1:\\n self.change_client_name(message[1], client)\\n else:\\n self.send_message('No se especifico nombre.', client.get_socket())\\n\\n\\n elif message[0] == Protocol.STATUS.value:\\n if len(message) > 1:\\n self.change_user_status(message[1], client)\\n else:\\n self.send_message('No se especifico status.', client.get_socket())\\n\\n\\n elif message[0] == Protocol.MESSAGE.value:\\n if len(message) > 2:\\n message_to_user = message[1]\\n userMessage = self.get_user_message(message, 2)\\n self.send_direct_message(message_to_user, userMessage, client)\\n else:\\n if len(message == 1):\\n self.send_message('No se especifico mensaje.', client.get_socket())\\n else:\\n self.send_message('No se especifico usuario ni mensaje.',\\n client.get_socket())\\n\\n\\n elif message[0] == Protocol.USERS.value:\\n self.send_clients(client)\\n\\n\\n elif message[0] == Protocol.PUBLICMESSAGE.value:\\n if len(message) > 1:\\n userMessage = self.get_user_message(message, 1)\\n self.send_public_message(userMessage)\\n else:\\n self.send_message('No se especifico mensaje.', client.get_socket())\\n\\n\\n elif message[0] == Protocol.CREATEROOM.value:\\n if len(message) > 1:\\n roomName = message[1]\\n self.create_room(roomName, client)\\n else:\\n self.send_message('No se especifico nombre de la sala.', client.get_socket())\\n\\n\\n elif message[0] == Protocol.INVITE.value:\\n if len(message) > 2 and self.verify_chat_room_existance(roomName):\\n roomName = message[1]\\n users_verified = self.get_unique_users(message)\\n sockets = self.get_sockets(users_verified)\\n if len(sockets) > 0:\\n self.invite_users(roomName, sockets, client)\\n else:\\n self.send_message('No existen los usuarios que quieres invitar.',\\n client.get_socket())\\n else:\\n if len(message) == 2:\\n self.send_message('No se especificaron los invitados a la sala.',\\n client.get_socket())\\n elif len(message) == 1:\\n self.send_message('No se especifico el nombre de la sala ni los invitados.',\\n client.get_socket())\\n elif self.verify_chat_room_existance(roomName) == False:\\n self.send_message('No existe una sala con ese nombre.',\\n client.get_socket())\\n\\n\\n elif message[0] == Protocol.JOINROOM.value:\\n if len(message) > 1:\\n roomName = message[1]\\n if self.verify_chat_room_existance(roomName):\\n room = self.get_room(roomName)\\n self.join_room(client.get_socket(), room)\\n else:\\n self.send_message('La sala no existe.', client.get_socket())\\n else:\\n self.send_message('No se especifico la sala.', client.get_socket())\\n\\n\\n elif message[0] == Protocol.ROOMMESSAGE.value:\\n if len(message) > 2:\\n roomName = message[1]\\n if self.verify_chat_room_existance(roomName):\\n room = self.get_room(roomName)\\n roomMessage = self.get_user_message(message, 2)\\n self.send_room_message(room, roomMessage)\\n else:\\n self.send_message('La sala no existe.', client.get_socket())\\n else:\\n if len(message) == 1:\\n self.send_message('No se especifico el nombre de la sala ni el mensaje.',\\n client.get_socket())\\n else:\\n self.send_message('No se especifico el mensaje.', client.get_socket())\\n\\n elif message[0] == Protocol.DISCONNECT.value:\\n if len(message) == 1:\\n self.send_message('Bye bye', client.get_socket())\\n self.kill_client(client)\\n client.get_socket().close()\\n print('Acaba de desconectarse ', client.get_ip())\\n connected = False\\n else:\\n self.send_message('Solo debes mandar DISCONNECT', client.get_socket())\\n\\n\\n else:\\n msg = ''\\n msg += 'Mensaje invalido. A continuacion tiene la lista de mensajes validos: '\\n msg += '| IDENTIFY nombre : para identificar usuario'\\n msg += '| STATUS status: asigna estado al usuario: ACTIVE, BUSY o AWAY'\\n msg += '| USERS: muestra los usuarios identificados'\\n msg += '| MESSAGE usuario mensaje: envia mensaje privado al usuario'\\n msg += '| PUBLICMESSAGE mensaje: envia el mensaje a todos los usuarios identificados'\\n msg += '| CREATEROOM nombre: crea una sala con ese nombre, siendo el dueno el usario que la creo'\\n msg += '| INVITE nombre usuario1 usuario2...: envia invitaciones a los usuarios para unirse a la sala'\\n msg += '| JOINROOM nombre: acepta la invitacion a la sala que fuiste invitado'\\n msg += '| ROOMMESSAGE nombre mensaje: envia mensaje a los usuarios de la sala'\\n msg += '| DISCONNECT: te desconectas del servidor'\\n self.send_message(msg, client.get_socket())\\n\\n if not message:\\n break\",\n \"def listening(self):\\n while True:\\n client_socket, address = self.s.accept()\\n print(\\\"Connection from \\\" + str(address[0]) + \\\" has been established.\\\")\\n\\n data = get_top_random_toy()\\n\\n msg = pickle.dumps(data)\\n\\n # prepend message length to message\\n msg = bytes(f'{len(msg):<{self.__HEADER_SIZE}}', \\\"utf-8\\\") + msg\\n client_socket.send(msg)\",\n \"def flush(self):\\n # TODO: use socketserver or something different.\\n # We are very limited by select here\\n \\n r, w, x = select.select([self.messageDirector.sock, self.clientAgent.sock] + list(self.clients), [], [], 0)\\n for sock in r:\\n if sock == self.messageDirector.sock:\\n sock, addr = sock.accept()\\n self.clients[sock] = MDClient(self.messageDirector, sock, addr)\\n self.messageDirector.clients.append(self.clients[sock])\\n \\n elif sock == self.clientAgent.sock:\\n sock, addr = sock.accept()\\n self.clients[sock] = Client(self.clientAgent, sock, addr)\\n self.clientAgent.clients.append(self.clients[sock])\\n \\n else:\\n client = self.clients[sock]\\n try:\\n data = sock.recv(2048)\\n except socket.error:\\n data = None\\n \\n if not data:\\n del self.clients[sock]\\n \\n if type(client) == MDClient:\\n self.messageDirector.clients.remove(client)\\n \\n elif type(client) == Client:\\n self.clientAgent.clients.remove(client)\\n \\n client.onLost()\\n \\n else:\\n client.onData(data)\",\n \"def collector_process_data(self, data):\\n for c in clients:\\n c.on_message(json.dumps(data))\",\n \"def send(self, data):\\n pass\",\n \"def sendMessage(self,data,destination):\\r\\n \\r\\n self.log('Signal','Sending command to networked node \\\"%s\\\"' % destination,'sendMessage')\\r\\n \\r\\n if destination in self.nodes:\\r\\n\\r\\n self.log('Signal','Sent command to %s cmd: %s' % (destination,data),'sendMessage')\\r\\n \\r\\n self.server.sendMessage(data,destination)\",\n \"def send_reply(self, message, data=None):\\n try:\\n client = self._active_clients[message[\\\"client-tag\\\"]]\\n except KeyError:\\n self._log.error(\\\"send: No active client %s message discarded\\\" % (\\n message[\\\"client-tag\\\"]\\n ))\\n else:\\n client.send(message, data)\",\n \"def send(self, data):\\n self.sock.send(data)\",\n \"def send(self, data):\\n self.sock.send(data)\",\n \"def handle_messages(data_socket):\\r\\n size = 1\\r\\n message_number = 1\\r\\n while size != 0:\\r\\n lines = read_message(data_socket)\\r\\n size = len(lines)\\r\\n if size != 0:\\r\\n file_name = str(message_number) + \\\".txt\\\"\\r\\n message_number += 1\\r\\n with open(file_name, \\\"wb\\\") as file:\\r\\n write_lines(lines, file)\\r\\n data_socket.sendall(b'A')\\r\\n data_socket.sendall(b'Q')\",\n \"def send(self, message):\\n self.logger.info(\\\"Sending to server: %s\\\" % message)\\n self.sendLine(message)\",\n \"def send(self, msg):\\n # keep track of the total sent\\n # so we can make sure the whole message is sent\\n msg = (msg+'\\\\n').encode('utf-8')\\n totalsent = 0\\n while totalsent < len(msg):\\n sent = self.sock.send(msg[totalsent:])\\n # it is bad if we still have things to send\\n # but do not send anything\\n if sent == 0:\\n raise RuntimeError(\\\"connection broken\\\")\\n totalsent += sent\",\n \"def send(event=None): #event is passed by binders.\\n try:\\n msg = my_msg.get()\\n my_msg.set(\\\" \\\") #Clears input field.\\n client_socket.send(bytes(msg, \\\"utf8\\\"))\\n \\n except:\\n \\n HOST = '10.0.0.8'\\n PORT = 8081\\n ADDR = (HOST, PORT)\\n \\n s = socket(AF_INET, SOCK_STREAM)\\n client_socket.bind(ADDR)\\n s.connect((HOST, PORT))\\n s.send(msg)\\n \\n if msg == \\\"{quit}\\\":\\n client_socket.close()\\n top.destroy()\",\n \"def send_message(self,data):\\n num_bytes = len(data)\\n message = WriteMessage()\\n message.write_uint32(num_bytes)\\n message.data.extend(data)\\n self.socket.sendall(message.data)\",\n \"def runNodesMessage(self):\\n while True:\\n for neighbour in self.nextIP:\\n socketNodes = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n while True:\\n try:\\n socketNodes.connect((neighbour, 5003))\\n socketNodes.send(self.message)\\n break\\n except TimeoutError:\\n pass\\n except ConnectionRefusedError:\\n pass\\n socketNodes.close()\\n break\",\n \"def _send_data(self):\\n pass\",\n \"def send_message():\\n try:\\n sock = socket(AF_INET, SOCK_STREAM)\\n sock.connect((SERVER_IP, SERVER_PORT))\\n print('[+] ' + SERVER_IP + ' connected!')\\n position = MESSAGE.encode('utf-8')\\n sock.send(bytes(position))\\n sock.close()\\n print('[+] Transfer completed!')\\n except Exception as e:\\n print('[-]', e)\",\n \"def send(connection, data):\\n connection.send(pickle.dumps(data))\",\n \"def send_message(self, message):\\n self.print_debug_message(message)\\n self.socket.send(message)\",\n \"def send_message(groupID, clientID, clientMessage, timestamp): # Unfinish\\n for socket in GROUP_LIST[groupID]:\\n message = ''\\n try :\\n socket.send(message)\\n except :\\n # broken socket connection may be, chat client pressed ctrl+c for example\\n socket.close()\\n CONNECTION_LIST.remove(socket)\",\n \"def send(self, data):\\n self.socket.sendall(data)\",\n \"def on_message(self, message):\\n print \\\"Client %s received a message : %s\\\" % (self.id, message)\",\n \"def receive():\\n while True:\\n try:\\n msg = client_socket.recv(BUFSIZ).decode(\\\"utf8\\\")\\n msg_list.insert(tkinter.END, msg)\\n \\n except OSError: # Possibly client has left the chat.\\n break\",\n \"def _custom_privmsg(self, data) :\\n try:\\n self.socket.send(data)\\n if self.dcctype == \\\"chat\\\":\\n self.socket.send(\\\"\\\\n\\\")\\n log('Sent %d bytes of data over DCC' % len(data), 2)\\n\\n except socket.error:\\n # Ouch!\\n self.disconnect(\\\"Connection reset by peer.\\\")\",\n \"def broadcast(self, clients, msg):\\n self.server.broadcast(clients, msg)\",\n \"def receive():\\r\\n while True:\\r\\n try:\\r\\n msg = client_socket.recv(BUFSIZ).decode(\\\"utf8\\\")\\r\\n msg_list.insert(tkinter.END, msg)\\r\\n except OSError: # Possibly client has left the chat.\\r\\n break\",\n \"def send (self, data):\\n return self.sending.send(data)\",\n \"def send_to_other_clients(self, msg):\\r\\n # get_nearby_roles\\r\\n role_id_2_ins = self.server.aoi_mgr.get_nearby_roles(self.role)\\r\\n\\r\\n for role_id in role_id_2_ins.keys():\\r\\n if role_id == self.client_pc_id:\\r\\n continue\\r\\n small_server = self.server.pc_id_2_small_server[role_id]\\r\\n small_server.send_to_client(msg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7056971","0.7019039","0.68477523","0.6810195","0.6644971","0.6635883","0.66154855","0.66057706","0.65976816","0.65917546","0.6570916","0.6568058","0.6564347","0.654368","0.6525813","0.6509941","0.6500165","0.6489766","0.64577127","0.6444351","0.64402354","0.6437696","0.643444","0.6404515","0.64041775","0.6390604","0.638037","0.6371336","0.63676363","0.6351187","0.63351554","0.63278645","0.63259095","0.62852764","0.6266842","0.6254565","0.6252355","0.62382364","0.6232578","0.623009","0.6214801","0.6205338","0.6205338","0.6205338","0.62025505","0.6182209","0.6176296","0.617622","0.61734855","0.6172405","0.6166945","0.61627394","0.6161743","0.61500204","0.61409944","0.6134089","0.6130024","0.6118745","0.60996115","0.6098362","0.6095459","0.6092709","0.60900766","0.6089575","0.6082596","0.6081292","0.607309","0.60651684","0.6061348","0.6058438","0.6049851","0.60430443","0.6011268","0.6001026","0.5999949","0.5996758","0.5992229","0.5990725","0.5990643","0.59795576","0.59795576","0.59756845","0.5972095","0.5969825","0.5968528","0.5962163","0.5958329","0.59570193","0.59568256","0.5953595","0.5946783","0.5943664","0.5936301","0.5936261","0.59280527","0.5927068","0.59241295","0.59227574","0.59138554","0.5913226"],"string":"[\n \"0.7056971\",\n \"0.7019039\",\n \"0.68477523\",\n \"0.6810195\",\n \"0.6644971\",\n \"0.6635883\",\n \"0.66154855\",\n \"0.66057706\",\n \"0.65976816\",\n \"0.65917546\",\n \"0.6570916\",\n \"0.6568058\",\n \"0.6564347\",\n \"0.654368\",\n \"0.6525813\",\n \"0.6509941\",\n \"0.6500165\",\n \"0.6489766\",\n \"0.64577127\",\n \"0.6444351\",\n \"0.64402354\",\n \"0.6437696\",\n \"0.643444\",\n \"0.6404515\",\n \"0.64041775\",\n \"0.6390604\",\n \"0.638037\",\n \"0.6371336\",\n \"0.63676363\",\n \"0.6351187\",\n \"0.63351554\",\n \"0.63278645\",\n \"0.63259095\",\n \"0.62852764\",\n \"0.6266842\",\n \"0.6254565\",\n \"0.6252355\",\n \"0.62382364\",\n \"0.6232578\",\n \"0.623009\",\n \"0.6214801\",\n \"0.6205338\",\n \"0.6205338\",\n \"0.6205338\",\n \"0.62025505\",\n \"0.6182209\",\n \"0.6176296\",\n \"0.617622\",\n \"0.61734855\",\n \"0.6172405\",\n \"0.6166945\",\n \"0.61627394\",\n \"0.6161743\",\n \"0.61500204\",\n \"0.61409944\",\n \"0.6134089\",\n \"0.6130024\",\n \"0.6118745\",\n \"0.60996115\",\n \"0.6098362\",\n \"0.6095459\",\n \"0.6092709\",\n \"0.60900766\",\n \"0.6089575\",\n \"0.6082596\",\n \"0.6081292\",\n \"0.607309\",\n \"0.60651684\",\n \"0.6061348\",\n \"0.6058438\",\n \"0.6049851\",\n \"0.60430443\",\n \"0.6011268\",\n \"0.6001026\",\n \"0.5999949\",\n \"0.5996758\",\n \"0.5992229\",\n \"0.5990725\",\n \"0.5990643\",\n \"0.59795576\",\n \"0.59795576\",\n \"0.59756845\",\n \"0.5972095\",\n \"0.5969825\",\n \"0.5968528\",\n \"0.5962163\",\n \"0.5958329\",\n \"0.59570193\",\n \"0.59568256\",\n \"0.5953595\",\n \"0.5946783\",\n \"0.5943664\",\n \"0.5936301\",\n \"0.5936261\",\n \"0.59280527\",\n \"0.5927068\",\n \"0.59241295\",\n \"0.59227574\",\n \"0.59138554\",\n \"0.5913226\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6016732"},"document_rank":{"kind":"string","value":"72"}}},{"rowIdx":15092,"cells":{"query":{"kind":"string","value":"Writes results to a file"},"document":{"kind":"string","value":"def write_to_file(file_name, bds, parameters=None, success=None, result=None, add_head=False, overwrite=False):\n\n if overwrite:\n f = open(file_name, \"w\")\n else:\n f = open(file_name, \"a\")\n\n # adds header for the results file\n if add_head:\n head_str = \"Iteration\"\n\n params = {}\n for bd in _BDS:\n head_str = ','.join([head_str, bd['name']])\n\n head_str = ','.join([head_str, 'success', _TARGET])\n\n f.write(\"{}\\n\".format(head_str))\n \n else:\n result_str = \"0\"\n for idx, _ in enumerate(_BDS):\n\n result_str = ','.join([result_str, \"%d\" % (int(parameters[0][idx]))])\n\n result_str = ','.join([result_str, \"%d\" % (success), \"%f\" % (result)])\n \n f.write(\"{}\\n\".format(result_str))\n\n f.close()"},"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 write_result(self, file_name):\n f = file(file_name, \"w\")\n f.write(self.m_result)\n f.close()","def write_results_to_disk(self, result_path,results):\n with open(result_path+\"/results.txt\",\"w+\") as out:\n\n for query_num in results:\n for doc_num in results[query_num]:\n out.write(str(query_num)+\" 0 \"+doc_num+\" 1 42.38 mt\\n\")\n out.close()","def write_results(results):\n with RESULTS_PATH.open(\"w\") as writer:\n csvwriter = csv.writer(writer)\n csvwriter.writerows(results)","def write_results_to_file(stocks_to_write):\n date = datetime.date.today()\n date_str = str(date.year) + '-' + str(date.month) + '-' + str(date.day)\n file_name_core = 'results-' + date_str\n\n with open(\"results/txt/\" + file_name_core + \".txt\",\n 'w') as txt_results_file:\n\n for stock in stocks_to_write:\n txt_results_file.write(stock.make_one_line_report() + \"\\n\")\n\n with open(\"results/csv/\" + file_name_core + \".csv\",\n 'w') as csv_results_file:\n\n writer = csv.writer(csv_results_file)\n writer.writerow(StockData.get_csv_data_headings())\n for stock in stocks_to_write:\n writer.writerow(stock.get_csv_data_list())","def write_results(filename, algorithm_name, qty_array, min_coins):\n with open(filename, 'a') as f:\n f.write('{0}\\n'.format(algorithm_name))\n f.write('{0}\\n'.format(qty_array))\n f.write('{0}\\n'.format(min_coins))","def write_result(file, result):\n name = result[0]\n dataset = result[1]\n file.write(name + \"\\n\")\n for data, values in dataset.items():\n file.write(\"%s %.3f %.3f \\n\" %(data, np.mean(values),np.std(values)))","def export(fileName, result):\n with open(fileName, 'a') as output:\n output.write(result)","def fileWrite(content):\n file = open('./result.txt', 'w')\n file.write(content)\n file.close()","def save_results(PATH, data, filename):\n with open(PATH + '/' + filename + \".txt\",\"w\") as file:\n file.write(\"Results of heuristic models with mean and standard deviation.\\n\")\n for result in data:\n write_result(file, result)\n file.close()\n print('results saved in:'+ PATH + '/' + filename + \".txt\")","def write_result_to_file(self):\n self.__test_result[Result.__RUN] = self.__run\n with open(self.__json_file_path, \"w+\") as outfile:\n json.dump(self.__test_result, outfile,\n ensure_ascii=False, indent=2)","def write_results_to_file(self, param):\n results_file = open('%stheoretical_results_%s.yml' % (directory, str(param)), 'w')\n results_file.write(yaml.dump(self.mean_time_to_absorbtion, default_flow_style=False))\n results_file.close()","def write_to_file(self, results):\n with open(self.outputFilename, \"w\") as csvFile:\n csvWriter = csv.writer(csvFile, delimiter=',') \n title_row = ('asset_id', 'component_id', 'latitude', 'longitude', 'installation_date', 'commissioning_date', 'street_name', 'cabinet_id', 'nominal_wattage', 'current_time', 'current_LogValue', 'current_IsLogValueOff') \n csvWriter.writerow(title_row)\n for record in results:\n csvWriter.writerow(record)","def writeResult(self, result_path, result):\r\n f = open(result_path, 'w')\r\n f.write(self.formatResult(result))\r\n f.write('\\n')\r\n f.close()","def write_results(detections, filename):\n start = time.time()\n\n boxes, labels, scores = detections\n with PathManager.open(filename, \"w\") as f:\n for key in boxes.keys():\n for box, label, score in zip(boxes[key], labels[key], scores[key]):\n f.write(\n \"%s,%.03f,%.03f,%.03f,%.03f,%d,%.04f\\n\"\n % (key, box[1], box[0], box[3], box[2], label, score)\n )\n\n logger.info(\"AVA results wrote to %s\" % filename)\n logger.info(\"\\ttook %d seconds.\" % (time.time() - start))","def write_result(fragment_list, results, output_file):\n if not output_file:\n output_file = results + os.sep + \"prodigal_filtered.txt\"\n try:\n with open(output_file, \"wt\") as output:\n for fragment in fragment_list:\n output.write(\"{1}{0}{2}{0}\".format(\n os.linesep, fragment[0], fill(fragment[1])))\n except IOError:\n sys.exit(\"Error cannot open {0}\".format(output_file))","def write_results_dat(self, output_path):\n\n def fstr(nb):\n data = '%E' % nb\n if data == 'NAN':\n nb, power = 0,0\n else:\n nb, power = data.split('E')\n nb = float(nb) /10\n power = int(power) + 1\n return '%.5fE%+03i' %(nb,power)\n\n line = '%s %s %s %i %i %i %i %s %s %s %s %s %i\\n' % (fstr(self.axsec), fstr(self.xerru), \n fstr(self.xerrc), self.nevents, self.nw, self.maxit, self.nunwgt,\n fstr(self.luminosity), fstr(self.wgt), fstr(self.xsec), fstr(self.maxwgt),\n fstr(self.th_maxwgt), self.th_nunwgt) \n fsock = open(output_path,'w') \n fsock.writelines(line)\n for i in range(len(self.ysec_iter)):\n line = '%s %s %s %s %s %s\\n' % (i+1, self.ysec_iter[i], self.yerr_iter[i], \n self.eff_iter[i], self.maxwgt_iter[i], self.yasec_iter[i]) \n fsock.writelines(line)","def save_result(file_name, text):\n\n filename = os.path.join(fileDir, 'tools/results/{}.txt'.format(file_name))\n\n with open(filename, \"a+\") as file:\n file.write(text)","def write_result(results_file, outcome, player_1, player_2):\n # append the outcome to the file\n with open(results_file, 'a') as f:\n # write the outcome from player 1's perspective\n f.write(\"%s %s\\n\" % (player_1, map_outcome(int(outcome))))\n # write the outcome from player 2's perspective (hence the not)\n f.write(\"%s %s\\n\" % (player_2, map_outcome(int(not outcome))))","def write_results(self, results, fname, folder=None):\n folder = folder or self.output_dir\n\n if not os.path.exists(folder):\n logger.info(\"creating {} (did not exist)\".format(folder))\n os.makedirs(folder)\n\n out_file = os.path.join(folder, fname)\n logger.info(\"writing to {}\".format(out_file))\n df = pd.DataFrame(results)\n df.to_csv(out_file, index=False)\n \n return None","def write_file(self):\n\n running_time = str(self.running_time_end - self.running_time_start)\n rounded_running_time = '{:.10}'.format(running_time)\n output = 'path_to_goal: ' + str(self.path_to_goal) + '\\n'\n output += 'cost_of_path: ' + str(self.cost_of_path) + '\\n'\n output += 'nodes_expanded: ' + str(self.nodes_expanded) + '\\n'\n output += 'fringe_size: ' + str(self.fringe_size) + '\\n'\n output += 'max_fringe_size: ' + str(self.max_fringe_size) + '\\n'\n output += 'search_depth: ' + str(self.search_depth) + '\\n'\n output += 'max_search_depth: ' + str(self.max_search_depth) + '\\n'\n output += 'running_time: ' + rounded_running_time + '\\n'\n\n system_name = system()\n if system_name == 'Windows':\n output += 'max_ram_usage: (Not available on Windows OS)'\n elif system_name == 'Linux':\n output += 'max_ram_usage: ' + \\\n str(getrusage(RUSAGE_SELF).ru_maxrss / 1024) + '\\n'\n\n file = open('output.txt', 'w+')\n file.write(output)\n print(output)","def dump_file(str_content, filename):\n\n with open(\"results/\" + filename, \"w\") as f:\n f.write(str_content)","def write_to_file(self, filename: str) -> None:","def _write_keypoint_results(keypoint_results, gt_folder, pred_folder):","def writeOutput(result, fname, output_dir):\n\n s = str(len(result)) + \"\\n\" + str(result).replace(\",\", \"\")[1:-1]\n fname = fname[:fname.rfind(\".\")] + \".txt\"\n os.makedirs(output_dir, exist_ok=True)\n with open(output_dir + fname, \"w\") as f:\n f.write(s)\n\n print(\"output written to: \" + output_dir + fname)","def write_results_file(self, results_list, file_path):\n # Replace references to the malware repo with a variable.\n malware_repo = mwcp.config.get(\"MALWARE_REPO\", None)\n if malware_repo:\n for results in results_list:\n # TODO: Refactor this\n input_file_path = results[INPUT_FILE_PATH]\n if input_file_path.startswith(malware_repo):\n input_file_path = \"{MALWARE_REPO}\" + input_file_path[len(malware_repo) :]\n results[INPUT_FILE_PATH] = input_file_path\n\n # Write updated data to results file\n # NOTE: We need to use dumps instead of dump to avoid TypeError.\n with open(file_path, \"w\", encoding=\"utf8\") as results_file:\n results_file.write(str(json.dumps(results_list, indent=4, sort_keys=True)))","def save_results(results, results_file: str):\n with open(results_file, \"wb\") as file:\n pickle.dump(results, file)","def dump_to_file(final_results):\n\t#Add prefix result\n\tif final_results[\"Results\"][\"Test passed\"] == True:\n\t\ttime_now = time.time()\n\t\touput_filepath = checklists_filepath.replace(\".json\", \"\", 1) + \"_\" + datetime.datetime.fromtimestamp(time_now).strftime('%Y-%m-%d_%Hh%Mm%Ss') + \"_PASSED.json\"\n\telse:\n\t\ttime_now = time.time()\n\t\touput_filepath = checklists_filepath.replace(\".json\", \"\", 1) + \"_\" + datetime.datetime.fromtimestamp(time_now).strftime('%Y-%m-%d_%Hh%Mm%Ss') + \"_FAILED.json\"\n\twith open(ouput_filepath, 'w') as fp:\n\t\tjson.dump(final_results, fp)\n\treturn ouput_filepath","def write_krun_results_file(results, filename):\n\n with bz2.BZ2File(filename, 'wb') as file_:\n file_.write(json.dumps(results, indent=4))","def output(results):\n\n text_file = open(\"problem_1_B_output.txt\", \"w\")\n\n out = \"\"\n\n for i, line in enumerate(results):\n\n string = \"Sample {}: {}, posterior probability of {:.4f}\".format(i + 1,\n line[0],\n line[1])\n\n out += (string + \"\\n\")\n\n text_file.write(out)\n\n text_file.close()","def save(self,filename):\n f = open(filename,'w')\n f.write('Test results for %s v%s\\n' % (self.description,self.version))\n f.write('Series ran by %s\\n\\n' % self.person_name)\n for result in self.values():\n f.write('%-70s : %s\\n' % (result.id,result.outcome))\n if result.outcome != Result.PASS:\n for (kind, annotation) in result.annotations.items():\n f.write('%s:\\n%s\\n' % (kind, as_utf8(annotation)))\n f.write('\\n')\n f.write('\\n\\nPasses: %i\\n' % self.get_pass_count())\n f.write('Fails: %i\\n' % self.get_fail_count())\n f.write('Errors: %i\\n' % self.get_error_count())\n f.write('Untested: %i\\n' % self.get_untested_count())\n f.write('Skipped: %i\\n' % self.get_skipped_count())\n f.close()","def save_results(results):\n json.dump(results, open(\"results.json\", \"w\"))","def write_results(gold, pred, ratings, text):\n\n f = open(\"results.txt\", \"w\")\n for g, p, r, t in zip(gold, pred, ratings, text):\n f.write(\"%d\\t%d\\t%d\\t%s\\n\" % (g,p,r,t))\n\n f.close()","def write_result(file_name, name, entries, extra_includes, src_file_names):\r\n\r\n with open(file_name, 'w', newline='\\n') as f:\r\n f.write('// Generated by %s\\n' % os.path.basename(__file__))\r\n f.write('// Based on %s: %s\\n' %\r\n ((\"this file\" if len(src_file_names) < 2 else\r\n \"these files\"), \", \".join(src_file_names)))\r\n methods = entries[0]\r\n if len(methods) != 0:\r\n f.write(to_PyMethodDef(name, methods, extra_includes))\r\n f.write('\\n')\r\n\r\n properties = entries[1]\r\n if len(properties) != 0:\r\n f.write('\\n')\r\n f.write(to_PyGetSetDef(name, properties))","def to_file(results: Solution, file: Path) -> Path:\n\n\tlogger = getLogger()\n\n\tfilepath = _create_filepath(file)\n\n\tlogger.info(f\"Writing the best results into '{filepath.name}'...\")\n\n\tnetwork_desc = Element(\"NetworkDescription\", {\"cost\": str(results.monetaryCost()), \"Redundancy_Ratio\":str(results.redundancySatisfiedRatio()), \"Deadlines_missed\":\"Yes\" if len(results.misses) > 0 else \"No\"})\n\tworst_wctt = list(results.streams)[0].WCTT\n\taverage_wctt = 0\n\tfor stream in results.streams:\n\t\tif stream.WCTT > worst_wctt:\n\t\t\tworst_wctt = stream.WCTT\n\t\taverage_wctt += stream.WCTT\n\taverage_wctt = average_wctt / len(results.streams)\n\n\tSubElement(network_desc, \"Worst_WCTT\", {\"Time\": str(worst_wctt), \"Unit\": \"Microseconds\"})\n\tSubElement(network_desc, \"Average_WCTT\", {\"Time\": str(average_wctt), \"Unit\": \"Microseconds\"})\n\n\tfor node in results.network:\n\t\tSubElement(network_desc, \"device\", {\"name\": node.name, \"type\": node.__class__.__name__})\n\n\tfor u, v, speed in results.network.edges(data=\"speed\"):\n\t\tSubElement(network_desc, \"link\", {\"src\": u.name, \"dest\": v.name, \"speed\": str(speed)})\n\n\tfor stream in results.streams:\n\t\tSubElement(network_desc, \"stream_times\", {\"id\": stream.id, \"WCTT\" : str(stream.WCTT)})\n\n\tfor time, streams in results.misses.items():\n\t\tmiss = SubElement(network_desc, \"miss\", {\"time\": str(time)})\n\n\t\tfor stream in streams:\n\t\t\tSubElement(miss, \"stream\", {\"id\": stream.id})\n\n\tnetwork_desc = _add_streams(network_desc, results)\n\n\troot = ElementTree(network_desc)\n\tindent(root, space=\"\\t\")\n\troot.write(filepath, encoding='utf-8', xml_declaration=True)\n\n\tlogger.info(\"done.\")\n\n\treturn filepath","def write_results(location, classes, results):\n check_out(location)\n # these are tuples consisting of list of values, mean of the values and std the values\n [accuracies, precisions, recalls, f1scores] = results\n\n file_name = []\n for class_ in classes:\n if class_.endswith('.hdf5'):\n # file_name.append(re.findall('\\d/(.*).hdf5$', class_)[0])\n file_name.append(re.findall('([%.\\w-]*).hdf5$', class_)[0])\n\n with open(os.path.join(location, '-'.join(file_name) + '.results'), 'w') as out_file:\n for class_id, class_ in enumerate(classes):\n out_file.write(file_name[class_id].split('_')[0] + '\\n')\n # out_file.write(infos)\n # out_file.write(' '.join([str(header_item) for header_item in headers[class_id]]))\n out_file.write('\\n\\n')\n\n out_file.write(\"Mean accuracy is {:.2f} with a std of ({:.2f}).\\n\".format(accuracies[1], accuracies[2]))\n out_file.write('\\n'.join([str(accuracy) for accuracy in accuracies[0]]) + '\\n')\n\n if len(classes) == 2:\n out_file.write(\"\\nMean precision is {:.2f} with a std of ({:.2f}).\\n\".format(precisions[1], precisions[2]))\n out_file.write('\\n'.join([str(precision) for precision in precisions[0]]) + '\\n')\n\n out_file.write(\"\\nMean recall is {:.2f} with a std of ({:.2f}).\\n\".format(recalls[1], recalls[2]))\n out_file.write('\\n'.join([str(recall) for recall in recalls[0]]) + '\\n')\n\n out_file.write(\"\\nMean f1 score is {:.2f} with a std of ({:.2f}).\\n\".format(f1scores[1], f1scores[2]))\n out_file.write('\\n'.join([str(f1score) for f1score in f1scores[0]]) + '\\n')","def print_json_file(results, file):\n print(\"Writing results to {}...\".format(file.name))\n for json in results:\n file.write(\"\\n########## Result for IP {} ##########\\n\".format(json['ip']))\n pprint.pprint(json, stream=file)\n file.write('######################################\\n')","def write_results(file_path, predictions):\n with open(file_path, \"w\") as csv_file:\n writer = csv.writer(csv_file, delimiter=\",\")\n writer.writerow([\"Id\", \"Bound\"])\n for id, bound in enumerate(predictions):\n writer.writerow([id, bound])","def write_output():\n f = open(OUTPUT_FILE, 'w')\n for case_index, words in get_output():\n f.write('Case #%d: %s\\n' % (case_index, ' '.join(words)))\n f.close()","def file_output(matches: list, output_file_name: str = 'matches.txt'):\n with open(\"test/Matches/\" + output_file_name, 'w') as f:\n for match in matches:\n for event in match.events:\n f.write(\"%s\\n\" % event.payload)\n f.write(\"\\n\")","def write_output(self):","def save_results(self, results, file_name, file_type):\n if file_type == 'csv':\n csv_filename = '{}.csv'.format(file_name)\n\n with open(csv_filename, 'w', newline='') as file:\n writer = csv.writer(file)\n writer.writerows(results)","def save_submission(results, file_name='submission.csv'):\n submission_path = path.join('..', 'output', file_name)\n results.to_csv(submission_path)","def write(self, filename):\n pass","def write(self, filename):\n pass","def write_result_to_file(duration_string, statistic):\n\n with open(\"scores.txt\", \"a\") as results_file:\n for player_name in statistic.keys():\n if player_name == 'draw':\n continue\n results_file.write(\"{} won {} number of times\\n\".format(player_name, statistic[player_name]))\n results_file.write(\"draws = {} times\\n\".format(statistic.get(\"draw\", 0)))\n results_file.write(\"{}\\n\".format(duration_string))\n today = date.today()\n results_file.write(\n \"This game was played on {}, {}, {}\\n\\n\".format(today.day, calendar.month_name[today.month], today.year))","def write_results(frame, r):\n # cast single precision to double precision\n if np.iscomplexobj(r):\n r = np.array(r, dtype=complex)\n else:\n r = np.array(r, dtype=float)\n data = np.array(r).flatten().view(float)\n filen = frame.f_globals[\"__file__\"]\n funcname = frame.f_code.co_name\n identifier = \"{}__{}\".format(filen.split(\"test_\", 1)[1][:-3],\n funcname)\n text = pathlib.Path(\"data.txt\")\n zipf = pathlib.Path(identifier+\".zip\")\n # remove existing files\n if text.exists():\n text.unlink()\n if zipf.exists():\n zipf.unlink()\n # save text\n np.savetxt(str(text), data, fmt=\"%.10f\")\n # make zip\n with zipfile.ZipFile(str(zipf),\n \"w\",\n compression=zipfile.ZIP_DEFLATED) as arc:\n arc.write(str(text))\n text.unlink()","def writeOut(self):\r\n with open(self.fname, 'w') as f:\r\n for i in range(10):\r\n score = self.getNextHighest()\r\n if score is not None:\r\n f.write('%s %s\\n' % (score.name,\r\n score.score))\r\n pass","def write_output(arr, filename):\n print('Started writing the output..')\n f = open(filename, 'w')\n for a in arr:\n f.write(str(a) + '\\n')\n f.close()\n print('Done!, Open the file to see the approved loans.')","def test_write_race_results_to_file():\n number = random.randint(1, 3)\n f1.write_race_results_to_file(number)\n with open(f\"results_for_race_{number}.txt\", encoding=\"utf-8\") as opened_file:\n list_of_lines = opened_file.readlines()\n assert len(list_of_lines) == 13","def create_file_output(self, results):\n for key, value in results.table_output.items():\n name_timestamp = key.split('&')\n _name = name_timestamp[0]\n timestamp = name_timestamp[1]\n file_name = output_file_prefix + \"-\" + _name + \".csv\"\n if file_name not in self.file_creation_set:\n self._header_written = False\n self.file_creation_set.update([file_name])\n for row in value:\n with open(file_name, 'a+') as file_to_write:\n row.update({'Timestamp': timestamp})\n _keys = row.keys()\n file_output = csv.DictWriter(file_to_write, _keys)\n if not self._header_written:\n file_output.writeheader()\n self._header_written = True\n file_output.writerow(row)\n file_to_write.close()\n return results","def save_results(predictions, filename):\n with open(filename, 'w') as f:\n f.write(\"id,ACTION\\n\")\n for i, pred in enumerate(predictions):\n f.write(\"%d,%f\\n\" % (i + 1, pred))","def writeOutput(self, output):","def write_to_output(output):\n try:\n # changing current directory to script directory\n OutputWrite.change_to_script_directory(__file__)\n # writing the output a file\n timestamp_in_secs = time.time()\n time_stamp_readable = datetime.datetime.fromtimestamp(\n timestamp_in_secs).strftime(\"%Y_%m_%d-%Ih_%Mm_%Ss_%p\")\n try:\n if not os.path.isdir('../results'):\n os.chdir('..')\n print('Current directory {0}'.format(os.getcwd()))\n os.mkdir('./results')\n OutputWrite.change_to_script_directory(__file__)\n except OSError as _ex_:\n print(\"Unable to create results directory {0}\".format(_ex_))\n abspath = os.path.abspath('..')\n print('abspath of ..', abspath)\n path = OutputWrite.create_dir_structure()\n file_name = os.path.join(path, 'output_' +\n time_stamp_readable)\n print('The file name after joining', file_name)\n with open(file_name, 'w') as file_obj:\n file_obj.write(output)\n\n except FileNotFoundError as err:\n print('Unable write the test results into the file {0}'.\n format(err))","def write(cls, vas):\n with open(Y, 'w') as f_i:\n for items in vas:\n f_i.write('%s ' % items)\n print(\"File written successfully. Check out \\\"output.txt\\\" file\")\n f_i.close()","def write_result(self, file_path):\n f = open(file_path, \"a\")\n f.write(\"{}\\t{}\\n\".format(*[self.name, str(self.ROC_AUC_value)]))\n f.close()","def _writeMockResultFile(result):\n with open(result.filename, 'w') as f:\n f.write('<?xml version=\"1.0\" encoding=\"UTF-8\"?>\\n')\n if len(result.suites) > 1 or result.noSuitesRoot is False:\n f.write('<testsuites>\\n')\n for suite in result.suites:\n f.write('<testsuite tests=\"'+str(suite.tests)+'\" failures=\"'+str(suite.fail)+'\" time=\"'+str(suite.time)+'\" errors=\"'+str(suite.errors)+'\" name=\"'+suite.name+'\">\\n')\n for case in suite.cases:\n f.write('<testcase name=\"'+case.name+'\" status=\"run\" time=\"'+str(case.time)+'\" classname=\"'+case.classname+'\">\\n')\n for error in case.errorList:\n f.write('<failure message=\"'+error.value+'\" type=\"'+error.value+'\"/>\\n')\n f.write('</testcase>\\n')\n f.write('</testsuite>\\n')\n if len(result.suites) > 1 or result.noSuitesRoot is False:\n f.write('</testsuites>\\n')","def save_to_file(result, date):\n try:\n os.mkdir('/Users/yueyang/Downloads/serp-626-75-json', mode=0o744)\n except FileExistsError:\n # print('Directory already exists.')