.get_data()\n y = self.value.get_data()\n rad= min(self.width/2.0,self.height/2.0)\n sx = x*rad+ self.x + self.width/2.0\n sy = y*rad+ self.y + self.height/2.0\n\n points = transpose(array((sx,sy)))\n self._cached_data_pts = points\n self._cache_valid = True\n return","def coordinates(self):","def island_aging(self):\n for y in self.island_map:\n for cell in y:\n cell.aging()","def generate_all_locations(grid, shape):","def hammer_points(neighbors = 3):\n tgtPts = cmds.ls(sl=True, fl=True)\n#---------------- add in soft selection? \n if not tgtPts:\n cmds.warning(\"Select one or more cvs\")\n return\n\n for tgtPt in tgtPts:\n\n #tgtPtPos = cmds.pointPosition(tgtPt)\n \n tgtNum = int(tgtPt.partition(\"[\")[2].rpartition(\"]\")[0])\n #tgtBase = tgtPt.partition(\"[\")[0]\n crv = tgtPts[0].partition(\".\")[0]\n \n ptPosListX = []\n ptPosListY = []\n ptPosListZ = []\n \n count = 0\n \n for x in range(-neighbors, neighbors+1):\n \n count += abs(x)\n \n if x != 0:\n origPt = \"{0}.cv[{1}]\".format(crv, tgtNum + x)\n origPtPos = cmds.pointPosition(origPt)\n \n for a in range(abs(x)):\n ptPosListX.append(origPtPos[0])\n ptPosListY.append(origPtPos[1])\n ptPosListZ.append(origPtPos[2])\n \n avgX = sum(ptPosListX)/(len(ptPosListX))\n avgY = sum(ptPosListY)/(len(ptPosListY))\n avgZ = sum(ptPosListZ)/(len(ptPosListZ))\n \n newPos = [avgX, avgY, avgZ]\n \n cmds.xform(tgtPt, ws=True, t=newPos)","def teselado(self,points):\n #muestrea todo el espacio de la envolvente para conseguir las fronteras de decision a intervalos regulares \n #get_hull\n area = boundingbox(points)\n #(min_x,min_y),(max_x,min_y),(max_x,max_y),(min_x,max_y)\n #sample inside hull\n lat_sample = np.arange(min_y, max_y, 0.001).tolist()\n lon_sample = np.arange(min_x,max_x, 0.001)\n sampling_space = [[x,y] for x in lat_sample for y in lon_sample]\n sampling_space = np.asarray(sampling_space, dtype=np.float32)\n prediction = kmeans_instance.predict(sampling_space)\n pol = [[list(sampling_space[index]) for index in [i for i, j in enumerate(prediction) if j == k]] for k in range(centers)]\n hull = [shapely.geometry.MultiPoint(pol[i]).convex_hull.exterior._get_coords() for i in range(len(pol))]","def find_area(self):\n min_lat_point = self.latitude_min\n max_lat_point = self.latitude_max\n min_lon_point = self.longitude_min\n max_lon_point = self.longitude_max\n self.rename_latitude()\n self.rename_longitude()\n all_lat_bounds = self.cube.coord('latitude').bounds\n all_lon_bounds = self.cube.coord('longitude').bounds\n # print(all_lat_bounds)\n # print(all_lon_bounds)\n for i, lat in enumerate(all_lat_bounds):\n for j, lon in enumerate(all_lon_bounds):\n lat_bounds = lat # 2D array of the lower and upper lat bounds\n lon_bounds = lon # 2D array of the lower and upper lon bounds\n if lat_bounds[0] <= min_lat_point < lat_bounds[1]:\n if lon_bounds[0] <= min_lon_point < lon_bounds[1]:\n nlat_min = i\n nlon_min = j\n else:\n pass\n else:\n pass\n\n for k, lat in enumerate(all_lat_bounds):\n for l, lon in enumerate(all_lon_bounds):\n lat_bounds = lat # 2D array of the lower and upper lat bounds\n lon_bounds = lon # 2D array of the lower and upper lon bounds\n if lat_bounds[0] <= max_lat_point < lat_bounds[1]:\n if lon_bounds[0] <= max_lon_point < lon_bounds[1]:\n nlat_max = k\n nlon_max = l\n else:\n pass\n else:\n pass\n\n area_subset = self.cube[:, nlat_min:nlat_max+1, nlon_min:nlon_max+1]\n # print(area_subset.coord('latitude').points)\n # print(area_subset.coord('longitude').points)\n area_mean = area_subset.collapsed(['latitude', 'longitude'],\n iris.analysis.MEAN)\n\n return area_mean","def island_feeding(self):\n for y in self.island_map:\n for cell in y:\n cell.feeding()","def _pick_visible_ids(self, xmin, ymin, xmax, ymax):\n #vtk.vtkSelectVisiblePoints()\n #vcs = vtk.vtkVisibleCellSelector()\n area_picker = vtk.vtkRenderedAreaPicker()\n area_picker.AreaPick(xmin, ymin, xmax, ymax, self.parent.rend)\n #area_picker.Pick()","def find_points(self):\n\n points = [\n (self.inner_radius, 0, \"straight\"),\n (self.inner_radius, self.height / 2, \"straight\"),\n (self.outer_radius, self.height / 2, \"straight\"),\n (self.outer_radius, self.arc_height / 2, \"circle\"),\n (self.mid_radius, 0, \"circle\"),\n (self.outer_radius, -self.arc_height / 2, \"straight\"),\n (self.outer_radius, -self.height / 2, \"straight\"),\n (self.inner_radius, -self.height / 2, \"straight\")\n ]\n\n self.points = points","def spatial(self):","def area(self):","def getAllPoints(self):\n self.getOrigin()\n self.computesWingsMeshPoints()\n self.computesFuselageMeshPoints()\n self.readsMaterials()\n self.assembleMatrices()\n self.computesWingConnexions()\n\n logger.debug(self.aircraftTotalMass)\n # self.plotSectionsPoints() # for debugging","def choose_starting_points(self, agent):\n # Left Side\n if agent % 4 == 1:\n if self.left_side[\"x_max\"] != self.left_side[\"x_min\"]:\n x = (self.left_side[\"x_max\"] + self.left_side[\"x_min\"])/2\n else:\n x = self.left_side[\"x_max\"]\n if self.left_side[\"y_max\"] != self.left_side[\"y_min\"]:\n y = (self.left_side[\"y_max\"] + self.left_side[\"y_min\"])/2\n else:\n y = self.left_side[\"y_max\"]\n # Right Side\n elif agent % 4 == 2:\n if self.right_side[\"x_max\"] != self.right_side[\"x_min\"]:\n x = (self.right_side[\"x_max\"] + self.right_side[\"x_min\"])/2\n else:\n x = self.right_side[\"x_max\"]\n if self.right_side[\"y_max\"] != self.right_side[\"y_min\"]:\n y = (self.right_side[\"y_max\"] + self.right_side[\"y_min\"])/2\n else:\n y = self.right_side[\"y_max\"]\n # Top\n elif agent % 4 == 3:\n if self.top[\"x_max\"] != self.top[\"x_min\"]:\n x = (self.top[\"x_max\"] + self.top[\"x_min\"])/2\n else:\n x = self.top[\"x_max\"]\n if self.top[\"y_max\"] != self.top[\"y_min\"]:\n y = (self.top[\"y_max\"] + self.top[\"y_min\"])/2\n else:\n y = self.top[\"y_max\"]\n # Bottom\n elif agent % 4 == 0:\n if self.bottom[\"x_max\"] != self.bottom[\"x_min\"]:\n x = (self.bottom[\"x_max\"] + self.bottom[\"x_min\"])/2\n else:\n x = self.bottom[\"x_max\"]\n if self.bottom[\"y_max\"] != self.bottom[\"y_min\"]:\n y = (self.bottom[\"y_max\"] + self.bottom[\"y_min\"])/2\n else:\n y = self.bottom[\"y_max\"]\n else:\n raise ValueError(\"Invalid number for sides!\")\n\n return x, y","def example_staypoints():\n p1 = Point(8.5067847, 47.4)\n p2 = Point(8.5067847, 47.5)\n p3 = Point(8.5067847, 47.6)\n p4 = Point(8.5067847, 47.7)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-01 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 07:00:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:00:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n t6 = pd.Timestamp(\"1971-01-02 10:00:00\", tz=\"utc\")\n\n list_dict = [\n {\"id\": 1, \"user_id\": 0, \"started_at\": t1, \"finished_at\": t2, \"geom\": p1},\n {\"id\": 5, \"user_id\": 0, \"started_at\": t2, \"finished_at\": t3, \"geom\": p2},\n {\"id\": 2, \"user_id\": 0, \"started_at\": t3, \"finished_at\": t4, \"geom\": p3},\n {\"id\": 6, \"user_id\": 0, \"started_at\": t4, \"finished_at\": t5, \"geom\": p2},\n {\"id\": 15, \"user_id\": 0, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1},\n {\"id\": 7, \"user_id\": 1, \"started_at\": t3, \"finished_at\": t4, \"geom\": p4},\n {\"id\": 80, \"user_id\": 1, \"started_at\": t4, \"finished_at\": t5, \"geom\": p2},\n {\"id\": 3, \"user_id\": 1, \"started_at\": t5, \"finished_at\": t6, \"geom\": p2},\n ]\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\"geom\", crs=\"EPSG:4326\")\n sp = sp.set_index(\"id\")\n sp.as_staypoints\n return sp","def nearPoints(self):\n # initialize the edge counter\n for edge in self.G.edges():\n self.edge_id__count[self.G[edge[0]][edge[1]].get(\"edgecounter\")] = 0\n \n for point in self.gps_points:\n nearest_edge = self.getNearestEdge(point)\n # print str(point.getAttributes().get(\"ID\")) + \"->\" + str(nearest_edge.getAttributes().get('Id'))\n self.addPointCountToEdge(nearest_edge)","def get_nodes(self, latlon=False):\n ids = np.where(np.isnan(self.data[:,:,:]))\n i_nan = ids[0][0] ; j_nan = ids[1][0]\n \n def area_neighbours(Area, i_nan, j_nan):\n rows = np.array(Area)[:,0]\n cols = np.array(Area)[:,1]\n rows_m = rows-1\n cols_m = cols-1\n rows_p = rows+1\n cols_p = cols+1\n \n p1 = np.array([rows_m,cols]).ravel().reshape(len(rows),2,order='F')\n p2 = np.array([rows_p,cols]).ravel().reshape(len(rows),2,order='F')\n p3 = np.array([rows,cols_m]).ravel().reshape(len(rows),2,order='F')\n p4 = np.array([rows,cols_p]).ravel().reshape(len(rows),2,order='F')\n cond1 = p1[:,0]<0\n cond2 = p2[:,0]>self.dimX-1\n cond3 = p3[:,1]<0\n cond4 = p4[:,1]>self.dimY-1\n if latlon:\n p3[:,1][cond3] = self.dimY-1\n p4[:,1][cond4] = 0\n else:\n p3[:,0][cond3] = i_nan\n p3[:,1][cond3] = j_nan\n p4[:,0][cond4] = i_nan\n p4[:,1][cond4] = j_nan\n p1[:,0][cond1] = i_nan\n p1[:,1][cond1] = j_nan\n p2[:,0][cond2] = i_nan\n p2[:,1][cond2] = j_nan\n p = np.concatenate((p1,p2,p3,p4)).tolist()\n return [i for i in p if i not in self.unavail]\n\n def area_max_correlation(Area, neighbours):\n Rmean = [] ; X = []\n for cell in neighbours:\n R = []\n new_cell = cell[0]*self.dimY + cell[1]\n if new_cell in self.gridcells:\n X.append(cell)\n IDm = np.where(self.gridcells==new_cell)\n Rmean.append(np.nanmean(self.corrs[cells_in_k,IDm]))\n try:\n Rmax = np.nanmax(Rmean)\n except ValueError:\n Rmax = np.nan\n return np.array(X), Rmean, Rmax\n \n def diag_indices(a, k):\n rows, cols = np.diag_indices_from(a)\n if k < 0:\n return rows[-k:], cols[:k]\n elif k > 0:\n return rows[:-k], cols[k:]\n else:\n return rows, cols\n\n #S T E P 1 (C R E A T E N O D E S)\n\n self.nodes = {}\n self.unavail = []\n if latlon:\n neighbour_corrs1 = self.corrs.diagonal(offset=1)\n neighbour_corrs2 = self.corrs.diagonal(offset=self.dimY-1)\n subset = np.arange(0,len(neighbour_corrs2),self.dimY)\n neighbour_corrs2 = neighbour_corrs2[subset]\n neighbour_corrs = np.concatenate((neighbour_corrs1,neighbour_corrs2))\n\n cellIDs1 = diag_indices(self.corrs,1)\n cellIDs2 = diag_indices(self.corrs,self.dimY-1)\n\n cellIDs = (np.concatenate((cellIDs1[0],cellIDs2[0][subset])),\\\n np.concatenate((cellIDs1[1],cellIDs2[1][subset])))\n else:\n neighbour_corrs = self.corrs.diagonal(offset=1)\n cellIDs = diag_indices(self.corrs,1)\n \n cellIDs = (self.gridcells[cellIDs[0]],self.gridcells[cellIDs[1]])\n k = 0\n neighbour_corrs,cellIDs1,cellIDs2 = list(zip(*sorted(zip(neighbour_corrs,cellIDs[0],cellIDs[1]),reverse=True)))\n cell_IDs = (cellIDs1,cellIDs2)\n np.random.seed(2)\n for it in range(len(neighbour_corrs)):\n cells_in_k = []\n i = cell_IDs[0][it]\n j = cell_IDs[1][it]\n r = neighbour_corrs[it]\n \n row_i = int(np.floor(i/self.dimY)) ; col_i = int(i % self.dimY)\n row_j = int(np.floor(j/self.dimY)) ; col_j = int(j % self.dimY)\n \n if ([row_i,col_i] not in self.unavail) & ([row_j,col_j] not in self.unavail):\n if r>self.tau:\n self.nodes.setdefault(k, []).append([row_i,col_i])\n self.nodes.setdefault(k, []).append([row_j,col_j])\n self.unavail.append([row_i,col_i])\n self.unavail.append([row_j,col_j])\n cells_in_k.extend(np.where(self.gridcells==i)[0])\n cells_in_k.extend(np.where(self.gridcells==j)[0])\n\n while True: #expand\n neighbours = area_neighbours(self.nodes[k], i_nan, j_nan)\n X, Rmean, Rmax = area_max_correlation(Area=self.nodes[k], neighbours=neighbours)\n if Rmax > self.tau:\n m = X[Rmean==Rmax].tolist()\n if len(m)>1:\n m = m[np.random.randint(low=0,high=len(m))]\n else:\n m = m[0]\n self.nodes.setdefault(k, []).append(m)\n self.unavail.append(m)\n cells_in_k.extend(np.where(self.gridcells==m[0]*self.dimY+m[1])[0])\n else:\n break\n if len(self.nodes[k]) <= 2:\n del self.nodes[k]\n k += 1\n else:\n break\n \n #S T E P 2 (M E R G E N O D E S)\n \n self.unavail = []\n while True:\n Rs = {}\n unavail_neighbours = {}\n num_cells = dict([(area,len(self.nodes[area])) if self.nodes[area] not in self.unavail else (area,np.inf) for area in self.nodes.keys()])\n maxID = min(num_cells.items(), key=operator.itemgetter(1))[0]\n if num_cells[maxID] > 175: #arbitrary choice?\n break\n else:\n cells_in_k = [np.where(self.gridcells==cell[0]*self.dimY+cell[1])[0] for cell in self.nodes[maxID]]\n neighbours = area_neighbours(self.nodes[maxID], i_nan, j_nan)\n for cell in neighbours:\n gcell = cell[0]*self.dimY + cell[1]\n Rmean = []\n cond1 = gcell in self.gridcells\n cond2 = cell not in self.nodes[maxID]\n cond3 = cell not in [k for k, g in itertools.groupby(sorted(itertools.chain(*unavail_neighbours.values())))]\n cond4 = len([area for area, cells in self.nodes.items() if cell in cells]) > 0\n if (cond1) & (cond2) & (cond3) & (cond4):\n nID = [area for area, cells in self.nodes.items() if cell in cells][0]\n unavail_neighbours[nID] = self.nodes[nID]\n X, Rmean, Rmax = area_max_correlation(Area=self.nodes[nID]+self.nodes[maxID], neighbours=self.nodes[nID]+self.nodes[maxID])\n if nID not in Rs: \n Rs[nID] = np.nanmean(Rmean)\n try:\n Rs_maxID = max(Rs.items(), key=operator.itemgetter(1))[0]\n if Rs[Rs_maxID] > self.tau:\n for cell in self.nodes.pop(Rs_maxID, None):\n self.nodes.setdefault(maxID, []).append([cell[0],cell[1]])\n else:\n self.unavail.append(self.nodes[maxID])\n except ValueError:\n self.unavail.append(self.nodes[maxID])","def feature_coords(features):\n coords_list = []\n for feature in features:\n coord_start = feature.location.nofuzzy_start\n coord_end = feature.location.nofuzzy_end\n coord_pair = (coord_start, coord_end)\n coords_list.append(coord_pair)\n ## consider adding some info to the log\n return coords_list","def fit (self,points):\n all_coordinates = [p.coordinates for p in points]\n for i in range(len(all_coordinates[0])):\n if (len(self.min_coordinate)<=i):\n self.min_coordinate.append(all_coordinates[0][i])\n self.max_coordinate.append(all_coordinates[0][i])\n for x in all_coordinates:\n if(self.max_coordinate[i]<x[i]):\n self.max_coordinate[i]=x[i]\n if(self.min_coordinate[i]>x[i]):\n self.min_coordinate[i]=x[i]","def getGridPoints(x, y, robot):\r\n roundedGrid = (round(x), round(y))\r\n total_radius = (robot.RADIUS + robot.BALL_RADIUS) / robot.grid.scale\r\n scanAmount = math.ceil(total_radius)\r\n scan = range(-scanAmount, scanAmount + 1)\r\n corners = ((0, 0), (0, 1), (1, 1), (1, 0))\r\n points = []\r\n for i in scan:\r\n for j in scan:\r\n for corner in corners:\r\n newX = roundedGrid[0] + i + corner[0]\r\n newY = roundedGrid[1] + j + corner[1]\r\n if grid_distance(newX, newY, x, y) < total_radius:\r\n points.append((newX, newY))\r\n\r\n return points","def find_points(self, angle):\n angle_rads = angle * 1.0 / 360 * 2 * pi\n points = []\n self.pic.setPenColor(255, 0, 0)\n cur_pos_x = self.xcenter\n cur_pos_y = self.ycenter\n in_line = False\n while self.in_bounds(cur_pos_x, cur_pos_y):\n if in_line:\n if not is_dark(self.pic, cur_pos_x, cur_pos_y):\n in_line = False\n points.append(self.distance(cur_pos_x, cur_pos_y))\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\n else:\n if is_dark(self.pic, cur_pos_x, cur_pos_y):\n in_line = True\n points.append(self.distance(cur_pos_x, cur_pos_y))\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\n cur_pos_x += 3 * cos(angle_rads)\n cur_pos_y += 3 * sin(angle_rads)\n return points","def find_crime_areas(segmented_points):\n\n # once all of the interpolated points are loaded into segmented_points\n # loop through them again to find out which places are high crime.\n bad_neighborhood_crime_index = 0.2\n\n for j in range(1, len(segmented_points)):\n print \"segmented_points[j]\", segmented_points[j]\n # ====================================================================\n # waypoint algorithm fleshing out\n # ====================================================================\n if segmented_points[j]['crime_index'] > bad_neighborhood_crime_index:\n # get the center of the geohash\n print \"This is a bad neighborhood\"\n\n # this is probably temporary, for display purposes\n segmented_points[j]['is_high_crime'] = True\n\n # do a conditional that if the bad neighborhood is at\n # len(segmented_points) we need to go get the end dict\n\n # now that we know what the bad neighborhood point is, let's get\n # the latitude, longitude from the point before and the point after\n if 'lat' not in segmented_points[j-1] or 'lng' not in segmented_points[j-1]:\n point_before = (segmented_points[j-1]['data']['start']['lat'],\n segmented_points[j-1]['data']['start']['lng'])\n else:\n point_before = (segmented_points[j-1]['lat'],\n segmented_points[j-1]['lng'])\n\n if 'lat' not in segmented_points[j+1] or 'lng' not in segmented_points[j+1]:\n point_after = (segmented_points[j+1]['data']['end']['lat'],\n segmented_points[j+1]['data']['end']['lng'])\n else:\n point_after = (segmented_points[j+1]['lat'],\n segmented_points[j+1]['lng'])\n\n current_point = (segmented_points[j]['lat'],\n segmented_points[j]['lng'])\n\n # before calling inspect_waypoints, check the deltas for the\n # step before and the step after to determine whether the function\n # needs to be called twice, or four times, and what direction to go\n # get the change in latitude and longitude between the before\n # and current point location\n delta_lat_before_current = current_point[0] - point_before[0]\n delta_lng_before_current = current_point[1] - point_before[1]\n\n # get the change in latitude and longitude between the before\n # and current point location\n delta_lat_after_current = point_after[0] - current_point[0]\n delta_lng_after_current = point_after[1] - current_point[1]\n\n delta_before_after = [delta_lat_before_current, delta_lng_before_current,\n delta_lat_after_current, delta_lng_after_current]\n\n segmented_points = check_directions_find_waypoint(current_point,\n segmented_points[j],\n delta_before_after,\n segmented_points)\n print \"this is segmented_points[0] returned\", segmented_points[0]\n return segmented_points[0]","def setPointsToTurn(self):\r\n for point in self.points:\r\n point.setActiveTurn()","def get_all_locations(self):","def getPointValues(self, *args, **kwargs):\n ...","def points_earned(self):\n delta_counts = self.alive_counts - self.initial_counts\n points = self.points_table * delta_counts\n points = points.reshape(-1,72) # unravel the points for easier sum\n return np.sum(points, axis=1) + super().current_points()","def get_all_offgrid_pin(self, pin, insufficient_list):\n #print(\"INSUFFICIENT LIST\",insufficient_list)\n # Find the coordinate with the most overlap\n any_overlap = set()\n for coord in insufficient_list:\n full_pin = self.convert_track_to_pin(coord)\n # Compute the overlap with that rectangle\n overlap_rect=pin.compute_overlap(full_pin)\n # Determine the max x or y overlap\n max_overlap = max(overlap_rect)\n if max_overlap>0:\n any_overlap.update([coord])\n \n return any_overlap","def add_points(grid, num_points):\n \n for i in range(num_points):\n # Coord for crit point\n rand_x = random.randint(0, GRID_WIDTH - 1)\n rand_y = random.randint(0, GRID_HEIGHT - 1)\n \n # Set value of crit point\n elev = (MAX_HEIGHT - MIN_HEIGHT) * random.random() + MIN_HEIGHT\n grid[rand_x][rand_y] = elev * PEAK_HEIGHT\n \n return grid","def grid(iant,xgrid=[0],ygrid=[0],sleep=4):\n d=Carma(iant).drive()\n d.setOffset(xgrid[0],ygrid[0])\n time.sleep(sleep)\n time.sleep(sleep)\n for y in ygrid:\n for x in xgrid:\n print x,y\n d.setOffset(x,y)\n time.sleep(sleep)","def graham_scan(self, show_progress):\n global anchor # point (x,y) with the smallest y value\n points = self.points\n\n min_index = None\n for i, (x, y) in enumerate(points):\n if min_index == None or y < points[min_index][1]:\n min_index = i\n if y == points[min_index][1] and x < points[min_index][0]:\n min_index = i\n anchor = points[min_index]\n sorted_points = self.quicksort(points)\n del sorted_points[sorted_points.index(anchor)]\n # the 'anchor' point and the point with smallest polar angle will always be on the hull\n self.hull_points = [anchor, sorted_points[0]]\n for s in sorted_points[1:]:\n while self.rotation(self.hull_points[-2], self.hull_points[-1], s) <= 0:\n del self.hull_points[-1] # backtrack\n self.hull_points.append(s)\n if show_progress:\n draw_scatter_plot(points, self.hull_points, self.color, self.title)\n plot.close()\n return self.hull_points","def setPointsActive(self):\r\n for point in self.points:\r\n point.setActive()","def attackArea(initPair, connect):\r\n area = []\r\n opts = ((1, 0), (0, 1), (1, 1), (1, -1))\r\n (x, y) = initPair\r\n\r\n for pair in opts:\r\n (x1, y1) = pair\r\n\r\n for s in (1, -1):\r\n for i in range(1, connect):\r\n x2 = x + x1 * i * s\r\n y2 = y + y1 * i * s\r\n area.append((x2, y2))\r\n\r\n return area","def get_dancefloor_area(self):\n cords = []\n\n x1 = self.coordinates[0]\n y1 = self.coordinates[1]\n x2 = self.coordinates[0] + self.width\n y2 = self.coordinates[1] + self.height\n if self.width <= 0:\n x1, x2 = x2, x1\n if self.height <= 0:\n y1, y2 = y2, y1\n\n for x in range(x1, x2):\n for y in range(y1, y2):\n if x % 20 == 0 and y % 20 == 0:\n cords.append([x, y])\n return cords","def __sample(self):\n # xvals are \"east\" vals and yvals are \"north\" vals on the map\n xvals = np.random.uniform(self._xmin, self._xmax, self._num_samples)\n yvals = np.random.uniform(self._ymin, self._ymax, self._num_samples)\n if self._target_altitude is None:\n zvals = np.random.uniform(self._zmin, self._zmax, self._num_samples)\n else:\n zvals = np.full(self._num_samples, self._target_altitude, dtype=float)\n \n samples = list(zip(xvals, yvals, zvals))\n\n pts = []\n for s in samples:\n in_collision = False\n idxs = list(self._obstacles_tree.query_radius(\n np.array([s[0], s[1]]).reshape(1, -1), r=self._max_poly_xy)[0])\n \n if len(idxs) > 0:\n for ind in idxs: \n p = self._polygons[int(ind)]\n if p.contains(s) and p.height >= s[2]:\n in_collision = True\n\n if not in_collision:\n pts.append(s)\n \n return pts","def _add_points(self):\n if not '_list_of_points' in self.__dict__.keys():\n self._list_of_points = [] \n for point in self['point'].items():\n self._list_of_points.append(point[1])","def point1(source, ants, elmin=15.0, showGUI=True, zoom=1, mode=0, \n nextSource=None, writeFITS=True, brightness=50,isInner=False,\n subtractBackground=False, ncoadd=1, dazbkg=2.0, centroidLoopMax=16,\n minsamples=None):\n global previous_source_\n antlist = helpers.makeList(ants)\n el = elevation(source)\n \n # Check for source too low, and bail out if it is\n if el < elmin:\n print 'Skipping %s because elevation(%.1f) is less than limit(%.1f)' \\\n %(source,el,elmin)\n return\n \n # Check for maximum elevation as well\n maxelLimit = 87.0\n if el > maxelLimit:\n print 'Skipping %s because elevation(%.1f) is greater than limit(%.1f)' \\\n %(source,el,maxelLimit)\n return\n\n # Print message\n msg = \"Pointing on %s at el=%5.1f (elmin = %5.1f); ants = %s\" % (source, el, elmin, str(ants))\n print msg\n SAC.scriptlog(msg)\n \n d = distance(source,previous_source_)\n print \" Distance from %s to %s is %.1f deg\" %(previous_source_,source,d)\n if mode > 0:\n if nextSource != None:\n print \" Next source will be %s\" % nextSource\n else:\n print \" Last source.\"\n # Send all antennas tracking to new source, even though some are\n # already there.\n if isInner :\n SAC.track(source)\n else : SAC.track(source, antlist, phaseCenter=False, waiton=SAC.NONE)\n antsTodo = antlist\n while len(antsTodo) > 0 :\n r = SAC.wait(SAC.TRACK, antsTodo, waiton=SAC.ANY)\n antsReady = r.ready\n antsTodo = r.notready\n for i in antsReady :\n show(source)\n #print \"Loading camera on antenna %d\" %i\n opticalSystem(i, auto=True, object=source, repeat=4, zoom=zoom,\n showGUI=showGUI, dontClose=True, \n brightness=brightness, ncoadd=ncoadd, dazbkg=dazbkg,\n subtractBackground=subtractBackground, \n centroidLoopMax=centroidLoopMax, minsamples=minsamples)\n # now that this ant is done, send it to next source\n if mode > 0:\n if nextSource != None: \n if isInner : SAC.track(nextSource)\n else : SAC.track(nextSource, i, waiton=SAC.NONE)\n previous_source_ = source","def sample_pin_position_range():\n #Create a sample goniometer\n g = TopazInHouseGoniometer()\n\n #Initialize the leg limits\n g.relative_sample_position = column([0.0, 0.0, 0.0])\n g.getplatepos(0.0, 0.0, 0.0)\n g.calculate_leg_xy_limits(visualize=True)\n\n# if True:\n# pylab.show()\n# return\n\n n = 17\n positions = np.linspace(-8, 8, n) #Range calculated in mm\n allowed = np.zeros( (n,n,n) )\n for (ix, x) in enumerate(positions):\n print \"Calculating x\", x\n for (iy, y) in enumerate(positions):\n for (iz, z) in enumerate(positions):\n #Set up\n g.relative_sample_position = column([x, y, z])\n allowed[ix,iy,iz] = g.are_angles_allowed([0., 0., 0.], return_reason=False)\n\n #Do a plot\n\n pylab.figure(1, figsize=[15,15])\n pylab.title(\"Allowable XZ sample positions\")\n for (iy, y) in enumerate(positions):\n print \"At y of\", y, \", # of points = \", np.sum( allowed[:, iy,:])\n if iy < 16:\n pylab.subplot(4,4,iy+1)\n pylab.pcolor(positions, positions, allowed[:, iy, :].transpose(), norm=pylab.Normalize(0, 1))\n pylab.xlabel(\"x\")\n pylab.ylabel(\"z\")\n pylab.title(\"y = %.3f mm\" % y)\n pylab.draw()\n pylab.axis('equal')\n pylab.show()\n #pylab.","def get_uncertain_point_coords_on_grid(uncertainty_map, num_points):\n R, _, H, W = uncertainty_map.shape\n h_step = 1.0 / float(H)\n w_step = 1.0 / float(W)\n\n num_points = min(H * W, num_points)\n point_indices = torch.topk(uncertainty_map.view(R, H * W), k=num_points, dim=1)[1]\n point_coords = torch.zeros(R, num_points, 2, dtype=torch.float, device=uncertainty_map.device)\n point_coords[:, :, 0] = w_step / 2.0 + (point_indices % W).to(torch.float) * w_step\n point_coords[:, :, 1] = h_step / 2.0 + (point_indices // W).to(torch.float) * h_step\n return point_indices, point_coords","def global_coords(self) -> GlobalCoordsABC:","def internal_points(self):\n return self.ghost_points()","def get_random_coordinates(self):\n array_shape = np.shape(self.cells) # type: tuple\n points_on_island = []\n for i in range(1, array_shape[0] - 1):\n for j in range(1, array_shape[1] - 1):\n points_on_island.append((i, j))\n random.shuffle(points_on_island)\n return points_on_island","def iter_points(self):\n for x in range(self.left, self.right + 1):\n for y in range(self.top, self.bottom + 1):\n yield Point(x, y)","def area(self):\n ...","def test_absolute_areas(self):\n\n assert len(self.test_shape.areas) == 4\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1","def pointsSetUp(self):\r\n self.background.draw(self.surface)\r\n for i in range(len(self.points)):\r\n self.points[i].organize()\r\n self.points[i].update()\r\n self.points[i].addNumber(i)\r\n self.points[i].setActiveTurn()","def __generate_distribution_grid(self) -> np.ndarray:\n\n x_min = self.occupancy_map.info.origin.position.x\n x_max = x_min + self.occupancy_map.info.width * self.occupancy_map.info.resolution\n y_min = self.occupancy_map.info.origin.position.y\n y_max = y_min + self.occupancy_map.info.height * \\\n self.occupancy_map.info.resolution\n\n # CREATE HOTSPOTS IF WANTED:\n if self.create_random_hotspots:\n if self.use_uniform_distr:\n rospy.logerr(\"*** WRAPPER MESSAGE ***\\n\\n\\t\" + rospy.get_caller_id()\n + \":\\n\\tIt does not make sense to create random hotspots if distribution should be uniform.\"\n + \"\\n\\tThe node will go on with uniform distribution. You can change this in the launch parameters.\\n\")\n self.number_of_random_hotspots = 1 # still needed to get the right edges\n elif self.number_of_random_hotspots <= 0:\n rospy.logerr(\"*** WRAPPER MESSAGE ***\\n\\n\\t\" + rospy.get_caller_id()\n + \":\\n\\tThe number of wanted hotspots is 0 or below. This is not possible.\"\n + \"\\n\\tThe node will go on with 1 random hotspot. You can change this in the launch parameters.\\n\")\n self.number_of_random_hotspots = 1\n self.hotspots = []\n for _ in range(self.number_of_random_hotspots):\n x, y, spread = self.__get_random_hotspot()\n self.hotspots.append([x, y, spread])\n\n if len(self.hotspots) < 1:\n if not self.use_uniform_distr:\n rospy.logerr(\"*** WRAPPER MESSAGE ***\\n\\n\\t\" + rospy.get_caller_id()\n + \":\\n\\tIf no hotspots should be generated randomly (and no uniform distribution),\\n\\tyou need to specify hotspots.\"\n + \"\\n\\tThe node will go on with 1 random hotspot. You can change this in the launch parameters.\\n\")\n x, y, spread = self.__get_random_hotspot()\n self.hotspots = [[x, y, spread]]\n\n hotspots_string = \"\\n\\tDirt generator - HOTSPOTS information:\"\n if self.use_uniform_distr:\n hotspots_string += \"\\n\\n\\tThe dirt distribution was set to be uniform. Hotspots do not exist.\"\n else:\n if self.create_random_hotspots:\n hotspots_string += \"\\n\\n\\tThe dirt distribution is based on the \" + str(len(\n self.hotspots)) + \" randomly created hotspot(s) [real world position in m]:\"\n else:\n hotspots_string += \"\\n\\tThe dirt distribution is based on the \" + \\\n str(len(self.hotspots)) + \\\n \" specified hotspot(s) [real world position in m]:\"\n for index, hotspot in enumerate(self.hotspots):\n hotspots_string += \"\\n\\t\\tHotspot %d: x=%.2f, y=%.2f, spread=%.2f\" % (\n index + 1, hotspot[0], hotspot[1], hotspot[2])\n print(hotspots_string + \"\\n\")\n\n # CREATING SEVERAL NORMAL DISTRIBUTIONS:\n # creating for each hotspot one normal distribution and then combine them to one for X and one for Y\n # for X:\n normal_distributions_attr_X = []\n for spot in self.hotspots:\n # each with [mean_x, std. deviation]\n normal_distributions_attr_X.append([spot[0], spot[2]])\n X_distributions = []\n for attributes in normal_distributions_attr_X:\n distr = stats.truncnorm((x_min - attributes[0]) / attributes[1], (\n x_max - attributes[0]) / attributes[1], loc=attributes[0], scale=attributes[1])\n X_distributions.append(distr.rvs(self.points_per_distr))\n X = np.concatenate(X_distributions)\n # to ensure that the distribution and later the grid is full sized (at a point at each end for full axis):\n X = np.append(X, [x_min, x_max])\n # for Y:\n normal_distributions_attr_Y = []\n for spot in self.hotspots:\n # each with [mean_y, std. deviation]\n normal_distributions_attr_Y.append([spot[1], spot[2]])\n Y_distributions = []\n for attributes in normal_distributions_attr_Y:\n distr = stats.truncnorm((y_min - attributes[0]) / attributes[1], (\n y_max - attributes[0]) / attributes[1], loc=attributes[0], scale=attributes[1])\n Y_distributions.append(distr.rvs(self.points_per_distr))\n Y = np.concatenate(Y_distributions)\n # to ensure that the distribution and later the grid is full sized (at a point at each end for full axis):\n Y = np.append(Y, [y_min, y_max])\n\n # # Plotting of the two separate distributions (rather for testing)\n # fig, ax = plt.subplots(2, sharex=True)\n # ax[0].hist(X, bins=self.occupancy_map.info.width)\n # ax[1].hist(Y, bins=self.occupancy_map.info.height)\n # plt.show() # will block the remaining process!\n\n # COMBINING TWO DISTRIBUTIONS TO A BIVARIATE DISTRIBUTION:\n H, xedges, yedges = np.histogram2d(\n X, Y, bins=[self.occupancy_map.info.width, self.occupancy_map.info.height], density=False)\n H = H.astype('int32')\n # inverse H, otherwise x and y would be the other way around (now, x are the columns, and y are rows, with row-major ordered)\n H = H.T\n # H starts with its inital element in (x_min, y_min) and then row-major ordered (see above): each row is one y and in there are all the column-x for this y\n\n if not self.strictly_hotspots:\n # if all cells should have at least a very small chance, no cell should have 0, which is why we add 1 to all of them\n H = H + 1\n\n if self.use_uniform_distr:\n H = np.ones((self.occupancy_map.info.height,\n self.occupancy_map.info.width), dtype=int)\n\n # already kick out all static occupied cells (walls, etc.) and replace them with -1\n for (cell_y, cell_x), _ in np.ndenumerate(H):\n if self.__cell_is_occupied(cell_x, cell_y):\n H[cell_y][cell_x] = -1\n\n if self.enable_printing:\n self.__print_probability_grid(H)\n\n # Creating image\n max_tick_number = 10\n x_axis_res = self.occupancy_map.info.resolution\n while (x_max - x_min) / x_axis_res > max_tick_number:\n x_axis_res *= 2\n x_axis = np.arange(\n x_min, x_max + self.occupancy_map.info.resolution, x_axis_res)\n y_axis_res = self.occupancy_map.info.resolution\n while (y_max - y_min) / y_axis_res > max_tick_number:\n y_axis_res *= 2\n y_axis = np.arange(\n y_min, y_max + self.occupancy_map.info.resolution, y_axis_res)\n\n figure_height = 10\n resize_factor_height = figure_height / self.occupancy_map.info.height\n fontsize = 26\n font_factor = 1.0 + (0.2 / resize_factor_height - 1.0) / 8\n\n fig = plt.figure(figsize=(2 * self.occupancy_map.info.width *\n resize_factor_height, self.occupancy_map.info.height * resize_factor_height))\n title = \"Dirt probability distribution in perspective of the initial map image\"\n fig.suptitle(title, fontsize=fontsize * font_factor, fontweight=\"bold\")\n\n viridis = cm.get_cmap('autumn_r', 256)\n newcolors = viridis(np.linspace(0, 1, 256))\n black = np.array([0, 0, 0, 1])\n white = np.array([1, 1, 1, 1])\n newcolors[:1, :] = black\n newcolors[1:20, :] = white\n cmap = colors.ListedColormap(newcolors)\n\n # actual bins/edges:\n ax = fig.add_subplot(1, 2, 1, aspect=\"equal\")\n ax.set_title(\"Actual cells\", size=(fontsize - 2)\n * font_factor, fontweight=\"bold\")\n mesh_X, mesh_Y = np.meshgrid(xedges, yedges)\n ax.pcolormesh(mesh_X, mesh_Y, H, cmap=cmap,\n edgecolors=\"k\", linewidths=0.5)\n ax.set_xlabel(\"X axis [m]\", fontsize=(fontsize - 4) * font_factor)\n ax.set_ylabel(\"Y axis [m]\", fontsize=(fontsize - 4) * font_factor)\n ax.set_xticks(x_axis)\n ax.set_yticks(y_axis)\n\n plt.tick_params(axis='both', which='major',\n labelsize=(fontsize - 6) * font_factor)\n\n # interpolated:\n ax = fig.add_subplot(1, 2, 2, aspect=\"equal\",\n xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])\n ax.set_title(\"Interpolated\", size=(fontsize - 2)\n * font_factor, fontweight=\"bold\")\n interpolated_image = NonUniformImage(\n ax, interpolation=\"bilinear\", cmap=cmap)\n xcenters = (xedges[:-1] + xedges[1:]) / 2\n ycenters = (yedges[:-1] + yedges[1:]) / 2\n interpolated_image.set_data(xcenters, ycenters, H)\n ax.images.append(interpolated_image)\n ax.set_xlabel(\"X axis [m]\", fontsize=(fontsize - 4) * font_factor)\n ax.set_ylabel(\"Y axis [m]\", fontsize=(fontsize - 4) * font_factor)\n ax.set_xticks(x_axis)\n ax.set_yticks(y_axis)\n\n plt.tick_params(axis='both', which='major',\n labelsize=(fontsize - 6) * font_factor)\n\n # get path to the output file\n rospack = rospkg.RosPack()\n pkg_path = rospack.get_path(self.output_pkg)\n image_path = pkg_path + \"/\" + self.distribution_folder + \\\n \"/\" + self.distribution_image\n\n plt.savefig(image_path)\n\n if self.enable_plotting:\n warning = \"Do not close this window until the end. Otherwise the generator node could crash!\"\n fig = plt.gcf()\n fig.canvas.set_window_title(warning)\n # show it (without blocking the remaining process)\n plt.ion()\n # plt.draw()\n plt.pause(0.001)\n # but cannot be closed without crashing this node!\n\n # each cell should have the probability of itself regarding the final distribution:\n # the occupied cells will have -1 and we do not want negative probabilities. So, we replace them with 0\n for (cell_y, cell_x), cell_value in np.ndenumerate(H):\n if cell_value < 0:\n H[cell_y][cell_x] = 0\n prob_grid = H / np.sum(H)\n\n rospy.loginfo(\n f\"*** WRAPPER MESSAGE ***\\n\\n\\t'{rospy.get_caller_id()}' has created the final probability distribution.\\n\"\n + \"\\tAn image of it was saved to (will be overridden each time!):\\n\"\n + image_path + \"\\n\")\n\n return prob_grid","def pick_area(data ,total_process, interval ,list_of_vars, list_of_areas, init_time=0, pr_height=None, ):\n \n \n \n #trying if the longitude values change from 0 to 360 or -180 to 180?\n \n if data['lon'].values[0] < 0:\n \n p_d = {'europe' : [0, 48, 30, 65],\n 'northamerica' : [-142,-42,0,60],\n 'australia' : [80,180,-50,10],\n 'gulfofmexico' : [-100,-75,18,31],\n 'carribeans' : [-85,-60,12,38], \n 'indianocean' : [30, 130,-35,35],\n 'NH' : [-180, 180 ,0,90]}\n \n # -180 to 180 change the values given in the dictionary to relevant\n else:\n \n p_d = {'europe' : [0, 48, 30, 65],\n 'northamerica' : [218,318,-10,70],\n 'australia' : [80,180,-50,10],\n 'gulfofmexico' : [260,285,14,37],\n 'carribeans' : [275,300,12,38], \n 'indianocean' : [30, 130,-35,35],\n 'NH' : [0, 360 ,0,90]}\n \n \n \n places_dict = {}\n #looping in the list of areas\n say_pl = 1\n for pl in list_of_areas:\n variables_l = {}\n #looping in the list of variables\n say_var =1\n for var in list_of_vars:\n #check if data contains 'lev' coords.\n try:\n \n #wrap the data\n single = data[var].sel(lon=slice(p_d[pl][0],p_d[pl][1]), \n lat=slice(p_d[pl][2],p_d[pl][3]), \n lev=pr_height).isel(time=slice(init_time, total_process, interval))\n \n #if no 'lev' coords exist.\n except:\n single = data[var].sel(lon=slice(p_d[pl][0],p_d[pl][1]), \n lat=slice(p_d[pl][2],p_d[pl][3]),).isel(time=slice(init_time, total_process, interval))\n \n #append a single variable given by the user\n variables_l[var] = single\n \n \n #append all the variables with respect to their area of interest.\n places_dict[pl] = variables_l\n \n #return\n return places_dict","def example_staypoints_merge():\n p1 = Point(8.5067847, 47.4)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-02 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 06:45:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:55:00\", tz=\"utc\")\n t45 = pd.Timestamp(\"1971-01-02 08:57:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n t6 = pd.Timestamp(\"1971-01-02 09:20:00\", tz=\"utc\")\n\n list_dict = [\n {\"id\": 1, \"user_id\": 0, \"started_at\": t1, \"finished_at\": t2, \"geom\": p1, \"location_id\": 1},\n {\"id\": 5, \"user_id\": 0, \"started_at\": t2, \"finished_at\": t2, \"geom\": p1, \"location_id\": 2},\n {\"id\": 2, \"user_id\": 0, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 6, \"user_id\": 0, \"started_at\": t4, \"finished_at\": t45, \"geom\": p1, \"location_id\": 2},\n {\"id\": 15, \"user_id\": 0, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 2},\n {\"id\": 7, \"user_id\": 1, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 80, \"user_id\": 1, \"started_at\": t45, \"finished_at\": t5, \"geom\": p1, \"location_id\": 2},\n {\"id\": 3, \"user_id\": 1, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 4},\n ]\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\"geom\", crs=\"EPSG:4326\")\n sp = sp.set_index(\"id\")\n sp.as_staypoints\n\n # generate empty triplegs for the merge function\n tpls = pd.DataFrame([], columns=[\"user_id\", \"started_at\", \"finished_at\"])\n return sp, tpls","def InitPointR(session):\n global point_r\n # points are only buildings\n q = session.query(melt.BuildAssoc).filter_by(point=1)\n point_r = set([it.osm_build for it in q.all()])","def gps_points(self):\n return self.waypoints.all().order_by('localtime')\n # return self.waypoints.filter(photo_id__isnull=True).order_by('localtime')","def sampleFromGrid(grid, xypoints, method='nearest'):\n samples = []\n for lon, lat in xypoints:\n sample = grid.getValue(lat, lon, method=method)\n samples.append(sample)\n\n return np.array(samples)","def Ez_area(position, angle, detect):\n# a = range(round(-2*Ez_height),round(2*Ez_height))\n# b = range(round(-2*Ez_height),round(2*Ez_height))\n# a_valid = []\n# b_valid= []\n \n # These are the grid points in a coordinate system based on the Ez's angle\n if detect:\n a_valid = [-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6]\n b_valid = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3]\n else:\n a_valid = [-9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4]#[-19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n b_valid = [-4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3, -2, -1, 0, 1, 2]#[-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4]\n positions = []\n# for i in a:\n# for j in b:\n# if (i > ((Ez_width/2)-Ez_height-detect_length) and abs(j) < (Ez_width/2+detect_length) and i < 0) or (i > 0 and np.sqrt(i**2 + j**2) < Ez_width/2+detect_length):\n# a_valid.append(i)\n# b_valid.append(j)\n# print('AAAA', a_valid)\n# print(' ')\n# print('BBBB', b_valid)\n# print(' ')\n \n # This is a coordinate transfromation to x,y\n for i in range(len(a_valid)):\n positions.append((int(round(a_valid[i]*np.cos(angle) + b_valid[i]*np.sin(angle) + position[0])), int(round(a_valid[i]*np.sin(angle) - b_valid[i]*np.cos(angle) + position[1]))))\n return positions","def recalc(self, viewer):\n # select only points within range of the current pan/zoom\n bbox = viewer.get_pan_rect()\n if bbox is None:\n self.path.points = []\n return\n\n start_x, stop_x = bbox[0][0], bbox[2][0]\n\n self.calc_points(viewer, start_x, stop_x)","def shot_geolocations(self) -> geopandas.array.GeometryArray:\n geolocations = np.array(\n [shapely.geometry.Point(lon, lat) for lon, lat in self.shot_lon_lat],\n dtype=shapely.geometry.Point,\n )\n\n return geopandas.array.GeometryArray(geolocations, crs=WGS84)","def get_all_coordinates(self):\n coordinates = []\n\n for relative_coordinate in self.shape:\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\n coordinates.append(co)\n return coordinates","def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\n\n mxmin = 9e10\n mxmax = -9e10\n mymin = 9e10\n mymax = -9e10\n for pshape in pshapes:\n pxmin, pymin, pxmax, pymax = pshape.bounds\n if pxmin < mxmin:\n mxmin = pxmin\n if pxmax > mxmax:\n mxmax = pxmax\n if pymin < mymin:\n mymin = pymin\n if pymax > mymax:\n mymax = pymax\n\n if not touch_center:\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\n img = rasterizeShapes(pshapes, geodict)\n #now get the numpy array of x/y coordinates where covgrid == 1\n idx = np.where(img == 1)[0]\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\n yespoints = list(zip(x.flatten(), y.flatten()))\n nrows = geodict.ny\n ncols = geodict.nx\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\n else:\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n ncols = len(xvar)\n nrows = len(yvar)\n if nmax is not None:\n if ncols*nrows > nmax:\n aspect = ncols/nrows\n ncols = np.sqrt(nmax*aspect)\n nrows = nmax/ncols\n ncols = int(ncols)\n nrows = int(nrows)\n #re-calculate dx here...\n tdx = (mxmax-mxmin)/ncols\n tdy = (mymax-mymin)/nrows\n dx = np.max(tdx, tdy)\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n\n #Get the \"yes\" points to sample from\n yespoints = []\n idx = []\n shapeidx = 0\n if pshapes[0].type == 'Polygon':\n #loop over shapes, projecting each one, then get the sample points\n for pshape in pshapes:\n if not shapeidx % 1000:\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\n shapeidx += 1\n pxmin, pymin, pxmax, pymax = pshape.bounds\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\n toprow = np.where((yvar - pymax) >= 0)[0][0]\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\n xmesh, ymesh = np.meshgrid(xp, yp)\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\n for point in xy:\n ix = np.where(xvar == point[0])[0][0]\n iy = np.where(yvar == point[1])[0][0]\n if pshape.contains(Point(point)):\n yespoints.append(point)\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\n else:\n yespoints = []\n for pshape in pshapes:\n yespoints.append(pshape.coords[0])\n\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)","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 fillIn(self):\n\n # Grabs first point (which is a shore) and prefills in hashes\n toBeAnalyzed = [self.points[0]]\n islandHash = defaultdict(list)\n islandHash[toBeAnalyzed[0].x].append(toBeAnalyzed[0].x)\n islandGridPoints = toBeAnalyzed[:]\n\n # Find all points not at pond-level.\n while toBeAnalyzed:\n gridPoint = toBeAnalyzed.pop()\n neighbors = self.analyzeData.iterateDiagonal(gridPoint.x,\n gridPoint.y)\n for _x, _y, elevation in neighbors:\n\n if elevation != self.pondElevation and _y not in\\\n islandHash[_x]:\n branch = GridPoint(_x, _y, elevation)\n islandHash[_x].append(_y)\n toBeAnalyzed.append(branch)\n islandGridPoints.append(branch)\n self.points = islandGridPoints","def get_points(self):\n\t\treturn self.points","def get_all_xy(self):\n self.all_y = list(\n zip(\n self.open,\n self.open,\n self.high,\n self.low,\n self.close,\n self.close,\n self.empty,\n )\n )\n if self.dates is not None:\n date_dif = []\n for i in range(len(self.dates) - 1):\n date_dif.append(self.dates[i + 1] - self.dates[i])\n date_dif_min = (min(date_dif)) / 5\n self.all_x = [\n [x - date_dif_min, x, x, x, x, x + date_dif_min, None]\n for x in self.dates\n ]\n else:\n self.all_x = [\n [x - 0.2, x, x, x, x, x + 0.2, None] for x in range(len(self.open))\n ]","def find_centroid_for_each(self):","def get_points(self):\r\n return self.points","def guarded_places(self):\n guarded = []\n for x in range(8):\n for y in range(8):\n if self.squares[x][y].piece and self.squares[x][y].piece.color != self.turn:\n squares = self.squares[x][y].piece.actions(self, (x, y), True)\n if self.squares[x][y].piece.name != 'pawn': # pawns capture in different areas than they move\n guarded.extend(squares[0])\n guarded.extend(squares[1])\n return guarded","def get_areas_for_coords_in_range(coordinates, all_coords, range_of_c):\n get_closest_point = get_closest_from_coords(coordinates)\n def aux(acc, coord):\n dictionary, infinite_areas = acc\n closest = get_closest_point(coord)\n if closest is not None:\n dictionary.setdefault(closest, set())\n dictionary[closest].add(coord)\n if (coord.x in {range_of_c.min_x, range_of_c.max_x} or\n coord.y in {range_of_c.min_y, range_of_c.max_y}):\n infinite_areas.add(closest)\n return dictionary, infinite_areas\n return reduce(aux, all_coords, ({}, set()))","def _collect_points(self, image, point_value=0):\n return zip(*np.where(image == point_value))","def xy(self):\n ...","def jiggle_on_grid_points(data, grid_polys):\n new_data = []\n centroids = np.array([t.centroid.coords for t in grid_polys])\n for t in data:\n x = t[1]\n y = t[2]\n idx = np.where(np.sum(centroids == t[1:], axis=1) == 2)[0]\n if idx:\n assert len(idx) == 1, \"Overlapping polygons are not supported\"\n idx = idx[0]\n this_datum = (t[0],) + random_points_within_poly(grid_polys[idx], 1)\n else:\n this_datum = t\n new_data.append(this_datum)\n\n return np.array(new_data)","def g_corners(self):\n return (point for point in self.p)","def grid_in_shape(up_lat,low_lat,left_long,right_long,shape,\n lat_resolution=15,long_resolution=60): \n \n longitudes = np.linspace(left_long,right_long,60)\n latitudes = np.linspace(low_lat,up_lat,15)\n prods = list(itertools.product(longitudes,latitudes))\n points = [shapely.geometry.Point(point) for point in prods]\n points_within = [point for point in points if shape.contains(point)]\n points_gdf = gpd.GeoDataFrame(geometry = points_within)\n \n return points_gdf","def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\n pass","def charuco_img_points(images, objpoint, board, a_dict):\r\n #Criteria for subpixel refinement\r\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)\r\n\r\n objpoints = [] # 3d point in world space\r\n imgpoints = [] # 2d point in image plane\r\n\r\n for img in images:\r\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n corners, ids, rejpoints = cv2.aruco.detectMarkers(gray, a_dict)\r\n if len(corners)>0:\r\n res2 = cv2.aruco.interpolateCornersCharuco(corners,ids,gray,board)\r\n if res2[1] is not None:\r\n cv2.cornerSubPix(gray,res2[1],(3,3),(-1,1),criteria)\r\n imgpoints.append(res2[1].T[:,0,:])\r\n objpoints.append(objpoint[:,res2[2].flatten()])\r\n cv2.aruco.drawDetectedCornersCharuco(img,res2[1],res2[2])\r\n cv2.imshow(\"frame\",img)\r\n cv2.waitKey(0)\r\n cv2.destroyAllWindows()\r\n\r\n return objpoints,imgpoints","def testGetPointsGeodesic(self):\n\n for i in range(10):\n self.basicGetPointsGeodesic()","def extend (self) :\n return (self.x_min, self.x_max, self.y_min, self.y_max)","def draw_known_points(self):\n if self.tracking:\n if self.current_tracked_point == None:\n return\n p = self.current_tracked_point\n x, y = (int(u) for u in p)\n cv2.circle(self.altered_image, (x, y), 10, (0, 0, 255), 1, cv2.LINE_AA)\n cv2.putText(self.altered_image, f'az:{x:.3f} alt:{y:.3f}', (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))\n self.statusBar().showMessage(f'az: {x:.3f} alt: {y:.3f}')\n else:\n for p in self.known_image_points:\n x, y = (int(u) for u in p)\n\n cv2.circle(self.altered_image, (x, y), 5, (0, 0, 255), 1, cv2.LINE_AA)\n cv2.putText(self.altered_image, f'x:{x} y:{y}', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255))","def __areas_to_pool(self, region_width, region_height, region_width_step, region_height_step):\n \n areas = [[(width_ind * region_width_step, height_ind * region_height_step,\n (width_ind + 1) * region_width_step if (width_ind + 1) < self.width else region_width,\n (height_ind + 1) * region_height_step if (height_ind + 1) < self.height else region_height) for width_ind in range(self.width)] for height_ind in range(self.height)]\n\n return areas","def get_neighbors(point):\n pt = point.copy()\n output= [point.copy() for i in range(4)]\n output[0:2] = map(Point.setY, output[0:2], [pt.getY()+ i for i in range(-1,2,2)])\n output[2:4]= map(Point.setX, output[2:4], [pt.getX()+ i for i in range(-1,2,2)])\n return output","def geocube():","def oneEntryPoints(self):\n points = []\n if self.isRed:\n for x in range(self.midWidth, self.width):\n for y in range(0, self.height):\n if (x, y) not in self.walls:\n adjacent = self.adjacentValidPoints(x, y)\n if len(adjacent) == 1:\n points.append((x, y))\n else:\n for x in range(0, self.midWidth-1):\n for y in range(0, self.height):\n if(x, y) not in self.walls:\n adjacent = self.adjacentValidPoints(x, y)\n if len(adjacent) == 1:\n points.append((x, y))\n return points","def query(self, points):\n return self.locate_points(points)","def get_points(self) -> typing.Iterable[float]:\n raise NotImplementedError()","def update_world_points(self, wpSet):\r\n self.world_points = np.empty((0, 5))\r\n for i, row in wpSet.correspondences.iterrows():\r\n Ids = row['ViewId']\r\n if self.id in Ids:\r\n point_2D = row['FeatureIndex'][Ids.index(self.id)]\r\n point_3D = wpSet.world_points[i]\r\n self.world_points = np.append(self.world_points, [np.hstack((point_2D, point_3D))], axis=0)","def collocation_points(self, **kw):\n pass","def getAreas(df):\n\n df_plu = df[df[\"strand\"]==\"+\"]\n df_min = df[df[\"strand\"]==\"-\"]\n df_plu_FA = FivePrimeArea(df_plu)\n df_min_FA = FivePrimeArea(df_min)\n df_plu_LA = ThreePrimeArea(df_plu)[[\"name\",\"LA_start\",\"LA_end\",\"LA_length\"]]\n df_min_LA = ThreePrimeArea(df_min)[[\"name\",\"LA_start\",\"LA_end\",\"LA_length\"]]\n df_plu = pd.merge(df_plu_FA,df_plu_LA,on=\"name\")\n df_min = pd.merge(df_min_FA,df_min_LA,on=\"name\")\n df = pd.concat([df_plu,df_min])\n return df","def to_points(self, divisions=100):","def detect_points(self):\r\n\r\n\t\r\n\r\n\t\tfeature_mask = np.zeros_like(self.gray) ## Create a mask so we only look for template features in the ROI\r\n\t\t\r\n\t\tfeature_mask[max(0,self.bb[1]):min(360,self.bb[1] + self.bb[3]),max(0,self.bb[0]):min(640,self.bb[0] + self.bb[2])] = 255\r\n\r\n\t\t# search for good points\r\n\t\tfeatures = cv2.goodFeaturesToTrack(self.gray, mask = feature_mask, **feature_params)\r\n\t\t# refine the corner locations\r\n\t\tcv2.cornerSubPix(self.gray,features, **subpix_params)\r\n\r\n\t\tself.features = features\r\n\r\n\t\tself.tracks = [[p] for p in features.reshape((-1,2))]\r\n\r\n\t\tself.prev_gray = self.gray","def __iter__(self):\n return self.points.__iter__()","def setHolesCoordinates(self):\r\n # productive\r\n profprint()\r\n self.p = [[0 for j in range(63)] for j in range(3)]\r\n self.p[0][0] = 35\r\n self.p[1][0] = 34\r\n self.p[0][1] = 25\r\n self.p[1][1] = 36.679\r\n self.p[0][2] = 17.679\r\n self.p[1][2] = 44\r\n self.p[0][3] = 15\r\n self.p[1][3] = 54\r\n self.p[0][4] = 17.679\r\n self.p[1][4] = 64\r\n self.p[0][5] = 25\r\n self.p[1][5] = 71.321\r\n self.p[0][6] = 35\r\n self.p[1][6] = 74\r\n self.p[0][7] = 45\r\n self.p[1][7] = 71.321\r\n self.p[0][8] = 52.321\r\n self.p[1][8] = 64\r\n self.p[0][9] = 55\r\n self.p[1][9] = 54\r\n self.p[0][10] = 52.321\r\n self.p[1][10] = 44\r\n self.p[0][11] = 45\r\n self.p[1][11] = 36.679\r\n self.p[0][12] = 29.791\r\n self.p[1][12] = 24.456\r\n self.p[0][13] = 20\r\n self.p[1][13] = 28.019\r\n self.p[0][14] = 12.019\r\n self.p[1][14] = 34.716\r\n self.p[0][15] = 6.809\r\n self.p[1][15] = 43.739\r\n self.p[0][16] = 5\r\n self.p[1][16] = 54\r\n self.p[0][17] = 6.809\r\n self.p[1][17] = 64.261\r\n self.p[0][18] = 12.019\r\n self.p[1][18] = 73.284\r\n self.p[0][19] = 20\r\n self.p[1][19] = 79.981\r\n self.p[0][20] = 29.791\r\n self.p[1][20] = 83.544\r\n self.p[0][21] = 40.209\r\n self.p[1][21] = 83.544\r\n self.p[0][22] = 50\r\n self.p[1][22] = 79.981\r\n self.p[0][23] = 57.981\r\n self.p[1][23] = 73.284\r\n self.p[0][24] = 63.191\r\n self.p[1][24] = 64.262\r\n self.p[0][25] = 65\r\n self.p[1][25] = 54\r\n self.p[0][26] = 63.191\r\n self.p[1][26] = 43.739\r\n self.p[0][27] = 57.981\r\n self.p[1][27] = 34.716\r\n self.p[0][28] = 50\r\n self.p[1][28] = 28.019\r\n self.p[0][29] = 40.209\r\n self.p[1][29] = 24.456\r\n self.p[0][30] = 35\r\n self.p[1][30] = 14\r\n self.p[0][31] = 24.647\r\n self.p[1][31] = 15.363\r\n self.p[0][32] = 15\r\n self.p[1][32] = 19.359\r\n self.p[0][33] = 15\r\n self.p[1][33] = 88.641\r\n self.p[0][34] = 24.647\r\n self.p[1][34] = 92.637\r\n self.p[0][35] = 35\r\n self.p[1][35] = 94\r\n self.p[0][36] = 45.353\r\n self.p[1][36] = 92.637\r\n self.p[0][37] = 55\r\n self.p[1][37] = 88.641\r\n self.p[0][38] = 55\r\n self.p[1][38] = 19.359\r\n self.p[0][39] = 45.353\r\n self.p[1][39] = 15.363\r\n self.p[0][40] = 30.642\r\n self.p[1][40] = 4.19\r\n self.p[0][41] = 22.059\r\n self.p[1][41] = 5.704\r\n self.p[0][42] = 22.059\r\n self.p[1][42] = 102.296\r\n self.p[0][43] = 30.642\r\n self.p[1][43] = 103.81\r\n self.p[0][44] = 39.358\r\n self.p[1][44] = 103.81\r\n self.p[0][45] = 47.941\r\n self.p[1][45] = 102.296\r\n self.p[0][46] = 47.941\r\n self.p[1][46] = 5.704\r\n self.p[0][47] = 39.358\r\n self.p[1][47] = 4.19\r\n self.p[0][48] = 29.7\r\n self.p[1][48] = 44.82\r\n self.p[0][49] = 24.4\r\n self.p[1][49] = 54\r\n self.p[0][50] = 29.7\r\n self.p[1][50] = 63.18\r\n self.p[0][51] = 40.3\r\n self.p[1][51] = 63.18\r\n self.p[0][52] = 45.6\r\n self.p[1][52] = 54\r\n self.p[0][53] = 40.3\r\n self.p[1][53] = 44.82\r\n self.p[0][54] = 35\r\n self.p[1][54] = 54\r\n self.p[0][55] = 9\r\n self.p[1][55] = 12\r\n self.p[0][56] = 5\r\n self.p[1][56] = 18\r\n self.p[0][57] = 5\r\n self.p[1][57] = 90\r\n self.p[0][58] = 9\r\n self.p[1][58] = 96\r\n self.p[0][59] = 61\r\n self.p[1][59] = 96\r\n self.p[0][60] = 65\r\n self.p[1][60] = 90\r\n self.p[0][61] = 65\r\n self.p[1][61] = 18\r\n self.p[0][62] = 61\r\n self.p[1][62] = 12\r\n\r\n return self.p","def getSearchSpaceCoords(self):","def draw_pins():\n\n pass","def point(ants, gui=True, zoom=1, mode=1, flap=True, \n elmin=15, writeFITS=True, brightness=50,isInner=False,\n subtractBackground=False,ncoadd=1,dazbkg=2.0,\n centroidLoopMax=16,minsamples=None):\n global stars_\n if len(stars_) == 0:\n print \"No stars have been selected, go use 'stars()'\"\n return\n else:\n print \"POINT:: using %d stars for ants=%s zoom=%g\" \\\n %(len(stars_),ants,zoom)\n previous_source_ = stars_[0][0]\n print previous_source_\n scnt = 0\n smax = len(stars_)\n antlist = helpers.makeList(ants)\n # Always turn on cameras (open flaps/caps) for all antenna\n # before looping over all stars\n SAC.camera(SAC.ON, antlist)\n for s in stars_:\n scnt = scnt + 1\n src = s[0]\n print \"POINT1(%s) %d/%d at %s\" % (src,scnt,smax,time.ctime())\n nextSrc = None\n if scnt < smax: nextSrc = stars_[scnt][0]\n try:\n point1(src, ants, showGUI=gui, zoom=zoom, mode=mode, \n nextSource=nextSrc, elmin=elmin,\n writeFITS=writeFITS, brightness=brightness,isInner=isInner,\n subtractBackground=subtractBackground,ncoadd=ncoadd,dazbkg=dazbkg,\n centroidLoopMax=centroidLoopMax,minsamples=minsamples)\n except KeyboardInterrupt, e:\n print \"Quitting point because of keyboard interrupt\"\n break\n except Exception, e:\n print \"POINT1 failed with exception: %s; skipping source %s\" \\\n %(e, s[0])\n if flap:\n # Turn off all cameras, close flaps/caps, go back to radio constants\n subarrayControl.s.camera(carma.antenna.common.OFF, antlist)","def calc_points(self, viewer, start_x, stop_x):\n # in case X axis is flipped\n start_x, stop_x = min(start_x, stop_x), max(start_x, stop_x)\n\n new_xlim = (start_x, stop_x)\n if new_xlim == self.plot_xlim:\n # X limits are the same, no need to recalculate points\n return\n\n self.plot_xlim = new_xlim\n\n points = self.get_data_points(points=self.points)\n if len(points) == 0:\n self.path.points = points\n return\n\n x_data, y_data = points.T\n\n # if we can determine the visible region shown on the plot\n # limit the points to those within the region\n if np.all(np.isfinite([start_x, stop_x])):\n idx = np.logical_and(x_data >= start_x, x_data <= stop_x)\n points = points[idx]\n\n if self.x_func is not None:\n # now find all points position in canvas X coord\n cpoints = self.get_cpoints(viewer, points=points)\n cx, cy = cpoints.T\n\n # Reduce each group of Y points that map to a unique X via a\n # function that reduces to a single value. The desirable function\n # will depend on the function of the plot, but mean() would be a\n # sensible default\n _, i = np.unique(cx, return_index=True)\n gr_pts = np.split(points, i)\n x_data = np.array([self.x_func(a.T[0]) for a in gr_pts\n if len(a) > 0])\n y_data = np.array([self.y_func(a.T[1]) for a in gr_pts\n if len(a) > 0])\n assert len(x_data) == len(y_data)\n\n points = np.array((x_data, y_data)).T\n\n self.path.points = points","def find_coordinates(self):\n\n raise NotImplementedError","def find_coordinates(self):\n\n raise NotImplementedError","def peaks_from_area(self, mask, keep=None, refine=True, Imin=None, **kwargs):\n y = numpy.round(self.keypoints.y).astype(int)\n x = numpy.round(self.keypoints.x).astype(int)\n is_inside = (mask[y, x]).astype(bool)\n if is_inside.sum() == 0:\n logger.error(\"No keypoint that region\")\n return []\n kp = self.keypoints[is_inside]\n if Imin:\n valid = kp.I >= Imin\n if valid.sum() == 0:\n logger.error(\"no keypoint match Intensity criteria in the region\")\n valid2 = self.raw[y, x] >= Imin\n good_kp = self.keypoints[numpy.logical_and(valid2, is_inside)]\n else:\n good_kp = kp[valid]\n else:\n good_kp = kp\n if keep and len(good_kp) > keep:\n order = numpy.argsort(good_kp.I)\n order = order[-1::-1][:keep] #keep only the most intense\n good_kp = good_kp[order]\n if refine:\n if self.bilinear is None:\n self.bilinear = Bilinear(self.raw)\n return [self.bilinear.local_maxi((i.y, i.x)) for i in good_kp]\n else:\n return [(i.y, i.x) for i in good_kp]","def get_coordinates(self, soup: BeautifulSoup) -> None:\n try:\n url = soup.find(\n \"a\", {\"title\": \"Open this area in Google Maps (opens a new window)\"}\n )[\"href\"]\n coordinates = url[url.find(\"=\") + 1 : url.find(\"&\")]\n coordinates = [float(n) for n in coordinates.split(\",\")]\n except (AttributeError, TypeError):\n coordinates = [None, None]\n self.__collected_dic[\"latitude\"].append(coordinates[0])\n self.__collected_dic[\"longitude\"].append(coordinates[1])","def find_keypoints ( gray , quality , ksize , blocksize , max_area = None ) :\n gray32 = np.float32(gray)\n points = cv2.goodFeaturesToTrack(gray32,maxCorners = 100, qualityLevel = quality ,minDistance = ksize , blockSize = blocksize )\n\n if points is None :\n return None , None , None\n\n if len(points) < 4 :\n return None , None , None\n\n oob = cv2.minAreaRect(points) \n\n if oob is None :\n return None, None , None\n\n oob_corners = get_oob_corners ( oob = oob )\n\n if oob_corners is None :\n return None, None , None\n\n if max_area is None :\n return points , oob , oob_corners\n\n area = get_polygon_area ( corners = oob_corners )\n\n if area > max_area :\n return None, None , None\n\n return points , oob , oob_corners","def draw_points(self, pic_path, points_data):\n # Pupil Finding here\n pupils = get_eye_locations_in_image(pic_path)\n img = cv2.imread(pic_path)\n frame_number = int(re.findall(r'\\d+', pic_path.split('/')[-1])[0])\n dets = detector(img)\n shape = None\n height, width, channels = img.shape\n\n for k, d in enumerate(dets):\n shape = predictor(img, d)\n\n if(not shape):\n return\n\n pointList = []\n c = 0\n for b in range(68):\n # sanitizing input points\n point = Point(shape.part(b).x, shape.part(b).y)\n points_data[c] = [point.x, point.y]\n c = c + 1\n # some points might be out of bound\n # so, move them to the closest boundary\n if(point.x < 0):\n point.x = 0\n elif(point.x >= width):\n point.x = width - 1\n if(point.y < 0):\n point.y = 0\n elif(point.y >= height):\n point.y = height - 1\n\n pointList.append(point)\n\n roll = findRoll(pointList)\n #print(\"roll is \" + str(roll) + ' angles')\n yaw = findYaw(pointList)\n #print(\"yaw is \" + str(yaw) + ' angles')\n pitch = findPitch(pointList)\n #print(\"pitch is \" + str(pitch) + ' angles')\n self.data[frame_number] = [roll, yaw, pitch]\n counter = 0\n for point in pointList:\n cv2.circle(img, (point.x, point.y), ImageProcessor.POINT_SIZE, ImageProcessor.POINT_COLOR, -1)\n counter = counter + 1\n\n self.draw_triangles(img, pointList)\n \n for pupil in pupils:\n cv2.circle(img, (pupil.left.x, pupil.left.y), 5, (0,0,255), -1)\n cv2.circle(img, (pupil.right.x, pupil.right.y), 5, (0,0,255), -1)\n points_data[-1] = [pupil.left.x, pupil.left.y]\n points_data[-2] = [pupil.right.x, pupil.right.y]\n #print(pupil.left.x, \", \", pupil.left.y)\n #print(pupil.right.x, \", \", pupil.right.y)\n\n cv2.imwrite(pic_path, img)","def get_coordinates(self):\n x_houses = []\n y_houses = []\n\n x_batt = []\n y_batt = []\n\n # turn dict to list so we can iterate through\n houses_list = list(self.houses.values())\n batteries_list = list(self.batteries.values())\n\n # for every house save coordinates to lists\n for house in houses_list:\n x_houses.append(house.x)\n y_houses.append(house.y)\n\n # for every battery save coordinates to lists\n for battery in batteries_list:\n x_batt.append(battery.x)\n y_batt.append(battery.y)\n\n return x_houses, y_houses, x_batt, y_batt","def _take_pieces(self, x, y):\n scores = []\n for p, (x1, y1) in self._get_surrounding(x, y):\n # If location is opponent's color and has no liberties, tally it up\n if p is self._next_turn and self.count_liberties(x1, y1) == 0:\n score = self._kill_group(x1, y1)\n scores.append(score)\n self._tally(score)\n return sum(scores)","def __load_geo(self):\n pass\n # process any splines? and turn them into arcs\n # http://www.mathopenref.com/constcirclecenter.html\n # find max dist between points\n # double it\n # select two segments\n # draw normal lines\n # find intersections, that is the center"],"string":"[\n \"def grid_points(self):\\n for i in range(self.rows):\\n for j in range(self.cols):\\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\\n if i == 0:\\n print_gps(max_lat,max_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(max_lat,min_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(min_lat,min_lon,\\\"grid\\\")\\n print_gps(min_lat,max_lon,\\\"grid\\\")\",\n \"def _gather_points(self):\\n # This is just a stub for now. We should really find the lines only\\n # inside the screen range here.\\n\\n x = self.index.get_data()\\n y = self.value.get_data()\\n rad= min(self.width/2.0,self.height/2.0)\\n sx = x*rad+ self.x + self.width/2.0\\n sy = y*rad+ self.y + self.height/2.0\\n\\n points = transpose(array((sx,sy)))\\n self._cached_data_pts = points\\n self._cache_valid = True\\n return\",\n \"def coordinates(self):\",\n \"def island_aging(self):\\n for y in self.island_map:\\n for cell in y:\\n cell.aging()\",\n \"def generate_all_locations(grid, shape):\",\n \"def hammer_points(neighbors = 3):\\n tgtPts = cmds.ls(sl=True, fl=True)\\n#---------------- add in soft selection? \\n if not tgtPts:\\n cmds.warning(\\\"Select one or more cvs\\\")\\n return\\n\\n for tgtPt in tgtPts:\\n\\n #tgtPtPos = cmds.pointPosition(tgtPt)\\n \\n tgtNum = int(tgtPt.partition(\\\"[\\\")[2].rpartition(\\\"]\\\")[0])\\n #tgtBase = tgtPt.partition(\\\"[\\\")[0]\\n crv = tgtPts[0].partition(\\\".