\n pass\n\n filename = '{0}.json'.format(date) #datetime.today().strftime('%m-%d-%Y'), query)\n with open(os.path.join('/Users/yueyang/Downloads/serp-626-75-json', filename), 'w') as f:\n json.dump(result, f, indent=4)\n print('Saved search results to {0}'.format(f.name))","def write_output(basis, filename):\n\n logging.info('Writing output to {}'.format(filename))\n\n basis.to_csv(filename)","def write_file(self):\n if self._write_file == None:\n return\n\n try:\n out = file(self._write_file, \"w\")\n except IOError, e:\n print e\n sys.exit(1)\n out.writelines(\"A cases\") \n out.close()","def exportEvaluation(self, results, url):\r\n # research\r\n profprint()\r\n if not os.path.exists(url):\r\n print \"creating new results file: \",url\r\n open(url, 'w').close()\r\n myfile = open(url, 'a')\r\n\r\n wr = csv.writer(myfile)\r\n r = numpy.array(results)\r\n if len(r.shape) == 1:\r\n wr.writerow(results)\r\n else:\r\n wr.writerows(results)","def write_results(self, results):\n predictions = open('hmm_results.csv', 'w')\n predictions.write(\"Type,Prediction\")\n for type in results:\n if type == 'O':\n continue\n predictions.write(\"\\n\" + str(type) + \",\")\n for interval in results[type]:\n predictions.write(str(interval) + \" \")\n predictions.close()","def write_results_to_file(test_results_dict, results_json_file, test_summary, summary_file):\n try:\n logging.info(\"Removing previous version of results file...\")\n if os.path.exists(results_json_file):\n os.remove(results_json_file)\n if os.path.exists(summary_file):\n os.remove(summary_file)\n except Exception as e:\n logging.error(\"Deleting file failed with error '{ERROR}'\".format(ERROR=e))\n\n try:\n logging.info(\"Writing test results to JSON file '{FILE}'...\".format(FILE=results_json_file))\n with open(results_json_file, 'w', encoding='utf-8') as results_json:\n json.dump(test_results_dict, results_json, ensure_ascii=False, indent=4)\n\n logging.info(\"Writing test summary to file '{FILE}'...\".format(FILE=summary_file))\n f = open(summary_file, \"w\")\n f.write(test_summary)\n f.close()\n except Exception as e:\n logging.error(\"Writing test results to files failed with error '{ERROR}'\".format(ERROR=e))","def __export_file(self, filename, output):\n outfile = open(filename, \"w\")\n outfile.write(output)\n outfile.close\n print(\"Output written to file: \" + filename + \"\\n\")","def write(self, outfile, rebasings=None):\r\n raise NotImplementedError()","def save_result(self, file_id, data):\n filename = path.join(\n self._ext_config['dirresults'],\n \"{0}.{1}\".format(file_id, self.type_file)\n )\n with open(filename, 'w') as file:\n file.write(data)\n logging.info(\"File %s has beed saved!\" % filename)","def write_results(filename):\n \n with open(filename, 'w', newline='') as fn:\n writer = csv.writer(fn)\n writer.writerows([('id', 'number_of_places_available'), ''])\n pair: Point = yield # where x is the user id and y is the number of places available\n while pair is not None:\n writer.writerow(pair)\n pair = yield","def exportEvaluation(self,results,url):\n profbox()\n if not os.path.exists(url):\n open(url, 'w').close()\n myfile = open(url, 'a')\n\n wr = csv.writer(myfile)\n r = numpy.array(results)\n if len(r.shape) == 1:\n wr.writerow(results)\n else:\n wr.writerows(results)","def _write_output_file(output: str, file_name: str):\n\tfile1 = open(file_name, 'w')\n\tfile1.write(output)\n\tfile1.close()","def sequences_to_file(results, outfile_name):\r\n\r\n f = open(outfile_name, 'w+')\r\n for label, seq in results:\r\n output_lines = []\r\n output_lines.append(\">%s\\n\" % label)\r\n output_lines.append(\"%s\\n\" % seq)\r\n f.writelines(output_lines)\r\n f.close()","def write_to_file(output, test_case_name, path):\n path_to_store = OutputWrite.make_test_dir(path, test_case_name)\n time_stamp = OutputWrite.get_time_stamp()\n try:\n LOG.debug('Changing the dir to {0}'.format(path_to_store))\n os.chdir(path_to_store)\n except Exception as _ex_:\n LOG.exception('Error :{0}'.format(_ex_))\n else:\n file_name = os.path.join(path_to_store, test_case_name +\n time_stamp)\n LOG.debug('The file name after joining = {0}'.format(file_name))\n try:\n LOG.debug('Writing Test case output to the file')\n with open(file_name, 'w') as file_obj:\n file_obj.write(output)\n except FileNotFoundError as _ex_:\n LOG.exception('Error : {0}'.format(_ex_))","def writeFile(fileName, profile, singleScores, bestMotifs, dnaScores, bestMotif):\n with open(fileName, 'w+') as f:\n f.write(strftime(\"Created on: %Y-%m-%d %H:%M:%S\\n\", localtime()))\n f.write('Best Motifs: ')\n f.write('\\n')\n json.dump(bestMotif, f)\n f.write('\\n')\n f.write('Motifs Profile: ')\n f.write('\\n')\n json.dump(profile, f)\n f.write('\\n')\n f.write('Single Scores: ')\n f.write('\\n')\n for i in range(0, len(singleScores)):\n json.dump(bestMotifs[i], f)\n f.write(': ')\n json.dump(singleScores[i], f)\n f.write('\\n')\n f.write('Motifs that have a better score than the worst scoring one: ')\n f.write('\\n')\n for scores in dnaScores:\n json.dump(scores, f)\n f.write('\\n')","def save_outputs(self):\n write_pickled(join(self.output_folder, \"results.pkl\"), self.get_results())","def append_to_file_storage(avm_results):\n with open(AVM_RESULTS, \"a\", encoding=\"utf-8\") as open_file:\n open_file.write(avm_results)","def write(self, f):\n if self.best_mhc_align:\n mhc_align_str = self.best_mhc_align.subject_str()\n mhc_score_str = str(self.best_mhc_align.bit_score)\n else:\n mhc_align_str = \".\"\n mhc_score_str = \"0\"\n\n if self.best_non_mhc_align:\n non_mhc_align_str = self.best_non_mhc_align.subject_str()\n non_mhc_score_str = str(self.best_non_mhc_align.bit_score)\n else:\n non_mhc_align_str = \".\"\n non_mhc_score_str = \"0\"\n \n f.write(\"\\t\".join([self.locus, self.short_samp_id, self.name,\n str(self.length), mhc_align_str, non_mhc_align_str,\n mhc_score_str, non_mhc_score_str,\n str(self.n_mhc_align), str(self.n_non_mhc_align)]) + \"\\n\")","def write_results(results):\n fields = results[0].keys()\n with open('results.csv', 'w') as f:\n dw = csv.DictWriter(f, fieldnames=fields, delimiter='|')\n dw.writer.writerow(list(dw.fieldnames))\n dw.writerows(results)","def write(self, out):","def writeAlltoFile(self):\n with open(self._fname, 'w') as f:\n for elem in self.getAll():\n line = self._writeGratoLine(elem)\n f.write(line + \"\\n\")\n f.close()","def writing_get_game(file_name, title):\n result = str(reports.get_game(file_name, title))\n with open (\"report_for_judy_part2.txt\", \"+a\") as f:\n f.write(result)\n f.write(\"\\n\")","def write(self, fname):\n pass","def _toFile(self):\n pass","def write(self, outputFile):\n \n try: \n f = open(outputFile + '.py', 'w')\n for trail in self.trails: \n f.write(\"[\")\n for index in trail:\n f.write(\"({0}, {1}), \".format(*index)) \n f.write(\"]\\n\")\n \n except IOError, e:\n msg = \"Exception encountered when attempting \" + \\\n \"to write data to file: {0}.\" + \\\n \"\\n\\t -- Exception was: {1}\" + \\\n \"\\n\\t For help use --help\".format(outputFile, e)\n raise Usage(e)","def outputFunc(filename, resultList):\n #assert len(parks) == 3\n \n f = open(filename, 'wt')\n \n try:\n writer = csv.writer(f)\n for i in range(len(resultList)):\n print resultList[0]\n writer.writerow(resultList[i])\n \n finally:\n f.close()","def save_results(self, path):\n create_folder(path)\n self.get_scores().to_csv(path + r'/scores.csv', index=False)\n self.get_results().to_csv(path + r'/results.csv', index=False)\n self.get_pivot_last_epoch().to_csv(path + r'/pivot_last_epoch.csv', index=True)","def WriteFlowResults(self, results):\n self._WriteFlowResultsOrErrors(self.flow_results, results)","def export_results(path: str):\n _, ext = Utils.get_filename_ext(path)\n\n if ext not in [\".md\", \".json\", \".csv\"]:\n click.echo(\"Output file must be of type markdown, csv or json.\\n\", err=True)\n return\n\n with open(path, \"w+\") as out:\n Exporter.write(Format(Utils.format_to_int(ext)), out)\n\n click.echo(f\"Successfully exported results to {path}\\n\")","def write_to_files():\n\t# Create output files\n\toutput = [None, \\\n\t\t open(\"priority-1.txt\", \"w\"), \\\n\t\t open(\"priority-2.txt\", \"w\"), \\\n\t\t open(\"priority-3.txt\", \"w\"), \\\n\t\t open(\"priority-4.txt\", \"w\"), \\\n\t\t open(\"priority-5.txt\", \"w\"), ]\n\n\t# Loop over all fields and write them to the correct file\n\tfor field in sorted(reportlog.keys()):\n\t\tpriority = reportlog[field]['priority']\n\t\tlabel = reportlog[field]['label']\n\n\t\toutput[priority].write(\"intphas_%s\\t%s\\n\" % (field, label))\n\t\toutput[priority].flush()\n\n\t# Close files\n\tfor i in [1,2,3,4,5]:\n\t\toutput[i].close()","def finalize_result(self):\n logging.debug(\"finalize_result()\")\n with open(self.html_file, \"a\") as result_file:\n result_file.write(\"<br/>Analyzis successful\")\n with open(self.txt_file, \"a\") as result_file:\n result_file.write(\"Analyzis successful\")","def write(self, file):\n\n # Initialize output buffer\n out = ''\n\n # Print specification\n for key, value in self.specification.items():\n out += f'{key} : {value}\\n'\n\n # Print the tour\n if self.tour:\n out += 'TOUR_SECTION\\n'\n for s in self.tour:\n out += str(s) + '\\n'\n out += '-1\\n'\n\n # Append EOF\n out += 'EOF\\n'\n\n # Write to file\n with open(file, 'w') as f:\n f.write(out)","def dump(self, filename=None):\n if filename is None:\n current_datetime = datetime.datetime.now()\n filename = current_datetime.strftime(\"results_%Y%m%d%H%M%S.csv\")\n print(\"Writing results to \\\"{}\\\"\".format(filename))\n identifiers = \"\"\n values = \"\"\n for entry in self.log.items():\n identifiers += ((\";\" if len(identifiers) > 0 else \"\")\n + str(entry[0]))\n values += \";\" + str(entry[1]) if len(values) > 0 else str(entry[1])\n with open(filename, 'a') as f:\n f.write(identifiers + \"\\n\")\n f.write(values + \"\\n\")","def output_results(self, filename):\n\n self.data.plot(title='Result of applying {} onto data set'.format(self.transformations[-1]))\n plt.savefig(\"results/{}.png\".format(filename))\n plt.close()","def write_file(self):\n rl_df, lift_df = self.create_df()\n\n number = re.findall('\\d+', self.url)[0]\n\n if self.write is True:\n with open('house_{}.csv'.format(number), 'w',\n encoding='utf-8-sig') as file:\n rl_df.to_csv(file, sep=';')\n with open('house_lifts_{}.csv'.format(number), 'w',\n encoding='utf-8-sig') as file2:\n lift_df.to_csv(file2, sep=';')","def make_file(self):\n\n f = open(get_output_path(), \"w\")\n \n f.write(self.export())\n \n f.close()\n\n return self","def write_output_file(self, index):\n ctx = self.block_store.make_local_output(self.expected_outputs[index])\n self.open_output_contexts[index] = ctx\n return ctx.get_filename()","def send_results_file_json(**kwargs):\n try:\n logging.debug(\"Opening json output file for writing\")\n with open(kwargs[\"output_file_json\"], \"w\") as file_json_open:\n logging.info(\n \"Writing to output json file: \" + kwargs[\"output_file_json\"]\n )\n file_json_open.write(kwargs[\"results_dataset_json\"])\n return True\n except IOError:\n logging.exception(\"Error writing results to json output file\")\n return False","def write_to_file(self, filepath, mode = \"a\"): \n if \"r\" in mode: \n print(\"Only accepts write and append modes\")\n return \n with open(filepath, mode) as f: \n f.write(\"{}\\n\".format(self.title))\n verified, seen, ratio = self.get_verified_ratio()\n f.write(\"Verified Names: {}\\n\".format(str(verified)))\n f.write(\"Names: {}\\n\".format(str(seen)))\n f.write(\"Ratio: {}\\n\".format(str(ratio)))","def write_the_contents_to_the_same_file(self):\n if not len(self.student_list):\n print('There is no contents to write')\n return\n\n if self._filename is None:\n self._filename = self.input_filename()\n\n with open(self._filename, 'w') as OUT:\n OUT.write(self.student_list.to_csv(date_format='%Y-%m-%d',\n sep='\\t', header=False, columns=self.columns_to_save))\n print(f'Data are saved into {self._filename!r}')","def log_results(self, path):\n pd.DataFrame(self.results).to_csv(path)","def save_to_file(self) -> None:\n abs_errors = 0\n art_errors = 0\n for i, doc in enumerate(tqdm(self.results[\"documents\"])):\n paper, abstract = self.__get_article_and_abstract(doc[\"eid\"])\n if abstract:\n abstract_file = open(self.out_dir + \"[{}] Abstract_{}.txt\".format(doc[\"date\"], doc[\"title\"]), \"w\")\n abstract_file.write(abstract)\n abstract_file.close()\n else:\n abs_errors += 1\n if paper:\n file = open(self.out_dir + \"[{}] {}.txt\".format(doc[\"date\"], doc[\"title\"]), \"w\")\n file.write(paper)\n file.close()\n else:\n art_errors += 1\n print(\"Not saved/found: {} abstracts, {} articles\".format(abs_errors, art_errors))","def write_output_file(self, query_nr, doc_scores,\n output_file_path, top_n=1000):\n \n # Sort by score and select the n highest scored documents\n top_n_doc_scores = dict(sorted(doc_scores.items(),\n key = itemgetter(1), reverse = True)[:top_n])\n \n with open(output_file_path, \"a\") as f:\n doc_rank = 1\n for cord_uid, doc_score in top_n_doc_scores.items():\n string = f\"{query_nr} Q0 {cord_uid} {doc_rank} {doc_score} TEST-RUN-0\\n\"\n f.write(string)\n doc_rank += 1","def write_output(series, filename):\n\n logging.info('Writing output')\n\n df = series.reset_index()\n\n df.columns = ['subject_id', 'classification']\n\n df.to_csv(filename, index=False)"],"string":"[\n \"def write_result(self, file_name):\\n f = file(file_name, \\\"w\\\")\\n f.write(self.m_result)\\n f.close()\",\n \"def write_results_to_disk(self, result_path,results):\\n with open(result_path+\\\"/results.txt\\\",\\\"w+\\\") as out:\\n\\n for query_num in results:\\n for doc_num in results[query_num]:\\n out.write(str(query_num)+\\\" 0 \\\"+doc_num+\\\" 1 42.38 mt\\\\n\\\")\\n out.close()\",\n \"def write_results(results):\\n with RESULTS_PATH.open(\\\"w\\\") as writer:\\n csvwriter = csv.writer(writer)\\n csvwriter.writerows(results)\",\n \"def write_results_to_file(stocks_to_write):\\n date = datetime.date.today()\\n date_str = str(date.year) + '-' + str(date.month) + '-' + str(date.day)\\n file_name_core = 'results-' + date_str\\n\\n with open(\\\"results/txt/\\\" + file_name_core + \\\".txt\\\",\\n 'w') as txt_results_file:\\n\\n for stock in stocks_to_write:\\n txt_results_file.write(stock.make_one_line_report() + \\\"\\\\n\\\")\\n\\n with open(\\\"results/csv/\\\" + file_name_core + \\\".csv\\\",\\n 'w') as csv_results_file:\\n\\n writer = csv.writer(csv_results_file)\\n writer.writerow(StockData.get_csv_data_headings())\\n for stock in stocks_to_write:\\n writer.writerow(stock.get_csv_data_list())\",\n \"def write_results(filename, algorithm_name, qty_array, min_coins):\\n with open(filename, 'a') as f:\\n f.write('{0}\\\\n'.format(algorithm_name))\\n f.write('{0}\\\\n'.format(qty_array))\\n f.write('{0}\\\\n'.format(min_coins))\",\n \"def write_result(file, result):\\n name = result[0]\\n dataset = result[1]\\n file.write(name + \\\"\\\\n\\\")\\n for data, values in dataset.items():\\n file.write(\\\"%s %.3f %.3f \\\\n\\\" %(data, np.mean(values),np.std(values)))\",\n \"def export(fileName, result):\\n with open(fileName, 'a') as output:\\n output.write(result)\",\n \"def fileWrite(content):\\n file = open('./result.txt', 'w')\\n file.write(content)\\n file.close()\",\n \"def save_results(PATH, data, filename):\\n with open(PATH + '/' + filename + \\\".txt\\\",\\\"w\\\") as file:\\n file.write(\\\"Results of heuristic models with mean and standard deviation.\\\\n\\\")\\n for result in data:\\n write_result(file, result)\\n file.close()\\n print('results saved in:'+ PATH + '/' + filename + \\\".txt\\\")\",\n \"def write_result_to_file(self):\\n self.__test_result[Result.__RUN] = self.__run\\n with open(self.__json_file_path, \\\"w+\\\") as outfile:\\n json.dump(self.__test_result, outfile,\\n ensure_ascii=False, indent=2)\",\n \"def write_results_to_file(self, param):\\n results_file = open('%stheoretical_results_%s.yml' % (directory, str(param)), 'w')\\n results_file.write(yaml.dump(self.mean_time_to_absorbtion, default_flow_style=False))\\n results_file.close()\",\n \"def write_to_file(self, results):\\n with open(self.outputFilename, \\\"w\\\") as csvFile:\\n csvWriter = csv.writer(csvFile, delimiter=',') \\n title_row = ('asset_id', 'component_id', 'latitude', 'longitude', 'installation_date', 'commissioning_date', 'street_name', 'cabinet_id', 'nominal_wattage', 'current_time', 'current_LogValue', 'current_IsLogValueOff') \\n csvWriter.writerow(title_row)\\n for record in results:\\n csvWriter.writerow(record)\",\n \"def writeResult(self, result_path, result):\\r\\n f = open(result_path, 'w')\\r\\n f.write(self.formatResult(result))\\r\\n f.write('\\\\n')\\r\\n f.close()\",\n \"def write_results(detections, filename):\\n start = time.time()\\n\\n boxes, labels, scores = detections\\n with PathManager.open(filename, \\\"w\\\") as f:\\n for key in boxes.keys():\\n for box, label, score in zip(boxes[key], labels[key], scores[key]):\\n f.write(\\n \\\"%s,%.03f,%.03f,%.03f,%.03f,%d,%.04f\\\\n\\\"\\n % (key, box[1], box[0], box[3], box[2], label, score)\\n )\\n\\n logger.info(\\\"AVA results wrote to %s\\\" % filename)\\n logger.info(\\\"\\\\ttook %d seconds.\\\" % (time.time() - start))\",\n \"def write_result(fragment_list, results, output_file):\\n if not output_file:\\n output_file = results + os.sep + \\\"prodigal_filtered.txt\\\"\\n try:\\n with open(output_file, \\\"wt\\\") as output:\\n for fragment in fragment_list:\\n output.write(\\\"{1}{0}{2}{0}\\\".format(\\n os.linesep, fragment[0], fill(fragment[1])))\\n except IOError:\\n sys.exit(\\\"Error cannot open {0}\\\".format(output_file))\",\n \"def write_results_dat(self, output_path):\\n\\n def fstr(nb):\\n data = '%E' % nb\\n if data == 'NAN':\\n nb, power = 0,0\\n else:\\n nb, power = data.split('E')\\n nb = float(nb) /10\\n power = int(power) + 1\\n return '%.5fE%+03i' %(nb,power)\\n\\n line = '%s %s %s %i %i %i %i %s %s %s %s %s %i\\\\n' % (fstr(self.axsec), fstr(self.xerru), \\n fstr(self.xerrc), self.nevents, self.nw, self.maxit, self.nunwgt,\\n fstr(self.luminosity), fstr(self.wgt), fstr(self.xsec), fstr(self.maxwgt),\\n fstr(self.th_maxwgt), self.th_nunwgt) \\n fsock = open(output_path,'w') \\n fsock.writelines(line)\\n for i in range(len(self.ysec_iter)):\\n line = '%s %s %s %s %s %s\\\\n' % (i+1, self.ysec_iter[i], self.yerr_iter[i], \\n self.eff_iter[i], self.maxwgt_iter[i], self.yasec_iter[i]) \\n fsock.writelines(line)\",\n \"def save_result(file_name, text):\\n\\n filename = os.path.join(fileDir, 'tools/results/{}.txt'.format(file_name))\\n\\n with open(filename, \\\"a+\\\") as file:\\n file.write(text)\",\n \"def write_result(results_file, outcome, player_1, player_2):\\n # append the outcome to the file\\n with open(results_file, 'a') as f:\\n # write the outcome from player 1's perspective\\n f.write(\\\"%s %s\\\\n\\\" % (player_1, map_outcome(int(outcome))))\\n # write the outcome from player 2's perspective (hence the not)\\n f.write(\\\"%s %s\\\\n\\\" % (player_2, map_outcome(int(not outcome))))\",\n \"def write_results(self, results, fname, folder=None):\\n folder = folder or self.output_dir\\n\\n if not os.path.exists(folder):\\n logger.info(\\\"creating {} (did not exist)\\\".format(folder))\\n os.makedirs(folder)\\n\\n out_file = os.path.join(folder, fname)\\n logger.info(\\\"writing to {}\\\".format(out_file))\\n df = pd.DataFrame(results)\\n df.to_csv(out_file, index=False)\\n \\n return None\",\n \"def write_file(self):\\n\\n running_time = str(self.running_time_end - self.running_time_start)\\n rounded_running_time = '{:.10}'.format(running_time)\\n output = 'path_to_goal: ' + str(self.path_to_goal) + '\\\\n'\\n output += 'cost_of_path: ' + str(self.cost_of_path) + '\\\\n'\\n output += 'nodes_expanded: ' + str(self.nodes_expanded) + '\\\\n'\\n output += 'fringe_size: ' + str(self.fringe_size) + '\\\\n'\\n output += 'max_fringe_size: ' + str(self.max_fringe_size) + '\\\\n'\\n output += 'search_depth: ' + str(self.search_depth) + '\\\\n'\\n output += 'max_search_depth: ' + str(self.max_search_depth) + '\\\\n'\\n output += 'running_time: ' + rounded_running_time + '\\\\n'\\n\\n system_name = system()\\n if system_name == 'Windows':\\n output += 'max_ram_usage: (Not available on Windows OS)'\\n elif system_name == 'Linux':\\n output += 'max_ram_usage: ' + \\\\\\n str(getrusage(RUSAGE_SELF).ru_maxrss / 1024) + '\\\\n'\\n\\n file = open('output.txt', 'w+')\\n file.write(output)\\n print(output)\",\n \"def dump_file(str_content, filename):\\n\\n with open(\\\"results/\\\" + filename, \\\"w\\\") as f:\\n f.write(str_content)\",\n \"def write_to_file(self, filename: str) -> None:\",\n \"def _write_keypoint_results(keypoint_results, gt_folder, pred_folder):\",\n \"def writeOutput(result, fname, output_dir):\\n\\n s = str(len(result)) + \\\"\\\\n\\\" + str(result).replace(\\\",\\\", \\\"\\\")[1:-1]\\n fname = fname[:fname.rfind(\\\".\\\")] + \\\".txt\\\"\\n os.makedirs(output_dir, exist_ok=True)\\n with open(output_dir + fname, \\\"w\\\") as f:\\n f.write(s)\\n\\n print(\\\"output written to: \\\" + output_dir + fname)\",\n \"def write_results_file(self, results_list, file_path):\\n # Replace references to the malware repo with a variable.\\n malware_repo = mwcp.config.get(\\\"MALWARE_REPO\\\", None)\\n if malware_repo:\\n for results in results_list:\\n # TODO: Refactor this\\n input_file_path = results[INPUT_FILE_PATH]\\n if input_file_path.startswith(malware_repo):\\n input_file_path = \\\"{MALWARE_REPO}\\\" + input_file_path[len(malware_repo) :]\\n results[INPUT_FILE_PATH] = input_file_path\\n\\n # Write updated data to results file\\n # NOTE: We need to use dumps instead of dump to avoid TypeError.\\n with open(file_path, \\\"w\\\", encoding=\\\"utf8\\\") as results_file:\\n results_file.write(str(json.dumps(results_list, indent=4, sort_keys=True)))\",\n \"def save_results(results, results_file: str):\\n with open(results_file, \\\"wb\\\") as file:\\n pickle.dump(results, file)\",\n \"def dump_to_file(final_results):\\n\\t#Add prefix result\\n\\tif final_results[\\\"Results\\\"][\\\"Test passed\\\"] == True:\\n\\t\\ttime_now = time.time()\\n\\t\\touput_filepath = checklists_filepath.replace(\\\".json\\\", \\\"\\\", 1) + \\\"_\\\" + datetime.datetime.fromtimestamp(time_now).strftime('%Y-%m-%d_%Hh%Mm%Ss') + \\\"_PASSED.json\\\"\\n\\telse:\\n\\t\\ttime_now = time.time()\\n\\t\\touput_filepath = checklists_filepath.replace(\\\".json\\\", \\\"\\\", 1) + \\\"_\\\" + datetime.datetime.fromtimestamp(time_now).strftime('%Y-%m-%d_%Hh%Mm%Ss') + \\\"_FAILED.json\\\"\\n\\twith open(ouput_filepath, 'w') as fp:\\n\\t\\tjson.dump(final_results, fp)\\n\\treturn ouput_filepath\",\n \"def write_krun_results_file(results, filename):\\n\\n with bz2.BZ2File(filename, 'wb') as file_:\\n file_.write(json.dumps(results, indent=4))\",\n \"def output(results):\\n\\n text_file = open(\\\"problem_1_B_output.txt\\\", \\\"w\\\")\\n\\n out = \\\"\\\"\\n\\n for i, line in enumerate(results):\\n\\n string = \\\"Sample {}: {}, posterior probability of {:.4f}\\\".format(i + 1,\\n line[0],\\n line[1])\\n\\n out += (string + \\\"\\\\n\\\")\\n\\n text_file.write(out)\\n\\n text_file.close()\",\n \"def save(self,filename):\\n f = open(filename,'w')\\n f.write('Test results for %s v%s\\\\n' % (self.description,self.version))\\n f.write('Series ran by %s\\\\n\\\\n' % self.person_name)\\n for result in self.values():\\n f.write('%-70s : %s\\\\n' % (result.id,result.outcome))\\n if result.outcome != Result.PASS:\\n for (kind, annotation) in result.annotations.items():\\n f.write('%s:\\\\n%s\\\\n' % (kind, as_utf8(annotation)))\\n f.write('\\\\n')\\n f.write('\\\\n\\\\nPasses: %i\\\\n' % self.get_pass_count())\\n f.write('Fails: %i\\\\n' % self.get_fail_count())\\n f.write('Errors: %i\\\\n' % self.get_error_count())\\n f.write('Untested: %i\\\\n' % self.get_untested_count())\\n f.write('Skipped: %i\\\\n' % self.get_skipped_count())\\n f.close()\",\n \"def save_results(results):\\n json.dump(results, open(\\\"results.json\\\", \\\"w\\\"))\",\n \"def write_results(gold, pred, ratings, text):\\n\\n f = open(\\\"results.txt\\\", \\\"w\\\")\\n for g, p, r, t in zip(gold, pred, ratings, text):\\n f.write(\\\"%d\\\\t%d\\\\t%d\\\\t%s\\\\n\\\" % (g,p,r,t))\\n\\n f.close()\",\n \"def write_result(file_name, name, entries, extra_includes, src_file_names):\\r\\n\\r\\n with open(file_name, 'w', newline='\\\\n') as f:\\r\\n f.write('// Generated by %s\\\\n' % os.path.basename(__file__))\\r\\n f.write('// Based on %s: %s\\\\n' %\\r\\n ((\\\"this file\\\" if len(src_file_names) < 2 else\\r\\n \\\"these files\\\"), \\\", \\\".join(src_file_names)))\\r\\n methods = entries[0]\\r\\n if len(methods) != 0:\\r\\n f.write(to_PyMethodDef(name, methods, extra_includes))\\r\\n f.write('\\\\n')\\r\\n\\r\\n properties = entries[1]\\r\\n if len(properties) != 0:\\r\\n f.write('\\\\n')\\r\\n f.write(to_PyGetSetDef(name, properties))\",\n \"def to_file(results: Solution, file: Path) -> Path:\\n\\n\\tlogger = getLogger()\\n\\n\\tfilepath = _create_filepath(file)\\n\\n\\tlogger.info(f\\\"Writing the best results into '{filepath.name}'...\\\")\\n\\n\\tnetwork_desc = Element(\\\"NetworkDescription\\\", {\\\"cost\\\": str(results.monetaryCost()), \\\"Redundancy_Ratio\\\":str(results.redundancySatisfiedRatio()), \\\"Deadlines_missed\\\":\\\"Yes\\\" if len(results.misses) > 0 else \\\"No\\\"})\\n\\tworst_wctt = list(results.streams)[0].WCTT\\n\\taverage_wctt = 0\\n\\tfor stream in results.streams:\\n\\t\\tif stream.WCTT > worst_wctt:\\n\\t\\t\\tworst_wctt = stream.WCTT\\n\\t\\taverage_wctt += stream.WCTT\\n\\taverage_wctt = average_wctt / len(results.streams)\\n\\n\\tSubElement(network_desc, \\\"Worst_WCTT\\\", {\\\"Time\\\": str(worst_wctt), \\\"Unit\\\": \\\"Microseconds\\\"})\\n\\tSubElement(network_desc, \\\"Average_WCTT\\\", {\\\"Time\\\": str(average_wctt), \\\"Unit\\\": \\\"Microseconds\\\"})\\n\\n\\tfor node in results.network:\\n\\t\\tSubElement(network_desc, \\\"device\\\", {\\\"name\\\": node.name, \\\"type\\\": node.__class__.__name__})\\n\\n\\tfor u, v, speed in results.network.edges(data=\\\"speed\\\"):\\n\\t\\tSubElement(network_desc, \\\"link\\\", {\\\"src\\\": u.name, \\\"dest\\\": v.name, \\\"speed\\\": str(speed)})\\n\\n\\tfor stream in results.streams:\\n\\t\\tSubElement(network_desc, \\\"stream_times\\\", {\\\"id\\\": stream.id, \\\"WCTT\\\" : str(stream.WCTT)})\\n\\n\\tfor time, streams in results.misses.items():\\n\\t\\tmiss = SubElement(network_desc, \\\"miss\\\", {\\\"time\\\": str(time)})\\n\\n\\t\\tfor stream in streams:\\n\\t\\t\\tSubElement(miss, \\\"stream\\\", {\\\"id\\\": stream.id})\\n\\n\\tnetwork_desc = _add_streams(network_desc, results)\\n\\n\\troot = ElementTree(network_desc)\\n\\tindent(root, space=\\\"\\\\t\\\")\\n\\troot.write(filepath, encoding='utf-8', xml_declaration=True)\\n\\n\\tlogger.info(\\\"done.\\\")\\n\\n\\treturn filepath\",\n \"def write_results(location, classes, results):\\n check_out(location)\\n # these are tuples consisting of list of values, mean of the values and std the values\\n [accuracies, precisions, recalls, f1scores] = results\\n\\n file_name = []\\n for class_ in classes:\\n if class_.endswith('.hdf5'):\\n # file_name.append(re.findall('\\\\d/(.*).hdf5$', class_)[0])\\n file_name.append(re.findall('([%.\\\\w-]*).hdf5$', class_)[0])\\n\\n with open(os.path.join(location, '-'.join(file_name) + '.results'), 'w') as out_file:\\n for class_id, class_ in enumerate(classes):\\n out_file.write(file_name[class_id].split('_')[0] + '\\\\n')\\n # out_file.write(infos)\\n # out_file.write(' '.join([str(header_item) for header_item in headers[class_id]]))\\n out_file.write('\\\\n\\\\n')\\n\\n out_file.write(\\\"Mean accuracy is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(accuracies[1], accuracies[2]))\\n out_file.write('\\\\n'.join([str(accuracy) for accuracy in accuracies[0]]) + '\\\\n')\\n\\n if len(classes) == 2:\\n out_file.write(\\\"\\\\nMean precision is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(precisions[1], precisions[2]))\\n out_file.write('\\\\n'.join([str(precision) for precision in precisions[0]]) + '\\\\n')\\n\\n out_file.write(\\\"\\\\nMean recall is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(recalls[1], recalls[2]))\\n out_file.write('\\\\n'.join([str(recall) for recall in recalls[0]]) + '\\\\n')\\n\\n out_file.write(\\\"\\\\nMean f1 score is {:.2f} with a std of ({:.2f}).\\\\n\\\".format(f1scores[1], f1scores[2]))\\n out_file.write('\\\\n'.join([str(f1score) for f1score in f1scores[0]]) + '\\\\n')\",\n \"def print_json_file(results, file):\\n print(\\\"Writing results to {}...\\\".format(file.name))\\n for json in results:\\n file.write(\\\"\\\\n########## Result for IP {} ##########\\\\n\\\".format(json['ip']))\\n pprint.pprint(json, stream=file)\\n file.write('######################################\\\\n')\",\n \"def write_results(file_path, predictions):\\n with open(file_path, \\\"w\\\") as csv_file:\\n writer = csv.writer(csv_file, delimiter=\\\",\\\")\\n writer.writerow([\\\"Id\\\", \\\"Bound\\\"])\\n for id, bound in enumerate(predictions):\\n writer.writerow([id, bound])\",\n \"def write_output():\\n f = open(OUTPUT_FILE, 'w')\\n for case_index, words in get_output():\\n f.write('Case #%d: %s\\\\n' % (case_index, ' '.join(words)))\\n f.close()\",\n \"def file_output(matches: list, output_file_name: str = 'matches.txt'):\\n with open(\\\"test/Matches/\\\" + output_file_name, 'w') as f:\\n for match in matches:\\n for event in match.events:\\n f.write(\\\"%s\\\\n\\\" % event.payload)\\n f.write(\\\"\\\\n\\\")\",\n \"def write_output(self):\",\n \"def save_results(self, results, file_name, file_type):\\n if file_type == 'csv':\\n csv_filename = '{}.csv'.format(file_name)\\n\\n with open(csv_filename, 'w', newline='') as file:\\n writer = csv.writer(file)\\n writer.writerows(results)\",\n \"def save_submission(results, file_name='submission.csv'):\\n submission_path = path.join('..', 'output', file_name)\\n results.to_csv(submission_path)\",\n \"def write(self, filename):\\n pass\",\n \"def write(self, filename):\\n pass\",\n \"def write_result_to_file(duration_string, statistic):\\n\\n with open(\\\"scores.txt\\\", \\\"a\\\") as results_file:\\n for player_name in statistic.keys():\\n if player_name == 'draw':\\n continue\\n results_file.write(\\\"{} won {} number of times\\\\n\\\".format(player_name, statistic[player_name]))\\n results_file.write(\\\"draws = {} times\\\\n\\\".format(statistic.get(\\\"draw\\\", 0)))\\n results_file.write(\\\"{}\\\\n\\\".format(duration_string))\\n today = date.today()\\n results_file.write(\\n \\\"This game was played on {}, {}, {}\\\\n\\\\n\\\".format(today.day, calendar.month_name[today.month], today.year))\",\n \"def write_results(frame, r):\\n # cast single precision to double precision\\n if np.iscomplexobj(r):\\n r = np.array(r, dtype=complex)\\n else:\\n r = np.array(r, dtype=float)\\n data = np.array(r).flatten().view(float)\\n filen = frame.f_globals[\\\"__file__\\\"]\\n funcname = frame.f_code.co_name\\n identifier = \\\"{}__{}\\\".format(filen.split(\\\"test_\\\", 1)[1][:-3],\\n funcname)\\n text = pathlib.Path(\\\"data.txt\\\")\\n zipf = pathlib.Path(identifier+\\\".zip\\\")\\n # remove existing files\\n if text.exists():\\n text.unlink()\\n if zipf.exists():\\n zipf.unlink()\\n # save text\\n np.savetxt(str(text), data, fmt=\\\"%.10f\\\")\\n # make zip\\n with zipfile.ZipFile(str(zipf),\\n \\\"w\\\",\\n compression=zipfile.ZIP_DEFLATED) as arc:\\n arc.write(str(text))\\n text.unlink()\",\n \"def writeOut(self):\\r\\n with open(self.fname, 'w') as f:\\r\\n for i in range(10):\\r\\n score = self.getNextHighest()\\r\\n if score is not None:\\r\\n f.write('%s %s\\\\n' % (score.name,\\r\\n score.score))\\r\\n pass\",\n \"def write_output(arr, filename):\\n print('Started writing the output..')\\n f = open(filename, 'w')\\n for a in arr:\\n f.write(str(a) + '\\\\n')\\n f.close()\\n print('Done!, Open the file to see the approved loans.')\",\n \"def test_write_race_results_to_file():\\n number = random.randint(1, 3)\\n f1.write_race_results_to_file(number)\\n with open(f\\\"results_for_race_{number}.txt\\\", encoding=\\\"utf-8\\\") as opened_file:\\n list_of_lines = opened_file.readlines()\\n assert len(list_of_lines) == 13\",\n \"def create_file_output(self, results):\\n for key, value in results.table_output.items():\\n name_timestamp = key.split('&')\\n _name = name_timestamp[0]\\n timestamp = name_timestamp[1]\\n file_name = output_file_prefix + \\\"-\\\" + _name + \\\".csv\\\"\\n if file_name not in self.file_creation_set:\\n self._header_written = False\\n self.file_creation_set.update([file_name])\\n for row in value:\\n with open(file_name, 'a+') as file_to_write:\\n row.update({'Timestamp': timestamp})\\n _keys = row.keys()\\n file_output = csv.DictWriter(file_to_write, _keys)\\n if not self._header_written:\\n file_output.writeheader()\\n self._header_written = True\\n file_output.writerow(row)\\n file_to_write.close()\\n return results\",\n \"def save_results(predictions, filename):\\n with open(filename, 'w') as f:\\n f.write(\\\"id,ACTION\\\\n\\\")\\n for i, pred in enumerate(predictions):\\n f.write(\\\"%d,%f\\\\n\\\" % (i + 1, pred))\",\n \"def writeOutput(self, output):\",\n \"def write_to_output(output):\\n try:\\n # changing current directory to script directory\\n OutputWrite.change_to_script_directory(__file__)\\n # writing the output a file\\n timestamp_in_secs = time.time()\\n time_stamp_readable = datetime.datetime.fromtimestamp(\\n timestamp_in_secs).strftime(\\\"%Y_%m_%d-%Ih_%Mm_%Ss_%p\\\")\\n try:\\n if not os.path.isdir('../results'):\\n os.chdir('..')\\n print('Current directory {0}'.format(os.getcwd()))\\n os.mkdir('./results')\\n OutputWrite.change_to_script_directory(__file__)\\n except OSError as _ex_:\\n print(\\\"Unable to create results directory {0}\\\".format(_ex_))\\n abspath = os.path.abspath('..')\\n print('abspath of ..', abspath)\\n path = OutputWrite.create_dir_structure()\\n file_name = os.path.join(path, 'output_' +\\n time_stamp_readable)\\n print('The file name after joining', file_name)\\n with open(file_name, 'w') as file_obj:\\n file_obj.write(output)\\n\\n except FileNotFoundError as err:\\n print('Unable write the test results into the file {0}'.\\n format(err))\",\n \"def write(cls, vas):\\n with open(Y, 'w') as f_i:\\n for items in vas:\\n f_i.write('%s ' % items)\\n print(\\\"File written successfully. Check out \\\\\\\"output.txt\\\\\\\" file\\\")\\n f_i.close()\",\n \"def write_result(self, file_path):\\n f = open(file_path, \\\"a\\\")\\n f.write(\\\"{}\\\\t{}\\\\n\\\".format(*[self.name, str(self.ROC_AUC_value)]))\\n f.close()\",\n \"def _writeMockResultFile(result):\\n with open(result.filename, 'w') as f:\\n f.write('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\\\n')\\n if len(result.suites) > 1 or result.noSuitesRoot is False:\\n f.write('<testsuites>\\\\n')\\n for suite in result.suites:\\n f.write('<testsuite tests=\\\"'+str(suite.tests)+'\\\" failures=\\\"'+str(suite.fail)+'\\\" time=\\\"'+str(suite.time)+'\\\" errors=\\\"'+str(suite.errors)+'\\\" name=\\\"'+suite.name+'\\\">\\\\n')\\n for case in suite.cases:\\n f.write('<testcase name=\\\"'+case.name+'\\\" status=\\\"run\\\" time=\\\"'+str(case.time)+'\\\" classname=\\\"'+case.classname+'\\\">\\\\n')\\n for error in case.errorList:\\n f.write('<failure message=\\\"'+error.value+'\\\" type=\\\"'+error.value+'\\\"/>\\\\n')\\n f.write('</testcase>\\\\n')\\n f.write('</testsuite>\\\\n')\\n if len(result.suites) > 1 or result.noSuitesRoot is False:\\n f.write('</testsuites>\\\\n')\",\n \"def save_to_file(result, date):\\n try:\\n os.mkdir('/Users/yueyang/Downloads/serp-626-75-json', mode=0o744)\\n except FileExistsError:\\n # print('Directory already exists.')\\n pass\\n\\n filename = '{0}.json'.format(date) #datetime.today().strftime('%m-%d-%Y'), query)\\n with open(os.path.join('/Users/yueyang/Downloads/serp-626-75-json', filename), 'w') as f:\\n json.dump(result, f, indent=4)\\n print('Saved search results to {0}'.format(f.name))\",\n \"def write_output(basis, filename):\\n\\n logging.info('Writing output to {}'.format(filename))\\n\\n basis.to_csv(filename)\",\n \"def write_file(self):\\n if self._write_file == None:\\n return\\n\\n try:\\n out = file(self._write_file, \\\"w\\\")\\n except IOError, e:\\n print e\\n sys.exit(1)\\n out.writelines(\\\"A cases\\\") \\n out.close()\",\n \"def exportEvaluation(self, results, url):\\r\\n # research\\r\\n profprint()\\r\\n if not os.path.exists(url):\\r\\n print \\\"creating new results file: \\\",url\\r\\n open(url, 'w').close()\\r\\n myfile = open(url, 'a')\\r\\n\\r\\n wr = csv.writer(myfile)\\r\\n r = numpy.array(results)\\r\\n if len(r.shape) == 1:\\r\\n wr.writerow(results)\\r\\n else:\\r\\n wr.writerows(results)\",\n \"def write_results(self, results):\\n predictions = open('hmm_results.csv', 'w')\\n predictions.write(\\\"Type,Prediction\\\")\\n for type in results:\\n if type == 'O':\\n continue\\n predictions.write(\\\"\\\\n\\\" + str(type) + \\\",\\\")\\n for interval in results[type]:\\n predictions.write(str(interval) + \\\" \\\")\\n predictions.close()\",\n \"def write_results_to_file(test_results_dict, results_json_file, test_summary, summary_file):\\n try:\\n logging.info(\\\"Removing previous version of results file...\\\")\\n if os.path.exists(results_json_file):\\n os.remove(results_json_file)\\n if os.path.exists(summary_file):\\n os.remove(summary_file)\\n except Exception as e:\\n logging.error(\\\"Deleting file failed with error '{ERROR}'\\\".format(ERROR=e))\\n\\n try:\\n logging.info(\\\"Writing test results to JSON file '{FILE}'...\\\".format(FILE=results_json_file))\\n with open(results_json_file, 'w', encoding='utf-8') as results_json:\\n json.dump(test_results_dict, results_json, ensure_ascii=False, indent=4)\\n\\n logging.info(\\\"Writing test summary to file '{FILE}'...\\\".format(FILE=summary_file))\\n f = open(summary_file, \\\"w\\\")\\n f.write(test_summary)\\n f.close()\\n except Exception as e:\\n logging.error(\\\"Writing test results to files failed with error '{ERROR}'\\\".format(ERROR=e))\",\n \"def __export_file(self, filename, output):\\n outfile = open(filename, \\\"w\\\")\\n outfile.write(output)\\n outfile.close\\n print(\\\"Output written to file: \\\" + filename + \\\"\\\\n\\\")\",\n \"def write(self, outfile, rebasings=None):\\r\\n raise NotImplementedError()\",\n \"def save_result(self, file_id, data):\\n filename = path.join(\\n self._ext_config['dirresults'],\\n \\\"{0}.{1}\\\".format(file_id, self.type_file)\\n )\\n with open(filename, 'w') as file:\\n file.write(data)\\n logging.info(\\\"File %s has beed saved!\\\" % filename)\",\n \"def write_results(filename):\\n \\n with open(filename, 'w', newline='') as fn:\\n writer = csv.writer(fn)\\n writer.writerows([('id', 'number_of_places_available'), ''])\\n pair: Point = yield # where x is the user id and y is the number of places available\\n while pair is not None:\\n writer.writerow(pair)\\n pair = yield\",\n \"def exportEvaluation(self,results,url):\\n profbox()\\n if not os.path.exists(url):\\n open(url, 'w').close()\\n myfile = open(url, 'a')\\n\\n wr = csv.writer(myfile)\\n r = numpy.array(results)\\n if len(r.shape) == 1:\\n wr.writerow(results)\\n else:\\n wr.writerows(results)\",\n \"def _write_output_file(output: str, file_name: str):\\n\\tfile1 = open(file_name, 'w')\\n\\tfile1.write(output)\\n\\tfile1.close()\",\n \"def sequences_to_file(results, outfile_name):\\r\\n\\r\\n f = open(outfile_name, 'w+')\\r\\n for label, seq in results:\\r\\n output_lines = []\\r\\n output_lines.append(\\\">%s\\\\n\\\" % label)\\r\\n output_lines.append(\\\"%s\\\\n\\\" % seq)\\r\\n f.writelines(output_lines)\\r\\n f.close()\",\n \"def write_to_file(output, test_case_name, path):\\n path_to_store = OutputWrite.make_test_dir(path, test_case_name)\\n time_stamp = OutputWrite.get_time_stamp()\\n try:\\n LOG.debug('Changing the dir to {0}'.format(path_to_store))\\n os.chdir(path_to_store)\\n except Exception as _ex_:\\n LOG.exception('Error :{0}'.format(_ex_))\\n else:\\n file_name = os.path.join(path_to_store, test_case_name +\\n time_stamp)\\n LOG.debug('The file name after joining = {0}'.format(file_name))\\n try:\\n LOG.debug('Writing Test case output to the file')\\n with open(file_name, 'w') as file_obj:\\n file_obj.write(output)\\n except FileNotFoundError as _ex_:\\n LOG.exception('Error : {0}'.format(_ex_))\",\n \"def writeFile(fileName, profile, singleScores, bestMotifs, dnaScores, bestMotif):\\n with open(fileName, 'w+') as f:\\n f.write(strftime(\\\"Created on: %Y-%m-%d %H:%M:%S\\\\n\\\", localtime()))\\n f.write('Best Motifs: ')\\n f.write('\\\\n')\\n json.dump(bestMotif, f)\\n f.write('\\\\n')\\n f.write('Motifs Profile: ')\\n f.write('\\\\n')\\n json.dump(profile, f)\\n f.write('\\\\n')\\n f.write('Single Scores: ')\\n f.write('\\\\n')\\n for i in range(0, len(singleScores)):\\n json.dump(bestMotifs[i], f)\\n f.write(': ')\\n json.dump(singleScores[i], f)\\n f.write('\\\\n')\\n f.write('Motifs that have a better score than the worst scoring one: ')\\n f.write('\\\\n')\\n for scores in dnaScores:\\n json.dump(scores, f)\\n f.write('\\\\n')\",\n \"def save_outputs(self):\\n write_pickled(join(self.output_folder, \\\"results.pkl\\\"), self.get_results())\",\n \"def append_to_file_storage(avm_results):\\n with open(AVM_RESULTS, \\\"a\\\", encoding=\\\"utf-8\\\") as open_file:\\n open_file.write(avm_results)\",\n \"def write(self, f):\\n if self.best_mhc_align:\\n mhc_align_str = self.best_mhc_align.subject_str()\\n mhc_score_str = str(self.best_mhc_align.bit_score)\\n else:\\n mhc_align_str = \\\".\\\"\\n mhc_score_str = \\\"0\\\"\\n\\n if self.best_non_mhc_align:\\n non_mhc_align_str = self.best_non_mhc_align.subject_str()\\n non_mhc_score_str = str(self.best_non_mhc_align.bit_score)\\n else:\\n non_mhc_align_str = \\\".\\\"\\n non_mhc_score_str = \\\"0\\\"\\n \\n f.write(\\\"\\\\t\\\".join([self.locus, self.short_samp_id, self.name,\\n str(self.length), mhc_align_str, non_mhc_align_str,\\n mhc_score_str, non_mhc_score_str,\\n str(self.n_mhc_align), str(self.n_non_mhc_align)]) + \\\"\\\\n\\\")\",\n \"def write_results(results):\\n fields = results[0].keys()\\n with open('results.csv', 'w') as f:\\n dw = csv.DictWriter(f, fieldnames=fields, delimiter='|')\\n dw.writer.writerow(list(dw.fieldnames))\\n dw.writerows(results)\",\n \"def write(self, out):\",\n \"def writeAlltoFile(self):\\n with open(self._fname, 'w') as f:\\n for elem in self.getAll():\\n line = self._writeGratoLine(elem)\\n f.write(line + \\\"\\\\n\\\")\\n f.close()\",\n \"def writing_get_game(file_name, title):\\n result = str(reports.get_game(file_name, title))\\n with open (\\\"report_for_judy_part2.txt\\\", \\\"+a\\\") as f:\\n f.write(result)\\n f.write(\\\"\\\\n\\\")\",\n \"def write(self, fname):\\n pass\",\n \"def _toFile(self):\\n pass\",\n \"def write(self, outputFile):\\n \\n try: \\n f = open(outputFile + '.py', 'w')\\n for trail in self.trails: \\n f.write(\\\"[\\\")\\n for index in trail:\\n f.write(\\\"({0}, {1}), \\\".format(*index)) \\n f.write(\\\"]\\\\n\\\")\\n \\n except IOError, e:\\n msg = \\\"Exception encountered when attempting \\\" + \\\\\\n \\\"to write data to file: {0}.\\\" + \\\\\\n \\\"\\\\n\\\\t -- Exception was: {1}\\\" + \\\\\\n \\\"\\\\n\\\\t For help use --help\\\".format(outputFile, e)\\n raise Usage(e)\",\n \"def outputFunc(filename, resultList):\\n #assert len(parks) == 3\\n \\n f = open(filename, 'wt')\\n \\n try:\\n writer = csv.writer(f)\\n for i in range(len(resultList)):\\n print resultList[0]\\n writer.writerow(resultList[i])\\n \\n finally:\\n f.close()\",\n \"def save_results(self, path):\\n create_folder(path)\\n self.get_scores().to_csv(path + r'/scores.csv', index=False)\\n self.get_results().to_csv(path + r'/results.csv', index=False)\\n self.get_pivot_last_epoch().to_csv(path + r'/pivot_last_epoch.csv', index=True)\",\n \"def WriteFlowResults(self, results):\\n self._WriteFlowResultsOrErrors(self.flow_results, results)\",\n \"def export_results(path: str):\\n _, ext = Utils.get_filename_ext(path)\\n\\n if ext not in [\\\".md\\\", \\\".json\\\", \\\".csv\\\"]:\\n click.echo(\\\"Output file must be of type markdown, csv or json.\\\\n\\\", err=True)\\n return\\n\\n with open(path, \\\"w+\\\") as out:\\n Exporter.write(Format(Utils.format_to_int(ext)), out)\\n\\n click.echo(f\\\"Successfully exported results to {path}\\\\n\\\")\",\n \"def write_to_files():\\n\\t# Create output files\\n\\toutput = [None, \\\\\\n\\t\\t open(\\\"priority-1.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-2.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-3.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-4.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-5.txt\\\", \\\"w\\\"), ]\\n\\n\\t# Loop over all fields and write them to the correct file\\n\\tfor field in sorted(reportlog.keys()):\\n\\t\\tpriority = reportlog[field]['priority']\\n\\t\\tlabel = reportlog[field]['label']\\n\\n\\t\\toutput[priority].write(\\\"intphas_%s\\\\t%s\\\\n\\\" % (field, label))\\n\\t\\toutput[priority].flush()\\n\\n\\t# Close files\\n\\tfor i in [1,2,3,4,5]:\\n\\t\\toutput[i].close()\",\n \"def finalize_result(self):\\n logging.debug(\\\"finalize_result()\\\")\\n with open(self.html_file, \\\"a\\\") as result_file:\\n result_file.write(\\\"<br/>Analyzis successful\\\")\\n with open(self.txt_file, \\\"a\\\") as result_file:\\n result_file.write(\\\"Analyzis successful\\\")\",\n \"def write(self, file):\\n\\n # Initialize output buffer\\n out = ''\\n\\n # Print specification\\n for key, value in self.specification.items():\\n out += f'{key} : {value}\\\\n'\\n\\n # Print the tour\\n if self.tour:\\n out += 'TOUR_SECTION\\\\n'\\n for s in self.tour:\\n out += str(s) + '\\\\n'\\n out += '-1\\\\n'\\n\\n # Append EOF\\n out += 'EOF\\\\n'\\n\\n # Write to file\\n with open(file, 'w') as f:\\n f.write(out)\",\n \"def dump(self, filename=None):\\n if filename is None:\\n current_datetime = datetime.datetime.now()\\n filename = current_datetime.strftime(\\\"results_%Y%m%d%H%M%S.csv\\\")\\n print(\\\"Writing results to \\\\\\\"{}\\\\\\\"\\\".format(filename))\\n identifiers = \\\"\\\"\\n values = \\\"\\\"\\n for entry in self.log.items():\\n identifiers += ((\\\";\\\" if len(identifiers) > 0 else \\\"\\\")\\n + str(entry[0]))\\n values += \\\";\\\" + str(entry[1]) if len(values) > 0 else str(entry[1])\\n with open(filename, 'a') as f:\\n f.write(identifiers + \\\"\\\\n\\\")\\n f.write(values + \\\"\\\\n\\\")\",\n \"def output_results(self, filename):\\n\\n self.data.plot(title='Result of applying {} onto data set'.format(self.transformations[-1]))\\n plt.savefig(\\\"results/{}.png\\\".format(filename))\\n plt.close()\",\n \"def write_file(self):\\n rl_df, lift_df = self.create_df()\\n\\n number = re.findall('\\\\d+', self.url)[0]\\n\\n if self.write is True:\\n with open('house_{}.csv'.format(number), 'w',\\n encoding='utf-8-sig') as file:\\n rl_df.to_csv(file, sep=';')\\n with open('house_lifts_{}.csv'.format(number), 'w',\\n encoding='utf-8-sig') as file2:\\n lift_df.to_csv(file2, sep=';')\",\n \"def make_file(self):\\n\\n f = open(get_output_path(), \\\"w\\\")\\n \\n f.write(self.export())\\n \\n f.close()\\n\\n return self\",\n \"def write_output_file(self, index):\\n ctx = self.block_store.make_local_output(self.expected_outputs[index])\\n self.open_output_contexts[index] = ctx\\n return ctx.get_filename()\",\n \"def send_results_file_json(**kwargs):\\n try:\\n logging.debug(\\\"Opening json output file for writing\\\")\\n with open(kwargs[\\\"output_file_json\\\"], \\\"w\\\") as file_json_open:\\n logging.info(\\n \\\"Writing to output json file: \\\" + kwargs[\\\"output_file_json\\\"]\\n )\\n file_json_open.write(kwargs[\\\"results_dataset_json\\\"])\\n return True\\n except IOError:\\n logging.exception(\\\"Error writing results to json output file\\\")\\n return False\",\n \"def write_to_file(self, filepath, mode = \\\"a\\\"): \\n if \\\"r\\\" in mode: \\n print(\\\"Only accepts write and append modes\\\")\\n return \\n with open(filepath, mode) as f: \\n f.write(\\\"{}\\\\n\\\".format(self.title))\\n verified, seen, ratio = self.get_verified_ratio()\\n f.write(\\\"Verified Names: {}\\\\n\\\".format(str(verified)))\\n f.write(\\\"Names: {}\\\\n\\\".format(str(seen)))\\n f.write(\\\"Ratio: {}\\\\n\\\".format(str(ratio)))\",\n \"def write_the_contents_to_the_same_file(self):\\n if not len(self.student_list):\\n print('There is no contents to write')\\n return\\n\\n if self._filename is None:\\n self._filename = self.input_filename()\\n\\n with open(self._filename, 'w') as OUT:\\n OUT.write(self.student_list.to_csv(date_format='%Y-%m-%d',\\n sep='\\\\t', header=False, columns=self.columns_to_save))\\n print(f'Data are saved into {self._filename!r}')\",\n \"def log_results(self, path):\\n pd.DataFrame(self.results).to_csv(path)\",\n \"def save_to_file(self) -> None:\\n abs_errors = 0\\n art_errors = 0\\n for i, doc in enumerate(tqdm(self.results[\\\"documents\\\"])):\\n paper, abstract = self.__get_article_and_abstract(doc[\\\"eid\\\"])\\n if abstract:\\n abstract_file = open(self.out_dir + \\\"[{}] Abstract_{}.txt\\\".format(doc[\\\"date\\\"], doc[\\\"title\\\"]), \\\"w\\\")\\n abstract_file.write(abstract)\\n abstract_file.close()\\n else:\\n abs_errors += 1\\n if paper:\\n file = open(self.out_dir + \\\"[{}] {}.txt\\\".format(doc[\\\"date\\\"], doc[\\\"title\\\"]), \\\"w\\\")\\n file.write(paper)\\n file.close()\\n else:\\n art_errors += 1\\n print(\\\"Not saved/found: {} abstracts, {} articles\\\".format(abs_errors, art_errors))\",\n \"def write_output_file(self, query_nr, doc_scores,\\n output_file_path, top_n=1000):\\n \\n # Sort by score and select the n highest scored documents\\n top_n_doc_scores = dict(sorted(doc_scores.items(),\\n key = itemgetter(1), reverse = True)[:top_n])\\n \\n with open(output_file_path, \\\"a\\\") as f:\\n doc_rank = 1\\n for cord_uid, doc_score in top_n_doc_scores.items():\\n string = f\\\"{query_nr} Q0 {cord_uid} {doc_rank} {doc_score} TEST-RUN-0\\\\n\\\"\\n f.write(string)\\n doc_rank += 1\",\n \"def write_output(series, filename):\\n\\n logging.info('Writing output')\\n\\n df = series.reset_index()\\n\\n df.columns = ['subject_id', 'classification']\\n\\n df.to_csv(filename, index=False)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77442235","0.77054715","0.7367398","0.7360259","0.73494154","0.73246366","0.7308568","0.72313744","0.7201102","0.71957","0.7158928","0.7124561","0.7120183","0.7078446","0.707296","0.7059509","0.7049528","0.69777733","0.69493216","0.688521","0.6858873","0.6823137","0.6817897","0.68089396","0.6794814","0.67901176","0.6786203","0.6784315","0.6777316","0.67513305","0.6749359","0.6743578","0.67267245","0.6718003","0.6713472","0.66967225","0.6690362","0.66408044","0.66303235","0.6629874","0.6621906","0.6621586","0.66165525","0.66165525","0.66149503","0.66075593","0.6599551","0.6586803","0.6579874","0.65678716","0.6560242","0.65562737","0.6553206","0.65395385","0.65389526","0.65388316","0.65355146","0.6529406","0.6523663","0.64994025","0.64933467","0.649268","0.64920825","0.6488638","0.64743084","0.64480376","0.6443427","0.6435595","0.6397515","0.6384635","0.6381284","0.63768566","0.6376092","0.63754857","0.63740224","0.6360595","0.63584185","0.6357402","0.63485163","0.6343518","0.6338277","0.6331469","0.63175154","0.63172024","0.6305074","0.63046247","0.62996477","0.6295635","0.62942064","0.62936175","0.62924576","0.62869346","0.62847686","0.6284087","0.6280401","0.62784714","0.62759495","0.62750626","0.62607455","0.6257729"],"string":"[\n \"0.77442235\",\n \"0.77054715\",\n \"0.7367398\",\n \"0.7360259\",\n \"0.73494154\",\n \"0.73246366\",\n \"0.7308568\",\n \"0.72313744\",\n \"0.7201102\",\n \"0.71957\",\n \"0.7158928\",\n \"0.7124561\",\n \"0.7120183\",\n \"0.7078446\",\n \"0.707296\",\n \"0.7059509\",\n \"0.7049528\",\n \"0.69777733\",\n \"0.69493216\",\n \"0.688521\",\n \"0.6858873\",\n \"0.6823137\",\n \"0.6817897\",\n \"0.68089396\",\n \"0.6794814\",\n \"0.67901176\",\n \"0.6786203\",\n \"0.6784315\",\n \"0.6777316\",\n \"0.67513305\",\n \"0.6749359\",\n \"0.6743578\",\n \"0.67267245\",\n \"0.6718003\",\n \"0.6713472\",\n \"0.66967225\",\n \"0.6690362\",\n \"0.66408044\",\n \"0.66303235\",\n \"0.6629874\",\n \"0.6621906\",\n \"0.6621586\",\n \"0.66165525\",\n \"0.66165525\",\n \"0.66149503\",\n \"0.66075593\",\n \"0.6599551\",\n \"0.6586803\",\n \"0.6579874\",\n \"0.65678716\",\n \"0.6560242\",\n \"0.65562737\",\n \"0.6553206\",\n \"0.65395385\",\n \"0.65389526\",\n \"0.65388316\",\n \"0.65355146\",\n \"0.6529406\",\n \"0.6523663\",\n \"0.64994025\",\n \"0.64933467\",\n \"0.649268\",\n \"0.64920825\",\n \"0.6488638\",\n \"0.64743084\",\n \"0.64480376\",\n \"0.6443427\",\n \"0.6435595\",\n \"0.6397515\",\n \"0.6384635\",\n \"0.6381284\",\n \"0.63768566\",\n \"0.6376092\",\n \"0.63754857\",\n \"0.63740224\",\n \"0.6360595\",\n \"0.63584185\",\n \"0.6357402\",\n \"0.63485163\",\n \"0.6343518\",\n \"0.6338277\",\n \"0.6331469\",\n \"0.63175154\",\n \"0.63172024\",\n \"0.6305074\",\n \"0.63046247\",\n \"0.62996477\",\n \"0.6295635\",\n \"0.62942064\",\n \"0.62936175\",\n \"0.62924576\",\n \"0.62869346\",\n \"0.62847686\",\n \"0.6284087\",\n \"0.6280401\",\n \"0.62784714\",\n \"0.62759495\",\n \"0.62750626\",\n \"0.62607455\",\n \"0.6257729\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6562216"},"document_rank":{"kind":"string","value":"50"}}},{"rowIdx":15093,"cells":{"query":{"kind":"string","value":"returning all elements names from ``ImmunizationRecommendation`` according specification, with preserving original sequence order."},"document":{"kind":"string","value":"def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"patient\",\n \"date\",\n \"authority\",\n \"recommendation\",\n ]"},"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 itemnames():\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\n return(g)","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"indication\", \"dosingGuideline\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"code\",\n \"status\",\n \"author\",\n \"intendedJurisdiction\",\n \"name\",\n \"relatedMedicationKnowledge\",\n \"associatedMedication\",\n \"productType\",\n \"monograph\",\n \"preparationInstruction\",\n \"cost\",\n \"monitoringProgram\",\n \"indicationGuideline\",\n \"medicineClassification\",\n \"packaging\",\n \"clinicalUseIssue\",\n \"storageGuideline\",\n \"regulatory\",\n \"definitional\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"regulatoryAuthority\",\n \"substitution\",\n \"schedule\",\n \"maxDispense\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"definition\",\n \"doseForm\",\n \"intendedRoute\",\n \"ingredient\",\n \"drugCharacteristic\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"instantiatesCanonical\",\n \"instantiatesUri\",\n \"basedOn\",\n \"priorRequest\",\n \"groupIdentifier\",\n \"status\",\n \"intent\",\n \"priority\",\n \"codeReference\",\n \"codeCodeableConcept\",\n \"parameter\",\n \"subject\",\n \"encounter\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"authoredOn\",\n \"requester\",\n \"performerType\",\n \"performer\",\n \"reasonCode\",\n \"reasonReference\",\n \"insurance\",\n \"supportingInfo\",\n \"note\",\n \"relevantHistory\",\n ]","def get_names(self):\n return [doc['name'] for doc in self.vocab]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"status\",\n \"category\",\n \"code\",\n \"subject\",\n \"context\",\n \"effectiveDateTime\",\n \"effectivePeriod\",\n \"issued\",\n \"performer\",\n \"specimen\",\n \"result\",\n \"imagingStudy\",\n \"image\",\n \"conclusion\",\n \"codedDiagnosis\",\n \"presentedForm\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"instantiatesCanonical\",\n \"instantiatesUri\",\n \"basedOn\",\n \"replaces\",\n \"groupIdentifier\",\n \"status\",\n \"intent\",\n \"priority\",\n \"code\",\n \"subject\",\n \"encounter\",\n \"authoredOn\",\n \"author\",\n \"reason\",\n \"goal\",\n \"note\",\n \"action\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"partOf\",\n \"status\",\n \"patient\",\n \"type\",\n \"suppliedItem\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"supplier\",\n \"destination\",\n \"receiver\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"issued\",\n \"applies\",\n \"type\",\n \"subType\",\n \"topic\",\n \"action\",\n \"actionReason\",\n \"securityLabel\",\n \"agent\",\n \"text\",\n \"valuedItem\",\n \"group\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"role\", \"actor\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"name\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"actor\", \"role\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"actor\", \"role\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"example\",\n \"name\",\n \"description\",\n \"acronym\",\n \"sourceUri\",\n \"sourceReference\",\n \"exampleFor\",\n ]","def elementNames(self):\n nel = self.nElements()\n return map(self.elementName,range(nel))","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"function\", \"actor\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"treatmentIntent\",\n \"dosage\",\n \"administrationTreatment\",\n \"patientCharacteristic\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"name\",\n \"title\",\n \"shortTitle\",\n \"subtitle\",\n \"status\",\n \"date\",\n \"description\",\n \"note\",\n \"useContext\",\n \"publisher\",\n \"contact\",\n \"author\",\n \"editor\",\n \"reviewer\",\n \"endorser\",\n \"relatedArtifact\",\n \"actual\",\n \"characteristicCombination\",\n \"characteristic\",\n \"handling\",\n \"category\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"cost\", \"packagedProduct\"]","def names(self):\n return list(item.name for item in self.mechanisms)","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"definitionUri\",\n \"definitionCanonical\",\n \"status\",\n \"partOf\",\n \"code\",\n \"subject\",\n \"context\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"performer\",\n \"performingOrganization\",\n \"requestingOrganization\",\n \"costCenter\",\n \"quantity\",\n \"bodysite\",\n \"factorOverride\",\n \"priceOverride\",\n \"overrideReason\",\n \"enterer\",\n \"enteredDate\",\n \"reason\",\n \"service\",\n \"productReference\",\n \"productCodeableConcept\",\n \"account\",\n \"note\",\n \"supportingInformation\",\n ]","def get_names(self):\n selected_masks = self._component_obj.get_support()\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]","def names(self) -> list[str]:","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"definitionReference\",\n \"definitionCanonical\",\n \"definitionCodeableConcept\",\n \"definitionExpression\",\n \"method\",\n \"device\",\n \"exclude\",\n \"timeFromStart\",\n \"groupMeasure\",\n ]","def get_seq_names(self) -> List[str]:\n return [seq.Name.lower() for seq in self.Sequencers]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"status\",\n \"issued\",\n \"applies\",\n \"subject\",\n \"topic\",\n \"authority\",\n \"domain\",\n \"type\",\n \"subType\",\n \"action\",\n \"actionReason\",\n \"decisionType\",\n \"contentDerivative\",\n \"securityLabel\",\n \"agent\",\n \"signer\",\n \"valuedItem\",\n \"term\",\n \"bindingAttachment\",\n \"bindingReference\",\n \"bindingReference\",\n \"bindingReference\",\n \"friendly\",\n \"legal\",\n \"rule\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"item\",\n \"type\",\n \"strengthRatio\",\n \"strengthCodeableConcept\",\n \"strengthQuantity\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"sourceString\",\n \"sourceUri\",\n \"classification\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"quantity\",\n \"range\",\n \"note\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"profile\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"name\",\n \"title\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"purpose\",\n \"useContext\",\n \"jurisdiction\",\n \"code\",\n \"search\",\n \"resource\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"title\",\n \"derivedFromUri\",\n \"partOf\",\n \"replaces\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"useContext\",\n \"jurisdiction\",\n \"copyright\",\n \"approvalDate\",\n \"lastReviewDate\",\n \"effectivePeriod\",\n \"code\",\n \"instance\",\n \"applicability\",\n \"propertyGroup\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"allowed\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"quantity\", \"period\"]","def _name_estimators(estimators):\n\n names = [type(estimator).__name__.lower() for estimator in estimators]\n namecount = defaultdict(int)\n for est, name in zip(estimators, names):\n namecount[name] += 1\n\n for k, v in list(six.iteritems(namecount)):\n if v == 1:\n del namecount[k]\n\n for i in reversed(range(len(estimators))):\n name = names[i]\n if name in namecount:\n names[i] += \"-%d\" % namecount[name]\n namecount[name] -= 1\n\n return list(zip(names, estimators))","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"reference\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"linkId\",\n \"prefix\",\n \"title\",\n \"description\",\n \"textEquivalent\",\n \"priority\",\n \"code\",\n \"documentation\",\n \"goal\",\n \"condition\",\n \"input\",\n \"output\",\n \"relatedAction\",\n \"timingDateTime\",\n \"timingAge\",\n \"timingPeriod\",\n \"timingDuration\",\n \"timingRange\",\n \"timingTiming\",\n \"location\",\n \"participant\",\n \"type\",\n \"groupingBehavior\",\n \"selectionBehavior\",\n \"requiredBehavior\",\n \"precheckBehavior\",\n \"cardinalityBehavior\",\n \"resource\",\n \"definitionCanonical\",\n \"definitionUri\",\n \"transform\",\n \"dynamicValue\",\n \"action\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"kind\", \"expression\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"vaccineCode\",\n \"targetDisease\",\n \"contraindicatedVaccineCode\",\n \"forecastStatus\",\n \"forecastReason\",\n \"dateCriterion\",\n \"description\",\n \"series\",\n \"doseNumberPositiveInt\",\n \"doseNumberString\",\n \"seriesDosesPositiveInt\",\n \"seriesDosesString\",\n \"supportingImmunization\",\n \"supportingPatientInformation\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"sequence\",\n \"text\",\n \"additionalInstruction\",\n \"patientInstruction\",\n \"timing\",\n \"asNeeded\",\n \"asNeededFor\",\n \"site\",\n \"route\",\n \"method\",\n \"doseAndRate\",\n \"maxDosePerPeriod\",\n \"maxDosePerAdministration\",\n \"maxDosePerLifetime\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"classifier\",\n \"label\",\n \"display\",\n \"citation\",\n \"document\",\n \"resource\",\n \"resourceReference\",\n \"publicationStatus\",\n \"publicationDate\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"productCategory\",\n \"productCode\",\n \"parent\",\n \"request\",\n \"identifier\",\n \"biologicalSourceEvent\",\n \"processingFacility\",\n \"division\",\n \"productStatus\",\n \"expirationDate\",\n \"collection\",\n \"storageTempRequirements\",\n \"property\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"reference\",\n \"note\",\n \"stabilityDuration\",\n \"environmentalSetting\",\n ]","def seqNames(self):\n\t\tseqDict = self.sequenceDict()\n\t\treturn seqDict.keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"applicability\",\n \"priceComponent\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"language\",\n \"expression\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"version\",\n \"name\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"useContext\",\n \"jurisdiction\",\n \"copyright\",\n \"fhirVersion\",\n \"dependency\",\n \"package\",\n \"global\",\n \"binary\",\n \"page\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"quantity\",\n \"itemCodeableConcept\",\n \"itemReference\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"low\", \"high\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"typeCanonical\",\n \"typeReference\",\n \"role\",\n \"function\",\n \"actorCanonical\",\n \"actorReference\",\n ]","def input_names(self) -> List[Union[str, int]]:\n return [x.name or i for i, x in enumerate(self.inputs)]","def names(self) -> List:\n ...","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"comment\", \"link\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"dosage\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"source\"]","def get_sequence_names(self):\r\n return Wiki().sequences_for_article_url(self.url).keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"code\",\n \"param\",\n \"documentation\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"name\",\n \"valueCodeableConcept\",\n \"valueQuantity\",\n \"valueRange\",\n ]","def get_input_names(self):\n return self._input_names","def get_specification_kinds(specifications):\n specifications.setdefault(\"manual event models\", {\"tags\": [\"manual event models\"]})\n return [\"manual event models\"]","def _get_exog_re_names(exog_re):\n if isinstance(exog_re, pd.DataFrame):\n return exog_re.columns.tolist()\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\n return [exog_re.name]\n elif isinstance(exog_re, list):\n return exog_re\n return [\"Z{0}\".format(k + 1) for k in range(exog_re.shape[1])]","def get_repeat_steps_names(self) -> List[str]:\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"collector\",\n \"source\",\n \"collectedDateTime\",\n \"collectedPeriod\",\n ]","def assign_choice_names(self):\r\n\r\n for index, choice in enumerate(\r\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\r\n ):\r\n # Set the name attribute for <choices>\r\n # \"bc\" is appended at the end to indicate that this is a\r\n # binary choice as opposed to a numtolerance_input, this convention\r\n # is used when grading the problem\r\n choice.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) + \"bc\"\r\n )\r\n # Set Name attributes for <numtolerance_input> elements\r\n # Look for all <numtolerance_inputs> inside this choice.\r\n numtolerance_inputs = choice.findall('numtolerance_input')\r\n # Look for all <decoy_input> inside this choice\r\n decoys = choice.findall('decoy_input')\r\n # <decoy_input> would only be used in choices which do not contain\r\n # <numtolerance_input>\r\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\r\n # Give each input inside of the choice a name combining\r\n # The ordinality of the choice, and the ordinality of the input\r\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\r\n for ind, child in enumerate(inputs):\r\n child.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) +\r\n \"_numtolerance_input_\" + str(ind)\r\n )","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"uri\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"party\", \"signature\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"name\",\n \"description\",\n \"resource\",\n ]","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def get_ordered_adversary_names(self) -> List[str]:\n pass","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"path\", \"expression\"]","def pyranose_names(self):\n output = set()\n for item in self.pyranoses():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def _list_of_availability_strings():\n names = [availability.name for availability in Availability]\n return names","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"effectiveDate\",\n \"type\",\n \"source\",\n \"costMoney\",\n \"costCodeableConcept\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"targetId\",\n \"relationship\",\n \"endRelationship\",\n \"offsetDuration\",\n \"offsetRange\",\n ]","def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\n identifiers = []\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\n\n for arr in arrs:\n node = arr.getElementsByTagName('str')[0]\n identifiers.append(node.firstChild.nodeValue)\n return identifiers","def expected_entity_names(emplacement,\n association,\n composition,\n violation,\n membership_organization,\n membership_person):\n return [\n emplacement[0].site.get_value().value.name,\n association[0].area.get_value().value.name,\n composition[0].child.get_value().value.name.get_value().value,\n truncatewords(violation.description.get_value(), 10),\n membership_organization.organization.get_value().value.name.get_value().value,\n membership_person[0].member.get_value().value.name.get_value().value\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"code\", \"value\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"code\",\n \"factor\",\n \"amount\",\n ]","def getNamesFromDescr(descr):\n i = getIter(descr)\n if not i:\n return\n\n try:\n item = i.next()\n while item:\n if isinstance(item[1], str):\n yield item[0]\n else:\n l = []\n for j in getNamesFromDescr(item[1]):\n l.append(j)\n r = (item[0], l)\n yield r\n item = i.next()\n except StopIteration:\n pass","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"doseRange\",\n \"doseQuantity\",\n \"rateRatio\",\n \"rateRange\",\n \"rateQuantity\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"source\",\n \"title\",\n \"kind\",\n \"type\",\n \"package\",\n \"format\",\n \"page\",\n ]","def names(cls) -> List[str]:","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"valueCodeableConcept\",\n \"valueQuantity\",\n \"valueRange\",\n ]","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def extract_names(collection):\n return (\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\n if n in groups else repr(n) for n in collection\n )","def name(self):\n\t\t# This is necessary for ColumnLists that are used\n\t\t# for CondDescs as well. Ideally, we'd do this on an\n\t\t# InputKeys basis and yield their names (because that's what\n\t\t# formal counts on), but it's probably not worth the effort.\n\t\treturn \"+\".join([f.name for f in self.inputKeys])","def xff_alternative_names(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"xff_alternative_names\")","def getParameterList(self):\n inputList = []\n for name, n in zip(self._names, self._inputs):\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\n return inputList","def get_input_names():\n names = [device.name for device in get_devices() if device.is_input]\n return list(sorted(names))","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"valueQuantity\",\n \"valueRange\",\n \"valueCodeableConcept\",\n ]","def get_column_names(self):\n names = []\n names.append(self.question_column + \"_agree_lot\")\n names.append(self.question_column + \"_agree_little\")\n names.append(self.question_column + \"_neither\")\n names.append(self.question_column + \"_dis_little\")\n names.append(self.question_column + \"_dis_lot\")\n return names","def __catalogue__(interface):\n names = []\n seen = set()\n for component in interface.__implementations__():\n for name in component.