\\\")[0]\\n \\n ptPosListX = []\\n ptPosListY = []\\n ptPosListZ = []\\n \\n count = 0\\n \\n for x in range(-neighbors, neighbors+1):\\n \\n count += abs(x)\\n \\n if x != 0:\\n origPt = \\\"{0}.cv[{1}]\\\".format(crv, tgtNum + x)\\n origPtPos = cmds.pointPosition(origPt)\\n \\n for a in range(abs(x)):\\n ptPosListX.append(origPtPos[0])\\n ptPosListY.append(origPtPos[1])\\n ptPosListZ.append(origPtPos[2])\\n \\n avgX = sum(ptPosListX)/(len(ptPosListX))\\n avgY = sum(ptPosListY)/(len(ptPosListY))\\n avgZ = sum(ptPosListZ)/(len(ptPosListZ))\\n \\n newPos = [avgX, avgY, avgZ]\\n \\n cmds.xform(tgtPt, ws=True, t=newPos)\",\n \"def teselado(self,points):\\n #muestrea todo el espacio de la envolvente para conseguir las fronteras de decision a intervalos regulares \\n #get_hull\\n area = boundingbox(points)\\n #(min_x,min_y),(max_x,min_y),(max_x,max_y),(min_x,max_y)\\n #sample inside hull\\n lat_sample = np.arange(min_y, max_y, 0.001).tolist()\\n lon_sample = np.arange(min_x,max_x, 0.001)\\n sampling_space = [[x,y] for x in lat_sample for y in lon_sample]\\n sampling_space = np.asarray(sampling_space, dtype=np.float32)\\n prediction = kmeans_instance.predict(sampling_space)\\n pol = [[list(sampling_space[index]) for index in [i for i, j in enumerate(prediction) if j == k]] for k in range(centers)]\\n hull = [shapely.geometry.MultiPoint(pol[i]).convex_hull.exterior._get_coords() for i in range(len(pol))]\",\n \"def find_area(self):\\n min_lat_point = self.latitude_min\\n max_lat_point = self.latitude_max\\n min_lon_point = self.longitude_min\\n max_lon_point = self.longitude_max\\n self.rename_latitude()\\n self.rename_longitude()\\n all_lat_bounds = self.cube.coord('latitude').bounds\\n all_lon_bounds = self.cube.coord('longitude').bounds\\n # print(all_lat_bounds)\\n # print(all_lon_bounds)\\n for i, lat in enumerate(all_lat_bounds):\\n for j, lon in enumerate(all_lon_bounds):\\n lat_bounds = lat # 2D array of the lower and upper lat bounds\\n lon_bounds = lon # 2D array of the lower and upper lon bounds\\n if lat_bounds[0] <= min_lat_point < lat_bounds[1]:\\n if lon_bounds[0] <= min_lon_point < lon_bounds[1]:\\n nlat_min = i\\n nlon_min = j\\n else:\\n pass\\n else:\\n pass\\n\\n for k, lat in enumerate(all_lat_bounds):\\n for l, lon in enumerate(all_lon_bounds):\\n lat_bounds = lat # 2D array of the lower and upper lat bounds\\n lon_bounds = lon # 2D array of the lower and upper lon bounds\\n if lat_bounds[0] <= max_lat_point < lat_bounds[1]:\\n if lon_bounds[0] <= max_lon_point < lon_bounds[1]:\\n nlat_max = k\\n nlon_max = l\\n else:\\n pass\\n else:\\n pass\\n\\n area_subset = self.cube[:, nlat_min:nlat_max+1, nlon_min:nlon_max+1]\\n # print(area_subset.coord('latitude').points)\\n # print(area_subset.coord('longitude').points)\\n area_mean = area_subset.collapsed(['latitude', 'longitude'],\\n iris.analysis.MEAN)\\n\\n return area_mean\",\n \"def island_feeding(self):\\n for y in self.island_map:\\n for cell in y:\\n cell.feeding()\",\n \"def _pick_visible_ids(self, xmin, ymin, xmax, ymax):\\n #vtk.vtkSelectVisiblePoints()\\n #vcs = vtk.vtkVisibleCellSelector()\\n area_picker = vtk.vtkRenderedAreaPicker()\\n area_picker.AreaPick(xmin, ymin, xmax, ymax, self.parent.rend)\\n #area_picker.Pick()\",\n \"def find_points(self):\\n\\n points = [\\n (self.inner_radius, 0, \\\"straight\\\"),\\n (self.inner_radius, self.height / 2, \\\"straight\\\"),\\n (self.outer_radius, self.height / 2, \\\"straight\\\"),\\n (self.outer_radius, self.arc_height / 2, \\\"circle\\\"),\\n (self.mid_radius, 0, \\\"circle\\\"),\\n (self.outer_radius, -self.arc_height / 2, \\\"straight\\\"),\\n (self.outer_radius, -self.height / 2, \\\"straight\\\"),\\n (self.inner_radius, -self.height / 2, \\\"straight\\\")\\n ]\\n\\n self.points = points\",\n \"def spatial(self):\",\n \"def area(self):\",\n \"def getAllPoints(self):\\n self.getOrigin()\\n self.computesWingsMeshPoints()\\n self.computesFuselageMeshPoints()\\n self.readsMaterials()\\n self.assembleMatrices()\\n self.computesWingConnexions()\\n\\n logger.debug(self.aircraftTotalMass)\\n # self.plotSectionsPoints() # for debugging\",\n \"def choose_starting_points(self, agent):\\n # Left Side\\n if agent % 4 == 1:\\n if self.left_side[\\\"x_max\\\"] != self.left_side[\\\"x_min\\\"]:\\n x = (self.left_side[\\\"x_max\\\"] + self.left_side[\\\"x_min\\\"])/2\\n else:\\n x = self.left_side[\\\"x_max\\\"]\\n if self.left_side[\\\"y_max\\\"] != self.left_side[\\\"y_min\\\"]:\\n y = (self.left_side[\\\"y_max\\\"] + self.left_side[\\\"y_min\\\"])/2\\n else:\\n y = self.left_side[\\\"y_max\\\"]\\n # Right Side\\n elif agent % 4 == 2:\\n if self.right_side[\\\"x_max\\\"] != self.right_side[\\\"x_min\\\"]:\\n x = (self.right_side[\\\"x_max\\\"] + self.right_side[\\\"x_min\\\"])/2\\n else:\\n x = self.right_side[\\\"x_max\\\"]\\n if self.right_side[\\\"y_max\\\"] != self.right_side[\\\"y_min\\\"]:\\n y = (self.right_side[\\\"y_max\\\"] + self.right_side[\\\"y_min\\\"])/2\\n else:\\n y = self.right_side[\\\"y_max\\\"]\\n # Top\\n elif agent % 4 == 3:\\n if self.top[\\\"x_max\\\"] != self.top[\\\"x_min\\\"]:\\n x = (self.top[\\\"x_max\\\"] + self.top[\\\"x_min\\\"])/2\\n else:\\n x = self.top[\\\"x_max\\\"]\\n if self.top[\\\"y_max\\\"] != self.top[\\\"y_min\\\"]:\\n y = (self.top[\\\"y_max\\\"] + self.top[\\\"y_min\\\"])/2\\n else:\\n y = self.top[\\\"y_max\\\"]\\n # Bottom\\n elif agent % 4 == 0:\\n if self.bottom[\\\"x_max\\\"] != self.bottom[\\\"x_min\\\"]:\\n x = (self.bottom[\\\"x_max\\\"] + self.bottom[\\\"x_min\\\"])/2\\n else:\\n x = self.bottom[\\\"x_max\\\"]\\n if self.bottom[\\\"y_max\\\"] != self.bottom[\\\"y_min\\\"]:\\n y = (self.bottom[\\\"y_max\\\"] + self.bottom[\\\"y_min\\\"])/2\\n else:\\n y = self.bottom[\\\"y_max\\\"]\\n else:\\n raise ValueError(\\\"Invalid number for sides!\\\")\\n\\n return x, y\",\n \"def example_staypoints():\\n p1 = Point(8.5067847, 47.4)\\n p2 = Point(8.5067847, 47.5)\\n p3 = Point(8.5067847, 47.6)\\n p4 = Point(8.5067847, 47.7)\\n\\n t1 = pd.Timestamp(\\\"1971-01-01 00:00:00\\\", tz=\\\"utc\\\")\\n t2 = pd.Timestamp(\\\"1971-01-01 05:00:00\\\", tz=\\\"utc\\\")\\n t3 = pd.Timestamp(\\\"1971-01-02 07:00:00\\\", tz=\\\"utc\\\")\\n t4 = pd.Timestamp(\\\"1971-01-02 08:00:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n t6 = pd.Timestamp(\\\"1971-01-02 10:00:00\\\", tz=\\\"utc\\\")\\n\\n list_dict = [\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t1, \\\"finished_at\\\": t2, \\\"geom\\\": p1},\\n {\\\"id\\\": 5, \\\"user_id\\\": 0, \\\"started_at\\\": t2, \\\"finished_at\\\": t3, \\\"geom\\\": p2},\\n {\\\"id\\\": 2, \\\"user_id\\\": 0, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p3},\\n {\\\"id\\\": 6, \\\"user_id\\\": 0, \\\"started_at\\\": t4, \\\"finished_at\\\": t5, \\\"geom\\\": p2},\\n {\\\"id\\\": 15, \\\"user_id\\\": 0, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1},\\n {\\\"id\\\": 7, \\\"user_id\\\": 1, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p4},\\n {\\\"id\\\": 80, \\\"user_id\\\": 1, \\\"started_at\\\": t4, \\\"finished_at\\\": t5, \\\"geom\\\": p2},\\n {\\\"id\\\": 3, \\\"user_id\\\": 1, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p2},\\n ]\\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\\\"geom\\\", crs=\\\"EPSG:4326\\\")\\n sp = sp.set_index(\\\"id\\\")\\n sp.as_staypoints\\n return sp\",\n \"def nearPoints(self):\\n # initialize the edge counter\\n for edge in self.G.edges():\\n self.edge_id__count[self.G[edge[0]][edge[1]].get(\\\"edgecounter\\\")] = 0\\n \\n for point in self.gps_points:\\n nearest_edge = self.getNearestEdge(point)\\n # print str(point.getAttributes().get(\\\"ID\\\")) + \\\"->\\\" + str(nearest_edge.getAttributes().get('Id'))\\n self.addPointCountToEdge(nearest_edge)\",\n \"def get_nodes(self, latlon=False):\\n ids = np.where(np.isnan(self.data[:,:,:]))\\n i_nan = ids[0][0] ; j_nan = ids[1][0]\\n \\n def area_neighbours(Area, i_nan, j_nan):\\n rows = np.array(Area)[:,0]\\n cols = np.array(Area)[:,1]\\n rows_m = rows-1\\n cols_m = cols-1\\n rows_p = rows+1\\n cols_p = cols+1\\n \\n p1 = np.array([rows_m,cols]).ravel().reshape(len(rows),2,order='F')\\n p2 = np.array([rows_p,cols]).ravel().reshape(len(rows),2,order='F')\\n p3 = np.array([rows,cols_m]).ravel().reshape(len(rows),2,order='F')\\n p4 = np.array([rows,cols_p]).ravel().reshape(len(rows),2,order='F')\\n cond1 = p1[:,0]<0\\n cond2 = p2[:,0]>self.dimX-1\\n cond3 = p3[:,1]<0\\n cond4 = p4[:,1]>self.dimY-1\\n if latlon:\\n p3[:,1][cond3] = self.dimY-1\\n p4[:,1][cond4] = 0\\n else:\\n p3[:,0][cond3] = i_nan\\n p3[:,1][cond3] = j_nan\\n p4[:,0][cond4] = i_nan\\n p4[:,1][cond4] = j_nan\\n p1[:,0][cond1] = i_nan\\n p1[:,1][cond1] = j_nan\\n p2[:,0][cond2] = i_nan\\n p2[:,1][cond2] = j_nan\\n p = np.concatenate((p1,p2,p3,p4)).tolist()\\n return [i for i in p if i not in self.unavail]\\n\\n def area_max_correlation(Area, neighbours):\\n Rmean = [] ; X = []\\n for cell in neighbours:\\n R = []\\n new_cell = cell[0]*self.dimY + cell[1]\\n if new_cell in self.gridcells:\\n X.append(cell)\\n IDm = np.where(self.gridcells==new_cell)\\n Rmean.append(np.nanmean(self.corrs[cells_in_k,IDm]))\\n try:\\n Rmax = np.nanmax(Rmean)\\n except ValueError:\\n Rmax = np.nan\\n return np.array(X), Rmean, Rmax\\n \\n def diag_indices(a, k):\\n rows, cols = np.diag_indices_from(a)\\n if k < 0:\\n return rows[-k:], cols[:k]\\n elif k > 0:\\n return rows[:-k], cols[k:]\\n else:\\n return rows, cols\\n\\n #S T E P 1 (C R E A T E N O D E S)\\n\\n self.nodes = {}\\n self.unavail = []\\n if latlon:\\n neighbour_corrs1 = self.corrs.diagonal(offset=1)\\n neighbour_corrs2 = self.corrs.diagonal(offset=self.dimY-1)\\n subset = np.arange(0,len(neighbour_corrs2),self.dimY)\\n neighbour_corrs2 = neighbour_corrs2[subset]\\n neighbour_corrs = np.concatenate((neighbour_corrs1,neighbour_corrs2))\\n\\n cellIDs1 = diag_indices(self.corrs,1)\\n cellIDs2 = diag_indices(self.corrs,self.dimY-1)\\n\\n cellIDs = (np.concatenate((cellIDs1[0],cellIDs2[0][subset])),\\\\\\n np.concatenate((cellIDs1[1],cellIDs2[1][subset])))\\n else:\\n neighbour_corrs = self.corrs.diagonal(offset=1)\\n cellIDs = diag_indices(self.corrs,1)\\n \\n cellIDs = (self.gridcells[cellIDs[0]],self.gridcells[cellIDs[1]])\\n k = 0\\n neighbour_corrs,cellIDs1,cellIDs2 = list(zip(*sorted(zip(neighbour_corrs,cellIDs[0],cellIDs[1]),reverse=True)))\\n cell_IDs = (cellIDs1,cellIDs2)\\n np.random.seed(2)\\n for it in range(len(neighbour_corrs)):\\n cells_in_k = []\\n i = cell_IDs[0][it]\\n j = cell_IDs[1][it]\\n r = neighbour_corrs[it]\\n \\n row_i = int(np.floor(i/self.dimY)) ; col_i = int(i % self.dimY)\\n row_j = int(np.floor(j/self.dimY)) ; col_j = int(j % self.dimY)\\n \\n if ([row_i,col_i] not in self.unavail) & ([row_j,col_j] not in self.unavail):\\n if r>self.tau:\\n self.nodes.setdefault(k, []).append([row_i,col_i])\\n self.nodes.setdefault(k, []).append([row_j,col_j])\\n self.unavail.append([row_i,col_i])\\n self.unavail.append([row_j,col_j])\\n cells_in_k.extend(np.where(self.gridcells==i)[0])\\n cells_in_k.extend(np.where(self.gridcells==j)[0])\\n\\n while True: #expand\\n neighbours = area_neighbours(self.nodes[k], i_nan, j_nan)\\n X, Rmean, Rmax = area_max_correlation(Area=self.nodes[k], neighbours=neighbours)\\n if Rmax > self.tau:\\n m = X[Rmean==Rmax].tolist()\\n if len(m)>1:\\n m = m[np.random.randint(low=0,high=len(m))]\\n else:\\n m = m[0]\\n self.nodes.setdefault(k, []).append(m)\\n self.unavail.append(m)\\n cells_in_k.extend(np.where(self.gridcells==m[0]*self.dimY+m[1])[0])\\n else:\\n break\\n if len(self.nodes[k]) <= 2:\\n del self.nodes[k]\\n k += 1\\n else:\\n break\\n \\n #S T E P 2 (M E R G E N O D E S)\\n \\n self.unavail = []\\n while True:\\n Rs = {}\\n unavail_neighbours = {}\\n num_cells = dict([(area,len(self.nodes[area])) if self.nodes[area] not in self.unavail else (area,np.inf) for area in self.nodes.keys()])\\n maxID = min(num_cells.items(), key=operator.itemgetter(1))[0]\\n if num_cells[maxID] > 175: #arbitrary choice?\\n break\\n else:\\n cells_in_k = [np.where(self.gridcells==cell[0]*self.dimY+cell[1])[0] for cell in self.nodes[maxID]]\\n neighbours = area_neighbours(self.nodes[maxID], i_nan, j_nan)\\n for cell in neighbours:\\n gcell = cell[0]*self.dimY + cell[1]\\n Rmean = []\\n cond1 = gcell in self.gridcells\\n cond2 = cell not in self.nodes[maxID]\\n cond3 = cell not in [k for k, g in itertools.groupby(sorted(itertools.chain(*unavail_neighbours.values())))]\\n cond4 = len([area for area, cells in self.nodes.items() if cell in cells]) > 0\\n if (cond1) & (cond2) & (cond3) & (cond4):\\n nID = [area for area, cells in self.nodes.items() if cell in cells][0]\\n unavail_neighbours[nID] = self.nodes[nID]\\n X, Rmean, Rmax = area_max_correlation(Area=self.nodes[nID]+self.nodes[maxID], neighbours=self.nodes[nID]+self.nodes[maxID])\\n if nID not in Rs: \\n Rs[nID] = np.nanmean(Rmean)\\n try:\\n Rs_maxID = max(Rs.items(), key=operator.itemgetter(1))[0]\\n if Rs[Rs_maxID] > self.tau:\\n for cell in self.nodes.pop(Rs_maxID, None):\\n self.nodes.setdefault(maxID, []).append([cell[0],cell[1]])\\n else:\\n self.unavail.append(self.nodes[maxID])\\n except ValueError:\\n self.unavail.append(self.nodes[maxID])\",\n \"def feature_coords(features):\\n coords_list = []\\n for feature in features:\\n coord_start = feature.location.nofuzzy_start\\n coord_end = feature.location.nofuzzy_end\\n coord_pair = (coord_start, coord_end)\\n coords_list.append(coord_pair)\\n ## consider adding some info to the log\\n return coords_list\",\n \"def fit (self,points):\\n all_coordinates = [p.coordinates for p in points]\\n for i in range(len(all_coordinates[0])):\\n if (len(self.min_coordinate)<=i):\\n self.min_coordinate.append(all_coordinates[0][i])\\n self.max_coordinate.append(all_coordinates[0][i])\\n for x in all_coordinates:\\n if(self.max_coordinate[i]<x[i]):\\n self.max_coordinate[i]=x[i]\\n if(self.min_coordinate[i]>x[i]):\\n self.min_coordinate[i]=x[i]\",\n \"def getGridPoints(x, y, robot):\\r\\n roundedGrid = (round(x), round(y))\\r\\n total_radius = (robot.RADIUS + robot.BALL_RADIUS) / robot.grid.scale\\r\\n scanAmount = math.ceil(total_radius)\\r\\n scan = range(-scanAmount, scanAmount + 1)\\r\\n corners = ((0, 0), (0, 1), (1, 1), (1, 0))\\r\\n points = []\\r\\n for i in scan:\\r\\n for j in scan:\\r\\n for corner in corners:\\r\\n newX = roundedGrid[0] + i + corner[0]\\r\\n newY = roundedGrid[1] + j + corner[1]\\r\\n if grid_distance(newX, newY, x, y) < total_radius:\\r\\n points.append((newX, newY))\\r\\n\\r\\n return points\",\n \"def find_points(self, angle):\\n angle_rads = angle * 1.0 / 360 * 2 * pi\\n points = []\\n self.pic.setPenColor(255, 0, 0)\\n cur_pos_x = self.xcenter\\n cur_pos_y = self.ycenter\\n in_line = False\\n while self.in_bounds(cur_pos_x, cur_pos_y):\\n if in_line:\\n if not is_dark(self.pic, cur_pos_x, cur_pos_y):\\n in_line = False\\n points.append(self.distance(cur_pos_x, cur_pos_y))\\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\\n else:\\n if is_dark(self.pic, cur_pos_x, cur_pos_y):\\n in_line = True\\n points.append(self.distance(cur_pos_x, cur_pos_y))\\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\\n cur_pos_x += 3 * cos(angle_rads)\\n cur_pos_y += 3 * sin(angle_rads)\\n return points\",\n \"def find_crime_areas(segmented_points):\\n\\n # once all of the interpolated points are loaded into segmented_points\\n # loop through them again to find out which places are high crime.\\n bad_neighborhood_crime_index = 0.2\\n\\n for j in range(1, len(segmented_points)):\\n print \\\"segmented_points[j]\\\", segmented_points[j]\\n # ====================================================================\\n # waypoint algorithm fleshing out\\n # ====================================================================\\n if segmented_points[j]['crime_index'] > bad_neighborhood_crime_index:\\n # get the center of the geohash\\n print \\\"This is a bad neighborhood\\\"\\n\\n # this is probably temporary, for display purposes\\n segmented_points[j]['is_high_crime'] = True\\n\\n # do a conditional that if the bad neighborhood is at\\n # len(segmented_points) we need to go get the end dict\\n\\n # now that we know what the bad neighborhood point is, let's get\\n # the latitude, longitude from the point before and the point after\\n if 'lat' not in segmented_points[j-1] or 'lng' not in segmented_points[j-1]:\\n point_before = (segmented_points[j-1]['data']['start']['lat'],\\n segmented_points[j-1]['data']['start']['lng'])\\n else:\\n point_before = (segmented_points[j-1]['lat'],\\n segmented_points[j-1]['lng'])\\n\\n if 'lat' not in segmented_points[j+1] or 'lng' not in segmented_points[j+1]:\\n point_after = (segmented_points[j+1]['data']['end']['lat'],\\n segmented_points[j+1]['data']['end']['lng'])\\n else:\\n point_after = (segmented_points[j+1]['lat'],\\n segmented_points[j+1]['lng'])\\n\\n current_point = (segmented_points[j]['lat'],\\n segmented_points[j]['lng'])\\n\\n # before calling inspect_waypoints, check the deltas for the\\n # step before and the step after to determine whether the function\\n # needs to be called twice, or four times, and what direction to go\\n # get the change in latitude and longitude between the before\\n # and current point location\\n delta_lat_before_current = current_point[0] - point_before[0]\\n delta_lng_before_current = current_point[1] - point_before[1]\\n\\n # get the change in latitude and longitude between the before\\n # and current point location\\n delta_lat_after_current = point_after[0] - current_point[0]\\n delta_lng_after_current = point_after[1] - current_point[1]\\n\\n delta_before_after = [delta_lat_before_current, delta_lng_before_current,\\n delta_lat_after_current, delta_lng_after_current]\\n\\n segmented_points = check_directions_find_waypoint(current_point,\\n segmented_points[j],\\n delta_before_after,\\n segmented_points)\\n print \\\"this is segmented_points[0] returned\\\", segmented_points[0]\\n return segmented_points[0]\",\n \"def setPointsToTurn(self):\\r\\n for point in self.points:\\r\\n point.setActiveTurn()\",\n \"def get_all_locations(self):\",\n \"def getPointValues(self, *args, **kwargs):\\n ...\",\n \"def points_earned(self):\\n delta_counts = self.alive_counts - self.initial_counts\\n points = self.points_table * delta_counts\\n points = points.reshape(-1,72) # unravel the points for easier sum\\n return np.sum(points, axis=1) + super().current_points()\",\n \"def get_all_offgrid_pin(self, pin, insufficient_list):\\n #print(\\\"INSUFFICIENT LIST\\\",insufficient_list)\\n # Find the coordinate with the most overlap\\n any_overlap = set()\\n for coord in insufficient_list:\\n full_pin = self.convert_track_to_pin(coord)\\n # Compute the overlap with that rectangle\\n overlap_rect=pin.compute_overlap(full_pin)\\n # Determine the max x or y overlap\\n max_overlap = max(overlap_rect)\\n if max_overlap>0:\\n any_overlap.update([coord])\\n \\n return any_overlap\",\n \"def add_points(grid, num_points):\\n \\n for i in range(num_points):\\n # Coord for crit point\\n rand_x = random.randint(0, GRID_WIDTH - 1)\\n rand_y = random.randint(0, GRID_HEIGHT - 1)\\n \\n # Set value of crit point\\n elev = (MAX_HEIGHT - MIN_HEIGHT) * random.random() + MIN_HEIGHT\\n grid[rand_x][rand_y] = elev * PEAK_HEIGHT\\n \\n return grid\",\n \"def grid(iant,xgrid=[0],ygrid=[0],sleep=4):\\n d=Carma(iant).drive()\\n d.setOffset(xgrid[0],ygrid[0])\\n time.sleep(sleep)\\n time.sleep(sleep)\\n for y in ygrid:\\n for x in xgrid:\\n print x,y\\n d.setOffset(x,y)\\n time.sleep(sleep)\",\n \"def graham_scan(self, show_progress):\\n global anchor # point (x,y) with the smallest y value\\n points = self.points\\n\\n min_index = None\\n for i, (x, y) in enumerate(points):\\n if min_index == None or y < points[min_index][1]:\\n min_index = i\\n if y == points[min_index][1] and x < points[min_index][0]:\\n min_index = i\\n anchor = points[min_index]\\n sorted_points = self.quicksort(points)\\n del sorted_points[sorted_points.index(anchor)]\\n # the 'anchor' point and the point with smallest polar angle will always be on the hull\\n self.hull_points = [anchor, sorted_points[0]]\\n for s in sorted_points[1:]:\\n while self.rotation(self.hull_points[-2], self.hull_points[-1], s) <= 0:\\n del self.hull_points[-1] # backtrack\\n self.hull_points.append(s)\\n if show_progress:\\n draw_scatter_plot(points, self.hull_points, self.color, self.title)\\n plot.close()\\n return self.hull_points\",\n \"def setPointsActive(self):\\r\\n for point in self.points:\\r\\n point.setActive()\",\n \"def attackArea(initPair, connect):\\r\\n area = []\\r\\n opts = ((1, 0), (0, 1), (1, 1), (1, -1))\\r\\n (x, y) = initPair\\r\\n\\r\\n for pair in opts:\\r\\n (x1, y1) = pair\\r\\n\\r\\n for s in (1, -1):\\r\\n for i in range(1, connect):\\r\\n x2 = x + x1 * i * s\\r\\n y2 = y + y1 * i * s\\r\\n area.append((x2, y2))\\r\\n\\r\\n return area\",\n \"def get_dancefloor_area(self):\\n cords = []\\n\\n x1 = self.coordinates[0]\\n y1 = self.coordinates[1]\\n x2 = self.coordinates[0] + self.width\\n y2 = self.coordinates[1] + self.height\\n if self.width <= 0:\\n x1, x2 = x2, x1\\n if self.height <= 0:\\n y1, y2 = y2, y1\\n\\n for x in range(x1, x2):\\n for y in range(y1, y2):\\n if x % 20 == 0 and y % 20 == 0:\\n cords.append([x, y])\\n return cords\",\n \"def __sample(self):\\n # xvals are \\\"east\\\" vals and yvals are \\\"north\\\" vals on the map\\n xvals = np.random.uniform(self._xmin, self._xmax, self._num_samples)\\n yvals = np.random.uniform(self._ymin, self._ymax, self._num_samples)\\n if self._target_altitude is None:\\n zvals = np.random.uniform(self._zmin, self._zmax, self._num_samples)\\n else:\\n zvals = np.full(self._num_samples, self._target_altitude, dtype=float)\\n \\n samples = list(zip(xvals, yvals, zvals))\\n\\n pts = []\\n for s in samples:\\n in_collision = False\\n idxs = list(self._obstacles_tree.query_radius(\\n np.array([s[0], s[1]]).reshape(1, -1), r=self._max_poly_xy)[0])\\n \\n if len(idxs) > 0:\\n for ind in idxs: \\n p = self._polygons[int(ind)]\\n if p.contains(s) and p.height >= s[2]:\\n in_collision = True\\n\\n if not in_collision:\\n pts.append(s)\\n \\n return pts\",\n \"def _add_points(self):\\n if not '_list_of_points' in self.__dict__.keys():\\n self._list_of_points = [] \\n for point in self['point'].items():\\n self._list_of_points.append(point[1])\",\n \"def point1(source, ants, elmin=15.0, showGUI=True, zoom=1, mode=0, \\n nextSource=None, writeFITS=True, brightness=50,isInner=False,\\n subtractBackground=False, ncoadd=1, dazbkg=2.0, centroidLoopMax=16,\\n minsamples=None):\\n global previous_source_\\n antlist = helpers.makeList(ants)\\n el = elevation(source)\\n \\n # Check for source too low, and bail out if it is\\n if el < elmin:\\n print 'Skipping %s because elevation(%.1f) is less than limit(%.1f)' \\\\\\n %(source,el,elmin)\\n return\\n \\n # Check for maximum elevation as well\\n maxelLimit = 87.0\\n if el > maxelLimit:\\n print 'Skipping %s because elevation(%.1f) is greater than limit(%.1f)' \\\\\\n %(source,el,maxelLimit)\\n return\\n\\n # Print message\\n msg = \\\"Pointing on %s at el=%5.1f (elmin = %5.1f); ants = %s\\\" % (source, el, elmin, str(ants))\\n print msg\\n SAC.scriptlog(msg)\\n \\n d = distance(source,previous_source_)\\n print \\\" Distance from %s to %s is %.1f deg\\\" %(previous_source_,source,d)\\n if mode > 0:\\n if nextSource != None:\\n print \\\" Next source will be %s\\\" % nextSource\\n else:\\n print \\\" Last source.\\\"\\n # Send all antennas tracking to new source, even though some are\\n # already there.\\n if isInner :\\n SAC.track(source)\\n else : SAC.track(source, antlist, phaseCenter=False, waiton=SAC.NONE)\\n antsTodo = antlist\\n while len(antsTodo) > 0 :\\n r = SAC.wait(SAC.TRACK, antsTodo, waiton=SAC.ANY)\\n antsReady = r.ready\\n antsTodo = r.notready\\n for i in antsReady :\\n show(source)\\n #print \\\"Loading camera on antenna %d\\\" %i\\n opticalSystem(i, auto=True, object=source, repeat=4, zoom=zoom,\\n showGUI=showGUI, dontClose=True, \\n brightness=brightness, ncoadd=ncoadd, dazbkg=dazbkg,\\n subtractBackground=subtractBackground, \\n centroidLoopMax=centroidLoopMax, minsamples=minsamples)\\n # now that this ant is done, send it to next source\\n if mode > 0:\\n if nextSource != None: \\n if isInner : SAC.track(nextSource)\\n else : SAC.track(nextSource, i, waiton=SAC.NONE)\\n previous_source_ = source\",\n \"def sample_pin_position_range():\\n #Create a sample goniometer\\n g = TopazInHouseGoniometer()\\n\\n #Initialize the leg limits\\n g.relative_sample_position = column([0.0, 0.0, 0.0])\\n g.getplatepos(0.0, 0.0, 0.0)\\n g.calculate_leg_xy_limits(visualize=True)\\n\\n# if True:\\n# pylab.show()\\n# return\\n\\n n = 17\\n positions = np.linspace(-8, 8, n) #Range calculated in mm\\n allowed = np.zeros( (n,n,n) )\\n for (ix, x) in enumerate(positions):\\n print \\\"Calculating x\\\", x\\n for (iy, y) in enumerate(positions):\\n for (iz, z) in enumerate(positions):\\n #Set up\\n g.relative_sample_position = column([x, y, z])\\n allowed[ix,iy,iz] = g.are_angles_allowed([0., 0., 0.], return_reason=False)\\n\\n #Do a plot\\n\\n pylab.figure(1, figsize=[15,15])\\n pylab.title(\\\"Allowable XZ sample positions\\\")\\n for (iy, y) in enumerate(positions):\\n print \\\"At y of\\\", y, \\\", # of points = \\\", np.sum( allowed[:, iy,:])\\n if iy < 16:\\n pylab.subplot(4,4,iy+1)\\n pylab.pcolor(positions, positions, allowed[:, iy, :].transpose(), norm=pylab.Normalize(0, 1))\\n pylab.xlabel(\\\"x\\\")\\n pylab.ylabel(\\\"z\\\")\\n pylab.title(\\\"y = %.3f mm\\\" % y)\\n pylab.draw()\\n pylab.axis('equal')\\n pylab.show()\\n #pylab.\",\n \"def get_uncertain_point_coords_on_grid(uncertainty_map, num_points):\\n R, _, H, W = uncertainty_map.shape\\n h_step = 1.0 / float(H)\\n w_step = 1.0 / float(W)\\n\\n num_points = min(H * W, num_points)\\n point_indices = torch.topk(uncertainty_map.view(R, H * W), k=num_points, dim=1)[1]\\n point_coords = torch.zeros(R, num_points, 2, dtype=torch.float, device=uncertainty_map.device)\\n point_coords[:, :, 0] = w_step / 2.0 + (point_indices % W).to(torch.float) * w_step\\n point_coords[:, :, 1] = h_step / 2.0 + (point_indices // W).to(torch.float) * h_step\\n return point_indices, point_coords\",\n \"def global_coords(self) -> GlobalCoordsABC:\",\n \"def internal_points(self):\\n return self.ghost_points()\",\n \"def get_random_coordinates(self):\\n array_shape = np.shape(self.cells) # type: tuple\\n points_on_island = []\\n for i in range(1, array_shape[0] - 1):\\n for j in range(1, array_shape[1] - 1):\\n points_on_island.append((i, j))\\n random.shuffle(points_on_island)\\n return points_on_island\",\n \"def iter_points(self):\\n for x in range(self.left, self.right + 1):\\n for y in range(self.top, self.bottom + 1):\\n yield Point(x, y)\",\n \"def area(self):\\n ...\",\n \"def test_absolute_areas(self):\\n\\n assert len(self.test_shape.areas) == 4\\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1\",\n \"def pointsSetUp(self):\\r\\n self.background.draw(self.surface)\\r\\n for i in range(len(self.points)):\\r\\n self.points[i].organize()\\r\\n self.points[i].update()\\r\\n self.points[i].addNumber(i)\\r\\n self.points[i].setActiveTurn()\",\n \"def __generate_distribution_grid(self) -> np.ndarray:\\n\\n x_min = self.occupancy_map.info.origin.position.x\\n x_max = x_min + self.occupancy_map.info.width * self.occupancy_map.info.resolution\\n y_min = self.occupancy_map.info.origin.position.y\\n y_max = y_min + self.occupancy_map.info.height * \\\\\\n self.occupancy_map.info.resolution\\n\\n # CREATE HOTSPOTS IF WANTED:\\n if self.create_random_hotspots:\\n if self.use_uniform_distr:\\n rospy.logerr(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\t\\\" + rospy.get_caller_id()\\n + \\\":\\\\n\\\\tIt does not make sense to create random hotspots if distribution should be uniform.\\\"\\n + \\\"\\\\n\\\\tThe node will go on with uniform distribution. You can change this in the launch parameters.\\\\n\\\")\\n self.number_of_random_hotspots = 1 # still needed to get the right edges\\n elif self.number_of_random_hotspots <= 0:\\n rospy.logerr(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\t\\\" + rospy.get_caller_id()\\n + \\\":\\\\n\\\\tThe number of wanted hotspots is 0 or below. This is not possible.\\\"\\n + \\\"\\\\n\\\\tThe node will go on with 1 random hotspot. You can change this in the launch parameters.\\\\n\\\")\\n self.number_of_random_hotspots = 1\\n self.hotspots = []\\n for _ in range(self.number_of_random_hotspots):\\n x, y, spread = self.__get_random_hotspot()\\n self.hotspots.append([x, y, spread])\\n\\n if len(self.hotspots) < 1:\\n if not self.use_uniform_distr:\\n rospy.logerr(\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\t\\\" + rospy.get_caller_id()\\n + \\\":\\\\n\\\\tIf no hotspots should be generated randomly (and no uniform distribution),\\\\n\\\\tyou need to specify hotspots.\\\"\\n + \\\"\\\\n\\\\tThe node will go on with 1 random hotspot. You can change this in the launch parameters.\\\\n\\\")\\n x, y, spread = self.__get_random_hotspot()\\n self.hotspots = [[x, y, spread]]\\n\\n hotspots_string = \\\"\\\\n\\\\tDirt generator - HOTSPOTS information:\\\"\\n if self.use_uniform_distr:\\n hotspots_string += \\\"\\\\n\\\\n\\\\tThe dirt distribution was set to be uniform. Hotspots do not exist.\\\"\\n else:\\n if self.create_random_hotspots:\\n hotspots_string += \\\"\\\\n\\\\n\\\\tThe dirt distribution is based on the \\\" + str(len(\\n self.hotspots)) + \\\" randomly created hotspot(s) [real world position in m]:\\\"\\n else:\\n hotspots_string += \\\"\\\\n\\\\tThe dirt distribution is based on the \\\" + \\\\\\n str(len(self.hotspots)) + \\\\\\n \\\" specified hotspot(s) [real world position in m]:\\\"\\n for index, hotspot in enumerate(self.hotspots):\\n hotspots_string += \\\"\\\\n\\\\t\\\\tHotspot %d: x=%.2f, y=%.2f, spread=%.2f\\\" % (\\n index + 1, hotspot[0], hotspot[1], hotspot[2])\\n print(hotspots_string + \\\"\\\\n\\\")\\n\\n # CREATING SEVERAL NORMAL DISTRIBUTIONS:\\n # creating for each hotspot one normal distribution and then combine them to one for X and one for Y\\n # for X:\\n normal_distributions_attr_X = []\\n for spot in self.hotspots:\\n # each with [mean_x, std. deviation]\\n normal_distributions_attr_X.append([spot[0], spot[2]])\\n X_distributions = []\\n for attributes in normal_distributions_attr_X:\\n distr = stats.truncnorm((x_min - attributes[0]) / attributes[1], (\\n x_max - attributes[0]) / attributes[1], loc=attributes[0], scale=attributes[1])\\n X_distributions.append(distr.rvs(self.points_per_distr))\\n X = np.concatenate(X_distributions)\\n # to ensure that the distribution and later the grid is full sized (at a point at each end for full axis):\\n X = np.append(X, [x_min, x_max])\\n # for Y:\\n normal_distributions_attr_Y = []\\n for spot in self.hotspots:\\n # each with [mean_y, std. deviation]\\n normal_distributions_attr_Y.append([spot[1], spot[2]])\\n Y_distributions = []\\n for attributes in normal_distributions_attr_Y:\\n distr = stats.truncnorm((y_min - attributes[0]) / attributes[1], (\\n y_max - attributes[0]) / attributes[1], loc=attributes[0], scale=attributes[1])\\n Y_distributions.append(distr.rvs(self.points_per_distr))\\n Y = np.concatenate(Y_distributions)\\n # to ensure that the distribution and later the grid is full sized (at a point at each end for full axis):\\n Y = np.append(Y, [y_min, y_max])\\n\\n # # Plotting of the two separate distributions (rather for testing)\\n # fig, ax = plt.subplots(2, sharex=True)\\n # ax[0].hist(X, bins=self.occupancy_map.info.width)\\n # ax[1].hist(Y, bins=self.occupancy_map.info.height)\\n # plt.show() # will block the remaining process!\\n\\n # COMBINING TWO DISTRIBUTIONS TO A BIVARIATE DISTRIBUTION:\\n H, xedges, yedges = np.histogram2d(\\n X, Y, bins=[self.occupancy_map.info.width, self.occupancy_map.info.height], density=False)\\n H = H.astype('int32')\\n # inverse H, otherwise x and y would be the other way around (now, x are the columns, and y are rows, with row-major ordered)\\n H = H.T\\n # H starts with its inital element in (x_min, y_min) and then row-major ordered (see above): each row is one y and in there are all the column-x for this y\\n\\n if not self.strictly_hotspots:\\n # if all cells should have at least a very small chance, no cell should have 0, which is why we add 1 to all of them\\n H = H + 1\\n\\n if self.use_uniform_distr:\\n H = np.ones((self.occupancy_map.info.height,\\n self.occupancy_map.info.width), dtype=int)\\n\\n # already kick out all static occupied cells (walls, etc.) and replace them with -1\\n for (cell_y, cell_x), _ in np.ndenumerate(H):\\n if self.__cell_is_occupied(cell_x, cell_y):\\n H[cell_y][cell_x] = -1\\n\\n if self.enable_printing:\\n self.__print_probability_grid(H)\\n\\n # Creating image\\n max_tick_number = 10\\n x_axis_res = self.occupancy_map.info.resolution\\n while (x_max - x_min) / x_axis_res > max_tick_number:\\n x_axis_res *= 2\\n x_axis = np.arange(\\n x_min, x_max + self.occupancy_map.info.resolution, x_axis_res)\\n y_axis_res = self.occupancy_map.info.resolution\\n while (y_max - y_min) / y_axis_res > max_tick_number:\\n y_axis_res *= 2\\n y_axis = np.arange(\\n y_min, y_max + self.occupancy_map.info.resolution, y_axis_res)\\n\\n figure_height = 10\\n resize_factor_height = figure_height / self.occupancy_map.info.height\\n fontsize = 26\\n font_factor = 1.0 + (0.2 / resize_factor_height - 1.0) / 8\\n\\n fig = plt.figure(figsize=(2 * self.occupancy_map.info.width *\\n resize_factor_height, self.occupancy_map.info.height * resize_factor_height))\\n title = \\\"Dirt probability distribution in perspective of the initial map image\\\"\\n fig.suptitle(title, fontsize=fontsize * font_factor, fontweight=\\\"bold\\\")\\n\\n viridis = cm.get_cmap('autumn_r', 256)\\n newcolors = viridis(np.linspace(0, 1, 256))\\n black = np.array([0, 0, 0, 1])\\n white = np.array([1, 1, 1, 1])\\n newcolors[:1, :] = black\\n newcolors[1:20, :] = white\\n cmap = colors.ListedColormap(newcolors)\\n\\n # actual bins/edges:\\n ax = fig.add_subplot(1, 2, 1, aspect=\\\"equal\\\")\\n ax.set_title(\\\"Actual cells\\\", size=(fontsize - 2)\\n * font_factor, fontweight=\\\"bold\\\")\\n mesh_X, mesh_Y = np.meshgrid(xedges, yedges)\\n ax.pcolormesh(mesh_X, mesh_Y, H, cmap=cmap,\\n edgecolors=\\\"k\\\", linewidths=0.5)\\n ax.set_xlabel(\\\"X axis [m]\\\", fontsize=(fontsize - 4) * font_factor)\\n ax.set_ylabel(\\\"Y axis [m]\\\", fontsize=(fontsize - 4) * font_factor)\\n ax.set_xticks(x_axis)\\n ax.set_yticks(y_axis)\\n\\n plt.tick_params(axis='both', which='major',\\n labelsize=(fontsize - 6) * font_factor)\\n\\n # interpolated:\\n ax = fig.add_subplot(1, 2, 2, aspect=\\\"equal\\\",\\n xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])\\n ax.set_title(\\\"Interpolated\\\", size=(fontsize - 2)\\n * font_factor, fontweight=\\\"bold\\\")\\n interpolated_image = NonUniformImage(\\n ax, interpolation=\\\"bilinear\\\", cmap=cmap)\\n xcenters = (xedges[:-1] + xedges[1:]) / 2\\n ycenters = (yedges[:-1] + yedges[1:]) / 2\\n interpolated_image.set_data(xcenters, ycenters, H)\\n ax.images.append(interpolated_image)\\n ax.set_xlabel(\\\"X axis [m]\\\", fontsize=(fontsize - 4) * font_factor)\\n ax.set_ylabel(\\\"Y axis [m]\\\", fontsize=(fontsize - 4) * font_factor)\\n ax.set_xticks(x_axis)\\n ax.set_yticks(y_axis)\\n\\n plt.tick_params(axis='both', which='major',\\n labelsize=(fontsize - 6) * font_factor)\\n\\n # get path to the output file\\n rospack = rospkg.RosPack()\\n pkg_path = rospack.get_path(self.output_pkg)\\n image_path = pkg_path + \\\"/\\\" + self.distribution_folder + \\\\\\n \\\"/\\\" + self.distribution_image\\n\\n plt.savefig(image_path)\\n\\n if self.enable_plotting:\\n warning = \\\"Do not close this window until the end. Otherwise the generator node could crash!\\\"\\n fig = plt.gcf()\\n fig.canvas.set_window_title(warning)\\n # show it (without blocking the remaining process)\\n plt.ion()\\n # plt.draw()\\n plt.pause(0.001)\\n # but cannot be closed without crashing this node!\\n\\n # each cell should have the probability of itself regarding the final distribution:\\n # the occupied cells will have -1 and we do not want negative probabilities. So, we replace them with 0\\n for (cell_y, cell_x), cell_value in np.ndenumerate(H):\\n if cell_value < 0:\\n H[cell_y][cell_x] = 0\\n prob_grid = H / np.sum(H)\\n\\n rospy.loginfo(\\n f\\\"*** WRAPPER MESSAGE ***\\\\n\\\\n\\\\t'{rospy.get_caller_id()}' has created the final probability distribution.\\\\n\\\"\\n + \\\"\\\\tAn image of it was saved to (will be overridden each time!):\\\\n\\\"\\n + image_path + \\\"\\\\n\\\")\\n\\n return prob_grid\",\n \"def pick_area(data ,total_process, interval ,list_of_vars, list_of_areas, init_time=0, pr_height=None, ):\\n \\n \\n \\n #trying if the longitude values change from 0 to 360 or -180 to 180?\\n \\n if data['lon'].values[0] < 0:\\n \\n p_d = {'europe' : [0, 48, 30, 65],\\n 'northamerica' : [-142,-42,0,60],\\n 'australia' : [80,180,-50,10],\\n 'gulfofmexico' : [-100,-75,18,31],\\n 'carribeans' : [-85,-60,12,38], \\n 'indianocean' : [30, 130,-35,35],\\n 'NH' : [-180, 180 ,0,90]}\\n \\n # -180 to 180 change the values given in the dictionary to relevant\\n else:\\n \\n p_d = {'europe' : [0, 48, 30, 65],\\n 'northamerica' : [218,318,-10,70],\\n 'australia' : [80,180,-50,10],\\n 'gulfofmexico' : [260,285,14,37],\\n 'carribeans' : [275,300,12,38], \\n 'indianocean' : [30, 130,-35,35],\\n 'NH' : [0, 360 ,0,90]}\\n \\n \\n \\n places_dict = {}\\n #looping in the list of areas\\n say_pl = 1\\n for pl in list_of_areas:\\n variables_l = {}\\n #looping in the list of variables\\n say_var =1\\n for var in list_of_vars:\\n #check if data contains 'lev' coords.\\n try:\\n \\n #wrap the data\\n single = data[var].sel(lon=slice(p_d[pl][0],p_d[pl][1]), \\n lat=slice(p_d[pl][2],p_d[pl][3]), \\n lev=pr_height).isel(time=slice(init_time, total_process, interval))\\n \\n #if no 'lev' coords exist.\\n except:\\n single = data[var].sel(lon=slice(p_d[pl][0],p_d[pl][1]), \\n lat=slice(p_d[pl][2],p_d[pl][3]),).isel(time=slice(init_time, total_process, interval))\\n \\n #append a single variable given by the user\\n variables_l[var] = single\\n \\n \\n #append all the variables with respect to their area of interest.\\n places_dict[pl] = variables_l\\n \\n #return\\n return places_dict\",\n \"def example_staypoints_merge():\\n p1 = Point(8.5067847, 47.4)\\n\\n t1 = pd.Timestamp(\\\"1971-01-01 00:00:00\\\", tz=\\\"utc\\\")\\n t2 = pd.Timestamp(\\\"1971-01-02 05:00:00\\\", tz=\\\"utc\\\")\\n t3 = pd.Timestamp(\\\"1971-01-02 06:45:00\\\", tz=\\\"utc\\\")\\n t4 = pd.Timestamp(\\\"1971-01-02 08:55:00\\\", tz=\\\"utc\\\")\\n t45 = pd.Timestamp(\\\"1971-01-02 08:57:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n t6 = pd.Timestamp(\\\"1971-01-02 09:20:00\\\", tz=\\\"utc\\\")\\n\\n list_dict = [\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t1, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 1},\\n {\\\"id\\\": 5, \\\"user_id\\\": 0, \\\"started_at\\\": t2, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 2, \\\"user_id\\\": 0, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 6, \\\"user_id\\\": 0, \\\"started_at\\\": t4, \\\"finished_at\\\": t45, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 15, \\\"user_id\\\": 0, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 7, \\\"user_id\\\": 1, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 80, \\\"user_id\\\": 1, \\\"started_at\\\": t45, \\\"finished_at\\\": t5, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 3, \\\"user_id\\\": 1, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 4},\\n ]\\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\\\"geom\\\", crs=\\\"EPSG:4326\\\")\\n sp = sp.set_index(\\\"id\\\")\\n sp.as_staypoints\\n\\n # generate empty triplegs for the merge function\\n tpls = pd.DataFrame([], columns=[\\\"user_id\\\", \\\"started_at\\\", \\\"finished_at\\\"])\\n return sp, tpls\",\n \"def InitPointR(session):\\n global point_r\\n # points are only buildings\\n q = session.query(melt.BuildAssoc).filter_by(point=1)\\n point_r = set([it.osm_build for it in q.all()])\",\n \"def gps_points(self):\\n return self.waypoints.all().order_by('localtime')\\n # return self.waypoints.filter(photo_id__isnull=True).order_by('localtime')\",\n \"def sampleFromGrid(grid, xypoints, method='nearest'):\\n samples = []\\n for lon, lat in xypoints:\\n sample = grid.getValue(lat, lon, method=method)\\n samples.append(sample)\\n\\n return np.array(samples)\",\n \"def Ez_area(position, angle, detect):\\n# a = range(round(-2*Ez_height),round(2*Ez_height))\\n# b = range(round(-2*Ez_height),round(2*Ez_height))\\n# a_valid = []\\n# b_valid= []\\n \\n # These are the grid points in a coordinate system based on the Ez's angle\\n if detect:\\n a_valid = [-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6]\\n b_valid = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3]\\n else:\\n a_valid = [-9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4]#[-19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -13, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -12, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -9, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -7, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9]\\n b_valid = [-4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3, -2, -1, 0, 1, 2]#[-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4]\\n positions = []\\n# for i in a:\\n# for j in b:\\n# if (i > ((Ez_width/2)-Ez_height-detect_length) and abs(j) < (Ez_width/2+detect_length) and i < 0) or (i > 0 and np.sqrt(i**2 + j**2) < Ez_width/2+detect_length):\\n# a_valid.append(i)\\n# b_valid.append(j)\\n# print('AAAA', a_valid)\\n# print(' ')\\n# print('BBBB', b_valid)\\n# print(' ')\\n \\n # This is a coordinate transfromation to x,y\\n for i in range(len(a_valid)):\\n positions.append((int(round(a_valid[i]*np.cos(angle) + b_valid[i]*np.sin(angle) + position[0])), int(round(a_valid[i]*np.sin(angle) - b_valid[i]*np.cos(angle) + position[1]))))\\n return positions\",\n \"def recalc(self, viewer):\\n # select only points within range of the current pan/zoom\\n bbox = viewer.get_pan_rect()\\n if bbox is None:\\n self.path.points = []\\n return\\n\\n start_x, stop_x = bbox[0][0], bbox[2][0]\\n\\n self.calc_points(viewer, start_x, stop_x)\",\n \"def shot_geolocations(self) -> geopandas.array.GeometryArray:\\n geolocations = np.array(\\n [shapely.geometry.Point(lon, lat) for lon, lat in self.shot_lon_lat],\\n dtype=shapely.geometry.Point,\\n )\\n\\n return geopandas.array.GeometryArray(geolocations, crs=WGS84)\",\n \"def get_all_coordinates(self):\\n coordinates = []\\n\\n for relative_coordinate in self.shape:\\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\\n coordinates.append(co)\\n return coordinates\",\n \"def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\\n\\n mxmin = 9e10\\n mxmax = -9e10\\n mymin = 9e10\\n mymax = -9e10\\n for pshape in pshapes:\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n if pxmin < mxmin:\\n mxmin = pxmin\\n if pxmax > mxmax:\\n mxmax = pxmax\\n if pymin < mymin:\\n mymin = pymin\\n if pymax > mymax:\\n mymax = pymax\\n\\n if not touch_center:\\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\\n img = rasterizeShapes(pshapes, geodict)\\n #now get the numpy array of x/y coordinates where covgrid == 1\\n idx = np.where(img == 1)[0]\\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\\n yespoints = list(zip(x.flatten(), y.flatten()))\\n nrows = geodict.ny\\n ncols = geodict.nx\\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\\n else:\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n ncols = len(xvar)\\n nrows = len(yvar)\\n if nmax is not None:\\n if ncols*nrows > nmax:\\n aspect = ncols/nrows\\n ncols = np.sqrt(nmax*aspect)\\n nrows = nmax/ncols\\n ncols = int(ncols)\\n nrows = int(nrows)\\n #re-calculate dx here...\\n tdx = (mxmax-mxmin)/ncols\\n tdy = (mymax-mymin)/nrows\\n dx = np.max(tdx, tdy)\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n\\n #Get the \\\"yes\\\" points to sample from\\n yespoints = []\\n idx = []\\n shapeidx = 0\\n if pshapes[0].type == 'Polygon':\\n #loop over shapes, projecting each one, then get the sample points\\n for pshape in pshapes:\\n if not shapeidx % 1000:\\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\\n shapeidx += 1\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\\n toprow = np.where((yvar - pymax) >= 0)[0][0]\\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\\n xmesh, ymesh = np.meshgrid(xp, yp)\\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\\n for point in xy:\\n ix = np.where(xvar == point[0])[0][0]\\n iy = np.where(yvar == point[1])[0][0]\\n if pshape.contains(Point(point)):\\n yespoints.append(point)\\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\\n else:\\n yespoints = []\\n for pshape in pshapes:\\n yespoints.append(pshape.coords[0])\\n\\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)\",\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 fillIn(self):\\n\\n # Grabs first point (which is a shore) and prefills in hashes\\n toBeAnalyzed = [self.points[0]]\\n islandHash = defaultdict(list)\\n islandHash[toBeAnalyzed[0].x].append(toBeAnalyzed[0].x)\\n islandGridPoints = toBeAnalyzed[:]\\n\\n # Find all points not at pond-level.\\n while toBeAnalyzed:\\n gridPoint = toBeAnalyzed.pop()\\n neighbors = self.analyzeData.iterateDiagonal(gridPoint.x,\\n gridPoint.y)\\n for _x, _y, elevation in neighbors:\\n\\n if elevation != self.pondElevation and _y not in\\\\\\n islandHash[_x]:\\n branch = GridPoint(_x, _y, elevation)\\n islandHash[_x].append(_y)\\n toBeAnalyzed.append(branch)\\n islandGridPoints.append(branch)\\n self.points = islandGridPoints\",\n \"def get_points(self):\\n\\t\\treturn self.points\",\n \"def get_all_xy(self):\\n self.all_y = list(\\n zip(\\n self.open,\\n self.open,\\n self.high,\\n self.low,\\n self.close,\\n self.close,\\n self.empty,\\n )\\n )\\n if self.dates is not None:\\n date_dif = []\\n for i in range(len(self.dates) - 1):\\n date_dif.append(self.dates[i + 1] - self.dates[i])\\n date_dif_min = (min(date_dif)) / 5\\n self.all_x = [\\n [x - date_dif_min, x, x, x, x, x + date_dif_min, None]\\n for x in self.dates\\n ]\\n else:\\n self.all_x = [\\n [x - 0.2, x, x, x, x, x + 0.2, None] for x in range(len(self.open))\\n ]\",\n \"def find_centroid_for_each(self):\",\n \"def get_points(self):\\r\\n return self.points\",\n \"def guarded_places(self):\\n guarded = []\\n for x in range(8):\\n for y in range(8):\\n if self.squares[x][y].piece and self.squares[x][y].piece.color != self.turn:\\n squares = self.squares[x][y].piece.actions(self, (x, y), True)\\n if self.squares[x][y].piece.name != 'pawn': # pawns capture in different areas than they move\\n guarded.extend(squares[0])\\n guarded.extend(squares[1])\\n return guarded\",\n \"def get_areas_for_coords_in_range(coordinates, all_coords, range_of_c):\\n get_closest_point = get_closest_from_coords(coordinates)\\n def aux(acc, coord):\\n dictionary, infinite_areas = acc\\n closest = get_closest_point(coord)\\n if closest is not None:\\n dictionary.setdefault(closest, set())\\n dictionary[closest].add(coord)\\n if (coord.x in {range_of_c.min_x, range_of_c.max_x} or\\n coord.y in {range_of_c.min_y, range_of_c.max_y}):\\n infinite_areas.add(closest)\\n return dictionary, infinite_areas\\n return reduce(aux, all_coords, ({}, set()))\",\n \"def _collect_points(self, image, point_value=0):\\n return zip(*np.where(image == point_value))\",\n \"def xy(self):\\n ...\",\n \"def jiggle_on_grid_points(data, grid_polys):\\n new_data = []\\n centroids = np.array([t.centroid.coords for t in grid_polys])\\n for t in data:\\n x = t[1]\\n y = t[2]\\n idx = np.where(np.sum(centroids == t[1:], axis=1) == 2)[0]\\n if idx:\\n assert len(idx) == 1, \\\"Overlapping polygons are not supported\\\"\\n idx = idx[0]\\n this_datum = (t[0],) + random_points_within_poly(grid_polys[idx], 1)\\n else:\\n this_datum = t\\n new_data.append(this_datum)\\n\\n return np.array(new_data)\",\n \"def g_corners(self):\\n return (point for point in self.p)\",\n \"def grid_in_shape(up_lat,low_lat,left_long,right_long,shape,\\n lat_resolution=15,long_resolution=60): \\n \\n longitudes = np.linspace(left_long,right_long,60)\\n latitudes = np.linspace(low_lat,up_lat,15)\\n prods = list(itertools.product(longitudes,latitudes))\\n points = [shapely.geometry.Point(point) for point in prods]\\n points_within = [point for point in points if shape.contains(point)]\\n points_gdf = gpd.GeoDataFrame(geometry = points_within)\\n \\n return points_gdf\",\n \"def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\\n pass\",\n \"def charuco_img_points(images, objpoint, board, a_dict):\\r\\n #Criteria for subpixel refinement\\r\\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)\\r\\n\\r\\n objpoints = [] # 3d point in world space\\r\\n imgpoints = [] # 2d point in image plane\\r\\n\\r\\n for img in images:\\r\\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\\r\\n corners, ids, rejpoints = cv2.aruco.detectMarkers(gray, a_dict)\\r\\n if len(corners)>0:\\r\\n res2 = cv2.aruco.interpolateCornersCharuco(corners,ids,gray,board)\\r\\n if res2[1] is not None:\\r\\n cv2.cornerSubPix(gray,res2[1],(3,3),(-1,1),criteria)\\r\\n imgpoints.append(res2[1].T[:,0,:])\\r\\n objpoints.append(objpoint[:,res2[2].flatten()])\\r\\n cv2.aruco.drawDetectedCornersCharuco(img,res2[1],res2[2])\\r\\n cv2.imshow(\\\"frame\\\",img)\\r\\n cv2.waitKey(0)\\r\\n cv2.destroyAllWindows()\\r\\n\\r\\n return objpoints,imgpoints\",\n \"def testGetPointsGeodesic(self):\\n\\n for i in range(10):\\n self.basicGetPointsGeodesic()\",\n \"def extend (self) :\\n return (self.x_min, self.x_max, self.y_min, self.y_max)\",\n \"def draw_known_points(self):\\n if self.tracking:\\n if self.current_tracked_point == None:\\n return\\n p = self.current_tracked_point\\n x, y = (int(u) for u in p)\\n cv2.circle(self.altered_image, (x, y), 10, (0, 0, 255), 1, cv2.LINE_AA)\\n cv2.putText(self.altered_image, f'az:{x:.3f} alt:{y:.3f}', (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))\\n self.statusBar().showMessage(f'az: {x:.3f} alt: {y:.3f}')\\n else:\\n for p in self.known_image_points:\\n x, y = (int(u) for u in p)\\n\\n cv2.circle(self.altered_image, (x, y), 5, (0, 0, 255), 1, cv2.LINE_AA)\\n cv2.putText(self.altered_image, f'x:{x} y:{y}', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255))\",\n \"def __areas_to_pool(self, region_width, region_height, region_width_step, region_height_step):\\n \\n areas = [[(width_ind * region_width_step, height_ind * region_height_step,\\n (width_ind + 1) * region_width_step if (width_ind + 1) < self.width else region_width,\\n (height_ind + 1) * region_height_step if (height_ind + 1) < self.height else region_height) for width_ind in range(self.width)] for height_ind in range(self.height)]\\n\\n return areas\",\n \"def get_neighbors(point):\\n pt = point.copy()\\n output= [point.copy() for i in range(4)]\\n output[0:2] = map(Point.setY, output[0:2], [pt.getY()+ i for i in range(-1,2,2)])\\n output[2:4]= map(Point.setX, output[2:4], [pt.getX()+ i for i in range(-1,2,2)])\\n return output\",\n \"def geocube():\",\n \"def oneEntryPoints(self):\\n points = []\\n if self.isRed:\\n for x in range(self.midWidth, self.width):\\n for y in range(0, self.height):\\n if (x, y) not in self.walls:\\n adjacent = self.adjacentValidPoints(x, y)\\n if len(adjacent) == 1:\\n points.append((x, y))\\n else:\\n for x in range(0, self.midWidth-1):\\n for y in range(0, self.height):\\n if(x, y) not in self.walls:\\n adjacent = self.adjacentValidPoints(x, y)\\n if len(adjacent) == 1:\\n points.append((x, y))\\n return points\",\n \"def query(self, points):\\n return self.locate_points(points)\",\n \"def get_points(self) -> typing.Iterable[float]:\\n raise NotImplementedError()\",\n \"def update_world_points(self, wpSet):\\r\\n self.world_points = np.empty((0, 5))\\r\\n for i, row in wpSet.correspondences.iterrows():\\r\\n Ids = row['ViewId']\\r\\n if self.id in Ids:\\r\\n point_2D = row['FeatureIndex'][Ids.index(self.id)]\\r\\n point_3D = wpSet.world_points[i]\\r\\n self.world_points = np.append(self.world_points, [np.hstack((point_2D, point_3D))], axis=0)\",\n \"def collocation_points(self, **kw):\\n pass\",\n \"def getAreas(df):\\n\\n df_plu = df[df[\\\"strand\\\"]==\\\"+\\\"]\\n df_min = df[df[\\\"strand\\\"]==\\\"-\\\"]\\n df_plu_FA = FivePrimeArea(df_plu)\\n df_min_FA = FivePrimeArea(df_min)\\n df_plu_LA = ThreePrimeArea(df_plu)[[\\\"name\\\",\\\"LA_start\\\",\\\"LA_end\\\",\\\"LA_length\\\"]]\\n df_min_LA = ThreePrimeArea(df_min)[[\\\"name\\\",\\\"LA_start\\\",\\\"LA_end\\\",\\\"LA_length\\\"]]\\n df_plu = pd.merge(df_plu_FA,df_plu_LA,on=\\\"name\\\")\\n df_min = pd.merge(df_min_FA,df_min_LA,on=\\\"name\\\")\\n df = pd.concat([df_plu,df_min])\\n return df\",\n \"def to_points(self, divisions=100):\",\n \"def detect_points(self):\\r\\n\\r\\n\\t\\r\\n\\r\\n\\t\\tfeature_mask = np.zeros_like(self.gray) ## Create a mask so we only look for template features in the ROI\\r\\n\\t\\t\\r\\n\\t\\tfeature_mask[max(0,self.bb[1]):min(360,self.bb[1] + self.bb[3]),max(0,self.bb[0]):min(640,self.bb[0] + self.bb[2])] = 255\\r\\n\\r\\n\\t\\t# search for good points\\r\\n\\t\\tfeatures = cv2.goodFeaturesToTrack(self.gray, mask = feature_mask, **feature_params)\\r\\n\\t\\t# refine the corner locations\\r\\n\\t\\tcv2.cornerSubPix(self.gray,features, **subpix_params)\\r\\n\\r\\n\\t\\tself.features = features\\r\\n\\r\\n\\t\\tself.tracks = [[p] for p in features.reshape((-1,2))]\\r\\n\\r\\n\\t\\tself.prev_gray = self.gray\",\n \"def __iter__(self):\\n return self.points.__iter__()\",\n \"def setHolesCoordinates(self):\\r\\n # productive\\r\\n profprint()\\r\\n self.p = [[0 for j in range(63)] for j in range(3)]\\r\\n self.p[0][0] = 35\\r\\n self.p[1][0] = 34\\r\\n self.p[0][1] = 25\\r\\n self.p[1][1] = 36.679\\r\\n self.p[0][2] = 17.679\\r\\n self.p[1][2] = 44\\r\\n self.p[0][3] = 15\\r\\n self.p[1][3] = 54\\r\\n self.p[0][4] = 17.679\\r\\n self.p[1][4] = 64\\r\\n self.p[0][5] = 25\\r\\n self.p[1][5] = 71.321\\r\\n self.p[0][6] = 35\\r\\n self.p[1][6] = 74\\r\\n self.p[0][7] = 45\\r\\n self.p[1][7] = 71.321\\r\\n self.p[0][8] = 52.321\\r\\n self.p[1][8] = 64\\r\\n self.p[0][9] = 55\\r\\n self.p[1][9] = 54\\r\\n self.p[0][10] = 52.321\\r\\n self.p[1][10] = 44\\r\\n self.p[0][11] = 45\\r\\n self.p[1][11] = 36.679\\r\\n self.p[0][12] = 29.791\\r\\n self.p[1][12] = 24.456\\r\\n self.p[0][13] = 20\\r\\n self.p[1][13] = 28.019\\r\\n self.p[0][14] = 12.019\\r\\n self.p[1][14] = 34.716\\r\\n self.p[0][15] = 6.809\\r\\n self.p[1][15] = 43.739\\r\\n self.p[0][16] = 5\\r\\n self.p[1][16] = 54\\r\\n self.p[0][17] = 6.809\\r\\n self.p[1][17] = 64.261\\r\\n self.p[0][18] = 12.019\\r\\n self.p[1][18] = 73.284\\r\\n self.p[0][19] = 20\\r\\n self.p[1][19] = 79.981\\r\\n self.p[0][20] = 29.791\\r\\n self.p[1][20] = 83.544\\r\\n self.p[0][21] = 40.209\\r\\n self.p[1][21] = 83.544\\r\\n self.p[0][22] = 50\\r\\n self.p[1][22] = 79.981\\r\\n self.p[0][23] = 57.981\\r\\n self.p[1][23] = 73.284\\r\\n self.p[0][24] = 63.191\\r\\n self.p[1][24] = 64.262\\r\\n self.p[0][25] = 65\\r\\n self.p[1][25] = 54\\r\\n self.p[0][26] = 63.191\\r\\n self.p[1][26] = 43.739\\r\\n self.p[0][27] = 57.981\\r\\n self.p[1][27] = 34.716\\r\\n self.p[0][28] = 50\\r\\n self.p[1][28] = 28.019\\r\\n self.p[0][29] = 40.209\\r\\n self.p[1][29] = 24.456\\r\\n self.p[0][30] = 35\\r\\n self.p[1][30] = 14\\r\\n self.p[0][31] = 24.647\\r\\n self.p[1][31] = 15.363\\r\\n self.p[0][32] = 15\\r\\n self.p[1][32] = 19.359\\r\\n self.p[0][33] = 15\\r\\n self.p[1][33] = 88.641\\r\\n self.p[0][34] = 24.647\\r\\n self.p[1][34] = 92.637\\r\\n self.p[0][35] = 35\\r\\n self.p[1][35] = 94\\r\\n self.p[0][36] = 45.353\\r\\n self.p[1][36] = 92.637\\r\\n self.p[0][37] = 55\\r\\n self.p[1][37] = 88.641\\r\\n self.p[0][38] = 55\\r\\n self.p[1][38] = 19.359\\r\\n self.p[0][39] = 45.353\\r\\n self.p[1][39] = 15.363\\r\\n self.p[0][40] = 30.642\\r\\n self.p[1][40] = 4.19\\r\\n self.p[0][41] = 22.059\\r\\n self.p[1][41] = 5.704\\r\\n self.p[0][42] = 22.059\\r\\n self.p[1][42] = 102.296\\r\\n self.p[0][43] = 30.642\\r\\n self.p[1][43] = 103.81\\r\\n self.p[0][44] = 39.358\\r\\n self.p[1][44] = 103.81\\r\\n self.p[0][45] = 47.941\\r\\n self.p[1][45] = 102.296\\r\\n self.p[0][46] = 47.941\\r\\n self.p[1][46] = 5.704\\r\\n self.p[0][47] = 39.358\\r\\n self.p[1][47] = 4.19\\r\\n self.p[0][48] = 29.7\\r\\n self.p[1][48] = 44.82\\r\\n self.p[0][49] = 24.4\\r\\n self.p[1][49] = 54\\r\\n self.p[0][50] = 29.7\\r\\n self.p[1][50] = 63.18\\r\\n self.p[0][51] = 40.3\\r\\n self.p[1][51] = 63.18\\r\\n self.p[0][52] = 45.6\\r\\n self.p[1][52] = 54\\r\\n self.p[0][53] = 40.3\\r\\n self.p[1][53] = 44.82\\r\\n self.p[0][54] = 35\\r\\n self.p[1][54] = 54\\r\\n self.p[0][55] = 9\\r\\n self.p[1][55] = 12\\r\\n self.p[0][56] = 5\\r\\n self.p[1][56] = 18\\r\\n self.p[0][57] = 5\\r\\n self.p[1][57] = 90\\r\\n self.p[0][58] = 9\\r\\n self.p[1][58] = 96\\r\\n self.p[0][59] = 61\\r\\n self.p[1][59] = 96\\r\\n self.p[0][60] = 65\\r\\n self.p[1][60] = 90\\r\\n self.p[0][61] = 65\\r\\n self.p[1][61] = 18\\r\\n self.p[0][62] = 61\\r\\n self.p[1][62] = 12\\r\\n\\r\\n return self.p\",\n \"def getSearchSpaceCoords(self):\",\n \"def draw_pins():\\n\\n pass\",\n \"def point(ants, gui=True, zoom=1, mode=1, flap=True, \\n elmin=15, writeFITS=True, brightness=50,isInner=False,\\n subtractBackground=False,ncoadd=1,dazbkg=2.0,\\n centroidLoopMax=16,minsamples=None):\\n global stars_\\n if len(stars_) == 0:\\n print \\\"No stars have been selected, go use 'stars()'\\\"\\n return\\n else:\\n print \\\"POINT:: using %d stars for ants=%s zoom=%g\\\" \\\\\\n %(len(stars_),ants,zoom)\\n previous_source_ = stars_[0][0]\\n print previous_source_\\n scnt = 0\\n smax = len(stars_)\\n antlist = helpers.makeList(ants)\\n # Always turn on cameras (open flaps/caps) for all antenna\\n # before looping over all stars\\n SAC.camera(SAC.ON, antlist)\\n for s in stars_:\\n scnt = scnt + 1\\n src = s[0]\\n print \\\"POINT1(%s) %d/%d at %s\\\" % (src,scnt,smax,time.ctime())\\n nextSrc = None\\n if scnt < smax: nextSrc = stars_[scnt][0]\\n try:\\n point1(src, ants, showGUI=gui, zoom=zoom, mode=mode, \\n nextSource=nextSrc, elmin=elmin,\\n writeFITS=writeFITS, brightness=brightness,isInner=isInner,\\n subtractBackground=subtractBackground,ncoadd=ncoadd,dazbkg=dazbkg,\\n centroidLoopMax=centroidLoopMax,minsamples=minsamples)\\n except KeyboardInterrupt, e:\\n print \\\"Quitting point because of keyboard interrupt\\\"\\n break\\n except Exception, e:\\n print \\\"POINT1 failed with exception: %s; skipping source %s\\\" \\\\\\n %(e, s[0])\\n if flap:\\n # Turn off all cameras, close flaps/caps, go back to radio constants\\n subarrayControl.s.camera(carma.antenna.common.OFF, antlist)\",\n \"def calc_points(self, viewer, start_x, stop_x):\\n # in case X axis is flipped\\n start_x, stop_x = min(start_x, stop_x), max(start_x, stop_x)\\n\\n new_xlim = (start_x, stop_x)\\n if new_xlim == self.plot_xlim:\\n # X limits are the same, no need to recalculate points\\n return\\n\\n self.plot_xlim = new_xlim\\n\\n points = self.get_data_points(points=self.points)\\n if len(points) == 0:\\n self.path.points = points\\n return\\n\\n x_data, y_data = points.T\\n\\n # if we can determine the visible region shown on the plot\\n # limit the points to those within the region\\n if np.all(np.isfinite([start_x, stop_x])):\\n idx = np.logical_and(x_data >= start_x, x_data <= stop_x)\\n points = points[idx]\\n\\n if self.x_func is not None:\\n # now find all points position in canvas X coord\\n cpoints = self.get_cpoints(viewer, points=points)\\n cx, cy = cpoints.T\\n\\n # Reduce each group of Y points that map to a unique X via a\\n # function that reduces to a single value. The desirable function\\n # will depend on the function of the plot, but mean() would be a\\n # sensible default\\n _, i = np.unique(cx, return_index=True)\\n gr_pts = np.split(points, i)\\n x_data = np.array([self.x_func(a.T[0]) for a in gr_pts\\n if len(a) > 0])\\n y_data = np.array([self.y_func(a.T[1]) for a in gr_pts\\n if len(a) > 0])\\n assert len(x_data) == len(y_data)\\n\\n points = np.array((x_data, y_data)).T\\n\\n self.path.points = points\",\n \"def find_coordinates(self):\\n\\n raise NotImplementedError\",\n \"def find_coordinates(self):\\n\\n raise NotImplementedError\",\n \"def peaks_from_area(self, mask, keep=None, refine=True, Imin=None, **kwargs):\\n y = numpy.round(self.keypoints.y).astype(int)\\n x = numpy.round(self.keypoints.x).astype(int)\\n is_inside = (mask[y, x]).astype(bool)\\n if is_inside.sum() == 0:\\n logger.error(\\\"No keypoint that region\\\")\\n return []\\n kp = self.keypoints[is_inside]\\n if Imin:\\n valid = kp.I >= Imin\\n if valid.sum() == 0:\\n logger.error(\\\"no keypoint match Intensity criteria in the region\\\")\\n valid2 = self.raw[y, x] >= Imin\\n good_kp = self.keypoints[numpy.logical_and(valid2, is_inside)]\\n else:\\n good_kp = kp[valid]\\n else:\\n good_kp = kp\\n if keep and len(good_kp) > keep:\\n order = numpy.argsort(good_kp.I)\\n order = order[-1::-1][:keep] #keep only the most intense\\n good_kp = good_kp[order]\\n if refine:\\n if self.bilinear is None:\\n self.bilinear = Bilinear(self.raw)\\n return [self.bilinear.local_maxi((i.y, i.x)) for i in good_kp]\\n else:\\n return [(i.y, i.x) for i in good_kp]\",\n \"def get_coordinates(self, soup: BeautifulSoup) -> None:\\n try:\\n url = soup.find(\\n \\\"a\\\", {\\\"title\\\": \\\"Open this area in Google Maps (opens a new window)\\\"}\\n )[\\\"href\\\"]\\n coordinates = url[url.find(\\\"=\\\") + 1 : url.find(\\\"&\\\")]\\n coordinates = [float(n) for n in coordinates.split(\\\",\\\")]\\n except (AttributeError, TypeError):\\n coordinates = [None, None]\\n self.__collected_dic[\\\"latitude\\\"].append(coordinates[0])\\n self.__collected_dic[\\\"longitude\\\"].append(coordinates[1])\",\n \"def find_keypoints ( gray , quality , ksize , blocksize , max_area = None ) :\\n gray32 = np.float32(gray)\\n points = cv2.goodFeaturesToTrack(gray32,maxCorners = 100, qualityLevel = quality ,minDistance = ksize , blockSize = blocksize )\\n\\n if points is None :\\n return None , None , None\\n\\n if len(points) < 4 :\\n return None , None , None\\n\\n oob = cv2.minAreaRect(points) \\n\\n if oob is None :\\n return None, None , None\\n\\n oob_corners = get_oob_corners ( oob = oob )\\n\\n if oob_corners is None :\\n return None, None , None\\n\\n if max_area is None :\\n return points , oob , oob_corners\\n\\n area = get_polygon_area ( corners = oob_corners )\\n\\n if area > max_area :\\n return None, None , None\\n\\n return points , oob , oob_corners\",\n \"def draw_points(self, pic_path, points_data):\\n # Pupil Finding here\\n pupils = get_eye_locations_in_image(pic_path)\\n img = cv2.imread(pic_path)\\n frame_number = int(re.findall(r'\\\\d+', pic_path.split('/')[-1])[0])\\n dets = detector(img)\\n shape = None\\n height, width, channels = img.shape\\n\\n for k, d in enumerate(dets):\\n shape = predictor(img, d)\\n\\n if(not shape):\\n return\\n\\n pointList = []\\n c = 0\\n for b in range(68):\\n # sanitizing input points\\n point = Point(shape.part(b).x, shape.part(b).y)\\n points_data[c] = [point.x, point.y]\\n c = c + 1\\n # some points might be out of bound\\n # so, move them to the closest boundary\\n if(point.x < 0):\\n point.x = 0\\n elif(point.x >= width):\\n point.x = width - 1\\n if(point.y < 0):\\n point.y = 0\\n elif(point.y >= height):\\n point.y = height - 1\\n\\n pointList.append(point)\\n\\n roll = findRoll(pointList)\\n #print(\\\"roll is \\\" + str(roll) + ' angles')\\n yaw = findYaw(pointList)\\n #print(\\\"yaw is \\\" + str(yaw) + ' angles')\\n pitch = findPitch(pointList)\\n #print(\\\"pitch is \\\" + str(pitch) + ' angles')\\n self.data[frame_number] = [roll, yaw, pitch]\\n counter = 0\\n for point in pointList:\\n cv2.circle(img, (point.x, point.y), ImageProcessor.POINT_SIZE, ImageProcessor.POINT_COLOR, -1)\\n counter = counter + 1\\n\\n self.draw_triangles(img, pointList)\\n \\n for pupil in pupils:\\n cv2.circle(img, (pupil.left.x, pupil.left.y), 5, (0,0,255), -1)\\n cv2.circle(img, (pupil.right.x, pupil.right.y), 5, (0,0,255), -1)\\n points_data[-1] = [pupil.left.x, pupil.left.y]\\n points_data[-2] = [pupil.right.x, pupil.right.y]\\n #print(pupil.left.x, \\\", \\\", pupil.left.y)\\n #print(pupil.right.x, \\\", \\\", pupil.right.y)\\n\\n cv2.imwrite(pic_path, img)\",\n \"def get_coordinates(self):\\n x_houses = []\\n y_houses = []\\n\\n x_batt = []\\n y_batt = []\\n\\n # turn dict to list so we can iterate through\\n houses_list = list(self.houses.values())\\n batteries_list = list(self.batteries.values())\\n\\n # for every house save coordinates to lists\\n for house in houses_list:\\n x_houses.append(house.x)\\n y_houses.append(house.y)\\n\\n # for every battery save coordinates to lists\\n for battery in batteries_list:\\n x_batt.append(battery.x)\\n y_batt.append(battery.y)\\n\\n return x_houses, y_houses, x_batt, y_batt\",\n \"def _take_pieces(self, x, y):\\n scores = []\\n for p, (x1, y1) in self._get_surrounding(x, y):\\n # If location is opponent's color and has no liberties, tally it up\\n if p is self._next_turn and self.count_liberties(x1, y1) == 0:\\n score = self._kill_group(x1, y1)\\n scores.append(score)\\n self._tally(score)\\n return sum(scores)\",\n \"def __load_geo(self):\\n pass\\n # process any splines? and turn them into arcs\\n # http://www.mathopenref.com/constcirclecenter.html\\n # find max dist between points\\n # double it\\n # select two segments\\n # draw normal lines\\n # find intersections, that is the center\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60352486","0.60089654","0.5957634","0.57922155","0.5735454","0.56912833","0.5677051","0.5659202","0.5644068","0.5619629","0.5581817","0.55763596","0.5568444","0.5551165","0.55458266","0.55394226","0.55220485","0.5515554","0.55153877","0.55147487","0.55110705","0.54576766","0.54534495","0.5445197","0.5436135","0.540998","0.5395972","0.5391023","0.5391011","0.53902036","0.53865933","0.53676295","0.53617775","0.5354485","0.5346112","0.53351355","0.5334359","0.5327337","0.5323527","0.5316133","0.5315485","0.5314741","0.5312208","0.5303954","0.53031224","0.53006035","0.5298225","0.5293558","0.5287215","0.52828366","0.5282792","0.5281652","0.52797765","0.52632785","0.52614987","0.5254608","0.52366626","0.52224857","0.5208873","0.52085435","0.5208473","0.52013886","0.5196598","0.51788014","0.5174316","0.5172234","0.5160261","0.51496947","0.51466906","0.5145851","0.5139596","0.51364475","0.51344687","0.5131036","0.51278865","0.51237446","0.51167506","0.51127285","0.5108524","0.5107631","0.5101478","0.5097581","0.50964445","0.50958747","0.5089302","0.5085445","0.5084894","0.50769955","0.5075742","0.50756264","0.50617695","0.50589633","0.50555575","0.50555575","0.5055159","0.5053417","0.5051034","0.5042832","0.5039416","0.5039049","0.5037143"],"string":"[\n \"0.60352486\",\n \"0.60089654\",\n \"0.5957634\",\n \"0.57922155\",\n \"0.5735454\",\n \"0.56912833\",\n \"0.5677051\",\n \"0.5659202\",\n \"0.5644068\",\n \"0.5619629\",\n \"0.5581817\",\n \"0.55763596\",\n \"0.5568444\",\n \"0.5551165\",\n \"0.55458266\",\n \"0.55394226\",\n \"0.55220485\",\n \"0.5515554\",\n \"0.55153877\",\n \"0.55147487\",\n \"0.55110705\",\n \"0.54576766\",\n \"0.54534495\",\n \"0.5445197\",\n \"0.5436135\",\n \"0.540998\",\n \"0.5395972\",\n \"0.5391023\",\n \"0.5391011\",\n \"0.53902036\",\n \"0.53865933\",\n \"0.53676295\",\n \"0.53617775\",\n \"0.5354485\",\n \"0.5346112\",\n \"0.53351355\",\n \"0.5334359\",\n \"0.5327337\",\n \"0.5323527\",\n \"0.5316133\",\n \"0.5315485\",\n \"0.5314741\",\n \"0.5312208\",\n \"0.5303954\",\n \"0.53031224\",\n \"0.53006035\",\n \"0.5298225\",\n \"0.5293558\",\n \"0.5287215\",\n \"0.52828366\",\n \"0.5282792\",\n \"0.5281652\",\n \"0.52797765\",\n \"0.52632785\",\n \"0.52614987\",\n \"0.5254608\",\n \"0.52366626\",\n \"0.52224857\",\n \"0.5208873\",\n \"0.52085435\",\n \"0.5208473\",\n \"0.52013886\",\n \"0.5196598\",\n \"0.51788014\",\n \"0.5174316\",\n \"0.5172234\",\n \"0.5160261\",\n \"0.51496947\",\n \"0.51466906\",\n \"0.5145851\",\n \"0.5139596\",\n \"0.51364475\",\n \"0.51344687\",\n \"0.5131036\",\n \"0.51278865\",\n \"0.51237446\",\n \"0.51167506\",\n \"0.51127285\",\n \"0.5108524\",\n \"0.5107631\",\n \"0.5101478\",\n \"0.5097581\",\n \"0.50964445\",\n \"0.50958747\",\n \"0.5089302\",\n \"0.5085445\",\n \"0.5084894\",\n \"0.50769955\",\n \"0.5075742\",\n \"0.50756264\",\n \"0.50617695\",\n \"0.50589633\",\n \"0.50555575\",\n \"0.50555575\",\n \"0.5055159\",\n \"0.5053417\",\n \"0.5051034\",\n \"0.5042832\",\n \"0.5039416\",\n \"0.5039049\",\n \"0.5037143\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":87497,"cells":{"query":{"kind":"string","value":"Generator of a new area"},"document":{"kind":"string","value":"def _generate(self, center_idx):\n center = self._not_included[center_idx]\n area = [center]\n others = []\n for i in range(len(self._not_included)):\n if i == center_idx:\n continue\n candidate = self._not_included[i]\n dis_geo = ((center['x'] - candidate['x']) ** 2 + (center['y'] - candidate['y']) ** 2) ** 0.5 \\\n / self._max_distance\n if center['value'] == 0.0:\n if candidate['value'] != 0.0:\n others.append(candidate)\n continue\n pass\n # if dis_geo > self._max_threshold:\n # others.append(candidate)\n # continue\n dis_val = (center['value'] - candidate['value']) / (self._value_extends[1] - self._value_extends[0])\n if abs(dis_val) > self._max_threshold:\n others.append(candidate)\n continue\n dif = self._alpha * dis_geo + (1 - self._alpha) * dis_val\n if dif <= self._base_threshold:\n area.append(candidate)\n pass\n else:\n others.append(candidate)\n pass\n pass\n self.leaves.append(area)\n self._not_included = others\n if len(self._not_included) == 0:\n return -1\n else:\n return int(rand() * len(self._not_included))"},"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 area(self):","def area(self):\n ...","def __areas_to_pool(self, region_width, region_height, region_width_step, region_height_step):\n \n areas = [[(width_ind * region_width_step, height_ind * region_height_step,\n (width_ind + 1) * region_width_step if (width_ind + 1) < self.width else region_width,\n (height_ind + 1) * region_height_step if (height_ind + 1) < self.height else region_height) for width_ind in range(self.width)] for height_ind in range(self.height)]\n\n return areas","def area(self):\n raise NotImplementedError","def generate_all_locations(grid, shape):","def test_area(self):\n r = Rectangle(5, 6)\n self.assertEqual(r.area(), 30)\n w = randrange(10) + 1\n h = randrange(10) + 1\n r.width = w\n r.height = h\n self.assertEqual(r.area(), w * h)\n w = randrange(10) + 1\n h = randrange(10) + 1\n r = Rectangle(w, h, 7, 8, 9)\n self.assertEqual(r.area(), w * h)\n w = randrange(10) + 1\n h = randrange(10) + 1\n r = Rectangle(w, h, y=7, x=8, id=9)\n self.assertEqual(r.area(), w * h)","def demo_1(radii):\n areas = []\n for r in radii:\n a = area(r) # populate list one-by-one in for loop\n areas.append(a)\n print a","def area(self):\n raise Exception(\"area() is not implemented\")","def area(self):\n raise Exception(\"area() is not implemented\")","def _area(self):\n self.area = 0.0\n for sail in self.sails:\n self.area += sail.area","def display(self):\n for i in range(self.height - 1, 0, -1):\n for j in range(self.width):\n # yield i, j - 1, self.grid[i][j - 1]\n yield j, i, self.dungeon.tile(Point(j, i))\n\n \"\"\"\n def __iter__(self):\n for i in range(self.height):\n for j in range(self.width):\n yield Point(x=self.x + j, y=self.y + i)\n \"\"\"","def area(base, height):\n\n return base * height","def generatePolygons():","def area(self):\n raise Exception('area() is not implemented')","def area(self):\n raise Exception('area() is not implemented')","def draw_area(horiz, vertic, step, obj, screen):\n start_h = horiz\n start_w = vertic\n Area = []\n Cols = []\n for i in range(0,10):\n for i in range(0,10):\n Cols.append(obj(start_w, start_h, 20, 20, screen, '1'))\n start_h += step\n Area.append(Cols)\n Cols = []\n start_h = horiz\n start_w += step\n return Area","def test_multi_area(self):\n pass","def area(self):\n\n raise Exception(\"area() is not implemented\")","def area(self):\n\n raise Exception(\"area() is not implemented\")","def area(self, tileID):\n pass","def build(self):\n self._start = np.zeros_like(self._rooms.shape)\n self._start[0] = random.randrange(self._rooms.shape[0])\n position = self._start\n egress = Direction.South\n distance = 1\n while position in self:\n room = self[position]\n room.egress = egress\n room.distance = distance\n yield position\n options = {}\n for direction in Direction.range():\n if direction != egress:\n new_position = position + direction.offset()\n if new_position in self:\n if self.is_sealed(new_position):\n options[direction] = new_position\n if options:\n direction = random.choice(tuple(options.keys()))\n room.remove_wall(direction)\n position = options[direction]\n egress = direction.reverse()\n distance += 1\n else:\n position += egress.offset()\n if position in self:\n egress = self[position].egress\n distance = self[position].distance","def constructRectangle(self, area: int) -> List[int]:\n w = int(area ** 0.5)\n while area % w:\n w -= 1\n\n return [area // w, w]","def test_absolute_areas(self):\n\n assert len(self.test_shape.areas) == 4\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1","def area(self):\n\n raise Exception('Not implemented')","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):\n self.quadrants_count = 4\n self.quadrant_positions_count = 9\n self.play_area = [Quadrant(self.quadrant_positions_count) for _ in range(self.quadrants_count)]","def point_to_square_loop(source, area):\n half_diag = np.sqrt(area/2)\n xyz_hor = rotation(source[3]+90.0, 0.0)*half_diag\n xyz_ver = rotation(source[3], source[4]+90.0)*half_diag\n points = source[:3] + np.stack(\n [xyz_hor, xyz_ver, -xyz_hor, -xyz_ver, xyz_hor])\n return points","def area(width, height):\n return width * height","def _create_room(new_map, room):\n for x in range(room.x1 + 1, room.x2):\n for y in range(room.y1 + 1, room.y2):\n new_map.terrain[x][y] = 1","def area(self):\n return (self.baselength1 + self.baselength2)*self.height/2","def NewBlock(self):\n for i in self.matrix:\n if 2 in i:\n return()\n blockType = self.bag.Choose()\n subtractor = {\"I\" : 4, \"J\" : 3, \"L\" : 3, \"O\" : 2, \"S\" : 3, \"T\" : 3, \"Z\": 3}\n x = random.randint(0, self.width - subtractor.get(blockType))\n coords = []\n if blockType == \"I\":\n coords = [(x + i, 0) for i in range(4)]\n elif blockType == \"J\":\n coords = [(x + i, 0) for i in range(3)]\n coords.append((x, 1))\n elif blockType == \"L\":\n coords = [(x + i, 0) for i in range(3)]\n coords.append((x + 2, 1))\n elif blockType == \"O\":\n coords = [(x, 0), (x + 1, 0), (x, 1), (x + 1, 1)]\n elif blockType == \"Z\":\n coords = [(x, 0), (x + 1, 0), (x + 1, 1), (x + 2, 1)]\n elif blockType == \"S\":\n coords = [(x + 1, 0), (x + 2, 0), (x, 1), (x + 1, 1)]\n elif blockType == \"T\":\n coords = [(x, 0), (x + 1, 0), (x + 2, 0), (x + 1, 1)]\n self.coords = coords\n return(coords)","def test_area(self):\n r1 = Rectangle(3, 2)\n self.assertEqual(r1.area(), 6)\n\n r2 = Rectangle(2, 10)\n self.assertEqual(r2.area(), 20)\n\n r3 = Rectangle(10, 10)\n self.assertEqual(r3.area(), 100)","def test_absolute_shape_areas(self):\n\n assert self.test_shape.area == pytest.approx((math.pi * (10**2) * 2) + (math.pi * (2 * 10) * 30))\n assert len(self.test_shape.areas) == 3\n assert self.test_shape.areas.count(pytest.approx(math.pi * (10**2))) == 2\n assert self.test_shape.areas.count(pytest.approx(math.pi * (2 * 10) * 30)) == 1","def get_area(self):\n raise NotImplementedError()","def generate_level(self):\n for _ in range(AMOUNT_REGIONS_TO_DRAW):\n self._generate_next_blocks()","def initialize_areas(self):\n self._areas[1] = copy.copy(self._areas[0])","def g_xy(self):\n for x in range(self.size.x):\n for y in range(self.size.y):\n yield self.p[0] + Vect(x, y)","def area(self):\n self.convert_window(\"Area\", \"square meters\", [\"acres\", \"ares\", \"circular inches\", \"hectares\", \"hides\", \"roods\", \"square centimeters\", \"square feet(US & UK)\", \"square feet(US survey)\", \"square inches\", \"square kilometers\", \"square meters\", \"square miles\", \"square millimeters\", \"square of timber\", \"square rods or poles\", \"square yards\", \"townships\"])","def gamearea_maker(self, shape=input('Circle or square?\\n'), dim=int(input('Dimension \\\n (radius or side length ): \\n'))):\n\n self.gameAreaDim = dim # sort arguments\n self.gameAreaShape = shape\n self.cursor.penup()\n self.cursor.shape('circle')\n self.cursor.color('white')\n self.cursor.turtlesize(0.2)\n self.cursor.st()\n self.slither.positions = [(0, 0)]\n self.screen.tracer(0, 0)\n self.screen.setup(self.screensize[0], self.screensize[1], 0, 0)\n self.screen.bgcolor(self.mapEdgeColor)\n self.screen.title = 'David\\'s slither game'\n self.area.ht()\n\n if self.gameAreaShape in ['circle', 'Circle', 'C', 'c', 'cir', 'Cir', 'a really big big big circle', '1']:\n self.gameAreaShape = 'circle'\n self.area.penup()\n self.area.setposition(0, -self.gameAreaDim)\n self.area.pencolor(self.edgeRed)\n self.area.pensize(1)\n self.area.color(self.gameAreaColor)\n self.area.begin_fill()\n self.area.circle(self.gameAreaDim) # draw a circle area with radius\n self.area.end_fill()\n\n else:\n self.gameAreaShape = 'square'\n self.area.penup()\n self.area.setposition(-1/2*self.gameAreaDim, -1/2*self.gameAreaDim) # goes to the left bottom corner of\n # the game area.\n self.area.setheading(0)\n self.area.pencolor(self.edgeRed)\n self.area.pensize(1)\n self.area.color(self.gameAreaColor)\n self.area.begin_fill()\n self.area.forward(self.gameAreaDim)\n self.area.setheading(90)\n self.area.forward(self.gameAreaDim)\n self.area.setheading(180)\n self.area.forward(self.gameAreaDim) # draw a square with side length\n self.area.setheading(270)\n self.area.forward(self.gameAreaDim)\n self.area.end_fill()","def explainAreaSmall(self):\n \n #EXPLANATION NO. 1\n #fadeout the non-required areas\n self.play(FadeOut(area_ABC_copy), FadeOut(area_ABD_copy),\n FadeOut(geq_2), FadeOut(geq_1),\n FadeOut(area_ABC), FadeOut(area_ABD))\n \n #expand the required area\n self.play(area_ABE_copy.animate.scale(2).move_to(RIGHT*2))\n\n #surrounding text\n abe_text_1 = always_redraw(\n lambda : MathTex(\"=\", \"\\\\text{Area of } \\\\triangle ABE\").scale(0.8).next_to(area_ABE_copy, RIGHT)\n )\n\n #half base height\n abe_text_2 = always_redraw(\n lambda : MathTex(\"=\", \"\\\\dfrac{1}{2}\", \"\\\\times\", \"\\\\text{base}\", \"\\\\times\", \"\\\\text{height}\").scale(0.8).next_to(area_ABE_copy, RIGHT)\n )\n\n #write texts\n self.play(Write(abe_text_1))\n self.wait()\n self.play(ReplacementTransform(abe_text_1[0], abe_text_2[0]),\n ReplacementTransform(abe_text_1[1:], abe_text_2[1:]))\n self.wait()\n\n #defining braces\n abe_base_brace = always_redraw(\n lambda : Brace(radius_ang, DOWN)\n )\n abe_base_brace_label = always_redraw(\n lambda : MathTex(\"R\\\\cos\\\\theta\").scale(0.6).next_to(abe_base_brace, DOWN)\n )\n abe_height_brace = always_redraw(\n lambda : Brace(radius_ang, LEFT)\n )\n abe_height_brace_label = always_redraw(\n lambda : MathTex(\"R\\\\sin\\\\theta\").scale(0.6).next_to(abe_height_brace, LEFT)\n )\n\n self.play(Write(abe_base_brace), Write(abe_height_brace))\n self.play(Write(abe_base_brace_label), Write(abe_height_brace_label))\n self.wait()\n\n \n #back to editing the equation\n abe_text_3 = always_redraw(\n lambda : MathTex(\"=\", \"\\\\dfrac{1}{2}\", \"\\\\times\", \"R\\\\cos\\\\theta\", \"\\\\times\", \"R\\\\sin\\\\theta\").scale(0.8).next_to(area_ABE_copy, RIGHT)\n )\n\n self.play(ReplacementTransform(abe_text_2[0:], abe_text_3[0:]))\n self.wait(0.5)\n self.play(FadeOut(abe_base_brace), FadeOut(abe_height_brace),\n FadeOut(abe_base_brace_label), FadeOut(abe_height_brace_label))\n \n abe_text_4 = always_redraw(\n lambda : MathTex(\"=\", \"\\\\dfrac{1}{2}\", \"\\\\times\", \"\\\\cos x\", \"\\\\times\", \"\\\\sin x\").scale(0.8).next_to(area_ABE_copy, RIGHT)\n )\n self.play(ReplacementTransform(abe_text_3[0:], abe_text_4[0:]))\n\n abe_text_5 = always_redraw(\n lambda : MathTex(\"=\", \"\\\\dfrac{1}{2}\", \"\\\\sin x\", \"\\\\cos x\").scale(0.8).next_to(area_ABE_copy, RIGHT)\n )\n self.play(ReplacementTransform(abe_text_4[0:2], abe_text_5[0:2]),\n ReplacementTransform(abe_text_4[2:], abe_text_5[2:]))\n\n #vgroup for drawing box\n abe_group = VGroup(abe_text_5, area_ABE_copy)\n abe_formula_box = SurroundingRectangle(abe_group, color=PINK)\n\n self.play(Write(abe_formula_box))\n self.wait()\n\n #remove all elements\n self.play(FadeOut(abe_formula_box), FadeOut(abe_text_5), FadeOut(area_ABE_copy), FadeOut(area_ABE))","def hexagon_generator(edge_length, offset):\n x, y = offset\n for angle in range(0, 360, 60):\n x += math.cos(math.radians(angle)) * edge_length\n y += math.sin(math.radians(angle)) * edge_length\n yield x, y","def area(self):\n return self.length*self.length","def pattern_generator(self, x: int, y: int) -> float:\n dis_corners = self.distance_from_corners(x, y)\n dis_center = self.distance_from_center(x, y)\n key = (dis_corners - dis_center)\n return [x / key, y / key]","def test_rectangle(self):\n result = shape_area.rectangle_area(6,7)\n self.assertEqual(result,26)","def new_round(self):\n # Incrementar la ronda actual\n self.num_round += 1\n # Actualizar los puntos de generacion de aliens\n if self.num_round == 1:\n self.total_alien_points = 3\n else:\n self.total_alien_points += int(self.num_round / 2)\n self.alien_points = self.total_alien_points\n\n # Establecemos los aliens que vamos a generar en esta ronda\n while self.alien_points > 0:\n max_interval_value = 3\n # Comprobamos que no haya pocos puntos para generar aliens\n # y limitamos la generacion de aliens de alto nivel\n if self.alien_points <= 2:\n max_interval_value = 2\n elif self.alien_points <= 2:\n max_interval_value = 1\n max_interval_value = min(max_interval_value, self.round_based_alien_limitation())\n number = random.randint(0, 1)\n if number == 0 or number == 1:\n self.aliens_to_generate.append(0)\n self.alien_points -= 1\n elif number == 2:\n self.aliens_to_generate.append(1)\n self.alien_points -= 2\n elif number == 3:\n self.aliens_to_generate.append(2)\n self.alien_points -= 3","def FillupArea(self):\r\n\r\n drillsize = self.FromUserUnit(float(self.m_txtViaDrillSize.GetValue()))\r\n viasize = self.FromUserUnit(float(self.m_txtViaSize.GetValue()))\r\n step_x = self.FromUserUnit(float(self.m_txtHSpacing.GetValue()))\r\n step_y = self.FromUserUnit(float(self.m_txtVSpacing.GetValue()))\r\n clearance = self.FromUserUnit(float(self.m_txtClearance.GetValue()))\r\n self.randomize = self.m_chkRandomize.GetValue()\r\n self.clearance = clearance\r\n bbox = self.area.GetBoundingBox()\r\n top = bbox.GetTop()\r\n bottom = bbox.GetBottom()\r\n right = bbox.GetRight()\r\n left = bbox.GetLeft()\r\n netname = self.m_cbNet.GetStringSelection()\r\n netcode = self.board.GetNetcodeFromNetname(netname)\r\n # commit = pcbnew.COMMIT()\r\n viacount = 0\r\n x = left\r\n\r\n # Cycle trough area bounding box checking and implanting vias\r\n layer = self.area.GetLayer()\r\n\r\n while x <= right:\r\n y = top\r\n while y <= bottom:\r\n if self.randomize:\r\n xp = x + random.uniform(-1, 1) * step_x / 5\r\n yp = y + random.uniform(-1, 1) * step_y / 5\r\n else:\r\n xp = x\r\n yp = y\r\n\r\n if hasattr(pcbnew, 'VECTOR2I'):\r\n p = pcbnew.VECTOR2I(xp, yp)\r\n else:\r\n if(hasattr(pcbnew, 'wxPoint')):\r\n p = pcbnew.wxPoint(xp, yp)\r\n\r\n if self.area.HitTestFilledArea(layer, p, 0):\r\n via = pcbnew.PCB_VIA(self.board)\r\n via.SetPosition(p)\r\n via.SetLayer(layer)\r\n via.SetNetCode(netcode)\r\n # Set up via with clearance added to its size-> bounding box check will be OK in worst case, may be too conservative, but additional checks are possible if needed\r\n # TODO: possibly take the clearance from the PCB settings instead of the dialog\r\n # Clearance is all around -> *2\r\n via.SetDrill(drillsize + 2 * clearance)\r\n via.SetWidth(viasize + 2 * clearance)\r\n # via.SetTimeStamp(__timecode__)\r\n if not self.CheckOverlap(via):\r\n # Check clearance only if clearance value differs from 0 (disabled)\r\n if (clearance == 0) or self.CheckClearance(via, self.area, clearance):\r\n via.SetWidth(viasize)\r\n via.SetDrill(drillsize)\r\n self.board.Add(via)\r\n # commit.Add(via)\r\n self.pcb_group.AddItem(via)\r\n viacount += 1\r\n y += step_y\r\n x += step_x\r\n\r\n if viacount > 0:\r\n wx.MessageBox(_(u\"Implanted: %d vias!\") % viacount)\r\n # commit.Push()\r\n pcbnew.Refresh()\r\n else:\r\n wx.MessageBox(_(u\"No vias implanted!\"))","def house ():\n\n poly (3,300,\"red\")\n penup()\n setposition(0,-300)\n pendown()\n poly (4,300,\"brown\")\n penup()\n setposition(100,-300)\n pendown()\n poly(4,100,\"green\") \n\n return None","def area(self):\n\t\treturn self.width * self.height","def make_asteroid_generator(\n *,\n x: float,\n y: float,\n width: float,\n height: float,\n interval_ms: float,\n initial_asteroids: int = 0,\n) -> GameObject:\n\n game_object = game_objects.new_object()\n\n def spawn_asteroid():\n make_asteroid(\n x=random.uniform(x, x + width),\n y=random.uniform(y, y + height),\n vx=random.gauss(0, 0.03),\n vy=random.gauss(0, 0.03),\n )\n\n for _ in range(initial_asteroids):\n spawn_asteroid()\n\n def generate_asteroids():\n while True:\n yield resume_after(delay_ms=interval_ms)\n spawn_asteroid()\n\n GameObjectCoroutine(game_object, generate_asteroids()).start()\n\n return game_object","def area(self):\n num_rows = self.row_end - self.row_start\n num_cols = self.col_end - self.col_start\n area = num_rows*num_cols\n return area","def area(self):\n\t\treturn self.width() * self.height()","def create_room(room):\n global map\n for x in range(room.x1+1, room.x2):\n for y in range(room.y1+1, room.y2):\n map[x][y].blocked = False\n map[x][y].block_sight = False","def area(self):\n\t\treturn self.height * self.height","def __iter__(self):\n pt = (self.x, self.y)\n for i in pt:\n yield i","def asteroidCreator(numCorner,win):\n\n xCoor = []\n yCoor = []\n\n # Creating coordinates of the points\n coorRange = [i for i in range(-10,10) if i not in [0]] # to avoid 0\n\n for i in range(numCorner):\n xCoor.append(round(random.choice(coorRange)*random.uniform(0.01,1),2))\n yCoor.append(round(random.choice(coorRange)*random.uniform(0.01,1),2))\n\n # Sorting the coordinates\n bubbleSort(xCoor,len(xCoor))\n bubbleSort(yCoor,len(yCoor))\n\n\n # Isolating the extreme points\n xSmallest = xCoor.pop(0)\n xLargest = xCoor.pop()\n\n ySmallest = yCoor.pop(0)\n yLargest = yCoor.pop()\n\n # Shuffle the coordinates\n random.shuffle(xCoor)\n random.shuffle(yCoor)\n\n # Divide them into two sets\n xCoorLower = xCoor[:len(xCoor)//2]\n xCoorUpper = xCoor[len(xCoor)//2:]\n\n yCoorLower = yCoor[:len(yCoor)//2]\n yCoorUpper = yCoor[len(yCoor)//2:]\n\n # Append back the extreme points, and sort them again\n xCoorLower.append(xSmallest)\n xCoorLower.append(xLargest)\n xCoorUpper.append(xSmallest)\n xCoorUpper.append(xLargest)\n\n yCoorLower.append(ySmallest)\n yCoorLower.append(yLargest)\n yCoorUpper.append(ySmallest)\n yCoorUpper.append(yLargest)\n\n bubbleSort(xCoorLower,len(xCoorLower))\n bubbleSort(xCoorUpper,len(xCoorUpper))\n bubbleSort(yCoorLower,len(yCoorLower))\n bubbleSort(yCoorUpper,len(yCoorUpper))\n\n # Getting the vector lengths out of the points\n # We will get vectors in 4 directions from 4 lists\n xVectorLengths = []\n yVectorLengths = []\n\n for i in range(len(xCoorLower)-1):\n xVectorLengths.append(xCoorLower[i]-xCoorLower[i+1])\n for i in range(len(xCoorUpper)-1):\n xVectorLengths.append(xCoorUpper[i+1]-xCoorUpper[i])\n for i in range(len(yCoorLower)-1):\n yVectorLengths.append(yCoorLower[i]-yCoorLower[i+1])\n for i in range(len(yCoorUpper)-1):\n yVectorLengths.append(yCoorUpper[i+1]-yCoorUpper[i])\n\n random.shuffle(xVectorLengths)\n random.shuffle(yVectorLengths)\n\n # Creating the vectors\n vectors = []\n defaultVector = [0,0]\n\n for i in range(len(xVectorLengths)):\n defaultVector[0] = round(xVectorLengths[i],2)\n defaultVector[1] = round(yVectorLengths[i],2)\n vectors.append(defaultVector.copy())\n\n # Sorting vectors by their angle\n sortedVectors = []\n quadrant1 = []\n quadrant2 = []\n quadrant3 = []\n quadrant4 = []\n\n ### Dividing them by quadrants\n for vector in vectors:\n if vector[0] >= 0 and vector[1] >= 0:\n quadrant1.append(vector)\n elif vector[0] <= 0 and vector[1] >= 0:\n quadrant2.append(vector)\n elif vector[0] <= 0 and vector[1] <= 0:\n quadrant3.append(vector)\n elif vector[0] >= 0 and vector[1] <= 0:\n quadrant4.append(vector)\n\n ### Sorting them inside the quadrants\n quadrant1 = angleSort(quadrant1,1,len(quadrant1))\n quadrant2 = angleSort(quadrant2,2,len(quadrant2))\n quadrant3 = angleSort(quadrant3,3,len(quadrant3))\n quadrant4 = angleSort(quadrant4,4,len(quadrant4))\n\n ### Adding them up in order\n for vector in quadrant1:\n sortedVectors.append(vector)\n for vector in quadrant2:\n sortedVectors.append(vector)\n for vector in quadrant3:\n sortedVectors.append(vector)\n for vector in quadrant4:\n sortedVectors.append(vector)\n\n # Creating the points for the polygon\n points = []\n points = vectorsToPoints(sortedVectors,points)\n\n rightEdge = 0\n leftEdge = 0\n upperEdge = 0\n lowerEdge = 0\n\n # getting the boundaries for the asteroid\n for point in points:\n if point[0] > rightEdge:\n rightEdge = point[0]\n elif point[0] < leftEdge:\n leftEdge = point[0]\n if point[1] > upperEdge:\n upperEdge = point[1]\n elif point[1] < lowerEdge:\n lowerEdge = point[1]\n\n # Width and height are only required since it is a child of rotating_block class\n width = rightEdge - leftEdge\n height = upperEdge - lowerEdge\n\n centerPoint = [(rightEdge + leftEdge) / 2 , (upperEdge + lowerEdge) / 2]\n\n asteroid = pho.Asteroid(win,width,height,points,centerPoint[0],centerPoint[1])\n\n return asteroid","def area(self):\n return(self.__width * self.__height)","def decorate_scene():\n make_polygon( (100,100),(120,140),(270,70) )\n make_polygon( (300,10), (300,550), (340,452),(380,300), (330,50))\n make_polygon( (200,450), (100,450), (100,500), (200,500) )\n make_polygon( (130,320), (150,300), (140,280) )\n return","def create_block():\n global BLOCK\n posx = SEG_SIZE * random.randint(1, (WIDTH-SEG_SIZE) / SEG_SIZE)\n posy = SEG_SIZE * random.randint(1, (HEIGHT-SEG_SIZE) / SEG_SIZE)\n BLOCK = c.create_oval(posx, posy,\n posx+SEG_SIZE, posy+SEG_SIZE,\n fill=\"red\")\n # print(posx, posy)\n return posx, posy","def rectangle_area(base, height):\n return (base * height)","def __maze_generator(self):\n grid = []\n for row in range(self.__row_count):\n new_row = []\n for col in range(self.__col_count):\n new_row.append(RoomFactory.create_room([row, col]))\n if col > 0:\n new_row[col].left = new_row[col - 1]\n new_row[col - 1].right = new_row[col]\n if row > 0:\n new_row[col].up = grid[row - 1][col]\n grid[row - 1][col].down = new_row[col]\n grid.append(new_row)\n return grid","def getArea(rob):\r\n def dfs(visit, i, j):\r\n visit.add((i, j))\r\n for k in range(4):\r\n newi, newj = i + x[k], j + y[k]\r\n if (newi, newj) in visit or not rob.move(k):\r\n continue\r\n dfs(visit, newi, newj)\r\n rob.move((k + 2) % 4)\r\n visit = set()\r\n dfs(visit, 0, 0)\r\n return len(visit)","def make_board(self):\n generate = lambda: random.randint(1, 100) in range(1, self.p_pit+1)\n some_number = self.some_number\n agent = Agent(some_number)\n agent.program = Oozeplorer_Percept(agent)\n self.add_agent(agent)\n gold = Gold()\n self.add_thing(gold, None)\n for row in range(1, some_number + 1):\n for col in range(1, some_number + 1):\n valid_spot = (row, col) != gold.location and (row, col) != (1, 1)\n if valid_spot and generate():\n t_pt = Pit()\n t_pt.location = (row, col)\n self.things.append(t_pt)","def createEntrances(self):\n\t\tfor x in range(self.width):\n\t\t\tif self.isFloor(x, 1):\n\t\t\t\tself.setFloor(x, 0)\n\t\t\t\tbreak\n\t\tfor x in range(self.width - 1, 0, -1):\n\t\t\tif self.isFloor(x, self.height - 2):\n\t\t\t\tself.setFloor(x, self.height - 1)\n\t\t\t\tbreak","def stage_1_generator(option):\n # Stage Size + Player Starting Position\n STAGE_1 = ([10,10], [1,1])\n\n # Non-Ocean tiles\n STAGE_1_TILES = { \n \"1,2\":\"rock\",\n \"1,3\":\"mountain\",\n \"2,4\":\"rock\",\n \"2,7\":\"rock\",\n \"2,8\":\"rock\",\n \"3,3\":\"rock\",\n \"3,4\":\"rock\",\n \"3,8\":\"mountain\",\n \"3,9\":\"rock\",\n \"3,10\":\"rock\",\n \"4,4\":\"rock\",\n \"4,5\":\"mountain\",\n \"5,6\":\"rock\",\n \"6,1\":\"rock\",\n \"6,2\":\"rock\",\n \"6,7\":\"rock\",\n \"6,10\":\"rock\",\n \"7,1\":\"rock\",\n \"7,2\":\"rock\",\n \"7,6\":\"rock\",\n \"7,10\":\"rock\",\n \"8,5\":\"rock\",\n \"8,6\":\"rock\",\n \n \"8,10\":\"rock\",\n \"9,1\":\"sign\",\n \"9,3\":\"rock\",\n \"9,4\":\"rock\",\n \"9,9\":\"rock\",\n \"9,10\":\"rock\",\n \"10,1\":\"cave\",\n \"10,3\":\"rock\",\n \"10,4\":\"cave\",\n \"10,8\":\"rock\",\n \"10,9\":\"rock\",\n \"10,10\":\"rock\",\n \n \"1,10\":\"end\",\n }\n\n # Special Tiles that trigger an event\n STAGE_1_SPECIAL = { \n \"1,10\":\"end\",\n \"9,1\":\"sign_cave\",\n \"10,1\":\"cave_entrance_1\",\n \"10,4\":\"cave_entrance_2\",\n \"1,9\":\"dark_water\",\n \"2,9\":\"dark_water\",\n \"2,10\":\"dark_water\"\n }\n\n # Decide what data to return\n if option == \"stage\":\n return STAGE_1\n elif option == \"tiles\":\n return STAGE_1_TILES\n elif option == \"special\":\n return STAGE_1_SPECIAL\n else:\n print(\"Something Broke! map_generator_1\")","def get_area(self):\n dic = {}\n for _ in range(self.tries):\n x = randint(0, self.width - 1)\n y = randint(0, self.height - 1)\n target = self.service.info(x, y)\n if target in dic.keys():\n dic[target] += 1\n else:\n dic[target] = 1\n for index in dic.keys():\n dic[index] = dic[index] / self.tries * self.area\n return dic","def old_data(self) -> Generator[Tuple[int, int], Tuple[bool, bool], None]:\n x = self.x_start\n y = self.y_start\n yield x, y\n while True:\n x += self.x_rate\n y += self.y_rate\n # Get whether the object has collided with a wall\n horizontal_hit, vertical_hit = yield int(x), int(y)\n # Simply reverse the x/y rate depending on the wall type\n # TODO: could add a little variance in the angle with every bounce\n if horizontal_hit:\n self.y_rate *= -1\n if vertical_hit:\n self.x_rate *= -1","def village_gen(x1, villages, y_list, z1, halfDoorsInVillage, emptySpaces, axis, tick, cat):\n cat2 = cat[\"data\"]\n doors_coords_lists = []\n doors_set = set()\n for y in y_list:\n doors_coords_lists += village_doors_coordinates(x1, villages, y, z1, halfDoorsInVillage, emptySpaces, axis)\n for vill_coords_list in doors_coords_lists:\n for single_door_coord in vill_coords_list:\n doors_set.add(tuple(single_door_coord))\n del_door(cat2['Villages'], doors_set)\n for coordinates_list in doors_coords_lists:\n vil = Village.create_village(tick)\n for x, y, z in coordinates_list:\n vil.add_door(create_door(tick, x, y, z))\n cat2['Villages'].append(vil.get_vil())","def area(self):\r\n return self.width * self.height","def set_area(self, area=0.0):\n self.area = area","def area(self):\n return self.width*self.height","def boundaries_next(*args):\n return _ida_hexrays.boundaries_next(*args)","def __init__(self, total_length:int, initial_y:int, screen, number:int, debug:bool = False):\n\n #Call the superclass\n super().__init__()\n\n #Create the group of blocks based on x and y and add them to the group\n for k in range(number):\n for i in range(-1,2):\n for j in range(-2,3):\n self.add(Block(total_length * (k+1) // (number+1) + 10*j, initial_y + 10*i, screen, debug = debug))","def nextGen(self):\n\n p = []\n while len(p) < len(self.p):\n #select mates and produce offspring\n p1, p2 = self.select()\n offspring = self.mate(p1, p2)\n\n #put the offspring in the next generation (with mutation)\n for child in offspring:\n child=self.mutate(child)\n p.append(child)\n \n\n # the world belongs to the new generation\n return p","def area(self):\n return (self.width * self.height)","def create_block(self, location_list, POI_locations):\n\n \n for i in range(len(location_list)):\n this_cell = self.grid.get_cell_list_contents(location_list[i])\n\n for agent in this_cell:\n if type(agent) is nodeAgent:\n agent.block = True\n\n for i in POI_locations:\n agent.locations[i] = 10000","def attackArea(initPair, connect):\r\n area = []\r\n opts = ((1, 0), (0, 1), (1, 1), (1, -1))\r\n (x, y) = initPair\r\n\r\n for pair in opts:\r\n (x1, y1) = pair\r\n\r\n for s in (1, -1):\r\n for i in range(1, connect):\r\n x2 = x + x1 * i * s\r\n y2 = y + y1 * i * s\r\n area.append((x2, y2))\r\n\r\n return area","def get_dancefloor_area(self):\n cords = []\n\n x1 = self.coordinates[0]\n y1 = self.coordinates[1]\n x2 = self.coordinates[0] + self.width\n y2 = self.coordinates[1] + self.height\n if self.width <= 0:\n x1, x2 = x2, x1\n if self.height <= 0:\n y1, y2 = y2, y1\n\n for x in range(x1, x2):\n for y in range(y1, y2):\n if x % 20 == 0 and y % 20 == 0:\n cords.append([x, y])\n return cords","def __iter__(self):\n while (self.pointsleft > 0):\n current = min(self.pointsleft, self.settings.LOCALSKIPNUM)\n for i in range(current):\n self.add(self.fabric.getcoordinate())\n self.pointsleft -= self.settings.LOCALSKIPNUM\n self.pointscontroller.set(self.graph)\n yield self.graph","def _create_drawing_area(self):\n\n self.drawing_x = -self.size/2 + self.margin\n self.drawing_y = self.size/2 - self.margin\n self.drawing_width = self.size - self.margin * 2\n self.drawing_height = (self.size/2 + self.flat_fragment) - self.margin * 2\n \n self.drawing_x_step = self.drawing_width \n self.drawing_y_step = self.drawing_height","def generate(self):\n self.generate_points()\n self.generate_edges()","def genHome(grid, house):\n house.random_x(grid)\n house.random_y(grid)\n return generic.placeHouse(grid, house)","def getLandmarkGenerator(shape, frame_width, frame_height):\n\n # load landmarks file\n shape = read_table(shape, delim_whitespace=True, header=None)\n\n # deduce number of landmarks from file dimension\n _, d = shape.shape\n n_points = (d - 2) / 2\n\n # t is the time sent by the frame generator\n t = yield\n\n shapes = []\n currentT = None\n\n for _, row in shape.iterrows():\n\n T = float(row[0])\n identifier = int(row[1])\n landmarks = np.float32(list(pairwise(\n [coordinate for coordinate in row[2:]])))\n landmarks[:, 0] = np.round(landmarks[:, 0] * frame_width)\n landmarks[:, 1] = np.round(landmarks[:, 1] * frame_height)\n\n # load all shapes from current frame\n # and only those shapes\n if T == currentT or currentT is None:\n shapes.append((identifier, landmarks))\n currentT = T\n continue\n\n # once all shapes at current time are loaded\n # wait until t reaches current time\n # then returns all shapes at once\n\n while True:\n\n # wait...\n if currentT > t:\n t = yield t, []\n continue\n\n # return all shapes at once\n t = yield currentT, shapes\n\n # reset current time and corresponding shapes\n shapes = [(identifier, landmarks)]\n currentT = T\n break\n\n while True:\n t = yield t, []","def shape(self):","def shape(self):","def generate_grid():\n y_offset = -10\n for a in range(20):\n # Line 1\n # Adds offset to the x position of the squares\n x_offset = 10\n for b in range(1):\n # Adds offset to the y position of the squares\n y_offset += 20\n for c in range(20):\n # Prints a row of squares(5 squares along the x)\n square(x_offset, y_offset, 20, 20, dark_green)\n for d in range(1):\n # Adds x offset for the next line of squares on the y axis\n x_offset += 40\n # Line 2 (needs 2 lines because the offset of each line)\n # Adds offset to the x position of the squares\n x_offset = 30\n for e in range(1):\n # Adds offset to the y position of the squares\n y_offset += 20\n for f in range(20):\n # Prints a row of squares(5 squares along the x)\n square(x_offset, y_offset, 20, 20, dark_green)\n for g in range(1):\n # Adds x offset for the next line of squares on the y axis\n x_offset += 40","def Intarea( xc, yc, r, x0, x1, y0, y1):\n\n#\n# Shift the objects so that the circle is at the origin.\n#\n x0 = x0 - xc\n y0 = y0 - yc\n x1 = x1 - xc\n y1 = y1 - yc\n\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )","def new_tile(self):\n rowm, colm = self.get_ava_index()\n value = 2 if random() <= 0.90 else 4\n self.set_tile(rowm, colm, value)\n print rowm,colm,value","def boundaries_new(*args):\n return _ida_hexrays.boundaries_new(*args)","def __init__(self, NW, SE):\n self.type = \"HOLD AREA\"\n self.NW = NW\n self.SE = SE\n self.ctr = [(self.NW[0] + self.SE[0]) / 2, (self.NW[1] + self.SE[1]) / 2]","def clean_area(screen,origin,width,height,color):\r\n ox,oy = origin\r\n points = [(ox,oy),(ox+width,oy),(ox+width,oy+height),(ox,oy+height),(ox,oy)]\r\n pygame.draw.polygon(screen, color, points, 0)","def advance(self):\n\t\tself.level += 1\t\n\t\t\"\"\"Creating the new wave of asteroids. Each level starts with the number of asteroids equal to the level number. So, the first level starts with only one asteroid, the second with two, and so on. Now, creating a bunch of asteroids is easy, but I need to make sure that no new asteroid is created right on top of the ship. Otherwise, the ship will explode just as the new level begins.\"\"\"\n\t\t# amount of space around ship to preserve when creating asteroids\n\t\tBUFFER = 150 #BUFFER is a constant for the amount of safe space needed around the ship. BUFFER=x_min+y_min\n\t\t# create new asteroids\n\t\tfor i in range(self.level):\n\t\t\t# calculate an x and y at least BUFFER distance from the ship\n\t\t\t# choose minimum distance along x-axis and y-axis\n\t\t\tx_min = random.randrange(BUFFER)# x_min is the minimum distance the new asteroid should be from the ship along the x-axis,\n\t\t\ty_min = BUFFER - x_min # y_min is the minimum distance that the new asteroid should be from the ship alongthe y-axis\n\t\t\t# choose distance along x-axis and y-axis based on minimum distance\n\t\t\tx_distance = random.randrange(x_min, games.screen.width - x_min) # x_distance is the distance from the ship for the new asteroid along the x-axis, It is a randomly\n\t\t\t#selected number that ensures that the new asteroid will be at least x_min distance from the ship\n\t\t\ty_distance = random.randrange(y_min, games.screen.height - y_min) # y_distance is the distance from the ship for the new asteroid along the y-axis. It is a randomly #selected number that ensures that the new asteroid will be at least y_min distance from the ship\n\t\t\t# calculate location based on distance\n\t\t\tx = self.ship.x + x_distance #x is the x-coordinate for the new asteroid\n\t\t\ty = self.ship.y + y_distance #y is the y-coordinate for the new asteroid\n\t\t\t# wrap around screen, if necessary\n\t\t\tx %= games.screen.width\n\t\t\ty %= games.screen.height\n\t\t\t# create the asteroid\n\t\t\tnew_asteroid = Asteroid(game = self,x = x, y = y,size = Asteroid.LARGE)\n\t\t\tgames.screen.add(new_asteroid)\n\t\t\t# display level number\n\t\tlevel_message = games.Message(value = \"Level \" + str(self.level),\n\t\t\t\t\t\t\t\t\t\t size = 40, \n\t\t\t\t\t\t\t\t\t\t color = color.yellow,\n\t\t\t\t\t\t\t\t\t\t x = games.screen.width/2,\n\t\t\t\t\t\t\t\t\t\t y = games.screen.width/10,\n\t\t\t\t\t\t\t\t\t\t lifetime = 3 * games.screen.fps,\n\t\t\t\t\t\t\t\t\t\t is_collideable = False)\n\t\tgames.screen.add(level_message)\n\t\t\t\n\t\t\t# play new level sound (except at first level)\n\t\tif self.level > 1:\n\t\t\tself.sound.play()","def iter_coords():\n yield (0, 0)\n incr = 0\n x = 1\n y = 0\n\n while True:\n incr += 2\n\n top = y + incr - 1\n bot = y - 1\n left = x - incr\n right = x\n\n yield (x, y)\n while y < top:\n y += 1\n yield (x, y)\n\n while x > left:\n x -= 1\n yield (x, y)\n\n while y > bot:\n y -= 1\n yield (x, y)\n\n while x < right:\n x += 1\n yield (x, y)\n\n x += 1","def area(self):\n return (self.__width * self.__height)","def area(self):\n return (self.__width * self.__height)","def nine_regions(self):\n\n coordinateList = []\n\n # Top left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\n coordinateList.append( [x, y] )\n\n # Top center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] ) \n coordinateList.append( [x, y] )\n\n # Top right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * ( 1.0 - self.ratioTopLeft[IDX_X] ) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\n coordinateList.append( [x, y] )\n\n # Center left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Center right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\n coordinateList.append( [x, y] )\n\n # Bottom left.\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n # Bottom center.\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n # Bottom right.\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\n coordinateList.append( [x, y] )\n\n return coordinateList","def generate_area_file(app):\n sphinx.util.logging.getLogger('make_areas').info('Generating areas file...')\n\n output = Path(app.srcdir) / 'api' / 'areas.rst'\n with output.open('w') as table:\n header = f\"\"\"\n .. (comment): DO NOT EDIT this file.\n .. It is auto-generated by running : {__file__}\n .. Please adjust by making changes there.\n .. It is included in the repository only to aid detection of changes.\n .. _api-areas:\n\n MetPy Area List\n ===============\n\n\n \"\"\"\n\n table.write(textwrap.dedent(header))\n\n for ind, area in enumerate(named_areas):\n extent = named_areas[area].bounds\n longname = named_areas[area].description\n\n area_header = area + ' - ' + longname + '\\n'\n table.write(area_header)\n table.write('-' * len(area_header) + '\\n\\n')\n\n if area in states_provinces:\n code = textwrap.dedent(f\"\"\"\n .. plot::\n\n import matplotlib.pyplot as plt\n import cartopy.crs as ccrs\n import cartopy.feature as cfeature\n from metpy.plots import named_areas\n\n proj = named_areas['{area}'].projection\n\n plt.figure(figsize=(8, 10))\n ax = plt.subplot(111, projection=proj)\n ax.set_extent({extent}, ccrs.PlateCarree())\n ax.add_feature(cfeature.STATES.with_scale('10m'), edgecolor='grey',\n linewidth=0.75)\n ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=1.1)\n ax.add_feature(cfeature.BORDERS.with_scale('10m'), edgecolor='black')\n ax.set_title(f'area={area} name={named_areas[area].description}')\n\n\n \"\"\")\n\n else:\n code = textwrap.dedent(f\"\"\"\n .. plot::\n\n import matplotlib.pyplot as plt\n import cartopy.crs as ccrs\n import cartopy.feature as cfeature\n from metpy.plots import named_areas\n\n proj = named_areas['{area}'].projection\n\n plt.figure(figsize=(8, 10))\n ax = plt.subplot(111, projection=proj)\n ax.set_extent({extent}, ccrs.PlateCarree())\n ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=1.1)\n ax.add_feature(cfeature.BORDERS.with_scale('10m'), edgecolor='black')\n ax.set_title(f'area={area} name={named_areas[area].description}')\n\n\n \"\"\")\n table.write(code)\n if not app.config.metpy_generate_all_areas and ind >= 1:\n break","def area(self):\n return self.__height * self.__width","def area(self):\n return self.__height * self.__width","def area(self):\n return self.__height * self.__width","def __init__(self, area_extents: np.ndarray):\n self.area_extents = area_extents"],"string":"[\n \"def area(self):\",\n \"def area(self):\\n ...\",\n \"def __areas_to_pool(self, region_width, region_height, region_width_step, region_height_step):\\n \\n areas = [[(width_ind * region_width_step, height_ind * region_height_step,\\n (width_ind + 1) * region_width_step if (width_ind + 1) < self.width else region_width,\\n (height_ind + 1) * region_height_step if (height_ind + 1) < self.height else region_height) for width_ind in range(self.width)] for height_ind in range(self.height)]\\n\\n return areas\",\n \"def area(self):\\n raise NotImplementedError\",\n \"def generate_all_locations(grid, shape):\",\n \"def test_area(self):\\n r = Rectangle(5, 6)\\n self.assertEqual(r.area(), 30)\\n w = randrange(10) + 1\\n h = randrange(10) + 1\\n r.width = w\\n r.height = h\\n self.assertEqual(r.area(), w * h)\\n w = randrange(10) + 1\\n h = randrange(10) + 1\\n r = Rectangle(w, h, 7, 8, 9)\\n self.assertEqual(r.area(), w * h)\\n w = randrange(10) + 1\\n h = randrange(10) + 1\\n r = Rectangle(w, h, y=7, x=8, id=9)\\n self.assertEqual(r.area(), w * h)\",\n \"def demo_1(radii):\\n areas = []\\n for r in radii:\\n a = area(r) # populate list one-by-one in for loop\\n areas.append(a)\\n print a\",\n \"def area(self):\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self):\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def _area(self):\\n self.area = 0.0\\n for sail in self.sails:\\n self.area += sail.area\",\n \"def display(self):\\n for i in range(self.height - 1, 0, -1):\\n for j in range(self.width):\\n # yield i, j - 1, self.grid[i][j - 1]\\n yield j, i, self.dungeon.tile(Point(j, i))\\n\\n \\\"\\\"\\\"\\n def __iter__(self):\\n for i in range(self.height):\\n for j in range(self.width):\\n yield Point(x=self.x + j, y=self.y + i)\\n \\\"\\\"\\\"\",\n \"def area(base, height):\\n\\n return base * height\",\n \"def generatePolygons():\",\n \"def area(self):\\n raise Exception('area() is not implemented')\",\n \"def area(self):\\n raise Exception('area() is not implemented')\",\n \"def draw_area(horiz, vertic, step, obj, screen):\\n start_h = horiz\\n start_w = vertic\\n Area = []\\n Cols = []\\n for i in range(0,10):\\n for i in range(0,10):\\n Cols.append(obj(start_w, start_h, 20, 20, screen, '1'))\\n start_h += step\\n Area.append(Cols)\\n Cols = []\\n start_h = horiz\\n start_w += step\\n return Area\",\n \"def test_multi_area(self):\\n pass\",\n \"def area(self):\\n\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self):\\n\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self, tileID):\\n pass\",\n \"def build(self):\\n self._start = np.zeros_like(self._rooms.shape)\\n self._start[0] = random.randrange(self._rooms.shape[0])\\n position = self._start\\n egress = Direction.South\\n distance = 1\\n while position in self:\\n room = self[position]\\n room.egress = egress\\n room.distance = distance\\n yield position\\n options = {}\\n for direction in Direction.range():\\n if direction != egress:\\n new_position = position + direction.offset()\\n if new_position in self:\\n if self.is_sealed(new_position):\\n options[direction] = new_position\\n if options:\\n direction = random.choice(tuple(options.keys()))\\n room.remove_wall(direction)\\n position = options[direction]\\n egress = direction.reverse()\\n distance += 1\\n else:\\n position += egress.offset()\\n if position in self:\\n egress = self[position].egress\\n distance = self[position].distance\",\n \"def constructRectangle(self, area: int) -> List[int]:\\n w = int(area ** 0.5)\\n while area % w:\\n w -= 1\\n\\n return [area // w, w]\",\n \"def test_absolute_areas(self):\\n\\n assert len(self.test_shape.areas) == 4\\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1\",\n \"def area(self):\\n\\n raise Exception('Not implemented')\",\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):\\n self.quadrants_count = 4\\n self.quadrant_positions_count = 9\\n self.play_area = [Quadrant(self.quadrant_positions_count) for _ in range(self.quadrants_count)]\",\n \"def point_to_square_loop(source, area):\\n half_diag = np.sqrt(area/2)\\n xyz_hor = rotation(source[3]+90.0, 0.0)*half_diag\\n xyz_ver = rotation(source[3], source[4]+90.0)*half_diag\\n points = source[:3] + np.stack(\\n [xyz_hor, xyz_ver, -xyz_hor, -xyz_ver, xyz_hor])\\n return points\",\n \"def area(width, height):\\n return width * height\",\n \"def _create_room(new_map, room):\\n for x in range(room.x1 + 1, room.x2):\\n for y in range(room.y1 + 1, room.y2):\\n new_map.terrain[x][y] = 1\",\n \"def area(self):\\n return (self.baselength1 + self.baselength2)*self.height/2\",\n \"def NewBlock(self):\\n for i in self.matrix:\\n if 2 in i:\\n return()\\n blockType = self.bag.Choose()\\n subtractor = {\\\"I\\\" : 4, \\\"J\\\" : 3, \\\"L\\\" : 3, \\\"O\\\" : 2, \\\"S\\\" : 3, \\\"T\\\" : 3, \\\"Z\\\": 3}\\n x = random.randint(0, self.width - subtractor.get(blockType))\\n coords = []\\n if blockType == \\\"I\\\":\\n coords = [(x + i, 0) for i in range(4)]\\n elif blockType == \\\"J\\\":\\n coords = [(x + i, 0) for i in range(3)]\\n coords.append((x, 1))\\n elif blockType == \\\"L\\\":\\n coords = [(x + i, 0) for i in range(3)]\\n coords.append((x + 2, 1))\\n elif blockType == \\\"O\\\":\\n coords = [(x, 0), (x + 1, 0), (x, 1), (x + 1, 1)]\\n elif blockType == \\\"Z\\\":\\n coords = [(x, 0), (x + 1, 0), (x + 1, 1), (x + 2, 1)]\\n elif blockType == \\\"S\\\":\\n coords = [(x + 1, 0), (x + 2, 0), (x, 1), (x + 1, 1)]\\n elif blockType == \\\"T\\\":\\n coords = [(x, 0), (x + 1, 0), (x + 2, 0), (x + 1, 1)]\\n self.coords = coords\\n return(coords)\",\n \"def test_area(self):\\n r1 = Rectangle(3, 2)\\n self.assertEqual(r1.area(), 6)\\n\\n r2 = Rectangle(2, 10)\\n self.assertEqual(r2.area(), 20)\\n\\n r3 = Rectangle(10, 10)\\n self.assertEqual(r3.area(), 100)\",\n \"def test_absolute_shape_areas(self):\\n\\n assert self.test_shape.area == pytest.approx((math.pi * (10**2) * 2) + (math.pi * (2 * 10) * 30))\\n assert len(self.test_shape.areas) == 3\\n assert self.test_shape.areas.count(pytest.approx(math.pi * (10**2))) == 2\\n assert self.test_shape.areas.count(pytest.approx(math.pi * (2 * 10) * 30)) == 1\",\n \"def get_area(self):\\n raise NotImplementedError()\",\n \"def generate_level(self):\\n for _ in range(AMOUNT_REGIONS_TO_DRAW):\\n self._generate_next_blocks()\",\n \"def initialize_areas(self):\\n self._areas[1] = copy.copy(self._areas[0])\",\n \"def g_xy(self):\\n for x in range(self.size.x):\\n for y in range(self.size.y):\\n yield self.p[0] + Vect(x, y)\",\n \"def area(self):\\n self.convert_window(\\\"Area\\\", \\\"square meters\\\", [\\\"acres\\\", \\\"ares\\\", \\\"circular inches\\\", \\\"hectares\\\", \\\"hides\\\", \\\"roods\\\", \\\"square centimeters\\\", \\\"square feet(US & UK)\\\", \\\"square feet(US survey)\\\", \\\"square inches\\\", \\\"square kilometers\\\", \\\"square meters\\\", \\\"square miles\\\", \\\"square millimeters\\\", \\\"square of timber\\\", \\\"square rods or poles\\\", \\\"square yards\\\", \\\"townships\\\"])\",\n \"def gamearea_maker(self, shape=input('Circle or square?\\\\n'), dim=int(input('Dimension \\\\\\n (radius or side length ): \\\\n'))):\\n\\n self.gameAreaDim = dim # sort arguments\\n self.gameAreaShape = shape\\n self.cursor.penup()\\n self.cursor.shape('circle')\\n self.cursor.color('white')\\n self.cursor.turtlesize(0.2)\\n self.cursor.st()\\n self.slither.positions = [(0, 0)]\\n self.screen.tracer(0, 0)\\n self.screen.setup(self.screensize[0], self.screensize[1], 0, 0)\\n self.screen.bgcolor(self.mapEdgeColor)\\n self.screen.title = 'David\\\\'s slither game'\\n self.area.ht()\\n\\n if self.gameAreaShape in ['circle', 'Circle', 'C', 'c', 'cir', 'Cir', 'a really big big big circle', '1']:\\n self.gameAreaShape = 'circle'\\n self.area.penup()\\n self.area.setposition(0, -self.gameAreaDim)\\n self.area.pencolor(self.edgeRed)\\n self.area.pensize(1)\\n self.area.color(self.gameAreaColor)\\n self.area.begin_fill()\\n self.area.circle(self.gameAreaDim) # draw a circle area with radius\\n self.area.end_fill()\\n\\n else:\\n self.gameAreaShape = 'square'\\n self.area.penup()\\n self.area.setposition(-1/2*self.gameAreaDim, -1/2*self.gameAreaDim) # goes to the left bottom corner of\\n # the game area.\\n self.area.setheading(0)\\n self.area.pencolor(self.edgeRed)\\n self.area.pensize(1)\\n self.area.color(self.gameAreaColor)\\n self.area.begin_fill()\\n self.area.forward(self.gameAreaDim)\\n self.area.setheading(90)\\n self.area.forward(self.gameAreaDim)\\n self.area.setheading(180)\\n self.area.forward(self.gameAreaDim) # draw a square with side length\\n self.area.setheading(270)\\n self.area.forward(self.gameAreaDim)\\n self.area.end_fill()\",\n \"def explainAreaSmall(self):\\n \\n #EXPLANATION NO. 1\\n #fadeout the non-required areas\\n self.play(FadeOut(area_ABC_copy), FadeOut(area_ABD_copy),\\n FadeOut(geq_2), FadeOut(geq_1),\\n FadeOut(area_ABC), FadeOut(area_ABD))\\n \\n #expand the required area\\n self.play(area_ABE_copy.animate.scale(2).move_to(RIGHT*2))\\n\\n #surrounding text\\n abe_text_1 = always_redraw(\\n lambda : MathTex(\\\"=\\\", \\\"\\\\\\\\text{Area of } \\\\\\\\triangle ABE\\\").scale(0.8).next_to(area_ABE_copy, RIGHT)\\n )\\n\\n #half base height\\n abe_text_2 = always_redraw(\\n lambda : MathTex(\\\"=\\\", \\\"\\\\\\\\dfrac{1}{2}\\\", \\\"\\\\\\\\times\\\", \\\"\\\\\\\\text{base}\\\", \\\"\\\\\\\\times\\\", \\\"\\\\\\\\text{height}\\\").scale(0.8).next_to(area_ABE_copy, RIGHT)\\n )\\n\\n #write texts\\n self.play(Write(abe_text_1))\\n self.wait()\\n self.play(ReplacementTransform(abe_text_1[0], abe_text_2[0]),\\n ReplacementTransform(abe_text_1[1:], abe_text_2[1:]))\\n self.wait()\\n\\n #defining braces\\n abe_base_brace = always_redraw(\\n lambda : Brace(radius_ang, DOWN)\\n )\\n abe_base_brace_label = always_redraw(\\n lambda : MathTex(\\\"R\\\\\\\\cos\\\\\\\\theta\\\").scale(0.6).next_to(abe_base_brace, DOWN)\\n )\\n abe_height_brace = always_redraw(\\n lambda : Brace(radius_ang, LEFT)\\n )\\n abe_height_brace_label = always_redraw(\\n lambda : MathTex(\\\"R\\\\\\\\sin\\\\\\\\theta\\\").scale(0.6).next_to(abe_height_brace, LEFT)\\n )\\n\\n self.play(Write(abe_base_brace), Write(abe_height_brace))\\n self.play(Write(abe_base_brace_label), Write(abe_height_brace_label))\\n self.wait()\\n\\n \\n #back to editing the equation\\n abe_text_3 = always_redraw(\\n lambda : MathTex(\\\"=\\\", \\\"\\\\\\\\dfrac{1}{2}\\\", \\\"\\\\\\\\times\\\", \\\"R\\\\\\\\cos\\\\\\\\theta\\\", \\\"\\\\\\\\times\\\", \\\"R\\\\\\\\sin\\\\\\\\theta\\\").scale(0.8).next_to(area_ABE_copy, RIGHT)\\n )\\n\\n self.play(ReplacementTransform(abe_text_2[0:], abe_text_3[0:]))\\n self.wait(0.5)\\n self.play(FadeOut(abe_base_brace), FadeOut(abe_height_brace),\\n FadeOut(abe_base_brace_label), FadeOut(abe_height_brace_label))\\n \\n abe_text_4 = always_redraw(\\n lambda : MathTex(\\\"=\\\", \\\"\\\\\\\\dfrac{1}{2}\\\", \\\"\\\\\\\\times\\\", \\\"\\\\\\\\cos x\\\", \\\"\\\\\\\\times\\\", \\\"\\\\\\\\sin x\\\").scale(0.8).next_to(area_ABE_copy, RIGHT)\\n )\\n self.play(ReplacementTransform(abe_text_3[0:], abe_text_4[0:]))\\n\\n abe_text_5 = always_redraw(\\n lambda : MathTex(\\\"=\\\", \\\"\\\\\\\\dfrac{1}{2}\\\", \\\"\\\\\\\\sin x\\\", \\\"\\\\\\\\cos x\\\").scale(0.8).next_to(area_ABE_copy, RIGHT)\\n )\\n self.play(ReplacementTransform(abe_text_4[0:2], abe_text_5[0:2]),\\n ReplacementTransform(abe_text_4[2:], abe_text_5[2:]))\\n\\n #vgroup for drawing box\\n abe_group = VGroup(abe_text_5, area_ABE_copy)\\n abe_formula_box = SurroundingRectangle(abe_group, color=PINK)\\n\\n self.play(Write(abe_formula_box))\\n self.wait()\\n\\n #remove all elements\\n self.play(FadeOut(abe_formula_box), FadeOut(abe_text_5), FadeOut(area_ABE_copy), FadeOut(area_ABE))\",\n \"def hexagon_generator(edge_length, offset):\\n x, y = offset\\n for angle in range(0, 360, 60):\\n x += math.cos(math.radians(angle)) * edge_length\\n y += math.sin(math.radians(angle)) * edge_length\\n yield x, y\",\n \"def area(self):\\n return self.length*self.length\",\n \"def pattern_generator(self, x: int, y: int) -> float:\\n dis_corners = self.distance_from_corners(x, y)\\n dis_center = self.distance_from_center(x, y)\\n key = (dis_corners - dis_center)\\n return [x / key, y / key]\",\n \"def test_rectangle(self):\\n result = shape_area.rectangle_area(6,7)\\n self.assertEqual(result,26)\",\n \"def new_round(self):\\n # Incrementar la ronda actual\\n self.num_round += 1\\n # Actualizar los puntos de generacion de aliens\\n if self.num_round == 1:\\n self.total_alien_points = 3\\n else:\\n self.total_alien_points += int(self.num_round / 2)\\n self.alien_points = self.total_alien_points\\n\\n # Establecemos los aliens que vamos a generar en esta ronda\\n while self.alien_points > 0:\\n max_interval_value = 3\\n # Comprobamos que no haya pocos puntos para generar aliens\\n # y limitamos la generacion de aliens de alto nivel\\n if self.alien_points <= 2:\\n max_interval_value = 2\\n elif self.alien_points <= 2:\\n max_interval_value = 1\\n max_interval_value = min(max_interval_value, self.round_based_alien_limitation())\\n number = random.randint(0, 1)\\n if number == 0 or number == 1:\\n self.aliens_to_generate.append(0)\\n self.alien_points -= 1\\n elif number == 2:\\n self.aliens_to_generate.append(1)\\n self.alien_points -= 2\\n elif number == 3:\\n self.aliens_to_generate.append(2)\\n self.alien_points -= 3\",\n \"def FillupArea(self):\\r\\n\\r\\n drillsize = self.FromUserUnit(float(self.m_txtViaDrillSize.GetValue()))\\r\\n viasize = self.FromUserUnit(float(self.m_txtViaSize.GetValue()))\\r\\n step_x = self.FromUserUnit(float(self.m_txtHSpacing.GetValue()))\\r\\n step_y = self.FromUserUnit(float(self.m_txtVSpacing.GetValue()))\\r\\n clearance = self.FromUserUnit(float(self.m_txtClearance.GetValue()))\\r\\n self.randomize = self.m_chkRandomize.GetValue()\\r\\n self.clearance = clearance\\r\\n bbox = self.area.GetBoundingBox()\\r\\n top = bbox.GetTop()\\r\\n bottom = bbox.GetBottom()\\r\\n right = bbox.GetRight()\\r\\n left = bbox.GetLeft()\\r\\n netname = self.m_cbNet.GetStringSelection()\\r\\n netcode = self.board.GetNetcodeFromNetname(netname)\\r\\n # commit = pcbnew.COMMIT()\\r\\n viacount = 0\\r\\n x = left\\r\\n\\r\\n # Cycle trough area bounding box checking and implanting vias\\r\\n layer = self.area.GetLayer()\\r\\n\\r\\n while x <= right:\\r\\n y = top\\r\\n while y <= bottom:\\r\\n if self.randomize:\\r\\n xp = x + random.uniform(-1, 1) * step_x / 5\\r\\n yp = y + random.uniform(-1, 1) * step_y / 5\\r\\n else:\\r\\n xp = x\\r\\n yp = y\\r\\n\\r\\n if hasattr(pcbnew, 'VECTOR2I'):\\r\\n p = pcbnew.VECTOR2I(xp, yp)\\r\\n else:\\r\\n if(hasattr(pcbnew, 'wxPoint')):\\r\\n p = pcbnew.wxPoint(xp, yp)\\r\\n\\r\\n if self.area.HitTestFilledArea(layer, p, 0):\\r\\n via = pcbnew.PCB_VIA(self.board)\\r\\n via.SetPosition(p)\\r\\n via.SetLayer(layer)\\r\\n via.SetNetCode(netcode)\\r\\n # Set up via with clearance added to its size-> bounding box check will be OK in worst case, may be too conservative, but additional checks are possible if needed\\r\\n # TODO: possibly take the clearance from the PCB settings instead of the dialog\\r\\n # Clearance is all around -> *2\\r\\n via.SetDrill(drillsize + 2 * clearance)\\r\\n via.SetWidth(viasize + 2 * clearance)\\r\\n # via.SetTimeStamp(__timecode__)\\r\\n if not self.CheckOverlap(via):\\r\\n # Check clearance only if clearance value differs from 0 (disabled)\\r\\n if (clearance == 0) or self.CheckClearance(via, self.area, clearance):\\r\\n via.SetWidth(viasize)\\r\\n via.SetDrill(drillsize)\\r\\n self.board.Add(via)\\r\\n # commit.Add(via)\\r\\n self.pcb_group.AddItem(via)\\r\\n viacount += 1\\r\\n y += step_y\\r\\n x += step_x\\r\\n\\r\\n if viacount > 0:\\r\\n wx.MessageBox(_(u\\\"Implanted: %d vias!\\\") % viacount)\\r\\n # commit.Push()\\r\\n pcbnew.Refresh()\\r\\n else:\\r\\n wx.MessageBox(_(u\\\"No vias implanted!\\\"))\",\n \"def house ():\\n\\n poly (3,300,\\\"red\\\")\\n penup()\\n setposition(0,-300)\\n pendown()\\n poly (4,300,\\\"brown\\\")\\n penup()\\n setposition(100,-300)\\n pendown()\\n poly(4,100,\\\"green\\\") \\n\\n return None\",\n \"def area(self):\\n\\t\\treturn self.width * self.height\",\n \"def make_asteroid_generator(\\n *,\\n x: float,\\n y: float,\\n width: float,\\n height: float,\\n interval_ms: float,\\n initial_asteroids: int = 0,\\n) -> GameObject:\\n\\n game_object = game_objects.new_object()\\n\\n def spawn_asteroid():\\n make_asteroid(\\n x=random.uniform(x, x + width),\\n y=random.uniform(y, y + height),\\n vx=random.gauss(0, 0.03),\\n vy=random.gauss(0, 0.03),\\n )\\n\\n for _ in range(initial_asteroids):\\n spawn_asteroid()\\n\\n def generate_asteroids():\\n while True:\\n yield resume_after(delay_ms=interval_ms)\\n spawn_asteroid()\\n\\n GameObjectCoroutine(game_object, generate_asteroids()).start()\\n\\n return game_object\",\n \"def area(self):\\n num_rows = self.row_end - self.row_start\\n num_cols = self.col_end - self.col_start\\n area = num_rows*num_cols\\n return area\",\n \"def area(self):\\n\\t\\treturn self.width() * self.height()\",\n \"def create_room(room):\\n global map\\n for x in range(room.x1+1, room.x2):\\n for y in range(room.y1+1, room.y2):\\n map[x][y].blocked = False\\n map[x][y].block_sight = False\",\n \"def area(self):\\n\\t\\treturn self.height * self.height\",\n \"def __iter__(self):\\n pt = (self.x, self.y)\\n for i in pt:\\n yield i\",\n \"def asteroidCreator(numCorner,win):\\n\\n xCoor = []\\n yCoor = []\\n\\n # Creating coordinates of the points\\n coorRange = [i for i in range(-10,10) if i not in [0]] # to avoid 0\\n\\n for i in range(numCorner):\\n xCoor.append(round(random.choice(coorRange)*random.uniform(0.01,1),2))\\n yCoor.append(round(random.choice(coorRange)*random.uniform(0.01,1),2))\\n\\n # Sorting the coordinates\\n bubbleSort(xCoor,len(xCoor))\\n bubbleSort(yCoor,len(yCoor))\\n\\n\\n # Isolating the extreme points\\n xSmallest = xCoor.pop(0)\\n xLargest = xCoor.pop()\\n\\n ySmallest = yCoor.pop(0)\\n yLargest = yCoor.pop()\\n\\n # Shuffle the coordinates\\n random.shuffle(xCoor)\\n random.shuffle(yCoor)\\n\\n # Divide them into two sets\\n xCoorLower = xCoor[:len(xCoor)//2]\\n xCoorUpper = xCoor[len(xCoor)//2:]\\n\\n yCoorLower = yCoor[:len(yCoor)//2]\\n yCoorUpper = yCoor[len(yCoor)//2:]\\n\\n # Append back the extreme points, and sort them again\\n xCoorLower.append(xSmallest)\\n xCoorLower.append(xLargest)\\n xCoorUpper.append(xSmallest)\\n xCoorUpper.append(xLargest)\\n\\n yCoorLower.append(ySmallest)\\n yCoorLower.append(yLargest)\\n yCoorUpper.append(ySmallest)\\n yCoorUpper.append(yLargest)\\n\\n bubbleSort(xCoorLower,len(xCoorLower))\\n bubbleSort(xCoorUpper,len(xCoorUpper))\\n bubbleSort(yCoorLower,len(yCoorLower))\\n bubbleSort(yCoorUpper,len(yCoorUpper))\\n\\n # Getting the vector lengths out of the points\\n # We will get vectors in 4 directions from 4 lists\\n xVectorLengths = []\\n yVectorLengths = []\\n\\n for i in range(len(xCoorLower)-1):\\n xVectorLengths.append(xCoorLower[i]-xCoorLower[i+1])\\n for i in range(len(xCoorUpper)-1):\\n xVectorLengths.append(xCoorUpper[i+1]-xCoorUpper[i])\\n for i in range(len(yCoorLower)-1):\\n yVectorLengths.append(yCoorLower[i]-yCoorLower[i+1])\\n for i in range(len(yCoorUpper)-1):\\n yVectorLengths.append(yCoorUpper[i+1]-yCoorUpper[i])\\n\\n random.shuffle(xVectorLengths)\\n random.shuffle(yVectorLengths)\\n\\n # Creating the vectors\\n vectors = []\\n defaultVector = [0,0]\\n\\n for i in range(len(xVectorLengths)):\\n defaultVector[0] = round(xVectorLengths[i],2)\\n defaultVector[1] = round(yVectorLengths[i],2)\\n vectors.append(defaultVector.copy())\\n\\n # Sorting vectors by their angle\\n sortedVectors = []\\n quadrant1 = []\\n quadrant2 = []\\n quadrant3 = []\\n quadrant4 = []\\n\\n ### Dividing them by quadrants\\n for vector in vectors:\\n if vector[0] >= 0 and vector[1] >= 0:\\n quadrant1.append(vector)\\n elif vector[0] <= 0 and vector[1] >= 0:\\n quadrant2.append(vector)\\n elif vector[0] <= 0 and vector[1] <= 0:\\n quadrant3.append(vector)\\n elif vector[0] >= 0 and vector[1] <= 0:\\n quadrant4.append(vector)\\n\\n ### Sorting them inside the quadrants\\n quadrant1 = angleSort(quadrant1,1,len(quadrant1))\\n quadrant2 = angleSort(quadrant2,2,len(quadrant2))\\n quadrant3 = angleSort(quadrant3,3,len(quadrant3))\\n quadrant4 = angleSort(quadrant4,4,len(quadrant4))\\n\\n ### Adding them up in order\\n for vector in quadrant1:\\n sortedVectors.append(vector)\\n for vector in quadrant2:\\n sortedVectors.append(vector)\\n for vector in quadrant3:\\n sortedVectors.append(vector)\\n for vector in quadrant4:\\n sortedVectors.append(vector)\\n\\n # Creating the points for the polygon\\n points = []\\n points = vectorsToPoints(sortedVectors,points)\\n\\n rightEdge = 0\\n leftEdge = 0\\n upperEdge = 0\\n lowerEdge = 0\\n\\n # getting the boundaries for the asteroid\\n for point in points:\\n if point[0] > rightEdge:\\n rightEdge = point[0]\\n elif point[0] < leftEdge:\\n leftEdge = point[0]\\n if point[1] > upperEdge:\\n upperEdge = point[1]\\n elif point[1] < lowerEdge:\\n lowerEdge = point[1]\\n\\n # Width and height are only required since it is a child of rotating_block class\\n width = rightEdge - leftEdge\\n height = upperEdge - lowerEdge\\n\\n centerPoint = [(rightEdge + leftEdge) / 2 , (upperEdge + lowerEdge) / 2]\\n\\n asteroid = pho.Asteroid(win,width,height,points,centerPoint[0],centerPoint[1])\\n\\n return asteroid\",\n \"def area(self):\\n return(self.__width * self.__height)\",\n \"def decorate_scene():\\n make_polygon( (100,100),(120,140),(270,70) )\\n make_polygon( (300,10), (300,550), (340,452),(380,300), (330,50))\\n make_polygon( (200,450), (100,450), (100,500), (200,500) )\\n make_polygon( (130,320), (150,300), (140,280) )\\n return\",\n \"def create_block():\\n global BLOCK\\n posx = SEG_SIZE * random.randint(1, (WIDTH-SEG_SIZE) / SEG_SIZE)\\n posy = SEG_SIZE * random.randint(1, (HEIGHT-SEG_SIZE) / SEG_SIZE)\\n BLOCK = c.create_oval(posx, posy,\\n posx+SEG_SIZE, posy+SEG_SIZE,\\n fill=\\\"red\\\")\\n # print(posx, posy)\\n return posx, posy\",\n \"def rectangle_area(base, height):\\n return (base * height)\",\n \"def __maze_generator(self):\\n grid = []\\n for row in range(self.__row_count):\\n new_row = []\\n for col in range(self.__col_count):\\n new_row.append(RoomFactory.create_room([row, col]))\\n if col > 0:\\n new_row[col].left = new_row[col - 1]\\n new_row[col - 1].right = new_row[col]\\n if row > 0:\\n new_row[col].up = grid[row - 1][col]\\n grid[row - 1][col].down = new_row[col]\\n grid.append(new_row)\\n return grid\",\n \"def getArea(rob):\\r\\n def dfs(visit, i, j):\\r\\n visit.add((i, j))\\r\\n for k in range(4):\\r\\n newi, newj = i + x[k], j + y[k]\\r\\n if (newi, newj) in visit or not rob.move(k):\\r\\n continue\\r\\n dfs(visit, newi, newj)\\r\\n rob.move((k + 2) % 4)\\r\\n visit = set()\\r\\n dfs(visit, 0, 0)\\r\\n return len(visit)\",\n \"def make_board(self):\\n generate = lambda: random.randint(1, 100) in range(1, self.p_pit+1)\\n some_number = self.some_number\\n agent = Agent(some_number)\\n agent.program = Oozeplorer_Percept(agent)\\n self.add_agent(agent)\\n gold = Gold()\\n self.add_thing(gold, None)\\n for row in range(1, some_number + 1):\\n for col in range(1, some_number + 1):\\n valid_spot = (row, col) != gold.location and (row, col) != (1, 1)\\n if valid_spot and generate():\\n t_pt = Pit()\\n t_pt.location = (row, col)\\n self.things.append(t_pt)\",\n \"def createEntrances(self):\\n\\t\\tfor x in range(self.width):\\n\\t\\t\\tif self.isFloor(x, 1):\\n\\t\\t\\t\\tself.setFloor(x, 0)\\n\\t\\t\\t\\tbreak\\n\\t\\tfor x in range(self.width - 1, 0, -1):\\n\\t\\t\\tif self.isFloor(x, self.height - 2):\\n\\t\\t\\t\\tself.setFloor(x, self.height - 1)\\n\\t\\t\\t\\tbreak\",\n \"def stage_1_generator(option):\\n # Stage Size + Player Starting Position\\n STAGE_1 = ([10,10], [1,1])\\n\\n # Non-Ocean tiles\\n STAGE_1_TILES = { \\n \\\"1,2\\\":\\\"rock\\\",\\n \\\"1,3\\\":\\\"mountain\\\",\\n \\\"2,4\\\":\\\"rock\\\",\\n \\\"2,7\\\":\\\"rock\\\",\\n \\\"2,8\\\":\\\"rock\\\",\\n \\\"3,3\\\":\\\"rock\\\",\\n \\\"3,4\\\":\\\"rock\\\",\\n \\\"3,8\\\":\\\"mountain\\\",\\n \\\"3,9\\\":\\\"rock\\\",\\n \\\"3,10\\\":\\\"rock\\\",\\n \\\"4,4\\\":\\\"rock\\\",\\n \\\"4,5\\\":\\\"mountain\\\",\\n \\\"5,6\\\":\\\"rock\\\",\\n \\\"6,1\\\":\\\"rock\\\",\\n \\\"6,2\\\":\\\"rock\\\",\\n \\\"6,7\\\":\\\"rock\\\",\\n \\\"6,10\\\":\\\"rock\\\",\\n \\\"7,1\\\":\\\"rock\\\",\\n \\\"7,2\\\":\\\"rock\\\",\\n \\\"7,6\\\":\\\"rock\\\",\\n \\\"7,10\\\":\\\"rock\\\",\\n \\\"8,5\\\":\\\"rock\\\",\\n \\\"8,6\\\":\\\"rock\\\",\\n \\n \\\"8,10\\\":\\\"rock\\\",\\n \\\"9,1\\\":\\\"sign\\\",\\n \\\"9,3\\\":\\\"rock\\\",\\n \\\"9,4\\\":\\\"rock\\\",\\n \\\"9,9\\\":\\\"rock\\\",\\n \\\"9,10\\\":\\\"rock\\\",\\n \\\"10,1\\\":\\\"cave\\\",\\n \\\"10,3\\\":\\\"rock\\\",\\n \\\"10,4\\\":\\\"cave\\\",\\n \\\"10,8\\\":\\\"rock\\\",\\n \\\"10,9\\\":\\\"rock\\\",\\n \\\"10,10\\\":\\\"rock\\\",\\n \\n \\\"1,10\\\":\\\"end\\\",\\n }\\n\\n # Special Tiles that trigger an event\\n STAGE_1_SPECIAL = { \\n \\\"1,10\\\":\\\"end\\\",\\n \\\"9,1\\\":\\\"sign_cave\\\",\\n \\\"10,1\\\":\\\"cave_entrance_1\\\",\\n \\\"10,4\\\":\\\"cave_entrance_2\\\",\\n \\\"1,9\\\":\\\"dark_water\\\",\\n \\\"2,9\\\":\\\"dark_water\\\",\\n \\\"2,10\\\":\\\"dark_water\\\"\\n }\\n\\n # Decide what data to return\\n if option == \\\"stage\\\":\\n return STAGE_1\\n elif option == \\\"tiles\\\":\\n return STAGE_1_TILES\\n elif option == \\\"special\\\":\\n return STAGE_1_SPECIAL\\n else:\\n print(\\\"Something Broke! map_generator_1\\\")\",\n \"def get_area(self):\\n dic = {}\\n for _ in range(self.tries):\\n x = randint(0, self.width - 1)\\n y = randint(0, self.height - 1)\\n target = self.service.info(x, y)\\n if target in dic.keys():\\n dic[target] += 1\\n else:\\n dic[target] = 1\\n for index in dic.keys():\\n dic[index] = dic[index] / self.tries * self.area\\n return dic\",\n \"def old_data(self) -> Generator[Tuple[int, int], Tuple[bool, bool], None]:\\n x = self.x_start\\n y = self.y_start\\n yield x, y\\n while True:\\n x += self.x_rate\\n y += self.y_rate\\n # Get whether the object has collided with a wall\\n horizontal_hit, vertical_hit = yield int(x), int(y)\\n # Simply reverse the x/y rate depending on the wall type\\n # TODO: could add a little variance in the angle with every bounce\\n if horizontal_hit:\\n self.y_rate *= -1\\n if vertical_hit:\\n self.x_rate *= -1\",\n \"def village_gen(x1, villages, y_list, z1, halfDoorsInVillage, emptySpaces, axis, tick, cat):\\n cat2 = cat[\\\"data\\\"]\\n doors_coords_lists = []\\n doors_set = set()\\n for y in y_list:\\n doors_coords_lists += village_doors_coordinates(x1, villages, y, z1, halfDoorsInVillage, emptySpaces, axis)\\n for vill_coords_list in doors_coords_lists:\\n for single_door_coord in vill_coords_list:\\n doors_set.add(tuple(single_door_coord))\\n del_door(cat2['Villages'], doors_set)\\n for coordinates_list in doors_coords_lists:\\n vil = Village.create_village(tick)\\n for x, y, z in coordinates_list:\\n vil.add_door(create_door(tick, x, y, z))\\n cat2['Villages'].append(vil.get_vil())\",\n \"def area(self):\\r\\n return self.width * self.height\",\n \"def set_area(self, area=0.0):\\n self.area = area\",\n \"def area(self):\\n return self.width*self.height\",\n \"def boundaries_next(*args):\\n return _ida_hexrays.boundaries_next(*args)\",\n \"def __init__(self, total_length:int, initial_y:int, screen, number:int, debug:bool = False):\\n\\n #Call the superclass\\n super().