__names__:\n if name not in seen:\n names.append(name)\n seen.add(name)\n names.sort(key=(lambda n: str(n)))\n return names","def get_comp_names(self, names):\n names = names[:]\n comp_names = {}\n for r_block in self.frd.result_blocks:\n if r_block.name in names and r_block.name not in comp_names:\n comp_names[r_block.name] = []\n for ent in r_block.entities:\n if ent.ictype == 2 and ent.icind1 == 0:\n continue\n comp_names[r_block.name].append(ent.name)\n names.remove(r_block.name)\n if not names:\n break\n return comp_names"],"string":"[\n \"def itemnames():\\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\\n return(g)\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"indication\\\", \\\"dosingGuideline\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"code\\\",\\n \\\"status\\\",\\n \\\"author\\\",\\n \\\"intendedJurisdiction\\\",\\n \\\"name\\\",\\n \\\"relatedMedicationKnowledge\\\",\\n \\\"associatedMedication\\\",\\n \\\"productType\\\",\\n \\\"monograph\\\",\\n \\\"preparationInstruction\\\",\\n \\\"cost\\\",\\n \\\"monitoringProgram\\\",\\n \\\"indicationGuideline\\\",\\n \\\"medicineClassification\\\",\\n \\\"packaging\\\",\\n \\\"clinicalUseIssue\\\",\\n \\\"storageGuideline\\\",\\n \\\"regulatory\\\",\\n \\\"definitional\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"regulatoryAuthority\\\",\\n \\\"substitution\\\",\\n \\\"schedule\\\",\\n \\\"maxDispense\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"definition\\\",\\n \\\"doseForm\\\",\\n \\\"intendedRoute\\\",\\n \\\"ingredient\\\",\\n \\\"drugCharacteristic\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"instantiatesCanonical\\\",\\n \\\"instantiatesUri\\\",\\n \\\"basedOn\\\",\\n \\\"priorRequest\\\",\\n \\\"groupIdentifier\\\",\\n \\\"status\\\",\\n \\\"intent\\\",\\n \\\"priority\\\",\\n \\\"codeReference\\\",\\n \\\"codeCodeableConcept\\\",\\n \\\"parameter\\\",\\n \\\"subject\\\",\\n \\\"encounter\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"authoredOn\\\",\\n \\\"requester\\\",\\n \\\"performerType\\\",\\n \\\"performer\\\",\\n \\\"reasonCode\\\",\\n \\\"reasonReference\\\",\\n \\\"insurance\\\",\\n \\\"supportingInfo\\\",\\n \\\"note\\\",\\n \\\"relevantHistory\\\",\\n ]\",\n \"def get_names(self):\\n return [doc['name'] for doc in self.vocab]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"status\\\",\\n \\\"category\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"effectiveDateTime\\\",\\n \\\"effectivePeriod\\\",\\n \\\"issued\\\",\\n \\\"performer\\\",\\n \\\"specimen\\\",\\n \\\"result\\\",\\n \\\"imagingStudy\\\",\\n \\\"image\\\",\\n \\\"conclusion\\\",\\n \\\"codedDiagnosis\\\",\\n \\\"presentedForm\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"instantiatesCanonical\\\",\\n \\\"instantiatesUri\\\",\\n \\\"basedOn\\\",\\n \\\"replaces\\\",\\n \\\"groupIdentifier\\\",\\n \\\"status\\\",\\n \\\"intent\\\",\\n \\\"priority\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"encounter\\\",\\n \\\"authoredOn\\\",\\n \\\"author\\\",\\n \\\"reason\\\",\\n \\\"goal\\\",\\n \\\"note\\\",\\n \\\"action\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"partOf\\\",\\n \\\"status\\\",\\n \\\"patient\\\",\\n \\\"type\\\",\\n \\\"suppliedItem\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"supplier\\\",\\n \\\"destination\\\",\\n \\\"receiver\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"issued\\\",\\n \\\"applies\\\",\\n \\\"type\\\",\\n \\\"subType\\\",\\n \\\"topic\\\",\\n \\\"action\\\",\\n \\\"actionReason\\\",\\n \\\"securityLabel\\\",\\n \\\"agent\\\",\\n \\\"text\\\",\\n \\\"valuedItem\\\",\\n \\\"group\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"role\\\", \\\"actor\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"name\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"actor\\\", \\\"role\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"actor\\\", \\\"role\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"example\\\",\\n \\\"name\\\",\\n \\\"description\\\",\\n \\\"acronym\\\",\\n \\\"sourceUri\\\",\\n \\\"sourceReference\\\",\\n \\\"exampleFor\\\",\\n ]\",\n \"def elementNames(self):\\n nel = self.nElements()\\n return map(self.elementName,range(nel))\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"function\\\", \\\"actor\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"treatmentIntent\\\",\\n \\\"dosage\\\",\\n \\\"administrationTreatment\\\",\\n \\\"patientCharacteristic\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"name\\\",\\n \\\"title\\\",\\n \\\"shortTitle\\\",\\n \\\"subtitle\\\",\\n \\\"status\\\",\\n \\\"date\\\",\\n \\\"description\\\",\\n \\\"note\\\",\\n \\\"useContext\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"author\\\",\\n \\\"editor\\\",\\n \\\"reviewer\\\",\\n \\\"endorser\\\",\\n \\\"relatedArtifact\\\",\\n \\\"actual\\\",\\n \\\"characteristicCombination\\\",\\n \\\"characteristic\\\",\\n \\\"handling\\\",\\n \\\"category\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"cost\\\", \\\"packagedProduct\\\"]\",\n \"def names(self):\\n return list(item.name for item in self.mechanisms)\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"definitionUri\\\",\\n \\\"definitionCanonical\\\",\\n \\\"status\\\",\\n \\\"partOf\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"performer\\\",\\n \\\"performingOrganization\\\",\\n \\\"requestingOrganization\\\",\\n \\\"costCenter\\\",\\n \\\"quantity\\\",\\n \\\"bodysite\\\",\\n \\\"factorOverride\\\",\\n \\\"priceOverride\\\",\\n \\\"overrideReason\\\",\\n \\\"enterer\\\",\\n \\\"enteredDate\\\",\\n \\\"reason\\\",\\n \\\"service\\\",\\n \\\"productReference\\\",\\n \\\"productCodeableConcept\\\",\\n \\\"account\\\",\\n \\\"note\\\",\\n \\\"supportingInformation\\\",\\n ]\",\n \"def get_names(self):\\n selected_masks = self._component_obj.get_support()\\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]\",\n \"def names(self) -> list[str]:\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"definitionReference\\\",\\n \\\"definitionCanonical\\\",\\n \\\"definitionCodeableConcept\\\",\\n \\\"definitionExpression\\\",\\n \\\"method\\\",\\n \\\"device\\\",\\n \\\"exclude\\\",\\n \\\"timeFromStart\\\",\\n \\\"groupMeasure\\\",\\n ]\",\n \"def get_seq_names(self) -> List[str]:\\n return [seq.Name.lower() for seq in self.Sequencers]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"status\\\",\\n \\\"issued\\\",\\n \\\"applies\\\",\\n \\\"subject\\\",\\n \\\"topic\\\",\\n \\\"authority\\\",\\n \\\"domain\\\",\\n \\\"type\\\",\\n \\\"subType\\\",\\n \\\"action\\\",\\n \\\"actionReason\\\",\\n \\\"decisionType\\\",\\n \\\"contentDerivative\\\",\\n \\\"securityLabel\\\",\\n \\\"agent\\\",\\n \\\"signer\\\",\\n \\\"valuedItem\\\",\\n \\\"term\\\",\\n \\\"bindingAttachment\\\",\\n \\\"bindingReference\\\",\\n \\\"bindingReference\\\",\\n \\\"bindingReference\\\",\\n \\\"friendly\\\",\\n \\\"legal\\\",\\n \\\"rule\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"item\\\",\\n \\\"type\\\",\\n \\\"strengthRatio\\\",\\n \\\"strengthCodeableConcept\\\",\\n \\\"strengthQuantity\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"sourceString\\\",\\n \\\"sourceUri\\\",\\n \\\"classification\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"quantity\\\",\\n \\\"range\\\",\\n \\\"note\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"profile\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"name\\\",\\n \\\"title\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"purpose\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"code\\\",\\n \\\"search\\\",\\n \\\"resource\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"title\\\",\\n \\\"derivedFromUri\\\",\\n \\\"partOf\\\",\\n \\\"replaces\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"copyright\\\",\\n \\\"approvalDate\\\",\\n \\\"lastReviewDate\\\",\\n \\\"effectivePeriod\\\",\\n \\\"code\\\",\\n \\\"instance\\\",\\n \\\"applicability\\\",\\n \\\"propertyGroup\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"allowed\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"quantity\\\", \\\"period\\\"]\",\n \"def _name_estimators(estimators):\\n\\n names = [type(estimator).__name__.lower() for estimator in estimators]\\n namecount = defaultdict(int)\\n for est, name in zip(estimators, names):\\n namecount[name] += 1\\n\\n for k, v in list(six.iteritems(namecount)):\\n if v == 1:\\n del namecount[k]\\n\\n for i in reversed(range(len(estimators))):\\n name = names[i]\\n if name in namecount:\\n names[i] += \\\"-%d\\\" % namecount[name]\\n namecount[name] -= 1\\n\\n return list(zip(names, estimators))\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"reference\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"linkId\\\",\\n \\\"prefix\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"textEquivalent\\\",\\n \\\"priority\\\",\\n \\\"code\\\",\\n \\\"documentation\\\",\\n \\\"goal\\\",\\n \\\"condition\\\",\\n \\\"input\\\",\\n \\\"output\\\",\\n \\\"relatedAction\\\",\\n \\\"timingDateTime\\\",\\n \\\"timingAge\\\",\\n \\\"timingPeriod\\\",\\n \\\"timingDuration\\\",\\n \\\"timingRange\\\",\\n \\\"timingTiming\\\",\\n \\\"location\\\",\\n \\\"participant\\\",\\n \\\"type\\\",\\n \\\"groupingBehavior\\\",\\n \\\"selectionBehavior\\\",\\n \\\"requiredBehavior\\\",\\n \\\"precheckBehavior\\\",\\n \\\"cardinalityBehavior\\\",\\n \\\"resource\\\",\\n \\\"definitionCanonical\\\",\\n \\\"definitionUri\\\",\\n \\\"transform\\\",\\n \\\"dynamicValue\\\",\\n \\\"action\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"kind\\\", \\\"expression\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"vaccineCode\\\",\\n \\\"targetDisease\\\",\\n \\\"contraindicatedVaccineCode\\\",\\n \\\"forecastStatus\\\",\\n \\\"forecastReason\\\",\\n \\\"dateCriterion\\\",\\n \\\"description\\\",\\n \\\"series\\\",\\n \\\"doseNumberPositiveInt\\\",\\n \\\"doseNumberString\\\",\\n \\\"seriesDosesPositiveInt\\\",\\n \\\"seriesDosesString\\\",\\n \\\"supportingImmunization\\\",\\n \\\"supportingPatientInformation\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"sequence\\\",\\n \\\"text\\\",\\n \\\"additionalInstruction\\\",\\n \\\"patientInstruction\\\",\\n \\\"timing\\\",\\n \\\"asNeeded\\\",\\n \\\"asNeededFor\\\",\\n \\\"site\\\",\\n \\\"route\\\",\\n \\\"method\\\",\\n \\\"doseAndRate\\\",\\n \\\"maxDosePerPeriod\\\",\\n \\\"maxDosePerAdministration\\\",\\n \\\"maxDosePerLifetime\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"classifier\\\",\\n \\\"label\\\",\\n \\\"display\\\",\\n \\\"citation\\\",\\n \\\"document\\\",\\n \\\"resource\\\",\\n \\\"resourceReference\\\",\\n \\\"publicationStatus\\\",\\n \\\"publicationDate\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"productCategory\\\",\\n \\\"productCode\\\",\\n \\\"parent\\\",\\n \\\"request\\\",\\n \\\"identifier\\\",\\n \\\"biologicalSourceEvent\\\",\\n \\\"processingFacility\\\",\\n \\\"division\\\",\\n \\\"productStatus\\\",\\n \\\"expirationDate\\\",\\n \\\"collection\\\",\\n \\\"storageTempRequirements\\\",\\n \\\"property\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"reference\\\",\\n \\\"note\\\",\\n \\\"stabilityDuration\\\",\\n \\\"environmentalSetting\\\",\\n ]\",\n \"def seqNames(self):\\n\\t\\tseqDict = self.sequenceDict()\\n\\t\\treturn seqDict.keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"applicability\\\",\\n \\\"priceComponent\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"language\\\",\\n \\\"expression\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"version\\\",\\n \\\"name\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"copyright\\\",\\n \\\"fhirVersion\\\",\\n \\\"dependency\\\",\\n \\\"package\\\",\\n \\\"global\\\",\\n \\\"binary\\\",\\n \\\"page\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"quantity\\\",\\n \\\"itemCodeableConcept\\\",\\n \\\"itemReference\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"low\\\", \\\"high\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"typeCanonical\\\",\\n \\\"typeReference\\\",\\n \\\"role\\\",\\n \\\"function\\\",\\n \\\"actorCanonical\\\",\\n \\\"actorReference\\\",\\n ]\",\n \"def input_names(self) -> List[Union[str, int]]:\\n return [x.name or i for i, x in enumerate(self.inputs)]\",\n \"def names(self) -> List:\\n ...\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"comment\\\", \\\"link\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"dosage\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"source\\\"]\",\n \"def get_sequence_names(self):\\r\\n return Wiki().sequences_for_article_url(self.url).keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"code\\\",\\n \\\"param\\\",\\n \\\"documentation\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"name\\\",\\n \\\"valueCodeableConcept\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n ]\",\n \"def get_input_names(self):\\n return self._input_names\",\n \"def get_specification_kinds(specifications):\\n specifications.setdefault(\\\"manual event models\\\", {\\\"tags\\\": [\\\"manual event models\\\"]})\\n return [\\\"manual event models\\\"]\",\n \"def _get_exog_re_names(exog_re):\\n if isinstance(exog_re, pd.DataFrame):\\n return exog_re.columns.tolist()\\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\\n return [exog_re.name]\\n elif isinstance(exog_re, list):\\n return exog_re\\n return [\\\"Z{0}\\\".format(k + 1) for k in range(exog_re.shape[1])]\",\n \"def get_repeat_steps_names(self) -> List[str]:\\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"collector\\\",\\n \\\"source\\\",\\n \\\"collectedDateTime\\\",\\n \\\"collectedPeriod\\\",\\n ]\",\n \"def assign_choice_names(self):\\r\\n\\r\\n for index, choice in enumerate(\\r\\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\\r\\n ):\\r\\n # Set the name attribute for <choices>\\r\\n # \\\"bc\\\" is appended at the end to indicate that this is a\\r\\n # binary choice as opposed to a numtolerance_input, this convention\\r\\n # is used when grading the problem\\r\\n choice.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) + \\\"bc\\\"\\r\\n )\\r\\n # Set Name attributes for <numtolerance_input> elements\\r\\n # Look for all <numtolerance_inputs> inside this choice.\\r\\n numtolerance_inputs = choice.findall('numtolerance_input')\\r\\n # Look for all <decoy_input> inside this choice\\r\\n decoys = choice.findall('decoy_input')\\r\\n # <decoy_input> would only be used in choices which do not contain\\r\\n # <numtolerance_input>\\r\\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\\r\\n # Give each input inside of the choice a name combining\\r\\n # The ordinality of the choice, and the ordinality of the input\\r\\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\\r\\n for ind, child in enumerate(inputs):\\r\\n child.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) +\\r\\n \\\"_numtolerance_input_\\\" + str(ind)\\r\\n )\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"uri\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"party\\\", \\\"signature\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"name\\\",\\n \\\"description\\\",\\n \\\"resource\\\",\\n ]\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def get_ordered_adversary_names(self) -> List[str]:\\n pass\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"path\\\", \\\"expression\\\"]\",\n \"def pyranose_names(self):\\n output = set()\\n for item in self.pyranoses():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def _list_of_availability_strings():\\n names = [availability.name for availability in Availability]\\n return names\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"effectiveDate\\\",\\n \\\"type\\\",\\n \\\"source\\\",\\n \\\"costMoney\\\",\\n \\\"costCodeableConcept\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"targetId\\\",\\n \\\"relationship\\\",\\n \\\"endRelationship\\\",\\n \\\"offsetDuration\\\",\\n \\\"offsetRange\\\",\\n ]\",\n \"def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\\n identifiers = []\\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\\n\\n for arr in arrs:\\n node = arr.getElementsByTagName('str')[0]\\n identifiers.append(node.firstChild.nodeValue)\\n return identifiers\",\n \"def expected_entity_names(emplacement,\\n association,\\n composition,\\n violation,\\n membership_organization,\\n membership_person):\\n return [\\n emplacement[0].site.get_value().value.name,\\n association[0].area.get_value().value.name,\\n composition[0].child.get_value().value.name.get_value().value,\\n truncatewords(violation.description.get_value(), 10),\\n membership_organization.organization.get_value().value.name.get_value().value,\\n membership_person[0].member.get_value().value.name.get_value().value\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"code\\\", \\\"value\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"code\\\",\\n \\\"factor\\\",\\n \\\"amount\\\",\\n ]\",\n \"def getNamesFromDescr(descr):\\n i = getIter(descr)\\n if not i:\\n return\\n\\n try:\\n item = i.next()\\n while item:\\n if isinstance(item[1], str):\\n yield item[0]\\n else:\\n l = []\\n for j in getNamesFromDescr(item[1]):\\n l.append(j)\\n r = (item[0], l)\\n yield r\\n item = i.next()\\n except StopIteration:\\n pass\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"doseRange\\\",\\n \\\"doseQuantity\\\",\\n \\\"rateRatio\\\",\\n \\\"rateRange\\\",\\n \\\"rateQuantity\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"source\\\",\\n \\\"title\\\",\\n \\\"kind\\\",\\n \\\"type\\\",\\n \\\"package\\\",\\n \\\"format\\\",\\n \\\"page\\\",\\n ]\",\n \"def names(cls) -> List[str]:\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"valueCodeableConcept\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n ]\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def extract_names(collection):\\n return (\\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\\n if n in groups else repr(n) for n in collection\\n )\",\n \"def name(self):\\n\\t\\t# This is necessary for ColumnLists that are used\\n\\t\\t# for CondDescs as well. Ideally, we'd do this on an\\n\\t\\t# InputKeys basis and yield their names (because that's what\\n\\t\\t# formal counts on), but it's probably not worth the effort.\\n\\t\\treturn \\\"+\\\".join([f.name for f in self.inputKeys])\",\n \"def xff_alternative_names(self) -> pulumi.Output[Sequence[str]]:\\n return pulumi.get(self, \\\"xff_alternative_names\\\")\",\n \"def getParameterList(self):\\n inputList = []\\n for name, n in zip(self._names, self._inputs):\\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\\n return inputList\",\n \"def get_input_names():\\n names = [device.name for device in get_devices() if device.is_input]\\n return list(sorted(names))\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n \\\"valueCodeableConcept\\\",\\n ]\",\n \"def get_column_names(self):\\n names = []\\n names.append(self.question_column + \\\"_agree_lot\\\")\\n names.append(self.question_column + \\\"_agree_little\\\")\\n names.append(self.question_column + \\\"_neither\\\")\\n names.append(self.question_column + \\\"_dis_little\\\")\\n names.append(self.question_column + \\\"_dis_lot\\\")\\n return names\",\n \"def __catalogue__(interface):\\n names = []\\n seen = set()\\n for component in interface.__implementations__():\\n for name in component.__names__:\\n if name not in seen:\\n names.append(name)\\n seen.add(name)\\n names.sort(key=(lambda n: str(n)))\\n return names\",\n \"def get_comp_names(self, names):\\n names = names[:]\\n comp_names = {}\\n for r_block in self.frd.result_blocks:\\n if r_block.name in names and r_block.name not in comp_names:\\n comp_names[r_block.name] = []\\n for ent in r_block.entities:\\n if ent.ictype == 2 and ent.icind1 == 0:\\n continue\\n comp_names[r_block.name].append(ent.name)\\n names.remove(r_block.name)\\n if not names:\\n break\\n return comp_names\"\n]","isConversational":false},"negative_scores":{"kind":"list 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``ImmunizationRecommendationRecommendation`` according specification, with preserving original sequence order."},"document":{"kind":"string","value":"def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"vaccineCode\",\n \"targetDisease\",\n \"contraindicatedVaccineCode\",\n \"forecastStatus\",\n \"forecastReason\",\n \"dateCriterion\",\n \"description\",\n \"series\",\n \"doseNumberPositiveInt\",\n \"doseNumberString\",\n \"seriesDosesPositiveInt\",\n \"seriesDosesString\",\n \"supportingImmunization\",\n \"supportingPatientInformation\",\n ]"},"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 itemnames():\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\n return(g)","def get_names(self):\n return [doc['name'] for doc in self.vocab]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"indication\", \"dosingGuideline\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"patient\",\n \"date\",\n \"authority\",\n \"recommendation\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"code\",\n \"status\",\n \"author\",\n \"intendedJurisdiction\",\n \"name\",\n \"relatedMedicationKnowledge\",\n \"associatedMedication\",\n \"productType\",\n \"monograph\",\n \"preparationInstruction\",\n \"cost\",\n \"monitoringProgram\",\n \"indicationGuideline\",\n \"medicineClassification\",\n \"packaging\",\n \"clinicalUseIssue\",\n \"storageGuideline\",\n \"regulatory\",\n \"definitional\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"regulatoryAuthority\",\n \"substitution\",\n \"schedule\",\n \"maxDispense\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"definition\",\n \"doseForm\",\n \"intendedRoute\",\n \"ingredient\",\n \"drugCharacteristic\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"instantiatesCanonical\",\n \"instantiatesUri\",\n \"basedOn\",\n \"priorRequest\",\n \"groupIdentifier\",\n \"status\",\n \"intent\",\n \"priority\",\n \"codeReference\",\n \"codeCodeableConcept\",\n \"parameter\",\n \"subject\",\n \"encounter\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"authoredOn\",\n \"requester\",\n \"performerType\",\n \"performer\",\n \"reasonCode\",\n \"reasonReference\",\n \"insurance\",\n \"supportingInfo\",\n \"note\",\n \"relevantHistory\",\n ]","def get_names(self):\n selected_masks = self._component_obj.get_support()\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]","def elementNames(self):\n nel = self.nElements()\n return map(self.elementName,range(nel))","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"status\",\n \"category\",\n \"code\",\n \"subject\",\n \"context\",\n \"effectiveDateTime\",\n \"effectivePeriod\",\n \"issued\",\n \"performer\",\n \"specimen\",\n \"result\",\n \"imagingStudy\",\n \"image\",\n \"conclusion\",\n \"codedDiagnosis\",\n \"presentedForm\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"partOf\",\n \"status\",\n \"patient\",\n \"type\",\n \"suppliedItem\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"supplier\",\n \"destination\",\n \"receiver\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"name\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"instantiatesCanonical\",\n \"instantiatesUri\",\n \"basedOn\",\n \"replaces\",\n \"groupIdentifier\",\n \"status\",\n \"intent\",\n \"priority\",\n \"code\",\n \"subject\",\n \"encounter\",\n \"authoredOn\",\n \"author\",\n \"reason\",\n \"goal\",\n \"note\",\n \"action\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"cost\", \"packagedProduct\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"actor\", \"role\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"actor\", \"role\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"role\", \"actor\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"issued\",\n \"applies\",\n \"type\",\n \"subType\",\n \"topic\",\n \"action\",\n \"actionReason\",\n \"securityLabel\",\n \"agent\",\n \"text\",\n \"valuedItem\",\n \"group\",\n ]","def _name_estimators(estimators):\n\n names = [type(estimator).__name__.lower() for estimator in estimators]\n namecount = defaultdict(int)\n for est, name in zip(estimators, names):\n namecount[name] += 1\n\n for k, v in list(six.iteritems(namecount)):\n if v == 1:\n del namecount[k]\n\n for i in reversed(range(len(estimators))):\n name = names[i]\n if name in namecount:\n names[i] += \"-%d\" % namecount[name]\n namecount[name] -= 1\n\n return list(zip(names, estimators))","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"definitionUri\",\n \"definitionCanonical\",\n \"status\",\n \"partOf\",\n \"code\",\n \"subject\",\n \"context\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"performer\",\n \"performingOrganization\",\n \"requestingOrganization\",\n \"costCenter\",\n \"quantity\",\n \"bodysite\",\n \"factorOverride\",\n \"priceOverride\",\n \"overrideReason\",\n \"enterer\",\n \"enteredDate\",\n \"reason\",\n \"service\",\n \"productReference\",\n \"productCodeableConcept\",\n \"account\",\n \"note\",\n \"supportingInformation\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"function\", \"actor\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"treatmentIntent\",\n \"dosage\",\n \"administrationTreatment\",\n \"patientCharacteristic\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"example\",\n \"name\",\n \"description\",\n \"acronym\",\n \"sourceUri\",\n \"sourceReference\",\n \"exampleFor\",\n ]","def get_seq_names(self) -> List[str]:\n return [seq.Name.lower() for seq in self.Sequencers]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"name\",\n \"title\",\n \"shortTitle\",\n \"subtitle\",\n \"status\",\n \"date\",\n \"description\",\n \"note\",\n \"useContext\",\n \"publisher\",\n \"contact\",\n \"author\",\n \"editor\",\n \"reviewer\",\n \"endorser\",\n \"relatedArtifact\",\n \"actual\",\n \"characteristicCombination\",\n \"characteristic\",\n \"handling\",\n \"category\",\n ]","def names(self) -> list[str]:","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"sourceString\",\n \"sourceUri\",\n \"classification\",\n ]","def names(self):\n return list(item.name for item in self.mechanisms)","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"definitionReference\",\n \"definitionCanonical\",\n \"definitionCodeableConcept\",\n \"definitionExpression\",\n \"method\",\n \"device\",\n \"exclude\",\n \"timeFromStart\",\n \"groupMeasure\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"profile\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"quantity\", \"period\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"status\",\n \"issued\",\n \"applies\",\n \"subject\",\n \"topic\",\n \"authority\",\n \"domain\",\n \"type\",\n \"subType\",\n \"action\",\n \"actionReason\",\n \"decisionType\",\n \"contentDerivative\",\n \"securityLabel\",\n \"agent\",\n \"signer\",\n \"valuedItem\",\n \"term\",\n \"bindingAttachment\",\n \"bindingReference\",\n \"bindingReference\",\n \"bindingReference\",\n \"friendly\",\n \"legal\",\n \"rule\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"quantity\",\n \"range\",\n \"note\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"classifier\",\n \"label\",\n \"display\",\n \"citation\",\n \"document\",\n \"resource\",\n \"resourceReference\",\n \"publicationStatus\",\n \"publicationDate\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"item\",\n \"type\",\n \"strengthRatio\",\n \"strengthCodeableConcept\",\n \"strengthQuantity\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"reference\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"allowed\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"title\",\n \"derivedFromUri\",\n \"partOf\",\n \"replaces\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"useContext\",\n \"jurisdiction\",\n \"copyright\",\n \"approvalDate\",\n \"lastReviewDate\",\n \"effectivePeriod\",\n \"code\",\n \"instance\",\n \"applicability\",\n \"propertyGroup\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"kind\", \"expression\"]","def _get_exog_re_names(exog_re):\n if isinstance(exog_re, pd.DataFrame):\n return exog_re.columns.tolist()\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\n return [exog_re.name]\n elif isinstance(exog_re, list):\n return exog_re\n return [\"Z{0}\".format(k + 1) for k in range(exog_re.shape[1])]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"name\",\n \"title\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"purpose\",\n \"useContext\",\n \"jurisdiction\",\n \"code\",\n \"search\",\n \"resource\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"applicability\",\n \"priceComponent\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"productCategory\",\n \"productCode\",\n \"parent\",\n \"request\",\n \"identifier\",\n \"biologicalSourceEvent\",\n \"processingFacility\",\n \"division\",\n \"productStatus\",\n \"expirationDate\",\n \"collection\",\n \"storageTempRequirements\",\n \"property\",\n ]","def seqNames(self):\n\t\tseqDict = self.sequenceDict()\n\t\treturn seqDict.keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"linkId\",\n \"prefix\",\n \"title\",\n \"description\",\n \"textEquivalent\",\n \"priority\",\n \"code\",\n \"documentation\",\n \"goal\",\n \"condition\",\n \"input\",\n \"output\",\n \"relatedAction\",\n \"timingDateTime\",\n \"timingAge\",\n \"timingPeriod\",\n \"timingDuration\",\n \"timingRange\",\n \"timingTiming\",\n \"location\",\n \"participant\",\n \"type\",\n \"groupingBehavior\",\n \"selectionBehavior\",\n \"requiredBehavior\",\n \"precheckBehavior\",\n \"cardinalityBehavior\",\n \"resource\",\n \"definitionCanonical\",\n \"definitionUri\",\n \"transform\",\n \"dynamicValue\",\n \"action\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"sequence\",\n \"text\",\n \"additionalInstruction\",\n \"patientInstruction\",\n \"timing\",\n \"asNeeded\",\n \"asNeededFor\",\n \"site\",\n \"route\",\n \"method\",\n \"doseAndRate\",\n \"maxDosePerPeriod\",\n \"maxDosePerAdministration\",\n \"maxDosePerLifetime\",\n ]","def assign_choice_names(self):\r\n\r\n for index, choice in enumerate(\r\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\r\n ):\r\n # Set the name attribute for <choices>\r\n # \"bc\" is appended at the end to indicate that this is a\r\n # binary choice as opposed to a numtolerance_input, this convention\r\n # is used when grading the problem\r\n choice.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) + \"bc\"\r\n )\r\n # Set Name attributes for <numtolerance_input> elements\r\n # Look for all <numtolerance_inputs> inside this choice.\r\n numtolerance_inputs = choice.findall('numtolerance_input')\r\n # Look for all <decoy_input> inside this choice\r\n decoys = choice.findall('decoy_input')\r\n # <decoy_input> would only be used in choices which do not contain\r\n # <numtolerance_input>\r\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\r\n # Give each input inside of the choice a name combining\r\n # The ordinality of the choice, and the ordinality of the input\r\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\r\n for ind, child in enumerate(inputs):\r\n child.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) +\r\n \"_numtolerance_input_\" + str(ind)\r\n )","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"reference\",\n \"note\",\n \"stabilityDuration\",\n \"environmentalSetting\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"language\",\n \"expression\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"version\",\n \"name\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"useContext\",\n \"jurisdiction\",\n \"copyright\",\n \"fhirVersion\",\n \"dependency\",\n \"package\",\n \"global\",\n \"binary\",\n \"page\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"typeCanonical\",\n \"typeReference\",\n \"role\",\n \"function\",\n \"actorCanonical\",\n \"actorReference\",\n ]","def names(self) -> List:\n ...","def pyranose_names(self):\n output = set()\n for item in self.pyranoses():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def input_names(self) -> List[Union[str, int]]:\n return [x.name or i for i, x in enumerate(self.inputs)]","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def elements_sequence(cls):\n return [\"id\", \"extension\", \"low\", \"high\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"quantity\",\n \"itemCodeableConcept\",\n \"itemReference\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"comment\", \"link\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"dosage\"]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"source\"]","def get_specification_kinds(specifications):\n specifications.setdefault(\"manual event models\", {\"tags\": [\"manual event models\"]})\n return [\"manual event models\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"name\",\n \"valueCodeableConcept\",\n \"valueQuantity\",\n \"valueRange\",\n ]","def get_repeat_steps_names(self) -> List[str]:\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]","def get_sequence_names(self):\r\n return Wiki().sequences_for_article_url(self.url).keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"code\",\n \"param\",\n \"documentation\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"uri\"]","def get_input_names(self):\n return self._input_names","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"party\", \"signature\"]","def name(self):\n\t\t# This is necessary for ColumnLists that are used\n\t\t# for CondDescs as well. Ideally, we'd do this on an\n\t\t# InputKeys basis and yield their names (because that's what\n\t\t# formal counts on), but it's probably not worth the effort.\n\t\treturn \"+\".join([f.name for f in self.inputKeys])","def _make_param_names(self, exog_re):\n exog_names = list(self.exog_names)\n exog_re_names = _get_exog_re_names(exog_re)\n param_names = []\n\n jj = self.k_fe\n for i in range(len(exog_re_names)):\n for j in range(i + 1):\n if i == j:\n param_names.append(exog_re_names[i] + \" RE\")\n else:\n param_names.append(exog_re_names[j] + \" RE x \" +\n exog_re_names[i] + \" RE\")\n jj += 1\n\n return exog_names + param_names, exog_re_names, param_names","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"path\", \"expression\"]","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_ordered_adversary_names(self) -> List[str]:\n pass","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"name\",\n \"description\",\n \"resource\",\n ]","def get_comp_names(self, names):\n names = names[:]\n comp_names = {}\n for r_block in self.frd.result_blocks:\n if r_block.name in names and r_block.name not in comp_names:\n comp_names[r_block.name] = []\n for ent in r_block.entities:\n if ent.ictype == 2 and ent.icind1 == 0:\n continue\n comp_names[r_block.name].append(ent.name)\n names.remove(r_block.name)\n if not names:\n break\n return comp_names","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"collector\",\n \"source\",\n \"collectedDateTime\",\n \"collectedPeriod\",\n ]","def rank_names(self):\n return ['Domain', # 0\n 'Phylum', # 1\n 'Class', # 2\n 'Order', # 3\n 'Family', # 4\n 'Genus', # 5\n 'Species'] # 6","def extract_names(collection):\n return (\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\n if n in groups else repr(n) for n in collection\n )","def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\n identifiers = []\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\n\n for arr in arrs:\n node = arr.getElementsByTagName('str')[0]\n identifiers.append(node.firstChild.nodeValue)\n return identifiers","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"targetId\",\n \"relationship\",\n \"endRelationship\",\n \"offsetDuration\",\n \"offsetRange\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"effectiveDate\",\n \"type\",\n \"source\",\n \"costMoney\",\n \"costCodeableConcept\",\n ]","def get_all_clf_names(self) -> List[str]:\n\n model_names = []\n for i in range(self.clf_cnt):\n entry_name = \"target_name_{}\".format(str(i + 1))\n model_names.append(\n self.read_config_entry(\n self.config,\n ConfigKey.SML_SETTINGS.value,\n entry_name,\n data_type=Dtypes.STR.value,\n )\n )\n return model_names","def get_names(self):\n\n # log.debug(str(inspect.stack()[1][3]) + \" --> OC.get_names()\")\n return [x.options['name'] for x in self.get_list()]","def _list_of_availability_strings():\n names = [availability.name for availability in Availability]\n return names","def names(cls) -> List[str]:","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"doseRange\",\n \"doseQuantity\",\n \"rateRatio\",\n \"rateRange\",\n \"rateQuantity\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"code\", \"value\"]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"code\",\n \"factor\",\n \"amount\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"type\",\n \"valueCodeableConcept\",\n \"valueQuantity\",\n \"valueRange\",\n ]","def getParameterList(self):\n inputList = []\n for name, n in zip(self._names, self._inputs):\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\n return inputList","def reservoir_names(self):\n return self._reservoirs","def getNamesFromDescr(descr):\n i = getIter(descr)\n if not i:\n return\n\n try:\n item = i.next()\n while item:\n if isinstance(item[1], str):\n yield item[0]\n else:\n l = []\n for j in getNamesFromDescr(item[1]):\n l.append(j)\n r = (item[0], l)\n yield r\n item = i.next()\n except StopIteration:\n pass","def expected_entity_names(emplacement,\n association,\n composition,\n violation,\n membership_organization,\n membership_person):\n return [\n emplacement[0].site.get_value().value.name,\n association[0].area.get_value().value.name,\n composition[0].child.get_value().value.name.get_value().value,\n truncatewords(violation.description.get_value(), 10),\n membership_organization.organization.get_value().value.name.get_value().value,\n membership_person[0].member.get_value().value.name.get_value().value\n ]","def get_column_names(self):\n names = []\n names.append(self.question_column + \"_agree_lot\")\n names.append(self.question_column + \"_agree_little\")\n names.append(self.question_column + \"_neither\")\n names.append(self.question_column + \"_dis_little\")\n names.append(self.question_column + \"_dis_lot\")\n return names"],"string":"[\n \"def itemnames():\\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\\n return(g)\",\n \"def get_names(self):\\n return [doc['name'] for doc in self.vocab]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"indication\\\", \\\"dosingGuideline\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"patient\\\",\\n \\\"date\\\",\\n \\\"authority\\\",\\n \\\"recommendation\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"code\\\",\\n \\\"status\\\",\\n \\\"author\\\",\\n \\\"intendedJurisdiction\\\",\\n \\\"name\\\",\\n \\\"relatedMedicationKnowledge\\\",\\n \\\"associatedMedication\\\",\\n \\\"productType\\\",\\n \\\"monograph\\\",\\n \\\"preparationInstruction\\\",\\n \\\"cost\\\",\\n \\\"monitoringProgram\\\",\\n \\\"indicationGuideline\\\",\\n \\\"medicineClassification\\\",\\n \\\"packaging\\\",\\n \\\"clinicalUseIssue\\\",\\n \\\"storageGuideline\\\",\\n \\\"regulatory\\\",\\n \\\"definitional\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"regulatoryAuthority\\\",\\n \\\"substitution\\\",\\n \\\"schedule\\\",\\n \\\"maxDispense\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"definition\\\",\\n \\\"doseForm\\\",\\n \\\"intendedRoute\\\",\\n \\\"ingredient\\\",\\n \\\"drugCharacteristic\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"instantiatesCanonical\\\",\\n \\\"instantiatesUri\\\",\\n \\\"basedOn\\\",\\n \\\"priorRequest\\\",\\n \\\"groupIdentifier\\\",\\n \\\"status\\\",\\n \\\"intent\\\",\\n \\\"priority\\\",\\n \\\"codeReference\\\",\\n \\\"codeCodeableConcept\\\",\\n \\\"parameter\\\",\\n \\\"subject\\\",\\n \\\"encounter\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"authoredOn\\\",\\n \\\"requester\\\",\\n \\\"performerType\\\",\\n \\\"performer\\\",\\n \\\"reasonCode\\\",\\n \\\"reasonReference\\\",\\n \\\"insurance\\\",\\n \\\"supportingInfo\\\",\\n \\\"note\\\",\\n \\\"relevantHistory\\\",\\n ]\",\n \"def get_names(self):\\n selected_masks = self._component_obj.get_support()\\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]\",\n \"def elementNames(self):\\n nel = self.nElements()\\n return map(self.elementName,range(nel))\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"status\\\",\\n \\\"category\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"effectiveDateTime\\\",\\n \\\"effectivePeriod\\\",\\n \\\"issued\\\",\\n \\\"performer\\\",\\n \\\"specimen\\\",\\n \\\"result\\\",\\n \\\"imagingStudy\\\",\\n \\\"image\\\",\\n \\\"conclusion\\\",\\n \\\"codedDiagnosis\\\",\\n \\\"presentedForm\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"partOf\\\",\\n \\\"status\\\",\\n \\\"patient\\\",\\n \\\"type\\\",\\n \\\"suppliedItem\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"supplier\\\",\\n \\\"destination\\\",\\n \\\"receiver\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"name\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"instantiatesCanonical\\\",\\n \\\"instantiatesUri\\\",\\n \\\"basedOn\\\",\\n \\\"replaces\\\",\\n \\\"groupIdentifier\\\",\\n \\\"status\\\",\\n \\\"intent\\\",\\n \\\"priority\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"encounter\\\",\\n \\\"authoredOn\\\",\\n \\\"author\\\",\\n \\\"reason\\\",\\n \\\"goal\\\",\\n \\\"note\\\",\\n \\\"action\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"cost\\\", \\\"packagedProduct\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"actor\\\", \\\"role\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"actor\\\", \\\"role\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"role\\\", \\\"actor\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"issued\\\",\\n \\\"applies\\\",\\n \\\"type\\\",\\n \\\"subType\\\",\\n \\\"topic\\\",\\n \\\"action\\\",\\n \\\"actionReason\\\",\\n \\\"securityLabel\\\",\\n \\\"agent\\\",\\n \\\"text\\\",\\n \\\"valuedItem\\\",\\n \\\"group\\\",\\n ]\",\n \"def _name_estimators(estimators):\\n\\n names = [type(estimator).__name__.lower() for estimator in estimators]\\n namecount = defaultdict(int)\\n for est, name in zip(estimators, names):\\n namecount[name] += 1\\n\\n for k, v in list(six.iteritems(namecount)):\\n if v == 1:\\n del namecount[k]\\n\\n for i in reversed(range(len(estimators))):\\n name = names[i]\\n if name in namecount:\\n names[i] += \\\"-%d\\\" % namecount[name]\\n namecount[name] -= 1\\n\\n return list(zip(names, estimators))\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"definitionUri\\\",\\n \\\"definitionCanonical\\\",\\n \\\"status\\\",\\n \\\"partOf\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"performer\\\",\\n \\\"performingOrganization\\\",\\n \\\"requestingOrganization\\\",\\n \\\"costCenter\\\",\\n \\\"quantity\\\",\\n \\\"bodysite\\\",\\n \\\"factorOverride\\\",\\n \\\"priceOverride\\\",\\n \\\"overrideReason\\\",\\n \\\"enterer\\\",\\n \\\"enteredDate\\\",\\n \\\"reason\\\",\\n \\\"service\\\",\\n \\\"productReference\\\",\\n \\\"productCodeableConcept\\\",\\n \\\"account\\\",\\n \\\"note\\\",\\n \\\"supportingInformation\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"function\\\", \\\"actor\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"treatmentIntent\\\",\\n \\\"dosage\\\",\\n \\\"administrationTreatment\\\",\\n \\\"patientCharacteristic\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"example\\\",\\n \\\"name\\\",\\n \\\"description\\\",\\n \\\"acronym\\\",\\n \\\"sourceUri\\\",\\n \\\"sourceReference\\\",\\n \\\"exampleFor\\\",\\n ]\",\n \"def get_seq_names(self) -> List[str]:\\n return [seq.Name.lower() for seq in self.Sequencers]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"name\\\",\\n \\\"title\\\",\\n \\\"shortTitle\\\",\\n \\\"subtitle\\\",\\n \\\"status\\\",\\n \\\"date\\\",\\n \\\"description\\\",\\n \\\"note\\\",\\n \\\"useContext\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"author\\\",\\n \\\"editor\\\",\\n \\\"reviewer\\\",\\n \\\"endorser\\\",\\n \\\"relatedArtifact\\\",\\n \\\"actual\\\",\\n \\\"characteristicCombination\\\",\\n \\\"characteristic\\\",\\n \\\"handling\\\",\\n \\\"category\\\",\\n ]\",\n \"def names(self) -> list[str]:\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"sourceString\\\",\\n \\\"sourceUri\\\",\\n \\\"classification\\\",\\n ]\",\n \"def names(self):\\n return list(item.name for item in self.mechanisms)\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"definitionReference\\\",\\n \\\"definitionCanonical\\\",\\n \\\"definitionCodeableConcept\\\",\\n \\\"definitionExpression\\\",\\n \\\"method\\\",\\n \\\"device\\\",\\n \\\"exclude\\\",\\n \\\"timeFromStart\\\",\\n \\\"groupMeasure\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"profile\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"quantity\\\", \\\"period\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"status\\\",\\n \\\"issued\\\",\\n \\\"applies\\\",\\n \\\"subject\\\",\\n \\\"topic\\\",\\n \\\"authority\\\",\\n \\\"domain\\\",\\n \\\"type\\\",\\n \\\"subType\\\",\\n \\\"action\\\",\\n \\\"actionReason\\\",\\n \\\"decisionType\\\",\\n \\\"contentDerivative\\\",\\n \\\"securityLabel\\\",\\n \\\"agent\\\",\\n \\\"signer\\\",\\n \\\"valuedItem\\\",\\n \\\"term\\\",\\n \\\"bindingAttachment\\\",\\n \\\"bindingReference\\\",\\n \\\"bindingReference\\\",\\n \\\"bindingReference\\\",\\n \\\"friendly\\\",\\n \\\"legal\\\",\\n \\\"rule\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"quantity\\\",\\n \\\"range\\\",\\n \\\"note\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"classifier\\\",\\n \\\"label\\\",\\n \\\"display\\\",\\n \\\"citation\\\",\\n \\\"document\\\",\\n \\\"resource\\\",\\n \\\"resourceReference\\\",\\n \\\"publicationStatus\\\",\\n \\\"publicationDate\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"item\\\",\\n \\\"type\\\",\\n \\\"strengthRatio\\\",\\n \\\"strengthCodeableConcept\\\",\\n \\\"strengthQuantity\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"reference\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"allowed\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"title\\\",\\n \\\"derivedFromUri\\\",\\n \\\"partOf\\\",\\n \\\"replaces\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"copyright\\\",\\n \\\"approvalDate\\\",\\n \\\"lastReviewDate\\\",\\n \\\"effectivePeriod\\\",\\n \\\"code\\\",\\n \\\"instance\\\",\\n \\\"applicability\\\",\\n \\\"propertyGroup\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"kind\\\", \\\"expression\\\"]\",\n \"def _get_exog_re_names(exog_re):\\n if isinstance(exog_re, pd.DataFrame):\\n return exog_re.columns.tolist()\\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\\n return [exog_re.name]\\n elif isinstance(exog_re, list):\\n return exog_re\\n return [\\\"Z{0}\\\".format(k + 1) for k in range(exog_re.shape[1])]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"name\\\",\\n \\\"title\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"purpose\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"code\\\",\\n \\\"search\\\",\\n \\\"resource\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"applicability\\\",\\n \\\"priceComponent\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"productCategory\\\",\\n \\\"productCode\\\",\\n \\\"parent\\\",\\n \\\"request\\\",\\n \\\"identifier\\\",\\n \\\"biologicalSourceEvent\\\",\\n \\\"processingFacility\\\",\\n \\\"division\\\",\\n \\\"productStatus\\\",\\n \\\"expirationDate\\\",\\n \\\"collection\\\",\\n \\\"storageTempRequirements\\\",\\n \\\"property\\\",\\n ]\",\n \"def seqNames(self):\\n\\t\\tseqDict = self.sequenceDict()\\n\\t\\treturn seqDict.keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"linkId\\\",\\n \\\"prefix\\\",\\n \\\"title\\\",\\n \\\"description\\\",\\n \\\"textEquivalent\\\",\\n \\\"priority\\\",\\n \\\"code\\\",\\n \\\"documentation\\\",\\n \\\"goal\\\",\\n \\\"condition\\\",\\n \\\"input\\\",\\n \\\"output\\\",\\n \\\"relatedAction\\\",\\n \\\"timingDateTime\\\",\\n \\\"timingAge\\\",\\n \\\"timingPeriod\\\",\\n \\\"timingDuration\\\",\\n \\\"timingRange\\\",\\n \\\"timingTiming\\\",\\n \\\"location\\\",\\n \\\"participant\\\",\\n \\\"type\\\",\\n \\\"groupingBehavior\\\",\\n \\\"selectionBehavior\\\",\\n \\\"requiredBehavior\\\",\\n \\\"precheckBehavior\\\",\\n \\\"cardinalityBehavior\\\",\\n \\\"resource\\\",\\n \\\"definitionCanonical\\\",\\n \\\"definitionUri\\\",\\n \\\"transform\\\",\\n \\\"dynamicValue\\\",\\n \\\"action\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"sequence\\\",\\n \\\"text\\\",\\n \\\"additionalInstruction\\\",\\n \\\"patientInstruction\\\",\\n \\\"timing\\\",\\n \\\"asNeeded\\\",\\n \\\"asNeededFor\\\",\\n \\\"site\\\",\\n \\\"route\\\",\\n \\\"method\\\",\\n \\\"doseAndRate\\\",\\n \\\"maxDosePerPeriod\\\",\\n \\\"maxDosePerAdministration\\\",\\n \\\"maxDosePerLifetime\\\",\\n ]\",\n \"def assign_choice_names(self):\\r\\n\\r\\n for index, choice in enumerate(\\r\\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\\r\\n ):\\r\\n # Set the name attribute for <choices>\\r\\n # \\\"bc\\\" is appended at the end to indicate that this is a\\r\\n # binary choice as opposed to a numtolerance_input, this convention\\r\\n # is used when grading the problem\\r\\n choice.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) + \\\"bc\\\"\\r\\n )\\r\\n # Set Name attributes for <numtolerance_input> elements\\r\\n # Look for all <numtolerance_inputs> inside this choice.\\r\\n numtolerance_inputs = choice.findall('numtolerance_input')\\r\\n # Look for all <decoy_input> inside this choice\\r\\n decoys = choice.findall('decoy_input')\\r\\n # <decoy_input> would only be used in choices which do not contain\\r\\n # <numtolerance_input>\\r\\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\\r\\n # Give each input inside of the choice a name combining\\r\\n # The ordinality of the choice, and the ordinality of the input\\r\\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\\r\\n for ind, child in enumerate(inputs):\\r\\n child.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) +\\r\\n \\\"_numtolerance_input_\\\" + str(ind)\\r\\n )\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"reference\\\",\\n \\\"note\\\",\\n \\\"stabilityDuration\\\",\\n \\\"environmentalSetting\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"language\\\",\\n \\\"expression\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"version\\\",\\n \\\"name\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"copyright\\\",\\n \\\"fhirVersion\\\",\\n \\\"dependency\\\",\\n \\\"package\\\",\\n \\\"global\\\",\\n \\\"binary\\\",\\n \\\"page\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"typeCanonical\\\",\\n \\\"typeReference\\\",\\n \\\"role\\\",\\n \\\"function\\\",\\n \\\"actorCanonical\\\",\\n \\\"actorReference\\\",\\n ]\",\n \"def names(self) -> List:\\n ...\",\n \"def pyranose_names(self):\\n output = set()\\n for item in self.pyranoses():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def input_names(self) -> List[Union[str, int]]:\\n return [x.name or i for i, x in enumerate(self.inputs)]\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"low\\\", \\\"high\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"quantity\\\",\\n \\\"itemCodeableConcept\\\",\\n \\\"itemReference\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"comment\\\", \\\"link\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"dosage\\\"]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"source\\\"]\",\n \"def get_specification_kinds(specifications):\\n specifications.setdefault(\\\"manual event models\\\", {\\\"tags\\\": [\\\"manual event models\\\"]})\\n return [\\\"manual event models\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"name\\\",\\n \\\"valueCodeableConcept\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n ]\",\n \"def get_repeat_steps_names(self) -> List[str]:\\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]\",\n \"def get_sequence_names(self):\\r\\n return Wiki().sequences_for_article_url(self.url).keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"code\\\",\\n \\\"param\\\",\\n \\\"documentation\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"uri\\\"]\",\n \"def get_input_names(self):\\n return self._input_names\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"party\\\", \\\"signature\\\"]\",\n \"def name(self):\\n\\t\\t# This is necessary for ColumnLists that are used\\n\\t\\t# for CondDescs as well. Ideally, we'd do this on an\\n\\t\\t# InputKeys basis and yield their names (because that's what\\n\\t\\t# formal counts on), but it's probably not worth the effort.\\n\\t\\treturn \\\"+\\\".join([f.name for f in self.inputKeys])\",\n \"def _make_param_names(self, exog_re):\\n exog_names = list(self.exog_names)\\n exog_re_names = _get_exog_re_names(exog_re)\\n param_names = []\\n\\n jj = self.k_fe\\n for i in range(len(exog_re_names)):\\n for j in range(i + 1):\\n if i == j:\\n param_names.append(exog_re_names[i] + \\\" RE\\\")\\n else:\\n param_names.append(exog_re_names[j] + \\\" RE x \\\" +\\n exog_re_names[i] + \\\" RE\\\")\\n jj += 1\\n\\n return exog_names + param_names, exog_re_names, param_names\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"path\\\", \\\"expression\\\"]\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_ordered_adversary_names(self) -> List[str]:\\n pass\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"name\\\",\\n \\\"description\\\",\\n \\\"resource\\\",\\n ]\",\n \"def get_comp_names(self, names):\\n names = names[:]\\n comp_names = {}\\n for r_block in self.frd.result_blocks:\\n if r_block.name in names and r_block.name not in comp_names:\\n comp_names[r_block.name] = []\\n for ent in r_block.entities:\\n if ent.ictype == 2 and ent.icind1 == 0:\\n continue\\n comp_names[r_block.name].append(ent.name)\\n names.remove(r_block.name)\\n if not names:\\n break\\n return comp_names\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"collector\\\",\\n \\\"source\\\",\\n \\\"collectedDateTime\\\",\\n \\\"collectedPeriod\\\",\\n ]\",\n \"def rank_names(self):\\n return ['Domain', # 0\\n 'Phylum', # 1\\n 'Class', # 2\\n 'Order', # 3\\n 'Family', # 4\\n 'Genus', # 5\\n 'Species'] # 6\",\n \"def extract_names(collection):\\n return (\\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\\n if n in groups else repr(n) for n in collection\\n )\",\n \"def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\\n identifiers = []\\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\\n\\n for arr in arrs:\\n node = arr.getElementsByTagName('str')[0]\\n identifiers.append(node.firstChild.nodeValue)\\n return identifiers\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"targetId\\\",\\n \\\"relationship\\\",\\n \\\"endRelationship\\\",\\n \\\"offsetDuration\\\",\\n \\\"offsetRange\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"effectiveDate\\\",\\n \\\"type\\\",\\n \\\"source\\\",\\n \\\"costMoney\\\",\\n \\\"costCodeableConcept\\\",\\n ]\",\n \"def get_all_clf_names(self) -> List[str]:\\n\\n model_names = []\\n for i in range(self.clf_cnt):\\n entry_name = \\\"target_name_{}\\\".format(str(i + 1))\\n model_names.append(\\n self.read_config_entry(\\n self.config,\\n ConfigKey.SML_SETTINGS.value,\\n entry_name,\\n data_type=Dtypes.STR.value,\\n )\\n )\\n return model_names\",\n \"def get_names(self):\\n\\n # log.debug(str(inspect.stack()[1][3]) + \\\" --> OC.get_names()\\\")\\n return [x.options['name'] for x in self.get_list()]\",\n \"def _list_of_availability_strings():\\n names = [availability.name for availability in Availability]\\n return names\",\n \"def names(cls) -> List[str]:\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"doseRange\\\",\\n \\\"doseQuantity\\\",\\n \\\"rateRatio\\\",\\n \\\"rateRange\\\",\\n \\\"rateQuantity\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"code\\\", \\\"value\\\"]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"code\\\",\\n \\\"factor\\\",\\n \\\"amount\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"type\\\",\\n \\\"valueCodeableConcept\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n ]\",\n \"def getParameterList(self):\\n inputList = []\\n for name, n in zip(self._names, self._inputs):\\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\\n return inputList\",\n \"def reservoir_names(self):\\n return self._reservoirs\",\n \"def getNamesFromDescr(descr):\\n i = getIter(descr)\\n if not i:\\n return\\n\\n try:\\n item = i.next()\\n while item:\\n if isinstance(item[1], str):\\n yield item[0]\\n else:\\n l = []\\n for j in getNamesFromDescr(item[1]):\\n l.append(j)\\n r = (item[0], l)\\n yield r\\n item = i.next()\\n except StopIteration:\\n pass\",\n \"def expected_entity_names(emplacement,\\n association,\\n composition,\\n violation,\\n membership_organization,\\n membership_person):\\n return [\\n emplacement[0].site.get_value().value.name,\\n association[0].area.get_value().value.name,\\n composition[0].child.get_value().value.name.get_value().value,\\n truncatewords(violation.description.get_value(), 10),\\n membership_organization.organization.get_value().value.name.get_value().value,\\n membership_person[0].member.get_value().value.name.get_value().value\\n ]\",\n \"def get_column_names(self):\\n names = []\\n names.append(self.question_column + \\\"_agree_lot\\\")\\n names.append(self.question_column + \\\"_agree_little\\\")\\n names.append(self.question_column + \\\"_neither\\\")\\n names.append(self.question_column + \\\"_dis_little\\\")\\n names.append(self.question_column + \\\"_dis_lot\\\")\\n return names\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55996305","0.5533983","0.5532082","0.5531071","0.551983","0.55109936","0.5510303","0.5445824","0.54457086","0.54309005","0.54169387","0.5382732","0.5362275","0.53310806","0.5324852","0.5321274","0.5321274","0.53174865","0.53172743","0.53057075","0.5292066","0.5282227","0.52704674","0.52679956","0.5266378","0.52632535","0.5256607","0.52529067","0.52529067","0.524481","0.5232253","0.521007","0.51918936","0.51889026","0.51870644","0.5179257","0.5177771","0.5173314","0.5171869","0.51718026","0.5169937","0.5162864","0.51577365","0.5154056","0.51492995","0.51423883","0.51343405","0.5125526","0.51001686","0.50676745","0.50631756","0.5057568","0.5056535","0.50501716","0.50459355","0.5042023","0.50407207","0.5033866","0.5033866","0.5030489","0.5030152","0.50263286","0.50222224","0.5015575","0.50052464","0.5003371","0.5003371","0.49855357","0.4982142","0.49810028","0.49708533","0.49632606","0.49588004","0.4956564","0.49440455","0.49353418","0.4930729","0.49214888","0.4918324","0.49126855","0.49100605","0.49035263","0.48928764","0.48894674","0.48880273","0.48736778","0.48727503","0.48690596","0.48643368","0.4863242","0.4860555","0.48569226","0.484653","0.48300713","0.48257795","0.4825549","0.48249134","0.48206082","0.48188257","0.4816935"],"string":"[\n \"0.55996305\",\n \"0.5533983\",\n \"0.5532082\",\n \"0.5531071\",\n \"0.551983\",\n \"0.55109936\",\n \"0.5510303\",\n \"0.5445824\",\n \"0.54457086\",\n \"0.54309005\",\n \"0.54169387\",\n \"0.5382732\",\n \"0.5362275\",\n \"0.53310806\",\n \"0.5324852\",\n \"0.5321274\",\n \"0.5321274\",\n \"0.53174865\",\n \"0.53172743\",\n \"0.53057075\",\n \"0.5292066\",\n \"0.5282227\",\n \"0.52704674\",\n \"0.52679956\",\n \"0.5266378\",\n \"0.52632535\",\n \"0.5256607\",\n \"0.52529067\",\n \"0.52529067\",\n \"0.524481\",\n \"0.5232253\",\n \"0.521007\",\n \"0.51918936\",\n \"0.51889026\",\n \"0.51870644\",\n \"0.5179257\",\n \"0.5177771\",\n \"0.5173314\",\n \"0.5171869\",\n \"0.51718026\",\n \"0.5169937\",\n \"0.5162864\",\n \"0.51577365\",\n \"0.5154056\",\n \"0.51492995\",\n \"0.51423883\",\n \"0.51343405\",\n \"0.5125526\",\n \"0.51001686\",\n \"0.50676745\",\n \"0.50631756\",\n \"0.5057568\",\n \"0.5056535\",\n \"0.50501716\",\n \"0.50459355\",\n \"0.5042023\",\n \"0.50407207\",\n \"0.5033866\",\n \"0.5033866\",\n \"0.5030489\",\n \"0.5030152\",\n \"0.50263286\",\n \"0.50222224\",\n \"0.5015575\",\n \"0.50052464\",\n \"0.5003371\",\n \"0.5003371\",\n \"0.49855357\",\n \"0.4982142\",\n \"0.49810028\",\n \"0.49708533\",\n \"0.49632606\",\n \"0.49588004\",\n \"0.4956564\",\n \"0.49440455\",\n \"0.49353418\",\n \"0.4930729\",\n \"0.49214888\",\n \"0.4918324\",\n \"0.49126855\",\n \"0.49100605\",\n \"0.49035263\",\n \"0.48928764\",\n \"0.48894674\",\n \"0.48880273\",\n \"0.48736778\",\n \"0.48727503\",\n \"0.48690596\",\n \"0.48643368\",\n \"0.4863242\",\n \"0.4860555\",\n \"0.48569226\",\n \"0.484653\",\n \"0.48300713\",\n \"0.48257795\",\n \"0.4825549\",\n \"0.48249134\",\n \"0.48206082\",\n \"0.48188257\",\n \"0.4816935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5134457"},"document_rank":{"kind":"string","value":"46"}}},{"rowIdx":15095,"cells":{"query":{"kind":"string","value":"returning all elements names from ``ImmunizationRecommendationRecommendationDateCriterion`` according specification, with preserving original sequence order."},"document":{"kind":"string","value":"def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"code\", \"value\"]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_date_pred():\r\n \r\n date_now = dt.datetime.now()\r\n date_pred = [date_now - dt.timedelta(days=1)+dt.timedelta(days=i) for i in range(8)]\r\n month_pred = [item.month for item in date_pred]\r\n day_pred = [item.day for item in date_pred]\r\n \r\n return date_pred,month_pred,day_pred","def acquisitions_from_ifg_dates(ifg_dates):\n acq_dates = []\n for ifg_date in ifg_dates: # loop through the dates for each ifg\n dates = ifg_date.split('_') # split into two YYYYMMDDs\n for date in dates: # loop through each of these\n if date not in acq_dates: # if it't not already in the list...\n acq_dates.append(date) # add to it\n return acq_dates","def _get_exog_re_names(exog_re):\n if isinstance(exog_re, pd.DataFrame):\n return exog_re.columns.tolist()\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\n return [exog_re.name]\n elif isinstance(exog_re, list):\n return exog_re\n return [\"Z{0}\".format(k + 1) for k in range(exog_re.shape[1])]","def calibration_dates(self):\n return sorted(self._calibration_dict.keys())","def elementNames(self):\n nel = self.nElements()\n return map(self.elementName,range(nel))","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"vaccineCode\",\n \"targetDisease\",\n \"contraindicatedVaccineCode\",\n \"forecastStatus\",\n \"forecastReason\",\n \"dateCriterion\",\n \"description\",\n \"series\",\n \"doseNumberPositiveInt\",\n \"doseNumberString\",\n \"seriesDosesPositiveInt\",\n \"seriesDosesString\",\n \"supportingImmunization\",\n \"supportingPatientInformation\",\n ]","def period_names_list(periods):\r\n return [period_name(period) for period in periods]","def __getFormerNames(parsed: BeautifulSoup) -> list:\n\n # Former names container\n former_container = parsed.find('p', class_='identInfo')\n\n # List for former names\n former_names = list()\n\n # Building RegEx for former name sentence\n former_sentence_re = re.compile(r'(formerly:.+?\\(filings through .+?\\))')\n # Getting sentence matches\n former_sentences = former_sentence_re.findall(former_container.text)\n \n # Building RegEx for name and filings-through date extraction\n name_and_date_re = re.compile(r'formerly:(.*)\\(.*(\\d{4}-\\d{2}-\\d{2})')\n # Extracting former name and filings-through date for each sentence\n for sentence in former_sentences:\n matches = name_and_date_re.findall(sentence)\n former_name = dict()\n former_name['former_name'] = matches[0][0].strip()\n former_name['filings_through'] = matches[0][1]\n former_names += [former_name]\n\n return former_names","def assign_choice_names(self):\r\n\r\n for index, choice in enumerate(\r\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\r\n ):\r\n # Set the name attribute for <choices>\r\n # \"bc\" is appended at the end to indicate that this is a\r\n # binary choice as opposed to a numtolerance_input, this convention\r\n # is used when grading the problem\r\n choice.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) + \"bc\"\r\n )\r\n # Set Name attributes for <numtolerance_input> elements\r\n # Look for all <numtolerance_inputs> inside this choice.\r\n numtolerance_inputs = choice.findall('numtolerance_input')\r\n # Look for all <decoy_input> inside this choice\r\n decoys = choice.findall('decoy_input')\r\n # <decoy_input> would only be used in choices which do not contain\r\n # <numtolerance_input>\r\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\r\n # Give each input inside of the choice a name combining\r\n # The ordinality of the choice, and the ordinality of the input\r\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\r\n for ind, child in enumerate(inputs):\r\n child.set(\r\n \"name\",\r\n self.answer_id + \"_choiceinput_\" + str(index) +\r\n \"_numtolerance_input_\" + str(ind)\r\n )","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"regulatoryAuthority\",\n \"substitution\",\n \"schedule\",\n \"maxDispense\",\n ]","def get_all_criteria_names(group):\n return (\n criterion.findtext(\"name\")\n for criterion in group.findall(\"criteria/criterion\") if\n criterion.findtext(\"search_type\") != \"member of\")","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"collector\",\n \"source\",\n \"collectedDateTime\",\n \"collectedPeriod\",\n ]","def dates(self):\n pass","def getConditionNames():\n return _conditions.keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"status\",\n \"category\",\n \"code\",\n \"subject\",\n \"context\",\n \"effectiveDateTime\",\n \"effectivePeriod\",\n \"issued\",\n \"performer\",\n \"specimen\",\n \"result\",\n \"imagingStudy\",\n \"image\",\n \"conclusion\",\n \"codedDiagnosis\",\n \"presentedForm\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"patient\",\n \"date\",\n \"authority\",\n \"recommendation\",\n ]","def get_repeat_steps_names(self) -> List[str]:\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]","def _make_param_names(self, exog_re):\n exog_names = list(self.exog_names)\n exog_re_names = _get_exog_re_names(exog_re)\n param_names = []\n\n jj = self.k_fe\n for i in range(len(exog_re_names)):\n for j in range(i + 1):\n if i == j:\n param_names.append(exog_re_names[i] + \" RE\")\n else:\n param_names.append(exog_re_names[j] + \" RE x \" +\n exog_re_names[i] + \" RE\")\n jj += 1\n\n return exog_names + param_names, exog_re_names, param_names","def difficultydates(user_preferences: dict, matcher: NodeMatcher):\r\n dates = user_preferences[\"dificultad_citas\"]\r\n equal_styles = list(matcher.match(\"User\"), dificultad = dates)\r\n return equal_styles","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"instantiatesCanonical\",\n \"instantiatesUri\",\n \"basedOn\",\n \"priorRequest\",\n \"groupIdentifier\",\n \"status\",\n \"intent\",\n \"priority\",\n \"codeReference\",\n \"codeCodeableConcept\",\n \"parameter\",\n \"subject\",\n \"encounter\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"authoredOn\",\n \"requester\",\n \"performerType\",\n \"performer\",\n \"reasonCode\",\n \"reasonReference\",\n \"insurance\",\n \"supportingInfo\",\n \"note\",\n \"relevantHistory\",\n ]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"quantity\", \"period\"]","def dateReview(soup: str, nb:int):\n dateR = []\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\n 'itemprop': 'datePublished'})), ',')).replace(\"['[\", '').replace(\"]']\", '')\n dat = (format_date(dat))\n\n if (dat) > (datetime.now() - timedelta(nb)):\n top = span.findAll('time', attrs={'itemprop': 'datePublished'})\n dateR.append(recovTextBetweenTags(str(top), ','))\n\n return dateR","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"basedOn\",\n \"partOf\",\n \"status\",\n \"patient\",\n \"type\",\n \"suppliedItem\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"supplier\",\n \"destination\",\n \"receiver\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"description\",\n \"definitionReference\",\n \"definitionCanonical\",\n \"definitionCodeableConcept\",\n \"definitionExpression\",\n \"method\",\n \"device\",\n \"exclude\",\n \"timeFromStart\",\n \"groupMeasure\",\n ]","def get_column_names(self):\n names = []\n names.append(self.question_column + \"_agree_lot\")\n names.append(self.question_column + \"_agree_little\")\n names.append(self.question_column + \"_neither\")\n names.append(self.question_column + \"_dis_little\")\n names.append(self.question_column + \"_dis_lot\")\n return names","def _detect_columns_to_fold_dates(self):\n result = list()\n for index in range(len(self._column_names)):\n column_name = self._column_names[index]\n # do not want 12 to be parsed as date, minimum length should be 4 (year in YYYY format)\n if len(column_name) >= 4:\n try:\n # for now strict parsing is true, otherwise it'll parse 'year' as valid date.\n # in future, we'll have to specify date formats\n parsed_column_as_date = dateparser.parse(column_name, settings={'STRICT_PARSING': True})\n if parsed_column_as_date:\n # column_name has been parsed as a valid date, it is a candidate for fold\n result.append(index)\n except:\n # something went wrong, doesn't matter what\n pass\n return result","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_seq_names(self) -> List[str]:\n return [seq.Name.lower() for seq in self.Sequencers]","def _name_estimators(estimators):\n\n names = [type(estimator).__name__.lower() for estimator in estimators]\n namecount = defaultdict(int)\n for est, name in zip(estimators, names):\n namecount[name] += 1\n\n for k, v in list(six.iteritems(namecount)):\n if v == 1:\n del namecount[k]\n\n for i in reversed(range(len(estimators))):\n name = names[i]\n if name in namecount:\n names[i] += \"-%d\" % namecount[name]\n namecount[name] -= 1\n\n return list(zip(names, estimators))","def EventContentMissionExcelStartCompleteConditionParameterNameVector(builder, numElems):\n return StartCompleteConditionParameterNameVector(builder, numElems)","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"classifier\",\n \"label\",\n \"display\",\n \"citation\",\n \"document\",\n \"resource\",\n \"resourceReference\",\n \"publicationStatus\",\n \"publicationDate\",\n ]","def getBuilderNames(categories=None):","def names(self) -> list[str]:","def get_ordered_adversary_names(self) -> List[str]:\n pass","def retrieve_names(self, categorical_column, num_list):\n return [self.num_to_name[categorical_column][i] for i in num_list]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"definitionUri\",\n \"definitionCanonical\",\n \"status\",\n \"partOf\",\n \"code\",\n \"subject\",\n \"context\",\n \"occurrenceDateTime\",\n \"occurrencePeriod\",\n \"occurrenceTiming\",\n \"performer\",\n \"performingOrganization\",\n \"requestingOrganization\",\n \"costCenter\",\n \"quantity\",\n \"bodysite\",\n \"factorOverride\",\n \"priceOverride\",\n \"overrideReason\",\n \"enterer\",\n \"enteredDate\",\n \"reason\",\n \"service\",\n \"productReference\",\n \"productCodeableConcept\",\n \"account\",\n \"note\",\n \"supportingInformation\",\n ]","def expected_entity_names(emplacement,\n association,\n composition,\n violation,\n membership_organization,\n membership_person):\n return [\n emplacement[0].site.get_value().value.name,\n association[0].area.get_value().value.name,\n composition[0].child.get_value().value.name.get_value().value,\n truncatewords(violation.description.get_value(), 10),\n membership_organization.organization.get_value().value.name.get_value().value,\n membership_person[0].member.get_value().value.name.get_value().value\n ]","def get_names(self):\n return [doc['name'] for doc in self.vocab]","def get_names(self):\n selected_masks = self._component_obj.get_support()\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"identifier\",\n \"code\",\n \"status\",\n \"author\",\n \"intendedJurisdiction\",\n \"name\",\n \"relatedMedicationKnowledge\",\n \"associatedMedication\",\n \"productType\",\n \"monograph\",\n \"preparationInstruction\",\n \"cost\",\n \"monitoringProgram\",\n \"indicationGuideline\",\n \"medicineClassification\",\n \"packaging\",\n \"clinicalUseIssue\",\n \"storageGuideline\",\n \"regulatory\",\n \"definitional\",\n ]","def seqNames(self):\n\t\tseqDict = self.sequenceDict()\n\t\treturn seqDict.keys()","def date_prediction(config):\n if config['functionality'] == 'best_flights':\n departure_flight_date = date(config['departure_flight']['departure_date'][0],\n config['departure_flight']['departure_date'][1],\n config['departure_flight']['departure_date'][2])\n return_flight_date = date(config['return_flight']['departure_date'][0],\n config['return_flight']['departure_date'][1],\n config['return_flight']['departure_date'][2])\n div = config['prediction_period_days'] / 7\n dates_search = []\n for x in range(0, div + 1):\n dates_search.append(\n [(departure_flight_date + datetime.timedelta(days=x * 7)),\n (return_flight_date + datetime.timedelta(days=x * 7))])\n for i in dates_search:\n i[0] = str(i[0])\n year, month, day = i[0].split(\"-\")\n i[0] = \"%s/%s/%s\" % (day, month, year)\n i[1] = str(i[1])\n year, month, day = i[1].split(\"-\")\n i[1] = \"%s/%s/%s\" % (day, month, year)\n return dates_search\n elif config['functionality'] == 'flight_trends':\n departure_flight_date = date(\n config['departure_flight']['departure_date'][0],\n config['departure_flight']['departure_date'][1],\n config['departure_flight']['departure_date'][2])\n return_flight_date = date(config['return_flight']['departure_date'][0],\n config['return_flight']['departure_date'][1],\n config['return_flight']['departure_date'][2])\n dates_search = []\n for x in range(0, config['prediction_period_days']):\n dates_search.append(\n [(departure_flight_date + datetime.timedelta(days=x)),\n (return_flight_date + datetime.timedelta(days=x))])\n for i in dates_search:\n i[0] = str(i[0])\n year, month, day = i[0].split(\"-\")\n i[0] = \"%s/%s/%s\" % (day, month, year)\n i[1] = str(i[1])\n year, month, day = i[1].split(\"-\")\n i[1] = \"%s/%s/%s\" % (day, month, year)\n return dates_search","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"effectiveDate\",\n \"type\",\n \"source\",\n \"costMoney\",\n \"costCodeableConcept\",\n ]","def checkDate(list):\n\n removelist = []\n datelist = []\n\n for entry in list:\n fitsKeyWords = ['DATE', 'OBSCLASS']\n header = getFitsHeader(entry, fitsKeyWords)\n date = header[1]\n obsclass = header[2]\n if obsclass=='science':\n if not datelist or not datelist[-1]==date:\n datelist.append(date)\n\n for entry in list:\n fitsKeyWords = ['DATE', 'OBSCLASS']\n header = getFitsHeader(entry, fitsKeyWords)\n date = header[1]\n obsclass = header[2]\n if not obsclass=='science' and date not in datelist:\n removelist.append(entry)\n\n return removelist","def names(self) -> List:\n ...","def _list_of_availability_strings():\n names = [availability.name for availability in Availability]\n return names","def _get_case_dates(self):\n path = \"//path/to/text/text()\"\n return [\n convert_date_string(date_string)\n for date_string in self.html.xpath(path)\n ]","def getParameterList(self):\n inputList = []\n for name, n in zip(self._names, self._inputs):\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\n return inputList","def get_specification_kinds(specifications):\n specifications.setdefault(\"manual event models\", {\"tags\": [\"manual event models\"]})\n return [\"manual event models\"]","def get_serviceIDs_for_date(date):\n global SDHandler\n return SDHandler.effective_service_ids(date);","def get_original_names(self, name_index: int) -> Set[str]:\n return self.match_helper.get_original_names(name_index)","def get_dates(self):\n now = datetime.now()\n if now.month > 6 and now.month < 9:\n now = datetime(now.year, 6, 1)\n\n term = ReadingsTerm()\n out = list(term.get_year_interval(now)) + [now.month]\n return out","def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\n identifiers = []\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\n\n for arr in arrs:\n node = arr.getElementsByTagName('str')[0]\n identifiers.append(node.firstChild.nodeValue)\n return identifiers","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"definition\",\n \"doseForm\",\n \"intendedRoute\",\n \"ingredient\",\n \"drugCharacteristic\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"title\",\n \"derivedFromUri\",\n \"partOf\",\n \"replaces\",\n \"status\",\n \"experimental\",\n \"date\",\n \"publisher\",\n \"contact\",\n \"description\",\n \"useContext\",\n \"jurisdiction\",\n \"copyright\",\n \"approvalDate\",\n \"lastReviewDate\",\n \"effectivePeriod\",\n \"code\",\n \"instance\",\n \"applicability\",\n \"propertyGroup\",\n ]","def _already_summarised_months(self, product_name: str) -> Set[date]:\n\n existing_product = self.get_product_summary(product_name)\n if not existing_product:\n return set()\n\n return {\n r.start_day\n for r in self._engine.execute(\n select([TIME_OVERVIEW.c.start_day]).where(\n TIME_OVERVIEW.c.product_ref == existing_product.id_\n )\n )\n }","def get_sequence_names(self):\r\n return Wiki().sequences_for_article_url(self.url).keys()","def get_cols_for_datetime(train: NumpyOrPandas) -> Tuple[List[str], List[str]]:\n base_dates = get_columns_by_role(train, \"Datetime\", base_date=True)\n datetimes = get_columns_by_role(train, \"Datetime\", base_date=False) + get_columns_by_role(\n train, \"Datetime\", base_date=True, base_feats=True\n )\n\n return base_dates, datetimes","def parameter_names(self) -> List[str]:","def names(self):\n labels = [\n \"$X_{%i}$\" % i if d.name is None else d.name\n for i, d in enumerate(self.dimensions)\n ]\n return labels","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"indication\", \"dosingGuideline\"]","def _create_parameter_names(self):\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"sequence\",\n \"text\",\n \"additionalInstruction\",\n \"patientInstruction\",\n \"timing\",\n \"asNeeded\",\n \"asNeededFor\",\n \"site\",\n \"route\",\n \"method\",\n \"doseAndRate\",\n \"maxDosePerPeriod\",\n \"maxDosePerAdministration\",\n \"maxDosePerLifetime\",\n ]","def get_names(self):\n\n # log.debug(str(inspect.stack()[1][3]) + \" --> OC.get_names()\")\n return [x.options['name'] for x in self.get_list()]","def name(self):\n\t\t# This is necessary for ColumnLists that are used\n\t\t# for CondDescs as well. Ideally, we'd do this on an\n\t\t# InputKeys basis and yield their names (because that's what\n\t\t# formal counts on), but it's probably not worth the effort.