__init__()\\n\\n #Create the group of blocks based on x and y and add them to the group\\n for k in range(number):\\n for i in range(-1,2):\\n for j in range(-2,3):\\n self.add(Block(total_length * (k+1) // (number+1) + 10*j, initial_y + 10*i, screen, debug = debug))\",\n \"def nextGen(self):\\n\\n p = []\\n while len(p) < len(self.p):\\n #select mates and produce offspring\\n p1, p2 = self.select()\\n offspring = self.mate(p1, p2)\\n\\n #put the offspring in the next generation (with mutation)\\n for child in offspring:\\n child=self.mutate(child)\\n p.append(child)\\n \\n\\n # the world belongs to the new generation\\n return p\",\n \"def area(self):\\n return (self.width * self.height)\",\n \"def create_block(self, location_list, POI_locations):\\n\\n \\n for i in range(len(location_list)):\\n this_cell = self.grid.get_cell_list_contents(location_list[i])\\n\\n for agent in this_cell:\\n if type(agent) is nodeAgent:\\n agent.block = True\\n\\n for i in POI_locations:\\n agent.locations[i] = 10000\",\n \"def attackArea(initPair, connect):\\r\\n area = []\\r\\n opts = ((1, 0), (0, 1), (1, 1), (1, -1))\\r\\n (x, y) = initPair\\r\\n\\r\\n for pair in opts:\\r\\n (x1, y1) = pair\\r\\n\\r\\n for s in (1, -1):\\r\\n for i in range(1, connect):\\r\\n x2 = x + x1 * i * s\\r\\n y2 = y + y1 * i * s\\r\\n area.append((x2, y2))\\r\\n\\r\\n return area\",\n \"def get_dancefloor_area(self):\\n cords = []\\n\\n x1 = self.coordinates[0]\\n y1 = self.coordinates[1]\\n x2 = self.coordinates[0] + self.width\\n y2 = self.coordinates[1] + self.height\\n if self.width <= 0:\\n x1, x2 = x2, x1\\n if self.height <= 0:\\n y1, y2 = y2, y1\\n\\n for x in range(x1, x2):\\n for y in range(y1, y2):\\n if x % 20 == 0 and y % 20 == 0:\\n cords.append([x, y])\\n return cords\",\n \"def __iter__(self):\\n while (self.pointsleft > 0):\\n current = min(self.pointsleft, self.settings.LOCALSKIPNUM)\\n for i in range(current):\\n self.add(self.fabric.getcoordinate())\\n self.pointsleft -= self.settings.LOCALSKIPNUM\\n self.pointscontroller.set(self.graph)\\n yield self.graph\",\n \"def _create_drawing_area(self):\\n\\n self.drawing_x = -self.size/2 + self.margin\\n self.drawing_y = self.size/2 - self.margin\\n self.drawing_width = self.size - self.margin * 2\\n self.drawing_height = (self.size/2 + self.flat_fragment) - self.margin * 2\\n \\n self.drawing_x_step = self.drawing_width \\n self.drawing_y_step = self.drawing_height\",\n \"def generate(self):\\n self.generate_points()\\n self.generate_edges()\",\n \"def genHome(grid, house):\\n house.random_x(grid)\\n house.random_y(grid)\\n return generic.placeHouse(grid, house)\",\n \"def getLandmarkGenerator(shape, frame_width, frame_height):\\n\\n # load landmarks file\\n shape = read_table(shape, delim_whitespace=True, header=None)\\n\\n # deduce number of landmarks from file dimension\\n _, d = shape.shape\\n n_points = (d - 2) / 2\\n\\n # t is the time sent by the frame generator\\n t = yield\\n\\n shapes = []\\n currentT = None\\n\\n for _, row in shape.iterrows():\\n\\n T = float(row[0])\\n identifier = int(row[1])\\n landmarks = np.float32(list(pairwise(\\n [coordinate for coordinate in row[2:]])))\\n landmarks[:, 0] = np.round(landmarks[:, 0] * frame_width)\\n landmarks[:, 1] = np.round(landmarks[:, 1] * frame_height)\\n\\n # load all shapes from current frame\\n # and only those shapes\\n if T == currentT or currentT is None:\\n shapes.append((identifier, landmarks))\\n currentT = T\\n continue\\n\\n # once all shapes at current time are loaded\\n # wait until t reaches current time\\n # then returns all shapes at once\\n\\n while True:\\n\\n # wait...\\n if currentT > t:\\n t = yield t, []\\n continue\\n\\n # return all shapes at once\\n t = yield currentT, shapes\\n\\n # reset current time and corresponding shapes\\n shapes = [(identifier, landmarks)]\\n currentT = T\\n break\\n\\n while True:\\n t = yield t, []\",\n \"def shape(self):\",\n \"def shape(self):\",\n \"def generate_grid():\\n y_offset = -10\\n for a in range(20):\\n # Line 1\\n # Adds offset to the x position of the squares\\n x_offset = 10\\n for b in range(1):\\n # Adds offset to the y position of the squares\\n y_offset += 20\\n for c in range(20):\\n # Prints a row of squares(5 squares along the x)\\n square(x_offset, y_offset, 20, 20, dark_green)\\n for d in range(1):\\n # Adds x offset for the next line of squares on the y axis\\n x_offset += 40\\n # Line 2 (needs 2 lines because the offset of each line)\\n # Adds offset to the x position of the squares\\n x_offset = 30\\n for e in range(1):\\n # Adds offset to the y position of the squares\\n y_offset += 20\\n for f in range(20):\\n # Prints a row of squares(5 squares along the x)\\n square(x_offset, y_offset, 20, 20, dark_green)\\n for g in range(1):\\n # Adds x offset for the next line of squares on the y axis\\n x_offset += 40\",\n \"def Intarea( xc, yc, r, x0, x1, y0, y1):\\n\\n#\\n# Shift the objects so that the circle is at the origin.\\n#\\n x0 = x0 - xc\\n y0 = y0 - yc\\n x1 = x1 - xc\\n y1 = y1 - yc\\n\\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )\",\n \"def new_tile(self):\\n rowm, colm = self.get_ava_index()\\n value = 2 if random() <= 0.90 else 4\\n self.set_tile(rowm, colm, value)\\n print rowm,colm,value\",\n \"def boundaries_new(*args):\\n return _ida_hexrays.boundaries_new(*args)\",\n \"def __init__(self, NW, SE):\\n self.type = \\\"HOLD AREA\\\"\\n self.NW = NW\\n self.SE = SE\\n self.ctr = [(self.NW[0] + self.SE[0]) / 2, (self.NW[1] + self.SE[1]) / 2]\",\n \"def clean_area(screen,origin,width,height,color):\\r\\n ox,oy = origin\\r\\n points = [(ox,oy),(ox+width,oy),(ox+width,oy+height),(ox,oy+height),(ox,oy)]\\r\\n pygame.draw.polygon(screen, color, points, 0)\",\n \"def advance(self):\\n\\t\\tself.level += 1\\t\\n\\t\\t\\\"\\\"\\\"Creating the new wave of asteroids. Each level starts with the number of asteroids equal to the level number. So, the first level starts with only one asteroid, the second with two, and so on. Now, creating a bunch of asteroids is easy, but I need to make sure that no new asteroid is created right on top of the ship. Otherwise, the ship will explode just as the new level begins.\\\"\\\"\\\"\\n\\t\\t# amount of space around ship to preserve when creating asteroids\\n\\t\\tBUFFER = 150 #BUFFER is a constant for the amount of safe space needed around the ship. BUFFER=x_min+y_min\\n\\t\\t# create new asteroids\\n\\t\\tfor i in range(self.level):\\n\\t\\t\\t# calculate an x and y at least BUFFER distance from the ship\\n\\t\\t\\t# choose minimum distance along x-axis and y-axis\\n\\t\\t\\tx_min = random.randrange(BUFFER)# x_min is the minimum distance the new asteroid should be from the ship along the x-axis,\\n\\t\\t\\ty_min = BUFFER - x_min # y_min is the minimum distance that the new asteroid should be from the ship alongthe y-axis\\n\\t\\t\\t# choose distance along x-axis and y-axis based on minimum distance\\n\\t\\t\\tx_distance = random.randrange(x_min, games.screen.width - x_min) # x_distance is the distance from the ship for the new asteroid along the x-axis, It is a randomly\\n\\t\\t\\t#selected number that ensures that the new asteroid will be at least x_min distance from the ship\\n\\t\\t\\ty_distance = random.randrange(y_min, games.screen.height - y_min) # y_distance is the distance from the ship for the new asteroid along the y-axis. It is a randomly #selected number that ensures that the new asteroid will be at least y_min distance from the ship\\n\\t\\t\\t# calculate location based on distance\\n\\t\\t\\tx = self.ship.x + x_distance #x is the x-coordinate for the new asteroid\\n\\t\\t\\ty = self.ship.y + y_distance #y is the y-coordinate for the new asteroid\\n\\t\\t\\t# wrap around screen, if necessary\\n\\t\\t\\tx %= games.screen.width\\n\\t\\t\\ty %= games.screen.height\\n\\t\\t\\t# create the asteroid\\n\\t\\t\\tnew_asteroid = Asteroid(game = self,x = x, y = y,size = Asteroid.LARGE)\\n\\t\\t\\tgames.screen.add(new_asteroid)\\n\\t\\t\\t# display level number\\n\\t\\tlevel_message = games.Message(value = \\\"Level \\\" + str(self.level),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t size = 40, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t color = color.yellow,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t x = games.screen.width/2,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t y = games.screen.width/10,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t lifetime = 3 * games.screen.fps,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t is_collideable = False)\\n\\t\\tgames.screen.add(level_message)\\n\\t\\t\\t\\n\\t\\t\\t# play new level sound (except at first level)\\n\\t\\tif self.level > 1:\\n\\t\\t\\tself.sound.play()\",\n \"def iter_coords():\\n yield (0, 0)\\n incr = 0\\n x = 1\\n y = 0\\n\\n while True:\\n incr += 2\\n\\n top = y + incr - 1\\n bot = y - 1\\n left = x - incr\\n right = x\\n\\n yield (x, y)\\n while y < top:\\n y += 1\\n yield (x, y)\\n\\n while x > left:\\n x -= 1\\n yield (x, y)\\n\\n while y > bot:\\n y -= 1\\n yield (x, y)\\n\\n while x < right:\\n x += 1\\n yield (x, y)\\n\\n x += 1\",\n \"def area(self):\\n return (self.__width * self.__height)\",\n \"def area(self):\\n return (self.__width * self.__height)\",\n \"def nine_regions(self):\\n\\n coordinateList = []\\n\\n # Top left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Top center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] ) \\n coordinateList.append( [x, y] )\\n\\n # Top right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * ( 1.0 - self.ratioTopLeft[IDX_X] ) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * self.ratioTopLeft[IDX_Y] )\\n coordinateList.append( [x, y] )\\n\\n # Center left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Center right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * 0.5 - self.regionSize[IDX_HEIGHT] / 2 )\\n coordinateList.append( [x, y] )\\n\\n # Bottom left.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * self.ratioTopLeft[IDX_X] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom center.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * 0.5 - self.regionSize[IDX_WIDTH] / 2 )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n # Bottom right.\\n x = (int)( self.oriImgSize[IDX_WIDTH] * (1.0 - self.ratioTopLeft[IDX_X]) - self.regionSize[IDX_WIDTH] )\\n y = (int)( self.oriImgSize[IDX_HEIGHT] * (1.0 - self.ratioTopLeft[IDX_Y]) - self.regionSize[IDX_HEIGHT] )\\n coordinateList.append( [x, y] )\\n\\n return coordinateList\",\n \"def generate_area_file(app):\\n sphinx.util.logging.getLogger('make_areas').info('Generating areas file...')\\n\\n output = Path(app.srcdir) / 'api' / 'areas.rst'\\n with output.open('w') as table:\\n header = f\\\"\\\"\\\"\\n .. (comment): DO NOT EDIT this file.\\n .. It is auto-generated by running : {__file__}\\n .. Please adjust by making changes there.\\n .. It is included in the repository only to aid detection of changes.\\n .. _api-areas:\\n\\n MetPy Area List\\n ===============\\n\\n\\n \\\"\\\"\\\"\\n\\n table.write(textwrap.dedent(header))\\n\\n for ind, area in enumerate(named_areas):\\n extent = named_areas[area].bounds\\n longname = named_areas[area].description\\n\\n area_header = area + ' - ' + longname + '\\\\n'\\n table.write(area_header)\\n table.write('-' * len(area_header) + '\\\\n\\\\n')\\n\\n if area in states_provinces:\\n code = textwrap.dedent(f\\\"\\\"\\\"\\n .. plot::\\n\\n import matplotlib.pyplot as plt\\n import cartopy.crs as ccrs\\n import cartopy.feature as cfeature\\n from metpy.plots import named_areas\\n\\n proj = named_areas['{area}'].projection\\n\\n plt.figure(figsize=(8, 10))\\n ax = plt.subplot(111, projection=proj)\\n ax.set_extent({extent}, ccrs.PlateCarree())\\n ax.add_feature(cfeature.STATES.with_scale('10m'), edgecolor='grey',\\n linewidth=0.75)\\n ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=1.1)\\n ax.add_feature(cfeature.BORDERS.with_scale('10m'), edgecolor='black')\\n ax.set_title(f'area={area} name={named_areas[area].description}')\\n\\n\\n \\\"\\\"\\\")\\n\\n else:\\n code = textwrap.dedent(f\\\"\\\"\\\"\\n .. plot::\\n\\n import matplotlib.pyplot as plt\\n import cartopy.crs as ccrs\\n import cartopy.feature as cfeature\\n from metpy.plots import named_areas\\n\\n proj = named_areas['{area}'].projection\\n\\n plt.figure(figsize=(8, 10))\\n ax = plt.subplot(111, projection=proj)\\n ax.set_extent({extent}, ccrs.PlateCarree())\\n ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=1.1)\\n ax.add_feature(cfeature.BORDERS.with_scale('10m'), edgecolor='black')\\n ax.set_title(f'area={area} name={named_areas[area].description}')\\n\\n\\n \\\"\\\"\\\")\\n table.write(code)\\n if not app.config.metpy_generate_all_areas and ind >= 1:\\n break\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def __init__(self, area_extents: np.ndarray):\\n self.area_extents = area_extents\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7147663","0.6909164","0.65530187","0.63511145","0.61740553","0.6143072","0.6023134","0.6012145","0.6012145","0.6009092","0.5989456","0.59823465","0.59821725","0.5962189","0.5962189","0.5938174","0.593229","0.5910889","0.5910889","0.5908722","0.5890229","0.58568406","0.5818903","0.58001167","0.57754564","0.577481","0.574579","0.5726873","0.5690797","0.56663793","0.5620298","0.5610193","0.5601383","0.5595219","0.5590649","0.5533742","0.55275863","0.55204505","0.55157036","0.5512582","0.5507243","0.5491094","0.549021","0.547505","0.54730046","0.5470502","0.54640496","0.5462648","0.54573894","0.54568136","0.5453421","0.5448584","0.54466856","0.5438427","0.5437318","0.54367095","0.54010606","0.5395506","0.53952086","0.53921795","0.53920525","0.53888375","0.53856325","0.53719896","0.5364536","0.53588265","0.53575224","0.5349705","0.5342257","0.5334832","0.5328681","0.532638","0.53188133","0.5318416","0.5317312","0.53153634","0.530922","0.53090686","0.530448","0.52985716","0.5298123","0.5293364","0.529045","0.529045","0.5283901","0.5281373","0.52809054","0.5276261","0.5270604","0.5265473","0.5256208","0.5247951","0.52475303","0.52475303","0.5244452","0.52412665","0.5236749","0.5236749","0.5236749","0.5234029"],"string":"[\n \"0.7147663\",\n \"0.6909164\",\n \"0.65530187\",\n \"0.63511145\",\n \"0.61740553\",\n \"0.6143072\",\n \"0.6023134\",\n \"0.6012145\",\n \"0.6012145\",\n \"0.6009092\",\n \"0.5989456\",\n \"0.59823465\",\n \"0.59821725\",\n \"0.5962189\",\n \"0.5962189\",\n \"0.5938174\",\n \"0.593229\",\n \"0.5910889\",\n \"0.5910889\",\n \"0.5908722\",\n \"0.5890229\",\n \"0.58568406\",\n \"0.5818903\",\n \"0.58001167\",\n \"0.57754564\",\n \"0.577481\",\n \"0.574579\",\n \"0.5726873\",\n \"0.5690797\",\n \"0.56663793\",\n \"0.5620298\",\n \"0.5610193\",\n \"0.5601383\",\n \"0.5595219\",\n \"0.5590649\",\n \"0.5533742\",\n \"0.55275863\",\n \"0.55204505\",\n \"0.55157036\",\n \"0.5512582\",\n \"0.5507243\",\n \"0.5491094\",\n \"0.549021\",\n \"0.547505\",\n \"0.54730046\",\n \"0.5470502\",\n \"0.54640496\",\n \"0.5462648\",\n \"0.54573894\",\n \"0.54568136\",\n \"0.5453421\",\n \"0.5448584\",\n \"0.54466856\",\n \"0.5438427\",\n \"0.5437318\",\n \"0.54367095\",\n \"0.54010606\",\n \"0.5395506\",\n \"0.53952086\",\n \"0.53921795\",\n \"0.53920525\",\n \"0.53888375\",\n \"0.53856325\",\n \"0.53719896\",\n \"0.5364536\",\n \"0.53588265\",\n \"0.53575224\",\n \"0.5349705\",\n \"0.5342257\",\n \"0.5334832\",\n \"0.5328681\",\n \"0.532638\",\n \"0.53188133\",\n \"0.5318416\",\n \"0.5317312\",\n \"0.53153634\",\n \"0.530922\",\n \"0.53090686\",\n \"0.530448\",\n \"0.52985716\",\n \"0.5298123\",\n \"0.5293364\",\n \"0.529045\",\n \"0.529045\",\n \"0.5283901\",\n \"0.5281373\",\n \"0.52809054\",\n \"0.5276261\",\n \"0.5270604\",\n \"0.5265473\",\n \"0.5256208\",\n \"0.5247951\",\n \"0.52475303\",\n \"0.52475303\",\n \"0.5244452\",\n \"0.52412665\",\n \"0.5236749\",\n \"0.5236749\",\n \"0.5236749\",\n \"0.5234029\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5819086"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":87498,"cells":{"query":{"kind":"string","value":"Find a suitable binarytree structure"},"document":{"kind":"string","value":"def linkage(self):\n self.tree = {}\n un_linked = []\n for i in range(len(self.leaves)):\n leaf = self.leaves[i]\n un_linked.append({\n 'id': i,\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': leaf,\n 'children': []\n })\n pass\n while len(un_linked) > 1:\n # for i in tqdm(range(len(un_linked))):\n # print(\"Linking... {} nodes left\".format(len(un_linked)))\n for node in un_linked:\n for d in node['set']:\n node['x'] += d['x']\n node['y'] += d['y']\n node['value'] += d['value']\n pass\n node['x'] /= len(node['set'])\n node['y'] /= len(node['set'])\n node['value'] /= len(node['set'])\n pass\n # min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2) \\\n # * self._alpha + (un_linked[1]['value'] - un_linked[0]['value']) * (1 - self._alpha)\n min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2)\n min_cp = [0, 1]\n for i in range(len(un_linked) - 1):\n for j in range(i + 1, len(un_linked)):\n # dif = self._alpha * ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n # + (un_linked[j]['x'] - un_linked[i]['x']) ** 2) \\\n # + (1 - self._alpha) * (un_linked[j]['value'] - un_linked[i]['value'])\n dif = ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n + (un_linked[j]['x'] - un_linked[i]['x']) ** 2)\n if dif < min_dif:\n min_dif = dif\n min_cp = [i, j]\n pass\n pass\n pass\n set_a = []\n for each in un_linked[min_cp[0]]['set']:\n set_a.append(each)\n pass\n for each in un_linked[min_cp[1]]['set']:\n set_a.append(each)\n pass\n next_un_linked = []\n new_children = []\n if len(un_linked[min_cp[0]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[0]]['children'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[0]]['id'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n if len(un_linked[min_cp[1]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[1]]['children'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[1]]['id'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n next_un_linked.append({\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': set_a,\n 'children': new_children\n })\n del un_linked[min_cp[0]]['set']\n del un_linked[min_cp[0]]['x']\n del un_linked[min_cp[0]]['y']\n # del un_linked[min_cp[0]]['value']\n del un_linked[min_cp[1]]['set']\n del un_linked[min_cp[1]]['x']\n del un_linked[min_cp[1]]['y']\n # del un_linked[min_cp[1]]['value']\n for s in range(len(un_linked)):\n if s not in min_cp:\n next_un_linked.append(un_linked[s])\n pass\n pass\n un_linked = next_un_linked\n pass\n del un_linked[0]['set']\n del un_linked[0]['x']\n del un_linked[0]['y']\n # del un_linked[0]['value']\n self.tree = un_linked[0]\n self._count = 0\n\n self.tree = self._resolve(self.tree)\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 binary_search_tree_run():\n\n # no need for Tree object as the Tree itself is a concept; its made of connected nodes\n # nodes are the object; connections are self contained\n\n def binary_insert(root, node):\n if root is None:\n root = node\n else:\n if root.data > node.data:\n if root.l_child is None:\n root.l_child = node\n else:\n binary_insert(root.l_child, node)\n else:\n if root.r_child is None:\n root.r_child = node\n else:\n binary_insert(root.r_child, node)\n\n def in_order_print(root):\n if not root:\n return\n in_order_print(root.l_child)\n print(root.data)\n in_order_print(root.r_child)","def test_tree_binary_tree() -> None:\n t = generate_binary_tree_resources(4, 3)\n field(t, (\"root\", \"ds\", \"f1\")).identity = \"email\"\n field(t, (\"root.0.1.0\", \"ds.0.1.0\", \"f1\")).identity = \"ssn\"\n field(t, (\"root.1.1\", \"ds.1.1\", \"f1\")).identity = \"user_id\"\n assert generate_traversal({\"email\": \"X\"}, *t)\n assert generate_traversal({\"ssn\": \"X\"}, *t)\n assert generate_traversal({\"user_id\": \"X\"}, *t)","def binary_tree():\n\n class Node(object):\n def __init__(self, data):\n self.left = None\n self.right = None\n self.data = data\n\n # Create a root\n root = Node(data=1)\n root.left = Node(data=2)\n root.right = Node(data=3)\n root.left.left = Node(data=4)\n \"\"\" Structure\n 1 <-- root\n / \\\n 2 3 \n / \n 4\n \"\"\"","def bulid_binary_tree(ast):\n def is_terminal(node):\n if 'children' in node.keys() and len(node['children']) > 0:\n return False\n return True\n\n brother_map = {0: -1}\n for index, node in enumerate(ast): # 顺序遍历每个AST中的node\n if not isinstance(node, dict) and node == 0: # 删除AST中最后的0标识\n del ast[index]\n break\n\n if is_terminal(node):\n # 表示该node为terminal\n node['isTerminal'] = True\n continue\n else:\n # 注： 存在四种token，有children list但是list的长度为0，暂时将其归为terminal\n node['left'] = -1\n node['right'] = brother_map.get(node['id'], -1) # 只为non-terminal node构建左右child\n node['isTerminal'] = False\n add_two_bits_info(ast, node, brother_map) # 向每个节点添加两bit的额外信息\n child_list = node['children']\n\n first_nt_i = None\n temp_nt_node_id = None\n for i, child_index in enumerate(child_list):\n # 找到该节点第一个non-terminal child node作为该node的left node\n if not is_terminal(ast[child_index]):\n node['left'] = child_index\n first_nt_i = i\n temp_nt_node_id = child_list[first_nt_i]\n break\n\n if first_nt_i != None:\n # 说明该node有non-terminal left child，所以为这个nt left child构建brother map\n assert isinstance(first_nt_i, int) \\\n and first_nt_i < len(child_list) \\\n and isinstance(temp_nt_node_id, int)\n\n for index in range(first_nt_i+1, len(child_list)):\n next_node_id = child_list[index]\n # 为该node的所有non-terminal children构建non-terminal right sibling\n if not is_terminal(ast[next_node_id]):\n #print('nt',next_node_id)\n brother_map[temp_nt_node_id] = next_node_id\n temp_nt_node_id = next_node_id\n #print(brother_map)\n\n # 将转化生成的binary tree添加节点，组成完全二叉树\n if (node['left'] == -1) and (node['right'] != -1):\n node['left'] = 'PAD_EMPTY'\n if (node['left'] != -1) and(node['right'] == -1):\n node['right'] = 'PAD_EMPTY'\n\n return ast","def test_binarytree_exists():\n assert BinaryTree","def as_binary_search_tree(self):\n root = self.get_root()\n bst = BinarySearchTree(root)\n\n for patient in self.dataset:\n bst.insert_node(patient)\n\n return bst","def tree_find(T, x):\n if T.is_empty:\n return False\n if x == T.label:\n return True\n if x < T.label:\n return tree_find(T.left, x)\n else:\n return tree_find(T.right, x)","def testBinarySearchTree():\n\n \"\"\"\n Example After Deletion\n 7\n / \\\n 1 4\n\n \"\"\"\n t = BinarySearchTree()\n t.insert(8)\n t.insert(3)\n t.insert(6)\n t.insert(1)\n t.insert(10)\n t.insert(14)\n t.insert(13)\n t.insert(4)\n t.insert(7)\n\n # Prints all the elements of the list in order traversal\n print(t.__str__())\n\n if t.getNode(6) is not None:\n print(\"The label 6 exists\")\n else:\n print(\"The label 6 doesn't exist\")\n\n if t.getNode(-1) is not None:\n print(\"The label -1 exists\")\n else:\n print(\"The label -1 doesn't exist\")\n\n if not t.empty():\n print((\"Max Value: \", t.getMax().getLabel()))\n print((\"Min Value: \", t.getMin().getLabel()))\n\n t.delete(13)\n t.delete(10)\n t.delete(8)\n t.delete(3)\n t.delete(6)\n t.delete(14)\n\n # Gets all the elements of the tree In pre order\n # And it prints them\n list = t.traversalTree(InPreOrder, t.root)\n for x in list:\n print(x)","def _properBST(self):\n if not self._isthisapropertree():\n return False\n return self._BSTproperties()[0]","def binary_search_tree():\n\n class Node(object):\n def __init__(self, key):\n self.left = None\n self.right = None\n self.key = key\n\n def insert(node, key):\n \"\"\" Insertion method for a binary search tree \"\"\"\n # If the tree is empty, return a new node\n if node is None:\n return Node(key)\n\n # Otherwise recur down the tree\n if key < node.key:\n node.left = insert(node.left, key)\n else:\n node.right = insert(node.right, key)\n\n # return the (unchanged) node pointer\n return node\n\n \"\"\" Let us create the following BST \n 50 \n / \\ \n 30 70 \n / \\ / \\ \n 20 40 60 80\n \"\"\"\n\n root = None\n root = insert(root, 50)\n root = insert(root, 30)\n root = insert(root, 20)\n root = insert(root, 40)\n root = insert(root, 70)\n root = insert(root, 60)\n root = insert(root, 80)","def build_UNIST_tree():\n root = LinkedBinaryTree()","def main():\n\n\t\"\"\"\n\ttree = BST(treetype=int)\n\tsubtree=BST(treetype=int)\n\n\ttree.make_minheight_tree(range(15))\n\n\tsubtree.make_minheight_tree(range(7))\n\n\tprint tree \n\tprint subtree\n\n\tprint tree.subtree_matching(subtree)\n\t\"\"\"","def tree_contains(T, x):","def test_binarytree_instantiate_null():\n b = BinaryTree()\n assert isinstance(b, BinaryTree)","def test_tree_balace_root():\n result = balance.generate_tree('test_input.txt')\n expected = 'tknk'\n\n assert expected == result['root'][0]","def test_binarytree_instantiate_single_value():\n b = BinaryTree(42)\n assert isinstance(b, BinaryTree)","def find_tree(sv, subtree, tree=Special):\r\n if tree is None: return False\r\n # scan all program\r\n if tree==Special:\r\n for nam in sv.Object_list: \r\n nod=sv.Object[nam]\r\n for c,v in nod.clauses: # scan clauses\r\n if find_tree(sv, subtree, c): return True # recurse on condition\r\n if find_tree(sv, subtree, v): return True # recurse on condition\r\n return False\r\n\r\n # explore a branch \r\n if tree==subtree: return True\r\n O,A,B=tree \r\n if O==Comma: # special case: list\r\n for x in A:\r\n if find_tree(sv, subtree, x): return True # recurse on each element\r\n else:\r\n if find_tree(sv, subtree, A): return True # recurse on first term\r\n if find_tree(sv, subtree, B): return True # recurse on second term\r\n return False","def known_bst():\n bst = BST()\n bst.insert_non_balance(5)\n bst.insert_non_balance(4)\n bst.insert_non_balance(2)\n bst.insert_non_balance(8)\n bst.insert_non_balance(7)\n bst.insert_non_balance(9)\n return bst, 3, 0","def searchTree(self, d):\n return searchTreeF(self, d)","def test_search_finds_node(bst_balanced):\n assert bst_balanced.search(1).val == 1","def _gen_test_tree_2():\n tree = BinaryNode(5)\n tree.left = BinaryNode(3)\n tree.left.left = BinaryNode(2)\n tree.left.left.left = BinaryNode(1)\n tree.left.right = BinaryNode(4)\n tree.right = BinaryNode(7)\n tree.right.left = BinaryNode(6)\n tree.right.right = BinaryNode(8)\n tree.right.right.right = BinaryNode(9)\n return tree","def search(T,k):\r\n for t in T.data:\r\n if k == t.word:\r\n return t\r\n if T.isLeaf:\r\n return None\r\n return search(T.child[findChildB(T,k)],k)","def getExactBranch(root, tag):\n tags = tag.split(':')\n if tags[0] == 'stats':\n tags = tags[1:]\n n = root\n for t in tags:\n newChildren = []\n for child in n.children:\n if child.nodeName == t or t == '*':\n newChildren.append(child)\n n.children = newChildren\n if n.children:\n n = n.children[0]\n if tags[-1] != '*':\n n.children = [] # prune off non-specified children tags","def test_binarytree_in_order_exists():\n assert BinaryTree.in_order","def __build_binary_tree(self, root, node_id, json_data):\n new_node = BinaryTree(value=json_data[node_id][\"value\"], left=None, right=None)\n if json_data[node_id][\"left\"] != None:\n new_node.left = self.__build_binary_tree(new_node, json_data[node_id][\"left\"], json_data)\n if json_data[node_id][\"right\"] != None:\n new_node.right = self.__build_binary_tree(new_node, json_data[node_id][\"right\"], json_data)\n return new_node","def deserialize(self, data):\n if len(data) == 0:\n return None\n levels = math.log(len(data) + 1)/math.log(2)\n #print(\"Total levels\", levels)\n root = TreeNode(data[0])\n stack = [root]\n curr_elem = 1\n if levels == 1:\n return root\n while len(stack) > 0:\n topelem = stack.pop(0)\n topelem.left = TreeNode(data[curr_elem])\n curr_elem += 1\n topelem.right = TreeNode(data[curr_elem])\n curr_elem += 1\n stack.append(topelem.left)\n stack.append(topelem.right)\n if curr_elem >= len(data):\n break\n return root","def _determine_struct_tree_subtype(data_type, obj, strict):\n if '.tag' not in obj:\n raise bv.ValidationError(\"missing '.tag' key\")\n if not isinstance(obj['.tag'], six.string_types):\n raise bv.ValidationError('expected string, got %s' %\n bv.generic_type_name(obj['.tag']),\n parent='.tag')\n\n # Find the subtype the tags refer to\n full_tags_tuple = (obj['.tag'],)\n if full_tags_tuple in data_type.definition._tag_to_subtype_:\n subtype = data_type.definition._tag_to_subtype_[full_tags_tuple]\n if isinstance(subtype, bv.StructTree):\n raise bv.ValidationError(\"tag '%s' refers to non-leaf subtype\" %\n ('.'.join(full_tags_tuple)))\n return subtype\n else:\n if strict:\n # In strict mode, the entirety of the tag hierarchy should\n # point to a known subtype.\n raise bv.ValidationError(\"unknown subtype '%s'\" %\n '.'.join(full_tags_tuple))\n else:\n # If subtype was not found, use the base.\n if data_type.definition._is_catch_all_:\n return data_type\n else:\n raise bv.ValidationError(\n \"unknown subtype '%s' and '%s' is not a catch-all\" %\n ('.'.join(full_tags_tuple), data_type.definition.__name__))","def find_tree(tree: dict,\n func: Callable,\n args: Optional[Tuple] = None,\n kwargs: Optional[Mapping] = None,\n how: Optional[str] = \"one\"\n ) -> Union[Sequence[dict], dict, None]:\n if how not in [\"one\", \"all\"]:\n raise ValueError(\"'how' must be set to either 'all' or 'one'\")\n if args is None:\n args = ()\n if kwargs is None:\n kwargs = {}\n if how == \"one\":\n result = _find_one_tree(tree, func, args, kwargs)\n else:\n result = _find_all_tree(tree, func, args, kwargs)\n return result","def find_node(self, type):\n new_node = self.cur_node\n node_order = {'ROOT': 1, 'ISA': 2, 'GS': 3, 'ST': 4, 'SEG':\n 5, 'ELE': 6}\n while node_order[type] > new_node[new_node.get_id()]:\n new_node = new_node.get_parent()\n #walk error tree to find place to append\n #if type == 'ISA':\n #return node","def test_instantiate_six_nodes():\n input = [13, 42, 7, 3, 9, 99]\n six = BinaryTree(input)\n assert isinstance(six, BinaryTree)","def deserialize(self, data):\n if not data:\n return None\n root = TreeNode(data[0])\n n = len(data)\n curr_lvl = [root]\n s = 1\n while curr_lvl and s < n:\n nxt_lvl = []\n for node in curr_lvl:\n if data[s] != None:\n node.left = TreeNode(data[s])\n nxt_lvl.append(node.left)\n s += 1\n if s >= n:\n break\n if data[s] != None:\n node.right = TreeNode(data[s])\n nxt_lvl.append(node.right)\n s += 1\n if s >= n:\n break\n curr_lvl = nxt_lvl\n return root","def __search_tree(word, index=0, node=None):\n if index + 1 > len(word):\n return node\n\n current_key = word[index]\n\n child_node = _Node.__find_key_in_level(node, current_key)\n\n if not child_node:\n return False\n\n return _Node.__search_tree(word, index + 1, child_node)","def find(self, value):\n # initialize node as root\n node = self.root\n\n # find value\n while node != None:\n\n # value found: return node\n if node.value == value:\n return node\n\n # value is smaller than node: search in left sub tree\n elif node.value > value:\n node = node.left\n\n # value is bigger than node: search in right sub tree\n else:\n node = node.right\n\n # value not found: return None\n return None","def build_tree(keys, ROOT_TABLE, i, j):\n\n # index rootu aktualneho podstromu (so suradnicami [i, j] v ROOT_TABLE)\n root_id = ROOT_TABLE.at[i, j]\n # novy podstrom\n search_tree = Tree(keys.get(root_id))\n\n # rekurzivne do laveho podstromu\n if i < root_id:\n search_tree.set_left_subtree(\n build_tree(keys, ROOT_TABLE, i, root_id - 1)) # root podstromu v bunke vlavo\n\n # rekurzivne do praveho podstromu\n if j > root_id:\n search_tree.set_right_subtree(\n build_tree(keys, ROOT_TABLE, root_id + 1, j)) # root podstromu v bunke dole\n\n return search_tree","def breadth_first_search(self, target: Dict) -> Optional[Node]:\n assist_queue = deque()\n assist_queue.append(self.root_node)\n while assist_queue:\n current_node: Node = assist_queue.popleft()\n flag = True\n for k, v in target.items():\n flag = flag and getattr(current_node, k) == v\n if not flag:\n break\n if flag:\n return current_node\n if current_node.children:\n for child in current_node.children:\n assist_queue.append(child)\n return None","def binarizetree(tree):\n queue = [tree]\n while queue:\n node = queue.pop(0)\n queue += node.nodelist\n # Construct binary tree\n if len(node.nodelist) == 2:\n node.lnode = node.nodelist[0]\n node.rnode = node.nodelist[1]\n # Parent node\n node.lnode.pnode = node\n node.rnode.pnode = node\n elif len(node.nodelist) > 2:\n # Remove one node from the nodelist\n node.lnode = node.nodelist.pop(0)\n newnode = SpanNode(node.nodelist[0].prop)\n newnode.nodelist += node.nodelist\n # Right-branching\n node.rnode = newnode\n # Parent node\n node.lnode.pnode = node\n node.rnode.pnode = node\n # Add to the head of the queue\n # So the code will keep branching\n # until the nodelist size is 2\n queue.insert(0, newnode)\n # Clear nodelist for the current node\n node.nodelist = []\n return tree","def tree_lookup(self, target_path, commit):\n segments = target_path.split(\"/\")\n tree_or_blob = commit.tree\n path = ''\n while segments:\n dirent = segments.pop(0)\n if isinstance(tree_or_blob, pygit2.Tree):\n if dirent in tree_or_blob:\n tree_or_blob = self.repo[tree_or_blob[dirent].oid]\n # self.logger.debug('%s in %s' % (dirent, path))\n if path:\n path += '/'\n path += dirent\n else:\n # This is probably because we were called on a\n # commit whose parent added a new directory.\n self.logger.debug(' %s not in %s in %s' %\n (dirent, path, commit.hex[:8]))\n return None\n else:\n self.logger.debug(' %s not a tree in %s' %\n (tree_or_blob, commit.hex[:8]))\n return None\n return tree_or_blob","def _best_case(): # pragma no cover\n setup_code = \"\"\"\nfrom __main__ import _best_case\nfrom bst import BinarySearchTree\nfrom bst import Node\n \"\"\"\n test_code = \"\"\"\nnew_tree = BinarySearchTree([4,2,3,1,6,5,7])\nnew_tree.search(7)\n \"\"\"\n return(timeit.repeat(setup=setup_code, stmt=test_code, number=100000))","def tree_BST(tree): \r\n if(tree==None):\r\n return True\r\n elif (left(tree)!=None):\r\n if(value(left(tree))>value(tree)):\r\n return False\r\n elif (right(tree)!=None):\r\n if(value(right(tree))<value(tree)):\r\n return False\r\n return tree_BST(left(tree))\r\n return tree_BST(right(tree))","def bst_wiki():\n from bbst import Bst\n tree = Bst([6, 7, 9, 8, 2, 1, 4, 3, 5])\n return tree","def test1():\n tree = t1make(VALNUM)\n tree.print()\n t1find(tree)","def construct_binary_tree(preorder, inorder):\n if len(preorder) == 0 or preorder == None or inorder == None:\n return None\n\n val = preorder[0]\n root = BinaryTreeNode(val)\n\n if len(preorder) > 1:\n inorder_root_index = inorder.index(val)\n inorder_left_sub_tree = inorder[:inorder_root_index]\n inorder_right_sub_tree = inorder[inorder_root_index+1:]\n preorder_left_sub_tree = preorder[1:len(inorder_left_sub_tree)+1]\n preorder_right_sub_tree = preorder[len(inorder_left_sub_tree) + 1:]\n root.left = construct_binary_tree(preorder_left_sub_tree, inorder_left_sub_tree)\n root.right = construct_binary_tree(preorder_right_sub_tree, inorder_right_sub_tree)\n return root","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n #Creamos las estructuras de datos necesarias (queue y set)\n openNodes = util.Queue()\n closedNodes = set([])\n\n #Guardamos el nodo inicial\n node = Node(problem.getStartState(), '', 0, None)\n\n #Metemos el nodo en la cola\n openNodes.push(node)\n\n #Iteramos para cada nodo de la pila\n while True:\n if openNodes.isEmpty():\n break #ERROR: throw exception\n else :\n #Sacamos el nodo de arriba de la pila\n node = openNodes.pop()\n if problem.isGoalState(node.name):\n break\n else: #Expandimos los nodos sucesores del nodo n si no estan en closed\n if nodeIsClosed(node, closedNodes) is False:\n for successor in problem.getSuccessors(node.name):\n n, p, c = successor\n succNode = Node(n, p, c, node)\n if nodeIsClosed(succNode, closedNodes) is False:\n #Metemos al sucesor en la cola\n openNodes.push(succNode)\n #Metemos el nodo n en closed\n closedNodes.add(node)\n\n #Devolvemos el camino al Goal\n return findPath(node)","def deserialize(self, data):\n if len(data) == 0:\n return None\n root = TreeNode(data[0])\n root.left = self.deserialize(data[1]) \n root.right = self.deserialize(data[2])\n return root","def tree_find(T, x):\n if T.label is None:\n return False\n else:\n return x == T.label \\\n or (x < T.label and tree_find(T[0], x)) \\\n or (x > T.label and tree_find(T[1], x))","def make_tree(arr):\n\n for i in range(len(arr)):\n arr, val = mid(arr)\n\n if i == 0: \n binary = BinaryNode(val)\n\n else:\n binary.insert(val)\n\n return binary","def deserialize(self, data):\n data = eval(data)\n length = len(data)\n k = 0\n root = []\n while k < length:\n if k == 0:\n root = TreeNode(data[k])\n k += 1\n floor = [root]\n else:\n nextfloor = []\n for node in floor:\n if node != None:\n if data[k] == None:\n node.left = data[k]\n else:\n node.left = TreeNode(data[k])\n k += 1\n nextfloor.append(node.left)\n if k >= length:\n return root\n if data[k] == None:\n node.right = data[k]\n else:\n node.right = TreeNode(data[k])\n nextfloor.append(node.right)\n k += 1\n if k >= length:\n return root\n \n floor = nextfloor\n \n return root","def testBinaryHelper(self):\n\n self.mockSplay() #disable splaying\n\n #test base-case empty\n self.assertIsNone(self.s.binaryHelper(None, None))\n self.assertIsNone(self.s.binaryHelper(None, self.s._root))\n\n #test base-case\n self.s.insert(0, None)\n self.assertIsNotNone(self.s.binaryHelper(0, self.s._root))\n\n #note: binary helper does not splay, but insert does\n n = randint(2,2147483647)\n self.s.insert(n, None)\n self.s.insert(-n, None)\n\n #find a node that is in the tree\n self.assertIsNotNone(self.s.binaryHelper(n, self.s._root))\n self.assertIsNotNone(self.s.binaryHelper(-n, self.s._root))\n #find the exact node\n self.assertEqual(self.s.binaryHelper(n, self.s._root), self.s._root.right)\n self.assertEqual(self.s.binaryHelper(-n, self.s._root), self.s._root.left)\n #find the parent of a non-existant node\n #pdb.set_trace()\n self.assertEqual(self.s.binaryHelper(-1, self.s._root), self.s._root.left)\n self.assertEqual(self.s.binaryHelper(n-1, self.s._root), \\\n self.s._root.right)\n #recursively find the parent of a non-existant node\n self.assertEqual(self.s.binaryHelper(n+1, self.s._root), \\\n self.s._root.right)\n self.assertEqual(self.s.binaryHelper(-n+1, self.s._root), \\\n self.s._root.left)","def binarize(tree):\n if isinstance(tree, str):\n return Tree('0',[tree])\n elif len(tree) == 1:\n# print(tree)\n# print('\\n')\n return binarize(tree[0])\n else:\n label = tree.label()\n# print(type(label))\n return reduce(lambda x, y: Tree(label, (binarize(x), binarize(y))), tree)","def _gen_test_tree_6():\n tree = BinaryNode(20)\n tree.left = BinaryNode(10)\n tree.right = BinaryNode(30)\n tree.left.right = BinaryNode(25)\n return tree","def find(self, data):\n \n def _step(current, target):\n \"\"\"Recursively approach the target node.\"\"\"\n \n if current is None: # Base case: target not found.\n return current\n if current == other: # Base case: target found!\n return current\n if target < current: # Recursively search to the left.\n return _step(current.left, target)\n else: # Recursively search to the right.\n return _step(current.right, target)\n \n if self.root is None: # Check for empty tree.\n raise ValueError(str(data) + \" is not in the tree.\")\n \n # Create a new node to use the KDTNode comparison operators.\n n = KDTNode(data)\n found = _step(self.root, n)\n if found is None: # Report the data was not found.\n raise ValueError(str(data) + \" is not in the tree.\")\n return found # Return the target node.","def tree_case_2(self, prune: int=1):\n\n types_h = self.Node('types.h')\n malloc_h = self.Node('malloc.h')\n stdlib_h = self.Node('stdlib.h', [types_h, malloc_h])\n bar_h = self.Node('bar.h', [stdlib_h])\n blat_h = self.Node('blat.h', [stdlib_h])\n blat_c = self.Node('blat.c', [blat_h, bar_h])\n blat_o = self.Node('blat.o', [blat_c])\n\n expect = \"\"\"\\\n+-blat.o\n +-blat.c\n +-blat.h\n | +-stdlib.h\n | +-types.h\n | +-malloc.h\n +-bar.h\n\"\"\"\n if prune:\n expect += \"\"\" +-[stdlib.h]\n\"\"\"\n else:\n expect += \"\"\" +-stdlib.h\n +-types.h\n +-malloc.h\n\"\"\"\n\n lines = expect.split('\\n')[:-1]\n lines = ['[E BSPACN ]' + l for l in lines]\n withtags = '\\n'.join(lines) + '\\n'\n\n return blat_o, expect, withtags","def list_to_binary_tree(list_input):\n pass","def deserialize(self, data):\n if len(data)==0 or data[0]==None:\n return None\n root=TreeNode(data[0])\n buf=[root]\n l=1\n while len(buf) and l<len(data):\n newbuf=[]\n for i in buf:\n if i.val==None:\n continue\n else:\n i.left=TreeNode(data[l])\n newbuf.append(i.left)\n l+=1\n if l>=len(data):\n break\n i.right=TreeNode(data[l])\n newbuf.append(i.right)\n l+=1\n return root","def deserialize(stringTree):\n global i #Global shared index \n\n if (stringTree[i] == \"#\"): #Empty node\n return None \n\n else:\n node = Node(stringTree[i]) #Create node of current index\n i+=1\n node.left = deserialize(stringTree) #Check left child\n i+=1\n node.right = deserialize(stringTree) #Check right child\n return node","def test_binarytree_pre_order_exists():\n assert BinaryTree.pre_order","def _gen_test_tree_4():\n tree = BinaryNode(5)\n tree.left = BinaryNode(3)\n tree.left.left = BinaryNode(2)\n tree.left.right = BinaryNode(10)\n tree.right = BinaryNode(9)\n tree.right.left = BinaryNode(6)\n tree.right.right = BinaryNode(8)\n return tree","def tree(obj):\n if isinstance(obj, (bool, int, float, str, bytes)):\n return obj\n elif isinstance(obj, (dt.date, dt.time)):\n return obj.isoformat()\n elif isinstance(obj, dict):\n return {k: tree(v) for k, v in obj.items()}\n elif isnamedtupleinstance(obj):\n return {f: tree(getattr(obj, f)) for f in obj._fields}\n elif isinstance(obj, (list, tuple, set)):\n return [tree(i) for i in obj]\n elif is_dataclass(obj):\n return {obj.__class__.__qualname__: tree(dataclassNonDefaults(obj))}\n else:\n return str(obj)","def search_for_tree(xml_obj, container):\n if isinstance(container, list):\n for child in xml_obj:\n if child.attrib[\"type\"] == \"dict\":\n temp_container = dict()\n search_for_tree(child, temp_container)\n container.append(temp_container)\n elif child.attrib[\"type\"] == \"list\":\n temp_container = list()\n search_for_tree(child, temp_container)\n container.append(temp_container)\n elif child.attrib[\"type\"] == \"int\":\n container.append(int(child.text))\n else:\n container.append(child.text)\n elif isinstance(container, dict):\n for child in xml_obj:\n if child.attrib[\"type\"] == \"dict\":\n temp_container = dict()\n search_for_tree(child, temp_container)\n container[child.tag] = temp_container\n elif child.attrib[\"type\"] == \"list\":\n temp_container = list()\n search_for_tree(child, temp_container)\n container[child.tag] = temp_container\n elif child.attrib[\"type\"] == \"int\":\n container[child.tag] = int(child.text)\n else:\n container[child.tag] = child.text\n return container","def search(self, word):\n q = []\n q2 = []\n p = self.root\n q.append(p)\n for c in word:\n if (not q):\n return False\n if (c == \".\"): # if it's a dot, add all possible nodes to the queue\n while (q):\n node = q.pop(0)\n for child in node.children:\n if (child != None):\n q2.append(child)\n else: # append only possibly correct nodes\n ind = ord(c) - ord('a')\n while (q):\n node = q.pop(0)\n if (node.children[ind] == None):\n continue\n q2.append(node.children[ind])\n q = q2\n q2 = []\n \n while (q):\n node = q.pop()\n if (node.value != 0):\n return True\n \n return False","def test_binarytree_post_order_exists():\n assert BinaryTree.post_order","def extract_tree(x, y, tilt, common):\n left_tree = None\n for k in range(y - 1, x, -1):\n if (x, k) in tilt:\n if (x, k) not in common:\n left_tree = extract_tree(x, k, tilt, common)\n break\n right_tree = None\n for k in range(x + 1, y):\n if (k, y) in tilt:\n if (k, y) not in common:\n right_tree = extract_tree(k, y, tilt, common)\n break\n return BinaryTree([left_tree, right_tree])","def test_binarytree_instantiate_given(given_list):\n assert isinstance(given_list, BinaryTree)","def BFTbin(tree):\n queue = [tree]\n bft_nodelist = []\n while queue:\n node = queue.pop(0)\n bft_nodelist.append(node)\n if node.lnode is not None:\n queue.append(node.lnode)\n if node.rnode is not None:\n queue.append(node.rnode)\n return bft_nodelist","def find(self, key, forDel=False):\n found = False\n root = self.root\n while not found:\n if key > root.key:\n if root.right is None:\n print(\"Key not found\")\n return False\n root = root.right\n elif key < root.key:\n if root.left is None:\n print(\"Key not found\")\n return False\n root = root.left\n\n elif root.key == key:\n found = True\n\n if forDel:\n return root\n return root.content","def _worst_case(): # pragma no cover\n setup_code = \"\"\"\nfrom __main__ import _best_case\nfrom bst import BinarySearchTree\nfrom bst import Node\n \"\"\"\n test_code = \"\"\"\nnew_tree = BinarySearchTree([1,2,3,4,5,6,7])\nnew_tree.search(7)\n \"\"\"\n return(timeit.repeat(setup=setup_code, stmt=test_code, number=100000))","def print_binary_tree_bfs(root):\n if not isinstance(root, BinaryTreeNode):\n return\n queue = Queue()\n queue.put(root)\n\n while not queue.empty():\n node = queue.get()\n print(node.val)\n if node.left != None:\n queue.put(node.left)\n if node.right != None:\n queue.put(node.right)","def test_is_bst(tree: BSTNode) -> None:\n output = [n.value for n in tree.in_order(tree)]\n assert output == sorted(output)","def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n #Creamos las estructuras de datos necesarias (stack y set)\n openNodes = util.Stack()\n closedNodes = set([])\n\n #Guardamos el nodo inicial\n node = Node(problem.getStartState(), '', 0, None)\n\n #Metemos el nodo en la pila\n openNodes.push(node)\n\n #Iteramos para cada nodo de la pila\n while True:\n if openNodes.isEmpty():\n break #ERROR: throw exception\n else :\n #Sacamos el nodo de arriba de la pila\n node = openNodes.pop()\n if problem.isGoalState(node.name):\n break\n else: #Expandimos los nodos sucesores del nodo n si no estan en closed\n for successor in problem.getSuccessors(node.name):\n n, p, c = successor\n succNode = Node(n, p, c, node)\n if nodeIsClosed(succNode, closedNodes) is False:\n #Metemos al sucesor en la pila\n openNodes.push(succNode)\n #Metemos el nodo n en closed\n closedNodes.add(node)\n\n #Devolvemos el camino al Goal\n return findPath(node)","def binaryTree(r):\r\n return [r, [], []]","def search(self, val):\n if type(val) not in [int, float]:\n raise TypeError('This tree accepts numbers only.')\n current_node = self._root\n while current_node:\n if val == current_node._data:\n return current_node\n if val > current_node._data:\n current_node = current_node._rkid\n else:\n current_node = current_node._lkid\n return","def test_search_returns_none_when_value_notin_tree_left(bst_all_to_left):\n assert bst_all_to_left.search(0) is None","def test_small_tree_has_right_child_child(small_tree):\n assert small_tree.root.right.right.value == 27","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n visited=[]\n \n node=dict()\n start=problem.getStartState()\n node['parent']=None\n node['direction']=None\n node['state']=start\n \n \n qu.push(node)\n lis.append(node)\n \n print qu.list\n while qu.isEmpty()!=True:\n node=qu.pop()\n pos=node['state']\n visited.append(pos)\n print visited\n if problem.isGoalState(pos):\n print \"found\"\n return getPath(problem,node)\n #break\n suc=problem.getSuccessors(pos)\n if suc ==None:\n continue \n \n print suc\n for step in suc:\n #if step not in dic :\n if step[0] not in visited:\n childnode={}\n childnode['parent']=pos\n childnode['direction']=step[1]\n childnode['state']=step[0]\n qu.push(childnode)\n lis.append(childnode)\n \n\n #util.raiseNotDefined()","def _recursiveSearch(self, data, node):\n\t\tif (not node):\n\t\t\treturn None\n\t\tif (node.value() == data):\n\t\t\treturn node\n\t\telif (data < node.value()):\n\t\t\treturn self._recursiveSearch(data, node.lchild())\n\t\telse:\n\t\t\treturn self._recursiveSearch(data, node.rchild())","def build_tree(elements):\n print(\"Building tree with these elements:\",elements)\n root = BinarySearchTreeNode(elements[0])\n\n for i in range(1, len(elements)):\n root.add_child(elements[i])\n\n return root","def _find_one_tree(tree: dict,\n func: Callable,\n args: Tuple,\n kwargs: Mapping,\n ) -> Union[dict, None]:\n frontier = []\n explored = set()\n for uid, item in tree.items():\n frontier.append((uid, item))\n while frontier:\n uid, item = frontier.pop()\n explored.add(uid)\n if func(item, *args, **kwargs):\n return item\n if \"children\" in item:\n for child_uid, child_item in item[\"children\"].items():\n if child_uid not in explored:\n frontier.append((child_uid, child_item))","def _gen_test_tree_3():\n tree = BinaryNode(5)\n tree.left = BinaryNode(1)\n tree.left.left = BinaryNode(2)\n tree.left.right = BinaryNode(3)\n tree.right = BinaryNode(7)\n tree.right.left = BinaryNode(8)\n tree.right.right = BinaryNode(9)\n return tree","def minimal_tree(array: list):\n bst = BST()\n def build(l, r):\n if l == r: bst.insert(array[l]); return\n m = (l+r)//2\n # insert into the tree\n bst.insert(array[m])\n # build recursively\n build(l, m)\n build(m+1, r)\n build(0, len(array)-1)\n return bst","def lower_binary_tree(self):\n return self.min_linear_extension().binary_search_tree_shape(left_to_right=False)","def first_found_merging(file_names, tree_name):\n\n tree_branches = {}\n for ifile, file_name in enumerate(file_names):\n file_ = uproot.open(file_name)\n if tree_name+\";1\" in file_.keys():\n tree = file_[tree_name].arrays()\n tree_branches = { k: tree[k] for k in tree.fields }\n break\n\n return tree_branches","def breadthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n from util import Queue\n q = Queue()\n mapper = {} #child_point : (parent_point, direction_to_child)\n q.push(problem.getStartState())\n mapper[problem.getStartState()] = None #root\n\n while (not q.isEmpty()):\n point = q.pop()\n\n if (problem.isGoalState(point)):\n c = point\n l = []\n while mapper[c] != None:\n tup = mapper[c]\n l.append(tup[1])\n c = tup[0]\n l.reverse()\n print l\n return l\n\n else:\n for child in problem.getSuccessors(point):\n if (child[0] not in mapper):\n q.push(child[0])\n mapper[child[0]] = (point, child[1])\n\n # util.raiseNotDefined()","def findTrees( Dir, treeList ):\n \t_dirSave = Dir\n\t#Storing the father->child info\n\ttry:\n\t\tlistOfKeys = Dir.GetListOfKeys()\n\t##-- Checking that is a Tag and Probe fit \n\t## file. IF it is not, then we find\n\t## some not expected TKeys diferent from TTree\n\texcept AttributeError:\n\t\tmessage = \"\"\"\\033[1;31mError: The root file is not an standard T&P NTuple file\\033[1;m\"\"\" \n\t\traise AttributeError, message\n \tfor key in Dir.GetListOfKeys():\n \t\tclassName = key.GetClassName()\n \t\tif key.IsFolder() and className != 'TTree': #Watch TTree is a folder\n \t\t\t##-- Extracting the Folder from Dir\n\t\t\t_subdir = Dir.Get(key.GetName())\n \t\t\t##-- And browsing inside recursively\n \t\t\ttreeList = findTrees(_subdir,treeList)\n \t\t\t##-- To avoid segmentation faults, we need to return\n\t\t\t## at the original directory in order to continue\n\t\t\t## extracting subdirectories (or elements of the directory)\n \t\t\t_dirSave.cd()\n \t\t##-- Storing in the dictionary the interesting objects\n \t\telif className == 'TTree':\n\t\t\t#Asuming unique id object-->complet path\n\t\t\ttreeList.append( Dir.GetPath().split(':/')[1]+'/'+key.GetName() )\n \n \treturn treeList","def getRoot(obj):","def test_bin_tree():\n n1 = BinTreeNode(1)\n n2 = BinTreeNode(2)\n n3 = BinTreeNode(3)\n n4 = BinTreeNode(4)\n n5 = BinTreeNode(5)\n n1.left = n2\n n1.right = n3\n n2.left = n4\n n3.right = n5\n t = BinTree(n1)\n print('pre order')\n preorder_trav(t.root)\n print('in order')\n inorder_trav(t.root)\n print('post order')\n postorder_trav(t.root)","def deserialize(self, data):\n # 아래 부분에서 빈 리스트를 잡지 못함\n # if not data: 로 빈 리스트가 입력이 되었을 때 잡으려고 했음.\n # 하지만 serialize를 거친 빈 리스트는 n이 추가되어서 desrialize를 호출하기 때문에\n # data[0] == 'n'으로 잡아야만 처음 serialize의 input이 빈 리스트인 것을 잡을 수 있음.\n if data[0] == 'n':\n return None\n\n root = TreeNode(data.pop(0))\n queue = collections.deque([root])\n\n while queue:\n node = queue.popleft()\n temp = data.pop(0)\n if temp is not 'n':\n node.left = TreeNode(int(temp))\n queue.append(node.left)\n\n temp = data.pop(0)\n if temp is not 'n':\n node.right = TreeNode(int(temp))\n queue.append(node.right)\n return root","def deserialize(self, data):\n if not data:\n return None\n data = data.split(' ')\n root = TreeNode(int(data[0]))\n stack = [root]\n\n i, child = 1, 'left'\n curr = root\n while i < len(data):\n if data[i] != 'None':\n if child == 'left':\n curr.left = TreeNode(int(data[i]))\n stack.append(curr.left)\n curr = curr.left\n else:\n curr.right = TreeNode(int(data[i]))\n stack.append(curr.right)\n curr = curr.right\n child = 'left'\n else:\n curr = stack.pop()\n child = 'right'\n i += 1\n return root","def balanced_tree(ordered):\n bt = BinaryTree()\n\n add_range(bt, ordered, 0, len(ordered)-1)\n\n return bt","def find_node(self, type):\n pass","def search_in_tree(root_elem, target):\r\n\t# Guard agains an empty tree\r\n\tif root_elem is None:\r\n\t\treturn False\r\n\tqueue = deque([root_elem])\r\n\twhile queue:\r\n\t\tcurrent = queue.popleft()\r\n\t\tif current.value == target:\r\n\t\t\treturn True\r\n\t\tif current.left:\r\n\t\t\tqueue.append(current.left)\r\n\t\tif current.right:\r\n\t\t\tqueue.append(current.right)\r\n\treturn False","def test_depth(known_bst):\n assert known_bst[0].depth() == 3","def test_binarytree_instantiate_list():\n input = [13, 42, 7]\n c = BinaryTree(input)\n assert isinstance(c, BinaryTree)","def huffman_code_tree(node, left=True, binString=''):\n if type(node) is str:\n return {node: binString}\n \n (l, r) = node.children()\n dico_binary = {}\n dico_binary.update(huffman_code_tree(l, True, binString + '0'))\n dico_binary.update(huffman_code_tree(r, False, binString + '1'))\n\n return dico_binary","def find_matching_structures(self, query):\r\n return self.structures.find(query)","def deserialize(self, data):\n\n myiter = iter(data.split())\n qe = Queue.Queue(maxsize=0)\n item = next(myiter)\n if item == \"null\":\n return None\n\n root = TreeNode(int(item))\n qe.put(root)\n\n while not qe.empty():\n node = qe.get()\n item = next(myiter)\n if item != \"null\":\n node.left = TreeNode(int(item))\n qe.put(node.left)\n\n item = next(myiter)\n if item != \"null\":\n node.right = TreeNode(int(item))\n qe.put(node.right)\n\n return root","def binary_search(node, value):\n aux_node = None\n while node is not None and node.value != value:\n if value < node.value:\n aux_node = node.left\n node = aux_node\n else:\n aux_node = node.right\n node = aux_node\n return node if node.value == value else None","def test_Tree():","def search(self, word):\n if not word:\n return False\n discrepancy = 1\n stack = [(child, word, discrepancy)\n for child in self.root.children.values()]\n while stack:\n curr_node, curr_word, curr_disc = stack.pop()\n if len(curr_word) == 1:\n if curr_node.nil:\n if curr_disc == 0 and curr_word == curr_node.key:\n return curr_node.nil\n elif curr_disc == 1 and curr_word != curr_node.key:\n return curr_node.nil\n else:\n for key, child in curr_node.children.items():\n if curr_word[0] == curr_node.key:\n stack.append((child, curr_word[1:], curr_disc))\n elif curr_disc> 0:\n stack.append((child, curr_word[1:], curr_disc - 1))\n return False","def deserialize1(self, data):\n if data == \"\":\n return None\n serial = data.split(\",\")\n stack = []\n for val in serial:\n if not stack:\n root = TreeNode(int(val))\n stack.append([root, True])\n else:\n node, state = stack[-1]\n if not node.left and state:\n if val == \"null\":\n stack[-1][1] = False\n else:\n left_node = TreeNode(int(val))\n node.left = left_node\n print(left_node.val)\n stack.append([left_node, True])\n else:\n stack.pop()\n if not val == \"null\":\n right_node = TreeNode(int(val))\n node.right = right_node\n print(right_node.val)\n stack.append([right_node, True])\n return root","def Trees__CheckBST():\n # Python2 ported to Python3 via 2to3-3.7\n # URL:https://www.hackerrank.com/challenges/ctci-is-binary-search-tree/problem\n # O(n) solution. Passes all test cases.\n # Tricky part with leaf in left side of root being bigger than root. (or right/smaller)\n # E.g:\n # 3\n # 2 6\n # 1 4 5 7\n # Note, the 4 is bigger than the parent 3. But in a proper BST parent\n # must be bigger than all items on parent's left side.\n # Keep track of last biggest element as we descend to children (& last smallest element)\n # val < last biggest (last_left) #for cases like 4\n # val > last smallest (last_right) # for mirror side.\n # Convieniently, this also ensures uniqueness.\n def checkBST(root):\n queue = []\n queue.append((root, None, None)) # node, last_left, last_right.\n while queue:\n node, last_left, last_right = queue.pop()\n if not node:\n continue\n if last_left and not node.data < last_left \\\n or last_right and not node.data > last_right:\n return False\n queue.append((node.left, node.data, last_right))\n queue.append((node.right, last_left, node.data))\n return True","def searchTreeF(node, d):\n if isinstance(node, DecisionTree):\n if node.i == 999: return node.mostCommon()\n if d[node.i] < node.v:\n return searchTreeF(node.lt, d)\n else:\n return searchTreeF(node.gt, d)\n else:\n return node"],"string":"[\n \"def binary_search_tree_run():\\n\\n # no need for Tree object as the Tree itself is a concept; its made of connected nodes\\n # nodes are the object; connections are self contained\\n\\n def binary_insert(root, node):\\n if root is None:\\n root = node\\n else:\\n if root.data > node.data:\\n if root.l_child is None:\\n root.l_child = node\\n else:\\n binary_insert(root.l_child, node)\\n else:\\n if root.r_child is None:\\n root.r_child = node\\n else:\\n binary_insert(root.r_child, node)\\n\\n def in_order_print(root):\\n if not root:\\n return\\n in_order_print(root.l_child)\\n print(root.data)\\n in_order_print(root.r_child)\",\n \"def test_tree_binary_tree() -> None:\\n t = generate_binary_tree_resources(4, 3)\\n field(t, (\\\"root\\\", \\\"ds\\\", \\\"f1\\\")).identity = \\\"email\\\"\\n field(t, (\\\"root.0.1.0\\\", \\\"ds.0.1.0\\\", \\\"f1\\\")).identity = \\\"ssn\\\"\\n field(t, (\\\"root.1.1\\\", \\\"ds.1.1\\\", \\\"f1\\\")).identity = \\\"user_id\\\"\\n assert generate_traversal({\\\"email\\\": \\\"X\\\"}, *t)\\n assert generate_traversal({\\\"ssn\\\": \\\"X\\\"}, *t)\\n assert generate_traversal({\\\"user_id\\\": \\\"X\\\"}, *t)\",\n \"def binary_tree():\\n\\n class Node(object):\\n def __init__(self, data):\\n self.left = None\\n self.right = None\\n self.data = data\\n\\n # Create a root\\n root = Node(data=1)\\n root.left = Node(data=2)\\n root.right = Node(data=3)\\n root.left.left = Node(data=4)\\n \\\"\\\"\\\" Structure\\n 1 <-- root\\n / \\\\\\n 2 3 \\n / \\n 4\\n \\\"\\\"\\\"\",\n \"def bulid_binary_tree(ast):\\n def is_terminal(node):\\n if 'children' in node.keys() and len(node['children']) > 0:\\n return False\\n return True\\n\\n brother_map = {0: -1}\\n for index, node in enumerate(ast): # 顺序遍历每个AST中的node\\n if not isinstance(node, dict) and node == 0: # 删除AST中最后的0标识\\n del ast[index]\\n break\\n\\n if is_terminal(node):\\n # 表示该node为terminal\\n node['isTerminal'] = True\\n continue\\n else:\\n # 注： 存在四种token，有children list但是list的长度为0，暂时将其归为terminal\\n node['left'] = -1\\n node['right'] = brother_map.get(node['id'], -1) # 只为non-terminal node构建左右child\\n node['isTerminal'] = False\\n add_two_bits_info(ast, node, brother_map) # 向每个节点添加两bit的额外信息\\n child_list = node['children']\\n\\n first_nt_i = None\\n temp_nt_node_id = None\\n for i, child_index in enumerate(child_list):\\n # 找到该节点第一个non-terminal child node作为该node的left node\\n if not is_terminal(ast[child_index]):\\n node['left'] = child_index\\n first_nt_i = i\\n temp_nt_node_id = child_list[first_nt_i]\\n break\\n\\n if first_nt_i != None:\\n # 说明该node有non-terminal left child，所以为这个nt left child构建brother map\\n assert isinstance(first_nt_i, int) \\\\\\n and first_nt_i < len(child_list) \\\\\\n and isinstance(temp_nt_node_id, int)\\n\\n for index in range(first_nt_i+1, len(child_list)):\\n next_node_id = child_list[index]\\n # 为该node的所有non-terminal children构建non-terminal right sibling\\n if not is_terminal(ast[next_node_id]):\\n #print('nt',next_node_id)\\n brother_map[temp_nt_node_id] = next_node_id\\n temp_nt_node_id = next_node_id\\n #print(brother_map)\\n\\n # 将转化生成的binary tree添加节点，组成完全二叉树\\n if (node['left'] == -1) and (node['right'] != -1):\\n node['left'] = 'PAD_EMPTY'\\n if (node['left'] != -1) and(node['right'] == -1):\\n node['right'] = 'PAD_EMPTY'\\n\\n return ast\",\n \"def test_binarytree_exists():\\n assert BinaryTree\",\n \"def as_binary_search_tree(self):\\n root = self.get_root()\\n bst = BinarySearchTree(root)\\n\\n for patient in self.dataset:\\n bst.insert_node(patient)\\n\\n return bst\",\n \"def tree_find(T, x):\\n if T.is_empty:\\n return False\\n if x == T.label:\\n return True\\n if x < T.label:\\n return tree_find(T.left, x)\\n else:\\n return tree_find(T.right, x)\",\n \"def testBinarySearchTree():\\n\\n \\\"\\\"\\\"\\n Example After Deletion\\n 7\\n / \\\\\\n 1 4\\n\\n \\\"\\\"\\\"\\n t = BinarySearchTree()\\n t.insert(8)\\n t.insert(3)\\n t.insert(6)\\n t.insert(1)\\n t.insert(10)\\n t.insert(14)\\n t.insert(13)\\n t.insert(4)\\n t.insert(7)\\n\\n # Prints all the elements of the list in order traversal\\n print(t.__str__())\\n\\n if t.getNode(6) is not None:\\n print(\\\"The label 6 exists\\\")\\n else:\\n print(\\\"The label 6 doesn't exist\\\")\\n\\n if t.getNode(-1) is not None:\\n print(\\\"The label -1 exists\\\")\\n else:\\n print(\\\"The label -1 doesn't exist\\\")\\n\\n if not t.empty():\\n print((\\\"Max Value: \\\", t.getMax().getLabel()))\\n print((\\\"Min Value: \\\", t.getMin().getLabel()))\\n\\n t.delete(13)\\n t.delete(10)\\n t.delete(8)\\n t.delete(3)\\n t.delete(6)\\n t.delete(14)\\n\\n # Gets all the elements of the tree In pre order\\n # And it prints them\\n list = t.traversalTree(InPreOrder, t.root)\\n for x in list:\\n print(x)\",\n \"def _properBST(self):\\n if not self._isthisapropertree():\\n return False\\n return self._BSTproperties()[0]\",\n \"def binary_search_tree():\\n\\n class Node(object):\\n def __init__(self, key):\\n self.left = None\\n self.right = None\\n self.key = key\\n\\n def insert(node, key):\\n \\\"\\\"\\\" Insertion method for a binary search tree \\\"\\\"\\\"\\n # If the tree is empty, return a new node\\n if node is None:\\n return Node(key)\\n\\n # Otherwise recur down the tree\\n if key < node.key:\\n node.left = insert(node.left, key)\\n else:\\n node.right = insert(node.right, key)\\n\\n # return the (unchanged) node pointer\\n return node\\n\\n \\\"\\\"\\\" Let us create the following BST \\n 50 \\n / \\\\ \\n 30 70 \\n / \\\\ / \\\\ \\n 20 40 60 80\\n \\\"\\\"\\\"\\n\\n root = None\\n root = insert(root, 50)\\n root = insert(root, 30)\\n root = insert(root, 20)\\n root = insert(root, 40)\\n root = insert(root, 70)\\n root = insert(root, 60)\\n root = insert(root, 80)\",\n \"def build_UNIST_tree():\\n root = LinkedBinaryTree()\",\n \"def main():\\n\\n\\t\\\"\\\"\\\"\\n\\ttree = BST(treetype=int)\\n\\tsubtree=BST(treetype=int)\\n\\n\\ttree.make_minheight_tree(range(15))\\n\\n\\tsubtree.make_minheight_tree(range(7))\\n\\n\\tprint tree \\n\\tprint subtree\\n\\n\\tprint tree.subtree_matching(subtree)\\n\\t\\\"\\\"\\\"\",\n \"def tree_contains(T, x):\",\n \"def test_binarytree_instantiate_null():\\n b = BinaryTree()\\n assert isinstance(b, BinaryTree)\",\n \"def test_tree_balace_root():\\n result = balance.generate_tree('test_input.txt')\\n expected = 'tknk'\\n\\n assert expected == result['root'][0]\",\n \"def test_binarytree_instantiate_single_value():\\n b = BinaryTree(42)\\n assert isinstance(b, BinaryTree)\",\n \"def find_tree(sv, subtree, tree=Special):\\r\\n if tree is None: return False\\r\\n # scan all program\\r\\n if tree==Special:\\r\\n for nam in sv.Object_list: \\r\\n nod=sv.Object[nam]\\r\\n for c,v in nod.clauses: # scan clauses\\r\\n if find_tree(sv, subtree, c): return True # recurse on condition\\r\\n if find_tree(sv, subtree, v): return True # recurse on condition\\r\\n return False\\r\\n\\r\\n # explore a branch \\r\\n if tree==subtree: return True\\r\\n O,A,B=tree \\r\\n if O==Comma: # special case: list\\r\\n for x in A:\\r\\n if find_tree(sv, subtree, x): return True # recurse on each element\\r\\n else:\\r\\n if find_tree(sv, subtree, A): return True # recurse on first term\\r\\n if find_tree(sv, subtree, B): return True # recurse on second term\\r\\n return False\",\n \"def known_bst():\\n bst = BST()\\n bst.insert_non_balance(5)\\n bst.insert_non_balance(4)\\n bst.insert_non_balance(2)\\n bst.insert_non_balance(8)\\n bst.insert_non_balance(7)\\n bst.insert_non_balance(9)\\n return bst, 3, 0\",\n \"def searchTree(self, d):\\n return searchTreeF(self, d)\",\n \"def test_search_finds_node(bst_balanced):\\n assert bst_balanced.search(1).val == 1\",\n \"def _gen_test_tree_2():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(3)\\n tree.left.left = BinaryNode(2)\\n tree.left.left.left = BinaryNode(1)\\n tree.left.right = BinaryNode(4)\\n tree.right = BinaryNode(7)\\n tree.right.left = BinaryNode(6)\\n tree.right.right = BinaryNode(8)\\n tree.right.right.right = BinaryNode(9)\\n return tree\",\n \"def search(T,k):\\r\\n for t in T.data:\\r\\n if k == t.word:\\r\\n return t\\r\\n if T.isLeaf:\\r\\n return None\\r\\n return search(T.child[findChildB(T,k)],k)\",\n \"def getExactBranch(root, tag):\\n tags = tag.split(':')\\n if tags[0] == 'stats':\\n tags = tags[1:]\\n n = root\\n for t in tags:\\n newChildren = []\\n for child in n.children:\\n if child.nodeName == t or t == '*':\\n newChildren.append(child)\\n n.children = newChildren\\n if n.children:\\n n = n.children[0]\\n if tags[-1] != '*':\\n n.children = [] # prune off non-specified children tags\",\n \"def test_binarytree_in_order_exists():\\n assert BinaryTree.in_order\",\n \"def __build_binary_tree(self, root, node_id, json_data):\\n new_node = BinaryTree(value=json_data[node_id][\\\"value\\\"], left=None, right=None)\\n if json_data[node_id][\\\"left\\\"] != None:\\n new_node.left = self.