\n\t\treturn \"+\".join([f.name for f in self.inputKeys])","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"treatmentIntent\",\n \"dosage\",\n \"administrationTreatment\",\n \"patientCharacteristic\",\n ]","def listBuilderNames():","def listBuilderNames():","def _generate_expanded_column_names(self):\n\n names = []\n # Get names of the descriptors\n des_names = [column for column in self.descriptor_dataframe][1:]\n\n # Generate expanded descriptor names for each compound\n for i in range(self.total_compounds):\n for des_name in des_names:\n name = 'compund_{}_{}'.format(i, des_name)\n names.append(name)\n\n return names","def find_by_requirement(self):\n to_return = []\n for uniq, initiate in self.initiates.iteritems():\n req = initiate.completed_requirements(self.requirements)\n to_return.append((uniq, '{}/{}'.format(len(req), len(self.requirements))))\n\n return to_return","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"applicability\",\n \"priceComponent\",\n ]","def get_debates_by_date(date: str) -> Generator[Tuple[int, str], None, None]:\n debates = sorted(listdir(\"hansard_gathering/processed_hansard_data/{}\".format(date)))\n filtered_debates: List[str] = [_file for _file in debates if not _file.endswith(\"-spans.txt\")]\n for idx, debate in enumerate(filtered_debates):\n if not debate.endswith(\"-spans.txt\"):\n yield (idx, debate)","def prepare(self):\n self.datelist = np.array(self.datelist)\n self.adulist = np.array(self.adulist)\n ast = np.argsort(self.datelist)\n return (self.datelist[ast], self.adulist[ast])","def get_names(dep):\n res = [dep.name]\n return res","def getDummyVarsDays(input_dict, prefix=\"DummyVars_\"):\n header_list = []\n feat_list = []\n for ind,feat in enumerate([0.0, 1.0, 2.0, 3.0]):\n header_list.append(prefix+str(ind))\n if input_dict.has_key(feat):\n feat_list.append(input_dict[feat])\n else:\n feat_list.append(0)\n return feat_list, header_list","def tbl_restrns_date(self,*expect_restrns):\n\n for count,restrn in enumerate(self.final_dataframe.keys()):\n \n if 'No Key Found' not in expect_restrns:\n \n if count in (2,3,4,5) and restrn in expect_restrns:\n \n \"\"\"\n 2 == FTNT, 3 == FARERULE, 4 == ALTRULE, 5 == GENRULE\n \"\"\"\n \n if len(self.final_dataframe[restrn]) != 0 and (self.final_dataframe[restrn].NOAPPL.isnull().any()):\n \"\"\"\n if table restrictions are not empty and NOAPLL is null for e.g. FR has records with NOAPP is null,\n then call to restrn_date function to caputre restriction dates\n \"\"\"\n \n self.restrn_date(restrn)\n \n elif count == 6:\n pass\n \n elif 'No Key Found' in expect_restrns:\n \n if count in (2,3,4,5) and restrn in expect_restrns and len(self.final_dataframe[restrn]) != 0 and (self.final_dataframe[restrn].NOAPPL.notnull().any()):\n \n self.restrn_date(restrn)\n \n elif count == 6:\n pass","def sortByDate(inlist):\n\n seq = []\n for i, each in enumerate(inlist):\n # Lightly parse each flight (just reads the preamble)\n # Putting the last 3 returns of MISlightly into the _ junk var\n flight, _, _, _ = parseMISlightly(each)\n seq.append(flight.takeoff)\n\n # Sort by takeoff time (flight.takeoff is a datetime obj!)\n newseq = np.argsort(seq)\n\n return newseq","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"title\",\n \"requirement\",\n \"relatedData\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"productCategory\",\n \"productCode\",\n \"parent\",\n \"request\",\n \"identifier\",\n \"biologicalSourceEvent\",\n \"processingFacility\",\n \"division\",\n \"productStatus\",\n \"expirationDate\",\n \"collection\",\n \"storageTempRequirements\",\n \"property\",\n ]","def getSelectedDates(view):\n context = aq_inner(view.context)\n request = view.request\n ctx_selected_dates = request.SESSION.get('rendezvous', {})\n uid = context.UID()\n if uid in ctx_selected_dates:\n return ctx_selected_dates[uid].keys()\n else:\n return context.getPropositionsByDates().keys()","def getLinIterVarNames( self ):\n\n self.updateAdb( )\n\n return self.iterNames.keys()","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"entityCodeableConcept\",\n \"entityReference\",\n \"identifier\",\n \"effectiveTime\",\n \"quantity\",\n \"unitPrice\",\n \"factor\",\n \"points\",\n \"net\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"name\",\n \"valueCodeableConcept\",\n \"valueQuantity\",\n \"valueRange\",\n ]","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"type\",\n \"doseRange\",\n \"doseQuantity\",\n \"rateRatio\",\n \"rateRange\",\n \"rateQuantity\",\n ]","def _evaluate_dates(self):\n if len(self.date_strings)==0: \n return \"\"\n\n # high confidence: filter for date with a time attached, expected only 1 unique time\n high_confidence = self._extract_high_conf_dates(set(self.date_strings))\n if high_confidence: \n return high_confidence\n\n #else pick majority vote using absolute\n majority_vote = self.date_strings.most_common(1)[0][0]\n return majority_vote","def _getAttributeNames(self):\n return sorted(self._field_map.keys())","def get_filtered(self, collection, xmlFormat):\n\t\tstart = \"2012-05-01T00:00:00Z\"\n\t\tend = \"2012-05-20T00:00:00Z\"\n\t\tquery = '/text//annotationRecord/service/date/@modified:[%s TO %s]' % (start, end)\n\t\t\n\t\treturn {\n\t\t\t'q' : query,\n\t\t\t\"verb\": \"Search\",\n\t\t\t\"xmlFormat\": xmlFormat,\n\t\t\t\"ky\": collection,\n\t\t\t'sortDescending' : '/text//annotationRecord/service/date/@modified'\n\t\t\t}","def name(self):\n return [o.name for o in self.obs]","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"type\", \"name\"]","def __init__(self, vardict):\n \n datevars = []\n for v in vardict:\n vf = v.VariableFormat\n for fmt in [\"DATE\", \"TIME\", \"QYR\", \"MOYR\", \"MONTH\"]:\n if vf.find(fmt) >= 0:\n datevars.append(v.VariableName)\n break\n self.datevars = \" \".join(datevars)","def elements_sequence(cls):\n return [\n \"id\",\n \"meta\",\n \"implicitRules\",\n \"language\",\n \"text\",\n \"contained\",\n \"extension\",\n \"modifierExtension\",\n \"url\",\n \"identifier\",\n \"version\",\n \"name\",\n \"title\",\n \"shortTitle\",\n \"subtitle\",\n \"status\",\n \"date\",\n \"description\",\n \"note\",\n \"useContext\",\n \"publisher\",\n \"contact\",\n \"author\",\n \"editor\",\n \"reviewer\",\n \"endorser\",\n \"relatedArtifact\",\n \"actual\",\n \"characteristicCombination\",\n \"characteristic\",\n \"handling\",\n \"category\",\n ]","def extract_names(collection):\n return (\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\n if n in groups else repr(n) for n in collection\n )","def elements_sequence(cls):\n return [\"id\", \"extension\", \"modifierExtension\", \"cost\", \"packagedProduct\"]","def get_input_names(self):\n return self._input_names","def get_course_cohort_names(course_key):\r\n return [c.name for c in get_course_cohorts(course_key)]","def get_comp_names(self, names):\n names = names[:]\n comp_names = {}\n for r_block in self.frd.result_blocks:\n if r_block.name in names and r_block.name not in comp_names:\n comp_names[r_block.name] = []\n for ent in r_block.entities:\n if ent.ictype == 2 and ent.icind1 == 0:\n continue\n comp_names[r_block.name].append(ent.name)\n names.remove(r_block.name)\n if not names:\n break\n return comp_names","def list_dates(product):\n\n if product == 'analysis_assim':\n files = _list_files(product)\n dates = []\n for f in files:\n date = _date_from_filename(f)\n dates.append(date)\n dates = list(set(dates)) # Get unique dates\n else:\n template = (HS_DATA_EXPLORER_URI + 'files_explorer/get-folder-contents'\n '/?selection_path=%2Fprojects%2Fwater%2Fnwm%2Fdata%2F{0}'\n '%3Ffolder&query_type=filesystem')\n if 'long_range' in product:\n product = 'long_range'\n uri = template.format(product)\n response = urlopen(uri).read()\n dates = re.findall(r'\\>([0-9]+)\\<', response)\n return sorted(dates)","def getNames(self) -> List[unicode]:\n ...","def get_filename(self, dt):\n if self.period:\n flist = self.files_matching(dt)\n else:\n raise NotImplementedError\n #Now figure out the priority.."],"string":"[\n \"def get_date_pred():\\r\\n \\r\\n date_now = dt.datetime.now()\\r\\n date_pred = [date_now - dt.timedelta(days=1)+dt.timedelta(days=i) for i in range(8)]\\r\\n month_pred = [item.month for item in date_pred]\\r\\n day_pred = [item.day for item in date_pred]\\r\\n \\r\\n return date_pred,month_pred,day_pred\",\n \"def acquisitions_from_ifg_dates(ifg_dates):\\n acq_dates = []\\n for ifg_date in ifg_dates: # loop through the dates for each ifg\\n dates = ifg_date.split('_') # split into two YYYYMMDDs\\n for date in dates: # loop through each of these\\n if date not in acq_dates: # if it't not already in the list...\\n acq_dates.append(date) # add to it\\n return acq_dates\",\n \"def _get_exog_re_names(exog_re):\\n if isinstance(exog_re, pd.DataFrame):\\n return exog_re.columns.tolist()\\n elif isinstance(exog_re, pd.Series) and exog_re.name is not None:\\n return [exog_re.name]\\n elif isinstance(exog_re, list):\\n return exog_re\\n return [\\\"Z{0}\\\".format(k + 1) for k in range(exog_re.shape[1])]\",\n \"def calibration_dates(self):\\n return sorted(self._calibration_dict.keys())\",\n \"def elementNames(self):\\n nel = self.nElements()\\n return map(self.elementName,range(nel))\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"vaccineCode\\\",\\n \\\"targetDisease\\\",\\n \\\"contraindicatedVaccineCode\\\",\\n \\\"forecastStatus\\\",\\n \\\"forecastReason\\\",\\n \\\"dateCriterion\\\",\\n \\\"description\\\",\\n \\\"series\\\",\\n \\\"doseNumberPositiveInt\\\",\\n \\\"doseNumberString\\\",\\n \\\"seriesDosesPositiveInt\\\",\\n \\\"seriesDosesString\\\",\\n \\\"supportingImmunization\\\",\\n \\\"supportingPatientInformation\\\",\\n ]\",\n \"def period_names_list(periods):\\r\\n return [period_name(period) for period in periods]\",\n \"def __getFormerNames(parsed: BeautifulSoup) -> list:\\n\\n # Former names container\\n former_container = parsed.find('p', class_='identInfo')\\n\\n # List for former names\\n former_names = list()\\n\\n # Building RegEx for former name sentence\\n former_sentence_re = re.compile(r'(formerly:.+?\\\\(filings through .+?\\\\))')\\n # Getting sentence matches\\n former_sentences = former_sentence_re.findall(former_container.text)\\n \\n # Building RegEx for name and filings-through date extraction\\n name_and_date_re = re.compile(r'formerly:(.*)\\\\(.*(\\\\d{4}-\\\\d{2}-\\\\d{2})')\\n # Extracting former name and filings-through date for each sentence\\n for sentence in former_sentences:\\n matches = name_and_date_re.findall(sentence)\\n former_name = dict()\\n former_name['former_name'] = matches[0][0].strip()\\n former_name['filings_through'] = matches[0][1]\\n former_names += [former_name]\\n\\n return former_names\",\n \"def assign_choice_names(self):\\r\\n\\r\\n for index, choice in enumerate(\\r\\n self.xml.xpath('//*[@id=$id]//choice', id=self.xml.get('id'))\\r\\n ):\\r\\n # Set the name attribute for <choices>\\r\\n # \\\"bc\\\" is appended at the end to indicate that this is a\\r\\n # binary choice as opposed to a numtolerance_input, this convention\\r\\n # is used when grading the problem\\r\\n choice.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) + \\\"bc\\\"\\r\\n )\\r\\n # Set Name attributes for <numtolerance_input> elements\\r\\n # Look for all <numtolerance_inputs> inside this choice.\\r\\n numtolerance_inputs = choice.findall('numtolerance_input')\\r\\n # Look for all <decoy_input> inside this choice\\r\\n decoys = choice.findall('decoy_input')\\r\\n # <decoy_input> would only be used in choices which do not contain\\r\\n # <numtolerance_input>\\r\\n inputs = numtolerance_inputs if numtolerance_inputs else decoys\\r\\n # Give each input inside of the choice a name combining\\r\\n # The ordinality of the choice, and the ordinality of the input\\r\\n # within that choice e.g. 1_2_1_choiceinput_0_numtolerance_input_1\\r\\n for ind, child in enumerate(inputs):\\r\\n child.set(\\r\\n \\\"name\\\",\\r\\n self.answer_id + \\\"_choiceinput_\\\" + str(index) +\\r\\n \\\"_numtolerance_input_\\\" + str(ind)\\r\\n )\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"regulatoryAuthority\\\",\\n \\\"substitution\\\",\\n \\\"schedule\\\",\\n \\\"maxDispense\\\",\\n ]\",\n \"def get_all_criteria_names(group):\\n return (\\n criterion.findtext(\\\"name\\\")\\n for criterion in group.findall(\\\"criteria/criterion\\\") if\\n criterion.findtext(\\\"search_type\\\") != \\\"member of\\\")\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"collector\\\",\\n \\\"source\\\",\\n \\\"collectedDateTime\\\",\\n \\\"collectedPeriod\\\",\\n ]\",\n \"def dates(self):\\n pass\",\n \"def getConditionNames():\\n return _conditions.keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"status\\\",\\n \\\"category\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"effectiveDateTime\\\",\\n \\\"effectivePeriod\\\",\\n \\\"issued\\\",\\n \\\"performer\\\",\\n \\\"specimen\\\",\\n \\\"result\\\",\\n \\\"imagingStudy\\\",\\n \\\"image\\\",\\n \\\"conclusion\\\",\\n \\\"codedDiagnosis\\\",\\n \\\"presentedForm\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"patient\\\",\\n \\\"date\\\",\\n \\\"authority\\\",\\n \\\"recommendation\\\",\\n ]\",\n \"def get_repeat_steps_names(self) -> List[str]:\\n return [step.StartingFrom.lower() for step in self.Sequence if isinstance(step, Repeater)]\",\n \"def _make_param_names(self, exog_re):\\n exog_names = list(self.exog_names)\\n exog_re_names = _get_exog_re_names(exog_re)\\n param_names = []\\n\\n jj = self.k_fe\\n for i in range(len(exog_re_names)):\\n for j in range(i + 1):\\n if i == j:\\n param_names.append(exog_re_names[i] + \\\" RE\\\")\\n else:\\n param_names.append(exog_re_names[j] + \\\" RE x \\\" +\\n exog_re_names[i] + \\\" RE\\\")\\n jj += 1\\n\\n return exog_names + param_names, exog_re_names, param_names\",\n \"def difficultydates(user_preferences: dict, matcher: NodeMatcher):\\r\\n dates = user_preferences[\\\"dificultad_citas\\\"]\\r\\n equal_styles = list(matcher.match(\\\"User\\\"), dificultad = dates)\\r\\n return equal_styles\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"instantiatesCanonical\\\",\\n \\\"instantiatesUri\\\",\\n \\\"basedOn\\\",\\n \\\"priorRequest\\\",\\n \\\"groupIdentifier\\\",\\n \\\"status\\\",\\n \\\"intent\\\",\\n \\\"priority\\\",\\n \\\"codeReference\\\",\\n \\\"codeCodeableConcept\\\",\\n \\\"parameter\\\",\\n \\\"subject\\\",\\n \\\"encounter\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"authoredOn\\\",\\n \\\"requester\\\",\\n \\\"performerType\\\",\\n \\\"performer\\\",\\n \\\"reasonCode\\\",\\n \\\"reasonReference\\\",\\n \\\"insurance\\\",\\n \\\"supportingInfo\\\",\\n \\\"note\\\",\\n \\\"relevantHistory\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"quantity\\\", \\\"period\\\"]\",\n \"def dateReview(soup: str, nb:int):\\n dateR = []\\n for span in soup.findAll('article', attrs={'itemprop': 'review'}):\\n dat = str(recovTextBetweenTags(str(span.findAll('time', attrs={\\n 'itemprop': 'datePublished'})), ',')).replace(\\\"['[\\\", '').replace(\\\"]']\\\", '')\\n dat = (format_date(dat))\\n\\n if (dat) > (datetime.now() - timedelta(nb)):\\n top = span.findAll('time', attrs={'itemprop': 'datePublished'})\\n dateR.append(recovTextBetweenTags(str(top), ','))\\n\\n return dateR\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"basedOn\\\",\\n \\\"partOf\\\",\\n \\\"status\\\",\\n \\\"patient\\\",\\n \\\"type\\\",\\n \\\"suppliedItem\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"supplier\\\",\\n \\\"destination\\\",\\n \\\"receiver\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"description\\\",\\n \\\"definitionReference\\\",\\n \\\"definitionCanonical\\\",\\n \\\"definitionCodeableConcept\\\",\\n \\\"definitionExpression\\\",\\n \\\"method\\\",\\n \\\"device\\\",\\n \\\"exclude\\\",\\n \\\"timeFromStart\\\",\\n \\\"groupMeasure\\\",\\n ]\",\n \"def get_column_names(self):\\n names = []\\n names.append(self.question_column + \\\"_agree_lot\\\")\\n names.append(self.question_column + \\\"_agree_little\\\")\\n names.append(self.question_column + \\\"_neither\\\")\\n names.append(self.question_column + \\\"_dis_little\\\")\\n names.append(self.question_column + \\\"_dis_lot\\\")\\n return names\",\n \"def _detect_columns_to_fold_dates(self):\\n result = list()\\n for index in range(len(self._column_names)):\\n column_name = self._column_names[index]\\n # do not want 12 to be parsed as date, minimum length should be 4 (year in YYYY format)\\n if len(column_name) >= 4:\\n try:\\n # for now strict parsing is true, otherwise it'll parse 'year' as valid date.\\n # in future, we'll have to specify date formats\\n parsed_column_as_date = dateparser.parse(column_name, settings={'STRICT_PARSING': True})\\n if parsed_column_as_date:\\n # column_name has been parsed as a valid date, it is a candidate for fold\\n result.append(index)\\n except:\\n # something went wrong, doesn't matter what\\n pass\\n return result\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_seq_names(self) -> List[str]:\\n return [seq.Name.lower() for seq in self.Sequencers]\",\n \"def _name_estimators(estimators):\\n\\n names = [type(estimator).__name__.lower() for estimator in estimators]\\n namecount = defaultdict(int)\\n for est, name in zip(estimators, names):\\n namecount[name] += 1\\n\\n for k, v in list(six.iteritems(namecount)):\\n if v == 1:\\n del namecount[k]\\n\\n for i in reversed(range(len(estimators))):\\n name = names[i]\\n if name in namecount:\\n names[i] += \\\"-%d\\\" % namecount[name]\\n namecount[name] -= 1\\n\\n return list(zip(names, estimators))\",\n \"def EventContentMissionExcelStartCompleteConditionParameterNameVector(builder, numElems):\\n return StartCompleteConditionParameterNameVector(builder, numElems)\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"classifier\\\",\\n \\\"label\\\",\\n \\\"display\\\",\\n \\\"citation\\\",\\n \\\"document\\\",\\n \\\"resource\\\",\\n \\\"resourceReference\\\",\\n \\\"publicationStatus\\\",\\n \\\"publicationDate\\\",\\n ]\",\n \"def getBuilderNames(categories=None):\",\n \"def names(self) -> list[str]:\",\n \"def get_ordered_adversary_names(self) -> List[str]:\\n pass\",\n \"def retrieve_names(self, categorical_column, num_list):\\n return [self.num_to_name[categorical_column][i] for i in num_list]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"definitionUri\\\",\\n \\\"definitionCanonical\\\",\\n \\\"status\\\",\\n \\\"partOf\\\",\\n \\\"code\\\",\\n \\\"subject\\\",\\n \\\"context\\\",\\n \\\"occurrenceDateTime\\\",\\n \\\"occurrencePeriod\\\",\\n \\\"occurrenceTiming\\\",\\n \\\"performer\\\",\\n \\\"performingOrganization\\\",\\n \\\"requestingOrganization\\\",\\n \\\"costCenter\\\",\\n \\\"quantity\\\",\\n \\\"bodysite\\\",\\n \\\"factorOverride\\\",\\n \\\"priceOverride\\\",\\n \\\"overrideReason\\\",\\n \\\"enterer\\\",\\n \\\"enteredDate\\\",\\n \\\"reason\\\",\\n \\\"service\\\",\\n \\\"productReference\\\",\\n \\\"productCodeableConcept\\\",\\n \\\"account\\\",\\n \\\"note\\\",\\n \\\"supportingInformation\\\",\\n ]\",\n \"def expected_entity_names(emplacement,\\n association,\\n composition,\\n violation,\\n membership_organization,\\n membership_person):\\n return [\\n emplacement[0].site.get_value().value.name,\\n association[0].area.get_value().value.name,\\n composition[0].child.get_value().value.name.get_value().value,\\n truncatewords(violation.description.get_value(), 10),\\n membership_organization.organization.get_value().value.name.get_value().value,\\n membership_person[0].member.get_value().value.name.get_value().value\\n ]\",\n \"def get_names(self):\\n return [doc['name'] for doc in self.vocab]\",\n \"def get_names(self):\\n selected_masks = self._component_obj.get_support()\\n return [feature_name for (selected, feature_name) in zip(selected_masks, self.input_feature_names) if selected]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"identifier\\\",\\n \\\"code\\\",\\n \\\"status\\\",\\n \\\"author\\\",\\n \\\"intendedJurisdiction\\\",\\n \\\"name\\\",\\n \\\"relatedMedicationKnowledge\\\",\\n \\\"associatedMedication\\\",\\n \\\"productType\\\",\\n \\\"monograph\\\",\\n \\\"preparationInstruction\\\",\\n \\\"cost\\\",\\n \\\"monitoringProgram\\\",\\n \\\"indicationGuideline\\\",\\n \\\"medicineClassification\\\",\\n \\\"packaging\\\",\\n \\\"clinicalUseIssue\\\",\\n \\\"storageGuideline\\\",\\n \\\"regulatory\\\",\\n \\\"definitional\\\",\\n ]\",\n \"def seqNames(self):\\n\\t\\tseqDict = self.sequenceDict()\\n\\t\\treturn seqDict.keys()\",\n \"def date_prediction(config):\\n if config['functionality'] == 'best_flights':\\n departure_flight_date = date(config['departure_flight']['departure_date'][0],\\n config['departure_flight']['departure_date'][1],\\n config['departure_flight']['departure_date'][2])\\n return_flight_date = date(config['return_flight']['departure_date'][0],\\n config['return_flight']['departure_date'][1],\\n config['return_flight']['departure_date'][2])\\n div = config['prediction_period_days'] / 7\\n dates_search = []\\n for x in range(0, div + 1):\\n dates_search.append(\\n [(departure_flight_date + datetime.timedelta(days=x * 7)),\\n (return_flight_date + datetime.timedelta(days=x * 7))])\\n for i in dates_search:\\n i[0] = str(i[0])\\n year, month, day = i[0].split(\\\"-\\\")\\n i[0] = \\\"%s/%s/%s\\\" % (day, month, year)\\n i[1] = str(i[1])\\n year, month, day = i[1].split(\\\"-\\\")\\n i[1] = \\\"%s/%s/%s\\\" % (day, month, year)\\n return dates_search\\n elif config['functionality'] == 'flight_trends':\\n departure_flight_date = date(\\n config['departure_flight']['departure_date'][0],\\n config['departure_flight']['departure_date'][1],\\n config['departure_flight']['departure_date'][2])\\n return_flight_date = date(config['return_flight']['departure_date'][0],\\n config['return_flight']['departure_date'][1],\\n config['return_flight']['departure_date'][2])\\n dates_search = []\\n for x in range(0, config['prediction_period_days']):\\n dates_search.append(\\n [(departure_flight_date + datetime.timedelta(days=x)),\\n (return_flight_date + datetime.timedelta(days=x))])\\n for i in dates_search:\\n i[0] = str(i[0])\\n year, month, day = i[0].split(\\\"-\\\")\\n i[0] = \\\"%s/%s/%s\\\" % (day, month, year)\\n i[1] = str(i[1])\\n year, month, day = i[1].split(\\\"-\\\")\\n i[1] = \\\"%s/%s/%s\\\" % (day, month, year)\\n return dates_search\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"effectiveDate\\\",\\n \\\"type\\\",\\n \\\"source\\\",\\n \\\"costMoney\\\",\\n \\\"costCodeableConcept\\\",\\n ]\",\n \"def checkDate(list):\\n\\n removelist = []\\n datelist = []\\n\\n for entry in list:\\n fitsKeyWords = ['DATE', 'OBSCLASS']\\n header = getFitsHeader(entry, fitsKeyWords)\\n date = header[1]\\n obsclass = header[2]\\n if obsclass=='science':\\n if not datelist or not datelist[-1]==date:\\n datelist.append(date)\\n\\n for entry in list:\\n fitsKeyWords = ['DATE', 'OBSCLASS']\\n header = getFitsHeader(entry, fitsKeyWords)\\n date = header[1]\\n obsclass = header[2]\\n if not obsclass=='science' and date not in datelist:\\n removelist.append(entry)\\n\\n return removelist\",\n \"def names(self) -> List:\\n ...\",\n \"def _list_of_availability_strings():\\n names = [availability.name for availability in Availability]\\n return names\",\n \"def _get_case_dates(self):\\n path = \\\"//path/to/text/text()\\\"\\n return [\\n convert_date_string(date_string)\\n for date_string in self.html.xpath(path)\\n ]\",\n \"def getParameterList(self):\\n inputList = []\\n for name, n in zip(self._names, self._inputs):\\n inputList += ['%s.x%d' % (name, i) for i in range(n)]\\n return inputList\",\n \"def get_specification_kinds(specifications):\\n specifications.setdefault(\\\"manual event models\\\", {\\\"tags\\\": [\\\"manual event models\\\"]})\\n return [\\\"manual event models\\\"]\",\n \"def get_serviceIDs_for_date(date):\\n global SDHandler\\n return SDHandler.effective_service_ids(date);\",\n \"def get_original_names(self, name_index: int) -> Set[str]:\\n return self.match_helper.get_original_names(name_index)\",\n \"def get_dates(self):\\n now = datetime.now()\\n if now.month > 6 and now.month < 9:\\n now = datetime(now.year, 6, 1)\\n\\n term = ReadingsTerm()\\n out = list(term.get_year_interval(now)) + [now.month]\\n return out\",\n \"def _get_named_arr_str_from_xml(self, xml, name, is_expected=True):\\n identifiers = []\\n arrs = self._get_named_arrs_from_xml(xml, name, is_expected)\\n\\n for arr in arrs:\\n node = arr.getElementsByTagName('str')[0]\\n identifiers.append(node.firstChild.nodeValue)\\n return identifiers\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"definition\\\",\\n \\\"doseForm\\\",\\n \\\"intendedRoute\\\",\\n \\\"ingredient\\\",\\n \\\"drugCharacteristic\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"title\\\",\\n \\\"derivedFromUri\\\",\\n \\\"partOf\\\",\\n \\\"replaces\\\",\\n \\\"status\\\",\\n \\\"experimental\\\",\\n \\\"date\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"description\\\",\\n \\\"useContext\\\",\\n \\\"jurisdiction\\\",\\n \\\"copyright\\\",\\n \\\"approvalDate\\\",\\n \\\"lastReviewDate\\\",\\n \\\"effectivePeriod\\\",\\n \\\"code\\\",\\n \\\"instance\\\",\\n \\\"applicability\\\",\\n \\\"propertyGroup\\\",\\n ]\",\n \"def _already_summarised_months(self, product_name: str) -> Set[date]:\\n\\n existing_product = self.get_product_summary(product_name)\\n if not existing_product:\\n return set()\\n\\n return {\\n r.start_day\\n for r in self._engine.execute(\\n select([TIME_OVERVIEW.c.start_day]).where(\\n TIME_OVERVIEW.c.product_ref == existing_product.id_\\n )\\n )\\n }\",\n \"def get_sequence_names(self):\\r\\n return Wiki().sequences_for_article_url(self.url).keys()\",\n \"def get_cols_for_datetime(train: NumpyOrPandas) -> Tuple[List[str], List[str]]:\\n base_dates = get_columns_by_role(train, \\\"Datetime\\\", base_date=True)\\n datetimes = get_columns_by_role(train, \\\"Datetime\\\", base_date=False) + get_columns_by_role(\\n train, \\\"Datetime\\\", base_date=True, base_feats=True\\n )\\n\\n return base_dates, datetimes\",\n \"def parameter_names(self) -> List[str]:\",\n \"def names(self):\\n labels = [\\n \\\"$X_{%i}$\\\" % i if d.name is None else d.name\\n for i, d in enumerate(self.dimensions)\\n ]\\n return labels\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"indication\\\", \\\"dosingGuideline\\\"]\",\n \"def _create_parameter_names(self):\\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"sequence\\\",\\n \\\"text\\\",\\n \\\"additionalInstruction\\\",\\n \\\"patientInstruction\\\",\\n \\\"timing\\\",\\n \\\"asNeeded\\\",\\n \\\"asNeededFor\\\",\\n \\\"site\\\",\\n \\\"route\\\",\\n \\\"method\\\",\\n \\\"doseAndRate\\\",\\n \\\"maxDosePerPeriod\\\",\\n \\\"maxDosePerAdministration\\\",\\n \\\"maxDosePerLifetime\\\",\\n ]\",\n \"def get_names(self):\\n\\n # log.debug(str(inspect.stack()[1][3]) + \\\" --> OC.get_names()\\\")\\n return [x.options['name'] for x in self.get_list()]\",\n \"def name(self):\\n\\t\\t# This is necessary for ColumnLists that are used\\n\\t\\t# for CondDescs as well. Ideally, we'd do this on an\\n\\t\\t# InputKeys basis and yield their names (because that's what\\n\\t\\t# formal counts on), but it's probably not worth the effort.\\n\\t\\treturn \\\"+\\\".join([f.name for f in self.inputKeys])\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"treatmentIntent\\\",\\n \\\"dosage\\\",\\n \\\"administrationTreatment\\\",\\n \\\"patientCharacteristic\\\",\\n ]\",\n \"def listBuilderNames():\",\n \"def listBuilderNames():\",\n \"def _generate_expanded_column_names(self):\\n\\n names = []\\n # Get names of the descriptors\\n des_names = [column for column in self.descriptor_dataframe][1:]\\n\\n # Generate expanded descriptor names for each compound\\n for i in range(self.total_compounds):\\n for des_name in des_names:\\n name = 'compund_{}_{}'.format(i, des_name)\\n names.append(name)\\n\\n return names\",\n \"def find_by_requirement(self):\\n to_return = []\\n for uniq, initiate in self.initiates.iteritems():\\n req = initiate.completed_requirements(self.requirements)\\n to_return.append((uniq, '{}/{}'.format(len(req), len(self.requirements))))\\n\\n return to_return\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"applicability\\\",\\n \\\"priceComponent\\\",\\n ]\",\n \"def get_debates_by_date(date: str) -> Generator[Tuple[int, str], None, None]:\\n debates = sorted(listdir(\\\"hansard_gathering/processed_hansard_data/{}\\\".format(date)))\\n filtered_debates: List[str] = [_file for _file in debates if not _file.endswith(\\\"-spans.txt\\\")]\\n for idx, debate in enumerate(filtered_debates):\\n if not debate.endswith(\\\"-spans.txt\\\"):\\n yield (idx, debate)\",\n \"def prepare(self):\\n self.datelist = np.array(self.datelist)\\n self.adulist = np.array(self.adulist)\\n ast = np.argsort(self.datelist)\\n return (self.datelist[ast], self.adulist[ast])\",\n \"def get_names(dep):\\n res = [dep.name]\\n return res\",\n \"def getDummyVarsDays(input_dict, prefix=\\\"DummyVars_\\\"):\\n header_list = []\\n feat_list = []\\n for ind,feat in enumerate([0.0, 1.0, 2.0, 3.0]):\\n header_list.append(prefix+str(ind))\\n if input_dict.has_key(feat):\\n feat_list.append(input_dict[feat])\\n else:\\n feat_list.append(0)\\n return feat_list, header_list\",\n \"def tbl_restrns_date(self,*expect_restrns):\\n\\n for count,restrn in enumerate(self.final_dataframe.keys()):\\n \\n if 'No Key Found' not in expect_restrns:\\n \\n if count in (2,3,4,5) and restrn in expect_restrns:\\n \\n \\\"\\\"\\\"\\n 2 == FTNT, 3 == FARERULE, 4 == ALTRULE, 5 == GENRULE\\n \\\"\\\"\\\"\\n \\n if len(self.final_dataframe[restrn]) != 0 and (self.final_dataframe[restrn].NOAPPL.isnull().any()):\\n \\\"\\\"\\\"\\n if table restrictions are not empty and NOAPLL is null for e.g. FR has records with NOAPP is null,\\n then call to restrn_date function to caputre restriction dates\\n \\\"\\\"\\\"\\n \\n self.restrn_date(restrn)\\n \\n elif count == 6:\\n pass\\n \\n elif 'No Key Found' in expect_restrns:\\n \\n if count in (2,3,4,5) and restrn in expect_restrns and len(self.final_dataframe[restrn]) != 0 and (self.final_dataframe[restrn].NOAPPL.notnull().any()):\\n \\n self.restrn_date(restrn)\\n \\n elif count == 6:\\n pass\",\n \"def sortByDate(inlist):\\n\\n seq = []\\n for i, each in enumerate(inlist):\\n # Lightly parse each flight (just reads the preamble)\\n # Putting the last 3 returns of MISlightly into the _ junk var\\n flight, _, _, _ = parseMISlightly(each)\\n seq.append(flight.takeoff)\\n\\n # Sort by takeoff time (flight.takeoff is a datetime obj!)\\n newseq = np.argsort(seq)\\n\\n return newseq\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"title\\\",\\n \\\"requirement\\\",\\n \\\"relatedData\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"productCategory\\\",\\n \\\"productCode\\\",\\n \\\"parent\\\",\\n \\\"request\\\",\\n \\\"identifier\\\",\\n \\\"biologicalSourceEvent\\\",\\n \\\"processingFacility\\\",\\n \\\"division\\\",\\n \\\"productStatus\\\",\\n \\\"expirationDate\\\",\\n \\\"collection\\\",\\n \\\"storageTempRequirements\\\",\\n \\\"property\\\",\\n ]\",\n \"def getSelectedDates(view):\\n context = aq_inner(view.context)\\n request = view.request\\n ctx_selected_dates = request.SESSION.get('rendezvous', {})\\n uid = context.UID()\\n if uid in ctx_selected_dates:\\n return ctx_selected_dates[uid].keys()\\n else:\\n return context.getPropositionsByDates().keys()\",\n \"def getLinIterVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.iterNames.keys()\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"entityCodeableConcept\\\",\\n \\\"entityReference\\\",\\n \\\"identifier\\\",\\n \\\"effectiveTime\\\",\\n \\\"quantity\\\",\\n \\\"unitPrice\\\",\\n \\\"factor\\\",\\n \\\"points\\\",\\n \\\"net\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"name\\\",\\n \\\"valueCodeableConcept\\\",\\n \\\"valueQuantity\\\",\\n \\\"valueRange\\\",\\n ]\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"type\\\",\\n \\\"doseRange\\\",\\n \\\"doseQuantity\\\",\\n \\\"rateRatio\\\",\\n \\\"rateRange\\\",\\n \\\"rateQuantity\\\",\\n ]\",\n \"def _evaluate_dates(self):\\n if len(self.date_strings)==0: \\n return \\\"\\\"\\n\\n # high confidence: filter for date with a time attached, expected only 1 unique time\\n high_confidence = self._extract_high_conf_dates(set(self.date_strings))\\n if high_confidence: \\n return high_confidence\\n\\n #else pick majority vote using absolute\\n majority_vote = self.date_strings.most_common(1)[0][0]\\n return majority_vote\",\n \"def _getAttributeNames(self):\\n return sorted(self._field_map.keys())\",\n \"def get_filtered(self, collection, xmlFormat):\\n\\t\\tstart = \\\"2012-05-01T00:00:00Z\\\"\\n\\t\\tend = \\\"2012-05-20T00:00:00Z\\\"\\n\\t\\tquery = '/text//annotationRecord/service/date/@modified:[%s TO %s]' % (start, end)\\n\\t\\t\\n\\t\\treturn {\\n\\t\\t\\t'q' : query,\\n\\t\\t\\t\\\"verb\\\": \\\"Search\\\",\\n\\t\\t\\t\\\"xmlFormat\\\": xmlFormat,\\n\\t\\t\\t\\\"ky\\\": collection,\\n\\t\\t\\t'sortDescending' : '/text//annotationRecord/service/date/@modified'\\n\\t\\t\\t}\",\n \"def name(self):\\n return [o.name for o in self.obs]\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"type\\\", \\\"name\\\"]\",\n \"def __init__(self, vardict):\\n \\n datevars = []\\n for v in vardict:\\n vf = v.VariableFormat\\n for fmt in [\\\"DATE\\\", \\\"TIME\\\", \\\"QYR\\\", \\\"MOYR\\\", \\\"MONTH\\\"]:\\n if vf.find(fmt) >= 0:\\n datevars.append(v.VariableName)\\n break\\n self.datevars = \\\" \\\".join(datevars)\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"meta\\\",\\n \\\"implicitRules\\\",\\n \\\"language\\\",\\n \\\"text\\\",\\n \\\"contained\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"url\\\",\\n \\\"identifier\\\",\\n \\\"version\\\",\\n \\\"name\\\",\\n \\\"title\\\",\\n \\\"shortTitle\\\",\\n \\\"subtitle\\\",\\n \\\"status\\\",\\n \\\"date\\\",\\n \\\"description\\\",\\n \\\"note\\\",\\n \\\"useContext\\\",\\n \\\"publisher\\\",\\n \\\"contact\\\",\\n \\\"author\\\",\\n \\\"editor\\\",\\n \\\"reviewer\\\",\\n \\\"endorser\\\",\\n \\\"relatedArtifact\\\",\\n \\\"actual\\\",\\n \\\"characteristicCombination\\\",\\n \\\"characteristic\\\",\\n \\\"handling\\\",\\n \\\"category\\\",\\n ]\",\n \"def extract_names(collection):\\n return (\\n '[{}]'.format(', '.join(map(repr, groups[n].entity_names)))\\n if n in groups else repr(n) for n in collection\\n )\",\n \"def elements_sequence(cls):\\n return [\\\"id\\\", \\\"extension\\\", \\\"modifierExtension\\\", \\\"cost\\\", \\\"packagedProduct\\\"]\",\n \"def get_input_names(self):\\n return self._input_names\",\n \"def get_course_cohort_names(course_key):\\r\\n return [c.name for c in get_course_cohorts(course_key)]\",\n \"def get_comp_names(self, names):\\n names = names[:]\\n comp_names = {}\\n for r_block in self.frd.result_blocks:\\n if r_block.name in names and r_block.name not in comp_names:\\n comp_names[r_block.name] = []\\n for ent in r_block.entities:\\n if ent.ictype == 2 and ent.icind1 == 0:\\n continue\\n comp_names[r_block.name].append(ent.name)\\n names.remove(r_block.name)\\n if not names:\\n break\\n return comp_names\",\n \"def list_dates(product):\\n\\n if product == 'analysis_assim':\\n files = _list_files(product)\\n dates = []\\n for f in files:\\n date = _date_from_filename(f)\\n dates.append(date)\\n dates = list(set(dates)) # Get unique dates\\n else:\\n template = (HS_DATA_EXPLORER_URI + 'files_explorer/get-folder-contents'\\n '/?selection_path=%2Fprojects%2Fwater%2Fnwm%2Fdata%2F{0}'\\n '%3Ffolder&query_type=filesystem')\\n if 'long_range' in product:\\n product = 'long_range'\\n uri = template.format(product)\\n response = urlopen(uri).read()\\n dates = re.findall(r'\\\\>([0-9]+)\\\\<', response)\\n return sorted(dates)\",\n \"def getNames(self) -> List[unicode]:\\n ...\",\n \"def get_filename(self, dt):\\n if self.period:\\n flist = self.files_matching(dt)\\n else:\\n raise NotImplementedError\\n #Now figure out the priority..\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5164994","0.49957657","0.49790373","0.49350888","0.489302","0.4817644","0.47879577","0.4780576","0.4754007","0.4715894","0.46958432","0.46928817","0.46879193","0.46861127","0.46854797","0.4663666","0.46342486","0.4606411","0.45946944","0.4579715","0.45713055","0.4559208","0.45524564","0.45495743","0.45215315","0.4520441","0.45160317","0.4514277","0.4512708","0.45117345","0.4484873","0.44709122","0.4458012","0.4456935","0.4454","0.44530684","0.4451064","0.44398907","0.44357717","0.4434478","0.44313115","0.4429381","0.4428598","0.4425762","0.44250757","0.44206718","0.44058982","0.44023106","0.43967608","0.43938473","0.4387467","0.43872982","0.43870416","0.4383829","0.4382481","0.43675384","0.436178","0.43598628","0.4359307","0.43553317","0.4352486","0.43476897","0.43408403","0.433705","0.43365982","0.4333823","0.43331343","0.43331343","0.43262598","0.43231916","0.4322249","0.43153968","0.43146065","0.43132222","0.43124643","0.43084377","0.43071225","0.42998165","0.42998165","0.4299097","0.42975193","0.42964873","0.4293951","0.4293951","0.42888665","0.4286226","0.42798346","0.427698","0.42765734","0.42753845","0.42744976","0.42734107","0.42614818","0.42590043","0.42586374","0.42524374","0.42420185","0.4241343","0.42385754","0.4233789","0.42333245"],"string":"[\n \"0.5164994\",\n \"0.49957657\",\n \"0.49790373\",\n \"0.49350888\",\n \"0.489302\",\n \"0.4817644\",\n \"0.47879577\",\n \"0.4780576\",\n \"0.4754007\",\n \"0.4715894\",\n \"0.46958432\",\n \"0.46928817\",\n \"0.46879193\",\n \"0.46861127\",\n \"0.46854797\",\n \"0.4663666\",\n \"0.46342486\",\n \"0.4606411\",\n \"0.45946944\",\n \"0.4579715\",\n \"0.45713055\",\n \"0.4559208\",\n \"0.45524564\",\n \"0.45495743\",\n \"0.45215315\",\n \"0.4520441\",\n \"0.45160317\",\n \"0.4514277\",\n \"0.4512708\",\n \"0.45117345\",\n \"0.4484873\",\n \"0.44709122\",\n \"0.4458012\",\n \"0.4456935\",\n \"0.4454\",\n \"0.44530684\",\n \"0.4451064\",\n \"0.44398907\",\n \"0.44357717\",\n \"0.4434478\",\n \"0.44313115\",\n \"0.4429381\",\n \"0.4428598\",\n \"0.4425762\",\n \"0.44250757\",\n \"0.44206718\",\n \"0.44058982\",\n \"0.44023106\",\n \"0.43967608\",\n \"0.43938473\",\n \"0.4387467\",\n \"0.43872982\",\n \"0.43870416\",\n \"0.4383829\",\n \"0.4382481\",\n \"0.43675384\",\n \"0.436178\",\n \"0.43598628\",\n \"0.4359307\",\n \"0.43553317\",\n \"0.4352486\",\n \"0.43476897\",\n \"0.43408403\",\n \"0.433705\",\n \"0.43365982\",\n \"0.4333823\",\n \"0.43331343\",\n \"0.43331343\",\n \"0.43262598\",\n \"0.43231916\",\n \"0.4322249\",\n \"0.43153968\",\n \"0.43146065\",\n \"0.43132222\",\n \"0.43124643\",\n \"0.43084377\",\n \"0.43071225\",\n \"0.42998165\",\n \"0.42998165\",\n \"0.4299097\",\n \"0.42975193\",\n \"0.42964873\",\n \"0.4293951\",\n \"0.4293951\",\n \"0.42888665\",\n \"0.4286226\",\n \"0.42798346\",\n \"0.427698\",\n \"0.42765734\",\n \"0.42753845\",\n \"0.42744976\",\n \"0.42734107\",\n \"0.42614818\",\n \"0.42590043\",\n \"0.42586374\",\n \"0.42524374\",\n \"0.42420185\",\n \"0.4241343\",\n \"0.42385754\",\n \"0.4233789\",\n \"0.42333245\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":15096,"cells":{"query":{"kind":"string","value":"default cal index from 19000101"},"document":{"kind":"string","value":"def __init__(self, code, start_date=\"1900-01-01\", end_date=\"2020-01-01\"):\n base = Base()\n self.datas = base.getData(\n code=code, start_date=start_date, end_date=end_date)\n self._index = 0\n self.period = 14"},"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 setDefaultIndex():\r\n return 0","def dayofyear(self) -> Index:\n warnings.warn(\n \"`dayofyear` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.dayofyear)","def day(self) -> Index:\n warnings.warn(\n \"`day` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.day)","def daysinmonth(self) -> Index:\n warnings.warn(\n \"`daysinmonth` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.daysinmonth)","def default_index(self):\n return self._default_index","def default_start_index(self):\n return self._default_start_index","def init_index(self):\n raise NotImplementedError","def day_05_a() -> int:\n return 0","def index(self, year=None):\n if year is not None:\n return year.start.year - self.year.start.year + 1\n else:\n return self.year.index()","def day_05_b() -> int:\n return 0","def index_label(frequency):\n return {'D': 'date',\n 'M': 'month'}[frequency]","def change_format_to_database_index(self, date):\n year = date[0:4] + ','\n month = date[4:6]\n day = date[6:8]\n if month[0] == '0':\n month = month[1]\n\n if day[0] == '0':\n day = day[1]\n\n day = ' ' + day + ','\n month = ' ' + month\n\n return year + day + month","def day_07_a() -> int:\n return 0","def create_index():","def day_07_b() -> int:\n return 0","def year(self) -> Index:\n return Index(self.to_series().dt.year)","def dayofweek(self) -> Index:\n warnings.warn(\n \"`dayofweek` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.dayofweek)","def day_06_a() -> int:\n return 0","def index_in_epoch(self):\n return self._index_in_epoch","def day_06_b() -> int:\n return 0","def set_dt_index(self, dt_0=None):\r\n self.set_datetime(dt_0)\r\n dt = pd.to_timedelta(self.data['t'], unit='sec') + self.dt_0\r\n self.data.index = dt\r\n self.data.index.name = 'dt'","def build_index():\n pass","def default_start_index(self, value):\n if not isinstance(value, int):\n raise TypeError('default_start_index attribute must be of int '\n 'type.')\n self._default_start_index = value","def read_monthly_indices_from_CLIMEXP(name_of_index):\n \n import urllib, datetime\n import xarray as xr\n import numpy as np\n import pandas as pd\n\n name_to_url = { \n 'M1i':'http://climexp.knmi.nl/data/iM1.dat',# 1910 ->\n 'M2i':'http://climexp.knmi.nl/data/iM2.dat',# 1910 ->\n 'M3i':'http://climexp.knmi.nl/data/iM3.dat',\n 'M4i':'http://climexp.knmi.nl/data/iM4.dat',# 1910 ->\n 'M5i':'http://climexp.knmi.nl/data/iM5.dat',\n 'M6i':'http://climexp.knmi.nl/data/iM6.dat', # 1910 ->\n 'NAO':'http://climexp.knmi.nl/data/inao.dat', # 1821 ->\n 'NINO12':'http://climexp.knmi.nl/data/inino2.dat',\n 'NINO3': 'http://climexp.knmi.nl/data/inino3.dat',\n 'NINO34':'http://climexp.knmi.nl/data/inino5.dat',\n 'NINO4': 'http://climexp.knmi.nl/data/inino4.dat',\n 'AMO1': 'http://climexp.knmi.nl/data/iamo_hadsst.dat',\n 'AMO2': 'http://climexp.knmi.nl/data/iamo_hadsst_ts.dat',\n 'PDO1': 'http://climexp.knmi.nl/data/ipdo.dat',\n 'PDO2': 'http://climexp.knmi.nl/data/ipdo_hadsst3.dat',\n 'SOI': 'http://climexp.knmi.nl/data/isoi.dat',\n } \n\n url_string = name_to_url[name_of_index]\n try:\n fp2 = urllib.request.urlopen(url_string)\n data_extracted = fp2.readlines()\n except: \n pass\n \n data_asarray = []\n for row in range(len(data_extracted)): \n try: dline = np.array(data_extracted[row].split()).astype(float) \n except: dline = []\n if(len(dline)>0):\n data_asarray.append(np.array(data_extracted[row].split()).astype(float))\n\n data = np.array(data_asarray); dates = np.array([])\n data_years = data[:,0].astype(int); \n\n if(data.shape[1] > 3): \n data_tser = data[:,1:13].ravel()\n \n for y in data_years: \n for m in range(1,13): \n dates = np.append(dates, datetime.date(y, m, 1))\n\n if(data.shape[1] <= 3): \n data_tser = data[:,2]\n \n for row in data: \n dates = np.append(dates, datetime.date(int(row[0]), int(row[1]), 1))\n\n data_tser[data_tser<-990] = np.nan\n if(name_of_index=='Volc'):\n data_tser[data_tser==0] = np.nan\n data_tser = np.sqrt(data_tser)\n\n data_tser[np.isinf(data_tser)] = np.nan\n data_tser = inpaint_nans(data_tser)\n\n\n date_range = [1800, 2150]; idxs = np.zeros(dates.shape, bool)\n for i,date in enumerate(dates):\n if((date.year >= date_range[0]) & (date.year <= date_range[1])): idxs[i] = True\n\n ds = xr.Dataset(data_vars = {name_of_index: ('time',data_tser[idxs])}, \n coords = {'time': dates[idxs].astype(np.datetime64)})\n \n return ds.resample(time='1M').mean()","def index(self, value, start=None, stop=None): # real signature unknown; restored from __doc__\n return 0","def _get_ea_index():\n ea_index_temp = {'Address': 5, 'Agency': 10, 'City': 4, 'Country': 3,\n 'Datacenter': 7, 'Division': 8, 'Interface Name': 13,\n 'Region_List': 2, 'Requester Email': 9, 'Site': 6,\n 'VLAN Description': 11, 'IPR Designation': 16}\n return ea_index_temp","def getOctaveIndex(self):\n return 0","def get_index_data():\n indexTickers = ['^DJI', '^RUA', '^GSPC', '^IXIC', '^SZSA', '^XCI', '^MSH']","def test_sparse_index(self):\n freq = 'MS'\n index = pd.date_range(start=\"2000-1-1\", freq=\"B\", periods=1000)\n tl = formatter.TimestampLocator(plot_index, freq=freq)\n test = tl._process(0, 900)\n new_ind = index[test]\n assert np.all(new_ind.day < 5)\n\n freq = 'M'\n index = pd.date_range(start=\"2000-1-1\", freq=\"B\", periods=1000)\n tl = formatter.TimestampLocator(plot_index, freq=freq)\n test = tl._process(0, 900)\n new_ind = index[test]\n assert np.all(new_ind.day > 25)","def to_ordinal(self):\n return ((self.month - 1) * 20) + self.day - 1","def _get_index_name(self, timestamp):\n if self.index_rollover.lower() == 'monthly':\n return \"%s-%s\" % (self.es_config['all_index'],\n timestamp.strftime(\"%m%Y\")) \n elif self.index_rollover.lower() == 'daily':\n return \"%s-%s\" % (self.es_config['all_index'],\n timestamp.strftime(\"%d%m%Y\")) \n else:\n return self.es_config['all_index']","def get_default_binning_reference(self, run_date, num_banks):\n datatypeutility.check_string_variable('Run date', run_date)\n if run_date.count('-') != 2:\n raise RuntimeError('{} is not a standard YYYY-MM-DD format'.format(run_date))\n\n if run_date >= self._ned_date:\n calib_date = self._ned_date\n else:\n calib_date = '1990-01-01'\n\n if (calib_date, num_banks) in self._default_tof_bins_dict:\n return self._default_tof_bins_dict[calib_date, num_banks]\n\n # hard coded default binning parameters\n ew_bin_params = '5000.,-0.001,70000.'\n if run_date >= self._ned_date:\n high_angle_params = '5000.,-0.0003,70000.'\n else:\n high_angle_params = None\n\n # create binning\n self._default_tof_bins_dict[calib_date, num_banks] = \\\n mantid_reduction.VulcanBinningHelper.create_nature_bins(num_banks=num_banks,\n east_west_binning_parameters=ew_bin_params,\n high_angle_binning_parameters=high_angle_params)\n\n print('[DB...BAT] Binning: {}'.format(self._default_tof_bins_dict[calib_date, num_banks]))\n\n return self._default_tof_bins_dict[calib_date, num_banks]","def align_index_to_local_cdays(self):\n self.setup_class()\n date_range = pd.date_range(start=self.tsdf.first_valid_index(),\n end=self.tsdf.last_valid_index(), freq=CDay(calendar=self.sweden))\n\n self.tsdf = self.tsdf.reindex(date_range, method='pad', copy=False)\n\n return self","def init_index(clear=False):\n return _run_indexer_func(\"init_index\", clear)","def sangawara_fromordinal(cls, ordinal):\n return (max(0, cls.day_fromordinal(ordinal) - 3) % 9) + 1","def NewStartingIndex(self) -> int:","def day_04_a() -> int:\n return get_min_hash(\"bgvyzdsv\", \"00000\")","def get_ind_returns():\n ind = pd.read_csv(\"ind30_m_vw_rets.csv\", header=0, index_col=0)/100\n ind.index = pd.to_datetime(ind.index, format=\"%Y%m\").to_period('M')\n ind.columns = ind.columns.str.strip()\n return ind","def toordinal(self):\n return 0","def toordinal(self):\n return 0","def day_01_a() -> int:\n return get_floor(read_instructions('aoc/aoc2015/input/01A.txt'))","def index(self) -> int:","def get_default():\n today = datetime.date.today()\n if today.month == 1:\n return YearMonth(today.year - 1, 12)\n return YearMonth(today.year, today.month - 1)","def hour(self) -> Index:\n warnings.warn(\n \"`hour` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.hour)","def expandcal(self):\n ind=np.zeros(self.spec.shape[0]).astype(int)\n for k in range(self.nscan):\n ind[self.getscanind(k)]=k\n ind[self.getcalind(k)]=k\n return ind","def is_year_start(self) -> Index:\n return Index(self.to_series().dt.is_year_start)","def model_zero(df, offset, harmonize_year=\"2015\"):\n # current decision is to return a simple offset, this will be a straight\n # line for all time periods. previous behavior was to set df[numcols] = 0,\n # i.e., report 0 if model reports 0.\n return constant_offset(df, offset)","def get_test_index():\n return list(range(305, 435))","def __create_index(self, p_data):\n if \"module\" not in p_data or \"data_flag\" not in p_data:\n return None\n\n _module = p_data[\"module\"]\n _flag = p_data[\"data_flag\"]\n _case_no = self.__session \\\n .query(func.max(TabRunTime.data_index)) \\\n .filter(_module == TabRunTime.module) \\\n .filter(_flag == TabRunTime.data_flag) \\\n .first()[0]\n\n if _case_no is None:\n _case_no = 0\n\n return str(int(_case_no) + 1)","def generate_reverse_index(self):","def get_idx_forecast(idx_start, ar_iteration, forecast_cycle, output_k):\n return idx_start + (forecast_cycle * ar_iteration) + output_k","def get_index_last_started_at(self, index_db_name: str) -> int:\n attr_name = INDEX_ATTR_PREFIX + index_db_name\n return int(self.get_value(attr_name) or NULL_INDEX_LAST_STARTED_AT)","def get_default_index(session_key):\n app_config = get_app_config(session_key)\n\n if app_config is None:\n return \"default\"\n else:\n return app_config.index","def get_rec(self):\n return {'cal': 0}","def generate_inv_index(people):\n pass","def buildIndex(filename, currentTime, baseDir):\n pathToFolder = baseDir + 'Collections/IndriIndices/'\n if not os.path.exists(pathToFolder):\n os.makedirs(pathToFolder)\n INDRI_BUILD_INDEX = '/mnt/bi-strg3/v/zivvasilisky/ziv/env/indri/indri/bin/IndriBuildIndex'\n CORPUS_PATH = filename\n CORPUS_CLASS = 'trectext'\n MEMORY = '1G'\n INDEX = pathToFolder + currentTime\n STEMMER = 'krovetz'\n run_bash_command(INDRI_BUILD_INDEX + ' -corpus.path='+CORPUS_PATH + ' -corpus.class='+CORPUS_CLASS + ' -index='+INDEX + ' -memory='+MEMORY + ' -stemmer.name=' + STEMMER)\n return INDEX","def loadInitial(self):\n PCRsDF = pd.read_excel(defaults.DATA_PATH + \"pcrs-20200415.xlsx\")\n ef.saveIndex(self.credentials, PCRsDF, \"pcrs\")","def setIndexMonth(self,index):\n self.indexMonth = index","def dd_start_value_map_nb(record, ts):\n return ts[record['start_idx'], record['col']]","def from_fixed(cls, date):\n count = mod(date - cls.CORRELATION, 365)\n day = mod(count, 20) + 1\n month = quotient(count, 20) + 1\n return AztecXihuitlDate(month, day)","def _init_index(self):\n\n if self._check_idx:\n self._index = bamnostic.bai.Bai(self._index_path)\n self.__nocoordinate = self._index.n_no_coor\n self.__mapped = sum(self._index.unmapped[mapped].n_mapped for mapped in self._index.unmapped) + self.nocoordinate\n self.__unmapped = sum(self._index.unmapped[unmapped].n_unmapped for unmapped in self._index.unmapped) + self.nocoordinate","def _reset_index():\r\n WIX = create_in(INDEX_NAME, BmarkSchema) # noqa\r","def _natural_index(self, index):\r\n if index < 0:\r\n return len(self.data) + index\r\n return index","def global_index(self):\n raise NotImplementedError","def default_startpos(self) -> Dict[AtomKey, numpy.array]:\n ...","def OldStartingIndex(self) -> int:","def complete_zeros(df_dm,year):\n df_dm.insert(1,year,0)\n return df_dm","def init_index(self, index_name):\n return Index(self, index_name)","def __init__(self):\n self.current_year = datetime.date.today().year\n self.random_column_mappings = collections.defaultdict(dict)","def test_index_at_20101206():\r\n A = np.random.standard_normal(40)\r\n #negative t0\r\n TS_A = ts.TimeSeries(A, t0=-20, sampling_interval=2)\r\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))\r\n #positive t0\r\n TS_A = ts.TimeSeries(A, t0=15, sampling_interval=2)\r\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))\r\n #no t0\r\n TS_A = ts.TimeSeries(A, sampling_interval=2)\r\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))","def get_calibration_index(self, vulcan_run_date):\n # check input\n datatypeutility.check_date_time('VULCAN run start time\\'s date', vulcan_run_date)\n\n # search the list\n date_list = sorted(self._calibration_dict.keys())\n if vulcan_run_date < date_list[0]:\n raise RuntimeError('Input VULCAN run date {0} is too early comparing to {1}'\n ''.format(vulcan_run_date, date_list[0]))\n\n # do a brute force search (as there are only very few of them) against starting date of calibration\n cal_date_index = None\n for i_date in range(len(date_list)-1, -1, -1):\n if vulcan_run_date > date_list[i_date]:\n cal_date_index = date_list[i_date]\n break\n # END-IF\n # END-FOR\n\n return cal_date_index","def init_date( self ) -> datetime:\n return datetime( 2011 ,2 ,1 )","def create_index(self):\r\n\r\n #### Begin functionality here\r\n\r\n return()","def MINSTD0(x):\n \n yield from lehmer(x,16807,2**31-1)","def example_nine():\n x = list(range(10**6))\n i = x.index(991234)","def updateDateIndex(map, accident):\n occurreddate = accident['Start_Time']\n accidentdate = datetime.datetime.strptime(occurreddate, '%Y-%m-%d %H:%M:%S')\n entry = om.get(map, accidentdate.date())\n if entry is None:\n datentry = newDataEntry2(accident)\n om.put(map, accidentdate.date(), datentry)\n else:\n datentry = me.getValue(entry)\n addDateIndex(datentry, accident)\n\n return map","def return_index(self, idx):\n return (\n self.timeseries[:, idx],\n self.ch_amount,\n self.freq,\n self.ch_name[idx],\n self.units[idx],\n self.start_time,\n )","def reindex(self):","def reindex(self):","def getcalind(self,blk=None):\n if blk!=None:\n blk=arr(blk)\n cblk=np.array([]).astype(int) # Initialize cal stare indices\n cs=self.ind['cs']\n ce=self.ind['ce']\n for k,val in enumerate(blk):\n ss=self.ind['ss'][val] # Scan start\n se=self.ind['se'][val] # Scan stop\n # Find leading cal stare \n ind=np.where(ce<=ss)[0]\n if ind.size>0:\n # If it exists, append it\n cblk=np.append(cblk,ind[-1])\n\n # Find trailing cal stare \n ind=np.where(cs>=se)[0]\n if ind.size>0:\n # If it exists, append it\n cblk=np.append(cblk,ind[0])\n else:\n cblk=None\n\n return np.unique(self.getind('cs','ce',blk=cblk))","def y2k_coding(n):\n if n < 60:\n return 2000 + n\n else:\n return 1900 + n","def convertIndex(df):\n df.index = pd.to_datetime(df['Date'])\n return df","def _index_build(self, index_def):\n for index_data in index_def['key']:\n if index_data[0] == '_id' and len(index_def['key']) == 1:\n return 'Default Index'\n else:\n if index_def.get('background') == True:\n return 'Background'\n else:\n return 'Foreground'","def calibration(self, cal: int, /) -> None:","def _document2internal_id(value):\n return (value - 1)//2","def _get_indexer_level_0(self, target) -> npt.NDArray[np.intp]:\n lev = self.levels[0]\n codes = self._codes[0]\n cat = Categorical.from_codes(codes=codes, categories=lev, validate=False)\n ci = Index(cat)\n return ci.get_indexer_for(target)","def _idxs_postformat_null(self):\n pass","def _init_default_tof_bins(self):\n self._default_tof_bins_dict = dict()\n\n self.get_default_binning_reference(self._ned_date, 3)\n\n return","def Anchor_day(year):\r\n day = (5 * ((year // 100) % 4) + 2) % 7\r\n return day","def month(self) -> Index:\n return Index(self.to_series().dt.month)","def calc_base_year_data(base_year_vehicles_df):\n pass","def _generate_new_dates(self, n: int) -> pd.DatetimeIndex:\n new_dates = [\n (self.training_series.time_index()[-1] + (i * self.training_series.freq())) for i in range(1, n + 1)\n ]\n return pd.DatetimeIndex(new_dates, freq=self.training_series.freq_str())","def updateDateIndex(map, accidente):\n occurreddate = accidente['Start_Time']\n accidentedate = datetime.datetime.strptime(occurreddate, '%Y-%m-%d %H:%M:%S')\n entry = om.get(map, accidentedate.date())\n if entry is None:\n datentry = newDataEntry(accidente)\n om.put(map, accidentedate.date(), datentry)\n else:\n datentry = me.getValue(entry)\n addDateIndex(datentry, accidente)\n return map","def getEditInitDefault(self):\n if self.initDefault in DateFormat.dateStampStrings:\n return DateFormat.dateStampStrings[1]\n return TextFormat.getEditInitDefault(self)","def __AOD04(self):\n\n assert self.__lmax <= 100\n\n k = np.zeros(101)\n\n k[0] = 0\n k[1] = 0\n k[2] = -0.308\n k[3] = -0.195\n k[4] = -0.132\n k[5] = -0.103\n k[6] = -0.089\n k[7] = -0.082\n k[8] = -0.078\n k[9] = -0.073\n\n for i in range(10, 18):\n k[i] = -(0.682 + 0.27 * (i - 10) / 8) / i\n for i in range(18, 32):\n k[i] = -(0.952 + 0.288 * (i - 18) / 14) / i\n for i in range(32, 56):\n k[i] = -(1.24 + 0.162 * (i - 32) / 24) / i\n for i in range(56, 101):\n k[i] = -(1.402 + 0.059 * (i - 56) / 44) / i\n\n # print(k[0:(self.__Nmax+1)])\n\n return k[0:(self.__lmax + 1)]","def test_get_index_of_day_empty_list(self):\n self._test_giod([], \"15.07.2013\", 0,\n -1, \"Empty list without next value.\")\n self._test_giod([], \"15.07.2013\", 1,\n -1, \"Empty list with next=1.\")\n self._test_giod([], \"15.07.2013\", -1,\n -1, \"Empty list with next=-1.\")","def name(self):\n return 'D07 Indice de calidad de vida por manzanas'","def test_counter_start_at_zero(self):\n pass","def day(self):\n return 0","def day(self):\n return 0","def year_lookup_bounds(self, value):\n first = '%s-01-01 00:00:00'\n # SQL Server doesn't support microseconds\n last = '%s-12-31 23:59:59'\n return [first % value, last % value]"],"string":"[\n \"def setDefaultIndex():\\r\\n return 0\",\n \"def dayofyear(self) -> Index:\\n warnings.warn(\\n \\\"`dayofyear` will return int32 index instead of int 64 index in 4.0.0.\\\",\\n FutureWarning,\\n )\\n return Index(self.to_series().dt.dayofyear)\",\n \"def day(self) -> Index:\\n warnings.warn(\\n \\\"`day` will return int32 index instead of int 64 index in 4.0.0.\\\",\\n FutureWarning,\\n )\\n return Index(self.to_series().dt.day)\",\n \"def daysinmonth(self) -> Index:\\n warnings.warn(\\n \\\"`daysinmonth` will return int32 index instead of int 64 index in 4.0.0.\\\",\\n FutureWarning,\\n )\\n return Index(self.to_series().dt.daysinmonth)\",\n \"def default_index(self):\\n return self._default_index\",\n \"def default_start_index(self):\\n return self._default_start_index\",\n \"def init_index(self):\\n raise NotImplementedError\",\n \"def day_05_a() -> int:\\n return 0\",\n \"def index(self, year=None):\\n if year is not None:\\n return year.start.year - self.year.start.year + 1\\n else:\\n return self.year.index()\",\n \"def day_05_b() -> int:\\n return 0\",\n \"def index_label(frequency):\\n return {'D': 'date',\\n 'M': 'month'}[frequency]\",\n \"def change_format_to_database_index(self, date):\\n year = date[0:4] + ','\\n month = date[4:6]\\n day = date[6:8]\\n if month[0] == '0':\\n month = month[1]\\n\\n if day[0] == '0':\\n day = day[1]\\n\\n day = ' ' + day + ','\\n month = ' ' + month\\n\\n return year + day + month\",\n \"def day_07_a() -> int:\\n return 0\",\n \"def create_index():\",\n \"def day_07_b() -> int:\\n return 0\",\n \"def year(self) -> Index:\\n return Index(self.to_series().dt.year)\",\n \"def dayofweek(self) -> Index:\\n warnings.warn(\\n \\\"`dayofweek` will return int32 index instead of int 64 index in 4.0.0.\\\",\\n FutureWarning,\\n )\\n return Index(self.to_series().dt.dayofweek)\",\n \"def day_06_a() -> int:\\n return 0\",\n \"def index_in_epoch(self):\\n return self._index_in_epoch\",\n \"def day_06_b() -> int:\\n return 0\",\n \"def set_dt_index(self, dt_0=None):\\r\\n self.set_datetime(dt_0)\\r\\n dt = pd.to_timedelta(self.data['t'], unit='sec') + self.dt_0\\r\\n self.data.index = dt\\r\\n self.data.index.name = 'dt'\",\n \"def build_index():\\n pass\",\n \"def default_start_index(self, value):\\n if not isinstance(value, int):\\n raise TypeError('default_start_index attribute must be of int '\\n 'type.')\\n self._default_start_index = value\",\n \"def read_monthly_indices_from_CLIMEXP(name_of_index):\\n \\n import urllib, datetime\\n import xarray as xr\\n import numpy as np\\n import pandas as pd\\n\\n name_to_url = { \\n 'M1i':'http://climexp.knmi.nl/data/iM1.dat',# 1910 ->\\n 'M2i':'http://climexp.knmi.nl/data/iM2.dat',# 1910 ->\\n 'M3i':'http://climexp.knmi.nl/data/iM3.dat',\\n 'M4i':'http://climexp.knmi.nl/data/iM4.dat',# 1910 ->\\n 'M5i':'http://climexp.knmi.nl/data/iM5.dat',\\n 'M6i':'http://climexp.knmi.nl/data/iM6.dat', # 1910 ->\\n 'NAO':'http://climexp.knmi.nl/data/inao.dat', # 1821 ->\\n 'NINO12':'http://climexp.knmi.nl/data/inino2.dat',\\n 'NINO3': 'http://climexp.knmi.nl/data/inino3.dat',\\n 'NINO34':'http://climexp.knmi.nl/data/inino5.dat',\\n 'NINO4': 'http://climexp.knmi.nl/data/inino4.dat',\\n 'AMO1': 'http://climexp.knmi.nl/data/iamo_hadsst.dat',\\n 'AMO2': 'http://climexp.knmi.nl/data/iamo_hadsst_ts.dat',\\n 'PDO1': 'http://climexp.knmi.nl/data/ipdo.dat',\\n 'PDO2': 'http://climexp.knmi.nl/data/ipdo_hadsst3.dat',\\n 'SOI': 'http://climexp.knmi.nl/data/isoi.dat',\\n } \\n\\n url_string = name_to_url[name_of_index]\\n try:\\n fp2 = urllib.request.urlopen(url_string)\\n data_extracted = fp2.readlines()\\n except: \\n pass\\n \\n data_asarray = []\\n for row in range(len(data_extracted)): \\n try: dline = np.array(data_extracted[row].split()).astype(float) \\n except: dline = []\\n if(len(dline)>0):\\n data_asarray.append(np.array(data_extracted[row].split()).astype(float))\\n\\n data = np.array(data_asarray); dates = np.array([])\\n data_years = data[:,0].astype(int); \\n\\n if(data.shape[1] > 3): \\n data_tser = data[:,1:13].ravel()\\n \\n for y in data_years: \\n for m in range(1,13): \\n dates = np.append(dates, datetime.date(y, m, 1))\\n\\n if(data.shape[1] <= 3): \\n data_tser = data[:,2]\\n \\n for row in data: \\n dates = np.append(dates, datetime.date(int(row[0]), int(row[1]), 1))\\n\\n data_tser[data_tser<-990] = np.nan\\n if(name_of_index=='Volc'):\\n data_tser[data_tser==0] = np.nan\\n data_tser = np.sqrt(data_tser)\\n\\n data_tser[np.isinf(data_tser)] = np.nan\\n data_tser = inpaint_nans(data_tser)\\n\\n\\n date_range = [1800, 2150]; idxs = np.zeros(dates.shape, bool)\\n for i,date in enumerate(dates):\\n if((date.year >= date_range[0]) & (date.year <= date_range[1])): idxs[i] = True\\n\\n ds = xr.Dataset(data_vars = {name_of_index: ('time',data_tser[idxs])}, \\n coords = {'time': dates[idxs].astype(np.datetime64)})\\n \\n return ds.resample(time='1M').mean()\",\n \"def index(self, value, start=None, stop=None): # real signature unknown; restored from __doc__\\n return 0\",\n \"def _get_ea_index():\\n ea_index_temp = {'Address': 5, 'Agency': 10, 'City': 4, 'Country': 3,\\n 'Datacenter': 7, 'Division': 8, 'Interface Name': 13,\\n 'Region_List': 2, 'Requester Email': 9, 'Site': 6,\\n 'VLAN Description': 11, 'IPR Designation': 16}\\n return ea_index_temp\",\n \"def getOctaveIndex(self):\\n return 0\",\n \"def get_index_data():\\n indexTickers = ['^DJI', '^RUA', '^GSPC', '^IXIC', '^SZSA', '^XCI', '^MSH']\",\n \"def test_sparse_index(self):\\n freq = 'MS'\\n index = pd.date_range(start=\\\"2000-1-1\\\", freq=\\\"B\\\", periods=1000)\\n tl = formatter.TimestampLocator(plot_index, freq=freq)\\n test = tl._process(0, 900)\\n new_ind = index[test]\\n assert np.all(new_ind.day < 5)\\n\\n freq = 'M'\\n index = pd.date_range(start=\\\"2000-1-1\\\", freq=\\\"B\\\", periods=1000)\\n tl = formatter.TimestampLocator(plot_index, freq=freq)\\n test = tl._process(0, 900)\\n new_ind = index[test]\\n assert np.all(new_ind.day > 25)\",\n \"def to_ordinal(self):\\n return ((self.month - 1) * 20) + self.day - 1\",\n \"def _get_index_name(self, timestamp):\\n if self.index_rollover.lower() == 'monthly':\\n return \\\"%s-%s\\\" % (self.es_config['all_index'],\\n timestamp.strftime(\\\"%m%Y\\\")) \\n elif self.index_rollover.lower() == 'daily':\\n return \\\"%s-%s\\\" % (self.es_config['all_index'],\\n timestamp.strftime(\\\"%d%m%Y\\\")) \\n else:\\n return self.es_config['all_index']\",\n \"def get_default_binning_reference(self, run_date, num_banks):\\n datatypeutility.check_string_variable('Run date', run_date)\\n if run_date.count('-') != 2:\\n raise RuntimeError('{} is not a standard YYYY-MM-DD format'.format(run_date))\\n\\n if run_date >= self._ned_date:\\n calib_date = self._ned_date\\n else:\\n calib_date = '1990-01-01'\\n\\n if (calib_date, num_banks) in self._default_tof_bins_dict:\\n return self._default_tof_bins_dict[calib_date, num_banks]\\n\\n # hard coded default binning parameters\\n ew_bin_params = '5000.,-0.001,70000.'\\n if run_date >= self._ned_date:\\n high_angle_params = '5000.,-0.0003,70000.'\\n else:\\n high_angle_params = None\\n\\n # create binning\\n self._default_tof_bins_dict[calib_date, num_banks] = \\\\\\n mantid_reduction.VulcanBinningHelper.create_nature_bins(num_banks=num_banks,\\n east_west_binning_parameters=ew_bin_params,\\n high_angle_binning_parameters=high_angle_params)\\n\\n print('[DB...BAT] Binning: {}'.format(self._default_tof_bins_dict[calib_date, num_banks]))\\n\\n return self._default_tof_bins_dict[calib_date, num_banks]\",\n \"def align_index_to_local_cdays(self):\\n self.setup_class()\\n date_range = pd.date_range(start=self.tsdf.first_valid_index(),\\n end=self.tsdf.last_valid_index(), freq=CDay(calendar=self.sweden))\\n\\n self.tsdf = self.tsdf.reindex(date_range, method='pad', copy=False)\\n\\n return self\",\n \"def init_index(clear=False):\\n return _run_indexer_func(\\\"init_index\\\", clear)\",\n \"def sangawara_fromordinal(cls, ordinal):\\n return (max(0, cls.day_fromordinal(ordinal) - 3) % 9) + 1\",\n \"def NewStartingIndex(self) -> int:\",\n \"def day_04_a() -> int:\\n return get_min_hash(\\\"bgvyzdsv\\\", \\\"00000\\\")\",\n \"def get_ind_returns():\\n ind = pd.read_csv(\\\"ind30_m_vw_rets.csv\\\", header=0, index_col=0)/100\\n ind.index = pd.to_datetime(ind.index, format=\\\"%Y%m\\\").to_period('M')\\n ind.columns = ind.columns.str.strip()\\n return ind\",\n \"def toordinal(self):\\n return 0\",\n \"def toordinal(self):\\n return 0\",\n \"def day_01_a() -> int:\\n return get_floor(read_instructions('aoc/aoc2015/input/01A.txt'))\",\n \"def index(self) -> int:\",\n \"def get_default():\\n today = datetime.date.today()\\n if today.month == 1:\\n return YearMonth(today.year - 1, 12)\\n return YearMonth(today.year, today.month - 1)\",\n \"def hour(self) -> Index:\\n warnings.warn(\\n \\\"`hour` will return int32 index instead of int 64 index in 4.0.0.\\\",\\n FutureWarning,\\n )\\n return Index(self.to_series().dt.hour)\",\n \"def expandcal(self):\\n ind=np.zeros(self.spec.shape[0]).astype(int)\\n for k in range(self.nscan):\\n ind[self.getscanind(k)]=k\\n ind[self.getcalind(k)]=k\\n return ind\",\n \"def is_year_start(self) -> Index:\\n return Index(self.to_series().dt.is_year_start)\",\n \"def model_zero(df, offset, harmonize_year=\\\"2015\\\"):\\n # current decision is to return a simple offset, this will be a straight\\n # line for all time periods. previous behavior was to set df[numcols] = 0,\\n # i.e., report 0 if model reports 0.\\n return constant_offset(df, offset)\",\n \"def get_test_index():\\n return list(range(305, 435))\",\n \"def __create_index(self, p_data):\\n if \\\"module\\\" not in p_data or \\\"data_flag\\\" not in p_data:\\n return None\\n\\n _module = p_data[\\\"module\\\"]\\n _flag = p_data[\\\"data_flag\\\"]\\n _case_no = self.__session \\\\\\n .query(func.max(TabRunTime.data_index)) \\\\\\n .filter(_module == TabRunTime.module) \\\\\\n .filter(_flag == TabRunTime.data_flag) \\\\\\n .first()[0]\\n\\n if _case_no is None:\\n _case_no = 0\\n\\n return str(int(_case_no) + 1)\",\n \"def generate_reverse_index(self):\",\n \"def get_idx_forecast(idx_start, ar_iteration, forecast_cycle, output_k):\\n return idx_start + (forecast_cycle * ar_iteration) + output_k\",\n \"def get_index_last_started_at(self, index_db_name: str) -> int:\\n attr_name = INDEX_ATTR_PREFIX + index_db_name\\n return int(self.get_value(attr_name) or NULL_INDEX_LAST_STARTED_AT)\",\n \"def get_default_index(session_key):\\n app_config = get_app_config(session_key)\\n\\n if app_config is None:\\n return \\\"default\\\"\\n else:\\n return app_config.index\",\n \"def get_rec(self):\\n return {'cal': 0}\",\n \"def generate_inv_index(people):\\n pass\",\n \"def buildIndex(filename, currentTime, baseDir):\\n pathToFolder = baseDir + 'Collections/IndriIndices/'\\n if not os.path.exists(pathToFolder):\\n os.makedirs(pathToFolder)\\n INDRI_BUILD_INDEX = '/mnt/bi-strg3/v/zivvasilisky/ziv/env/indri/indri/bin/IndriBuildIndex'\\n CORPUS_PATH = filename\\n CORPUS_CLASS = 'trectext'\\n MEMORY = '1G'\\n INDEX = pathToFolder + currentTime\\n STEMMER = 'krovetz'\\n run_bash_command(INDRI_BUILD_INDEX + ' -corpus.path='+CORPUS_PATH + ' -corpus.class='+CORPUS_CLASS + ' -index='+INDEX + ' -memory='+MEMORY + ' -stemmer.name=' + STEMMER)\\n return INDEX\",\n \"def loadInitial(self):\\n PCRsDF = pd.read_excel(defaults.DATA_PATH + \\\"pcrs-20200415.xlsx\\\")\\n ef.saveIndex(self.credentials, PCRsDF, \\\"pcrs\\\")\",\n \"def setIndexMonth(self,index):\\n self.indexMonth = index\",\n \"def dd_start_value_map_nb(record, ts):\\n return ts[record['start_idx'], record['col']]\",\n \"def from_fixed(cls, date):\\n count = mod(date - cls.CORRELATION, 365)\\n day = mod(count, 20) + 1\\n month = quotient(count, 20) + 1\\n return AztecXihuitlDate(month, day)\",\n \"def _init_index(self):\\n\\n if self._check_idx:\\n self._index = bamnostic.bai.Bai(self._index_path)\\n self.