__build_binary_tree(new_node, json_data[node_id][\\\"left\\\"], json_data)\\n if json_data[node_id][\\\"right\\\"] != None:\\n new_node.right = self.__build_binary_tree(new_node, json_data[node_id][\\\"right\\\"], json_data)\\n return new_node\",\n \"def deserialize(self, data):\\n if len(data) == 0:\\n return None\\n levels = math.log(len(data) + 1)/math.log(2)\\n #print(\\\"Total levels\\\", levels)\\n root = TreeNode(data[0])\\n stack = [root]\\n curr_elem = 1\\n if levels == 1:\\n return root\\n while len(stack) > 0:\\n topelem = stack.pop(0)\\n topelem.left = TreeNode(data[curr_elem])\\n curr_elem += 1\\n topelem.right = TreeNode(data[curr_elem])\\n curr_elem += 1\\n stack.append(topelem.left)\\n stack.append(topelem.right)\\n if curr_elem >= len(data):\\n break\\n return root\",\n \"def _determine_struct_tree_subtype(data_type, obj, strict):\\n if '.tag' not in obj:\\n raise bv.ValidationError(\\\"missing '.tag' key\\\")\\n if not isinstance(obj['.tag'], six.string_types):\\n raise bv.ValidationError('expected string, got %s' %\\n bv.generic_type_name(obj['.tag']),\\n parent='.tag')\\n\\n # Find the subtype the tags refer to\\n full_tags_tuple = (obj['.tag'],)\\n if full_tags_tuple in data_type.definition._tag_to_subtype_:\\n subtype = data_type.definition._tag_to_subtype_[full_tags_tuple]\\n if isinstance(subtype, bv.StructTree):\\n raise bv.ValidationError(\\\"tag '%s' refers to non-leaf subtype\\\" %\\n ('.'.join(full_tags_tuple)))\\n return subtype\\n else:\\n if strict:\\n # In strict mode, the entirety of the tag hierarchy should\\n # point to a known subtype.\\n raise bv.ValidationError(\\\"unknown subtype '%s'\\\" %\\n '.'.join(full_tags_tuple))\\n else:\\n # If subtype was not found, use the base.\\n if data_type.definition._is_catch_all_:\\n return data_type\\n else:\\n raise bv.ValidationError(\\n \\\"unknown subtype '%s' and '%s' is not a catch-all\\\" %\\n ('.'.join(full_tags_tuple), data_type.definition.__name__))\",\n \"def find_tree(tree: dict,\\n func: Callable,\\n args: Optional[Tuple] = None,\\n kwargs: Optional[Mapping] = None,\\n how: Optional[str] = \\\"one\\\"\\n ) -> Union[Sequence[dict], dict, None]:\\n if how not in [\\\"one\\\", \\\"all\\\"]:\\n raise ValueError(\\\"'how' must be set to either 'all' or 'one'\\\")\\n if args is None:\\n args = ()\\n if kwargs is None:\\n kwargs = {}\\n if how == \\\"one\\\":\\n result = _find_one_tree(tree, func, args, kwargs)\\n else:\\n result = _find_all_tree(tree, func, args, kwargs)\\n return result\",\n \"def find_node(self, type):\\n new_node = self.cur_node\\n node_order = {'ROOT': 1, 'ISA': 2, 'GS': 3, 'ST': 4, 'SEG':\\n 5, 'ELE': 6}\\n while node_order[type] > new_node[new_node.get_id()]:\\n new_node = new_node.get_parent()\\n #walk error tree to find place to append\\n #if type == 'ISA':\\n #return node\",\n \"def test_instantiate_six_nodes():\\n input = [13, 42, 7, 3, 9, 99]\\n six = BinaryTree(input)\\n assert isinstance(six, BinaryTree)\",\n \"def deserialize(self, data):\\n if not data:\\n return None\\n root = TreeNode(data[0])\\n n = len(data)\\n curr_lvl = [root]\\n s = 1\\n while curr_lvl and s < n:\\n nxt_lvl = []\\n for node in curr_lvl:\\n if data[s] != None:\\n node.left = TreeNode(data[s])\\n nxt_lvl.append(node.left)\\n s += 1\\n if s >= n:\\n break\\n if data[s] != None:\\n node.right = TreeNode(data[s])\\n nxt_lvl.append(node.right)\\n s += 1\\n if s >= n:\\n break\\n curr_lvl = nxt_lvl\\n return root\",\n \"def __search_tree(word, index=0, node=None):\\n if index + 1 > len(word):\\n return node\\n\\n current_key = word[index]\\n\\n child_node = _Node.__find_key_in_level(node, current_key)\\n\\n if not child_node:\\n return False\\n\\n return _Node.__search_tree(word, index + 1, child_node)\",\n \"def find(self, value):\\n # initialize node as root\\n node = self.root\\n\\n # find value\\n while node != None:\\n\\n # value found: return node\\n if node.value == value:\\n return node\\n\\n # value is smaller than node: search in left sub tree\\n elif node.value > value:\\n node = node.left\\n\\n # value is bigger than node: search in right sub tree\\n else:\\n node = node.right\\n\\n # value not found: return None\\n return None\",\n \"def build_tree(keys, ROOT_TABLE, i, j):\\n\\n # index rootu aktualneho podstromu (so suradnicami [i, j] v ROOT_TABLE)\\n root_id = ROOT_TABLE.at[i, j]\\n # novy podstrom\\n search_tree = Tree(keys.get(root_id))\\n\\n # rekurzivne do laveho podstromu\\n if i < root_id:\\n search_tree.set_left_subtree(\\n build_tree(keys, ROOT_TABLE, i, root_id - 1)) # root podstromu v bunke vlavo\\n\\n # rekurzivne do praveho podstromu\\n if j > root_id:\\n search_tree.set_right_subtree(\\n build_tree(keys, ROOT_TABLE, root_id + 1, j)) # root podstromu v bunke dole\\n\\n return search_tree\",\n \"def breadth_first_search(self, target: Dict) -> Optional[Node]:\\n assist_queue = deque()\\n assist_queue.append(self.root_node)\\n while assist_queue:\\n current_node: Node = assist_queue.popleft()\\n flag = True\\n for k, v in target.items():\\n flag = flag and getattr(current_node, k) == v\\n if not flag:\\n break\\n if flag:\\n return current_node\\n if current_node.children:\\n for child in current_node.children:\\n assist_queue.append(child)\\n return None\",\n \"def binarizetree(tree):\\n queue = [tree]\\n while queue:\\n node = queue.pop(0)\\n queue += node.nodelist\\n # Construct binary tree\\n if len(node.nodelist) == 2:\\n node.lnode = node.nodelist[0]\\n node.rnode = node.nodelist[1]\\n # Parent node\\n node.lnode.pnode = node\\n node.rnode.pnode = node\\n elif len(node.nodelist) > 2:\\n # Remove one node from the nodelist\\n node.lnode = node.nodelist.pop(0)\\n newnode = SpanNode(node.nodelist[0].prop)\\n newnode.nodelist += node.nodelist\\n # Right-branching\\n node.rnode = newnode\\n # Parent node\\n node.lnode.pnode = node\\n node.rnode.pnode = node\\n # Add to the head of the queue\\n # So the code will keep branching\\n # until the nodelist size is 2\\n queue.insert(0, newnode)\\n # Clear nodelist for the current node\\n node.nodelist = []\\n return tree\",\n \"def tree_lookup(self, target_path, commit):\\n segments = target_path.split(\\\"/\\\")\\n tree_or_blob = commit.tree\\n path = ''\\n while segments:\\n dirent = segments.pop(0)\\n if isinstance(tree_or_blob, pygit2.Tree):\\n if dirent in tree_or_blob:\\n tree_or_blob = self.repo[tree_or_blob[dirent].oid]\\n # self.logger.debug('%s in %s' % (dirent, path))\\n if path:\\n path += '/'\\n path += dirent\\n else:\\n # This is probably because we were called on a\\n # commit whose parent added a new directory.\\n self.logger.debug(' %s not in %s in %s' %\\n (dirent, path, commit.hex[:8]))\\n return None\\n else:\\n self.logger.debug(' %s not a tree in %s' %\\n (tree_or_blob, commit.hex[:8]))\\n return None\\n return tree_or_blob\",\n \"def _best_case(): # pragma no cover\\n setup_code = \\\"\\\"\\\"\\nfrom __main__ import _best_case\\nfrom bst import BinarySearchTree\\nfrom bst import Node\\n \\\"\\\"\\\"\\n test_code = \\\"\\\"\\\"\\nnew_tree = BinarySearchTree([4,2,3,1,6,5,7])\\nnew_tree.search(7)\\n \\\"\\\"\\\"\\n return(timeit.repeat(setup=setup_code, stmt=test_code, number=100000))\",\n \"def tree_BST(tree): \\r\\n if(tree==None):\\r\\n return True\\r\\n elif (left(tree)!=None):\\r\\n if(value(left(tree))>value(tree)):\\r\\n return False\\r\\n elif (right(tree)!=None):\\r\\n if(value(right(tree))<value(tree)):\\r\\n return False\\r\\n return tree_BST(left(tree))\\r\\n return tree_BST(right(tree))\",\n \"def bst_wiki():\\n from bbst import Bst\\n tree = Bst([6, 7, 9, 8, 2, 1, 4, 3, 5])\\n return tree\",\n \"def test1():\\n tree = t1make(VALNUM)\\n tree.print()\\n t1find(tree)\",\n \"def construct_binary_tree(preorder, inorder):\\n if len(preorder) == 0 or preorder == None or inorder == None:\\n return None\\n\\n val = preorder[0]\\n root = BinaryTreeNode(val)\\n\\n if len(preorder) > 1:\\n inorder_root_index = inorder.index(val)\\n inorder_left_sub_tree = inorder[:inorder_root_index]\\n inorder_right_sub_tree = inorder[inorder_root_index+1:]\\n preorder_left_sub_tree = preorder[1:len(inorder_left_sub_tree)+1]\\n preorder_right_sub_tree = preorder[len(inorder_left_sub_tree) + 1:]\\n root.left = construct_binary_tree(preorder_left_sub_tree, inorder_left_sub_tree)\\n root.right = construct_binary_tree(preorder_right_sub_tree, inorder_right_sub_tree)\\n return root\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n #Creamos las estructuras de datos necesarias (queue y set)\\n openNodes = util.Queue()\\n closedNodes = set([])\\n\\n #Guardamos el nodo inicial\\n node = Node(problem.getStartState(), '', 0, None)\\n\\n #Metemos el nodo en la cola\\n openNodes.push(node)\\n\\n #Iteramos para cada nodo de la pila\\n while True:\\n if openNodes.isEmpty():\\n break #ERROR: throw exception\\n else :\\n #Sacamos el nodo de arriba de la pila\\n node = openNodes.pop()\\n if problem.isGoalState(node.name):\\n break\\n else: #Expandimos los nodos sucesores del nodo n si no estan en closed\\n if nodeIsClosed(node, closedNodes) is False:\\n for successor in problem.getSuccessors(node.name):\\n n, p, c = successor\\n succNode = Node(n, p, c, node)\\n if nodeIsClosed(succNode, closedNodes) is False:\\n #Metemos al sucesor en la cola\\n openNodes.push(succNode)\\n #Metemos el nodo n en closed\\n closedNodes.add(node)\\n\\n #Devolvemos el camino al Goal\\n return findPath(node)\",\n \"def deserialize(self, data):\\n if len(data) == 0:\\n return None\\n root = TreeNode(data[0])\\n root.left = self.deserialize(data[1]) \\n root.right = self.deserialize(data[2])\\n return root\",\n \"def tree_find(T, x):\\n if T.label is None:\\n return False\\n else:\\n return x == T.label \\\\\\n or (x < T.label and tree_find(T[0], x)) \\\\\\n or (x > T.label and tree_find(T[1], x))\",\n \"def make_tree(arr):\\n\\n for i in range(len(arr)):\\n arr, val = mid(arr)\\n\\n if i == 0: \\n binary = BinaryNode(val)\\n\\n else:\\n binary.insert(val)\\n\\n return binary\",\n \"def deserialize(self, data):\\n data = eval(data)\\n length = len(data)\\n k = 0\\n root = []\\n while k < length:\\n if k == 0:\\n root = TreeNode(data[k])\\n k += 1\\n floor = [root]\\n else:\\n nextfloor = []\\n for node in floor:\\n if node != None:\\n if data[k] == None:\\n node.left = data[k]\\n else:\\n node.left = TreeNode(data[k])\\n k += 1\\n nextfloor.append(node.left)\\n if k >= length:\\n return root\\n if data[k] == None:\\n node.right = data[k]\\n else:\\n node.right = TreeNode(data[k])\\n nextfloor.append(node.right)\\n k += 1\\n if k >= length:\\n return root\\n \\n floor = nextfloor\\n \\n return root\",\n \"def testBinaryHelper(self):\\n\\n self.mockSplay() #disable splaying\\n\\n #test base-case empty\\n self.assertIsNone(self.s.binaryHelper(None, None))\\n self.assertIsNone(self.s.binaryHelper(None, self.s._root))\\n\\n #test base-case\\n self.s.insert(0, None)\\n self.assertIsNotNone(self.s.binaryHelper(0, self.s._root))\\n\\n #note: binary helper does not splay, but insert does\\n n = randint(2,2147483647)\\n self.s.insert(n, None)\\n self.s.insert(-n, None)\\n\\n #find a node that is in the tree\\n self.assertIsNotNone(self.s.binaryHelper(n, self.s._root))\\n self.assertIsNotNone(self.s.binaryHelper(-n, self.s._root))\\n #find the exact node\\n self.assertEqual(self.s.binaryHelper(n, self.s._root), self.s._root.right)\\n self.assertEqual(self.s.binaryHelper(-n, self.s._root), self.s._root.left)\\n #find the parent of a non-existant node\\n #pdb.set_trace()\\n self.assertEqual(self.s.binaryHelper(-1, self.s._root), self.s._root.left)\\n self.assertEqual(self.s.binaryHelper(n-1, self.s._root), \\\\\\n self.s._root.right)\\n #recursively find the parent of a non-existant node\\n self.assertEqual(self.s.binaryHelper(n+1, self.s._root), \\\\\\n self.s._root.right)\\n self.assertEqual(self.s.binaryHelper(-n+1, self.s._root), \\\\\\n self.s._root.left)\",\n \"def binarize(tree):\\n if isinstance(tree, str):\\n return Tree('0',[tree])\\n elif len(tree) == 1:\\n# print(tree)\\n# print('\\\\n')\\n return binarize(tree[0])\\n else:\\n label = tree.label()\\n# print(type(label))\\n return reduce(lambda x, y: Tree(label, (binarize(x), binarize(y))), tree)\",\n \"def _gen_test_tree_6():\\n tree = BinaryNode(20)\\n tree.left = BinaryNode(10)\\n tree.right = BinaryNode(30)\\n tree.left.right = BinaryNode(25)\\n return tree\",\n \"def find(self, data):\\n \\n def _step(current, target):\\n \\\"\\\"\\\"Recursively approach the target node.\\\"\\\"\\\"\\n \\n if current is None: # Base case: target not found.\\n return current\\n if current == other: # Base case: target found!\\n return current\\n if target < current: # Recursively search to the left.\\n return _step(current.left, target)\\n else: # Recursively search to the right.\\n return _step(current.right, target)\\n \\n if self.root is None: # Check for empty tree.\\n raise ValueError(str(data) + \\\" is not in the tree.\\\")\\n \\n # Create a new node to use the KDTNode comparison operators.\\n n = KDTNode(data)\\n found = _step(self.root, n)\\n if found is None: # Report the data was not found.\\n raise ValueError(str(data) + \\\" is not in the tree.\\\")\\n return found # Return the target node.\",\n \"def tree_case_2(self, prune: int=1):\\n\\n types_h = self.Node('types.h')\\n malloc_h = self.Node('malloc.h')\\n stdlib_h = self.Node('stdlib.h', [types_h, malloc_h])\\n bar_h = self.Node('bar.h', [stdlib_h])\\n blat_h = self.Node('blat.h', [stdlib_h])\\n blat_c = self.Node('blat.c', [blat_h, bar_h])\\n blat_o = self.Node('blat.o', [blat_c])\\n\\n expect = \\\"\\\"\\\"\\\\\\n+-blat.o\\n +-blat.c\\n +-blat.h\\n | +-stdlib.h\\n | +-types.h\\n | +-malloc.h\\n +-bar.h\\n\\\"\\\"\\\"\\n if prune:\\n expect += \\\"\\\"\\\" +-[stdlib.h]\\n\\\"\\\"\\\"\\n else:\\n expect += \\\"\\\"\\\" +-stdlib.h\\n +-types.h\\n +-malloc.h\\n\\\"\\\"\\\"\\n\\n lines = expect.split('\\\\n')[:-1]\\n lines = ['[E BSPACN ]' + l for l in lines]\\n withtags = '\\\\n'.join(lines) + '\\\\n'\\n\\n return blat_o, expect, withtags\",\n \"def list_to_binary_tree(list_input):\\n pass\",\n \"def deserialize(self, data):\\n if len(data)==0 or data[0]==None:\\n return None\\n root=TreeNode(data[0])\\n buf=[root]\\n l=1\\n while len(buf) and l<len(data):\\n newbuf=[]\\n for i in buf:\\n if i.val==None:\\n continue\\n else:\\n i.left=TreeNode(data[l])\\n newbuf.append(i.left)\\n l+=1\\n if l>=len(data):\\n break\\n i.right=TreeNode(data[l])\\n newbuf.append(i.right)\\n l+=1\\n return root\",\n \"def deserialize(stringTree):\\n global i #Global shared index \\n\\n if (stringTree[i] == \\\"#\\\"): #Empty node\\n return None \\n\\n else:\\n node = Node(stringTree[i]) #Create node of current index\\n i+=1\\n node.left = deserialize(stringTree) #Check left child\\n i+=1\\n node.right = deserialize(stringTree) #Check right child\\n return node\",\n \"def test_binarytree_pre_order_exists():\\n assert BinaryTree.pre_order\",\n \"def _gen_test_tree_4():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(3)\\n tree.left.left = BinaryNode(2)\\n tree.left.right = BinaryNode(10)\\n tree.right = BinaryNode(9)\\n tree.right.left = BinaryNode(6)\\n tree.right.right = BinaryNode(8)\\n return tree\",\n \"def tree(obj):\\n if isinstance(obj, (bool, int, float, str, bytes)):\\n return obj\\n elif isinstance(obj, (dt.date, dt.time)):\\n return obj.isoformat()\\n elif isinstance(obj, dict):\\n return {k: tree(v) for k, v in obj.items()}\\n elif isnamedtupleinstance(obj):\\n return {f: tree(getattr(obj, f)) for f in obj._fields}\\n elif isinstance(obj, (list, tuple, set)):\\n return [tree(i) for i in obj]\\n elif is_dataclass(obj):\\n return {obj.__class__.__qualname__: tree(dataclassNonDefaults(obj))}\\n else:\\n return str(obj)\",\n \"def search_for_tree(xml_obj, container):\\n if isinstance(container, list):\\n for child in xml_obj:\\n if child.attrib[\\\"type\\\"] == \\\"dict\\\":\\n temp_container = dict()\\n search_for_tree(child, temp_container)\\n container.append(temp_container)\\n elif child.attrib[\\\"type\\\"] == \\\"list\\\":\\n temp_container = list()\\n search_for_tree(child, temp_container)\\n container.append(temp_container)\\n elif child.attrib[\\\"type\\\"] == \\\"int\\\":\\n container.append(int(child.text))\\n else:\\n container.append(child.text)\\n elif isinstance(container, dict):\\n for child in xml_obj:\\n if child.attrib[\\\"type\\\"] == \\\"dict\\\":\\n temp_container = dict()\\n search_for_tree(child, temp_container)\\n container[child.tag] = temp_container\\n elif child.attrib[\\\"type\\\"] == \\\"list\\\":\\n temp_container = list()\\n search_for_tree(child, temp_container)\\n container[child.tag] = temp_container\\n elif child.attrib[\\\"type\\\"] == \\\"int\\\":\\n container[child.tag] = int(child.text)\\n else:\\n container[child.tag] = child.text\\n return container\",\n \"def search(self, word):\\n q = []\\n q2 = []\\n p = self.root\\n q.append(p)\\n for c in word:\\n if (not q):\\n return False\\n if (c == \\\".\\\"): # if it's a dot, add all possible nodes to the queue\\n while (q):\\n node = q.pop(0)\\n for child in node.children:\\n if (child != None):\\n q2.append(child)\\n else: # append only possibly correct nodes\\n ind = ord(c) - ord('a')\\n while (q):\\n node = q.pop(0)\\n if (node.children[ind] == None):\\n continue\\n q2.append(node.children[ind])\\n q = q2\\n q2 = []\\n \\n while (q):\\n node = q.pop()\\n if (node.value != 0):\\n return True\\n \\n return False\",\n \"def test_binarytree_post_order_exists():\\n assert BinaryTree.post_order\",\n \"def extract_tree(x, y, tilt, common):\\n left_tree = None\\n for k in range(y - 1, x, -1):\\n if (x, k) in tilt:\\n if (x, k) not in common:\\n left_tree = extract_tree(x, k, tilt, common)\\n break\\n right_tree = None\\n for k in range(x + 1, y):\\n if (k, y) in tilt:\\n if (k, y) not in common:\\n right_tree = extract_tree(k, y, tilt, common)\\n break\\n return BinaryTree([left_tree, right_tree])\",\n \"def test_binarytree_instantiate_given(given_list):\\n assert isinstance(given_list, BinaryTree)\",\n \"def BFTbin(tree):\\n queue = [tree]\\n bft_nodelist = []\\n while queue:\\n node = queue.pop(0)\\n bft_nodelist.append(node)\\n if node.lnode is not None:\\n queue.append(node.lnode)\\n if node.rnode is not None:\\n queue.append(node.rnode)\\n return bft_nodelist\",\n \"def find(self, key, forDel=False):\\n found = False\\n root = self.root\\n while not found:\\n if key > root.key:\\n if root.right is None:\\n print(\\\"Key not found\\\")\\n return False\\n root = root.right\\n elif key < root.key:\\n if root.left is None:\\n print(\\\"Key not found\\\")\\n return False\\n root = root.left\\n\\n elif root.key == key:\\n found = True\\n\\n if forDel:\\n return root\\n return root.content\",\n \"def _worst_case(): # pragma no cover\\n setup_code = \\\"\\\"\\\"\\nfrom __main__ import _best_case\\nfrom bst import BinarySearchTree\\nfrom bst import Node\\n \\\"\\\"\\\"\\n test_code = \\\"\\\"\\\"\\nnew_tree = BinarySearchTree([1,2,3,4,5,6,7])\\nnew_tree.search(7)\\n \\\"\\\"\\\"\\n return(timeit.repeat(setup=setup_code, stmt=test_code, number=100000))\",\n \"def print_binary_tree_bfs(root):\\n if not isinstance(root, BinaryTreeNode):\\n return\\n queue = Queue()\\n queue.put(root)\\n\\n while not queue.empty():\\n node = queue.get()\\n print(node.val)\\n if node.left != None:\\n queue.put(node.left)\\n if node.right != None:\\n queue.put(node.right)\",\n \"def test_is_bst(tree: BSTNode) -> None:\\n output = [n.value for n in tree.in_order(tree)]\\n assert output == sorted(output)\",\n \"def depthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n #Creamos las estructuras de datos necesarias (stack y set)\\n openNodes = util.Stack()\\n closedNodes = set([])\\n\\n #Guardamos el nodo inicial\\n node = Node(problem.getStartState(), '', 0, None)\\n\\n #Metemos el nodo en la pila\\n openNodes.push(node)\\n\\n #Iteramos para cada nodo de la pila\\n while True:\\n if openNodes.isEmpty():\\n break #ERROR: throw exception\\n else :\\n #Sacamos el nodo de arriba de la pila\\n node = openNodes.pop()\\n if problem.isGoalState(node.name):\\n break\\n else: #Expandimos los nodos sucesores del nodo n si no estan en closed\\n for successor in problem.getSuccessors(node.name):\\n n, p, c = successor\\n succNode = Node(n, p, c, node)\\n if nodeIsClosed(succNode, closedNodes) is False:\\n #Metemos al sucesor en la pila\\n openNodes.push(succNode)\\n #Metemos el nodo n en closed\\n closedNodes.add(node)\\n\\n #Devolvemos el camino al Goal\\n return findPath(node)\",\n \"def binaryTree(r):\\r\\n return [r, [], []]\",\n \"def search(self, val):\\n if type(val) not in [int, float]:\\n raise TypeError('This tree accepts numbers only.')\\n current_node = self._root\\n while current_node:\\n if val == current_node._data:\\n return current_node\\n if val > current_node._data:\\n current_node = current_node._rkid\\n else:\\n current_node = current_node._lkid\\n return\",\n \"def test_search_returns_none_when_value_notin_tree_left(bst_all_to_left):\\n assert bst_all_to_left.search(0) is None\",\n \"def test_small_tree_has_right_child_child(small_tree):\\n assert small_tree.root.right.right.value == 27\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n visited=[]\\n \\n node=dict()\\n start=problem.getStartState()\\n node['parent']=None\\n node['direction']=None\\n node['state']=start\\n \\n \\n qu.push(node)\\n lis.append(node)\\n \\n print qu.list\\n while qu.isEmpty()!=True:\\n node=qu.pop()\\n pos=node['state']\\n visited.append(pos)\\n print visited\\n if problem.isGoalState(pos):\\n print \\\"found\\\"\\n return getPath(problem,node)\\n #break\\n suc=problem.getSuccessors(pos)\\n if suc ==None:\\n continue \\n \\n print suc\\n for step in suc:\\n #if step not in dic :\\n if step[0] not in visited:\\n childnode={}\\n childnode['parent']=pos\\n childnode['direction']=step[1]\\n childnode['state']=step[0]\\n qu.push(childnode)\\n lis.append(childnode)\\n \\n\\n #util.raiseNotDefined()\",\n \"def _recursiveSearch(self, data, node):\\n\\t\\tif (not node):\\n\\t\\t\\treturn None\\n\\t\\tif (node.value() == data):\\n\\t\\t\\treturn node\\n\\t\\telif (data < node.value()):\\n\\t\\t\\treturn self._recursiveSearch(data, node.lchild())\\n\\t\\telse:\\n\\t\\t\\treturn self._recursiveSearch(data, node.rchild())\",\n \"def build_tree(elements):\\n print(\\\"Building tree with these elements:\\\",elements)\\n root = BinarySearchTreeNode(elements[0])\\n\\n for i in range(1, len(elements)):\\n root.add_child(elements[i])\\n\\n return root\",\n \"def _find_one_tree(tree: dict,\\n func: Callable,\\n args: Tuple,\\n kwargs: Mapping,\\n ) -> Union[dict, None]:\\n frontier = []\\n explored = set()\\n for uid, item in tree.items():\\n frontier.append((uid, item))\\n while frontier:\\n uid, item = frontier.pop()\\n explored.add(uid)\\n if func(item, *args, **kwargs):\\n return item\\n if \\\"children\\\" in item:\\n for child_uid, child_item in item[\\\"children\\\"].items():\\n if child_uid not in explored:\\n frontier.append((child_uid, child_item))\",\n \"def _gen_test_tree_3():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(1)\\n tree.left.left = BinaryNode(2)\\n tree.left.right = BinaryNode(3)\\n tree.right = BinaryNode(7)\\n tree.right.left = BinaryNode(8)\\n tree.right.right = BinaryNode(9)\\n return tree\",\n \"def minimal_tree(array: list):\\n bst = BST()\\n def build(l, r):\\n if l == r: bst.insert(array[l]); return\\n m = (l+r)//2\\n # insert into the tree\\n bst.insert(array[m])\\n # build recursively\\n build(l, m)\\n build(m+1, r)\\n build(0, len(array)-1)\\n return bst\",\n \"def lower_binary_tree(self):\\n return self.min_linear_extension().binary_search_tree_shape(left_to_right=False)\",\n \"def first_found_merging(file_names, tree_name):\\n\\n tree_branches = {}\\n for ifile, file_name in enumerate(file_names):\\n file_ = uproot.open(file_name)\\n if tree_name+\\\";1\\\" in file_.keys():\\n tree = file_[tree_name].arrays()\\n tree_branches = { k: tree[k] for k in tree.fields }\\n break\\n\\n return tree_branches\",\n \"def breadthFirstSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n from util import Queue\\n q = Queue()\\n mapper = {} #child_point : (parent_point, direction_to_child)\\n q.push(problem.getStartState())\\n mapper[problem.getStartState()] = None #root\\n\\n while (not q.isEmpty()):\\n point = q.pop()\\n\\n if (problem.isGoalState(point)):\\n c = point\\n l = []\\n while mapper[c] != None:\\n tup = mapper[c]\\n l.append(tup[1])\\n c = tup[0]\\n l.reverse()\\n print l\\n return l\\n\\n else:\\n for child in problem.getSuccessors(point):\\n if (child[0] not in mapper):\\n q.push(child[0])\\n mapper[child[0]] = (point, child[1])\\n\\n # util.raiseNotDefined()\",\n \"def findTrees( Dir, treeList ):\\n \\t_dirSave = Dir\\n\\t#Storing the father->child info\\n\\ttry:\\n\\t\\tlistOfKeys = Dir.GetListOfKeys()\\n\\t##-- Checking that is a Tag and Probe fit \\n\\t## file. IF it is not, then we find\\n\\t## some not expected TKeys diferent from TTree\\n\\texcept AttributeError:\\n\\t\\tmessage = \\\"\\\"\\\"\\\\033[1;31mError: The root file is not an standard T&P NTuple file\\\\033[1;m\\\"\\\"\\\" \\n\\t\\traise AttributeError, message\\n \\tfor key in Dir.GetListOfKeys():\\n \\t\\tclassName = key.GetClassName()\\n \\t\\tif key.IsFolder() and className != 'TTree': #Watch TTree is a folder\\n \\t\\t\\t##-- Extracting the Folder from Dir\\n\\t\\t\\t_subdir = Dir.Get(key.GetName())\\n \\t\\t\\t##-- And browsing inside recursively\\n \\t\\t\\ttreeList = findTrees(_subdir,treeList)\\n \\t\\t\\t##-- To avoid segmentation faults, we need to return\\n\\t\\t\\t## at the original directory in order to continue\\n\\t\\t\\t## extracting subdirectories (or elements of the directory)\\n \\t\\t\\t_dirSave.cd()\\n \\t\\t##-- Storing in the dictionary the interesting objects\\n \\t\\telif className == 'TTree':\\n\\t\\t\\t#Asuming unique id object-->complet path\\n\\t\\t\\ttreeList.append( Dir.GetPath().split(':/')[1]+'/'+key.GetName() )\\n \\n \\treturn treeList\",\n \"def getRoot(obj):\",\n \"def test_bin_tree():\\n n1 = BinTreeNode(1)\\n n2 = BinTreeNode(2)\\n n3 = BinTreeNode(3)\\n n4 = BinTreeNode(4)\\n n5 = BinTreeNode(5)\\n n1.left = n2\\n n1.right = n3\\n n2.left = n4\\n n3.right = n5\\n t = BinTree(n1)\\n print('pre order')\\n preorder_trav(t.root)\\n print('in order')\\n inorder_trav(t.root)\\n print('post order')\\n postorder_trav(t.root)\",\n \"def deserialize(self, data):\\n # 아래 부분에서 빈 리스트를 잡지 못함\\n # if not data: 로 빈 리스트가 입력이 되었을 때 잡으려고 했음.\\n # 하지만 serialize를 거친 빈 리스트는 n이 추가되어서 desrialize를 호출하기 때문에\\n # data[0] == 'n'으로 잡아야만 처음 serialize의 input이 빈 리스트인 것을 잡을 수 있음.\\n if data[0] == 'n':\\n return None\\n\\n root = TreeNode(data.pop(0))\\n queue = collections.deque([root])\\n\\n while queue:\\n node = queue.popleft()\\n temp = data.pop(0)\\n if temp is not 'n':\\n node.left = TreeNode(int(temp))\\n queue.append(node.left)\\n\\n temp = data.pop(0)\\n if temp is not 'n':\\n node.right = TreeNode(int(temp))\\n queue.append(node.right)\\n return root\",\n \"def deserialize(self, data):\\n if not data:\\n return None\\n data = data.split(' ')\\n root = TreeNode(int(data[0]))\\n stack = [root]\\n\\n i, child = 1, 'left'\\n curr = root\\n while i < len(data):\\n if data[i] != 'None':\\n if child == 'left':\\n curr.left = TreeNode(int(data[i]))\\n stack.append(curr.left)\\n curr = curr.left\\n else:\\n curr.right = TreeNode(int(data[i]))\\n stack.append(curr.right)\\n curr = curr.right\\n child = 'left'\\n else:\\n curr = stack.pop()\\n child = 'right'\\n i += 1\\n return root\",\n \"def balanced_tree(ordered):\\n bt = BinaryTree()\\n\\n add_range(bt, ordered, 0, len(ordered)-1)\\n\\n return bt\",\n \"def find_node(self, type):\\n pass\",\n \"def search_in_tree(root_elem, target):\\r\\n\\t# Guard agains an empty tree\\r\\n\\tif root_elem is None:\\r\\n\\t\\treturn False\\r\\n\\tqueue = deque([root_elem])\\r\\n\\twhile queue:\\r\\n\\t\\tcurrent = queue.popleft()\\r\\n\\t\\tif current.value == target:\\r\\n\\t\\t\\treturn True\\r\\n\\t\\tif current.left:\\r\\n\\t\\t\\tqueue.append(current.left)\\r\\n\\t\\tif current.right:\\r\\n\\t\\t\\tqueue.append(current.right)\\r\\n\\treturn False\",\n \"def test_depth(known_bst):\\n assert known_bst[0].depth() == 3\",\n \"def test_binarytree_instantiate_list():\\n input = [13, 42, 7]\\n c = BinaryTree(input)\\n assert isinstance(c, BinaryTree)\",\n \"def huffman_code_tree(node, left=True, binString=''):\\n if type(node) is str:\\n return {node: binString}\\n \\n (l, r) = node.children()\\n dico_binary = {}\\n dico_binary.update(huffman_code_tree(l, True, binString + '0'))\\n dico_binary.update(huffman_code_tree(r, False, binString + '1'))\\n\\n return dico_binary\",\n \"def find_matching_structures(self, query):\\r\\n return self.structures.find(query)\",\n \"def deserialize(self, data):\\n\\n myiter = iter(data.split())\\n qe = Queue.Queue(maxsize=0)\\n item = next(myiter)\\n if item == \\\"null\\\":\\n return None\\n\\n root = TreeNode(int(item))\\n qe.put(root)\\n\\n while not qe.empty():\\n node = qe.get()\\n item = next(myiter)\\n if item != \\\"null\\\":\\n node.left = TreeNode(int(item))\\n qe.put(node.left)\\n\\n item = next(myiter)\\n if item != \\\"null\\\":\\n node.right = TreeNode(int(item))\\n qe.put(node.right)\\n\\n return root\",\n \"def binary_search(node, value):\\n aux_node = None\\n while node is not None and node.value != value:\\n if value < node.value:\\n aux_node = node.left\\n node = aux_node\\n else:\\n aux_node = node.right\\n node = aux_node\\n return node if node.value == value else None\",\n \"def test_Tree():\",\n \"def search(self, word):\\n if not word:\\n return False\\n discrepancy = 1\\n stack = [(child, word, discrepancy)\\n for child in self.root.children.values()]\\n while stack:\\n curr_node, curr_word, curr_disc = stack.pop()\\n if len(curr_word) == 1:\\n if curr_node.nil:\\n if curr_disc == 0 and curr_word == curr_node.key:\\n return curr_node.nil\\n elif curr_disc == 1 and curr_word != curr_node.key:\\n return curr_node.nil\\n else:\\n for key, child in curr_node.children.items():\\n if curr_word[0] == curr_node.key:\\n stack.append((child, curr_word[1:], curr_disc))\\n elif curr_disc> 0:\\n stack.append((child, curr_word[1:], curr_disc - 1))\\n return False\",\n \"def deserialize1(self, data):\\n if data == \\\"\\\":\\n return None\\n serial = data.split(\\\",\\\")\\n stack = []\\n for val in serial:\\n if not stack:\\n root = TreeNode(int(val))\\n stack.append([root, True])\\n else:\\n node, state = stack[-1]\\n if not node.left and state:\\n if val == \\\"null\\\":\\n stack[-1][1] = False\\n else:\\n left_node = TreeNode(int(val))\\n node.left = left_node\\n print(left_node.val)\\n stack.append([left_node, True])\\n else:\\n stack.pop()\\n if not val == \\\"null\\\":\\n right_node = TreeNode(int(val))\\n node.right = right_node\\n print(right_node.val)\\n stack.append([right_node, True])\\n return root\",\n \"def Trees__CheckBST():\\n # Python2 ported to Python3 via 2to3-3.7\\n # URL:https://www.hackerrank.com/challenges/ctci-is-binary-search-tree/problem\\n # O(n) solution. Passes all test cases.\\n # Tricky part with leaf in left side of root being bigger than root. (or right/smaller)\\n # E.g:\\n # 3\\n # 2 6\\n # 1 4 5 7\\n # Note, the 4 is bigger than the parent 3. But in a proper BST parent\\n # must be bigger than all items on parent's left side.\\n # Keep track of last biggest element as we descend to children (& last smallest element)\\n # val < last biggest (last_left) #for cases like 4\\n # val > last smallest (last_right) # for mirror side.\\n # Convieniently, this also ensures uniqueness.\\n def checkBST(root):\\n queue = []\\n queue.append((root, None, None)) # node, last_left, last_right.\\n while queue:\\n node, last_left, last_right = queue.pop()\\n if not node:\\n continue\\n if last_left and not node.data < last_left \\\\\\n or last_right and not node.data > last_right:\\n return False\\n queue.append((node.left, node.data, last_right))\\n queue.append((node.right, last_left, node.data))\\n return True\",\n \"def searchTreeF(node, d):\\n if isinstance(node, DecisionTree):\\n if node.i == 999: return node.mostCommon()\\n if d[node.i] < node.v:\\n return searchTreeF(node.lt, d)\\n else:\\n return searchTreeF(node.gt, d)\\n else:\\n return node\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6735971","0.6530564","0.62307024","0.61076456","0.6053023","0.5996912","0.59570765","0.59391767","0.592461","0.5840478","0.5802106","0.5735963","0.5731404","0.5731134","0.5724618","0.57046956","0.56911415","0.5674273","0.56437874","0.56290257","0.56067574","0.5594903","0.55931586","0.5588603","0.55779153","0.55713964","0.5562085","0.5559227","0.5538773","0.5536375","0.55327207","0.553178","0.55290943","0.5524285","0.5523251","0.55191195","0.55156934","0.5514641","0.54998165","0.5498967","0.54969716","0.549076","0.5489813","0.5480899","0.5478876","0.5476586","0.5460506","0.5458162","0.5449119","0.5448588","0.5448398","0.5445298","0.54431766","0.542252","0.5413669","0.54058033","0.53951234","0.53864217","0.53843224","0.53834593","0.5376868","0.537637","0.5373888","0.5372485","0.5359588","0.5347602","0.53451765","0.534278","0.53413945","0.5339898","0.53336924","0.53287345","0.53227204","0.5319828","0.5319582","0.5318185","0.53181326","0.53121215","0.53109246","0.53109217","0.53101087","0.53085035","0.530824","0.53062135","0.53058755","0.53054553","0.53017294","0.52971405","0.5291437","0.5290588","0.5289702","0.5288349","0.5285031","0.52824926","0.52722555","0.5269921","0.52648544","0.52586335","0.5253681","0.5253059","0.52510786"],"string":"[\n \"0.6735971\",\n \"0.6530564\",\n \"0.62307024\",\n \"0.61076456\",\n \"0.6053023\",\n \"0.5996912\",\n \"0.59570765\",\n \"0.59391767\",\n \"0.592461\",\n \"0.5840478\",\n \"0.5802106\",\n \"0.5735963\",\n \"0.5731404\",\n \"0.5731134\",\n \"0.5724618\",\n \"0.57046956\",\n \"0.56911415\",\n \"0.5674273\",\n \"0.56437874\",\n \"0.56290257\",\n \"0.56067574\",\n \"0.5594903\",\n \"0.55931586\",\n \"0.5588603\",\n \"0.55779153\",\n \"0.55713964\",\n \"0.5562085\",\n \"0.5559227\",\n \"0.5538773\",\n \"0.5536375\",\n \"0.55327207\",\n \"0.553178\",\n \"0.55290943\",\n \"0.5524285\",\n \"0.5523251\",\n \"0.55191195\",\n \"0.55156934\",\n \"0.5514641\",\n \"0.54998165\",\n \"0.5498967\",\n \"0.54969716\",\n \"0.549076\",\n \"0.5489813\",\n \"0.5480899\",\n \"0.5478876\",\n \"0.5476586\",\n \"0.5460506\",\n \"0.5458162\",\n \"0.5449119\",\n \"0.5448588\",\n \"0.5448398\",\n \"0.5445298\",\n \"0.54431766\",\n \"0.542252\",\n \"0.5413669\",\n \"0.54058033\",\n \"0.53951234\",\n \"0.53864217\",\n \"0.53843224\",\n \"0.53834593\",\n \"0.5376868\",\n \"0.537637\",\n \"0.5373888\",\n \"0.5372485\",\n \"0.5359588\",\n \"0.5347602\",\n \"0.53451765\",\n \"0.534278\",\n \"0.53413945\",\n \"0.5339898\",\n \"0.53336924\",\n \"0.53287345\",\n \"0.53227204\",\n \"0.5319828\",\n \"0.5319582\",\n \"0.5318185\",\n \"0.53181326\",\n \"0.53121215\",\n \"0.53109246\",\n \"0.53109217\",\n \"0.53101087\",\n \"0.53085035\",\n \"0.530824\",\n \"0.53062135\",\n \"0.53058755\",\n \"0.53054553\",\n \"0.53017294\",\n \"0.52971405\",\n \"0.5291437\",\n \"0.5290588\",\n \"0.5289702\",\n \"0.5288349\",\n \"0.5285031\",\n \"0.52824926\",\n \"0.52722555\",\n \"0.5269921\",\n \"0.52648544\",\n \"0.52586335\",\n \"0.5253681\",\n \"0.5253059\",\n \"0.52510786\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":87499,"cells":{"query":{"kind":"string","value":"Run the c++ unit tests. This runs a subprocess because the HDF5 used by h5py clashed with the HDF5 linked to the c++ unit tests on some systems. h5py is used by some XDMF tests."},"document":{"kind":"string","value":"def run_cpp_unit_tests():\n # We set cwd in case the script is run from another directory. This is needed\n # when testing from the core.\n out_bytes = subprocess.check_output(\n ['python3', 'run_cpp_unit_tests.py'], cwd=abspath(dirname(__file__)))\n return out_bytes.decode('utf-8')"},"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 __main() :\n launchTests()","def test(c, index=0):\n exec_path = f'{ROOT_DIR}/gem5/build/GCN3_X86/gem5.opt' \n conf_path = f'{ROOT_DIR}/gem5/configs/example/apu_se.py'\n test_dir = TEST_PATHS[index]\n print(f'Testing {test_dir}...')\n \n # Try to find executable\n try_paths = [os.path.basename(test_dir), os.path.basename(test_dir) + '.o']\n try_paths = [[ex, os.path.join('bin', ex)] for ex in try_paths]\n try_paths = [ex for t in try_paths for ex in t]\n for ex in try_paths:\n if os.path.isfile(os.path.join(ROOT_DIR, test_dir, ex)):\n print(f' Found executable {ex}.')\n break\n else:\n raise RuntimeError(f'Executable for {test_dir} not found!')\n \n\n args = f'-n 2 --benchmark-root={test_dir}/{os.path.dirname(ex)} -c {os.path.basename(ex)}'\n _run(c, ' '.join([exec_path, f'-d runs/{os.path.basename(ex).split(\".\")[0]}', conf_path, args]))","def run_tests():\n test_command = \"pytest -s \" + os.path.join(root_path, \"cases\", \"test_cases.py::TestCases::test_cases\") + \" --html=\" + os.path.join(root_path, \"reports\", \"qa_testing_report.html\")\n\n subprocess.run(test_command, shell=True)","def main():\n run_test_summary1a()\n run_test_summary1c()\n run_test_summary1c()","def main():\n run_test_all()","def test(cline):\n print(\"Running unit tests.\")\n cline.run(\"TF_CPP_MIN_LOG_LEVEL=3 python3 -m unittest\")","def run_test(testname,testfunc,epoch,jd):\n import subprocess\n \n print '\\n\\n\\nRunning PYTHON test',testname\n pyres = testfunc(epoch,jd)\n \n\n cmd = './sofatests %s %.16g %.16g'%(testname,epoch,jd)\n print '\\nRunning C test',testname,'as','\"'+cmd+'\"' \n \n if pyres is None:\n os.system(cmd)\n else:\n cmpfunc = pyres[0]\n \n sp = subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE)\n cstdout,cstderr = sp.communicate()\n if sp.returncode==0:\n print 'C code complete - output:\\n',cstdout\n else:\n print 'Problem with C - retcode',sp.returncode,'- output:\\n',cstdout,'\\nstd err:',cstderr\n \n cmps = []\n cmps.append(cstdout)\n cmps.extend(pyres[1:])\n print 'Comparing python to C'\n cmpfunc(*cmps)\n print 'Comparison complete'","def RunUnitTests():\n if FLAGS.test_targets:\n tests = FLAGS.test_targets\n else:\n tests = shell_interfaces.GetStdout(\n 'bazel query kind(\"cc_test\", ...)').split()\n\n # Run coverage, joining all data into one file.\n subprocess.check_call(['bazel', 'coverage', '--instrument_test_targets',\n '--experimental_cc_coverage',\n '--combined_report=lcov',\n ('--coverage_report_generator=@bazel_tools//tools/tes'\n 't/CoverageOutputGenerator/java/com/google/devtools/'\n 'coverageoutputgenerator:Main')] + tests)","def main():\n vunit = vunit_pkg.VUnit.from_argv()\n vunit = map_sources(vunit)\n run_tests(vunit)","def test_script(self) -> None:\n main()","def test_core(c):\n def test_python(c, filename):\n \"\"\"Run a single python test file in tests/python.\"\"\"\n test_dir = 'tests/python'\n filepath = '{}/{}'.format(test_dir, filename)\n\n print('Restarting network before executing test {}'.format(filepath))\n rm_network(c)\n start_core(c)\n start_horizon(c)\n network(c)\n\n with c.cd(test_dir):\n print('Executing test {}'.format(filepath))\n c.run('pipenv run python {filename} \"{passphrase}\" {whitelist_seed}'.format(\n filename=filename,\n passphrase=PASSPHRASE,\n whitelist_seed=WHITELIST_SEED))\n\n test_python(c, 'test_base_reserve.py')\n test_python(c, 'test_tx_order_by_fee.py')\n test_python(c, 'test_tx_order_by_whitelist.py')\n test_python(c, 'test_tx_priority_for_whitelist_holder.py')\n test_python(c, 'test_whitelist_affected_on_next_ledger.py')\n\n # XXX obsolete\n # see source file for more information\n # test_python(c, 'test_multiple_cores.py')","def run_tests():\n os.environ['WORKDIR'] = CONFIG['workdir']\n os.environ['REPORTDIR'] = CONFIG['reportFolder']\n stdout = subprocess.DEVNULL\n if CONFIG['verbose']:\n stdout = None\n # cycle throught version\n total = 0\n valid = 0\n start = time.time()\n for version in utils.get_dirs(CONFIG['versionsFolder']):\n os.environ['VERSION'] = version\n utils.copy_dir(os.path.join(CONFIG['versionsFolder'], version), CONFIG['workdir']\\\n , CONFIG['clearWorkdir'])\n # cycle throught use case\n for usecase in utils.get_dirs(CONFIG['testsFolder']):\n os.environ['TESTDIR'] = usecase\n if not CONFIG['quiet']:\n print('UseCase test: {}'.format(usecase))\n log_msg('info', 'UseCase test: {}'.format(usecase))\n try:\n folder = os.path.join(CONFIG['testsFolder'], usecase)\n with open(os.path.join(folder, CONFIG['useConfig'])) as usefp:\n jconfig = json.load(usefp)\n # clear workdir if desired\n if 'clearWorkdir' in jconfig.keys() and jconfig['clearWorkdir']:\n utils.copy_dir(os.path.join(CONFIG['versionsFolder'], version)\\\n , CONFIG['workdir'], CONFIG['clearWorkdir'])\n # print('clearing')\n # raise\n cmd = ['py', os.path.join(folder, jconfig['entrypoint'])]\n total += 1\n if jconfig['runType'] == 'single':\n subprocess.run(cmd, stdout=stdout, stderr=subprocess.PIPE, check=True)\n else:\n for step in range(jconfig['numRuns']):\n if not CONFIG['quiet']:\n print('\\r >Step {}/{} '.format(step+1, jconfig['numRuns'])\\\n , end='', flush=True)\n log_msg('info', 'Step {}/{}'.format(step+1, jconfig['numRuns']))\n subprocess.run(cmd, stdout=stdout, stderr=subprocess.PIPE, check=True)\n if step+1 != jconfig['numRuns']:\n time.sleep(jconfig['interval'])\n except subprocess.CalledProcessError as excp:\n if not CONFIG['quiet']:\n print('Error msg:{}'\\\n .format(excp.stderr.decode().replace('\\r', '').replace('\\n', '|')))\n log_msg('error', excp.stderr.decode())\n else:\n valid += 1\n if not CONFIG['quiet']:\n print('{}.....Passed'.format(usecase))\n log_msg('info', '{} Passed'.format(usecase))\n\n elapse = time.time()-start\n log_msg('info', 'Ran {} tests in {:.3f}s with {} passed'.format(total, elapse, valid))\n print('-'*20)\n print('Ran {} tests in {:.3f}s with {} passed.'.format(total, elapse, valid))\n return total-valid","def exe_tests(self):\n self.rank = mpicom.rank()\n self.size = mpicom.size()\n if mpicom.parallel():\n self.test(\"libname\",os.path.split(mpicom.__file__)[1],\"mpicom.so\")\n else:\n self.test(\"libname\",os.path.split(mpicom.__file__)[1],\"mpistub.pyc\")\n self.test_broadcast()\n self.test_reduce()\n self.test_p2p()\n self.test_gather()\n self.test_scatter()\n #self.test_alltoall()","def run_tests(virtual_env):\n signal.signal(signal.SIGINT, signal.SIG_IGN)\n args = [\n 'python',\n 'setup.py',\n 'nosetests',\n '--with-coverage',\n '--with-xunit',\n ]\n subprocess.call(args, cwd=abspath(join(HOLLAND_ROOT, 'holland-core')), env=virtual_env)\n subprocess.call(['coverage', 'xml'], cwd=abspath(join(HOLLAND_ROOT, 'holland-core')), env=virtual_env)\n for plugin_dir in open(join(HOLLAND_ROOT, 'plugins', 'ACTIVE')):\n plugin_dir = plugin_dir.rstrip()\n plugin_path = join(HOLLAND_ROOT, 'plugins', plugin_dir)\n subprocess.call(args, cwd=plugin_path, env=virtual_env)\n subprocess.call(['coverage', 'xml'], cwd=plugin_path, env=virtual_env)\n for addon_dir in open(join(HOLLAND_ROOT, 'addons', 'ACTIVE')):\n addon_dir = addon_dir.rstrip()\n addon_path = join(HOLLAND_ROOT, 'addons', addon_dir)\n subprocess.call(args, cwd=addon_path, env=virtual_env)\n subprocess.call(['coverage', 'xml'], cwd=plugin_path, env=virtual_env)\n #return subprocess.call(args, env=virtual_env)","def runTest(self):\n unittest.main()\n ChoreTest.clean_up()","def run_main(): # pragma: no cover\n RunTestsCLI.run()","def run_unit(session):\n set_environment_variables(PYBAMM_ENV, session=session)\n session.run_always(\"pip\", \"install\", \"-e\", \".[all]\")\n if sys.platform == \"linux\":\n session.run_always(\"pip\", \"install\", \"-e\", \".[odes]\")\n session.run_always(\"pip\", \"install\", \"-e\", \".[jax]\")\n session.run(\"python\", \"run-tests.py\", \"--unit\")","def test_suite():\n testSuite = unittest.TestSuite()\n testSuite.addTest(test_h5fs(\"test_mode\"))\n testSuite.addTest(test_h5fs(\"test_path_splitting\"))\n testSuite.addTest(test_h5fs(\"test_link_mixing\"))\n return testSuite","def Cpp_test():\n pass","def test(self):\n for arch, python in self.python:\n self.run(f\"{python} -m pytest\")","def test():\n for cmd in [\n \"pytest --verbose --cov pike/ --cov-report term --cov-report html tests/\",\n ]:\n _run_in_venv(shlex.split(cmd))\n for linter in [[\"black\", \"--check\"], [\"flake8\"], [\"isort\", \"--check\"]]:\n _run_in_venv(linter + TEST_FILES)\n\n _run_in_venv(\n [\"mypy\", \"pike/\", \"tests/\", \"setup.py\", \"pikefile.py\", \"--show-error-codes\"]\n )\n _run_in_venv([\"mypy\", \"examples/\"])\n _run_in_venv([\"bandit\", \"-r\", \"pike/\"])","def runtests(ctx):\n run('pytest -s tests', pty=pty_available)\n run('flake8 --ignore E265,E266,E501 --exclude src, lib', pty=pty_available)","def test_py_compile_basic(self):\n self._test_py_compile('basic')","def run(self, test, env):\n\n raise NotImplementedError","def _run_unittest(tests, verbose, use_gdb, gtest_filter, gtest_list_tests):\n failed_tests = []\n unfound_tests = []\n for test in tests:\n index = 1\n while True:\n test_info = _read_test_info('%s.%d.json' % (test, index))\n if not test_info:\n # The format of test info file is [test name].[index].json, where index\n # is one of consecutive numbers from 1. If the test info file for index\n # 1 is not found, that means the corresponding test does not exist.\n if index == 1:\n unfound_tests.append(test)\n break\n command = _construct_command(test_info, gtest_filter, gtest_list_tests)\n if verbose:\n print 'Running:', command\n args = shlex.split(command)\n if use_gdb:\n unittest_util.run_gdb(args)\n else:\n returncode = subprocess.call(args)\n if returncode != 0:\n print 'FAILED: ' + test\n failed_tests.append('%s.%d' % (test, index))\n index += 1\n if unfound_tests:\n print 'The following tests were not found: \\n' + '\\n'.join(unfound_tests)\n if failed_tests:\n print 'The following tests failed: \\n' + '\\n'.join(failed_tests)\n if unfound_tests or failed_tests:\n return -1\n return 0","def test_testutils():\n build()\n sh(\"%s psutil\\\\tests\\\\test_testutils.py\" % PYTHON)","def setUpSuite():\n global _output_dir\n global _suite_configured\n\n if _suite_configured:\n return\n\n def remove_output_dir():\n global _output_dir\n if _output_dir != '':\n try:\n shutil.rmtree(_output_dir)\n except FileNotFoundError:\n pass\n\n atexit.register(remove_output_dir)\n _output_dir = tempfile.mkdtemp(dir=TESTS_DIR)\n\n os.environ['VOC_BUILD_DIR'] = os.path.join(_output_dir, 'build')\n os.environ['VOC_DIST_DIR'] = os.path.join(_output_dir, 'dist')\n\n # If the code has been precompiled, we don't have to\n # compile it as part of the test suite setup.\n precompile = os.environ.get('PRECOMPILE', 'true').lower() == 'true'\n if not precompile:\n _suite_configured = True\n return\n\n proc = subprocess.Popen(\n \"ant java\",\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n shell=True,\n )\n\n try:\n out, err = proc.communicate(timeout=30)\n except subprocess.TimeoutExpired:\n proc.kill()\n out, err = proc.communicate()\n raise\n\n if proc.returncode != 0:\n raise Exception(\"Error compiling java sources: \" + out.decode('ascii'))\n\n _suite_configured = True","def runTests(self):\n \n pass","def _initialise_testbench(argv_):\n _rlock.acquire()\n\n if \"COCOTB_LIBRARY_COVERAGE\" in os.environ:\n import coverage\n\n global _library_coverage\n _library_coverage = coverage.coverage(\n data_file=\".coverage.cocotb\",\n branch=True,\n include=[\"{}/*\".format(os.path.dirname(__file__))])\n _library_coverage.start()\n\n global argc, argv\n argv = argv_\n argc = len(argv)\n\n root_name = os.getenv(\"TOPLEVEL\")\n if root_name is not None:\n if root_name == \"\":\n root_name = None\n elif '.' in root_name:\n # Skip any library component of the toplevel\n root_name = root_name.split(\".\", 1)[1]\n\n # sys.path normally includes \"\" (the current directory), but does not appear to when python is embedded.\n # Add it back because users expect to be able to import files in their test directory.\n # TODO: move this to gpi_embed.cpp\n sys.path.insert(0, \"\")\n\n _setup_logging()\n\n # From https://www.python.org/dev/peps/pep-0565/#recommended-filter-settings-for-test-runners\n # If the user doesn't want to see these, they can always change the global\n # warning settings in their test module.\n if not sys.warnoptions:\n warnings.simplefilter(\"default\")\n\n from cocotb import simulator\n\n global SIM_NAME, SIM_VERSION\n SIM_NAME = simulator.get_simulator_product().strip()\n SIM_VERSION = simulator.get_simulator_version().strip()\n\n cocotb.log.info(\"Running on {} version {}\".format(SIM_NAME, SIM_VERSION))\n\n memcheck_port = os.getenv('MEMCHECK')\n if memcheck_port is not None:\n mem_debug(int(memcheck_port))\n\n log.info(\"Running tests with cocotb v%s from %s\" %\n (__version__, os.path.dirname(__file__)))\n\n # Create the base handle type\n\n process_plusargs()\n\n global scheduler\n scheduler = Scheduler()\n\n # Seed the Python random number generator to make this repeatable\n global RANDOM_SEED\n RANDOM_SEED = os.getenv('RANDOM_SEED')\n\n if RANDOM_SEED is None:\n if 'ntb_random_seed' in plusargs:\n RANDOM_SEED = eval(plusargs['ntb_random_seed'])\n elif 'seed' in plusargs:\n RANDOM_SEED = eval(plusargs['seed'])\n else:\n RANDOM_SEED = int(time.time())\n log.info(\"Seeding Python random module with %d\" % (RANDOM_SEED))\n else:\n RANDOM_SEED = int(RANDOM_SEED)\n log.info(\"Seeding Python random module with supplied seed %d\" % (RANDOM_SEED))\n random.seed(RANDOM_SEED)\n\n # Setup DUT object\n from cocotb import simulator\n\n handle = simulator.get_root_handle(root_name)\n if not handle:\n raise RuntimeError(\"Can not find root handle ({})\".format(root_name))\n\n global top\n top = cocotb.handle.SimHandle(handle)\n\n try:\n import pytest\n except ImportError:\n log.warning(\"Pytest not found, assertion rewriting will not occur\")\n else:\n try:\n # Install the assertion rewriting hook, which must be done before we\n # import the test modules.\n from _pytest.config import Config\n from _pytest.assertion import install_importhook\n pytest_conf = Config.fromdictargs([], {})\n install_importhook(pytest_conf)\n except Exception:\n log.exception(\n \"Configuring the assertion rewrite hook using pytest {} failed. \"\n \"Please file a bug report!\".format(pytest.__version__))\n\n # start Regression Manager\n global regression_manager\n regression_manager = RegressionManager.from_discovery(top)\n regression_manager.execute()\n\n _rlock.release()\n return True","def main():\n # add all new test suites per test module here\n suite_date = test_date.suite()\n suite_ng = test_ng.suite()\n suite_page = test_page.suite()\n suite_container = test_container.suite()\n\n # add the suite to be tested here\n alltests = unittest.TestSuite((suite_date,\n suite_ng,\n suite_page,\n suite_container))\n\n # run the suite\n runner = unittest.TextTestRunner()\n runner.run(alltests)","def execute_test_on_model(test,model):\n logger.info(\"running %r with %r\",test,model)\n\n #grab the executable\n executable = test.executable\n #profiling time thing\n timeblock = \"/usr/bin/time --format={\\\\\\\"usertime\\\\\\\":%U,\\\\\\\"memmax\\\\\\\":%M,\\\\\\\"memavg\\\\\\\":%K} \"\n\n # run the test in its own directory\n with test.in_dir():\n # So, I switched this to now use the file handlers in the popen command\n # directly in the hopes that this will prevent issues of the buffer not\n # clearing when a test with a lot of output is run.\n with test.processed_infile(model) as kim_stdin_file, open(STDOUT_FILE,'w') as stdout_file, open(STDERR_FILE,'w') as stderr_file:\n #grab the input file\n # output_info = test.out_dict\n start_time = time.time()\n process = Command(timeblock+ executable,stdin=kim_stdin_file,stdout=stdout_file,stderr=stderr_file)\n logger.info(\"launching run...\")\n try:\n process.run(timeout=RUNNER_TIMEOUT)\n except PipelineTimeout:\n logger.error(\"test %r timed out\",test)\n raise PipelineTimeout, \"your test timed out\"\n\n end_time = time.time()\n\n # It seems the test didn't finish\n # this probably doesn't end\n if process.poll() is None:\n process.kill()\n raise KIMRuntimeError, \"your test didn't terminate nicely\"\n\n return (end_time - start_time)","def runTest(self):\n self.setUp()\n self.test_JupyterNotebooks1()","def main():\n test_merge_quick_sort()\n test_compare()","def test(extra_args: list[str] | None = None, run_doctests: bool = False) -> None:\n pytest = import_optional_dependency(\"pytest\")\n import_optional_dependency(\"hypothesis\")\n cmd = [\"-m not slow and not network and not db\"]\n if extra_args:\n if not isinstance(extra_args, list):\n extra_args = [extra_args]\n cmd = extra_args\n if run_doctests:\n cmd = [\n \"--doctest-modules\",\n \"--doctest-cython\",\n f\"--ignore={os.path.join(PKG, 'tests')}\",\n ]\n cmd += [PKG]\n joined = \" \".join(cmd)\n print(f\"running: pytest {joined}\")\n sys.exit(pytest.main(cmd))","def RunTest(self):\n self.TestLs()\n self.TestTerminate()\n self.TestMultipleProcesses()","def main():\n try:\n unittest.main(testLoader=BetterLoader(), defaultTest='suite')\n except Exception:\n import sys\n import traceback\n traceback.print_exc()\n sys.exit(1)","def main(argv):\n global g_test_root_dir\n global g_temp_filename\n\n if len(argv) < 2:\n print(\"invoke this script as python collectUnitTestRunTime.py 10 'python run.py_path/run.py --wipe \"\n \"--test dir_to_test/test1,python run.py_path/run.py --wipe --test dir_to_test2/test2,...' True\\n\")\n sys.exit(1)\n else: # we may be in business\n repeat_number = int(argv[1]) # number of times to run a unit test\n command_lists = argv[2] # list of unit tests to run\n\n for command in command_lists.split(','): # for each command in the list\n # run command repeat_number of times and collect results into result_dict\n run_commands(command, repeat_number, g_temp_filename)","def main():\n test_runner = TestRunner(\n FLAGS.workspace, FLAGS.bench_home, imagenet_dir=FLAGS.train_data_dir)\n test_runner.run_tests(FLAGS.test_list.split(','))","def runtests():\r\n\r\n app_abspath = os.path.dirname(os.path.dirname(__file__))\r\n models_abspath = os.path.join(app_abspath, 'models.py')\r\n models_exists = os.path.isfile(models_abspath)\r\n urls_abspath = os.path.join(app_abspath, 'urls.py')\r\n urls_exists = os.path.isfile(urls_abspath)\r\n views_abspath = os.path.join(app_abspath, 'views')\r\n views_exists = os.path.isdir(views_abspath)\r\n tpls_abspath = os.path.join(app_abspath, 'templates')\r\n tpls_exists = os.path.isdir(tpls_abspath)\r\n\r\n for f in [models_abspath, urls_abspath]:\r\n if os.path.isfile(f):\r\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\r\n\r\n overwrite_project_language('ja')\r\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\r\n time.sleep(1)\r\n overwrite_project_language('en-us')\r\n time.sleep(1)\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\r\n time.sleep(2) # Give time for Django's AppCache to clear\r\n\r\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\r\n\r\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\r\n\r\n if models_exists:\r\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(models_abspath), shell=True)\r\n\r\n if urls_exists:\r\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\r\n\r\n if views_exists:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\r\n\r\n if tpls_exists:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\r\n else:\r\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\r\n\r\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\r\n\r\n sys.exit(test_status)","def run(full=False):\n # Call about to get all version info printed with tests\n about()\n import pytest\n # real_num_cpu = qset.num_cpus\n # real_thresh = qset.openmp_thresh\n # if qset.has_openmp:\n # For travis which VMs have only 1 cpu.\n # Make sure the openmp version of the functions are tested.\n # qset.num_cpus = 2\n # qset.openmp_thresh = 100\n\n test_options = [\"--verbosity=1\", \"--disable-pytest-warnings\", \"--pyargs\"]\n if not full:\n test_options += ['-m', 'not slow']\n pytest.main(test_options + [\"qutip\"])\n # runs tests in qutip.tests module only\n\n # Restore previous settings\n # if qset.has_openmp:\n # qset.num_cpus = real_num_cpu\n # qset.openmp_thresh = real_thresh","def main():\n fix_sys_path()\n result = unittest.TextTestRunner(verbosity=2).run(createTestSuite())\n\n if result.testsRun != EXPECTED_TEST_COUNT:\n raise Exception(\n 'Expected %s tests to be run, not %s.' % (EXPECTED_TEST_COUNT, result.testsRun))\n\n if len(result.errors) != 0 or len(result.failures) != 0:\n raise Exception(\n \"Functional test suite failed: %s errors, %s failures of %s tests run.\" % (\n len(result.errors), len(result.failures), result.testsRun))","def run_tests(self):\n import nose\n from pocketlint.formatcheck import main as pocket_main\n\n nose_args = ['nosetests']\n if self.verbose:\n nose_args.append('-v')\n else:\n nose_args.append('-q')\n\n module = self.test_suite\n if self.test_module:\n module = self.test_module\n\n nose_args.extend([\n '--with-coverage',\n '--cover-package=' + module,\n '--cover-erase',\n '--cover-test',\n module.replace('.', '/'),\n ])\n\n pocket_args = [\n 'README.rst',\n 'release-notes.rst',\n 'setup.py',\n ]\n for root, dirs, files in os.walk('chevah/keycert', topdown=False):\n for name in files:\n pocket_args.append(os.path.join(root, name))\n\n nose_code = nose.run(argv=nose_args)\n if nose_code:\n nose_code = 0\n else:\n nose_code = 1\n\n pocket_code = pocket_main(pocket_args)\n if not pocket_code:\n print('Linter OK')\n\n coverage_args = [\n 'report',\n '--include=chevah/keycert/tests/*',\n '--fail-under=100',\n ]\n covergate_code = load_entry_point(\n 'coverage', 'console_scripts', 'coverage')(argv=coverage_args)\n if not covergate_code:\n print('Tests coverage OK')\n\n sys.exit(pocket_code or nose_code or covergate_code)","def execute_tests():\n\n if len(sys.argv) > 1:\n # Filter test list based on command line requests\n tests_to_run = []\n for requested in sys.argv[1:]:\n for func, param in registered_tests:\n if param == requested:\n tests_to_run += [(func, param)]\n break\n else:\n print('Unknown test ' + requested)\n sys.exit(1)\n else:\n tests_to_run = registered_tests\n\n failing_tests = []\n for func, param in tests_to_run:\n print(param + (' ' * (OUTPUT_ALIGN - len(param))), end='')\n sys.stdout.flush()\n try:\n func(param)\n print(COLOR_GREEN + 'PASS' + COLOR_NONE)\n except KeyboardInterrupt:\n sys.exit(1)\n except TestException as exc:\n print(COLOR_RED + 'FAIL' + COLOR_NONE)\n failing_tests += [(param, exc.args[0])]\n except Exception as exc: # pylint: disable=W0703\n print(COLOR_RED + 'FAIL' + COLOR_NONE)\n failing_tests += [(param, 'Test threw exception:\\n' +\n traceback.format_exc())]\n\n if failing_tests:\n print('Failing tests:')\n for name, output in failing_tests:\n print(name)\n print(output)\n\n print(str(len(failing_tests)) + '/' +\n str(len(tests_to_run)) + ' tests failed')\n if failing_tests != []:\n sys.exit(1)","def runTest(self):\n\t\tself.setUp()\n\t\tself.test_postopProgramming1()","def unittest():\n from a6test import test_all\n test_all()","def run_tests():\n\n parser = argparse.ArgumentParser(description=\"Run tests for the T2K Data Manager.\")\n parser.add_argument(\n \"-w\", \"--write\", action=\"store_true\", help=\"do write tests. Default: read only\"\n )\n parser.add_argument(\n \"-t\",\n \"--tape\",\n action=\"store_true\",\n help=\"do write tape storage tests. Default: disks only\",\n )\n parser.add_argument(\n \"-p\",\n \"--parallel\",\n default=2,\n type=int,\n help=\"specify how many parallel processes to test. Defaul: 2\",\n )\n parser.add_argument(\n \"-b\", \"--backend\", default=None, help=\"specify which backend to use\"\n )\n\n args = parser.parse_args()\n if args.backend is not None:\n dm.config.backend = args.backend\n dm.backend = backends.get_backend(dm.config)\n\n run_read_only_tests(tape=args.tape, parallel=args.parallel)\n if args.write:\n run_read_write_tests(tape=args.tape, parallel=args.parallel)\n\n print_(\"All done.\")","def runtest(self):","def main():\n parser = argparse.ArgumentParser(\n description='Runs test for C++ implementation of M*')\n parser.add_argument('test_file', help='File describing test cases')\n parser.add_argument('output_file', help='Name of output file')\n parser.add_argument('num_processors', type=int, action='store',\n help='Number of processes to run on each node. ' +\n 'The local host running the primary server will ' +\n 'run one fewer worker processes')\n parser.add_argument('-i', action='store', type=float, default=1.0,\n help='Set inflation factor for the heuristic, ' +\n 'defaults to 1', metavar='INF', dest='inflation')\n parser.add_argument('-t', action='store', type=int, default=120,\n help='Set time limit for planning. Defaults to 2 ' +\n 'minutes', dest='time_limit')\n parser.add_argument('--hosts', action='store',\n default=('python', 'cobra', 'viper', 'anaconda'),\n help='Hostnames/IPs to use as processing nodes.',\n nargs='*', metavar='HOSTNAME')\n\n args = parser.parse_args()\n\n run_cpp_mstar_trial(args.test_file, args.output_file,\n inflation=args.inflation, time_limit=args.time_limit,\n hosts=args.hosts, num_processors=args.num_processors)","def unittest(core):\n\n # local import because nose uses pkg_resources that is very slow to import\n import nose.core\n\n config = core.config\n\n application_name = config.get(APPLICATION_NAME)\n application_package = config.get(APPLICATION_ROOT)\n\n extension_names = config.get(EXTENSION_NAMES)\n tests = config.get(TESTS)\n report_enabled = config.get(REPORT_ENABLED)\n report_file = config.get(REPORT_FILE)\n coverage_enabled = core.config.get(COVERAGE_ENABLED)\n verbose = core.config.get(DETAILS)\n exclude_systests = core.config.get(EXCLUDE_SYSTESTS)\n\n matching_packages = packages.find_packages(\n extension_names, core.extension_manager, application_name, application_package)\n command = create_nose_command(\n matching_packages, verbose, tests, report_enabled, report_file, exclude_systests)\n\n if coverage_enabled:\n coverage_file = core.config.get(COVERAGE_FILE)\n xml_enabled = core.config.get(COVERAGE_XML_ENABLED)\n xml_file = core.config.get(COVERAGE_XML_FILE)\n with coverage.coverage(matching_packages, sys.stdout, coverage_file, xml_enabled, xml_file):\n result = nose.core.run(argv=command)\n else:\n result = nose.core.run(argv=command)\n\n return 0 if result else 1","def test_5():","def main():\n\n parser = argparse.ArgumentParser(prog=\"run_test.py\",\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('id', help=\"Id of a test\")\n args = parser.parse_args()\n\n configure_logger()\n\n test_info = TESTS.get(args.id, None)\n if not test_info:\n test_info.log.error(f'{args.id} does not exist')\n exit(ErrorCode.CRITICAL)\n os.environ['DISPLAY'] = \":0.0\"\n\n test = Test(args.id, test_info)\n result = test.run()\n\n test.log.info('#' * 80)\n if not result:\n test.log.error('TEST FAILED')\n else:\n test.log.info('TEST PASSED')\n test.log.info('#' * 80)\n exit(not result)","def main():\n # Disable *.pyc files\n sys.dont_write_bytecode = True\n\n # Add \"..\" to module search path\n cur_dir = os.path.dirname(os.path.realpath(__file__))\n top_dir = os.path.abspath(os.path.join(cur_dir, os.pardir))\n sys.path.append(top_dir)\n\n # Parse command line arguments\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0,\n help=\"verbosity level, use: [-v | -vv | -vvv]\")\n parser.add_argument(\"-s\", \"--start-directory\", default=None,\n help=\"directory to start discovery\")\n parser.add_argument(\"-p\", \"--pattern\", default=\"test*.py\",\n help=\"pattern to match test files ('test*.py' default)\")\n parser.add_argument(\"test\", nargs=\"*\",\n help=\"test specs (e.g. module.TestCase.test_func)\")\n args = parser.parse_args()\n\n if not args.start_directory:\n args.start_directory = cur_dir\n\n if args.verbose > 2:\n logging.basicConfig(level=logging.DEBUG, format=\"DEBUG: %(message)s\")\n\n loader = unittest.TestLoader()\n if args.test:\n # Add particular tests\n for test in args.test:\n suite = unittest.TestSuite()\n suite.addTests(loader.loadTestsFromName(test))\n else:\n # Find all tests\n suite = loader.discover(args.start_directory, args.pattern)\n\n runner = unittest.TextTestRunner(verbosity=args.verbose)\n result = runner.run(suite)\n return result.wasSuccessful()","def test_run():\n # Only few steps for test\n timesteps = 128\n\n # Compute all sub testing conf\n envs = ['CartPole-v0']\n ml_platforms = ['torch', 'tf']\n agents = ['dqn', 'a2c']\n\n test_combinations = list(it.product(\n envs,\n ml_platforms,\n agents\n )\n )\n\n # Finally test them all\n for conf in test_combinations:\n env_str, ml_platform_str, agent_str = conf\n run(\n agent_str,\n ml_platform_str,\n env_str,\n 'dense',\n timesteps,\n './target/')","def runTest(self):\n self.setUp()\n self.test_visuThreeD1()","def run_tests(session):\n set_environment_variables(PYBAMM_ENV, session=session)\n session.run_always(\"pip\", \"install\", \"-e\", \".[all]\")\n if sys.platform == \"linux\" or sys.platform == \"darwin\":\n session.run_always(\"pip\", \"install\", \"-e\", \".[odes]\")\n session.run_always(\"pip\", \"install\", \"-e\", \".[jax]\")\n session.run(\"python\", \"run-tests.py\", \"--all\")","def run_tests():\n fail = []\n okay = []\n for i in os.listdir(\".\"):\n if i.find(\"_test_\") > -1 and i.endswith(\".py\"):\n if 0 != subprocess.call(\"python \" + i, shell=True):\n fail.append(i)\n else:\n okay.append(i)\n if fail:\n print(\"[ERROR] The following %u tests failed: %r\" % (len(fail), fail))\n return False\n print(\"[DONE] All %u tests completely successfully!\" % (len(okay)))\n return True","def main():\n dims = params['dims']\n\n for d in dims:\n print('**** Running test for d={0:d} ****'.format(d))\n run_test(d)","def run():\n\tsubsuite_list = []\n\tfor _, modname, _ in pkgutil.iter_modules(test.__path__):\n\t\tif modname.startswith(\"test_\"):\n\t\t\tmodule = importlib.import_module('test.' + modname)\n\t\t\tsubsuite = unittest.TestLoader().loadTestsFromModule(module)\n\t\t\tsubsuite_list.append(subsuite)\n\tsuite = unittest.TestSuite(subsuite_list)\n\n\tprint(\"Testing:\\n\")\n\tunittest.TextTestRunner(verbosity=2).run(suite)","def task_test(argv):\n run_tests(\"python2\", argv)\n run_tests(\"python3\", argv)","def test_memleaks():\n build()\n sh(\"%s psutil\\\\tests\\\\test_memleaks.py\" % PYTHON)","def runAll():\n\n loader = unittest.TestLoader()\n test_dir = pkg_resources.resource_filename('frvcpy.test','.')\n suite = loader.discover(test_dir)\n\n runner = unittest.TextTestRunner(verbosity=2)\n runner.run(suite)","def run_tests(self, test_labels):\n import pytest\n\n argv = []\n if self.verbosity == 0:\n argv.append('--quiet')\n if self.verbosity == 2:\n argv.append('--verbose')\n if self.verbosity == 3:\n argv.append('-vv')\n if self.failfast:\n argv.append('--exitfirst')\n if self.keepdb:\n argv.append('--reuse-db')\n\n argv.extend(test_labels)\n return pytest.main(argv)","def run_tests(file, samples):\n # Get the script dir, name and check if the file given exists\n test_dir = os.path.dirname(os.path.realpath(__file__))\n script_name = os.path.basename(__file__)\n if not os.path.isfile(os.path.join(test_dir, file)):\n sys.stderr.write('{0}: file \\'{1}\\' not found\\n'.format(script_name, file))\n sys.exit(0)\n\n result_dir = os.path.join(test_dir, 'results')\n if not os.path.exists(result_dir):\n os.mkdir(result_dir)\n\n # Get a path to the build dir to run iengine and cd into it\n filepath = os.path.join(test_dir, file)\n exe_path = os.path.join(os.path.join(test_dir, '..'), 'cmake-build-debug')\n os.chdir(exe_path)\n\n # Open csv files for writing to\n time_dist = open(os.path.join(result_dir, 'time.csv'), 'a')\n inference_dist = open(os.path.join(result_dir, 'inference.csv'), 'a')\n time_writer = csv.DictWriter(time_dist, delimiter=',', fieldnames=['method',\n 'file',\n 'sample',\n 'time'])\n inference_writer = csv.DictWriter(inference_dist, delimiter=',',\n fieldnames=['method', 'file',\n 'sample', 'inference_length'])\n time_writer.writeheader()\n inference_writer.writeheader()\n\n # Run through tests for all inference methods\n for method in ['FC', 'BC', 'TT']:\n timer = timeit.Timer(functools.partial(execute, filepath, method))\n avg = 0\n avg_path = 0\n\n # Run through all samples for the current inference method getting the execution\n # time and the number of inferences/models considered in the process\n for i in range(0, samples):\n print(timer.timeit(1))\n current, (result, err) = timer.timeit(1)\n avg += current * 1000\n result = result.decode('ascii').replace(',', '').replace(':', '')\n result_list = str.split(result)[1:]\n length = len(result_list)\n if method == 'TT':\n length = int(result_list[0])\n avg_path += length\n time_writer.writerow({'method': method, 'file': file, 'sample': i,\n 'time': current})\n inference_writer.writerow({'method': method, 'file': file, 'sample': i,\n 'inference_length': length})\n\n terminology = 'inferences'\n if method == 'TT':\n terminology = 'models'\n\n print('Method: {0}, Average time: {1:.3f}ms, Average {2}: {3}'.format(method,\n avg / samples, terminology, avg_path / samples))\n\n time_dist.close()\n inference_dist.close()","def run_tests(self):\n raise NotImplementedError","def test_all():\n for descr, args in INPUT_PARAMS.iteritems():\n func = run_summarize_h5lmt\n func.description = 'bin/summarize_h5lmt.py ' + descr\n yield func, args","def Run(self) -> None:\n logging.info(\"Running %s in a subprocess...\", self)\n self.stdout = tempfile.TemporaryFile()\n self.stderr = tempfile.TemporaryFile()\n self.begin_time = time.time()\n env = os.environ.copy()\n # Give each test program a separate test_tmpdir so they don't overwrite\n # each other when running in parallel.\n env[\"TEST_TMPDIR\"] = tempfile.mkdtemp()\n # Bazel's test sharding protocol:\n # https://docs.bazel.build/versions/master/test-encyclopedia.html\n if self.total_shards > 1:\n env[\"TEST_TOTAL_SHARDS\"] = str(self.total_shards)\n env[\"TEST_SHARD_INDEX\"] = str(self.shard_id)\n\n self.subprocess = subprocess.Popen(\n [_GetPython(), self.path], stdout=self.stdout, stderr=self.stderr,\n env=env)","def main():\n unittest.main(exit=False, verbosity=2)\n return 0","def test_run(self, _run_mock):\n hhmake = self.tool(hhsuite.HHMake,\n options={\"output\": self.OUT_HHM_FILE})\n hhmake.run({\"a3m\": self.IN_A3M_FILE})\n self.verify_common(\"hhmake\", hhmake)\n\n _, kw_args = hhmake.tool.call_args\n self.assertEqual(kw_args[\"options\"][\"input\"], self.IN_A3M_FILE)","def _Main():\n\n options, args = run_tests_util.ParseArgs('gtest')\n test_runner = run_tests_util.TestRunner(\n script_dir=SCRIPT_DIR,\n build_dir_var_name='GMOCK_BUILD_DIR',\n injected_build_dir_finder=GetGmockBuildDir)\n tests = test_runner.GetTestsToRun(args,\n options.configurations,\n options.built_configurations)\n if not tests:\n sys.exit(1) # Incorrect parameters given, abort execution.\n\n sys.exit(test_runner.RunTests(tests[0], tests[1]))","def run(cls): \n tests_to_run = cls.config.TESTS # A list of 5-tuple elements specifying the tests to run. See the\n # 'Test Setup' section in config.py.template for more info.\n test_group_name = \"Alchemist Tests\" # A short string identifier for this test run.\n output_dir = cls.config.OUTPUT_DIR # The output file where we write results.\n \n try:\n os.makedirs(output_dir,0o777)\n except:\n pass\n num_tests_to_run = len(tests_to_run)\n\n print(OUTPUT_DIVIDER_STRING)\n if num_tests_to_run == 1:\n print(\"Running %d test in %s\" % (num_tests_to_run, test_group_name))\n else:\n print(\"Running %d tests in %s\" % (num_tests_to_run, test_group_name))\n failed_tests = []\n\n cls.before_run_tests()\n \n spark_settings = []\n for i in cls.config.SPARK_SETTINGS:\n spark_settings.append(i.to_array()[0])\n \n output_settings = []\n for i in cls.config.OUTPUT_SETTINGS:\n output_settings.append(i.to_array()[0])\n \n main_class = \"altest.AlTest\"\n\n for meta_data, opt_sets in tests_to_run:\n print(OUTPUT_DIVIDER_STRING + '\\n')\n# print(\"Running test command: '%s' ... \" % main_class)\n \n meta = {}\n meta_pairs = [i.to_tuple() for i in meta_data]\n for mp in meta_pairs:\n meta[mp[0].replace('-', '_')] = mp[1].replace('0x20', ' ')\n \n meta_settings = []\n for i in meta_data:\n meta_settings.append(i.to_array()[0])\n \n# stdout_filename = \"%s/%s.out\" % (output_dir, meta['short_name'])\n# stderr_filename = \"%s/%s.err\" % (output_dir, meta['short_name'])\n# \n# out_file = open(output_dir + \"/\" + meta['short_name'] + \".out\", 'w')\n\n # Run a test for all combinations of the OptionSets given, then capture\n # and print the output.\n opt_set_arrays = [i.to_array() for i in opt_sets]\n for opt_list in itertools.product(*opt_set_arrays):\n\n cmd = cls.get_spark_submit_cmd(spark_settings, main_class, output_settings, meta_settings, opt_list)\n# print(\"\\nSetting env var SPARK_SUBMIT_OPTS: %s\" % java_opts_str)\n# test_env[\"SPARK_SUBMIT_OPTS\"] = java_opts_str\n print(\"Running command:\")\n print(\"%s\\n\" % cmd)\n Popen(cmd, shell=True, env=test_env).wait()\n\n try:\n src = output_dir + meta['short_name'] + '_latest/'\n src_files = os.listdir(src)\n src_file = src_files[0][:-4]\n new_dir = output_dir + src_file\n os.makedirs(new_dir)\n for file_name in src_files:\n full_file_name = os.path.join(src, file_name)\n if (os.path.isfile(full_file_name)):\n shutil.copy(full_file_name, new_dir)\n except:\n pass\n \n# result_string = cls.process_output(config, meta['short_name'], opt_list,\n# stdout_filename, stderr_filename)\n# print(OUTPUT_DIVIDER_STRING)\n# print(\"\\nResult: \" + result_string)\n# print(OUTPUT_DIVIDER_STRING)\n# if \"FAILED\" in result_string:\n# failed_tests.append(meta['short_name'])\n# \n# \n# out_file.write(result_string + \"\\n\")\n# out_file.flush()\n\n if num_tests_to_run == 1:\n print(\"Finished running %d test in %s.\" % (num_tests_to_run, test_group_name))\n else:\n print(\"Finished running %d tests in %s.\" % (num_tests_to_run, test_group_name))\n# print(\"\\nNumber of failed tests: %d, failed tests: %s\" %\n# (len(failed_tests), \",\".join(failed_tests)))\n print(OUTPUT_DIVIDER_STRING)","def run_test(test_name):\n\n print 'Running %s_test...' % test_name\n os.system('./test_%s.py' % test_name)\n print","def _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)","def test_main():\n # Setup\n # Exercise\n # Verify","def main(args):\n\n run_all_tests = len(args) == 1 and args[0] == 'all'\n\n python = 'python -m ' # how to call python \n if len(sys.argv) > 1 and sys.argv[1] == 'wine':\n python = 'C:\\\\Python26\\\\python.exe ' # works for wine\n \n data_path = ' ' + prepare_data(run_all_tests)\n \n command = ' bbob_pproc ' # + join_path(os.path.dirname(os.path.realpath(__file__)), 'rungeneric.py ')\n \n copy_latex_templates()\n print('LaTeX templates copied.')\n \n print('*** testing module bbob_pproc ***')\n t0 = time.time()\n print(python + command + '--conv' + ' --no-svg --settings=grayscale' +\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\n result = os.system(python + command + '--conv' + ' --no-svg --settings=grayscale' +\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\n print('** subtest 1 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on one algorithm with option --conv.'\n\n result = run_latex_template(\"templateBBOBarticle.tex\")\n assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\n\n t0 = time.time()\n print(python + command + '--no-svg --settings=grayscale' + join_path(data_path, 'RS.tgz'))\n result = os.system(python + command + '--no-svg --settings=grayscale' + join_path(data_path, 'RS.tgz'))\n print('** subtest 1 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on one bi-objective algorithm.'\n\n # Latex templates are not prepared yet for bi-objective case. \n# result = run_latex_template(\"templateBBOBarticle.tex\")\n# assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\n\n if run_all_tests: \n t0 = time.time()\n print(time.asctime())\n result = os.system(python + command + # ' --omit-single ' +\n join_path(data_path, 'BIPOP-CMA-ES_hansen_noiseless.tgz') +\n join_path(data_path, 'MCS_huyer_noiseless.tgz') +\n join_path(data_path, 'NEWUOA_ros_noiseless.tgz') +\n join_path(data_path, 'RANDOMSEARCH_auger_noiseless.tgz') +\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\n print('** subtest 2 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on many algorithms.'\n \n result = run_latex_template(\"templateBBOBmany.tex\")\n assert not result, 'Test failed: error while generating pdf from templateBBOBmany.tex.'\n \n t0 = time.time()\n result = os.system(python + command + '--conv' +\n join_path(data_path, 'SMAC-BBOB_hutter_noiseless.tgz') +\n join_path(data_path, 'lmm-CMA-ES_auger_noiseless.tgz'))\n print('** subtest 3 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on two algorithms with option --conv.'\n \n result = run_latex_template(\"templateBBOBcmp.tex\")\n assert not result, 'Test failed: error while generating pdf from templateBBOBcmp.tex.'\n \n t0 = time.time()\n result = os.system(python + command + ' --omit-single ' +\n join_path(data_path, 'DE-PSO_garcia-nieto_noiseless.tgz') +\n join_path(data_path, 'VNS_garcia-martinez_noiseless.tgz'))\n print('** subtest 4 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on two algorithms with option --omit-single.'\n \n result = run_latex_template(\"templateBBOBcmp.tex\")\n assert not result, 'Test failed: error while generating pdf from templateBBOBcmp.tex.'\n \n t0 = time.time()\n result = os.system(python + command + ' --expensive ' +\n join_path(data_path, 'VNS_garcia-martinez_noiseless.tgz'))\n print('** subtest 5 finished in ', time.time() - t0, ' seconds')\n assert result == 0, 'Test failed: rungeneric on one algorithm with option --expensive.'\n \n result = run_latex_template(\"templateBBOBarticle.tex\")\n assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\n\n print('launching doctest (it might be necessary to close a few pop up windows to finish)')\n t0 = time.time()\n\n if 1 < 3:\n failure_count = 0\n test_count = 0\n #doctest.testmod(report=True, verbose=True) # this is quite cool!\n # go through the py files in the bbob_pproc folder\n currentPath = os.getcwd() \n newPath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n os.chdir(newPath) \n for root, dirnames, filenames in os.walk(os.path.dirname(os.path.realpath(__file__))):\n for filename in fnmatch.filter(filenames, '*.py'):\n current_failure_count, current_test_count = doctest.testfile(\n os.path.join(root, filename), report=True, module_relative=False) \n failure_count += current_failure_count\n test_count += current_test_count\n if current_failure_count:\n print('doctest file \"%s\" failed' % os.path.join(root, filename))\n os.chdir(currentPath)\n else:\n stdout = sys.stdout\n fn = '_bbob_pproc_doctest_.txt'\n try:\n with open(fn, 'w') as f:\n sys.stdout = f\n doctest.testmod(report=True)\n finally:\n sys.stdout = stdout\n process_doctest_output(fn)\n print('** doctest finished in ', time.time() - t0, ' seconds')\n # print(' more info in file _bbob_pproc_doctest_.txt)')\n print('*** done testing module bbob_pproc ***')\n \n if (failure_count > 0):\n raise ValueError('%d of %d tests failed' % (failure_count, test_count))","def run_test(skip_run):\n # First off, we need the 'official' test data downloaded and unpacked\n # if necessary.\n if ref_test_data.install_and_check_test_files() != 0:\n return 1\n if install_inputs() != 0:\n return 1\n\n if not skip_run:\n sys.stdout.write(\"Executing CCC code...\\n\")\n ret = run.main()\n if ret != 0:\n return 1\n\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \"result\")\n sys.stdout.write(\"Comparing CCC results with 'official' results...\\n\")\n ret = compare_results.main([sys.argv[0], \"result\", test_result_dir])\n if ret != 0:\n return 1","def main():\n\n print(\"=\" * 80)\n print(\"DATA STRUCTURE TESTS\")\n test_module(structs.tests)\n test_module(structs.regularization)\n\n print(\"=\" * 80)\n print(\"END-TO-END TESTS\")\n test_module(globals())","def runTest(self):\n self.setUp()\n success = False\n message = ''\n try:\n self.command = [self.test_manager.executable_path]\n self.command.extend(self.test.spirv_args)\n\n process = subprocess.Popen(\n args=self.command,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=self.directory)\n output = process.communicate(self.stdin_shader)\n test_status = TestStatus(self.test_manager, process.returncode, output[0],\n output[1], self.directory, self.inputs,\n self.file_shaders)\n run_results = [\n getattr(self.test, test_method)(test_status)\n for test_method in get_all_test_methods(self.test.__class__)\n ]\n success, message = zip(*run_results)\n success = all(success)\n message = '\\n'.join(message)\n except Exception as e:\n success = False\n message = str(e)\n self.test_manager.notify_result(\n self, success,\n message + '\\nSTDOUT:\\n%s\\nSTDERR:\\n%s' % (output[0], output[1]))\n self.tearDown()","def test_pep8_conformance_unitests(self):\n\n print(\"\\r\\n\")\n\n # Get the path to current directory\n path = os.path.dirname(os.path.realpath(__file__))\n\n self.run_check(path)","def test_by_name(name):\n build()\n sh(\"%s -m unittest -v %s\" % (PYTHON, name))","def main(options, args):\n\n # Set up logging format.\n log_level = logging.INFO\n if options.verbose:\n log_level = logging.DEBUG\n logging.basicConfig(level=log_level,\n format='%(message)s')\n\n # Include all tests if none are specified.\n if not args:\n path = '/';\n else:\n path = ' '.join(args);\n\n adb_cmd = \"adb \";\n if options.adb_options:\n adb_cmd += options.adb_options\n\n # Re-generate the test list if --refresh-test-list is on\n if options.refresh_test_list:\n logging.info(\"Generating test list.\");\n generate_test_list_cmd_str = adb_cmd + \" shell am instrument -e class com.android.dumprendertree.LayoutTestsAutoTest#generateTestList -e path \\\"\" + path + \"\\\" -w com.android.dumprendertree/.LayoutTestsAutoRunner\"\n adb_output = subprocess.Popen(generate_test_list_cmd_str, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\n\n if adb_output.find('Process crashed') != -1:\n logging.info(\"Aborting because cannot generate test list.\\n\" + adb_output)\n sys.exit(1)\n\n\n logging.info(\"Running tests\")\n\n # Count crashed tests.\n crashed_tests = []\n\n timeout_ms = '5000'\n if options.time_out_ms:\n timeout_ms = options.time_out_ms\n\n # Run test until it's done\n\n run_layout_test_cmd_prefix = adb_cmd + \" shell am instrument\"\n\n run_layout_test_cmd_postfix = \" -e path \\\"\" + path + \"\\\" -e timeout \" + timeout_ms\n if options.rebaseline:\n run_layout_test_cmd_postfix += \" -e rebaseline true\"\n run_layout_test_cmd_postfix += \" -w com.android.dumprendertree/.LayoutTestsAutoRunner\"\n\n # Call LayoutTestsAutoTest::startLayoutTests.\n run_layout_test_cmd = run_layout_test_cmd_prefix + \" -e class com.android.dumprendertree.LayoutTestsAutoTest#startLayoutTests\" + run_layout_test_cmd_postfix\n\n adb_output = subprocess.Popen(run_layout_test_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\n while not DumpRenderTreeFinished(adb_cmd):\n # Get the running_test.txt\n logging.error(\"DumpRenderTree crashed, output:\\n\" + adb_output)\n\n shell_cmd_str = adb_cmd + \" shell cat /sdcard/android/running_test.txt\"\n crashed_test = subprocess.Popen(shell_cmd_str, shell=True, stdout=subprocess.PIPE).communicate()[0]\n \n logging.info(crashed_test + \" CRASHED\");\n crashed_tests.append(crashed_test);\n\n logging.info(\"Resuming layout test runner...\");\n # Call LayoutTestsAutoTest::resumeLayoutTests\n run_layout_test_cmd = run_layout_test_cmd_prefix + \" -e class com.android.dumprendertree.LayoutTestsAutoTest#resumeLayoutTests\" + run_layout_test_cmd_postfix\n\n adb_output = subprocess.Popen(run_layout_test_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\n\n if adb_output.find('INSTRUMENTATION_FAILED') != -1:\n logging.error(\"Error happened : \" + adb_output)\n sys.exit(1)\n\n logging.debug(adb_output);\n logging.info(\"Done\\n\");\n\n # Pull results from /sdcard\n results_dir = options.results_directory\n if not os.path.exists(results_dir):\n os.makedirs(results_dir)\n if not os.path.isdir(results_dir):\n logging.error(\"Cannot create results dir: \" + results_dir);\n sys.exit(1);\n\n result_files = [\"/sdcard/layout_tests_passed.txt\",\n \"/sdcard/layout_tests_failed.txt\",\n \"/sdcard/layout_tests_nontext.txt\"]\n for file in result_files: \n shell_cmd_str = adb_cmd + \" pull \" + file + \" \" + results_dir\n adb_output = subprocess.Popen(shell_cmd_str, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\n logging.debug(adb_output)\n \n # Create the crash list.\n fp = open(results_dir + \"/layout_tests_crashed.txt\", \"w\");\n fp.writelines('\\n'.join(crashed_tests))\n fp.close()\n\n # Count the number of tests in each category.\n passed_tests = CountLineNumber(results_dir + \"/layout_tests_passed.txt\")\n logging.info(str(passed_tests) + \" passed\")\n failed_tests = CountLineNumber(results_dir + \"/layout_tests_failed.txt\")\n logging.info(str(failed_tests) + \" failed\")\n crashed_tests = CountLineNumber(results_dir + \"/layout_tests_crashed.txt\")\n logging.info(str(crashed_tests) + \" crashed\")\n nontext_tests = CountLineNumber(results_dir + \"/layout_tests_nontext.txt\")\n logging.info(str(nontext_tests) + \" no dumpAsText\")\n\n logging.info(\"Results are stored under: \" + results_dir + \"\\n\")\n\n # Comparing results to references to find new fixes and regressions.\n results_dir = os.path.abspath(options.results_directory)\n ref_dir = options.ref_directory\n\n # if ref_dir is null, cannonify ref_dir to the script dir.\n if not ref_dir:\n script_self = sys.argv[0]\n script_dir = os.path.dirname(script_self)\n ref_dir = os.path.join(script_dir, \"results\")\n\n ref_dir = os.path.abspath(ref_dir)\n\n CompareResults(ref_dir, results_dir)","def parse_and_test(program: Text,\n name: Text,\n *,\n filename: Text,\n additional_search_paths: Tuple[str, ...] = (),\n raise_on_error: bool = True,\n test_filter: Optional[Text] = None,\n trace_all: bool = False,\n compare_jit: bool = True,\n seed: Optional[int] = None) -> bool:\n did_fail = False\n test_name = None\n type_info = None\n\n importer = import_helpers.Importer(additional_search_paths)\n ran = 0\n\n try:\n module = Parser(Scanner(filename, program), name).parse_module()\n type_info = cpp_typecheck.check_module(module, importer.cache,\n importer.additional_search_paths)\n\n ir_package = (\n ir_converter.convert_module_to_package(\n module, type_info, importer.cache, traverse_tests=True)\n if compare_jit else None)\n\n interpreter = Interpreter(\n module,\n type_info,\n importer.typecheck,\n importer.additional_search_paths,\n importer.cache,\n trace_all=trace_all,\n ir_package=ir_package)\n for test_name in module.get_test_names():\n if not _matches(test_name, test_filter):\n continue\n ran += 1\n print('[ RUN UNITTEST ]', test_name, file=sys.stderr)\n interpreter.run_test(test_name)\n print('[ OK ]', test_name, file=sys.stderr)\n\n if ir_package and module.get_quickchecks():\n if seed is None:\n # We want to guarantee non-determinism by default. See\n # https://abseil.io/docs/cpp/guides/random#stability-of-generated-sequences\n # for rationale.\n seed = int(os.getpid() * time.time())\n print(f'[ SEED: {seed} ]')\n for quickcheck in module.get_quickchecks():\n test_name = quickcheck.f.name.identifier\n print('[ RUN QUICKCHECK ]', test_name, file=sys.stderr)\n quickcheck_helpers.run_quickcheck(\n interpreter, ir_package, quickcheck, seed=seed)\n print('[ OK ]', test_name, file=sys.stderr)\n\n except (PositionalError, FailureError, CppParseError, ScanError,\n TypeInferenceError, XlsTypeError) as e:\n did_fail = True\n parser_helpers.pprint_positional_error(\n e, output=cast(io.IOBase, sys.stderr))\n if test_name:\n print(\n '[ FAILED ]',\n test_name,\n e.__class__.__name__,\n file=sys.stderr)\n if raise_on_error:\n raise\n finally:\n if type_info is not None:\n type_info.clear_type_info_refs_for_gc()\n\n print('[==================]', ran, 'test(s) ran.', file=sys.stderr)\n return did_fail","def test_5_bam(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, \"run_info-bam.yaml\")]\n subprocess.check_call(cl)","def test_make_mul_transp():\n tmp_dir = make_temp_dir()\n FILE_NAME = \"tmp_test_mul_transp\"\n NAME = os.path.join(tmp_dir, FILE_NAME)\n C_NAME, EXE_NAME = NAME + \".c\", NAME + \".exe\"\n \n def make_testprogram(lv, lm, n=1):\n generate = BitMatrixMulTransp().generate_c_mul_transp\n f = open(C_NAME, \"wt\")\n print(r\"\"\"#include <stdio.h>\n#include <stdint.h>\nint main(int argc, char **argv)\n{{\n uint_fast32_t i, v;\n static uint_fast32_t a[1000];\n sscanf(argv[1], \"%ld\", &v);\n for (i=0; i < argc-2; ++i) sscanf(argv[i+2], \"%ld\", a+i);\n {0}\n printf(\"%ld\\n\",v);\n return 0;\n}}\n\"\"\".format(generate(\"v\", \"a\", lv, lm, n)), file = f)\n f.close()\n #subprocess.check_output([\"gcc\", C_NAME, \"-o\", EXE_NAME])\n compile_testprogramm([C_NAME], EXE_NAME)\n checker = BitMatrixMulTransp()\n checker.set_matrix(lv, lm, n)\n return checker\n\n def run_testprogram(v, m):\n data = list(map(str, [v]+m)) \n res = subprocess.check_output([EXE_NAME] + data)\n return int(res) \n\n def test_testprogram(checker , v, m):\n res = run_testprogram(v, m)\n ref = checker.compute(v,m+[0]*64)\n assert ref == ref, (hex, ref)\n\n def del_testprogram():\n for f in [C_NAME, EXE_NAME]:\n os.remove(f)\n\n test_data_dict = {\n (4,4,1,0): [ (3, [2,3, 1, 1]) , ]\n }\n\n def test_data(lv, lm, n, n_tests=10):\n \"\"\"yield n_tests test cases (v, m) for given lv, lm, m0\"\"\"\n try:\n d = test_data_dict[(lv, lm, n, m0)]\n for v, m in d: yield v, m\n except:\n pass\n for i in range(n_tests):\n v = random.randint(0, 2**(n*lv)-1)\n m = [] \n for i in range(lm):\n m.append(random.randint(0, 2**(n*lv)-1))\n yield v, m\n\n def test_programs():\n \"\"\"yield cases (lv, lm, n) for a making a test program\"\"\"\n yield 4, 4, 1\n yield 16, 12, 2\n\n \n\n \n for lv, lm, n in test_programs():\n print(\"Make C program for case lv=%d, lm=%d, n=%d\" % (\n lv,lm,n))\n checker = make_testprogram(lv, lm, n) \n print (\"C program has been made, starting tests..\")\n for v, m in test_data(lv, lm, n):\n test_testprogram(checker, v, m)\n del_testprogram()\n print (\"passed\")\n\n kill_temp_dir(tmp_dir)","def main():\n # ============================================================================\n\n working_folder = os.path.dirname(os.path.realpath(__file__))\n os.chdir(working_folder)\n\n description_string =\\\n \"A set of python tests to fetch data from Open Weather Map\"\n epilog_string = (\"These tests should be run regularly to ensure that the \"\n \"API server is healthy\")\n\n parser = argparse.ArgumentParser(\n prog='OWM-API-test',\n description=description_string,\n epilog=epilog_string)\n\n parser.add_argument(\n \"--tb\",\n action='store',\n default='line',\n nargs='?',\n choices=['auto', 'long', 'short', 'no', 'line', 'native'],\n help='Set the traceback level for pytest',\n dest='traceback')\n\n parser.add_argument(\n \"-v\", \"--verbose\",\n action='store_true',\n help='Increase output verbosity',\n dest='verbose')\n\n parser.add_argument(\n \"-q\", \"--quiet\",\n action='store_true',\n help='Reduce output verbosity')\n\n args = parser.parse_args()\n\n # ============================================================================\n # construct pytest commands\n pytest_command = []\n if args.verbose:\n # we double increase verbosity to make it actually verbose\n pytest_command.extend([\"-v\", \"-v\"])\n # this only overwrites traceback argument if left at default\n if args.traceback == \"line\":\n args.traceback = \"long\"\n\n if args.quiet: # this overwrites and tb argument given\n args.traceback = \"no\"\n\n pytest_command.extend([\"--tb\", args.traceback])\n\n # ============================================================================\n\n printing_functions.test_declaration(\"Running Tests...\")\n run_local_tests(pytest_command)","def run_tests(output_dir, fstype):\n global options\n if options.debug:\n print \"Run NUMA test\"\n for num_disks in [2]:\n for num_dirs in range(1, 5):\n postmark = PostMarkTest(output_dir, fstype, num_disks, num_dirs)\n run_one_test(postmark)","def test():\n import unittest\n tests = unittest \n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def run_tests():\n testfiles = ['tests.test_overall']\n exclude = ['__init__.py', 'test_overall.py']\n for t in glob(pjoin('tests', '*.py')):\n if True not in [t.endswith(ex) for ex in exclude]:\n if basename(t).startswith('test_'):\n testfiles.append('tests.%s' % splitext(basename(t))[0])\n\n suites = []\n for file in testfiles:\n __import__(file)\n suites.append(sys.modules[file].suite)\n\n tests = unittest.TestSuite(suites)\n runner = unittest.TextTestRunner(verbosity=2)\n\n # Disable logging output\n logging.basicConfig(level=100)\n logging.disable(100)\n\n result = runner.run(tests)\n return result","def main() -> int:\n parser = argparse.ArgumentParser()\n parser.add_argument('--dir-metadata',\n type=pathlib.Path, required=True)\n\n args = parser.parse_args()\n\n with LockedMetadata(args.dir_metadata, __file__) as md:\n summary_dict = {}\n passing_tests = []\n failing_tests = []\n for f in md.tests_pickle_files:\n try:\n trr = TestRunResult.construct_from_pickle(f)\n summary_dict[f\"{trr.testname}.{trr.seed}\"] = \\\n ('PASS' if trr.passed else\n 'FAILED' + (\" {T}\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \"\"))\n if trr.passed:\n passing_tests.append(trr)\n else:\n failing_tests.append(trr)\n except RuntimeError as e:\n failing_tests.append(\n TestRunResult(\n name='broken_test',\n failure_message=str(e)\n ))\n\n md.regr_log = md.dir_run/'regr.log'\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\n\n # Write results as junit_xml\n with open(md.regr_log_junit,\n 'w',\n encoding='UTF-8') as junit_xml,\\\n open(md.regr_log_junit_merged,\n 'w',\n encoding='UTF-8') as junit_merged_xml:\n output_run_results_junit_xml(passing_tests, failing_tests,\n junit_xml,\n junit_merged_xml)\n\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\n # Write results as regr.log (custom logfile format)\n output_results_text(passing_tests, failing_tests, summary_dict,\n outfile)\n\n test_summary_dict = create_test_summary_dict(passing_tests +\n failing_tests)\n\n cov_summary_dict = {}\n if md.simulator == \"xlm\":\n cov_summary_dict = create_cov_summary_dict(md)\n else:\n print(\"Warning: Not generating coverage summary, unsupported \" \\\n f\"simulator {md.simulator}\")\n\n html_report_filename = md.dir_run/'report.html'\n with open(html_report_filename, 'w') as outfile:\n output_results_html(md, passing_tests + failing_tests,\n test_summary_dict, cov_summary_dict, outfile)\n\n json_report_filename = md.dir_run/'report.json'\n with open(json_report_filename, 'w') as json_report_file:\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\n json_report_file)\n\n svg_summary_filename = md.dir_run/'summary.svg'\n with open(svg_summary_filename, 'w') as svg_summary_file:\n output_results_svg(test_summary_dict, cov_summary_dict,\n svg_summary_file)\n\n # Print a summary line to the terminal\n print(gen_summary_line(passing_tests, failing_tests))\n\n # Succeed if no tests failed\n return 1 if failing_tests else 0","def main():\n print()\n print(\"Un-comment and re-comment calls in MAIN one by one as you work.\")\n print()\n\n # run_test_sum_until_prime_input()\n # run_test_next_prime()\n # run_test_sum_to_next_prime()\n # run_test_prime_gap()\n # run_test_wait_for_sum_of_cubes()","def run(self):\n if self.all:\n cmd = self.apply_options(self.test_all_cmd)\n self.call_and_exit(cmd)\n else:\n cmds = (self.apply_options(self.unit_test_cmd, (\"coverage\",)),)\n if self.coverage:\n cmds += (self.apply_options(self.coverage_cmd),)\n self.call_in_sequence(cmds)","def main():\n testlib = VorpatestLibrary()\n testlib.prepare_test()\n testlib.run_vorpaline(*sys.argv[1:])\n testlib.run_vorpastat()\n testlib.cleanup_test()","def test_run_full(mk_tmp_dirs):\n tmp_current_path, tmp_data_path, tmp_config_path = mk_tmp_dirs\n\n cfg_dir = path.join(tmp_data_path, 'cfgs')\n collect_pipeline_cfgs(cfg_dir)\n\n asn_path = path.join(DATAPATH, 'mosaic_long_asn.json')\n args = [\n path.join(cfg_dir, 'calwebb_image3.cfg'),\n asn_path,\n ]\n\n Step.from_cmdline(args)\n\n # Check for the CRF files\n with open(asn_path) as fh:\n asn = load_asn(fh)\n expfilenames = [\n path.split(path.splitext(member['expname'])[0])[1]\n for member in asn['products'][0]['members']\n ]\n crffilenames = []\n for expfilename in expfilenames:\n name = remove_suffix(path.splitext(expfilename)[0])[0]\n crffilenames.append(name + '_a3001_crf.fits')\n for crffilename in crffilenames:\n assert path.isfile(crffilename)\n\n # Check for the level3 products\n product_name = asn['products'][0]['name']\n assert path.isfile(product_name + '_cat.ecsv')\n assert path.isfile(product_name + '_i2d.fits')","def test():\n loader = unittest.TestLoader()\n suite = loader.discover(os.path.dirname(__file__))\n runner = unittest.TextTestRunner()\n runner.run(suite)","def test_python(c, filename):\n test_dir = 'tests/python'\n filepath = '{}/{}'.format(test_dir, filename)\n\n print('Restarting network before executing test {}'.format(filepath))\n rm_network(c)\n start_core(c)\n start_horizon(c)\n network(c)\n\n with c.cd(test_dir):\n print('Executing test {}'.format(filepath))\n c.run('pipenv run python {filename} \"{passphrase}\" {whitelist_seed}'.format(\n filename=filename,\n passphrase=PASSPHRASE,\n whitelist_seed=WHITELIST_SEED))","def main_test():\n full = unittest.TestSuite()\n full.addTest(unittest.makeSuite(TestToolOptions))\n full.addTest(unittest.makeSuite(TestBadConfiguration))\n full.addTest(unittest.makeSuite(TestBasicEndpoints))\n full.addTest(unittest.makeSuite(TestMultipleEPG))\n full.addTest(unittest.makeSuite(TestBasicExistingEndpoints))\n full.addTest(unittest.makeSuite(TestBasicExistingEndpointsAddPolicyLater))\n full.addTest(unittest.makeSuite(TestExportPolicyRemoval))\n full.addTest(unittest.makeSuite(TestBasicEndpointsWithContract))\n full.addTest(unittest.makeSuite(TestBasicEndpointMove))\n full.addTest(unittest.makeSuite(TestPolicyChangeProvidedContract))\n full.addTest(unittest.makeSuite(TestChangeL3Out))\n full.addTest(unittest.makeSuite(TestDuplicates))\n full.addTest(unittest.makeSuite(TestDuplicatesTwoL3Outs))\n full.addTest(unittest.makeSuite(TestDeletions))\n\n unittest.main()","def test_main():\r\n\r\n ## Put any test code here\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"--source_svc\", required=True)\r\n parser.add_argument(\"--target_svc\", required=True)\r\n parser.add_argument(\"--host\", required=True)\r\n args=vars(parser.parse_args())\r\n\r\n ## For this test I need the disk collections\r\n sourceVdiskCollection = getvdiskcollection(args[\"source_svc\"])\r\n #targetVdiskCollection = getvdiskcollection(args[\"target_svc\"])\r\n remove_vdisk_from_svc(args[\"source_svc\"], sourceVdiskCollection[\"web01_d01\"])\r\n remove_vdisk_from_svc(args[\"source_svc\"], sourceVdiskCollection[\"web01_r01\"])","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def runalltests():\n doctest.testmod()","def test():\n import unittest\n tests = unittest.TestLoader().discover(tests)\n unittest.TextTestRunner(verbosity=2).run(tests)","def run_tests(args, applog):\n try:\n additional_args = []\n additional_args.extend([\"--pyargs\", \"dent_os_testbed.test.test_suite\", \"--strict-markers\"])\n additional_args.append(\"--durations=0\")\n if args.stdout:\n additional_args.append(\"--capture=tee-sys\")\n\n suite_groups = args.suite_groups if args.suite_groups else PYTEST_SUITE_GROUPS.keys()\n for sg_name in suite_groups:\n sg = PYTEST_SUITE_GROUPS[sg_name]\n pytest_args = []\n if not sg:\n continue\n pytest._current_suite = sg_name\n pytest_args.append(\"-m\")\n markers_string = sg[0] + \"\".join([(\" or %s\" % suite) for suite in sg[1:]])\n pytest_args.append(markers_string)\n pytest_args.append(\"--html=%s/report_%s.html\" % (LOGDIR, sg_name))\n pytest_args.append(\"--junitxml=%s/junit_%s.xml\" % (LOGDIR, sg_name))\n pytest_args.append(\"--self-contained-html\")\n if args.suite_tests:\n pytest_args.append(\"-k\")\n pytest_args.append(args.suite_tests)\n input_args = additional_args + pytest_args\n applog.info(\"Triggering pytest with args : %s\" % input_args)\n pytest.main(input_args)\n except Exception as e:\n applog.exception(\"Error running tests\", exc_info=e)\n raise"],"string":"[\n \"def __main() :\\n launchTests()\",\n \"def test(c, index=0):\\n exec_path = f'{ROOT_DIR}/gem5/build/GCN3_X86/gem5.opt' \\n conf_path = f'{ROOT_DIR}/gem5/configs/example/apu_se.py'\\n test_dir = TEST_PATHS[index]\\n print(f'Testing {test_dir}...')\\n \\n # Try to find executable\\n try_paths = [os.path.basename(test_dir), os.path.basename(test_dir) + '.o']\\n try_paths = [[ex, os.path.join('bin', ex)] for ex in try_paths]\\n try_paths = [ex for t in try_paths for ex in t]\\n for ex in try_paths:\\n if os.path.isfile(os.path.join(ROOT_DIR, test_dir, ex)):\\n print(f' Found executable {ex}.')\\n break\\n else:\\n raise RuntimeError(f'Executable for {test_dir} not found!')\\n \\n\\n args = f'-n 2 --benchmark-root={test_dir}/{os.path.dirname(ex)} -c {os.path.basename(ex)}'\\n _run(c, ' '.join([exec_path, f'-d runs/{os.path.basename(ex).split(\\\".\\\")[0]}', conf_path, args]))\",\n \"def run_tests():\\n test_command = \\\"pytest -s \\\" + os.path.join(root_path, \\\"cases\\\", \\\"test_cases.py::TestCases::test_cases\\\") + \\\" --html=\\\" + os.path.join(root_path, \\\"reports\\\", \\\"qa_testing_report.html\\\")\\n\\n subprocess.run(test_command, shell=True)\",\n \"def main():\\n run_test_summary1a()\\n run_test_summary1c()\\n run_test_summary1c()\",\n \"def main():\\n run_test_all()\",\n \"def test(cline):\\n print(\\\"Running unit tests.\\\")\\n cline.run(\\\"TF_CPP_MIN_LOG_LEVEL=3 python3 -m unittest\\\")\",\n \"def run_test(testname,testfunc,epoch,jd):\\n import subprocess\\n \\n print '\\\\n\\\\n\\\\nRunning PYTHON test',testname\\n pyres = testfunc(epoch,jd)\\n \\n\\n cmd = './sofatests %s %.16g %.16g'%(testname,epoch,jd)\\n print '\\\\nRunning C test',testname,'as','\\\"'+cmd+'\\\"' \\n \\n if pyres is None:\\n os.system(cmd)\\n else:\\n cmpfunc = pyres[0]\\n \\n sp = subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE)\\n cstdout,cstderr = sp.communicate()\\n if sp.returncode==0:\\n print 'C code complete - output:\\\\n',cstdout\\n else:\\n print 'Problem with C - retcode',sp.returncode,'- output:\\\\n',cstdout,'\\\\nstd err:',cstderr\\n \\n cmps = []\\n cmps.append(cstdout)\\n cmps.extend(pyres[1:])\\n print 'Comparing python to C'\\n cmpfunc(*cmps)\\n print 'Comparison complete'\",\n \"def RunUnitTests():\\n if FLAGS.test_targets:\\n tests = FLAGS.test_targets\\n else:\\n tests = shell_interfaces.GetStdout(\\n 'bazel query kind(\\\"cc_test\\\", ...)').split()\\n\\n # Run coverage, joining all data into one file.\\n subprocess.check_call(['bazel', 'coverage', '--instrument_test_targets',\\n '--experimental_cc_coverage',\\n '--combined_report=lcov',\\n ('--coverage_report_generator=@bazel_tools//tools/tes'\\n 't/CoverageOutputGenerator/java/com/google/devtools/'\\n 'coverageoutputgenerator:Main')] + tests)\",\n \"def main():\\n vunit = vunit_pkg.VUnit.from_argv()\\n vunit = map_sources(vunit)\\n run_tests(vunit)\",\n \"def test_script(self) -> None:\\n main()\",\n \"def test_core(c):\\n def test_python(c, filename):\\n \\\"\\\"\\\"Run a single python test file in tests/python.\\\"\\\"\\\"\\n test_dir = 'tests/python'\\n filepath = '{}/{}'.format(test_dir, filename)\\n\\n print('Restarting network before executing test {}'.format(filepath))\\n rm_network(c)\\n start_core(c)\\n start_horizon(c)\\n network(c)\\n\\n with c.cd(test_dir):\\n print('Executing test {}'.format(filepath))\\n c.run('pipenv run python {filename} \\\"{passphrase}\\\" {whitelist_seed}'.format(\\n filename=filename,\\n passphrase=PASSPHRASE,\\n whitelist_seed=WHITELIST_SEED))\\n\\n test_python(c, 'test_base_reserve.py')\\n test_python(c, 'test_tx_order_by_fee.py')\\n test_python(c, 'test_tx_order_by_whitelist.py')\\n test_python(c, 'test_tx_priority_for_whitelist_holder.py')\\n test_python(c, 'test_whitelist_affected_on_next_ledger.py')\\n\\n # XXX obsolete\\n # see source file for more information\\n # test_python(c, 'test_multiple_cores.py')\",\n \"def run_tests():\\n os.environ['WORKDIR'] = CONFIG['workdir']\\n os.environ['REPORTDIR'] = CONFIG['reportFolder']\\n stdout = subprocess.DEVNULL\\n if CONFIG['verbose']:\\n stdout = None\\n # cycle throught version\\n total = 0\\n valid = 0\\n start = time.time()\\n for version in utils.get_dirs(CONFIG['versionsFolder']):\\n os.environ['VERSION'] = version\\n utils.copy_dir(os.path.join(CONFIG['versionsFolder'], version), CONFIG['workdir']\\\\\\n , CONFIG['clearWorkdir'])\\n # cycle throught use case\\n for usecase in utils.get_dirs(CONFIG['testsFolder']):\\n os.environ['TESTDIR'] = usecase\\n if not CONFIG['quiet']:\\n print('UseCase test: {}'.format(usecase))\\n log_msg('info', 'UseCase test: {}'.format(usecase))\\n try:\\n folder = os.path.join(CONFIG['testsFolder'], usecase)\\n with open(os.path.join(folder, CONFIG['useConfig'])) as usefp:\\n jconfig = json.load(usefp)\\n # clear workdir if desired\\n if 'clearWorkdir' in jconfig.keys() and jconfig['clearWorkdir']:\\n utils.copy_dir(os.path.join(CONFIG['versionsFolder'], version)\\\\\\n , CONFIG['workdir'], CONFIG['clearWorkdir'])\\n # print('clearing')\\n # raise\\n cmd = ['py', os.path.join(folder, jconfig['entrypoint'])]\\n total += 1\\n if jconfig['runType'] == 'single':\\n subprocess.run(cmd, stdout=stdout, stderr=subprocess.PIPE, check=True)\\n else:\\n for step in range(jconfig['numRuns']):\\n if not CONFIG['quiet']:\\n print('\\\\r >Step {}/{} '.format(step+1, jconfig['numRuns'])\\\\\\n , end='', flush=True)\\n log_msg('info', 'Step {}/{}'.format(step+1, jconfig['numRuns']))\\n subprocess.run(cmd, stdout=stdout, stderr=subprocess.PIPE, check=True)\\n if step+1 != jconfig['numRuns']:\\n time.sleep(jconfig['interval'])\\n except subprocess.CalledProcessError as excp:\\n if not CONFIG['quiet']:\\n print('Error msg:{}'\\\\\\n .format(excp.stderr.decode().replace('\\\\r', '').replace('\\\\n', '|')))\\n log_msg('error', excp.stderr.decode())\\n else:\\n valid += 1\\n if not CONFIG['quiet']:\\n print('{}.....Passed'.format(usecase))\\n log_msg('info', '{} Passed'.format(usecase))\\n\\n elapse = time.time()-start\\n log_msg('info', 'Ran {} tests in {:.3f}s with {} passed'.format(total, elapse, valid))\\n print('-'*20)\\n print('Ran {} tests in {:.3f}s with {} passed.'.format(total, elapse, valid))\\n return total-valid\",\n \"def exe_tests(self):\\n self.rank = mpicom.rank()\\n self.size = mpicom.size()\\n if mpicom.parallel():\\n self.test(\\\"libname\\\",os.path.split(mpicom.__file__)[1],\\\"mpicom.so\\\")\\n else:\\n self.test(\\\"libname\\\",os.path.split(mpicom.__file__)[1],\\\"mpistub.pyc\\\")\\n self.test_broadcast()\\n self.test_reduce()\\n self.test_p2p()\\n self.test_gather()\\n self.test_scatter()\\n #self.test_alltoall()\",\n \"def run_tests(virtual_env):\\n signal.signal(signal.SIGINT, signal.SIG_IGN)\\n args = [\\n 'python',\\n 'setup.py',\\n 'nosetests',\\n '--with-coverage',\\n '--with-xunit',\\n ]\\n subprocess.call(args, cwd=abspath(join(HOLLAND_ROOT, 'holland-core')), env=virtual_env)\\n subprocess.call(['coverage', 'xml'], cwd=abspath(join(HOLLAND_ROOT, 'holland-core')), env=virtual_env)\\n for plugin_dir in open(join(HOLLAND_ROOT, 'plugins', 'ACTIVE')):\\n plugin_dir = plugin_dir.rstrip()\\n plugin_path = join(HOLLAND_ROOT, 'plugins', plugin_dir)\\n subprocess.call(args, cwd=plugin_path, env=virtual_env)\\n subprocess.call(['coverage', 'xml'], cwd=plugin_path, env=virtual_env)\\n for addon_dir in open(join(HOLLAND_ROOT, 'addons', 'ACTIVE')):\\n addon_dir = addon_dir.rstrip()\\n addon_path = join(HOLLAND_ROOT, 'addons', addon_dir)\\n subprocess.call(args, cwd=addon_path, env=virtual_env)\\n subprocess.call(['coverage', 'xml'], cwd=plugin_path, env=virtual_env)\\n #return subprocess.call(args, env=virtual_env)\",\n \"def runTest(self):\\n unittest.main()\\n ChoreTest.clean_up()\",\n \"def run_main(): # pragma: no cover\\n RunTestsCLI.run()\",\n \"def run_unit(session):\\n set_environment_variables(PYBAMM_ENV, session=session)\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[all]\\\")\\n if sys.platform == \\\"linux\\\":\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[odes]\\\")\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[jax]\\\")\\n session.run(\\\"python\\\", \\\"run-tests.py\\\", \\\"--unit\\\")\",\n \"def test_suite():\\n testSuite = unittest.TestSuite()\\n testSuite.addTest(test_h5fs(\\\"test_mode\\\"))\\n testSuite.addTest(test_h5fs(\\\"test_path_splitting\\\"))\\n testSuite.addTest(test_h5fs(\\\"test_link_mixing\\\"))\\n return testSuite\",\n \"def Cpp_test():\\n pass\",\n \"def test(self):\\n for arch, python in self.python:\\n self.run(f\\\"{python} -m pytest\\\")\",\n \"def test():\\n for cmd in [\\n \\\"pytest --verbose --cov pike/ --cov-report term --cov-report html tests/\\\",\\n ]:\\n _run_in_venv(shlex.split(cmd))\\n for linter in [[\\\"black\\\", \\\"--check\\\"], [\\\"flake8\\\"], [\\\"isort\\\", \\\"--check\\\"]]:\\n _run_in_venv(linter + TEST_FILES)\\n\\n _run_in_venv(\\n [\\\"mypy\\\", \\\"pike/\\\", \\\"tests/\\\", \\\"setup.py\\\", \\\"pikefile.py\\\", \\\"--show-error-codes\\\"]\\n )\\n _run_in_venv([\\\"mypy\\\", \\\"examples/\\\"])\\n _run_in_venv([\\\"bandit\\\", \\\"-r\\\", \\\"pike/\\\"])\",\n \"def runtests(ctx):\\n run('pytest -s tests', pty=pty_available)\\n run('flake8 --ignore E265,E266,E501 --exclude src, lib', pty=pty_available)\",\n \"def test_py_compile_basic(self):\\n self._test_py_compile('basic')\",\n \"def run(self, test, env):\\n\\n raise NotImplementedError\",\n \"def _run_unittest(tests, verbose, use_gdb, gtest_filter, gtest_list_tests):\\n failed_tests = []\\n unfound_tests = []\\n for test in tests:\\n index = 1\\n while True:\\n test_info = _read_test_info('%s.%d.json' % (test, index))\\n if not test_info:\\n # The format of test info file is [test name].[index].json, where index\\n # is one of consecutive numbers from 1. If the test info file for index\\n # 1 is not found, that means the corresponding test does not exist.\\n if index == 1:\\n unfound_tests.append(test)\\n break\\n command = _construct_command(test_info, gtest_filter, gtest_list_tests)\\n if verbose:\\n print 'Running:', command\\n args = shlex.split(command)\\n if use_gdb:\\n unittest_util.run_gdb(args)\\n else:\\n returncode = subprocess.call(args)\\n if returncode != 0:\\n print 'FAILED: ' + test\\n failed_tests.append('%s.%d' % (test, index))\\n index += 1\\n if unfound_tests:\\n print 'The following tests were not found: \\\\n' + '\\\\n'.join(unfound_tests)\\n if failed_tests:\\n print 'The following tests failed: \\\\n' + '\\\\n'.join(failed_tests)\\n if unfound_tests or failed_tests:\\n return -1\\n return 0\",\n \"def test_testutils():\\n build()\\n sh(\\\"%s psutil\\\\\\\\tests\\\\\\\\test_testutils.py\\\" % PYTHON)\",\n \"def setUpSuite():\\n global _output_dir\\n global _suite_configured\\n\\n if _suite_configured:\\n return\\n\\n def remove_output_dir():\\n global _output_dir\\n if _output_dir != '':\\n try:\\n shutil.rmtree(_output_dir)\\n except FileNotFoundError:\\n pass\\n\\n atexit.register(remove_output_dir)\\n _output_dir = tempfile.mkdtemp(dir=TESTS_DIR)\\n\\n os.environ['VOC_BUILD_DIR'] = os.path.join(_output_dir, 'build')\\n os.environ['VOC_DIST_DIR'] = os.path.join(_output_dir, 'dist')\\n\\n # If the code has been precompiled, we don't have to\\n # compile it as part of the test suite setup.\\n precompile = os.environ.get('PRECOMPILE', 'true').lower() == 'true'\\n if not precompile:\\n _suite_configured = True\\n return\\n\\n proc = subprocess.Popen(\\n \\\"ant java\\\",\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT,\\n shell=True,\\n )\\n\\n try:\\n out, err = proc.communicate(timeout=30)\\n except subprocess.TimeoutExpired:\\n proc.kill()\\n out, err = proc.communicate()\\n raise\\n\\n if proc.returncode != 0:\\n raise Exception(\\\"Error compiling java sources: \\\" + out.decode('ascii'))\\n\\n _suite_configured = True\",\n \"def runTests(self):\\n \\n pass\",\n \"def _initialise_testbench(argv_):\\n _rlock.acquire()\\n\\n if \\\"COCOTB_LIBRARY_COVERAGE\\\" in os.environ:\\n import coverage\\n\\n global _library_coverage\\n _library_coverage = coverage.coverage(\\n data_file=\\\".coverage.cocotb\\\",\\n branch=True,\\n include=[\\\"{}/*\\\".format(os.path.dirname(__file__))])\\n _library_coverage.start()\\n\\n global argc, argv\\n argv = argv_\\n argc = len(argv)\\n\\n root_name = os.getenv(\\\"TOPLEVEL\\\")\\n if root_name is not None:\\n if root_name == \\\"\\\":\\n root_name = None\\n elif '.' in root_name:\\n # Skip any library component of the toplevel\\n root_name = root_name.split(\\\".\\\", 1)[1]\\n\\n # sys.path normally includes \\\"\\\" (the current directory), but does not appear to when python is embedded.\\n # Add it back because users expect to be able to import files in their test directory.\\n # TODO: move this to gpi_embed.cpp\\n sys.path.insert(0, \\\"\\\")\\n\\n _setup_logging()\\n\\n # From https://www.python.org/dev/peps/pep-0565/#recommended-filter-settings-for-test-runners\\n # If the user doesn't want to see these, they can always change the global\\n # warning settings in their test module.\\n if not sys.warnoptions:\\n warnings.simplefilter(\\\"default\\\")\\n\\n from cocotb import simulator\\n\\n global SIM_NAME, SIM_VERSION\\n SIM_NAME = simulator.get_simulator_product().strip()\\n SIM_VERSION = simulator.get_simulator_version().strip()\\n\\n cocotb.log.info(\\\"Running on {} version {}\\\".format(SIM_NAME, SIM_VERSION))\\n\\n memcheck_port = os.getenv('MEMCHECK')\\n if memcheck_port is not None:\\n mem_debug(int(memcheck_port))\\n\\n log.info(\\\"Running tests with cocotb v%s from %s\\\" %\\n (__version__, os.path.dirname(__file__)))\\n\\n # Create the base handle type\\n\\n process_plusargs()\\n\\n global scheduler\\n scheduler = Scheduler()\\n\\n # Seed the Python random number generator to make this repeatable\\n global RANDOM_SEED\\n RANDOM_SEED = os.getenv('RANDOM_SEED')\\n\\n if RANDOM_SEED is None:\\n if 'ntb_random_seed' in plusargs:\\n RANDOM_SEED = eval(plusargs['ntb_random_seed'])\\n elif 'seed' in plusargs:\\n RANDOM_SEED = eval(plusargs['seed'])\\n else:\\n RANDOM_SEED = int(time.time())\\n log.info(\\\"Seeding Python random module with %d\\\" % (RANDOM_SEED))\\n else:\\n RANDOM_SEED = int(RANDOM_SEED)\\n log.info(\\\"Seeding Python random module with supplied seed %d\\\" % (RANDOM_SEED))\\n random.seed(RANDOM_SEED)\\n\\n # Setup DUT object\\n from cocotb import simulator\\n\\n handle = simulator.get_root_handle(root_name)\\n if not handle:\\n raise RuntimeError(\\\"Can not find root handle ({})\\\".format(root_name))\\n\\n global top\\n top = cocotb.handle.SimHandle(handle)\\n\\n try:\\n import pytest\\n except ImportError:\\n log.warning(\\\"Pytest not found, assertion rewriting will not occur\\\")\\n else:\\n try:\\n # Install the assertion rewriting hook, which must be done before we\\n # import the test modules.\\n from _pytest.config import Config\\n from _pytest.assertion import install_importhook\\n pytest_conf = Config.fromdictargs([], {})\\n install_importhook(pytest_conf)\\n except Exception:\\n log.exception(\\n \\\"Configuring the assertion rewrite hook using pytest {} failed. \\\"\\n \\\"Please file a bug report!\\\".format(pytest.__version__))\\n\\n # start Regression Manager\\n global regression_manager\\n regression_manager = RegressionManager.from_discovery(top)\\n regression_manager.execute()\\n\\n _rlock.release()\\n return True\",\n \"def main():\\n # add all new test suites per test module here\\n suite_date = test_date.suite()\\n suite_ng = test_ng.suite()\\n suite_page = test_page.suite()\\n suite_container = test_container.suite()\\n\\n # add the suite to be tested here\\n alltests = unittest.TestSuite((suite_date,\\n suite_ng,\\n suite_page,\\n suite_container))\\n\\n # run the suite\\n runner = unittest.TextTestRunner()\\n runner.run(alltests)\",\n \"def execute_test_on_model(test,model):\\n logger.info(\\\"running %r with %r\\\",test,model)\\n\\n #grab the executable\\n executable = test.executable\\n #profiling time thing\\n timeblock = \\\"/usr/bin/time --format={\\\\\\\\\\\\\\\"usertime\\\\\\\\\\\\\\\":%U,\\\\\\\\\\\\\\\"memmax\\\\\\\\\\\\\\\":%M,\\\\\\\\\\\\\\\"memavg\\\\\\\\\\\\\\\":%K} \\\"\\n\\n # run the test in its own directory\\n with test.in_dir():\\n # So, I switched this to now use the file handlers in the popen command\\n # directly in the hopes that this will prevent issues of the buffer not\\n # clearing when a test with a lot of output is run.\\n with test.processed_infile(model) as kim_stdin_file, open(STDOUT_FILE,'w') as stdout_file, open(STDERR_FILE,'w') as stderr_file:\\n #grab the input file\\n # output_info = test.out_dict\\n start_time = time.time()\\n process = Command(timeblock+ executable,stdin=kim_stdin_file,stdout=stdout_file,stderr=stderr_file)\\n logger.info(\\\"launching run...\\\")\\n try:\\n process.run(timeout=RUNNER_TIMEOUT)\\n except PipelineTimeout:\\n logger.error(\\\"test %r timed out\\\",test)\\n raise PipelineTimeout, \\\"your test timed out\\\"\\n\\n end_time = time.time()\\n\\n # It seems the test didn't finish\\n # this probably doesn't end\\n if process.poll() is None:\\n process.kill()\\n raise KIMRuntimeError, \\\"your test didn't terminate nicely\\\"\\n\\n return (end_time - start_time)\",\n \"def runTest(self):\\n self.setUp()\\n self.test_JupyterNotebooks1()\",\n \"def main():\\n test_merge_quick_sort()\\n test_compare()\",\n \"def test(extra_args: list[str] | None = None, run_doctests: bool = False) -> None:\\n pytest = import_optional_dependency(\\\"pytest\\\")\\n import_optional_dependency(\\\"hypothesis\\\")\\n cmd = [\\\"-m not slow and not network and not db\\\"]\\n if extra_args:\\n if not isinstance(extra_args, list):\\n extra_args = [extra_args]\\n cmd = extra_args\\n if run_doctests:\\n cmd = [\\n \\\"--doctest-modules\\\",\\n \\\"--doctest-cython\\\",\\n f\\\"--ignore={os.path.join(PKG, 'tests')}\\\",\\n ]\\n cmd += [PKG]\\n joined = \\\" \\\".join(cmd)\\n print(f\\\"running: pytest {joined}\\\")\\n sys.exit(pytest.main(cmd))\",\n \"def RunTest(self):\\n self.TestLs()\\n self.TestTerminate()\\n self.TestMultipleProcesses()\",\n \"def main():\\n try:\\n unittest.main(testLoader=BetterLoader(), defaultTest='suite')\\n except Exception:\\n import sys\\n import traceback\\n traceback.print_exc()\\n sys.exit(1)\",\n \"def main(argv):\\n global g_test_root_dir\\n global g_temp_filename\\n\\n if len(argv) < 2:\\n print(\\\"invoke this script as python collectUnitTestRunTime.py 10 'python run.py_path/run.py --wipe \\\"\\n \\\"--test dir_to_test/test1,python run.py_path/run.py --wipe --test dir_to_test2/test2,...' True\\\\n\\\")\\n sys.exit(1)\\n else: # we may be in business\\n repeat_number = int(argv[1]) # number of times to run a unit test\\n command_lists = argv[2] # list of unit tests to run\\n\\n for command in command_lists.split(','): # for each command in the list\\n # run command repeat_number of times and collect results into result_dict\\n run_commands(command, repeat_number, g_temp_filename)\",\n \"def main():\\n test_runner = TestRunner(\\n FLAGS.workspace, FLAGS.bench_home, imagenet_dir=FLAGS.train_data_dir)\\n test_runner.run_tests(FLAGS.test_list.split(','))\",\n \"def runtests():\\r\\n\\r\\n app_abspath = os.path.dirname(os.path.dirname(__file__))\\r\\n models_abspath = os.path.join(app_abspath, 'models.py')\\r\\n models_exists = os.path.isfile(models_abspath)\\r\\n urls_abspath = os.path.join(app_abspath, 'urls.py')\\r\\n urls_exists = os.path.isfile(urls_abspath)\\r\\n views_abspath = os.path.join(app_abspath, 'views')\\r\\n views_exists = os.path.isdir(views_abspath)\\r\\n tpls_abspath = os.path.join(app_abspath, 'templates')\\r\\n tpls_exists = os.path.isdir(tpls_abspath)\\r\\n\\r\\n for f in [models_abspath, urls_abspath]:\\r\\n if os.path.isfile(f):\\r\\n subprocess.call('cp {} {}.orig'.format(f, f), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(views_abspath, views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('cp -r {} {}.orig'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n\\r\\n overwrite_project_language('ja')\\r\\n subprocess.call('python manage.py generatescaffold test_app I18nModel title:string', shell=True)\\r\\n time.sleep(1)\\r\\n overwrite_project_language('en-us')\\r\\n time.sleep(1)\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedNoTimestampModel title:string description:text --no-timestamps', shell=True)\\r\\n time.sleep(2) # Give time for Django's AppCache to clear\\r\\n\\r\\n subprocess.call('python manage.py generatescaffold test_app GeneratedModel title:string description:text', shell=True)\\r\\n\\r\\n test_status = subprocess.call('python manage.py test --with-selenium --with-selenium-fixtures --with-cherrypyliveserver --noinput', shell=True)\\r\\n\\r\\n if models_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(models_abspath, models_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(models_abspath), shell=True)\\r\\n\\r\\n if urls_exists:\\r\\n subprocess.call('mv {}.orig {}'.format(urls_abspath, urls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm {}'.format(urls_abspath), shell=True)\\r\\n\\r\\n if views_exists:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(views_abspath, views_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(views_abspath), shell=True)\\r\\n\\r\\n if tpls_exists:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n subprocess.call('mv {}.orig {}'.format(tpls_abspath, tpls_abspath), shell=True)\\r\\n else:\\r\\n subprocess.call('rm -rf {}'.format(tpls_abspath), shell=True)\\r\\n\\r\\n subprocess.call('rm {}/*.pyc'.format(app_abspath), shell=True)\\r\\n\\r\\n sys.exit(test_status)\",\n \"def run(full=False):\\n # Call about to get all version info printed with tests\\n about()\\n import pytest\\n # real_num_cpu = qset.num_cpus\\n # real_thresh = qset.openmp_thresh\\n # if qset.has_openmp:\\n # For travis which VMs have only 1 cpu.\\n # Make sure the openmp version of the functions are tested.\\n # qset.num_cpus = 2\\n # qset.openmp_thresh = 100\\n\\n test_options = [\\\"--verbosity=1\\\", \\\"--disable-pytest-warnings\\\", \\\"--pyargs\\\"]\\n if not full:\\n test_options += ['-m', 'not slow']\\n pytest.main(test_options + [\\\"qutip\\\"])\\n # runs tests in qutip.tests module only\\n\\n # Restore previous settings\\n # if qset.has_openmp:\\n # qset.num_cpus = real_num_cpu\\n # qset.openmp_thresh = real_thresh\",\n \"def main():\\n fix_sys_path()\\n result = unittest.TextTestRunner(verbosity=2).run(createTestSuite())\\n\\n if result.testsRun != EXPECTED_TEST_COUNT:\\n raise Exception(\\n 'Expected %s tests to be run, not %s.' % (EXPECTED_TEST_COUNT, result.testsRun))\\n\\n if len(result.errors) != 0 or len(result.failures) != 0:\\n raise Exception(\\n \\\"Functional test suite failed: %s errors, %s failures of %s tests run.\\\" % (\\n len(result.errors), len(result.failures), result.testsRun))\",\n \"def run_tests(self):\\n import nose\\n from pocketlint.formatcheck import main as pocket_main\\n\\n nose_args = ['nosetests']\\n if self.verbose:\\n nose_args.append('-v')\\n else:\\n nose_args.append('-q')\\n\\n module = self.test_suite\\n if self.test_module:\\n module = self.test_module\\n\\n nose_args.extend([\\n '--with-coverage',\\n '--cover-package=' + module,\\n '--cover-erase',\\n '--cover-test',\\n module.replace('.', '/'),\\n ])\\n\\n pocket_args = [\\n 'README.rst',\\n 'release-notes.rst',\\n 'setup.py',\\n ]\\n for root, dirs, files in os.walk('chevah/keycert', topdown=False):\\n for name in files:\\n pocket_args.append(os.path.join(root, name))\\n\\n nose_code = nose.run(argv=nose_args)\\n if nose_code:\\n nose_code = 0\\n else:\\n nose_code = 1\\n\\n pocket_code = pocket_main(pocket_args)\\n if not pocket_code:\\n print('Linter OK')\\n\\n coverage_args = [\\n 'report',\\n '--include=chevah/keycert/tests/*',\\n '--fail-under=100',\\n ]\\n covergate_code = load_entry_point(\\n 'coverage', 'console_scripts', 'coverage')(argv=coverage_args)\\n if not covergate_code:\\n print('Tests coverage OK')\\n\\n sys.exit(pocket_code or nose_code or covergate_code)\",\n \"def execute_tests():\\n\\n if len(sys.argv) > 1:\\n # Filter test list based on command line requests\\n tests_to_run = []\\n for requested in sys.argv[1:]:\\n for func, param in registered_tests:\\n if param == requested:\\n tests_to_run += [(func, param)]\\n break\\n else:\\n print('Unknown test ' + requested)\\n sys.exit(1)\\n else:\\n tests_to_run = registered_tests\\n\\n failing_tests = []\\n for func, param in tests_to_run:\\n print(param + (' ' * (OUTPUT_ALIGN - len(param))), end='')\\n sys.stdout.flush()\\n try:\\n func(param)\\n print(COLOR_GREEN + 'PASS' + COLOR_NONE)\\n except KeyboardInterrupt:\\n sys.exit(1)\\n except TestException as exc:\\n print(COLOR_RED + 'FAIL' + COLOR_NONE)\\n failing_tests += [(param, exc.args[0])]\\n except Exception as exc: # pylint: disable=W0703\\n print(COLOR_RED + 'FAIL' + COLOR_NONE)\\n failing_tests += [(param, 'Test threw exception:\\\\n' +\\n traceback.format_exc())]\\n\\n if failing_tests:\\n print('Failing tests:')\\n for name, output in failing_tests:\\n print(name)\\n print(output)\\n\\n print(str(len(failing_tests)) + '/' +\\n str(len(tests_to_run)) + ' tests failed')\\n if failing_tests != []:\\n sys.exit(1)\",\n \"def runTest(self):\\n\\t\\tself.setUp()\\n\\t\\tself.test_postopProgramming1()\",\n \"def unittest():\\n from a6test import test_all\\n test_all()\",\n \"def run_tests():\\n\\n parser = argparse.ArgumentParser(description=\\\"Run tests for the T2K Data Manager.\\\")\\n parser.add_argument(\\n \\\"-w\\\", \\\"--write\\\", action=\\\"store_true\\\", help=\\\"do write tests. Default: read only\\\"\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--tape\\\",\\n action=\\\"store_true\\\",\\n help=\\\"do write tape storage tests. Default: disks only\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--parallel\\\",\\n default=2,\\n type=int,\\n help=\\\"specify how many parallel processes to test. Defaul: 2\\\",\\n )\\n parser.add_argument(\\n \\\"-b\\\", \\\"--backend\\\", default=None, help=\\\"specify which backend to use\\\"\\n )\\n\\n args = parser.parse_args()\\n if args.backend is not None:\\n dm.config.backend = args.backend\\n dm.backend = backends.get_backend(dm.config)\\n\\n run_read_only_tests(tape=args.tape, parallel=args.parallel)\\n if args.write:\\n run_read_write_tests(tape=args.tape, parallel=args.parallel)\\n\\n print_(\\\"All done.\\\")\",\n \"def runtest(self):\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description='Runs test for C++ implementation of M*')\\n parser.add_argument('test_file', help='File describing test cases')\\n parser.add_argument('output_file', help='Name of output file')\\n parser.add_argument('num_processors', type=int, action='store',\\n help='Number of processes to run on each node. ' +\\n 'The local host running the primary server will ' +\\n 'run one fewer worker processes')\\n parser.add_argument('-i', action='store', type=float, default=1.0,\\n help='Set inflation factor for the heuristic, ' +\\n 'defaults to 1', metavar='INF', dest='inflation')\\n parser.add_argument('-t', action='store', type=int, default=120,\\n help='Set time limit for planning. Defaults to 2 ' +\\n 'minutes', dest='time_limit')\\n parser.add_argument('--hosts', action='store',\\n default=('python', 'cobra', 'viper', 'anaconda'),\\n help='Hostnames/IPs to use as processing nodes.',\\n nargs='*', metavar='HOSTNAME')\\n\\n args = parser.parse_args()\\n\\n run_cpp_mstar_trial(args.test_file, args.output_file,\\n inflation=args.inflation, time_limit=args.time_limit,\\n hosts=args.hosts, num_processors=args.num_processors)\",\n \"def unittest(core):\\n\\n # local import because nose uses pkg_resources that is very slow to import\\n import nose.core\\n\\n config = core.config\\n\\n application_name = config.get(APPLICATION_NAME)\\n application_package = config.get(APPLICATION_ROOT)\\n\\n extension_names = config.get(EXTENSION_NAMES)\\n tests = config.get(TESTS)\\n report_enabled = config.get(REPORT_ENABLED)\\n report_file = config.get(REPORT_FILE)\\n coverage_enabled = core.config.get(COVERAGE_ENABLED)\\n verbose = core.config.get(DETAILS)\\n exclude_systests = core.config.get(EXCLUDE_SYSTESTS)\\n\\n matching_packages = packages.find_packages(\\n extension_names, core.extension_manager, application_name, application_package)\\n command = create_nose_command(\\n matching_packages, verbose, tests, report_enabled, report_file, exclude_systests)\\n\\n if coverage_enabled:\\n coverage_file = core.config.get(COVERAGE_FILE)\\n xml_enabled = core.config.get(COVERAGE_XML_ENABLED)\\n xml_file = core.config.get(COVERAGE_XML_FILE)\\n with coverage.coverage(matching_packages, sys.stdout, coverage_file, xml_enabled, xml_file):\\n result = nose.core.run(argv=command)\\n else:\\n result = nose.core.run(argv=command)\\n\\n return 0 if result else 1\",\n \"def test_5():\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog=\\\"run_test.py\\\",\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('id', help=\\\"Id of a test\\\")\\n args = parser.parse_args()\\n\\n configure_logger()\\n\\n test_info = TESTS.get(args.id, None)\\n if not test_info:\\n test_info.log.error(f'{args.id} does not exist')\\n exit(ErrorCode.CRITICAL)\\n os.environ['DISPLAY'] = \\\":0.0\\\"\\n\\n test = Test(args.id, test_info)\\n result = test.run()\\n\\n test.log.info('#' * 80)\\n if not result:\\n test.log.error('TEST FAILED')\\n else:\\n test.log.info('TEST PASSED')\\n test.log.info('#' * 80)\\n exit(not result)\",\n \"def main():\\n # Disable *.pyc files\\n sys.dont_write_bytecode = True\\n\\n # Add \\\"..\\\" to module search path\\n cur_dir = os.path.dirname(os.path.realpath(__file__))\\n top_dir = os.path.abspath(os.path.join(cur_dir, os.pardir))\\n sys.path.append(top_dir)\\n\\n # Parse command line arguments\\n parser = argparse.ArgumentParser(description=__doc__)\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", action=\\\"count\\\", default=0,\\n help=\\\"verbosity level, use: [-v | -vv | -vvv]\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--start-directory\\\", default=None,\\n help=\\\"directory to start discovery\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--pattern\\\", default=\\\"test*.py\\\",\\n help=\\\"pattern to match test files ('test*.py' default)\\\")\\n parser.add_argument(\\\"test\\\", nargs=\\\"*\\\",\\n help=\\\"test specs (e.g. module.TestCase.test_func)\\\")\\n args = parser.parse_args()\\n\\n if not args.start_directory:\\n args.start_directory = cur_dir\\n\\n if args.verbose > 2:\\n logging.basicConfig(level=logging.DEBUG, format=\\\"DEBUG: %(message)s\\\")\\n\\n loader = unittest.TestLoader()\\n if args.test:\\n # Add particular tests\\n for test in args.test:\\n suite = unittest.TestSuite()\\n suite.addTests(loader.loadTestsFromName(test))\\n else:\\n # Find all tests\\n suite = loader.discover(args.start_directory, args.pattern)\\n\\n runner = unittest.TextTestRunner(verbosity=args.verbose)\\n result = runner.run(suite)\\n return result.wasSuccessful()\",\n \"def test_run():\\n # Only few steps for test\\n timesteps = 128\\n\\n # Compute all sub testing conf\\n envs = ['CartPole-v0']\\n ml_platforms = ['torch', 'tf']\\n agents = ['dqn', 'a2c']\\n\\n test_combinations = list(it.product(\\n envs,\\n ml_platforms,\\n agents\\n )\\n )\\n\\n # Finally test them all\\n for conf in test_combinations:\\n env_str, ml_platform_str, agent_str = conf\\n run(\\n agent_str,\\n ml_platform_str,\\n env_str,\\n 'dense',\\n timesteps,\\n './target/')\",\n \"def runTest(self):\\n self.setUp()\\n self.test_visuThreeD1()\",\n \"def run_tests(session):\\n set_environment_variables(PYBAMM_ENV, session=session)\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[all]\\\")\\n if sys.platform == \\\"linux\\\" or sys.platform == \\\"darwin\\\":\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[odes]\\\")\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[jax]\\\")\\n session.run(\\\"python\\\", \\\"run-tests.py\\\", \\\"--all\\\")\",\n \"def run_tests():\\n fail = []\\n okay = []\\n for i in os.listdir(\\\".\\\"):\\n if i.find(\\\"_test_\\\") > -1 and i.endswith(\\\".py\\\"):\\n if 0 != subprocess.call(\\\"python \\\" + i, shell=True):\\n fail.append(i)\\n else:\\n okay.append(i)\\n if fail:\\n print(\\\"[ERROR] The following %u tests failed: %r\\\" % (len(fail), fail))\\n return False\\n print(\\\"[DONE] All %u tests completely successfully!\\\" % (len(okay)))\\n return True\",\n \"def main():\\n dims = params['dims']\\n\\n for d in dims:\\n print('**** Running test for d={0:d} ****'.format(d))\\n run_test(d)\",\n \"def run():\\n\\tsubsuite_list = []\\n\\tfor _, modname, _ in pkgutil.iter_modules(test.__path__):\\n\\t\\tif modname.startswith(\\\"test_\\\"):\\n\\t\\t\\tmodule = importlib.import_module('test.' + modname)\\n\\t\\t\\tsubsuite = unittest.TestLoader().loadTestsFromModule(module)\\n\\t\\t\\tsubsuite_list.append(subsuite)\\n\\tsuite = unittest.TestSuite(subsuite_list)\\n\\n\\tprint(\\\"Testing:\\\\n\\\")\\n\\tunittest.TextTestRunner(verbosity=2).run(suite)\",\n \"def task_test(argv):\\n run_tests(\\\"python2\\\", argv)\\n run_tests(\\\"python3\\\", argv)\",\n \"def test_memleaks():\\n build()\\n sh(\\\"%s psutil\\\\\\\\tests\\\\\\\\test_memleaks.py\\\" % PYTHON)\",\n \"def runAll():\\n\\n loader = unittest.TestLoader()\\n test_dir = pkg_resources.resource_filename('frvcpy.test','.')\\n suite = loader.discover(test_dir)\\n\\n runner = unittest.TextTestRunner(verbosity=2)\\n runner.run(suite)\",\n \"def run_tests(self, test_labels):\\n import pytest\\n\\n argv = []\\n if self.verbosity == 0:\\n argv.append('--quiet')\\n if self.verbosity == 2:\\n argv.append('--verbose')\\n if self.verbosity == 3:\\n argv.append('-vv')\\n if self.failfast:\\n argv.append('--exitfirst')\\n if self.keepdb:\\n argv.append('--reuse-db')\\n\\n argv.extend(test_labels)\\n return pytest.main(argv)\",\n \"def run_tests(file, samples):\\n # Get the script dir, name and check if the file given exists\\n test_dir = os.path.dirname(os.path.realpath(__file__))\\n script_name = os.path.basename(__file__)\\n if not os.path.isfile(os.path.join(test_dir, file)):\\n sys.stderr.write('{0}: file \\\\'{1}\\\\' not found\\\\n'.format(script_name, file))\\n sys.exit(0)\\n\\n result_dir = os.path.join(test_dir, 'results')\\n if not os.path.exists(result_dir):\\n os.mkdir(result_dir)\\n\\n # Get a path to the build dir to run iengine and cd into it\\n filepath = os.path.join(test_dir, file)\\n exe_path = os.path.join(os.path.join(test_dir, '..'), 'cmake-build-debug')\\n os.chdir(exe_path)\\n\\n # Open csv files for writing to\\n time_dist = open(os.path.join(result_dir, 'time.csv'), 'a')\\n inference_dist = open(os.path.join(result_dir, 'inference.csv'), 'a')\\n time_writer = csv.DictWriter(time_dist, delimiter=',', fieldnames=['method',\\n 'file',\\n 'sample',\\n 'time'])\\n inference_writer = csv.DictWriter(inference_dist, delimiter=',',\\n fieldnames=['method', 'file',\\n 'sample', 'inference_length'])\\n time_writer.writeheader()\\n inference_writer.writeheader()\\n\\n # Run through tests for all inference methods\\n for method in ['FC', 'BC', 'TT']:\\n timer = timeit.Timer(functools.partial(execute, filepath, method))\\n avg = 0\\n avg_path = 0\\n\\n # Run through all samples for the current inference method getting the execution\\n # time and the number of inferences/models considered in the process\\n for i in range(0, samples):\\n print(timer.timeit(1))\\n current, (result, err) = timer.timeit(1)\\n avg += current * 1000\\n result = result.decode('ascii').replace(',', '').replace(':', '')\\n result_list = str.split(result)[1:]\\n length = len(result_list)\\n if method == 'TT':\\n length = int(result_list[0])\\n avg_path += length\\n time_writer.writerow({'method': method, 'file': file, 'sample': i,\\n 'time': current})\\n inference_writer.writerow({'method': method, 'file': file, 'sample': i,\\n 'inference_length': length})\\n\\n terminology = 'inferences'\\n if method == 'TT':\\n terminology = 'models'\\n\\n print('Method: {0}, Average time: {1:.3f}ms, Average {2}: {3}'.format(method,\\n avg / samples, terminology, avg_path / samples))\\n\\n time_dist.close()\\n inference_dist.close()\",\n \"def run_tests(self):\\n raise NotImplementedError\",\n \"def test_all():\\n for descr, args in INPUT_PARAMS.iteritems():\\n func = run_summarize_h5lmt\\n func.description = 'bin/summarize_h5lmt.py ' + descr\\n yield func, args\",\n \"def Run(self) -> None:\\n logging.info(\\\"Running %s in a subprocess...\\\", self)\\n self.stdout = tempfile.TemporaryFile()\\n self.stderr = tempfile.TemporaryFile()\\n self.begin_time = time.time()\\n env = os.environ.copy()\\n # Give each test program a separate test_tmpdir so they don't overwrite\\n # each other when running in parallel.\\n env[\\\"TEST_TMPDIR\\\"] = tempfile.mkdtemp()\\n # Bazel's test sharding protocol:\\n # https://docs.bazel.build/versions/master/test-encyclopedia.html\\n if self.total_shards > 1:\\n env[\\\"TEST_TOTAL_SHARDS\\\"] = str(self.total_shards)\\n env[\\\"TEST_SHARD_INDEX\\\"] = str(self.shard_id)\\n\\n self.subprocess = subprocess.Popen(\\n [_GetPython(), self.path], stdout=self.stdout, stderr=self.stderr,\\n env=env)\",\n \"def main():\\n unittest.main(exit=False, verbosity=2)\\n return 0\",\n \"def test_run(self, _run_mock):\\n hhmake = self.tool(hhsuite.HHMake,\\n options={\\\"output\\\": self.OUT_HHM_FILE})\\n hhmake.run({\\\"a3m\\\": self.IN_A3M_FILE})\\n self.verify_common(\\\"hhmake\\\", hhmake)\\n\\n _, kw_args = hhmake.tool.call_args\\n self.assertEqual(kw_args[\\\"options\\\"][\\\"input\\\"], self.IN_A3M_FILE)\",\n \"def _Main():\\n\\n options, args = run_tests_util.ParseArgs('gtest')\\n test_runner = run_tests_util.TestRunner(\\n script_dir=SCRIPT_DIR,\\n build_dir_var_name='GMOCK_BUILD_DIR',\\n injected_build_dir_finder=GetGmockBuildDir)\\n tests = test_runner.GetTestsToRun(args,\\n options.configurations,\\n options.built_configurations)\\n if not tests:\\n sys.exit(1) # Incorrect parameters given, abort execution.\\n\\n sys.exit(test_runner.RunTests(tests[0], tests[1]))\",\n \"def run(cls): \\n tests_to_run = cls.config.TESTS # A list of 5-tuple elements specifying the tests to run. See the\\n # 'Test Setup' section in config.py.template for more info.\\n test_group_name = \\\"Alchemist Tests\\\" # A short string identifier for this test run.\\n output_dir = cls.config.OUTPUT_DIR # The output file where we write results.\\n \\n try:\\n os.makedirs(output_dir,0o777)\\n except:\\n pass\\n num_tests_to_run = len(tests_to_run)\\n\\n print(OUTPUT_DIVIDER_STRING)\\n if num_tests_to_run == 1:\\n print(\\\"Running %d test in %s\\\" % (num_tests_to_run, test_group_name))\\n else:\\n print(\\\"Running %d tests in %s\\\" % (num_tests_to_run, test_group_name))\\n failed_tests = []\\n\\n cls.before_run_tests()\\n \\n spark_settings = []\\n for i in cls.config.SPARK_SETTINGS:\\n spark_settings.append(i.to_array()[0])\\n \\n output_settings = []\\n for i in cls.config.OUTPUT_SETTINGS:\\n output_settings.append(i.to_array()[0])\\n \\n main_class = \\\"altest.AlTest\\\"\\n\\n for meta_data, opt_sets in tests_to_run:\\n print(OUTPUT_DIVIDER_STRING + '\\\\n')\\n# print(\\\"Running test command: '%s' ... \\\" % main_class)\\n \\n meta = {}\\n meta_pairs = [i.to_tuple() for i in meta_data]\\n for mp in meta_pairs:\\n meta[mp[0].replace('-', '_')] = mp[1].replace('0x20', ' ')\\n \\n meta_settings = []\\n for i in meta_data:\\n meta_settings.append(i.to_array()[0])\\n \\n# stdout_filename = \\\"%s/%s.out\\\" % (output_dir, meta['short_name'])\\n# stderr_filename = \\\"%s/%s.err\\\" % (output_dir, meta['short_name'])\\n# \\n# out_file = open(output_dir + \\\"/\\\" + meta['short_name'] + \\\".out\\\", 'w')\\n\\n # Run a test for all combinations of the OptionSets given, then capture\\n # and print the output.\\n opt_set_arrays = [i.to_array() for i in opt_sets]\\n for opt_list in itertools.product(*opt_set_arrays):\\n\\n cmd = cls.get_spark_submit_cmd(spark_settings, main_class, output_settings, meta_settings, opt_list)\\n# print(\\\"\\\\nSetting env var SPARK_SUBMIT_OPTS: %s\\\" % java_opts_str)\\n# test_env[\\\"SPARK_SUBMIT_OPTS\\\"] = java_opts_str\\n print(\\\"Running command:\\\")\\n print(\\\"%s\\\\n\\\" % cmd)\\n Popen(cmd, shell=True, env=test_env).wait()\\n\\n try:\\n src = output_dir + meta['short_name'] + '_latest/'\\n src_files = os.listdir(src)\\n src_file = src_files[0][:-4]\\n new_dir = output_dir + src_file\\n os.makedirs(new_dir)\\n for file_name in src_files:\\n full_file_name = os.path.join(src, file_name)\\n if (os.path.isfile(full_file_name)):\\n shutil.copy(full_file_name, new_dir)\\n except:\\n pass\\n \\n# result_string = cls.process_output(config, meta['short_name'], opt_list,\\n# stdout_filename, stderr_filename)\\n# print(OUTPUT_DIVIDER_STRING)\\n# print(\\\"\\\\nResult: \\\" + result_string)\\n# print(OUTPUT_DIVIDER_STRING)\\n# if \\\"FAILED\\\" in result_string:\\n# failed_tests.append(meta['short_name'])\\n# \\n# \\n# out_file.write(result_string + \\\"\\\\n\\\")\\n# out_file.flush()\\n\\n if num_tests_to_run == 1:\\n print(\\\"Finished running %d test in %s.\\\" % (num_tests_to_run, test_group_name))\\n else:\\n print(\\\"Finished running %d tests in %s.\\\" % (num_tests_to_run, test_group_name))\\n# print(\\\"\\\\nNumber of failed tests: %d, failed tests: %s\\\" %\\n# (len(failed_tests), \\\",\\\".join(failed_tests)))\\n print(OUTPUT_DIVIDER_STRING)\",\n \"def run_test(test_name):\\n\\n print 'Running %s_test...' % test_name\\n os.system('./test_%s.py' % test_name)\\n print\",\n \"def _run_ci_test():\\n _run_install(False)\\n _run_coverage_html(False)\\n _run_typecheck_xml(False)\\n _run_lint(True)\",\n \"def test_main():\\n # Setup\\n # Exercise\\n # Verify\",\n \"def main(args):\\n\\n run_all_tests = len(args) == 1 and args[0] == 'all'\\n\\n python = 'python -m ' # how to call python \\n if len(sys.argv) > 1 and sys.argv[1] == 'wine':\\n python = 'C:\\\\\\\\Python26\\\\\\\\python.exe ' # works for wine\\n \\n data_path = ' ' + prepare_data(run_all_tests)\\n \\n command = ' bbob_pproc ' # + join_path(os.path.dirname(os.path.realpath(__file__)), 'rungeneric.py ')\\n \\n copy_latex_templates()\\n print('LaTeX templates copied.')\\n \\n print('*** testing module bbob_pproc ***')\\n t0 = time.time()\\n print(python + command + '--conv' + ' --no-svg --settings=grayscale' +\\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\\n result = os.system(python + command + '--conv' + ' --no-svg --settings=grayscale' +\\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\\n print('** subtest 1 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on one algorithm with option --conv.'\\n\\n result = run_latex_template(\\\"templateBBOBarticle.tex\\\")\\n assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\\n\\n t0 = time.time()\\n print(python + command + '--no-svg --settings=grayscale' + join_path(data_path, 'RS.tgz'))\\n result = os.system(python + command + '--no-svg --settings=grayscale' + join_path(data_path, 'RS.tgz'))\\n print('** subtest 1 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on one bi-objective algorithm.'\\n\\n # Latex templates are not prepared yet for bi-objective case. \\n# result = run_latex_template(\\\"templateBBOBarticle.tex\\\")\\n# assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\\n\\n if run_all_tests: \\n t0 = time.time()\\n print(time.asctime())\\n result = os.system(python + command + # ' --omit-single ' +\\n join_path(data_path, 'BIPOP-CMA-ES_hansen_noiseless.tgz') +\\n join_path(data_path, 'MCS_huyer_noiseless.tgz') +\\n join_path(data_path, 'NEWUOA_ros_noiseless.tgz') +\\n join_path(data_path, 'RANDOMSEARCH_auger_noiseless.tgz') +\\n join_path(data_path, 'BFGS_ros_noiseless.tgz'))\\n print('** subtest 2 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on many algorithms.'\\n \\n result = run_latex_template(\\\"templateBBOBmany.tex\\\")\\n assert not result, 'Test failed: error while generating pdf from templateBBOBmany.tex.'\\n \\n t0 = time.time()\\n result = os.system(python + command + '--conv' +\\n join_path(data_path, 'SMAC-BBOB_hutter_noiseless.tgz') +\\n join_path(data_path, 'lmm-CMA-ES_auger_noiseless.tgz'))\\n print('** subtest 3 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on two algorithms with option --conv.'\\n \\n result = run_latex_template(\\\"templateBBOBcmp.tex\\\")\\n assert not result, 'Test failed: error while generating pdf from templateBBOBcmp.tex.'\\n \\n t0 = time.time()\\n result = os.system(python + command + ' --omit-single ' +\\n join_path(data_path, 'DE-PSO_garcia-nieto_noiseless.tgz') +\\n join_path(data_path, 'VNS_garcia-martinez_noiseless.tgz'))\\n print('** subtest 4 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on two algorithms with option --omit-single.'\\n \\n result = run_latex_template(\\\"templateBBOBcmp.tex\\\")\\n assert not result, 'Test failed: error while generating pdf from templateBBOBcmp.tex.'\\n \\n t0 = time.time()\\n result = os.system(python + command + ' --expensive ' +\\n join_path(data_path, 'VNS_garcia-martinez_noiseless.tgz'))\\n print('** subtest 5 finished in ', time.time() - t0, ' seconds')\\n assert result == 0, 'Test failed: rungeneric on one algorithm with option --expensive.'\\n \\n result = run_latex_template(\\\"templateBBOBarticle.tex\\\")\\n assert not result, 'Test failed: error while generating pdf from templateBBOBarticle.tex.'\\n\\n print('launching doctest (it might be necessary to close a few pop up windows to finish)')\\n t0 = time.time()\\n\\n if 1 < 3:\\n failure_count = 0\\n test_count = 0\\n #doctest.testmod(report=True, verbose=True) # this is quite cool!\\n # go through the py files in the bbob_pproc folder\\n currentPath = os.getcwd() \\n newPath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\\n os.chdir(newPath) \\n for root, dirnames, filenames in os.walk(os.path.dirname(os.path.realpath(__file__))):\\n for filename in fnmatch.filter(filenames, '*.py'):\\n current_failure_count, current_test_count = doctest.testfile(\\n os.path.join(root, filename), report=True, module_relative=False) \\n failure_count += current_failure_count\\n test_count += current_test_count\\n if current_failure_count:\\n print('doctest file \\\"%s\\\" failed' % os.path.join(root, filename))\\n os.chdir(currentPath)\\n else:\\n stdout = sys.stdout\\n fn = '_bbob_pproc_doctest_.txt'\\n try:\\n with open(fn, 'w') as f:\\n sys.stdout = f\\n doctest.testmod(report=True)\\n finally:\\n sys.stdout = stdout\\n process_doctest_output(fn)\\n print('** doctest finished in ', time.time() - t0, ' seconds')\\n # print(' more info in file _bbob_pproc_doctest_.txt)')\\n print('*** done testing module bbob_pproc ***')\\n \\n if (failure_count > 0):\\n raise ValueError('%d of %d tests failed' % (failure_count, test_count))\",\n \"def run_test(skip_run):\\n # First off, we need the 'official' test data downloaded and unpacked\\n # if necessary.\\n if ref_test_data.install_and_check_test_files() != 0:\\n return 1\\n if install_inputs() != 0:\\n return 1\\n\\n if not skip_run:\\n sys.stdout.write(\\\"Executing CCC code...\\\\n\\\")\\n ret = run.main()\\n if ret != 0:\\n return 1\\n\\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \\\"result\\\")\\n sys.stdout.write(\\\"Comparing CCC results with 'official' results...\\\\n\\\")\\n ret = compare_results.main([sys.argv[0], \\\"result\\\", test_result_dir])\\n if ret != 0:\\n return 1\",\n \"def main():\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"DATA STRUCTURE TESTS\\\")\\n test_module(structs.tests)\\n test_module(structs.regularization)\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"END-TO-END TESTS\\\")\\n test_module(globals())\",\n \"def runTest(self):\\n self.setUp()\\n success = False\\n message = ''\\n try:\\n self.command = [self.test_manager.executable_path]\\n self.command.extend(self.test.spirv_args)\\n\\n process = subprocess.Popen(\\n args=self.command,\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n cwd=self.directory)\\n output = process.communicate(self.stdin_shader)\\n test_status = TestStatus(self.test_manager, process.returncode, output[0],\\n output[1], self.directory, self.inputs,\\n self.file_shaders)\\n run_results = [\\n getattr(self.test, test_method)(test_status)\\n for test_method in get_all_test_methods(self.test.__class__)\\n ]\\n success, message = zip(*run_results)\\n success = all(success)\\n message = '\\\\n'.join(message)\\n except Exception as e:\\n success = False\\n message = str(e)\\n self.test_manager.notify_result(\\n self, success,\\n message + '\\\\nSTDOUT:\\\\n%s\\\\nSTDERR:\\\\n%s' % (output[0], output[1]))\\n self.tearDown()\",\n \"def test_pep8_conformance_unitests(self):\\n\\n print(\\\"\\\\r\\\\n\\\")\\n\\n # Get the path to current directory\\n path = os.path.dirname(os.path.realpath(__file__))\\n\\n self.run_check(path)\",\n \"def test_by_name(name):\\n build()\\n sh(\\\"%s -m unittest -v %s\\\" % (PYTHON, name))\",\n \"def main(options, args):\\n\\n # Set up logging format.\\n log_level = logging.INFO\\n if options.verbose:\\n log_level = logging.DEBUG\\n logging.basicConfig(level=log_level,\\n format='%(message)s')\\n\\n # Include all tests if none are specified.\\n if not args:\\n path = '/';\\n else:\\n path = ' '.join(args);\\n\\n adb_cmd = \\\"adb \\\";\\n if options.adb_options:\\n adb_cmd += options.adb_options\\n\\n # Re-generate the test list if --refresh-test-list is on\\n if options.refresh_test_list:\\n logging.info(\\\"Generating test list.\\\");\\n generate_test_list_cmd_str = adb_cmd + \\\" shell am instrument -e class com.android.dumprendertree.LayoutTestsAutoTest#generateTestList -e path \\\\\\\"\\\" + path + \\\"\\\\\\\" -w com.android.dumprendertree/.LayoutTestsAutoRunner\\\"\\n adb_output = subprocess.Popen(generate_test_list_cmd_str, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\\n\\n if adb_output.find('Process crashed') != -1:\\n logging.info(\\\"Aborting because cannot generate test list.\\\\n\\\" + adb_output)\\n sys.exit(1)\\n\\n\\n logging.info(\\\"Running tests\\\")\\n\\n # Count crashed tests.\\n crashed_tests = []\\n\\n timeout_ms = '5000'\\n if options.time_out_ms:\\n timeout_ms = options.time_out_ms\\n\\n # Run test until it's done\\n\\n run_layout_test_cmd_prefix = adb_cmd + \\\" shell am instrument\\\"\\n\\n run_layout_test_cmd_postfix = \\\" -e path \\\\\\\"\\\" + path + \\\"\\\\\\\" -e timeout \\\" + timeout_ms\\n if options.rebaseline:\\n run_layout_test_cmd_postfix += \\\" -e rebaseline true\\\"\\n run_layout_test_cmd_postfix += \\\" -w com.android.dumprendertree/.LayoutTestsAutoRunner\\\"\\n\\n # Call LayoutTestsAutoTest::startLayoutTests.\\n run_layout_test_cmd = run_layout_test_cmd_prefix + \\\" -e class com.android.dumprendertree.LayoutTestsAutoTest#startLayoutTests\\\" + run_layout_test_cmd_postfix\\n\\n adb_output = subprocess.Popen(run_layout_test_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\\n while not DumpRenderTreeFinished(adb_cmd):\\n # Get the running_test.txt\\n logging.error(\\\"DumpRenderTree crashed, output:\\\\n\\\" + adb_output)\\n\\n shell_cmd_str = adb_cmd + \\\" shell cat /sdcard/android/running_test.txt\\\"\\n crashed_test = subprocess.Popen(shell_cmd_str, shell=True, stdout=subprocess.PIPE).communicate()[0]\\n \\n logging.info(crashed_test + \\\" CRASHED\\\");\\n crashed_tests.append(crashed_test);\\n\\n logging.info(\\\"Resuming layout test runner...\\\");\\n # Call LayoutTestsAutoTest::resumeLayoutTests\\n run_layout_test_cmd = run_layout_test_cmd_prefix + \\\" -e class com.android.dumprendertree.LayoutTestsAutoTest#resumeLayoutTests\\\" + run_layout_test_cmd_postfix\\n\\n adb_output = subprocess.Popen(run_layout_test_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\\n\\n if adb_output.find('INSTRUMENTATION_FAILED') != -1:\\n logging.error(\\\"Error happened : \\\" + adb_output)\\n sys.exit(1)\\n\\n logging.debug(adb_output);\\n logging.info(\\\"Done\\\\n\\\");\\n\\n # Pull results from /sdcard\\n results_dir = options.results_directory\\n if not os.path.exists(results_dir):\\n os.makedirs(results_dir)\\n if not os.path.isdir(results_dir):\\n logging.error(\\\"Cannot create results dir: \\\" + results_dir);\\n sys.exit(1);\\n\\n result_files = [\\\"/sdcard/layout_tests_passed.txt\\\",\\n \\\"/sdcard/layout_tests_failed.txt\\\",\\n \\\"/sdcard/layout_tests_nontext.txt\\\"]\\n for file in result_files: \\n shell_cmd_str = adb_cmd + \\\" pull \\\" + file + \\\" \\\" + results_dir\\n adb_output = subprocess.Popen(shell_cmd_str, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()[0]\\n logging.debug(adb_output)\\n \\n # Create the crash list.\\n fp = open(results_dir + \\\"/layout_tests_crashed.txt\\\", \\\"w\\\");\\n fp.writelines('\\\\n'.join(crashed_tests))\\n fp.close()\\n\\n # Count the number of tests in each category.\\n passed_tests = CountLineNumber(results_dir + \\\"/layout_tests_passed.txt\\\")\\n logging.info(str(passed_tests) + \\\" passed\\\")\\n failed_tests = CountLineNumber(results_dir + \\\"/layout_tests_failed.txt\\\")\\n logging.info(str(failed_tests) + \\\" failed\\\")\\n crashed_tests = CountLineNumber(results_dir + \\\"/layout_tests_crashed.txt\\\")\\n logging.info(str(crashed_tests) + \\\" crashed\\\")\\n nontext_tests = CountLineNumber(results_dir + \\\"/layout_tests_nontext.txt\\\")\\n logging.info(str(nontext_tests) + \\\" no dumpAsText\\\")\\n\\n logging.info(\\\"Results are stored under: \\\" + results_dir + \\\"\\\\n\\\")\\n\\n # Comparing results to references to find new fixes and regressions.\\n results_dir = os.path.abspath(options.results_directory)\\n ref_dir = options.ref_directory\\n\\n # if ref_dir is null, cannonify ref_dir to the script dir.\\n if not ref_dir:\\n script_self = sys.argv[0]\\n script_dir = os.path.dirname(script_self)\\n ref_dir = os.path.join(script_dir, \\\"results\\\")\\n\\n ref_dir = os.path.abspath(ref_dir)\\n\\n CompareResults(ref_dir, results_dir)\",\n \"def parse_and_test(program: Text,\\n name: Text,\\n *,\\n filename: Text,\\n additional_search_paths: Tuple[str, ...] = (),\\n raise_on_error: bool = True,\\n test_filter: Optional[Text] = None,\\n trace_all: bool = False,\\n compare_jit: bool = True,\\n seed: Optional[int] = None) -> bool:\\n did_fail = False\\n test_name = None\\n type_info = None\\n\\n importer = import_helpers.Importer(additional_search_paths)\\n ran = 0\\n\\n try:\\n module = Parser(Scanner(filename, program), name).parse_module()\\n type_info = cpp_typecheck.check_module(module, importer.cache,\\n importer.additional_search_paths)\\n\\n ir_package = (\\n ir_converter.convert_module_to_package(\\n module, type_info, importer.cache, traverse_tests=True)\\n if compare_jit else None)\\n\\n interpreter = Interpreter(\\n module,\\n type_info,\\n importer.typecheck,\\n importer.additional_search_paths,\\n importer.cache,\\n trace_all=trace_all,\\n ir_package=ir_package)\\n for test_name in module.get_test_names():\\n if not _matches(test_name, test_filter):\\n continue\\n ran += 1\\n print('[ RUN UNITTEST ]', test_name, file=sys.stderr)\\n interpreter.run_test(test_name)\\n print('[ OK ]', test_name, file=sys.stderr)\\n\\n if ir_package and module.get_quickchecks():\\n if seed is None:\\n # We want to guarantee non-determinism by default. See\\n # https://abseil.io/docs/cpp/guides/random#stability-of-generated-sequences\\n # for rationale.\\n seed = int(os.getpid() * time.time())\\n print(f'[ SEED: {seed} ]')\\n for quickcheck in module.get_quickchecks():\\n test_name = quickcheck.f.name.identifier\\n print('[ RUN QUICKCHECK ]', test_name, file=sys.stderr)\\n quickcheck_helpers.run_quickcheck(\\n interpreter, ir_package, quickcheck, seed=seed)\\n print('[ OK ]', test_name, file=sys.stderr)\\n\\n except (PositionalError, FailureError, CppParseError, ScanError,\\n TypeInferenceError, XlsTypeError) as e:\\n did_fail = True\\n parser_helpers.pprint_positional_error(\\n e, output=cast(io.IOBase, sys.stderr))\\n if test_name:\\n print(\\n '[ FAILED ]',\\n test_name,\\n e.__class__.__name__,\\n file=sys.stderr)\\n if raise_on_error:\\n raise\\n finally:\\n if type_info is not None:\\n type_info.clear_type_info_refs_for_gc()\\n\\n print('[==================]', ran, 'test(s) ran.', file=sys.stderr)\\n return did_fail\",\n \"def test_5_bam(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, \\\"run_info-bam.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def test_make_mul_transp():\\n tmp_dir = make_temp_dir()\\n FILE_NAME = \\\"tmp_test_mul_transp\\\"\\n NAME = os.path.join(tmp_dir, FILE_NAME)\\n C_NAME, EXE_NAME = NAME + \\\".c\\\", NAME + \\\".exe\\\"\\n \\n def make_testprogram(lv, lm, n=1):\\n generate = BitMatrixMulTransp().generate_c_mul_transp\\n f = open(C_NAME, \\\"wt\\\")\\n print(r\\\"\\\"\\\"#include <stdio.h>\\n#include <stdint.h>\\nint main(int argc, char **argv)\\n{{\\n uint_fast32_t i, v;\\n static uint_fast32_t a[1000];\\n sscanf(argv[1], \\\"%ld\\\", &v);\\n for (i=0; i < argc-2; ++i) sscanf(argv[i+2], \\\"%ld\\\", a+i);\\n {0}\\n printf(\\\"%ld\\\\n\\\",v);\\n return 0;\\n}}\\n\\\"\\\"\\\".format(generate(\\\"v\\\", \\\"a\\\", lv, lm, n)), file = f)\\n f.close()\\n #subprocess.check_output([\\\"gcc\\\", C_NAME, \\\"-o\\\", EXE_NAME])\\n compile_testprogramm([C_NAME], EXE_NAME)\\n checker = BitMatrixMulTransp()\\n checker.set_matrix(lv, lm, n)\\n return checker\\n\\n def run_testprogram(v, m):\\n data = list(map(str, [v]+m)) \\n res = subprocess.check_output([EXE_NAME] + data)\\n return int(res) \\n\\n def test_testprogram(checker , v, m):\\n res = run_testprogram(v, m)\\n ref = checker.compute(v,m+[0]*64)\\n assert ref == ref, (hex, ref)\\n\\n def del_testprogram():\\n for f in [C_NAME, EXE_NAME]:\\n os.remove(f)\\n\\n test_data_dict = {\\n (4,4,1,0): [ (3, [2,3, 1, 1]) , ]\\n }\\n\\n def test_data(lv, lm, n, n_tests=10):\\n \\\"\\\"\\\"yield n_tests test cases (v, m) for given lv, lm, m0\\\"\\\"\\\"\\n try:\\n d = test_data_dict[(lv, lm, n, m0)]\\n for v, m in d: yield v, m\\n except:\\n pass\\n for i in range(n_tests):\\n v = random.randint(0, 2**(n*lv)-1)\\n m = [] \\n for i in range(lm):\\n m.append(random.randint(0, 2**(n*lv)-1))\\n yield v, m\\n\\n def test_programs():\\n \\\"\\\"\\\"yield cases (lv, lm, n) for a making a test program\\\"\\\"\\\"\\n yield 4, 4, 1\\n yield 16, 12, 2\\n\\n \\n\\n \\n for lv, lm, n in test_programs():\\n print(\\\"Make C program for case lv=%d, lm=%d, n=%d\\\" % (\\n lv,lm,n))\\n checker = make_testprogram(lv, lm, n) \\n print (\\\"C program has been made, starting tests..\\\")\\n for v, m in test_data(lv, lm, n):\\n test_testprogram(checker, v, m)\\n del_testprogram()\\n print (\\\"passed\\\")\\n\\n kill_temp_dir(tmp_dir)\",\n \"def main():\\n # ============================================================================\\n\\n working_folder = os.path.dirname(os.path.realpath(__file__))\\n os.chdir(working_folder)\\n\\n description_string =\\\\\\n \\\"A set of python tests to fetch data from Open Weather Map\\\"\\n epilog_string = (\\\"These tests should be run regularly to ensure that the \\\"\\n \\\"API server is healthy\\\")\\n\\n parser = argparse.ArgumentParser(\\n prog='OWM-API-test',\\n description=description_string,\\n epilog=epilog_string)\\n\\n parser.add_argument(\\n \\\"--tb\\\",\\n action='store',\\n default='line',\\n nargs='?',\\n choices=['auto', 'long', 'short', 'no', 'line', 'native'],\\n help='Set the traceback level for pytest',\\n dest='traceback')\\n\\n parser.add_argument(\\n \\\"-v\\\", \\\"--verbose\\\",\\n action='store_true',\\n help='Increase output verbosity',\\n dest='verbose')\\n\\n parser.add_argument(\\n \\\"-q\\\", \\\"--quiet\\\",\\n action='store_true',\\n help='Reduce output verbosity')\\n\\n args = parser.parse_args()\\n\\n # ============================================================================\\n # construct pytest commands\\n pytest_command = []\\n if args.verbose:\\n # we double increase verbosity to make it actually verbose\\n pytest_command.extend([\\\"-v\\\", \\\"-v\\\"])\\n # this only overwrites traceback argument if left at default\\n if args.traceback == \\\"line\\\":\\n args.traceback = \\\"long\\\"\\n\\n if args.quiet: # this overwrites and tb argument given\\n args.traceback = \\\"no\\\"\\n\\n pytest_command.extend([\\\"--tb\\\", args.traceback])\\n\\n # ============================================================================\\n\\n printing_functions.test_declaration(\\\"Running Tests...\\\")\\n run_local_tests(pytest_command)\",\n \"def run_tests(output_dir, fstype):\\n global options\\n if options.debug:\\n print \\\"Run NUMA test\\\"\\n for num_disks in [2]:\\n for num_dirs in range(1, 5):\\n postmark = PostMarkTest(output_dir, fstype, num_disks, num_dirs)\\n run_one_test(postmark)\",\n \"def test():\\n import unittest\\n tests = unittest \\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def run_tests():\\n testfiles = ['tests.test_overall']\\n exclude = ['__init__.py', 'test_overall.py']\\n for t in glob(pjoin('tests', '*.py')):\\n if True not in [t.endswith(ex) for ex in exclude]:\\n if basename(t).startswith('test_'):\\n testfiles.append('tests.%s' % splitext(basename(t))[0])\\n\\n suites = []\\n for file in testfiles:\\n __import__(file)\\n suites.append(sys.modules[file].suite)\\n\\n tests = unittest.TestSuite(suites)\\n runner = unittest.TextTestRunner(verbosity=2)\\n\\n # Disable logging output\\n logging.basicConfig(level=100)\\n logging.disable(100)\\n\\n result = runner.run(tests)\\n return result\",\n \"def main() -> int:\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--dir-metadata',\\n type=pathlib.Path, required=True)\\n\\n args = parser.parse_args()\\n\\n with LockedMetadata(args.dir_metadata, __file__) as md:\\n summary_dict = {}\\n passing_tests = []\\n failing_tests = []\\n for f in md.tests_pickle_files:\\n try:\\n trr = TestRunResult.construct_from_pickle(f)\\n summary_dict[f\\\"{trr.testname}.{trr.seed}\\\"] = \\\\\\n ('PASS' if trr.passed else\\n 'FAILED' + (\\\" {T}\\\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \\\"\\\"))\\n if trr.passed:\\n passing_tests.append(trr)\\n else:\\n failing_tests.append(trr)\\n except RuntimeError as e:\\n failing_tests.append(\\n TestRunResult(\\n name='broken_test',\\n failure_message=str(e)\\n ))\\n\\n md.regr_log = md.dir_run/'regr.log'\\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\\n\\n # Write results as junit_xml\\n with open(md.regr_log_junit,\\n 'w',\\n encoding='UTF-8') as junit_xml,\\\\\\n open(md.regr_log_junit_merged,\\n 'w',\\n encoding='UTF-8') as junit_merged_xml:\\n output_run_results_junit_xml(passing_tests, failing_tests,\\n junit_xml,\\n junit_merged_xml)\\n\\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\\n # Write results as regr.log (custom logfile format)\\n output_results_text(passing_tests, failing_tests, summary_dict,\\n outfile)\\n\\n test_summary_dict = create_test_summary_dict(passing_tests +\\n failing_tests)\\n\\n cov_summary_dict = {}\\n if md.simulator == \\\"xlm\\\":\\n cov_summary_dict = create_cov_summary_dict(md)\\n else:\\n print(\\\"Warning: Not generating coverage summary, unsupported \\\" \\\\\\n f\\\"simulator {md.simulator}\\\")\\n\\n html_report_filename = md.dir_run/'report.html'\\n with open(html_report_filename, 'w') as outfile:\\n output_results_html(md, passing_tests + failing_tests,\\n test_summary_dict, cov_summary_dict, outfile)\\n\\n json_report_filename = md.dir_run/'report.json'\\n with open(json_report_filename, 'w') as json_report_file:\\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\\n json_report_file)\\n\\n svg_summary_filename = md.dir_run/'summary.svg'\\n with open(svg_summary_filename, 'w') as svg_summary_file:\\n output_results_svg(test_summary_dict, cov_summary_dict,\\n svg_summary_file)\\n\\n # Print a summary line to the terminal\\n print(gen_summary_line(passing_tests, failing_tests))\\n\\n # Succeed if no tests failed\\n return 1 if failing_tests else 0\",\n \"def main():\\n print()\\n print(\\\"Un-comment and re-comment calls in MAIN one by one as you work.\\\")\\n print()\\n\\n # run_test_sum_until_prime_input()\\n # run_test_next_prime()\\n # run_test_sum_to_next_prime()\\n # run_test_prime_gap()\\n # run_test_wait_for_sum_of_cubes()\",\n \"def run(self):\\n if self.all:\\n cmd = self.apply_options(self.test_all_cmd)\\n self.call_and_exit(cmd)\\n else:\\n cmds = (self.apply_options(self.unit_test_cmd, (\\\"coverage\\\",)),)\\n if self.coverage:\\n cmds += (self.apply_options(self.coverage_cmd),)\\n self.call_in_sequence(cmds)\",\n \"def main():\\n testlib = VorpatestLibrary()\\n testlib.prepare_test()\\n testlib.run_vorpaline(*sys.argv[1:])\\n testlib.run_vorpastat()\\n testlib.cleanup_test()\",\n \"def test_run_full(mk_tmp_dirs):\\n tmp_current_path, tmp_data_path, tmp_config_path = mk_tmp_dirs\\n\\n cfg_dir = path.join(tmp_data_path, 'cfgs')\\n collect_pipeline_cfgs(cfg_dir)\\n\\n asn_path = path.join(DATAPATH, 'mosaic_long_asn.json')\\n args = [\\n path.join(cfg_dir, 'calwebb_image3.cfg'),\\n asn_path,\\n ]\\n\\n Step.from_cmdline(args)\\n\\n # Check for the CRF files\\n with open(asn_path) as fh:\\n asn = load_asn(fh)\\n expfilenames = [\\n path.split(path.splitext(member['expname'])[0])[1]\\n for member in asn['products'][0]['members']\\n ]\\n crffilenames = []\\n for expfilename in expfilenames:\\n name = remove_suffix(path.splitext(expfilename)[0])[0]\\n crffilenames.append(name + '_a3001_crf.fits')\\n for crffilename in crffilenames:\\n assert path.isfile(crffilename)\\n\\n # Check for the level3 products\\n product_name = asn['products'][0]['name']\\n assert path.isfile(product_name + '_cat.ecsv')\\n assert path.isfile(product_name + '_i2d.fits')\",\n \"def test():\\n loader = unittest.TestLoader()\\n suite = loader.discover(os.path.dirname(__file__))\\n runner = unittest.TextTestRunner()\\n runner.run(suite)\",\n \"def test_python(c, filename):\\n test_dir = 'tests/python'\\n filepath = '{}/{}'.format(test_dir, filename)\\n\\n print('Restarting network before executing test {}'.format(filepath))\\n rm_network(c)\\n start_core(c)\\n start_horizon(c)\\n network(c)\\n\\n with c.cd(test_dir):\\n print('Executing test {}'.format(filepath))\\n c.run('pipenv run python {filename} \\\"{passphrase}\\\" {whitelist_seed}'.format(\\n filename=filename,\\n passphrase=PASSPHRASE,\\n whitelist_seed=WHITELIST_SEED))\",\n \"def main_test():\\n full = unittest.TestSuite()\\n full.addTest(unittest.makeSuite(TestToolOptions))\\n full.addTest(unittest.makeSuite(TestBadConfiguration))\\n full.addTest(unittest.makeSuite(TestBasicEndpoints))\\n full.addTest(unittest.makeSuite(TestMultipleEPG))\\n full.addTest(unittest.makeSuite(TestBasicExistingEndpoints))\\n full.addTest(unittest.makeSuite(TestBasicExistingEndpointsAddPolicyLater))\\n full.addTest(unittest.makeSuite(TestExportPolicyRemoval))\\n full.addTest(unittest.makeSuite(TestBasicEndpointsWithContract))\\n full.addTest(unittest.makeSuite(TestBasicEndpointMove))\\n full.addTest(unittest.makeSuite(TestPolicyChangeProvidedContract))\\n full.addTest(unittest.makeSuite(TestChangeL3Out))\\n full.addTest(unittest.makeSuite(TestDuplicates))\\n full.addTest(unittest.makeSuite(TestDuplicatesTwoL3Outs))\\n full.addTest(unittest.makeSuite(TestDeletions))\\n\\n unittest.main()\",\n \"def test_main():\\r\\n\\r\\n ## Put any test code here\\r\\n parser = argparse.ArgumentParser()\\r\\n parser.add_argument(\\\"--source_svc\\\", required=True)\\r\\n parser.add_argument(\\\"--target_svc\\\", required=True)\\r\\n parser.add_argument(\\\"--host\\\", required=True)\\r\\n args=vars(parser.parse_args())\\r\\n\\r\\n ## For this test I need the disk collections\\r\\n sourceVdiskCollection = getvdiskcollection(args[\\\"source_svc\\\"])\\r\\n #targetVdiskCollection = getvdiskcollection(args[\\\"target_svc\\\"])\\r\\n remove_vdisk_from_svc(args[\\\"source_svc\\\"], sourceVdiskCollection[\\\"web01_d01\\\"])\\r\\n remove_vdisk_from_svc(args[\\\"source_svc\\\"], sourceVdiskCollection[\\\"web01_r01\\\"])\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def runalltests():\\n doctest.testmod()\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover(tests)\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def run_tests(args, applog):\\n try:\\n additional_args = []\\n additional_args.extend([\\\"--pyargs\\\", \\\"dent_os_testbed.test.test_suite\\\", \\\"--strict-markers\\\"])\\n additional_args.append(\\\"--durations=0\\\")\\n if args.stdout:\\n additional_args.append(\\\"--capture=tee-sys\\\")\\n\\n suite_groups = args.suite_groups if args.suite_groups else PYTEST_SUITE_GROUPS.keys()\\n for sg_name in suite_groups:\\n sg = PYTEST_SUITE_GROUPS[sg_name]\\n pytest_args = []\\n if not sg:\\n continue\\n pytest._current_suite = sg_name\\n pytest_args.append(\\\"-m\\\")\\n markers_string = sg[0] + \\\"\\\".join([(\\\" or %s\\\" % suite) for suite in sg[1:]])\\n pytest_args.append(markers_string)\\n pytest_args.append(\\\"--html=%s/report_%s.html\\\" % (LOGDIR, sg_name))\\n pytest_args.append(\\\"--junitxml=%s/junit_%s.xml\\\" % (LOGDIR, sg_name))\\n pytest_args.append(\\\"--self-contained-html\\\")\\n if args.suite_tests:\\n pytest_args.append(\\\"-k\\\")\\n pytest_args.append(args.suite_tests)\\n input_args = additional_args + pytest_args\\n applog.info(\\\"Triggering pytest with args : %s\\\" % input_args)\\n pytest.main(input_args)\\n except Exception as e:\\n applog.exception(\\\"Error running tests\\\", exc_info=e)\\n raise\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6613631","0.6581835","0.6549095","0.64722","0.6412084","0.63910145","0.6268595","0.62387437","0.618074","0.6167369","0.61670023","0.6159595","0.61505693","0.61311626","0.61168665","0.60939276","0.6077586","0.6045904","0.60249716","0.6006832","0.6001748","0.5956125","0.5944554","0.59178305","0.59103006","0.5906406","0.59049207","0.5904044","0.58896774","0.5856654","0.5838548","0.5836129","0.5834856","0.5824811","0.5797206","0.5786323","0.57761407","0.5774293","0.5772424","0.5772181","0.57697195","0.5761773","0.5756925","0.5748228","0.57473546","0.5741184","0.5724891","0.57167786","0.5705492","0.56864625","0.5685213","0.56770784","0.56768566","0.5671812","0.5667422","0.5662738","0.5662137","0.56428933","0.56420004","0.5628282","0.5627042","0.5621014","0.56109273","0.5609372","0.560546","0.56003565","0.5598851","0.5596819","0.55952644","0.55948246","0.5594218","0.5592215","0.5590974","0.5587799","0.5584587","0.5583046","0.55796987","0.55654556","0.5565118","0.5563264","0.5558393","0.5540083","0.55355215","0.55344445","0.55313176","0.55235475","0.55166066","0.55127704","0.55111814","0.5508541","0.5504767","0.5497705","0.5485568","0.5479183","0.54756397","0.5469391","0.54613423","0.5451114","0.5450301","0.5448861"],"string":"[\n \"0.6613631\",\n \"0.6581835\",\n \"0.6549095\",\n \"0.64722\",\n \"0.6412084\",\n \"0.63910145\",\n \"0.6268595\",\n \"0.62387437\",\n \"0.618074\",\n \"0.6167369\",\n \"0.61670023\",\n \"0.6159595\",\n \"0.61505693\",\n \"0.61311626\",\n \"0.61168665\",\n \"0.60939276\",\n \"0.6077586\",\n \"0.6045904\",\n \"0.60249716\",\n \"0.6006832\",\n \"0.6001748\",\n \"0.5956125\",\n \"0.5944554\",\n \"0.59178305\",\n \"0.59103006\",\n \"0.5906406\",\n \"0.59049207\",\n \"0.5904044\",\n \"0.58896774\",\n \"0.5856654\",\n \"0.5838548\",\n \"0.5836129\",\n \"0.5834856\",\n \"0.5824811\",\n \"0.5797206\",\n \"0.5786323\",\n \"0.57761407\",\n \"0.5774293\",\n \"0.5772424\",\n \"0.5772181\",\n \"0.57697195\",\n \"0.5761773\",\n \"0.5756925\",\n \"0.5748228\",\n \"0.57473546\",\n \"0.5741184\",\n \"0.5724891\",\n \"0.57167786\",\n \"0.5705492\",\n \"0.56864625\",\n \"0.5685213\",\n \"0.56770784\",\n \"0.56768566\",\n \"0.5671812\",\n \"0.5667422\",\n \"0.5662738\",\n \"0.5662137\",\n \"0.56428933\",\n \"0.56420004\",\n \"0.5628282\",\n \"0.5627042\",\n \"0.5621014\",\n \"0.56109273\",\n \"0.5609372\",\n \"0.560546\",\n \"0.56003565\",\n \"0.5598851\",\n \"0.5596819\",\n \"0.55952644\",\n \"0.55948246\",\n \"0.5594218\",\n \"0.5592215\",\n \"0.5590974\",\n \"0.5587799\",\n \"0.5584587\",\n \"0.5583046\",\n \"0.55796987\",\n \"0.55654556\",\n \"0.5565118\",\n \"0.5563264\",\n \"0.5558393\",\n \"0.5540083\",\n \"0.55355215\",\n \"0.55344445\",\n \"0.55313176\",\n \"0.55235475\",\n \"0.55166066\",\n \"0.55127704\",\n \"0.55111814\",\n \"0.5508541\",\n \"0.5504767\",\n \"0.5497705\",\n \"0.5485568\",\n \"0.5479183\",\n \"0.54756397\",\n \"0.5469391\",\n \"0.54613423\",\n \"0.5451114\",\n \"0.5450301\",\n \"0.5448861\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67698425"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":874,"numItemsPerPage":100,"numTotalItems":95772,"offset":87400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTg3MjE3OCwianRpIjoiZTg4YWEzOWEtZTE1OS00NGNjLWFmZjEtMTFkNmZkYTcyMGYwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU4NzU3NzgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.yDQWhXwUKD7FCfmrBV-DANvySd69AsmYlVOpgaU9rodeeJNKVRDtoVkxlgEQQ4WaUAtF-_QCLVqez7Aeb3Y7Ag","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 |
|---|---|---|---|---|---|---|
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.775081",
"0.775081",
"0.7338297",
"0.73333067",
"0.7296288",
"0.7277521",
"0.7159291",
"0.71573424",
"0.714855",
"0.714855",
"0.71273863",
"0.71261555",
"0.7122034",
"0.71070296",
"0.7060047",
"0.7043975",
"0.7032398",
"0.7023222",
"0.6964265",
"0.6958815",
"0.68987364",
... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.85856134",
"0.7814518",
"0.77898884",
"0.7751367",
"0.7751367",
"0.7712228",
"0.76981676",
"0.76700574",
"0.7651133",
"0.7597206",
"0.75800353",
"0.7568254",
"0.7538184",
"0.75228703",
"0.7515832",
"0.7498764",
"0.74850684",
"0.74850684",
"0.7467648",
"0.74488163",
"0.7442... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, SmartDocumentRecognizerKvResult):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Multi class version of Logarithmic Loss metric. | def multiclass_log_loss(y_true, y_pred, eps=1e-15):
predictions = np.clip(y_pred, eps, 1 - eps)
# normalize row sums to 1
predictions /= predictions.sum(axis=1)[:, np.newaxis]
actual = np.zeros(y_pred.shape)
n_samples = actual.shape[0]
actual[np.arange(n_samples), y_true.astype(int)] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_loss(self):\n probabilities = self.probability_array().copy()\n # need to flip the probabilities for p < 0.5 with this binary case.\n # 1 - old_val is same as oldval*-1 + 1. Do in 2 steps:\n probabilities[np.equal(0, self.y)] *= -1\n probabilities[np.equal(0, self.y)] +=... | [
"0.7211753",
"0.6897029",
"0.6878165",
"0.67519027",
"0.66347975",
"0.6525428",
"0.6510028",
"0.64878744",
"0.64682496",
"0.6452781",
"0.64294994",
"0.64249194",
"0.6417653",
"0.6411695",
"0.64109415",
"0.6408834",
"0.6394666",
"0.6376997",
"0.6347142",
"0.6337753",
"0.625644... | 0.6180762 | 27 |
Only changeable constraint on the factory is the number of oscillators | def __init__(self, n): # this is equivalent to starting a random one
self.n = n
# From table S1 in the supplemental materials
# each c parameters is [body,limb]
self.cv0 = [0.3, 0.0]
self.cv1 = [0.2, 0.2]
self.cR0 = [0.196,0.131]
self.cR1 = [0.065,0.131]
#[[dbodylow,dbodyhigh],[dlimblow,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def circuitSat(C):",
"def force(self, osc):\n pass",
"def test_14_missing_nonbonded_handler(self, force_field):\n top = Molecule.from_smiles(\"CCCC\").to_topology()\n\n ff_no_vdw = copy.deepcopy(force_field)\n ff_no_electrostatics = copy.deepcopy(force_field)\n\n ff_no_vdw.de... | [
"0.5257939",
"0.5219081",
"0.5210399",
"0.51840174",
"0.5178341",
"0.51490057",
"0.5104576",
"0.5071589",
"0.5067967",
"0.5032624",
"0.50028354",
"0.499776",
"0.4982781",
"0.49815163",
"0.49683052",
"0.4964372",
"0.49502867",
"0.49500018",
"0.48942435",
"0.48927838",
"0.48829... | 0.55693984 | 0 |
Converts things back into numpy | def deserialize(self, indv):
for key in indv:
if key in self.evolvables and not key in self.scalars:
if not isinstance(indv[key], np.ndarray):
indv[key] = np.array(indv[key])
return indv | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataConvertToNumpy( self ):\n self.featureNumpy = np.asarray( self.feature )\n self.ClassNumpy = np.asarray( self.Class )",
"def to_numpy(self, **kwargs):\n pass",
"def to_numpy(x):\r\n return x.squeeze().detach().cpu().numpy()",
"def to_numpy(x):\n if isinstance(x, list):\n ... | [
"0.7307521",
"0.7142706",
"0.7033888",
"0.6991174",
"0.6965739",
"0.6851011",
"0.68024766",
"0.6748254",
"0.67444295",
"0.6721904",
"0.66844344",
"0.6681224",
"0.6677446",
"0.6631592",
"0.66057754",
"0.65985245",
"0.6587741",
"0.6528488",
"0.6525603",
"0.65188247",
"0.6505604... | 0.59111553 | 64 |
Randomly generates the parameters for a CPG | def make(self, ident, factor):
CPG = {}
CPG['ident'] = str(ident)
# will not be updated during simulation
CPG['w'] = np.random.rand(self.n * self.n) * factor # (n * n)
for ind in range(self.n*self.n):
if ind not in self.nonzeros:
CPG['w'][ind] = 0
CPG['phi'] = np.random.rand(self.n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_params(self, randomize=True):\n pass",
"def random():\n gauss_scale = 10**np.random.uniform(1, 3)\n lorentz_scale = 10**np.random.uniform(1, 3)\n cor_length_static = 10**np.random.uniform(0, 3)\n cor_length_dynamic = 10**np.random.uniform(0, 3)\n pars = dict(\n #backgrou... | [
"0.7374085",
"0.6787519",
"0.67266333",
"0.65888166",
"0.6582195",
"0.65486234",
"0.6505041",
"0.63892645",
"0.62967104",
"0.6269808",
"0.6220182",
"0.61841",
"0.6142248",
"0.6137274",
"0.60836434",
"0.60771555",
"0.60572374",
"0.6054203",
"0.6049343",
"0.60028",
"0.5997497",... | 0.5695323 | 57 |
Because you can't refer to the current list in a list comprehension | def cumulative_sum(self, lis):
new_list = []
for i in range(len(lis)):
if i == 0:
new_list.append(lis[i])
else:
new_list.append(new_list[i-1] + lis[i])
return new_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_iterable(self):",
"def iflatten(self):\n return _((e for es in self.array for e in es))",
"def _loop_over(var):\n\n if hasattr(var,\"__iter__\"):\n return var\n else:\n return [var]",
"def list_value(self) -> global___Expression.RepeatedValue:",
"def demonstrate_list_c... | [
"0.6044748",
"0.58948314",
"0.5760046",
"0.5743097",
"0.57196575",
"0.57158786",
"0.5652399",
"0.56166095",
"0.5599128",
"0.55789924",
"0.55121166",
"0.54547006",
"0.54447865",
"0.54377687",
"0.5420903",
"0.5420903",
"0.5420903",
"0.5420903",
"0.54136294",
"0.5407103",
"0.539... | 0.0 | -1 |
Gets a random number, but makes sure it's not 1 | def safe_rand(self):
rand_n = np.random.rand()
if rand_n == float(1):
rand_n -= 1e-10
return rand_n | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maybe(self):\n return random.getrandbits(1)",
"def random_number():\n return random.randint(0, 9999)",
"def random_number():\n random_num = random.choice(empty)\n return random_num",
"def getRandom(self) -> int:",
"def getRandom(self) -> int:",
"def gen_unused_random_num(self):\n ... | [
"0.7610754",
"0.73537326",
"0.72561806",
"0.7106875",
"0.7106875",
"0.7073731",
"0.70407534",
"0.70076555",
"0.6958358",
"0.6942665",
"0.6919138",
"0.6918378",
"0.6899967",
"0.68962634",
"0.68693054",
"0.68458706",
"0.67763877",
"0.675229",
"0.675229",
"0.6742172",
"0.6728175... | 0.7573157 | 1 |
Takes a list of cpgs (sorted from lowest fitness to highest) and mixes them into one. Note that crossover_prob is assumed to be sorted from lowest to highest and must sum to 1 | def mix(self, cpgs, ident, method='avg', mutation_prob=0.5, crossover_prob=[0.5, 0.5]):
# Available methods:
# note that mutation means selecting a new random scalar for a particular value
# avg -- takes the average of all of the keys in self.evolvables and then goes through all of their values and chooses ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _crossover(self, sel):\n offspring = []\n for p1, p2 in sel:\n p1 = copy.deepcopy(p1)\n p2 = copy.deepcopy(p2)\n\n tmp = self.op.crossover(\n copy.deepcopy(p1['individual']),\n copy.deepcopy(p2['individual']))\n if not tmp[... | [
"0.68679935",
"0.65376526",
"0.64644337",
"0.63389164",
"0.63024414",
"0.6294254",
"0.6285703",
"0.6275384",
"0.6211367",
"0.61408365",
"0.61313534",
"0.6043319",
"0.5995043",
"0.5923218",
"0.5911872",
"0.58270127",
"0.5813963",
"0.5800146",
"0.57875407",
"0.5761791",
"0.5756... | 0.5951956 | 13 |
Test for login of registereduser | def test_login(self):
with self.client:
# user registration
credentials = {
'username': 'EdwinKato',
'password': 'qwerty@123'
}
response = self.client.post('/api/v1/auth/login',
data=json.dump... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_registered_user_login(self):\n # register a user\n register_user(self, 'some', 'name', 'another@gmail.com', 'aaaAAA111')\n\n # test logging in registered user\n with self.client:\n response = login_user(self, 'another@gmail.com', 'aaaAAA111')\n data = json... | [
"0.79693913",
"0.7889857",
"0.78656715",
"0.77873445",
"0.7646755",
"0.7631561",
"0.7569677",
"0.7561159",
"0.75429523",
"0.7538628",
"0.75328666",
"0.75239885",
"0.75087404",
"0.75062454",
"0.7505584",
"0.7501152",
"0.7411829",
"0.7408641",
"0.7408641",
"0.7408641",
"0.74086... | 0.0 | -1 |
Test for login of nonregistered user | def test_non_registered_user_login(self):
with self.client:
response = self.client.post(
'/api/v1/auth/login',
data=json.dumps({
"password": "qwerty@123",
"username": "EdwinKyato"
}),
content_type... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_non_user_login(self):\n self.user.list_of_accounts = [{'username': 'Parseen',\n 'pwd': 'mypassword',\n 'email': 'david.parseen@yahoo.com'}]\n msg = self.user.login(\"nonuser@yahoo.com\", \"idontevenhaveone\")\n ... | [
"0.82027346",
"0.80158097",
"0.78653216",
"0.7857918",
"0.7857918",
"0.7857918",
"0.7857918",
"0.7823152",
"0.7721956",
"0.7632311",
"0.75731003",
"0.7550225",
"0.7539262",
"0.75207067",
"0.7428655",
"0.7382271",
"0.73642707",
"0.73642707",
"0.73642707",
"0.73642707",
"0.7362... | 0.6747234 | 80 |
constructor initialize all footer panel elements | def __init__(self, commander_window):
self.commander_window = commander_window
self.font = QtGui.QFont(self)
self.font.setBold(True)
self.font.setWeight(75)
self.setup_footer_panel() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_footer_panel(self):\r\n self.create_footer_push_button(\"F3 View\", \"F3\")\r\n self.create_footer_push_button(\"F4 Edit\", \"F4\")\r\n self.create_footer_push_button(\"F5 Copy\", \"F5\")\r\n self.create_footer_push_button(\"F6 move\", \"F6\")\r\n self.create_footer_pus... | [
"0.6983345",
"0.6700082",
"0.6700082",
"0.65975446",
"0.61720026",
"0.59887004",
"0.5947216",
"0.5929559",
"0.57196146",
"0.57102317",
"0.570946",
"0.5708194",
"0.5699149",
"0.56370294",
"0.5585587",
"0.5581658",
"0.55222774",
"0.5504313",
"0.54480815",
"0.54113233",
"0.53723... | 0.5995883 | 5 |
This method is meant to create all the footer elements on this class used only from constructor | def setup_footer_panel(self):
self.create_footer_push_button("F3 View", "F3")
self.create_footer_push_button("F4 Edit", "F4")
self.create_footer_push_button("F5 Copy", "F5")
self.create_footer_push_button("F6 move", "F6")
self.create_footer_push_button("F7 New Folder", "F7")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def footer(self):\n pass",
"def footer(self):\n pass",
"def WriteFooter(self):\n return",
"def writeFooter(self):\n pass",
"def get_footer(self):\n self.footer = '</div>' \\\n '</div>' \\\n '</div>' \\\n '<div cla... | [
"0.7731519",
"0.7731519",
"0.73877597",
"0.7227607",
"0.71163267",
"0.6826449",
"0.6709397",
"0.6580358",
"0.6502539",
"0.6416045",
"0.63860506",
"0.6339453",
"0.63274145",
"0.6317201",
"0.6308385",
"0.6304386",
"0.6247864",
"0.6222086",
"0.614161",
"0.6100365",
"0.6100365",
... | 0.6427684 | 9 |
This method will create a button into footer layout | def create_footer_push_button(self, text, shortcut=None, connection=None):
button = QtGui.QPushButton(self.commander_window.footer_container)
button.setFont(self.font)
button.setText(text)
if (shortcut is not None):
button.setShortcut(shortcut)
if connection is ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_footer_panel(self):\r\n self.create_footer_push_button(\"F3 View\", \"F3\")\r\n self.create_footer_push_button(\"F4 Edit\", \"F4\")\r\n self.create_footer_push_button(\"F5 Copy\", \"F5\")\r\n self.create_footer_push_button(\"F6 move\", \"F6\")\r\n self.create_footer_pus... | [
"0.7278086",
"0.6899661",
"0.6563083",
"0.6563083",
"0.6352914",
"0.6226975",
"0.6212638",
"0.6148312",
"0.6068258",
"0.60441864",
"0.6039494",
"0.60364026",
"0.6031277",
"0.60041994",
"0.5968427",
"0.59683055",
"0.59562105",
"0.59323615",
"0.5884248",
"0.5879663",
"0.5878796... | 0.73384935 | 0 |
Shows all access requests that are waiting for approval. | def get(self):
self.require_action_permitted('grant')
q = model.Account.all().filter('requested_actions !=', None)
requests = []
for account in q.fetch(100):
for action in account.requested_actions:
if check_action_permitted(self.account, 'grant'):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ListApprovalRequests(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details(\"Method not implemented!\")\n raise NotImplementedError(\"Method not implemented!\")",
"def list(self, request):\n\n to_approve_requests = Task.objects.filter(\n ... | [
"0.6685316",
"0.66019034",
"0.6032612",
"0.59691817",
"0.58465636",
"0.5791276",
"0.5645128",
"0.562636",
"0.5594557",
"0.55650765",
"0.55298376",
"0.5490953",
"0.5453304",
"0.5444376",
"0.5415444",
"0.5356109",
"0.5323455",
"0.5323349",
"0.5304046",
"0.5287117",
"0.52603364"... | 0.62942934 | 2 |
Grants or denies a single request. | def post(self):
action = self.request.get('action')
if not action:
raise ErrorMessage(404, 'missing action (requested_action) params')
self.require_action_permitted('grant')
account = model.Account.get(self.request.get('key'))
if not account:
raise Erro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_everyone_denied(self):",
"def denied_response(self, req):\n if req.remote_user:\n return HTTPForbidden(request=req)\n else:\n return HTTPUnauthorized(request=req)",
"def _enforce(self, req, action):\n try:\n self.policy.enforce(req.context, action, ... | [
"0.6248997",
"0.6164844",
"0.61547506",
"0.61528665",
"0.6093935",
"0.6086757",
"0.6039381",
"0.6008447",
"0.6007422",
"0.5984474",
"0.59157324",
"0.5846528",
"0.5808597",
"0.58016115",
"0.57766014",
"0.57766014",
"0.5766336",
"0.5743154",
"0.57299995",
"0.57168144",
"0.57073... | 0.5577951 | 33 |
Chain and apply a list of contexts | def nested(*contexts):
with ExitStack() as stack:
for ctx in contexts:
stack.enter_context(ctx())
yield contexts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enrich_context(self, ctx: Context) -> Context:\n new_ctx = Context(ctx.expressions[:], ctx.namespace)\n for _ in range(self.expression_levels):\n new_ctx.extend(list(self.properties(new_ctx)))\n new_ctx.extend(list(self.unary_ops(new_ctx)))\n new_ctx.extend(list(s... | [
"0.6205127",
"0.6081701",
"0.5970155",
"0.58488804",
"0.57370806",
"0.57311106",
"0.5634332",
"0.56243086",
"0.56219083",
"0.55781084",
"0.55662006",
"0.5537953",
"0.5528676",
"0.55269414",
"0.5448107",
"0.54284644",
"0.541528",
"0.541528",
"0.5412888",
"0.5401186",
"0.534449... | 0.6755412 | 0 |
Used to calculate the comoving distance to object at redshift z. Eqn 14 from Hogg, astroph/9905116. | def inv_efunc(z):
return 1. / sqrt(omega_m * (1. + z)**3 + omega_lam) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Comoving_Distance(self, z):\n return self.cd_interp(z)",
"def comoving_distance(self, z):\n\n # Calculate the integrand.\n def f(z1):\n return c_sl / self.H(z1)\n\n return _intf_0_z(f, z) / self._unit_distance",
"def distcalc(z,h=0.70,omegalambda=0.7,omegam=0.3,omegak... | [
"0.6934687",
"0.69317114",
"0.6930934",
"0.67977977",
"0.6541344",
"0.6534358",
"0.6342927",
"0.63420075",
"0.6299687",
"0.62785506",
"0.6253984",
"0.6174778",
"0.6135191",
"0.6090954",
"0.6088764",
"0.60628283",
"0.60309136",
"0.59934825",
"0.5951465",
"0.59276175",
"0.59216... | 0.0 | -1 |
Returns True if filename1 has a newer modification time than filename2. | def is_newer(filename1, filename2):
return os.stat(filename1).st_mtime > os.stat(filename2).st_mtime | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_newer(a, b):\n return os.stat(a).st_mtime >= os.stat(b).st_mtime",
"def _newer(a: str, b: str) -> bool:\n if not os.path.exists(a):\n return False\n if not os.path.exists(b):\n return True\n return os.path.getmtime(a) >= os.path.getmtime(b)",
"def file_is_modified(filename, lastu... | [
"0.8237509",
"0.8148441",
"0.80743307",
"0.80290496",
"0.79828465",
"0.7979174",
"0.79657996",
"0.7751328",
"0.77098334",
"0.7662028",
"0.75494844",
"0.75325197",
"0.7447955",
"0.729623",
"0.7292164",
"0.7270774",
"0.7239093",
"0.69227624",
"0.69209176",
"0.69137496",
"0.6862... | 0.905768 | 0 |
Run SWIG if its outputs are missing or out of date. | def maybe_run_swig(wrapper_filename, module_name, base_directory,
extra_deps=None):
module_filename = 'python/bllipparser/%s.py' % module_name
swig_filename = join(base_directory, 'swig', 'wrapper.i')
extra_deps = extra_deps or []
if exists(wrapper_filename) and exists(module_filename... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_python(self):\n if(self.input == \"\"):\n stdout = subprocess.run(\n [\"python\", self.id+\".py\"], stdout=subprocess.PIPE).stdout.decode('utf-8')\n self.output = stdout\n if(len(stdout) == 0):\n self.output = subprocess.run(\n ... | [
"0.52748847",
"0.5227529",
"0.5040352",
"0.50146484",
"0.4976926",
"0.4908266",
"0.48579365",
"0.4817482",
"0.48093262",
"0.4803266",
"0.47972724",
"0.47972095",
"0.47310168",
"0.47299778",
"0.47203964",
"0.4704658",
"0.46845943",
"0.46779928",
"0.46702075",
"0.4669818",
"0.4... | 0.5256493 | 1 |
Creates a new online controller. | def __init__(self, view: View, url: str, key: str, server_time_url: str, data_loader: DataLoader,
data_writer: DataWriter, ai_class: str, ai_params):
super().__init__(view)
self.__url = url
self.__key = key
self.__server_time_url = server_time_url
self.__data_lo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_controller() -> Controller:\n _controller = Controller()\n return _controller",
"def create_controller(self, typ):\n return self.controller_objects[typ]()",
"def garage_controller_create(self, name=None,\n driver=None, driver_info=None):\n location... | [
"0.74706405",
"0.6701814",
"0.6299631",
"0.62110835",
"0.61677057",
"0.5994035",
"0.5972941",
"0.59482515",
"0.5916179",
"0.5785861",
"0.5690907",
"0.56186634",
"0.56096566",
"0.55698556",
"0.55606925",
"0.5510889",
"0.5510889",
"0.5509001",
"0.5492552",
"0.54767066",
"0.5416... | 0.0 | -1 |
Create a FASTA header for a sequence in a otu DNA FASTA file downloadable from Virtool. | def format_fasta_entry(otu_name, isolate_name, sequence_id, sequence):
return f">{otu_name}|{isolate_name}|{sequence_id}|{len(sequence)}\n{sequence}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_header(args):\n header = os.path.join(args.output_dir,'header.sam')\n args.header = header\n header_handle = open(header,'w')\n header_handle.write('@HD\\tVN:1.4\\n')\n joined_sam = open(os.path.join(args.output_dir, 'watson_joinedAligned.out.sam'))\n merged_sam = open(os.path.join(args.... | [
"0.69783336",
"0.69290286",
"0.6795281",
"0.66700673",
"0.6468582",
"0.6380044",
"0.634823",
"0.62475306",
"0.6224319",
"0.6206568",
"0.6201669",
"0.6077374",
"0.6075202",
"0.6057273",
"0.60009295",
"0.5996821",
"0.5993501",
"0.5920932",
"0.5891195",
"0.5866044",
"0.5844946",... | 0.5927973 | 17 |
Format a FASTA filename of the form "otu.isolate.sequence_id.fa". | def format_fasta_filename(*args):
if len(args) > 3:
raise ValueError("Unexpected number of filename parts")
if len(args) == 0:
raise ValueError("At least one filename part required")
filename = ".".join(args).replace(" ", "_") + ".fa"
return filename.lower() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_filename(prefix, suffix, seq_len, uncased):\n seq_str = \"seq-{}\".format(seq_len)\n if uncased:\n case_str = \"uncased\"\n else:\n case_str = \"cased\"\n\n file_name = \"{}.{}.{}.{}\".format(prefix, seq_str, case_str, suffix)\n\n return file_name",
"def format_filename(self, s):\n v... | [
"0.7300104",
"0.67765343",
"0.6772814",
"0.6697145",
"0.6529021",
"0.6502655",
"0.6502655",
"0.6502655",
"0.64751655",
"0.6464903",
"0.64495075",
"0.6445141",
"0.6358297",
"0.62694526",
"0.62530184",
"0.62435997",
"0.6228077",
"0.6186597",
"0.61775064",
"0.6148595",
"0.614143... | 0.8101146 | 0 |
Generate a FASTA filename and body for the sequences associated with the isolate identified by the passed ``otu_id`` and ``isolate_id``. | async def generate_isolate_fasta(db, otu_id, isolate_id):
_, isolate_name = await get_otu_and_isolate_names(db, otu_id, isolate_id)
otu = await db.otus.find_one({"_id": otu_id, "isolates.id": isolate_id}, ["name", "isolates"])
fasta = list()
async for sequence in db.sequences.find({"otu_id": otu_id, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def generate_sequence_fasta(db, sequence_id):\n sequence = await db.sequences.find_one(sequence_id, [\"sequence\", \"otu_id\", \"isolate_id\"])\n\n if not sequence:\n raise virtool.errors.DatabaseError(\"Sequence does not exist\")\n\n otu_name, isolate_name = await get_otu_and_isolate_names(d... | [
"0.69245267",
"0.6894119",
"0.5912291",
"0.5337424",
"0.51358384",
"0.5091706",
"0.5017148",
"0.49292922",
"0.490532",
"0.4845423",
"0.47710133",
"0.47625893",
"0.47573465",
"0.4747771",
"0.47446316",
"0.47379455",
"0.47282097",
"0.47108236",
"0.470876",
"0.47039667",
"0.4700... | 0.8190999 | 0 |
Generate a FASTA filename and body for the sequence associated with the passed ``sequence_id``. | async def generate_sequence_fasta(db, sequence_id):
sequence = await db.sequences.find_one(sequence_id, ["sequence", "otu_id", "isolate_id"])
if not sequence:
raise virtool.errors.DatabaseError("Sequence does not exist")
otu_name, isolate_name = await get_otu_and_isolate_names(db, sequence["otu_id... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_seq_file(seq_id, sequence, directory):\n f_fasta = open(directory + '/'+ seq_id + '.fasta', 'w')\n f_fasta.write(str(sequence))\n f_fasta.close()",
"def generate_fasta(sequences, fasta_path):\n\n with open(fasta_path, 'w+') as f:\n for i in range(len(sequences)):\n f.write(... | [
"0.6654648",
"0.5991678",
"0.5893759",
"0.55443513",
"0.55410385",
"0.5519095",
"0.54996574",
"0.5497378",
"0.546989",
"0.5423846",
"0.54024166",
"0.5382011",
"0.5371277",
"0.53569347",
"0.53467464",
"0.53463346",
"0.52885425",
"0.5284846",
"0.52747065",
"0.52720994",
"0.5260... | 0.7679342 | 0 |
Generate a FASTA filename and body for the sequences associated with the otu identified by the passed ``otu_id``. | async def generate_otu_fasta(db, otu_id):
otu = await db.otus.find_one(otu_id, ["name", "isolates"])
if not otu:
raise virtool.errors.DatabaseError("OTU does not exist")
fasta = list()
for isolate in otu["isolates"]:
async for sequence in db.sequences.find({"otu_id": otu_id, "isolate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def generate_sequence_fasta(db, sequence_id):\n sequence = await db.sequences.find_one(sequence_id, [\"sequence\", \"otu_id\", \"isolate_id\"])\n\n if not sequence:\n raise virtool.errors.DatabaseError(\"Sequence does not exist\")\n\n otu_name, isolate_name = await get_otu_and_isolate_names(d... | [
"0.683279",
"0.6485399",
"0.5569074",
"0.5483403",
"0.54582524",
"0.54195255",
"0.5392835",
"0.53337544",
"0.5320824",
"0.5282094",
"0.5202332",
"0.51656145",
"0.5165108",
"0.51585346",
"0.5126089",
"0.5122553",
"0.51130253",
"0.50874543",
"0.5049329",
"0.49892703",
"0.496523... | 0.7690048 | 0 |
This function checks if a given number is a Nigerian phone number. | def is_Nigerian(args1):
if len(args1) == 0:
return "Invalid input!, input must not be empty."
if (args1.isalnum() and not(args1.isnumeric())) or args1.isalpha():
return "Invalid Input!, input must be a string of numbers."
#creating a matching pattern for nigerian numbers by making use of reg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ni_number_check(number):\n ni_nuber = re.match(r\"^\\s*[a-zA-Z]{2}(?:\\s*\\d\\s*){6}[a-zA-Z]?\\s*$\", number)\n if ni_nuber:\n return True\n return False",
"def is_phonenumber(number):\n\n phonenum = re.compile(r'^(\\d{3})-(\\d{3})-(\\d{4})$')\n if phonenum.match(number)... | [
"0.774606",
"0.74679166",
"0.69906837",
"0.6815787",
"0.65633965",
"0.65529764",
"0.64875734",
"0.64522296",
"0.6425909",
"0.64103776",
"0.63558793",
"0.63498324",
"0.6343741",
"0.6288755",
"0.6271888",
"0.62652934",
"0.6176938",
"0.6169064",
"0.61534375",
"0.6152027",
"0.612... | 0.7525907 | 1 |
Should take the actor and the thing being used. | def test_init(self):
user = object()
used = object()
a = Use(user, used)
self.assertEqual(a.user, user)
self.assertEqual(a.used, used) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def actor(self):\n raise NotImplementedError()",
"def actor():\n return Actor()",
"def is_actor():\n return False",
"def is_actor(self):\n return True",
"def act(self) -> None:\n pass",
"def act(self):\n pass",
"def test_actor_matches_activity(self):",
"def act(self):\... | [
"0.6666154",
"0.66368026",
"0.6558715",
"0.6442945",
"0.6266615",
"0.6225885",
"0.60172606",
"0.5948542",
"0.56657785",
"0.5661286",
"0.5650924",
"0.559296",
"0.55888397",
"0.5577099",
"0.54633677",
"0.5461658",
"0.54506063",
"0.5445267",
"0.5445267",
"0.5441684",
"0.5418124"... | 0.0 | -1 |
Should call the .use method on used with user as the argument. | def test_call(self):
user = object()
used = Fake()
called = []
used.use = lambda u: called.append(u)
a = Use(user, used)
a()
self.assertEqual(called, [user], "Should have called used.use(user)") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_serve_user(self):\n pass",
"def execute(self, user):\n pass",
"def use(target, name):",
"def use(self, target, name):\n return self.usable.use(target, name)",
"def test_user(self):\n return True",
"def test_func(self, user, **kwargs):\n return self.get_object()... | [
"0.65986025",
"0.6208207",
"0.6177418",
"0.6163876",
"0.6136552",
"0.6130325",
"0.6091194",
"0.6085136",
"0.6056721",
"0.60412604",
"0.6028056",
"0.5941651",
"0.58077157",
"0.5778681",
"0.57705474",
"0.5755981",
"0.5677145",
"0.56585205",
"0.5647163",
"0.5641119",
"0.56390595... | 0.74292713 | 0 |
Test all functions parse. | def test_parses(func):
quoting.parse_function(func) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_simple_parse(self):\n pass",
"def main():\n\ttest() #test ParseError",
"def test_parse(self): \n\n results = self.parser.parse()\n self.assertEqual(results, test_case_data['parse_output'])",
"def test_basic_parsers():",
"def test_parse_function_pass(self):\n def fun... | [
"0.73987454",
"0.72671455",
"0.7213445",
"0.7200013",
"0.7099595",
"0.66740584",
"0.6665561",
"0.6617835",
"0.65762043",
"0.64718693",
"0.6446804",
"0.64256203",
"0.6404218",
"0.62964827",
"0.6247",
"0.62115675",
"0.62110454",
"0.6163633",
"0.61307585",
"0.6125878",
"0.611448... | 0.6957294 | 5 |
Test gradients of singleargument scalar functions. | def test_grad_unary(func, motion, optimized, preserve_result, a):
utils.test_reverse_array(func, motion, optimized, preserve_result, a) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n return math_ops.asin(x) + 1.0\n\n... | [
"0.69838196",
"0.62551624",
"0.6240577",
"0.6208231",
"0.61972624",
"0.61963433",
"0.6193286",
"0.6189036",
"0.6149409",
"0.60993594",
"0.60622007",
"0.6057781",
"0.60543615",
"0.6035959",
"0.60253406",
"0.6017511",
"0.60147274",
"0.5983582",
"0.5977976",
"0.59656",
"0.595884... | 0.64167225 | 1 |
Test gradients of twoargument scalar functions. | def test_grad_binary(func, motion, optimized, preserve_result, a, b):
utils.test_reverse_array(func, motion, optimized, preserve_result, a, b) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n return math_ops.asin(x) + 1.0\n\n... | [
"0.7452785",
"0.6717652",
"0.6628295",
"0.6502615",
"0.6466579",
"0.64543253",
"0.63532865",
"0.63481534",
"0.6294409",
"0.6281945",
"0.62719196",
"0.62702024",
"0.6231899",
"0.6228445",
"0.6183046",
"0.6182493",
"0.61806935",
"0.6167682",
"0.61419064",
"0.6122806",
"0.611600... | 0.6223911 | 14 |
Test gradients of threeargument scalar functions. | def test_grad_ternary(func, motion, optimized, preserve_result, a, b, c):
utils.test_reverse_array(func, motion, optimized, preserve_result, a, b, c) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grad_f3(x):\n pass",
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n re... | [
"0.6737468",
"0.6493623",
"0.6323118",
"0.6316329",
"0.6293697",
"0.6276132",
"0.6223059",
"0.6057779",
"0.5999899",
"0.59681517",
"0.59443873",
"0.59325856",
"0.59301656",
"0.59234667",
"0.5898176",
"0.58958566",
"0.58935535",
"0.5885172",
"0.5879116",
"0.58456063",
"0.58116... | 0.5770117 | 25 |
Test gradients of vector functions. | def test_grad_vector(func, motion, optimized, preserve_result, x):
utils.test_reverse_array(func, motion, optimized, preserve_result, x) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_numerical_gradient_vectorized(self):\n W, X, b, Y, expected_grad_W, expected_grad_b = vectorized_test_data()\n\n fn_to_optimize = lambda W: vectorized_loss(\n X, Y, W, b)\n grad_W = numerical_gradient(fn_to_optimize, W)\n np.testing.assert_allclose(grad_W, expected_g... | [
"0.70717275",
"0.70420396",
"0.6982868",
"0.68923724",
"0.6890963",
"0.6879641",
"0.6844028",
"0.65803635",
"0.6568304",
"0.6504972",
"0.6501379",
"0.64940745",
"0.64732254",
"0.63941187",
"0.63763016",
"0.6368085",
"0.6366209",
"0.63571703",
"0.6356648",
"0.63120234",
"0.630... | 0.7636104 | 0 |
Test gradients of square matrix functions. | def test_grad_square_matrix(func, motion, optimized, preserve_result, sqm):
utils.test_reverse_array(func, motion, optimized, preserve_result, sqm) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_grad(self):\r\n for shp0, shp1 in [((1,), (2,)),\r\n ((3,), (1,)),\r\n ((1,), (1,)),\r\n ((3,), (2,)),\r\n ((3, 2), (1, 1)),\r\n ((3, 2), (1, 4)),\r\n ... | [
"0.71215576",
"0.6692214",
"0.65100783",
"0.6492603",
"0.64668906",
"0.6455008",
"0.6402786",
"0.64009535",
"0.6388172",
"0.6335372",
"0.6319555",
"0.6312385",
"0.6283996",
"0.62804395",
"0.61972606",
"0.6187361",
"0.61780065",
"0.61689115",
"0.6151857",
"0.613323",
"0.613008... | 0.77920055 | 0 |
Test gradients of functions with scalar and integer input. | def test_grad_binary_int(func, motion, optimized, preserve_result, a, n):
utils.test_reverse_array(func, motion, optimized, preserve_result, a, n) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n return math_ops.asin(x) + 1.0\n\n... | [
"0.7141352",
"0.65598017",
"0.6515951",
"0.6444053",
"0.6311751",
"0.6303223",
"0.62829095",
"0.62594473",
"0.6215275",
"0.62092286",
"0.6166996",
"0.6150048",
"0.61389536",
"0.6121117",
"0.6120639",
"0.61168426",
"0.6020767",
"0.598313",
"0.59708667",
"0.59692293",
"0.596718... | 0.5751722 | 45 |
Test gradients of functions with single tensor input. | def test_grad_unary_tensor(func, motion, optimized, preserve_result, t):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0,), t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n return math_ops.asin(x) + 1.0\n\n... | [
"0.75575894",
"0.70530087",
"0.70420367",
"0.6837651",
"0.67773134",
"0.6739765",
"0.6699057",
"0.66702545",
"0.66556704",
"0.66472477",
"0.6573843",
"0.6523481",
"0.649424",
"0.6487613",
"0.645774",
"0.64325905",
"0.643159",
"0.6396742",
"0.6388076",
"0.63814676",
"0.6348040... | 0.68327725 | 4 |
Test gradients of reduction functions. | def test_grad_unary_reduction(func, motion, optimized, preserve_result,
timage, boolean):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0,),
timage, boolean) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_gradient_convergence(self):\n pass",
"def test_grad(self):\r\n for shp0, shp1 in [((1,), (2,)),\r\n ((3,), (1,)),\r\n ((1,), (1,)),\r\n ((3,), (2,)),\r\n ((3, 2), (1, 1)),\r\n ... | [
"0.73162276",
"0.7096802",
"0.6853156",
"0.67028135",
"0.6701313",
"0.66994137",
"0.6695083",
"0.66722023",
"0.66373664",
"0.66370106",
"0.65923834",
"0.6569986",
"0.65372443",
"0.6479069",
"0.64675087",
"0.6438433",
"0.6349279",
"0.63451445",
"0.6326558",
"0.6315513",
"0.631... | 0.6766216 | 3 |
Test gradients of functions with binary tensor inputs. | def test_grad_binary_tensor(func, motion, optimized, preserve_result, t1, t2):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),
t1, t2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_op.constant(1.0 - 1e-8, dtype=dtypes.float32)\n\n @def_function.function\n def asinp1(x):\n # asin()'s gradient overflows at the value close to 1.0.\n return math_ops.asin(x) + 1.0\n\n... | [
"0.7174986",
"0.69963866",
"0.6909422",
"0.6872271",
"0.6760931",
"0.6759554",
"0.67556065",
"0.6745516",
"0.6740587",
"0.67286974",
"0.66507494",
"0.6592925",
"0.65785694",
"0.65090275",
"0.65043676",
"0.6460004",
"0.6445297",
"0.64399576",
"0.6439512",
"0.6423145",
"0.63291... | 0.7402149 | 0 |
Test gradients of functions with binary matrix inputs. | def test_grad_matmul(func, motion, optimized, preserve_result, mat1, mat2,
boolean1, boolean2):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),
mat1, mat2, boolean1, boolean2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_grad_binary(func, motion, optimized, preserve_result, a, b):\n utils.test_reverse_array(func, motion, optimized, preserve_result, a, b)",
"def test_grad_binary_tensor(func, motion, optimized, preserve_result, t1, t2):\n tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),\n ... | [
"0.70167184",
"0.6937823",
"0.67399496",
"0.67363566",
"0.6702786",
"0.67019135",
"0.6695494",
"0.66286254",
"0.6604258",
"0.6593175",
"0.6582183",
"0.65545446",
"0.6515961",
"0.64890236",
"0.6457533",
"0.64294976",
"0.642394",
"0.6420689",
"0.6417929",
"0.6411399",
"0.638161... | 0.6872022 | 2 |
Test gradients of functions with binary matrix inputs. | def test_grad_matmul_higherdim(func, motion, optimized, preserve_result,
timage1, timage2, boolean1, boolean2):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),
timage1, timage2, boolean1, boolean2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_grad_binary(func, motion, optimized, preserve_result, a, b):\n utils.test_reverse_array(func, motion, optimized, preserve_result, a, b)",
"def test_grad_binary_tensor(func, motion, optimized, preserve_result, t1, t2):\n tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),\n ... | [
"0.70167184",
"0.6937823",
"0.6872022",
"0.67399496",
"0.67363566",
"0.6702786",
"0.67019135",
"0.6695494",
"0.66286254",
"0.6604258",
"0.6593175",
"0.6582183",
"0.65545446",
"0.6515961",
"0.64890236",
"0.6457533",
"0.64294976",
"0.6420689",
"0.6417929",
"0.6411399",
"0.63816... | 0.642394 | 17 |
Test gradients of functions with binary tensor inputs. | def test_grad_tensor_broadcast(func, motion, optimized, preserve_result, s,
t):
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),
s, t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_grad_binary_tensor(func, motion, optimized, preserve_result, t1, t2):\n tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0, 1),\n t1, t2)",
"def testNestedFunctionGradientCall(self):\n check_numerics_callback.enable_check_numerics()\n\n x = constant_... | [
"0.7404276",
"0.71765655",
"0.69961417",
"0.6910999",
"0.6869918",
"0.6762966",
"0.67546713",
"0.6744153",
"0.6739438",
"0.6729621",
"0.66494566",
"0.6591663",
"0.65795594",
"0.6510418",
"0.65035886",
"0.64594465",
"0.64457124",
"0.6440613",
"0.6439113",
"0.6423183",
"0.63270... | 0.67602366 | 6 |
Test gradients of image functions. | def test_grad_image(func, motion, optimized, preserve_result, timage, tkernel,
conv2dstrides):
# TODO: Upgrade utils.py to allow simultaneous testing of uneven args.
tfe_utils.test_rev_tensor(func, motion, optimized, preserve_result, (0,),
timage, tkernel, conv2dstrid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_gradient_convergence(self):\n pass",
"def test_gradients(self):\n # Load from files.\n flow_ab = read_flow_file('pwcnet/warp/test_data/flow_ab.flo')\n img_b = read_image('pwcnet/warp/test_data/image_b.png', as_float=True)\n\n H = img_b.shape[0]\n W = img_b.shape... | [
"0.71023715",
"0.6835922",
"0.6640372",
"0.6603771",
"0.6524708",
"0.65058905",
"0.6494919",
"0.63947445",
"0.63629365",
"0.63115203",
"0.62794995",
"0.6276175",
"0.62534803",
"0.62271285",
"0.6223386",
"0.6205185",
"0.62044686",
"0.6195168",
"0.61621594",
"0.6155193",
"0.613... | 0.7155387 | 0 |
Generator that iterates over an iterable in size chunks >>> list(chunks('ABCDEFG', size=3)) [('A', 'B', 'C'), ('D', 'E', 'F'), ('G',)] >>> list(chunks([1, 2, 3, 4, 5, 6], size=2)) [(1, 2), (3, 4), (5, 6)] | def chunks(iterable, size=20):
stop = object()
for chunk in recipes.grouper(size, iterable, fillvalue=stop):
if chunk[-1] is stop:
is_not_stop = functools.partial(operator.is_not, stop)
yield tuple(itertools.takewhile(is_not_stop, chunk))
break
yield chunk | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chunks(iterator, size):\n for index in range(0, len(iterator), size):\n yield iterator[index:index + size]",
"def iter_chunks(iterable, size):\n it = iter(iterable)\n while True:\n chunk = tuple(itertools.islice(it, size))\n if len(chunk) == 0:\n break\n yield ... | [
"0.84125316",
"0.8280994",
"0.8176167",
"0.7985401",
"0.7982482",
"0.7932622",
"0.78810847",
"0.78195244",
"0.78195244",
"0.78195244",
"0.7808499",
"0.7795705",
"0.77784157",
"0.7770861",
"0.7755475",
"0.77362293",
"0.7682972",
"0.7598768",
"0.7581873",
"0.7565406",
"0.755539... | 0.0 | -1 |
Drop Quasi Zero Features Returns a passed pandas DataFrame without columns containing too few nonzero values. | def drop_quasi_zero(df, thresh=0.05):
drop_list = []
for el in df.columns.values:
non_zero = df[el][df[el] != 0].shape[0] / df.shape[0]
if non_zero < thresh:
drop_list.append(el)
print('Dropping column: {} | Non-zero values ratio: {}%'.format(
el, round(10... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_zero_features(df,no_zeros = 1):\n thing = df.astype(bool).sum(axis=0) # number of nonzeros in each column\n idx = pd.Index(thing) #Index format\n location = idx.get_loc(no_zeros) # Set all elements that are 1.0 to True, rest to False.\n loc_of_one = np.asarray(np.nonzero(location)) #Array of... | [
"0.8001528",
"0.6879542",
"0.685985",
"0.6835183",
"0.6821461",
"0.68055904",
"0.67482245",
"0.67280966",
"0.6656785",
"0.66356796",
"0.6563634",
"0.65300715",
"0.6447803",
"0.64278233",
"0.64013696",
"0.63996434",
"0.6344614",
"0.63372946",
"0.63230413",
"0.6318931",
"0.6315... | 0.77936864 | 1 |
Regularized Logarithmic Transformation Returns a logarithmic transformation that deals elegantly with very small positive values. | def log_trans(vec):
m = vec[vec != 0]
c = int(np.log(min(m)))
d = 10 ^ c
return np.log(vec + d) - c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logarithmic():\n return Equivalency(\n [(dimensionless_unscaled, function_units.dex, np.log10, lambda x: 10.0**x)],\n \"logarithmic\",\n )",
"def transform(self, original_input):\n return super(LogarithmicDimension, self).transform(np.log(original_input))",
"def safelog(x):\n #r... | [
"0.75638396",
"0.7200353",
"0.686215",
"0.68412685",
"0.6811226",
"0.671013",
"0.6648036",
"0.64976794",
"0.6454982",
"0.6424051",
"0.64047575",
"0.638714",
"0.63495034",
"0.6339063",
"0.6285992",
"0.62852067",
"0.62727803",
"0.6262116",
"0.62470746",
"0.62452745",
"0.6198531... | 0.6616752 | 7 |
Drop Outliers by ZScore Deletes observations classified as outliers. The classification method analyzes if the observation is out of the established zscore bounds. | def drop_outliers_z_score(df, z=3):
n_initial_rows = df.shape[0]
drop_list = set()
print('-' * 25)
print('OUTLIERS DELETION: Z-SCORE METHOD\n')
for el in df.columns.values:
drop_list = drop_list | \
set(df[el][np.abs(df[el]-df[el].mean()) >=
(z*df[el].std... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_outliers(self, data, sd_val):\n data = data.dropna()\n data = data[(np.abs(stats.zscore(data)) < float(sd_val)).all(axis=1)]\n return data",
"def identify_and_handel_outliers(self):\n col_list = [] # This will hold the column names created for the administration of the modi... | [
"0.7309114",
"0.7176904",
"0.71419543",
"0.7014585",
"0.66157347",
"0.66115505",
"0.6607007",
"0.6549644",
"0.643559",
"0.6377236",
"0.6328624",
"0.630123",
"0.62695014",
"0.6264223",
"0.62510294",
"0.6243806",
"0.62305784",
"0.6151706",
"0.61425245",
"0.61235154",
"0.6117694... | 0.7543709 | 0 |
Drop Outliers by Quantiles Deletes observations classified as outliers. The classification method analyzes if the observation is out of the established quantile bounds. | def drop_outliers_quantile(df, upper=0.99, lower=0):
n_initial_rows = df.shape[0]
drop_list = set()
quant_upper = df.quantile(upper)
quant_lower = df.quantile(lower)
print('-' * 25)
print('OUTLIERS DELETION: QUANTILE METHOD\n')
for el in df.columns.values:
drop_list = drop_list | \... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_outliers(data):\n upper_boundary = np.quantile(data, 0.992)\n lower_boundary = np.quantile(data, 0.008)\n selection = data[(data > lower_boundary) & (data < upper_boundary)]\n standard_dev = np.std(selection)\n median = np.median(selection)\n data[(median + 4.5 * standard_dev < data) |... | [
"0.7241458",
"0.6982997",
"0.6830516",
"0.6804271",
"0.6767851",
"0.67002904",
"0.6677959",
"0.66053724",
"0.65986574",
"0.6532611",
"0.65269667",
"0.651718",
"0.65114",
"0.650066",
"0.6455752",
"0.6363624",
"0.62836605",
"0.62783426",
"0.6254968",
"0.61966586",
"0.6195618",
... | 0.73276263 | 0 |
Count Nan's Per Feature Returns a pandas DataFrame containing the absolute and percentual number of missing values per column. It can also show this information in a plot if show_plot=True. | def check_nan(df, show_plot=False):
void = pd.DataFrame(np.sum(df.isna()), columns=['absolute'])
void['percentage'] = round((void.absolute / df.shape[0]) * 100, 2)
if show_plot:
print('\n\n')
plt.figure(figsize=(12, 5))
plt.plot(void.index.values, void.percentage.values, 'ro')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_nan_counts(data, cols, null_col_suffix=''):\n nulls_df = pd.DataFrame(pd.isnull(data[cols]).sum())\n nulls_df.columns = ['null_counts'+null_col_suffix]\n nulls_df['feature'] = nulls_df.index\n nulls_df.reset_index(inplace=True, drop=True)\n return nulls_df",
"def nan_val_summary(df):\n ... | [
"0.76617587",
"0.76107836",
"0.7382069",
"0.7363311",
"0.7303697",
"0.6956843",
"0.6882129",
"0.6876723",
"0.672093",
"0.6697947",
"0.66910964",
"0.66730636",
"0.66213566",
"0.6581595",
"0.65028274",
"0.6355325",
"0.6343527",
"0.6327267",
"0.6289938",
"0.62678796",
"0.6197389... | 0.7220725 | 5 |
Drop Quasi Null Features Returns a passed pandas DataFrame without columns containing too many Null values. | def drop_nan(df, perc=20):
check = check_nan(df, show_plot=False)
return df.drop(check[check.percentage > perc].index.values, axis=1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_cleaning(df):\n df = df.dropna(how='all')\n \n return df",
"def df_cleaner(df):\n return df.dropna()",
"def df_cleaner(df):\n df = df.dropna()\n return df",
"def removeNull(self):\n self.data.dropna(axis=0, how='any')",
"def remove_null_cols(df, thresh=0.08):\n \n # ... | [
"0.7871674",
"0.7850133",
"0.7708419",
"0.7245343",
"0.7152289",
"0.71420413",
"0.71229476",
"0.706161",
"0.6958182",
"0.69297075",
"0.69206834",
"0.68892694",
"0.6867685",
"0.685136",
"0.6831037",
"0.6807297",
"0.68061906",
"0.6749607",
"0.67389345",
"0.6709776",
"0.6659096"... | 0.0 | -1 |
Perform Automated Downsampling Returns a set of balanced x_train and y_train datasets for classification. As of now, it only works for binary classifications. | def downsampling(x_train, y_train, random_state=42):
sampling = pd.concat([x_train, y_train], axis=1)
big = sampling[y_train == y_train.value_counts().index[0]]
small = sampling[y_train == y_train.value_counts().index[1]]
downsampled = resample(big,
replace=False,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_data(self, x_data, y_data, max_seq_len, digits, seq_len,\n n_samples, use_one_hot, class_partition,\n upsample_control):\n # modify seq_len in case we do upsampling control\n if upsample_control:\n upsample_factor = seq_len\n ... | [
"0.64318675",
"0.6423297",
"0.6305304",
"0.6246317",
"0.6061455",
"0.60597426",
"0.6001533",
"0.59941304",
"0.596125",
"0.59551734",
"0.59408695",
"0.5918505",
"0.59173226",
"0.5902152",
"0.58708876",
"0.5862264",
"0.57767594",
"0.5743646",
"0.5723611",
"0.5707372",
"0.570502... | 0.7455317 | 0 |
List Most Correlated Features Returns a pandas Series with the most correlated features to a certain target variable. The function will return features with a correlation value bigger than some threshold, which can be adjusted. | def corr_list(df, target, thresh=0.1, sort=True, fill=True):
if fill:
interest = df.corr().fillna(0)[target]
else:
interest = df.corr()[target]
interest = interest[np.abs(interest) > thresh]
if len(interest) > 0:
if sort:
return interest.sort_values(ascending=False)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_top_corr_features(self, feature_name):\n\n correlations = abs(self.train_correlations[feature_name][self.feature_names]) # abs!\n correlations.sort_values(ascending=False, inplace=True)\n return correlations.index[1:] # exlude itsself",
"def remove_highly_correlated_vars_fast(df, cor... | [
"0.64598227",
"0.64072794",
"0.61176175",
"0.60476017",
"0.5959691",
"0.5944587",
"0.57532865",
"0.56677884",
"0.5658192",
"0.56417507",
"0.5623593",
"0.558363",
"0.5528225",
"0.5513537",
"0.54229397",
"0.5422274",
"0.541264",
"0.53154373",
"0.5312354",
"0.52972484",
"0.52403... | 0.5943275 | 6 |
Cleaning of extra dots Deletes extra dots (thousands dot) in float values being represented as unprocessed strings. | def rm_dot(item):
if len(str(item).split('.')) > 2:
return item.replace('.', '', 1)
else:
return item | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_value(self, value):\n return float(value.replace('.', '').replace(',', '.'))",
"def condense_floating_points(css):\n log.debug(\"Condensing all floating point values.\")\n return re.sub(r\"(:|\\s)0+\\.(\\d+)\", r\"\\1.\\2\", css)",
"def replace_dot(data):\n data = re.sub(\"[.]\", \" .... | [
"0.7477636",
"0.68418944",
"0.65526754",
"0.6344429",
"0.6334262",
"0.6282358",
"0.62550396",
"0.6232874",
"0.62304324",
"0.61961925",
"0.6167261",
"0.615144",
"0.6147928",
"0.6097535",
"0.6063996",
"0.5992138",
"0.5967721",
"0.5933247",
"0.59322363",
"0.591937",
"0.5913577",... | 0.64362913 | 3 |
Check categorical cardinality Checks the cardinality of categorical features of a given dataset. Returns two dictionaries, one for features with low cardinality and another for features with high cardinality. The low/high cardinality criteria can be tunned with the `threshold` parameter. | def check_cardinality(df, cat_cols, threshold=8):
high_card = {}
low_card = {}
for col in cat_cols:
rank = len(Counter(df[col]))
if rank <= threshold:
low_card[col] = rank
else:
high_card[col] = rank
return low_card, high_card | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_counts(self, X: np.ndarray) -> Dict[int, np.ndarray]:\n return {f: np.bincount(X[:, f].astype(int), minlength=n_cat) for f, n_cat in\n self.categories_per_feature.items()}",
"def test_cardinality(self):\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categor... | [
"0.60110897",
"0.59391886",
"0.58878255",
"0.55651134",
"0.5557575",
"0.5502532",
"0.5467048",
"0.5462119",
"0.5437828",
"0.5422825",
"0.540756",
"0.53921795",
"0.5321203",
"0.530417",
"0.5298471",
"0.529479",
"0.526825",
"0.5265717",
"0.52566886",
"0.5253142",
"0.52381426",
... | 0.7394644 | 0 |
Perform OneHot Encoding Wrapper for perfoming OneHot Encoding on a set of categorical columns from a given DataFrame. The original categorical column is dropped. Good for encoding categorical features with low cardinality. | def one_hot_enc(df, ohe_cols):
for col in ohe_cols:
df_ohe = pd.get_dummies(df[col], preffix=col)
df = pd.concat([df, df_ohe], axis=1)
df.drop(col, axis=1, inplace=True)
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onehot_enc(df, categorical_columns, categories):\n noncategorical_cols = [col for col in df.columns if col not in categorical_columns]\n \n enc = OneHotEncoder(categories=categories,\n sparse=False,\n handle_unknown='ignore')\n y = enc.fit_transform(df[... | [
"0.83821774",
"0.8090642",
"0.80746007",
"0.80546844",
"0.8051921",
"0.7987664",
"0.79627573",
"0.7930709",
"0.788882",
"0.78858125",
"0.7865726",
"0.7839437",
"0.78283674",
"0.781988",
"0.77600044",
"0.7727394",
"0.76851445",
"0.7651386",
"0.7616133",
"0.76030165",
"0.755102... | 0.7897891 | 8 |
Perform Bniary Encoding Wrapper for perfoming Binary Encoding on a set of categorical columns from a given DataFrame. The original categorical columns are dropped. Good for encoding categorical features with high cardinality. | def binary_encoding(df, bin_cols):
for col in bin_cols:
enc = BinaryEncoder(cols=col)
bin_enc = enc.fit_transform(df[col])
df = pd.concat([df, bin_enc], axis=1)
df.drop(col, axis=1, inplace=True)
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encoding_df(df, cols):\n import pandas as pd\n df = df[cols]\n obj_df = df.select_dtypes(include=['object']).copy()\n num_var = df.select_dtypes(include=['int','float']).copy()\n cat_var = pd.get_dummies(obj_df, columns = obj_df.columns)\n encoded_df = pd.concat([num_var, cat_var], axis=1, so... | [
"0.7325643",
"0.7214714",
"0.71435875",
"0.69621587",
"0.6918023",
"0.69116336",
"0.6879287",
"0.6841618",
"0.68229026",
"0.68160236",
"0.6795417",
"0.67443526",
"0.6730028",
"0.67107",
"0.6649362",
"0.65972847",
"0.6576219",
"0.65755546",
"0.6571882",
"0.6562026",
"0.6538636... | 0.7727909 | 0 |
Get sparse matrix of scores Returns a sparse score matrix of implicit Client > Product interactions. The scores are wightened by the correlation of each feature with the target feature. It is also possible to filter the values from the target feature we want to be considered in the building of the sparse matrix. | def get_corr_sparse_df(df, client_id, product_id, tgt_feature,
tgt_filter=True, tgt_val=1):
# Auxiliary tables declaration
df_keys = df[[client_id, product_id, tgt_feature]]
df_score = df.drop([client_id, product_id, tgt_feature], axis=1)
# Calculating the score weight matrix, us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_sparse(self):\n from divisi2.sparse import SparseMatrix\n return SparseMatrix(self, self.row_labels, self.col_labels)",
"def analyze_sensitivity_sparse_grid(sparse_grid,max_order=2):\n from pyapprox.multivariate_polynomials import \\\n define_poly_options_from_variable_transformati... | [
"0.6489858",
"0.63272566",
"0.6297267",
"0.61061746",
"0.6096833",
"0.5966724",
"0.5915552",
"0.58811307",
"0.5846046",
"0.5798666",
"0.57851624",
"0.5760531",
"0.57405245",
"0.5736884",
"0.573338",
"0.57287127",
"0.5685769",
"0.56659234",
"0.56512487",
"0.5630169",
"0.561970... | 0.7354125 | 0 |
Evaluate and perform dataset sclaing Evaluates if a given dataset needs scaling by checking the differences in the orders of magnitudes of its features. Also, can perform Robust Scaling, returning the transformed dataset. | def evaluate_for_scaling(df, transform=False, lower_quant=25, upper_quant=75):
df_std_mean = df.mean().std()
df_median_mean = df.mean().median()
if df_std_mean > df_median_mean:
if transform:
# Scaling the features
rsc = RobustScaler(quantile_range=(lower_quant,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def standardize_data(X_train, X_test):\n scaler = StandardScaler()\n scaler.fit(X_train)\n X_train = scaler.transform(X_train)\n # apply same transformation to test data\n X_test = scaler.transform(X_test)\n return X_train, X_test",
"def X_scale_train_test(self, do_data=False):\n\n X_sca... | [
"0.67595345",
"0.6736341",
"0.6708757",
"0.6693353",
"0.66485626",
"0.6570267",
"0.6493276",
"0.64883626",
"0.64744467",
"0.64196485",
"0.63689077",
"0.63577473",
"0.63289684",
"0.6326573",
"0.6268254",
"0.62646896",
"0.62071",
"0.6206559",
"0.6201114",
"0.6168693",
"0.614723... | 0.6680995 | 4 |
Determine Number of Principal Components Returns a PCAtransformed dataset with enough Principal Components to explain a certain amout of variability. Used for sparse datasets, leveraging the properties of the TruncatedSVD transformer from sklearn. | def explained_variability(df, threshold=0.95):
# Using all components initially
transf = TruncatedSVD(n_components=df.shape[1]-1,
random_state=42).fit(df)
s = pd.DataFrame(transf.explained_variance_ratio_,
index=range(1, df.shape[1]),
colum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_PCA(data, ncomp):\n import sklearn.decomposition as dc\n \n pca = dc.PCA(n_components=ncomp, whiten=False, svd_solver='full')\n cps = pca.fit_transform(data)\n svl = pca.singular_values_\n return cps,pca,svl",
"def do_pca(x_data, n_class):\n\n run_pca = decomposition.PCA(n_componen... | [
"0.706759",
"0.6988584",
"0.69768095",
"0.6925743",
"0.67113084",
"0.66486746",
"0.662425",
"0.6620902",
"0.65606546",
"0.655598",
"0.6546643",
"0.65301853",
"0.6490808",
"0.64430857",
"0.6430357",
"0.64184636",
"0.63982505",
"0.6388121",
"0.63843805",
"0.6367074",
"0.6322675... | 0.0 | -1 |
Returns object id for given address or None when unknown source | def lookup_object(self, address):
obj_id = self.source_map.get(address)
if not obj_id:
# Register invalid event source
if self.source_map:
self.invalid_sources[address] += 1
return None
return obj_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grab_external_id(stix_object, source_name):\n for external_reference in stix_object.get(\"external_references\", []):\n if external_reference.get(\"source_name\") == source_name:\n return external_reference[\"external_id\"]",
"def getSourceForId(context, identifier):\n nearest = getat... | [
"0.66619503",
"0.6295734",
"0.62073255",
"0.6147287",
"0.6119063",
"0.6066387",
"0.600836",
"0.6007095",
"0.5943731",
"0.58872235",
"0.58872235",
"0.58104545",
"0.57378054",
"0.57162756",
"0.57081634",
"0.57081634",
"0.57081634",
"0.568439",
"0.568439",
"0.568439",
"0.568439"... | 0.81914216 | 0 |
Spool message to be sent | def register_message(self, object, timestamp, data):
self.perf_metrics["events_out"] += 1
self.messages += [{
"ts": timestamp,
"object": object,
"data": data
}] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def sendCommand(ser, msg):\n ser.write(\"%s\\r\\n\" % (msg))\n return",
"def sendpm(self, the_id, msg):\r\n the_id = Client.touserid(the_id)\r\n self.tx_cmd(FCTYPE.PMESG, th... | [
"0.64589876",
"0.64589876",
"0.64589876",
"0.6368447",
"0.63330376",
"0.6296989",
"0.62598866",
"0.6227509",
"0.6222479",
"0.6178442",
"0.61577266",
"0.6136637",
"0.61044085",
"0.61024183",
"0.60762924",
"0.6011487",
"0.6005013",
"0.600252",
"0.6001607",
"0.59993094",
"0.5981... | 0.0 | -1 |
Periodic task to send collected messages to fmwriter | def send_messages(self):
if self.messages:
messages, self.messages = self.messages, []
self.mpub("events.%s" % config.pool, messages) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n while True:\n try:\n sleep(influx_settings.write_freq)\n self._redis2influx()\n except InterruptExceptions as ex:\n raise ex",
"def trigger_update(self):\n update_thread = Thread(target=self.process_queued_msg)\n ... | [
"0.613982",
"0.61360604",
"0.6054681",
"0.6047653",
"0.60263085",
"0.6020203",
"0.6016649",
"0.5977986",
"0.59506196",
"0.5898428",
"0.58631",
"0.58552927",
"0.5847367",
"0.58328557",
"0.5831154",
"0.58268714",
"0.5758967",
"0.5752461",
"0.5745754",
"0.569751",
"0.5676762",
... | 0.6156687 | 0 |
Periodic task to request object mappings | def get_object_mappings(self):
self.logger.debug("Requesting object mappings")
sm = yield self.omap.get_trap_mappings(config.pool)
if sm != self.source_map:
self.logger.debug("Setting object mappings to: %s", sm)
self.source_map = sm | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def task():",
"def _schedule(self,task_dict):\n times = [time(), None, None, None] # (schedule timestamp, execution timestamp, stop timestamp, get timestamp)\n result_id = self._extract_features.remote(self, times) # calculation is started in new remote task \n task_dict[result_id] = self._i... | [
"0.61706334",
"0.60808724",
"0.58790255",
"0.5862278",
"0.5862278",
"0.5748848",
"0.5642705",
"0.5589115",
"0.55680954",
"0.5532347",
"0.5512871",
"0.5512871",
"0.5512871",
"0.54854304",
"0.54598445",
"0.54582804",
"0.5439082",
"0.5416327",
"0.54149127",
"0.5397255",
"0.53971... | 0.0 | -1 |
Report invalid event sources | def report_invalid_sources(self):
if not self.invalid_sources:
return
total = sum(self.invalid_sources[s] for s in self.invalid_sources)
self.logger.info(
"Dropping %d messages with invalid sources: %s",
total,
", ".join("%s: %s" % (s, self.invalid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sources_not_ok_on_connection_error(self):\n measurement = self.measurement(\n self.metric(),\n sources=[\n {\n \"source_uuid\": SOURCE_ID,\n \"value\": None,\n \"total\": None,\n \"parse... | [
"0.65968525",
"0.64882016",
"0.6418916",
"0.6381476",
"0.62577367",
"0.5951416",
"0.58868337",
"0.57699937",
"0.57269937",
"0.56809556",
"0.56666833",
"0.5643618",
"0.56132454",
"0.55951136",
"0.55816895",
"0.5566584",
"0.55154705",
"0.5508068",
"0.5502415",
"0.5494327",
"0.5... | 0.758512 | 0 |
Add a list of items to the list of all library items | def add_items(self, library_items):
for item in library_items:
self._all_items.append(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_items(self, items):\n for item in items:\n self.add(item)",
"def add_items(self, items):\n for item in items:\n self.addItem(item)\n # end for item in items",
"def addItems(*args):",
"def extend(self, items):\n\t\tfor item in items:\n\t\t\tself.append(item)"... | [
"0.76198214",
"0.7341822",
"0.7276178",
"0.7267924",
"0.71445847",
"0.70814025",
"0.706996",
"0.70159686",
"0.6767956",
"0.6730525",
"0.66963893",
"0.66547024",
"0.6601904",
"0.65674746",
"0.6306901",
"0.6306901",
"0.6306901",
"0.6264611",
"0.6227412",
"0.62267005",
"0.621254... | 0.8472733 | 0 |
Remove a list of items from the catalog | def remove_items(self, library_items):
for item in library_items:
try:
self._all_items.remove(item)
except ValueError:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_items(todofile, items):\n for item in filter(lambda x: x.itemid in items, todofile.fetch_items()):\n todofile.remove_todo(item)",
"def removeItem(*args):",
"def removeItem(*args):",
"def removeItems(c, items):\n\t\tcontainer.containersToSave[c['id_item_container']] = item.inventory.remov... | [
"0.7018876",
"0.69360423",
"0.69360423",
"0.687769",
"0.6809874",
"0.67999625",
"0.6712689",
"0.66521966",
"0.6628607",
"0.66213995",
"0.6594071",
"0.6592304",
"0.65764713",
"0.6541679",
"0.65117013",
"0.65044403",
"0.6392971",
"0.62988776",
"0.62521005",
"0.62501127",
"0.624... | 0.6773232 | 6 |
Search for items and add any matching items to a list | def search_items(self, filter_text, type_filter=None):
output = []
for item in self._all_items:
if type_filter:
if item.match(filter_text) and item.resource_type == type_filter:
output.append(item)
else:
if item.match(filter_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_items_to_find(self):\n self.items_to_find = ['sole', 'farina', 'innaffiatoio']",
"def find_all_items(items: WebElements, value_list: List[str]=PARAMS_LEAGUES) -> WebElements:\n items_list = []\n for item in items:\n if any([True if word in item.text.lower() else False for word in valu... | [
"0.7003679",
"0.635711",
"0.58783317",
"0.5858773",
"0.58105254",
"0.57913166",
"0.573767",
"0.5697082",
"0.5669749",
"0.5646335",
"0.563685",
"0.5634067",
"0.5554156",
"0.55335087",
"0.55306315",
"0.5501929",
"0.5493303",
"0.5480856",
"0.5478738",
"0.5444283",
"0.5380661",
... | 0.54567516 | 19 |
True/False whether the item is a match for the filter_text match should be case insensitive and should search all attributes of the class. Depending on the attribute, match requires an exact match or partial match. match needs to be redefined for any subclasses. Please see the note/notebook case study from Chapter 2 as... | def match(self, filter_text):
return filter_text.lower() in self.name.lower() or \
filter_text.lower() == self.isbn.lower() or \
filter_text.lower() in (str(tag).lower() for tag in self.tags) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, filter_text):\n\n return filter_text.lower() in self.director.lower() or \\\n filter_text.lower() in self.actor.lower() or \\\n super().match(filter_text)",
"def match(self, filter_text):\n\n return filter_text.lower() in self.artist.lower() or \\\n ... | [
"0.69324505",
"0.6889434",
"0.63976663",
"0.63642013",
"0.6214803",
"0.5999873",
"0.5973541",
"0.5937157",
"0.58969986",
"0.589467",
"0.58461",
"0.58175755",
"0.57801634",
"0.5702319",
"0.5633824",
"0.5630518",
"0.5629107",
"0.5620056",
"0.5602705",
"0.5573345",
"0.5557129",
... | 0.66317177 | 2 |
Return a well formatted string representation of the item All instance variables are included. All subclasses must provide a __str__ method | def __str__(self):
return f'Name: {self.name}\nISBN: {self.isbn}\nItem Type: {self.resource_type}\nTags: {", ".join(self.tags)}\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return str(self.item)",
"def __repr__(self):\n return '<Item {0} : {1}>'.format(self.item_id, self.item_name)",
"def __repr__(self):\n return '\\n<Item Code: =%s Item Name: =%s>' % (self.item_code, self.item_name)",
"def __str__(self):\n\n return \"{}: {}\".fo... | [
"0.8661985",
"0.8456193",
"0.8331384",
"0.83095175",
"0.8259899",
"0.8235929",
"0.8235929",
"0.8140813",
"0.8089503",
"0.80138934",
"0.7948992",
"0.7872446",
"0.782787",
"0.7735162",
"0.7620142",
"0.7596037",
"0.7588032",
"0.7571623",
"0.75450194",
"0.75450194",
"0.75450194",... | 0.8060703 | 9 |
Return a short string representation of the item String contains only the name of the item and the type of the item I.E. Moby Dick eBook All subclasses must provide a to_short_string method | def to_short_string(self):
return f'{self.name} - {self.resource_type}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return self._itemType",
"def __str__(self):\n return str(self.item)",
"def __str__(self):\n return f'Name: {self.name}\\nISBN: {self.isbn}\\nItem Type: {self.resource_type}\\nTags: {\", \".join(self.tags)}\\n'",
"def __str__(self):\n return self.item_name",
... | [
"0.7117714",
"0.7058652",
"0.6880865",
"0.6847068",
"0.6796854",
"0.6796854",
"0.6796854",
"0.67721087",
"0.6702772",
"0.66898733",
"0.6665129",
"0.6491782",
"0.64446867",
"0.64440036",
"0.641076",
"0.6404244",
"0.6358782",
"0.6358782",
"0.62972945",
"0.62821275",
"0.6269284"... | 0.68870425 | 2 |
Initialize a new book object | def __init__(self, name, isbn, author, tags=None):
super().__init__(name, isbn, tags)
self.author = author
self.resource_type = "Book" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._book = Library.functions.new_()",
"def __init__(self, bookAuthor, ISBN, numPurchased, bookTitle, numCheckedOut = 0, retailPrice = 0):\n\n self.bookAuthor = bookAuthor\n self.bookISBN = ISBN\n self.numberPurchased = numPurchased\n self.numberCheckedOu... | [
"0.7738984",
"0.7383594",
"0.73780483",
"0.70157796",
"0.69490784",
"0.6875649",
"0.68458223",
"0.6771472",
"0.6601914",
"0.6483411",
"0.6417341",
"0.63756514",
"0.6345037",
"0.63199055",
"0.626232",
"0.62504995",
"0.6247069",
"0.6246545",
"0.6223746",
"0.62212014",
"0.618638... | 0.7439095 | 1 |
An extension of the match function in the base LibraryItem class. Also searches for the filter text in the author variable. | def match(self, filter_text):
return filter_text.lower() in self.author.lower() or \
super().match(filter_text) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, filter_text):\n\n return filter_text.lower() in self.artist.lower() or \\\n super().match(filter_text)",
"def match(self, filter_text):\n return filter_text.lower() in self.name.lower() or \\\n filter_text.lower() == self.isbn.lower() or \\\n filter_... | [
"0.682007",
"0.6550258",
"0.6416015",
"0.63797086",
"0.6266647",
"0.59596294",
"0.58739334",
"0.58389205",
"0.58080375",
"0.5803565",
"0.56252295",
"0.5614221",
"0.5611868",
"0.5611807",
"0.5606387",
"0.5595165",
"0.5577767",
"0.5544993",
"0.54778427",
"0.5456448",
"0.5431750... | 0.7305958 | 0 |
Return a well formatted string representation of the item All instance variables are included. All subclasses must provide a __str__ method | def __str__(self):
return super().__str__() + f'Author: {self.author}\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return str(self.item)",
"def __repr__(self):\n return '<Item {0} : {1}>'.format(self.item_id, self.item_name)",
"def __repr__(self):\n return '\\n<Item Code: =%s Item Name: =%s>' % (self.item_code, self.item_name)",
"def __str__(self):\n\n return \"{}: {}\".fo... | [
"0.8661985",
"0.8456193",
"0.8331384",
"0.83095175",
"0.8259899",
"0.8235929",
"0.8235929",
"0.8140813",
"0.8089503",
"0.8060703",
"0.80138934",
"0.7948992",
"0.7872446",
"0.782787",
"0.7735162",
"0.7620142",
"0.7596037",
"0.7588032",
"0.7571623",
"0.75450194",
"0.75450194",
... | 0.0 | -1 |
Initialize a DVDMovie Item | def __init__(self, name, isbn, director, actor, tags=None):
super().__init__(name, isbn, tags)
self.director = director
self.actor = actor
self.resource_type = "DVD" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, C4Director, item_id):\n self.director = C4Director\n self.item_id = item_id",
"def __init__(self, C4Director, item_id):\n self.director = C4Director\n self.item_id = item_id",
"def __init__(self):\n # Item Dictionary (key: call number, value: Item object)\n... | [
"0.68161535",
"0.68161535",
"0.64105874",
"0.63517463",
"0.6327458",
"0.63026476",
"0.62970006",
"0.6288919",
"0.62316924",
"0.62074465",
"0.6139625",
"0.6041508",
"0.5999405",
"0.59802437",
"0.59486175",
"0.5944403",
"0.5904814",
"0.58922637",
"0.58280015",
"0.5824133",
"0.5... | 0.5621799 | 46 |
An extension of the LibraryItem's match method. Also searches for filter text in the director and actor fields. | def match(self, filter_text):
return filter_text.lower() in self.director.lower() or \
filter_text.lower() in self.actor.lower() or \
super().match(filter_text) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, item):",
"def match(self, filter_text):\n\n return filter_text.lower() in self.artist.lower() or \\\n super().match(filter_text)",
"def match(self, filter_text):\n\n return filter_text.lower() in self.author.lower() or \\\n super().match(filter_text)",
"def... | [
"0.66584975",
"0.66140664",
"0.6375227",
"0.62277186",
"0.60852814",
"0.5842212",
"0.5810459",
"0.58054554",
"0.56884336",
"0.56876177",
"0.5668758",
"0.5642446",
"0.56119794",
"0.56106514",
"0.55795777",
"0.55734146",
"0.5569108",
"0.5545362",
"0.5492105",
"0.5452527",
"0.54... | 0.7091767 | 0 |
Return a well formatted string representation of the item All instance variables are included. All subclasses must provide a __str__ method | def __str__(self):
return super().__str__() + f'Lead Actor: {self.actor}\nDirector: {self.director}\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return str(self.item)",
"def __repr__(self):\n return '<Item {0} : {1}>'.format(self.item_id, self.item_name)",
"def __repr__(self):\n return '\\n<Item Code: =%s Item Name: =%s>' % (self.item_code, self.item_name)",
"def __str__(self):\n\n return \"{}: {}\".fo... | [
"0.8661985",
"0.8456193",
"0.8331384",
"0.83095175",
"0.8259899",
"0.8235929",
"0.8235929",
"0.8140813",
"0.8089503",
"0.8060703",
"0.80138934",
"0.7948992",
"0.7872446",
"0.782787",
"0.7735162",
"0.7620142",
"0.7596037",
"0.7588032",
"0.7571623",
"0.75450194",
"0.75450194",
... | 0.0 | -1 |
Initialize a MusicCD item. | def __init__(self, name, isbn, artist, num_discs, tags=None):
super().__init__(name, isbn, tags)
self.artist = artist
self.num_discs = num_discs
self.resource_type = "Music CD" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, **kwargs):\n self.item = Item(**kwargs)\n self._working_dir = None",
"def __init__(self):\n # Item Dictionary (key: call number, value: Item object)\n self.item_list = {}\n\n # Add some items manually for testing purposes.\n book1 = Book(\"In praise of... | [
"0.6313415",
"0.62220263",
"0.5928097",
"0.58955693",
"0.5831417",
"0.5831417",
"0.5783373",
"0.5757864",
"0.5751746",
"0.5706846",
"0.5677021",
"0.5675513",
"0.56697357",
"0.56654",
"0.5658594",
"0.56570214",
"0.56510967",
"0.5628711",
"0.5588841",
"0.55882436",
"0.55713534"... | 0.6329616 | 0 |
An extension of the Library Item's match method. Also searches for the filter text in the artist variable | def match(self, filter_text):
return filter_text.lower() in self.artist.lower() or \
super().match(filter_text) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, item):",
"def find_artist(self):\n item = self.clementine_artists.currentItem()\n if not item:\n return\n self.artist_buffer = item\n search = item.text(0)\n self._parent.current_data = search\n if self.artist_map[item.text(0)]:\n ok... | [
"0.69125444",
"0.6398688",
"0.6256071",
"0.6176387",
"0.6136926",
"0.61072534",
"0.6032576",
"0.6002995",
"0.5977466",
"0.59103507",
"0.59099007",
"0.58667594",
"0.58499247",
"0.58097106",
"0.5808899",
"0.5755709",
"0.569537",
"0.5620561",
"0.5617918",
"0.5600141",
"0.5568909... | 0.74232924 | 0 |
Return a well formatted string representation of the item All instance variables are included. All subclasses must provide a __str__ method | def __str__(self):
return super().__str__() + f'Recording Artist: {self.artist}\nNumber of Discs: {self.num_discs}\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return str(self.item)",
"def __repr__(self):\n return '<Item {0} : {1}>'.format(self.item_id, self.item_name)",
"def __repr__(self):\n return '\\n<Item Code: =%s Item Name: =%s>' % (self.item_code, self.item_name)",
"def __str__(self):\n\n return \"{}: {}\".fo... | [
"0.86619437",
"0.8456832",
"0.83310175",
"0.83086365",
"0.8260541",
"0.8235759",
"0.8235759",
"0.81405205",
"0.8088825",
"0.8059674",
"0.8013039",
"0.79483455",
"0.78710866",
"0.78280276",
"0.77361995",
"0.7619677",
"0.7594185",
"0.75878334",
"0.7569862",
"0.75448054",
"0.754... | 0.0 | -1 |
BFS using a for loop | def cloneGraph2(self, node):
if not node:
return None
cloned = {}
queue = [node]
for original_node in queue:
if original_node.label not in cloned:
cloned[original_node.label] = UndirectedGraphNode(original_node.label)
for neighbor in original_node.neighbors:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bfs_iterative(graph,start):\n\tvisited = set()\n\twatched = set()\n\tnodes_queue = [start] # List that helps as queue\n\twatched.add(start)\n\t\n\twhile nodes_queue:\n\t\tcurrent_node = nodes_queue.pop(0)\n\n\t\tprint(\"visiting\",current_node)\n\t\tvisited.add(current_node)\n\t\t\n\t\tfor adjacent_node in gra... | [
"0.7547011",
"0.739157",
"0.7352518",
"0.73296833",
"0.72997516",
"0.7285272",
"0.72228265",
"0.7205614",
"0.7166409",
"0.7165715",
"0.71539867",
"0.710455",
"0.70918494",
"0.70626235",
"0.7026808",
"0.7024267",
"0.7004307",
"0.6999509",
"0.698091",
"0.69739",
"0.6968559",
... | 0.0 | -1 |
Check whether the input string is a valid JSON | def is_json(json_str: str) -> bool:
try:
json.loads(json_str)
except ValueError:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_correct_json(string):\n if len(string) == 0:\n return False\n\n if string[0] is not '{' and string[0] is not '[':\n return False\n\n try:\n json.loads(string)\n except ValueError:\n return False\n\n return True",
"def __valid_json(string):\n try:\n ... | [
"0.88037795",
"0.87745696",
"0.8589823",
"0.8078066",
"0.7820956",
"0.78165317",
"0.74995846",
"0.7473835",
"0.74669105",
"0.7455147",
"0.73179656",
"0.7150155",
"0.6894489",
"0.6852388",
"0.68200266",
"0.67260844",
"0.6717202",
"0.66565275",
"0.6613865",
"0.6610069",
"0.6563... | 0.84249747 | 3 |
Get Config setting from predefined variables or environment parameters. | def init_config_values():
global HEADER, PROCESS_PROGRAM_NAME, METADATA_HANDLE_EVENT_NAME
global APPINSIGHTS_INSTRUMENTATIONKEY
global DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL, DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN
global ADX_INGEST_QUEUE_URL_LIST, ADX_INGEST_QUEUE_SAS_TOKEN
global CONCURRENT_ENQUEUE_TAS... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config(setting, config=CONFIG):\n try:\n return config[setting]\n except KeyError:\n raise ImproperlyConfigured(\"Set the {} setting\".format(setting))",
"def get_config(config):\n global _config\n return _config",
"def get_config():\n return CONFIG",
"def get_config_var(... | [
"0.7611529",
"0.7403772",
"0.73666954",
"0.73517406",
"0.7271565",
"0.7270964",
"0.724717",
"0.71440154",
"0.71114945",
"0.7067627",
"0.70576876",
"0.70354825",
"0.70275337",
"0.6970136",
"0.69563943",
"0.6925892",
"0.6882713",
"0.6837568",
"0.68029976",
"0.6720337",
"0.67193... | 0.0 | -1 |
download blob file content as string | def get_blob_content(container_name: str, blob_path: str) -> str:
global BLOB_SERVICE_CLIENT
# TODO: Should add retry policy here
if not BLOB_SERVICE_CLIENT:
logging.info(
f"{HEADER} Initialize blob service client for {DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL}")
BLOB_SERVICE_CLIENT ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def blob_download(blob_url):\n blob = storage.Object.from_url(blob_url)\n blobc = blob.download()\n return blobc",
"def download_blob(bucket_name, source_blob_name):\n storage_client = storage.Client()\n\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n r... | [
"0.7356397",
"0.6980018",
"0.6911951",
"0.68267864",
"0.6805651",
"0.6737355",
"0.6728277",
"0.66589123",
"0.65999615",
"0.65836155",
"0.6581424",
"0.658025",
"0.6546602",
"0.6525553",
"0.65234154",
"0.65129626",
"0.6471623",
"0.6471",
"0.64411074",
"0.64331",
"0.6397154",
... | 0.6978253 | 2 |
Shrink (reduce size) checkpoint files size | def get_shrinked_checkpoint_content(content, lines_to_keep)->str:
i = 0
original_lines = content.splitlines()
newlines = []
for line in reversed(original_lines):
if i < lines_to_keep:
newlines.append(line)
i += 1
else:
break #reached max lines to kee... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shrink_file(in_filepath, api_key=None, out_filepath=None):\n info = get_shrink_file_info(in_filepath, api_key, out_filepath)\n write_shrunk_file(info)\n return info",
"def check_size():\n\n if os.path.getsize(config['LOG_PATH']) > config['LOG_MAXSIZE']:\n os.remove(config['LOG_PATH'])",
... | [
"0.58451694",
"0.5770589",
"0.5551063",
"0.55098546",
"0.5465791",
"0.5463636",
"0.53963625",
"0.5392331",
"0.537304",
"0.5330672",
"0.5321509",
"0.5310033",
"0.53080124",
"0.5300658",
"0.5292845",
"0.5289114",
"0.52819055",
"0.5275576",
"0.5267911",
"0.52384543",
"0.5223316"... | 0.0 | -1 |
update blob file by replace existing file | def update_blob_content(container_name: str, blob_path: str, content: str):
global BLOB_SERVICE_CLIENT
if not BLOB_SERVICE_CLIENT:
logging.info(
f"{HEADER} Initialize blob service client for {DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL}")
BLOB_SERVICE_CLIENT = BlobServiceClient(DATABRICK... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_record_update_file(appctx, db, record_with_file_processed, obj_name, content):\n record = CernSearchRecord.get_record(record_with_file_processed.id)\n initial_file_name = \"hello.txt\"\n initial_file = record.files[initial_file_name].obj # type: ObjectVersion\n initial_file_content = record.f... | [
"0.6568326",
"0.65407366",
"0.6406529",
"0.6354509",
"0.6352969",
"0.61979395",
"0.61885375",
"0.61471003",
"0.6096474",
"0.60016054",
"0.599357",
"0.5979346",
"0.5972336",
"0.5951576",
"0.5928227",
"0.58855575",
"0.58786947",
"0.5861474",
"0.58392084",
"0.58236605",
"0.57857... | 0.60457766 | 9 |
Convert the abfss path to https path style | def convert_abfss_path_to_https(abfss_path: str) -> str:
pattern = r'abfss:\/\/([^@]+)@([^.]+)[^\/]+\/(.+)'
regex = re.compile(pattern)
match = regex.search(abfss_path)
if not match:
raise ValueError(f"Invalid abfss path {abfss_path}")
container = match.group(1)
storage_account = match.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def https(url):\n if url[:8] == 'https://':\n return url\n if url[:7] != 'http://':\n return False\n return 'https://' + url[7:]",
"def path_ssl(self):\n return HOMEASSISTANT_SSL.format(HASSIO_SHARE_INT)",
"def convertToURL( cPathname ):\n if len( cPathname ) > 1:\n if c... | [
"0.62642324",
"0.61140555",
"0.61136925",
"0.6104507",
"0.609913",
"0.6059806",
"0.6051831",
"0.6046826",
"0.60012305",
"0.59881335",
"0.5960421",
"0.5948716",
"0.59205675",
"0.59025985",
"0.5899939",
"0.5899413",
"0.5885915",
"0.5857693",
"0.584945",
"0.5841724",
"0.57719195... | 0.73538405 | 0 |
Generate queue messages from Databricks ouutput metadata file content | def generate_metadata_queue_messages(event_time: str, metadata_file_content: str) -> List[str]:
ingest_queue_msg_list = []
current_part_num = 100000 #Max part number
global MAX_COMPACT_FILE_RECORDS
for line in reversed(metadata_file_content.splitlines()):
logging.info(f"{HEADER} Processing meta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_metadata_msg_enqueue_tasks(queue_msg_list: List[str],\n queue_client_list: List[QueueClient],\n tc: TelemetryClient) -> None:\n\n tasks = []\n for idx, queue_msg in enumerate(queue_msg_list):\n output_obj = json.loads(queue_ms... | [
"0.64029324",
"0.59525144",
"0.5745095",
"0.55602175",
"0.5504239",
"0.54586256",
"0.5446348",
"0.5404002",
"0.5401268",
"0.5355826",
"0.53395224",
"0.52417713",
"0.5229069",
"0.52107525",
"0.51790047",
"0.51555395",
"0.5095745",
"0.5088324",
"0.50780344",
"0.50593895",
"0.50... | 0.7086968 | 0 |
Async to send messages to storage queue | async def send_queue_messages(queue_client, base64_message, queue_msg):
try:
await queue_client.send_message(base64_message)
except Exception: # pylint: disable=bare-except
logging.exception(f"{HEADER} Failed to send message {queue_msg} to queue")
# Raise exception to let azure function ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def send(self):",
"async def send(self, message):",
"def send_msg(self, my_queue, my_msg):",
"def async(self, *args, **kwargs):\n uid = str(uuid.uuid4())\n message = {\n 'uid': uid,\n 'name': self.name,\n 'args': self.serializer.serialize(args),\n ... | [
"0.7073917",
"0.6974844",
"0.68638635",
"0.6803254",
"0.67270863",
"0.6582076",
"0.6537732",
"0.6507097",
"0.64683974",
"0.64622647",
"0.6417503",
"0.63880336",
"0.6262368",
"0.6172687",
"0.6127086",
"0.6076321",
"0.6046184",
"0.60391587",
"0.6025878",
"0.60185766",
"0.597763... | 0.6072268 | 16 |
Send queue messages to target queues | def gen_metadata_msg_enqueue_tasks(queue_msg_list: List[str],
queue_client_list: List[QueueClient],
tc: TelemetryClient) -> None:
tasks = []
for idx, queue_msg in enumerate(queue_msg_list):
output_obj = json.loads(queue_msg)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_msg(self, my_queue, my_msg):",
"def send_command_queue(self, command_queue):\n for command in command_queue:\n self._send_string(command)\n\n self._done_sending()",
"def send_command_queue(self, command_queue):\n for command in command_queue:\n self._send_str... | [
"0.7450541",
"0.72386956",
"0.72386956",
"0.71769816",
"0.6649021",
"0.6503821",
"0.6503487",
"0.6501781",
"0.6476404",
"0.64479864",
"0.64465857",
"0.6426796",
"0.6396762",
"0.63950366",
"0.6379228",
"0.62869745",
"0.6278462",
"0.62457585",
"0.62373036",
"0.619211",
"0.61710... | 0.0 | -1 |
limit the concurrent tasks with semaphore | async def gather_with_concurrency(n, tasks):
semaphore = asyncio.Semaphore(n)
async def sem_task(task):
async with semaphore:
await task
await asyncio.gather(*(sem_task(task) for task in tasks)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_ripe_request_tokens(sema: mp.Semaphore, limit: int, finish_event: threading.Event):\n logger.debug('generate thread started')\n while not finish_event.is_set():\n time.sleep(2 / limit)\n try:\n sema.release()\n sema.release()\n except ValueError:\n ... | [
"0.6282102",
"0.6080879",
"0.60440874",
"0.59983575",
"0.59436536",
"0.5899353",
"0.5844908",
"0.5839281",
"0.5807662",
"0.573292",
"0.571161",
"0.566539",
"0.5640771",
"0.55995965",
"0.5567172",
"0.55225784",
"0.5494984",
"0.5481205",
"0.54737425",
"0.539674",
"0.53499925",
... | 0.6391296 | 0 |
Close queue clients connection | def close_queue_clients(queue_client_list: List[QueueClient], loop: asyncio.AbstractEventLoop):
client_close_tasks = []
for client in queue_client_list:
close_task = asyncio.ensure_future(client.close())
client_close_tasks.append(close_task)
loop.run_until_complete(gather_with_concurrency(1,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _close_clients(self):\n for client in self.clients:\n client.close()",
"def closeConnecetion(self):\n client.close()",
"def close(self):\n for conn in self._conns:\n conn.send((self.CLOSE,()))",
"def __close_connection_with_clients(self):\n for current_client in self._... | [
"0.74559975",
"0.74452204",
"0.7294628",
"0.7047551",
"0.6944966",
"0.677886",
"0.6763271",
"0.6743381",
"0.67432",
"0.67000616",
"0.6695658",
"0.6674143",
"0.66615736",
"0.66608673",
"0.6640044",
"0.6629691",
"0.66181344",
"0.6616973",
"0.66131306",
"0.6568658",
"0.6536984",... | 0.74610734 | 0 |
Main function, triggered by Azure Storage Queue, parsed queue content and try to download the databricks output metadata file to get each succefully processed file location. Then enqueue to ingest queue for ingestion to ADX on later Azure function. If the file is checkpoint compact file, the code will shrink the file s... | def main(msg: func.QueueMessage) -> None:
code_start_time = time.time()
logging.info('Python queue trigger function processed a queue item: %s',
msg.get_body().decode('utf-8'))
# modify the log level of azure sdk requests
logging.getLogger('azure').setLevel(logging.WARNING)
init_con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_metadata_queue_messages(event_time: str, metadata_file_content: str) -> List[str]:\n ingest_queue_msg_list = []\n current_part_num = 100000 #Max part number\n global MAX_COMPACT_FILE_RECORDS\n\n for line in reversed(metadata_file_content.splitlines()):\n logging.info(f\"{HEADER} Pro... | [
"0.6682282",
"0.6162708",
"0.6032964",
"0.5799686",
"0.5781316",
"0.5746755",
"0.5729667",
"0.57098687",
"0.5676677",
"0.5572735",
"0.5548825",
"0.5534193",
"0.55331063",
"0.55202967",
"0.55051416",
"0.5490857",
"0.5485531",
"0.54404837",
"0.54099286",
"0.5386266",
"0.5369767... | 0.7072427 | 0 |
Parse Text file and yield 1 line at a time. | def parse_text_file(file_path):
with open(file_path, 'r') as f:
for line in f:
line = line.rstrip()
if not line:
continue
yield line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def txt_line_iterator(path):\n with tf.io.gfile.GFile(path) as f:\n for line in f:\n yield line.strip()",
"def parse(self, f):\n \n for line in f:\n self.parse_line(line)",
"def _gen_txt_data(self, f):\n\t\treader = iter(f)\n\n\t\tfor line_num, line in enumerate(reader):\n\t\t... | [
"0.7017607",
"0.66851646",
"0.6601368",
"0.6538029",
"0.64616096",
"0.64355737",
"0.64092755",
"0.6401797",
"0.63687634",
"0.6289389",
"0.6269863",
"0.62646693",
"0.6264008",
"0.6262496",
"0.6234503",
"0.62180173",
"0.6187032",
"0.6181076",
"0.6180821",
"0.6168177",
"0.616333... | 0.75814044 | 0 |
Import a data set, as its origin data points, in shape of | def fit(self, data):
self.data = data
self._not_included = data
if self._expected_area == -1:
self._expected_area = int(len(data) ** 0.5)
pass
self._x_extends = [data[0]['x'], data[0]['x']]
self._y_extends = [data[0]['y'], data[0]['y']]
self._value... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_dataset(fpath):\r\n data = read_csv(fpath)\r\n print(data.head())\r\n print(data.shape)\r\n return data",
"def read_data_set():\n # shapes of datasets -- [] means expanded form:\n # - X: J\n # - net.R: J [x J x 1]\n # - F_DIST: J x J x num_features\n # - F_DIST_w1: J x J x n... | [
"0.6362504",
"0.6320649",
"0.62760663",
"0.6209483",
"0.60866785",
"0.6017939",
"0.5989352",
"0.59728557",
"0.59344363",
"0.59291804",
"0.5890433",
"0.5882488",
"0.58824617",
"0.58749497",
"0.5872966",
"0.58425945",
"0.58192444",
"0.5801761",
"0.5795395",
"0.57905436",
"0.578... | 0.0 | -1 |
Gather all points in some autogrowing areas | def _cluster(self):
self._not_included = self.data
self.leaves = []
flag = int(rand() * len(self.data))
flag = self._generate(flag)
while len(self._not_included) > 0:
flag = self._generate(flag)
if flag == -1:
break
pass
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grid_points(self):\n for i in range(self.rows):\n for j in range(self.cols):\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\n if i == 0:\n print_gps(max_lat,max_lon,\"grid\")\n if j == 0:\n ... | [
"0.60352486",
"0.60089654",
"0.5957634",
"0.57922155",
"0.5735454",
"0.56912833",
"0.5677051",
"0.5659202",
"0.5644068",
"0.5619629",
"0.5581817",
"0.55763596",
"0.5568444",
"0.5551165",
"0.55458266",
"0.55394226",
"0.55220485",
"0.5515554",
"0.55153877",
"0.55147487",
"0.551... | 0.0 | -1 |
Generator of a new area | def _generate(self, center_idx):
center = self._not_included[center_idx]
area = [center]
others = []
for i in range(len(self._not_included)):
if i == center_idx:
continue
candidate = self._not_included[i]
dis_geo = ((center['x'] - candi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def area(self):",
"def area(self):\n ...",
"def __areas_to_pool(self, region_width, region_height, region_width_step, region_height_step):\n \n areas = [[(width_ind * region_width_step, height_ind * region_height_step,\n (width_ind + 1) * region_width_step if (width_ind + 1) ... | [
"0.7147663",
"0.6909164",
"0.65530187",
"0.63511145",
"0.61740553",
"0.6143072",
"0.6023134",
"0.6012145",
"0.6012145",
"0.6009092",
"0.5989456",
"0.59823465",
"0.59821725",
"0.5962189",
"0.5962189",
"0.5938174",
"0.593229",
"0.5910889",
"0.5910889",
"0.5908722",
"0.5890229",... | 0.5819086 | 22 |
Find a suitable binarytree structure | def linkage(self):
self.tree = {}
un_linked = []
for i in range(len(self.leaves)):
leaf = self.leaves[i]
un_linked.append({
'id': i,
'x': 0,
'y': 0,
'value': 0,
'set': leaf,
'c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binary_search_tree_run():\n\n # no need for Tree object as the Tree itself is a concept; its made of connected nodes\n # nodes are the object; connections are self contained\n\n def binary_insert(root, node):\n if root is None:\n root = node\n else:\n if root.data >... | [
"0.6735971",
"0.6530564",
"0.62307024",
"0.61076456",
"0.6053023",
"0.5996912",
"0.59570765",
"0.59391767",
"0.592461",
"0.5840478",
"0.5802106",
"0.5735963",
"0.5731404",
"0.5731134",
"0.5724618",
"0.57046956",
"0.56911415",
"0.5674273",
"0.56437874",
"0.56290257",
"0.560675... | 0.0 | -1 |
Run the c++ unit tests. This runs a subprocess because the HDF5 used by h5py clashed with the HDF5 linked to the c++ unit tests on some systems. h5py is used by some XDMF tests. | def run_cpp_unit_tests():
# We set cwd in case the script is run from another directory. This is needed
# when testing from the core.
out_bytes = subprocess.check_output(
['python3', 'run_cpp_unit_tests.py'], cwd=abspath(dirname(__file__)))
return out_bytes.decode('utf-8') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __main() :\n launchTests()",
"def test(c, index=0):\n exec_path = f'{ROOT_DIR}/gem5/build/GCN3_X86/gem5.opt' \n conf_path = f'{ROOT_DIR}/gem5/configs/example/apu_se.py'\n test_dir = TEST_PATHS[index]\n print(f'Testing {test_dir}...')\n \n # Try to find executable\n try_paths = [os.pat... | [
"0.6613631",
"0.6581835",
"0.6549095",
"0.64722",
"0.6412084",
"0.63910145",
"0.6268595",
"0.62387437",
"0.618074",
"0.6167369",
"0.61670023",
"0.6159595",
"0.61505693",
"0.61311626",
"0.61168665",
"0.60939276",
"0.6077586",
"0.6045904",
"0.60249716",
"0.6006832",
"0.6001748"... | 0.67698425 | 0 |
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