__nocoordinate = self._index.n_no_coor\\n self.__mapped = sum(self._index.unmapped[mapped].n_mapped for mapped in self._index.unmapped) + self.nocoordinate\\n self.__unmapped = sum(self._index.unmapped[unmapped].n_unmapped for unmapped in self._index.unmapped) + self.nocoordinate\",\n \"def _reset_index():\\r\\n WIX = create_in(INDEX_NAME, BmarkSchema) # noqa\\r\",\n \"def _natural_index(self, index):\\r\\n if index < 0:\\r\\n return len(self.data) + index\\r\\n return index\",\n \"def global_index(self):\\n raise NotImplementedError\",\n \"def default_startpos(self) -> Dict[AtomKey, numpy.array]:\\n ...\",\n \"def OldStartingIndex(self) -> int:\",\n \"def complete_zeros(df_dm,year):\\n df_dm.insert(1,year,0)\\n return df_dm\",\n \"def init_index(self, index_name):\\n return Index(self, index_name)\",\n \"def __init__(self):\\n self.current_year = datetime.date.today().year\\n self.random_column_mappings = collections.defaultdict(dict)\",\n \"def test_index_at_20101206():\\r\\n A = np.random.standard_normal(40)\\r\\n #negative t0\\r\\n TS_A = ts.TimeSeries(A, t0=-20, sampling_interval=2)\\r\\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))\\r\\n #positive t0\\r\\n TS_A = ts.TimeSeries(A, t0=15, sampling_interval=2)\\r\\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))\\r\\n #no t0\\r\\n TS_A = ts.TimeSeries(A, sampling_interval=2)\\r\\n npt.assert_equal(TS_A.time.index_at(TS_A.time), np.arange(40))\",\n \"def get_calibration_index(self, vulcan_run_date):\\n # check input\\n datatypeutility.check_date_time('VULCAN run start time\\\\'s date', vulcan_run_date)\\n\\n # search the list\\n date_list = sorted(self._calibration_dict.keys())\\n if vulcan_run_date < date_list[0]:\\n raise RuntimeError('Input VULCAN run date {0} is too early comparing to {1}'\\n ''.format(vulcan_run_date, date_list[0]))\\n\\n # do a brute force search (as there are only very few of them) against starting date of calibration\\n cal_date_index = None\\n for i_date in range(len(date_list)-1, -1, -1):\\n if vulcan_run_date > date_list[i_date]:\\n cal_date_index = date_list[i_date]\\n break\\n # END-IF\\n # END-FOR\\n\\n return cal_date_index\",\n \"def init_date( self ) -> datetime:\\n return datetime( 2011 ,2 ,1 )\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n\\r\\n return()\",\n \"def MINSTD0(x):\\n \\n yield from lehmer(x,16807,2**31-1)\",\n \"def example_nine():\\n x = list(range(10**6))\\n i = x.index(991234)\",\n \"def updateDateIndex(map, accident):\\n occurreddate = accident['Start_Time']\\n accidentdate = datetime.datetime.strptime(occurreddate, '%Y-%m-%d %H:%M:%S')\\n entry = om.get(map, accidentdate.date())\\n if entry is None:\\n datentry = newDataEntry2(accident)\\n om.put(map, accidentdate.date(), datentry)\\n else:\\n datentry = me.getValue(entry)\\n addDateIndex(datentry, accident)\\n\\n return map\",\n \"def return_index(self, idx):\\n return (\\n self.timeseries[:, idx],\\n self.ch_amount,\\n self.freq,\\n self.ch_name[idx],\\n self.units[idx],\\n self.start_time,\\n )\",\n \"def reindex(self):\",\n \"def reindex(self):\",\n \"def getcalind(self,blk=None):\\n if blk!=None:\\n blk=arr(blk)\\n cblk=np.array([]).astype(int) # Initialize cal stare indices\\n cs=self.ind['cs']\\n ce=self.ind['ce']\\n for k,val in enumerate(blk):\\n ss=self.ind['ss'][val] # Scan start\\n se=self.ind['se'][val] # Scan stop\\n # Find leading cal stare \\n ind=np.where(ce<=ss)[0]\\n if ind.size>0:\\n # If it exists, append it\\n cblk=np.append(cblk,ind[-1])\\n\\n # Find trailing cal stare \\n ind=np.where(cs>=se)[0]\\n if ind.size>0:\\n # If it exists, append it\\n cblk=np.append(cblk,ind[0])\\n else:\\n cblk=None\\n\\n return np.unique(self.getind('cs','ce',blk=cblk))\",\n \"def y2k_coding(n):\\n if n < 60:\\n return 2000 + n\\n else:\\n return 1900 + n\",\n \"def convertIndex(df):\\n df.index = pd.to_datetime(df['Date'])\\n return df\",\n \"def _index_build(self, index_def):\\n for index_data in index_def['key']:\\n if index_data[0] == '_id' and len(index_def['key']) == 1:\\n return 'Default Index'\\n else:\\n if index_def.get('background') == True:\\n return 'Background'\\n else:\\n return 'Foreground'\",\n \"def calibration(self, cal: int, /) -> None:\",\n \"def _document2internal_id(value):\\n return (value - 1)//2\",\n \"def _get_indexer_level_0(self, target) -> npt.NDArray[np.intp]:\\n lev = self.levels[0]\\n codes = self._codes[0]\\n cat = Categorical.from_codes(codes=codes, categories=lev, validate=False)\\n ci = Index(cat)\\n return ci.get_indexer_for(target)\",\n \"def _idxs_postformat_null(self):\\n pass\",\n \"def _init_default_tof_bins(self):\\n self._default_tof_bins_dict = dict()\\n\\n self.get_default_binning_reference(self._ned_date, 3)\\n\\n return\",\n \"def Anchor_day(year):\\r\\n day = (5 * ((year // 100) % 4) + 2) % 7\\r\\n return day\",\n \"def month(self) -> Index:\\n return Index(self.to_series().dt.month)\",\n \"def calc_base_year_data(base_year_vehicles_df):\\n pass\",\n \"def _generate_new_dates(self, n: int) -> pd.DatetimeIndex:\\n new_dates = [\\n (self.training_series.time_index()[-1] + (i * self.training_series.freq())) for i in range(1, n + 1)\\n ]\\n return pd.DatetimeIndex(new_dates, freq=self.training_series.freq_str())\",\n \"def updateDateIndex(map, accidente):\\n occurreddate = accidente['Start_Time']\\n accidentedate = datetime.datetime.strptime(occurreddate, '%Y-%m-%d %H:%M:%S')\\n entry = om.get(map, accidentedate.date())\\n if entry is None:\\n datentry = newDataEntry(accidente)\\n om.put(map, accidentedate.date(), datentry)\\n else:\\n datentry = me.getValue(entry)\\n addDateIndex(datentry, accidente)\\n return map\",\n \"def getEditInitDefault(self):\\n if self.initDefault in DateFormat.dateStampStrings:\\n return DateFormat.dateStampStrings[1]\\n return TextFormat.getEditInitDefault(self)\",\n \"def __AOD04(self):\\n\\n assert self.__lmax <= 100\\n\\n k = np.zeros(101)\\n\\n k[0] = 0\\n k[1] = 0\\n k[2] = -0.308\\n k[3] = -0.195\\n k[4] = -0.132\\n k[5] = -0.103\\n k[6] = -0.089\\n k[7] = -0.082\\n k[8] = -0.078\\n k[9] = -0.073\\n\\n for i in range(10, 18):\\n k[i] = -(0.682 + 0.27 * (i - 10) / 8) / i\\n for i in range(18, 32):\\n k[i] = -(0.952 + 0.288 * (i - 18) / 14) / i\\n for i in range(32, 56):\\n k[i] = -(1.24 + 0.162 * (i - 32) / 24) / i\\n for i in range(56, 101):\\n k[i] = -(1.402 + 0.059 * (i - 56) / 44) / i\\n\\n # print(k[0:(self.__Nmax+1)])\\n\\n return k[0:(self.__lmax + 1)]\",\n \"def test_get_index_of_day_empty_list(self):\\n self._test_giod([], \\\"15.07.2013\\\", 0,\\n -1, \\\"Empty list without next value.\\\")\\n self._test_giod([], \\\"15.07.2013\\\", 1,\\n -1, \\\"Empty list with next=1.\\\")\\n self._test_giod([], \\\"15.07.2013\\\", -1,\\n -1, \\\"Empty list with next=-1.\\\")\",\n \"def name(self):\\n return 'D07 Indice de calidad de vida por manzanas'\",\n \"def test_counter_start_at_zero(self):\\n pass\",\n \"def day(self):\\n return 0\",\n \"def day(self):\\n return 0\",\n \"def year_lookup_bounds(self, value):\\n first = '%s-01-01 00:00:00'\\n # SQL Server doesn't support microseconds\\n last = '%s-12-31 23:59:59'\\n return [first % value, last % value]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.625253","0.6234539","0.61408883","0.5848705","0.58286357","0.57312787","0.57171273","0.5658349","0.556045","0.5558227","0.555326","0.55059904","0.54892826","0.54743004","0.540458","0.5399076","0.53972256","0.53660876","0.53427213","0.5341637","0.52783215","0.52656335","0.52632785","0.5253664","0.52439344","0.5237953","0.521762","0.5208615","0.52050215","0.51924694","0.5185984","0.5160546","0.514229","0.5138648","0.5122532","0.5118486","0.5111915","0.51072794","0.5096095","0.5096095","0.50945663","0.5067886","0.50579333","0.5027559","0.5015198","0.5012644","0.49905324","0.49824232","0.498057","0.4971275","0.49665016","0.4964113","0.4963677","0.49511138","0.4931546","0.49170902","0.49137658","0.4906975","0.48919904","0.48841646","0.48770797","0.48764995","0.48692676","0.48685136","0.48676667","0.48604146","0.4857498","0.48540172","0.48522413","0.48504826","0.48465967","0.4845064","0.48432338","0.48276553","0.48272833","0.48180118","0.48151532","0.4811632","0.4811632","0.48021987","0.47995093","0.479603","0.4791998","0.479095","0.4788705","0.47885364","0.47851837","0.47816554","0.47739246","0.47738537","0.47729728","0.47721305","0.47712308","0.4768617","0.47683203","0.47610646","0.47567868","0.47528565","0.47521633","0.47521633","0.47465914"],"string":"[\n \"0.625253\",\n \"0.6234539\",\n \"0.61408883\",\n \"0.5848705\",\n \"0.58286357\",\n \"0.57312787\",\n \"0.57171273\",\n \"0.5658349\",\n \"0.556045\",\n \"0.5558227\",\n \"0.555326\",\n \"0.55059904\",\n \"0.54892826\",\n \"0.54743004\",\n \"0.540458\",\n \"0.5399076\",\n \"0.53972256\",\n \"0.53660876\",\n \"0.53427213\",\n \"0.5341637\",\n \"0.52783215\",\n \"0.52656335\",\n \"0.52632785\",\n \"0.5253664\",\n \"0.52439344\",\n \"0.5237953\",\n \"0.521762\",\n \"0.5208615\",\n \"0.52050215\",\n \"0.51924694\",\n \"0.5185984\",\n \"0.5160546\",\n \"0.514229\",\n \"0.5138648\",\n \"0.5122532\",\n \"0.5118486\",\n \"0.5111915\",\n \"0.51072794\",\n \"0.5096095\",\n \"0.5096095\",\n \"0.50945663\",\n \"0.5067886\",\n \"0.50579333\",\n \"0.5027559\",\n \"0.5015198\",\n \"0.5012644\",\n \"0.49905324\",\n \"0.49824232\",\n \"0.498057\",\n \"0.4971275\",\n \"0.49665016\",\n \"0.4964113\",\n \"0.4963677\",\n \"0.49511138\",\n \"0.4931546\",\n \"0.49170902\",\n \"0.49137658\",\n 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successive Poisson processes."},"document":{"kind":"string","value":"def successive_poisson(tau1, tau2, size=1):\n # Draw samples out of first exponential distribution: t1\n t1 = np.random.exponential(tau1, size)\n\n # Draw samples out of second exponential distribution: t2\n t2 = np.random.exponential(tau2, size)\n\n return t1 + t2"},"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 inter_arrival_times(self):\n # this function returns arrival times between two subsequent tuples in ms\n # task mean_inter_arrival_time std_inter_arrival_time\n if self.inter_arrival_time is None:\n if self.tuple_arrival is None:\n self.tuple_arrivals()\n self.inter_arrival_time = convert_throughput_to_inter_arr_times(self.tuple_arrival)\n\n return self.inter_arrival_time","def receive_and_probing_time(self):\r\n latest_completion = 0\r\n for probe in self.__probes.values():\r\n\t\t \t if probe.complete():\r\n\t\t\t \t\t latest_completion = max(latest_completion, probe.completion_time)\r\n return latest_completion - self.__arrival_time","def calculate_waiting_times(self):\n\n # let's count time in general units\n wait_times = []\n current = wait_time = self.processes[0].arrival_time\n\n for p in self.processes:\n\n # We won't actually wait :)\n # if the arrival\n wait_time = wait_time - p.arrival_time\n\n # The first process has wait time of zero\n wait_times.append(wait_time)\n wait_time = current + p.burst_time\n\n # Wait time for next process\n current = wait_time\n\n return wait_times","def compute_start_and_finish_times(a, n_t, current_time):\n duration = n_t.getc()\n speed = a.getv()\n current_location = a.getz()\n task_loc = n_t.getloc()\n dist = np.sqrt((task_loc[0] - current_location[0]) ** 2 + (task_loc[1] - current_location[1]) ** 2)\n travel_time = dist / speed\n start_time = current_time + travel_time\n finish_time = start_time + duration\n return start_time, finish_time","def probing_time(self):\r\n earliest_launch = (time.time() * 1000)**2\r\n latest_completion = 0\r\n for probe in self.__probes.values():\r\n if probe.complete():\r\n earliest_launch = min(earliest_launch, probe.launch_time)\r\n latest_completion = max(latest_completion, probe.completion_time)\r\n return latest_completion - earliest_launch","def part_two():\n tasks = {}\n current_time = 0\n while G.nodes():\n # noinspection PyCallingNonCallable\n candidate_next_tasks = [task for task in G.nodes()\n if task not in tasks.keys() and G.in_degree(task) == 0]\n if candidate_next_tasks and len(tasks) < 5:\n next_task = sorted(candidate_next_tasks)[0]\n tasks[next_task] = ord(next_task) - 4\n else:\n min_task_time = min(tasks.values())\n current_time += min_task_time\n completed_task = dict(zip(tasks.values(), tasks.keys()))[min_task_time]\n tasks = {k: v - min_task_time for k, v in tasks.items() if k != completed_task}\n G.remove_node(completed_task)\n return current_time","def particleCollisionTime(self, first_element, second_element):\n\n # Quantities required in following formula\n r = self.measure.distance(first_element, second_element)\n r2 = np.dot(r, r)\n #v = relativeVelocity(first_element, second_element, self.vel)\n v = self.measure.relativeVelocity(first_element, second_element)\n v2 = np.dot(v, v)\n b = np.dot(r, v)\n d = 2*self.particle_radius\n # We name 'inner_term' everything that will fall under the square root \n inner_term = b*b - v2*(r2 - d*d)\n \n # We need to filter non-valid results\n if (inner_term<0 or v2<=0 or first_element==second_element):\n t = 'inf'\n else:\n # The following formula has been taken from Eq: 14.2.2 in\n # 'The Art of Molecular Dynamics Simulations', D. Rapaport.\n t = (-b - np.sqrt(inner_term))/v2\n t = infIfNegative(t) # The collision ocurred in the past \n return t","def successive_poisson(tau1, tau2, size=1):\n # Draw samples out of first exponential distribution: t1\n t1 = np.random.exponential(tau1, size=1)\n\n # Draw samples out of second exponential distribution: t2\n t2 = np.random.exponential(tau2, size=1)\n\n return t1 + t2","def successive_poisson(tau1, tau2, size=1):\n # Draw samples out of first exponential distribution: t1\n t1 = np.random.exponential(tau1, size=size)\n\n # Draw samples out of second exponential distribution: t2\n t2 = np.random.exponential(tau2, size=size)\n\n return t1 + t2","def eta(self):\n\t\tresult = []\n\t\ttime = 0\n\t\tposition = self\n\t\tfor delay, star, order, num_ships in self.orders:\n\t\t\ttime += int(math.ceil(star.distance(position) / self.galaxy.fleet_speed))\n\t\t\tposition = star\n\t\t\tresult.append(time)\n\t\t\ttime += delay\n\t\treturn result","def ps_arrival_times(distance, depth, init_sec):\n\n # use taup to get the theoretical arrival times for P & S\n TauPy_model = TauPyModel('iasp91')\n tt = TauPy_model.get_travel_times(\n distance_in_degree=0.001 * distance / 111.11, source_depth_in_km=depth)\n tiemp = []\n tiems = []\n # from all possible P arrivals select the earliest one\n for i2 in xrange(0, len(tt)):\n if tt.__getitem__(i2).__dict__['name'] == 'P' or \\\n tt.__getitem__(i2).__dict__['name'] == 'p' or \\\n tt.__getitem__(i2).__dict__['name'] == 'Pdiff' or \\\n tt.__getitem__(i2).__dict__['name'] == 'PKiKP' or\\\n tt.__getitem__(i2).__dict__['name'] == 'PKIKP' or \\\n tt.__getitem__(i2).__dict__['name'] == 'PP' or \\\n tt.__getitem__(i2).__dict__['name'] == 'Pb' or \\\n tt.__getitem__(i2).__dict__['name'] == 'Pn' or \\\n tt.__getitem__(i2).__dict__['name'] == 'Pg':\n tiem_p = tt.__getitem__(i2).__dict__['time']\n tiemp.append(tiem_p)\n arriv_p = np.floor(init_sec + min(tiemp))\n\n # from all possible S arrivals select the earliest one\n for i3 in xrange(0, len(tt)):\n if tt.__getitem__(i3).__dict__['name'] == 'S' or \\\n tt.__getitem__(i3).__dict__['name'] == 's' or \\\n tt.__getitem__(i3).__dict__['name'] == 'Sdiff' or \\\n tt.__getitem__(i3).__dict__['name'] == 'SKiKS' or \\\n tt.__getitem__(i3).__dict__['name'] == 'SKIKS' or \\\n tt.__getitem__(i3).__dict__['name'] == 'SS' or \\\n tt.__getitem__(i3).__dict__['name'] == 'Sb' or \\\n tt.__getitem__(i3).__dict__['name'] == 'Sn' or \\\n tt.__getitem__(i3).__dict__['name'] == 'Sg':\n tiem_s = tt.__getitem__(i3).__dict__['time']\n tiems.append(tiem_s)\n arriv_s = np.floor(init_sec + min(tiems))\n return arriv_p, arriv_s","def time_diff_procs(numprocs):\n conv_times = dict()\n for proc in numprocs:\n start_time = datetime.datetime.now()\n convolve_spectra(spectrum_path, band, vsini, R, epsilon, fwhm_lim, plot, numprocs=proc)\n end_time = datetime.datetime.now()\n conv_times[proc] = end_time - start_time\n return conv_times","def successive_poisson(tau1, tau2, size=1):\r\n # Draw samples out of first exponential distribution: t1\r\n t1 = np.random.exponential(tau1,size=tau1)\r\n\r\n # Draw samples out of second exponential distribution: t2\r\n t2 = np.random.exponential(tau2,size=tau2)\r\n\r\n return t1 + t2","def test_spring_forward_2(self):\n # Exact crossover time:\n # datetime.datetime(2011, 3, 13, 2, 0, 0, tzinfo=pytz.utc)\n # This test will use times on either side of it.\n\n # From the PST vantage point, the run time is 0.6 hours away:\n s1a, s1b = self.hours_to_job_at_datetime(\n 'tz_config_test_job_spring_forward', 2011, 3, 13, 1, 55, 0)\n\n # This means the job at the nonexistent time 2:30 will be run at the\n # \"new\" 3:30\n self._assert_range(s1a, 0.59, 0.61)\n\n # From the PDT vantage point, the next run time is 23.4 hours away,\n # because the job has already been run.\n s2a, s2b = self.hours_to_job_at_datetime(\n 'tz_config_test_job_spring_forward', 2011, 3, 13, 3, 05, 0)\n\n self._assert_range(s1b - s1a, 22.99, 23.01)\n self._assert_range(s2b - s2a, 23.99, 24.01)\n\n # Since the second measurement is taken after the job runs, we check\n # the 'second' run from the first measurement against the 'first' run\n # of the first measurement.\n # Like test_spring_forward, the times should differ by 0.2 hours.\n self._assert_range(s1b - s2a, 0.19, 0.21)","def test_poisson(self):\n nt = 50\n ns = 1000\n num_giter = 5\n net = self.poisson\n\n times = []\n for i in range(ns):\n arrv = net.sample (nt)\n obs = arrv.subset (lambda a,e: a.is_last_in_queue(e), copy_evt)\n gsmp = net.gibbs_resample (arrv, 0, num_giter)\n resampled = gsmp[-1]\n evts = resampled.events_of_task (2)\n times.append (evts[0].d)\n \n exact_sample = [ numpy.random.gamma (shape=3, scale=0.5) for i in xrange (ns) ]\n times.sort()\n exact_sample.sort()\n \n print summarize(times)\n print summarize(exact_sample)\n \n netutils.check_quantiles (self, exact_sample, times, ns)","def on_time_step(self, event):\n live_birth_rate = self.live_birth_rate.at[self.clock().year]\n still_birth_rate = self.still_birth_rate.at[self.clock().year]\n\n step_size = event.step_size / pd.Timedelta(seconds=SECONDS_PER_YEAR)\n\n mean_births = (live_birth_rate + still_birth_rate) * step_size\n # Assume births occur as a Poisson process\n r = np.random.RandomState(seed=self.randomness.get_seed()) # does this always seed the PRNG with the same seed?\n live_births = r.poisson(live_birth_rate * step_size)\n stillbirths = r.poisson(still_birth_rate * step_size)\n print(live_births, stillbirths)\n simulants_to_add = live_births + stillbirths\n if simulants_to_add > 0:\n vital_status = ['alive'] * live_births + ['stillborn'] * stillbirths\n\n self.simulant_creator(simulants_to_add,\n population_configuration={\n 'age_start': 0,\n 'age_end': 0,\n 'alive': vital_status, # store this in the user_data dictionary\n # for use in the on_initialize_simulants method\n 'sim_state': 'time_step',\n })","def packet_arrival():\r\n return 1.0","def get_ready_time(self):\n\n # historical data\n data_pickup = self.data[self.data[\"ReadyTimePickup\"] != 0] # work on pickup data only\n pickup_arrival_times = {\n location: [\n (datetime.datetime.strptime(str(int(row[\"ReadyTimePickup\"])), '%H%M').replace(year=1970,\n month=1,\n day=1,\n tzinfo=gmt8)\n - datetime.datetime.now().replace(\n year=1970,\n month=1,\n day=1,\n hour=0,\n minute=0,\n second=0,\n microsecond=0,\n tzinfo=gmt8)\n ).total_seconds()\n for _, row in data_pickup[data_pickup[\"PostalCode\"] == location].iterrows()\n ]\n for location in self.pc_pickup_jobs\n }\n\n # mean\n pickup_arrival_time_mean = {\n location: mean_time(pickup_arrival_times[location])\n for location in self.pc_pickup_jobs\n }\n # variance\n pickup_arrival_time_var = {\n location: np.std(pickup_arrival_times[location])/2\n for location in self.pc_pickup_jobs\n }\n\n return pickup_arrival_time_mean, pickup_arrival_time_var","def M_D_1(arrival_time,max_time,service_time=1/90):\n #conversion in seconds\n max_seconds = max_time*60*60\n sim_time = 0.0 # simulation time\n t_1 = 0.0 # time for next event (arrival)\n t_2 = max_seconds # time for next event (departure)\n t_n = 0.0 #last event time--> tempo dell'ultimo avvenimento generico\n t_b = 0.0 # last start of busy time--> tempo in cui la queue inizia ad essere non vuota per l'ultima volta\n c = 0 # numero di servizi completati\n queue_aircraft = [] # number of aircraft in the queue\n aircraft = 0\n arrival = [] # time of arrival\n attesa = [] # attesa per gli aerei-->NON SICURO CHE SI CALCOLI COSI'\n # simulation loop\n while(sim_time < max_seconds):\n if(t_1<t_2): #event1:arrival\n sim_time = t_1\n arrival.append(t_1)\n aircraft += 1\n queue_aircraft.append(aircraft)\n t_n = sim_time\n t_1 = sim_time + rm.expovariate(arrival_time)\n if(aircraft==1):\n t_b = sim_time\n t_2 = sim_time + 1/service_time\n else:\n sim_time = t_2\n aircraft = aircraft -1\n queue_aircraft.append(aircraft)\n t_n = sim_time\n attesa.append( t_2 - arrival[c])\n c+=1\n if(aircraft>0):\n t_2=sim_time + 1/service_time\n else:\n t_2 = max_seconds\n\n\n\n\n return queue_aircraft,arrival,attesa","def part2() -> int:\n sleepiest_minute = None\n sleeping_guard = None\n sleep_frequency = 0\n\n for guard, times in sleep_times.items():\n if len(times) == 0:\n continue\n _sleepiest_minute = max(\n sleep_times[guard], key=sleep_times[guard].count)\n _sleep_frequency = sleep_times[guard].count(_sleepiest_minute)\n\n if _sleep_frequency > sleep_frequency:\n sleepiest_minute = _sleepiest_minute\n sleeping_guard = guard\n sleep_frequency = _sleep_frequency\n\n assert sleeping_guard is not None and sleepiest_minute is not None\n return sleeping_guard * sleepiest_minute","def _calcPlungerMoveTime(self, move_steps):\n sd = self.sim_state\n start_speed = sd['start_speed']\n top_speed = sd['top_speed']\n cutoff_speed = sd['cutoff_speed']\n slope = sd['slope']\n microstep = sd['microstep']\n\n slope *= 2500.0\n if microstep:\n move_steps = move_steps / 8.0\n theo_top_speed = sqrt((4.0 * move_steps*slope) + start_speed ** 2.0)\n # If theoretical top speed will not exceed cutoff speed\n if theo_top_speed < cutoff_speed:\n move_t = theo_top_speed - (start_speed/slope)\n else:\n theo_top_speed = sqrt(((2.0*move_steps*slope) +\n ((start_speed**2.0+cutoff_speed**2.0)/2.0)))\n # If theoretical top speed with exceed cutoff speed but not\n # reach the set top speed\n if cutoff_speed < theo_top_speed < top_speed:\n move_t = ((1 / slope) * (2.0 * theo_top_speed - start_speed -\n cutoff_speed))\n # If start speed, top speed, and cutoff speed are all the same\n elif start_speed == top_speed == cutoff_speed:\n move_t = (2.0 * move_steps) / top_speed\n # Otherwise, calculate time spent in each phase (start, constant,\n # ramp down)\n else:\n ramp_up_halfsteps = ((top_speed ** 2.0 - start_speed ** 2.0) /\n (2.0 * slope))\n ramp_down_halfsteps = ((top_speed ** 2.0 - cutoff_speed ** 2.0) /\n (2.0 * slope))\n if (ramp_up_halfsteps + ramp_down_halfsteps) < (2.0 * top_speed):\n ramp_up_t = (top_speed - start_speed) / slope\n ramp_down_t = (top_speed - cutoff_speed) / slope\n constant_halfsteps = (2.0 * move_steps - ramp_up_halfsteps -\n ramp_down_halfsteps)\n constant_t = constant_halfsteps / top_speed\n move_t = ramp_up_t + ramp_down_t + constant_t\n return move_t","def expected_time_in_markov_state_ignoring_class_2_arrivals(\n state, lambda_1, mu, num_of_servers, system_capacity\n):\n if state[1] == system_capacity:\n return 1 / (min(state[1], num_of_servers) * mu)\n return 1 / (min(state[1], num_of_servers) * mu + lambda_1)","def prob_class_1_arrival(state, lambda_1, mu, num_of_servers):\n return lambda_1 / (lambda_1 + (mu * min(state[1], num_of_servers)))","def queue_times(self):\r\n return [task.scheduler_launch_time - self.__arrival_time\r\n for task in self.__tasks.values() if task.complete()]","def elapseTime(self, gameState):\n\n \"*** YOUR CODE HERE ***\"\n\n allPossible = util.Counter()\n\n for oldPos in self.legalPositions:\n actions = gameState.getLegalActions(agentIndex)\n successorStates = [gameState.generateSuccessor(action) for action in actions]\n newPosDist = {}\n for state in successorStates:\n position = state.getAgentPosition(agentIndex)\n prob = 1.0/len(actions)\n newPosDist[position] = prob\n\n for newPos, prob in newPosDist.items():\n allPossible[newPos] += prob * self.beliefs[oldPos]\n\n allPossible.normalize()\n self.beliefs = allPossible","def time_step(self):\n\n rho_rel = np.abs(self.rho_dt / self.rho)\n rho_rel_max = np.max(rho_rel)\n e_rel = np.abs(self.e_dt / self.e)\n e_rel_max = np.max(e_rel)\n x_rel = np.abs(self.u / self.dx)\n x_rel_max = np.max(x_rel)\n y_rel = np.abs(self.w / self.dy)\n y_rel_max = np.max(y_rel)\n rel = [rho_rel_max, e_rel_max, x_rel_max, y_rel_max]\n delta = np.max(np.abs(rel))\n\n if 0.1 <= delta <= 1e3:\n self.dt = self.p / delta\n else:\n self.dt = self.p","def getTimes():","def getTimes():","def getTimes():","def duration(self):\r\n return self.t2 - self.t1","def prob(E1, E2, t):\n return 1 if E1 > E2 else np.exp((E1 - E2) / t)","def example2():\n arrive_time=rand_arr_time.rand_arr_time(6,100000,1000) # Get packet arrive time, with option 2, 100000 packets, expected in 1000 seconds.\n return arrive_time","def pcr_delta_time_ms(pcr_t1, pcr_t2, offset = 0):\n return float(pcr_t2-pcr_t1)/90000.0 + offset","def calculate_turnaround_times(self, wts=None):\n trts = []\n\n if wts == None:\n wts = self.calculate_waiting_times()\n\n for p in self.processes:\n waiting_time = wts.pop(0)\n trt = waiting_time + p.burst_time\n trts.append(trt)\n\n return trts","def response_time(self):\r\n if self.__arrival_time == INVALID_TIME:\r\n self.__logger.debug(\"Request %s missing arrival time\" % self.__id)\r\n return INVALID_TIME_DELTA\r\n completion_time = self.__arrival_time\r\n for task_id, task in self.__tasks.items():\r\n if task.completion_time == INVALID_TIME:\r\n self.__logger.debug((\"Task %s in request %s missing completion \"\r\n \"time\") % (task_id, self.__id))\r\n return INVALID_TIME_DELTA\r\n task_completion_time = task.adjusted_completion_time()\r\n #if task.scheduler_launch_time > task.node_monitor_launch_time:\r\n #self.__logger.warn((\"Task %s suggests clock skew: scheduler launch time %d, node \"\r\n # \"monitor launch time %d\") %\r\n\r\n #(task_id, task.scheduler_launch_time,\r\n # task.node_monitor_launch_time))\r\n completion_time = max(completion_time, task_completion_time)\r\n return completion_time - self.__arrival_time","def part2(input):\n ps = PlanetSystem(input)\n c = ps.total_cycle_time()\n return c","def get_time_walking(self):\n return self.time_step_to_enqueue - self.time_entered","def stack_ps(ps1, ps2, keep_unique = False, fill_time = False, message = True):\n # create deepcopies to avoid changing original instances\n \n ps1 = copy.deepcopy(ps1)\n ps2 = copy.deepcopy(ps2)\n \n # create datetime information in PS instances\n \n try:\n _ = getattr(ps1, \"datetime\")\n except AttributeError:\n ps1.createTimeDate()\n \n try: \n _ = getattr(ps2, \"datetime\")\n except AttributeError:\n ps2.createTimeDate()\n \n # check time resolutions\n res1 = (dt.datetime.strptime(ps1.datetime['data'][1], ps1.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])).seconds\n res2 = (dt.datetime.strptime(ps2.datetime['data'][1], ps2.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])).seconds\n \n if abs(res1-res2) > 60:\n if message:\n print( (\"warning: resolutions differ %d seconds\")%(abs(res1-res2)) )\n \n # check if ps1 is \"older\" than ps2\n \n reversed_order = False\n cut = None\n \n if dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units']) < dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']):\n # ps2 starts after ps1 ends\n timediff = (dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units'])).total_seconds()\n elif dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units']) < dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']):\n # ps1 starts after ps2 ends (user has inadvertently switched the order of the instances)\n reversed_order = True\n timediff = (dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units'])).total_seconds()\n else:\n # yikes! The particle sizer instances have overlapping data\n # it is assumed that ps2 data replaces ps1 data starting \n # from the overlapping time\n cut, cutdate = tt.findNearestDate(ps1.datetime['data'], ps2.datetime['data'][0]) \n fill_time = False\n \n #print(timediff, 1.5*res1)\n # check if filling is required\n if fill_time is True:\n # check time difference\n if reversed_order:\n # ps1 starts after ps2 ends\n if timediff > 1.5*res2:\n # the time gap between two instances has to be\n # larger than twice the normal resolution\n numdates = int(np.ceil(timediff/res2))\n base = dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])\n date_list = [base - dt.timedelta(seconds=res2*x) for x in range(numdates)]\n date_list = list(reversed(date_list[1:]))# because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\n datetimelist = [dt.datetime.strftime(dl, ps2.datetime['units']) for dl in date_list]\n ps2.datetime['data'] = np.append(ps2.datetime['data'], datetimelist)\n timelist = [dt.datetime.strftime(dl, ps2.time['units']) for dl in date_list]\n ps2.time['data'] = np.append(ps2.time['data'], timelist)\n datelist = [dt.datetime.strftime(dl, ps2.date['units']) for dl in date_list]\n ps2.date['data'] = np.append(ps2.date['data'], datelist)\n else:\n fill_time = False\n else:\n if timediff > 1.5*res1:\n # the time gap between two instances has to be\n # larger than twice the normal resolution\n numdates = int(np.ceil(timediff/res1))\n base = dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])\n date_list = [base - dt.timedelta(seconds=res1*x) for x in range(numdates)]\n date_list = list(reversed(date_list[1:])) # because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\n datetimelist = [dt.datetime.strftime(dl, ps1.datetime['units']) for dl in date_list]\n ps1.datetime['data'] = np.append(ps1.datetime['data'], datetimelist)\n timelist = [dt.datetime.strftime(dl, ps1.time['units']) for dl in date_list]\n ps1.time['data'] = np.append(ps1.time['data'], timelist)\n datelist = [dt.datetime.strftime(dl, ps1.date['units']) for dl in date_list]\n ps1.date['data'] = np.append(ps1.date['data'], datelist)\n else:\n fill_time = False\n \n if message:\n print(\"reversed order:\", reversed_order)\n # check which attributes are similar in both instances\n if reversed_order:\n # ps1 starts after ps2 ends\n new_ps = copy.deepcopy(ps2)\n for attribute in ps1.__dict__.keys():\n if attribute in ps2.__dict__.keys():\n afield = getattr(new_ps, attribute)\n if attribute == 'diameter':\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\n \n for var in new_ps.data['variables']:\n if fill_time is True:\n add = np.ma.zeros((ps2.data[var]['data'].shape[0],len(date_list))) \n add[:] = np.nan\n newdata = np.append(ps2.data[var]['data'],add,axis=1)\n ps2.data[var]['data'] = newdata\n \n sh1 = ps1.data[var]['data'].shape\n sh2 = ps2.data[var]['data'].shape\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\n new_field = np.ma.zeros(newfields)\n new_field[:] = np.ma.masked\n \n new_field[st21:st22, 0:ps2.data[var]['data'][:,:cut].shape[1]] = ps2.data[var]['data'][:,:cut]\n new_field[st11:st12, ps2.data[var]['data'][:,:cut].shape[1]:] = ps1.data[var]['data']\n \n new_ps.data[var]['data'] = new_field\n \n afield['data'] = diamlist\n \n elif attribute == 'data':\n # data has been appended with diameters\n pass\n else:\n try:\n field_ps2 = getattr(ps2, attribute)\n field_ps1 = getattr(ps1, attribute)\n except TypeError:\n if attribute == 'header':\n pass\n else:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n try:\n data_ps2 = field_ps2['data']\n data_ps1 = field_ps1['data']\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\n elif fill_time:\n add = np.ma.zeros(len(date_list))\n add[:] = np.nan\n afield['data'] = np.append(np.append(data_ps2[:cut],add), data_ps1)\n else:\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\n except:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n \n else:\n if keep_unique:\n newattribute = getattr(ps1,attribute)\n newattribute['time'] = ps1['datetime']['data']\n setattr(new_ps, attribute, newattribute)\n else:\n pass\n if keep_unique is False:\n # get rid of attributes which were in ps2 but not in ps1\n for attribute in ps2.__dict__.keys():\n if attribute in ps1.__dict__.keys():\n pass\n else:\n delattr(new_ps, attribute)\n \n \n else:\n # ps2 starts after ps1 ends\n new_ps = copy.deepcopy(ps1)\n for attribute in ps2.__dict__.keys():\n if attribute in ps1.__dict__.keys():\n afield = getattr(new_ps, attribute)\n if attribute == 'diameter':\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\n for var in new_ps.data['variables']:\n if fill_time is True:\n add = np.ma.zeros((ps1.data[var]['data'].shape[0],len(date_list))) \n add[:] = np.nan\n newdata = np.append(ps1.data[var]['data'],add,axis=1)\n ps1.data[var]['data'] = newdata\n \n sh1 = ps1.data[var]['data'].shape\n sh2 = ps2.data[var]['data'].shape\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\n new_field = np.ma.zeros(newfields)\n new_field[:] = np.ma.masked\n \n new_field[st11:st12, 0:ps1.data[var]['data'][:,:cut].shape[1]] = ps1.data[var]['data'][:,:cut]\n new_field[st21:st22, ps1.data[var]['data'][:,:cut].shape[1]:] = ps2.data[var]['data']\n \n new_ps.data[var]['data'] = new_field\n \n afield['data'] = diamlist\n \n elif attribute == 'data':\n # data has been appended with diameters\n pass\n else:\n try:\n field_ps2 = getattr(ps2, attribute)\n field_ps1 = getattr(ps1, attribute)\n except TypeError:\n if attribute == 'header':\n pass\n else:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n try:\n data_ps2 = field_ps2['data']\n data_ps1 = field_ps1['data']\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\n elif fill_time:\n add = np.ma.zeros(len(date_list))\n add[:] = np.nan\n afield['data'] = np.append(np.append(data_ps1[:cut],add), data_ps2)\n else:\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\n except:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n \n else:\n if keep_unique:\n newattribute = getattr(ps2,attribute)\n newattribute['time'] = ps2['datetime']['data']\n setattr(new_ps, attribute,newattribute)\n else:\n pass\n if keep_unique is False:\n # get rid of attributes which were in ps2 but not in ps1\n for attribute in ps1.__dict__.keys():\n if attribute in ps2.__dict__.keys():\n pass\n else:\n delattr(new_ps, attribute)\n \n new_ps.sample['data'] = np.arange(1.0, len(new_ps.datetime['data'])+1)\n new_ps.instrument_type = ps1.instrument_type.split('_')[0] + '_concatenated'\n \n if message:\n print('filltime: ', fill_time)\n \n return new_ps","def compute_trajectory():\n pass","def evaluate_time(self, pid, edge):\n return self.get_process_speed(pid) * edge.get_time()","def run_v2(self):\n start_time = dt.datetime.now()\n dic = {}\n for key in self.first :\n dic[key] = 0\n dic[key] = 0\n\n for idx in range(0, len(self.first)) :\n dic[self.first[idx]] = dic[self.first[idx]] + 1\n dic[self.second[idx]] = dic[self.second[idx]] + 1\n\n ans = 0\n\n for key in dic.keys():\n ans = max(ans , dic[key])\n\n print(ans)\n\n end_time = dt.datetime.now()\n print(\"time cost : {0}\".format(end_time - start_time))","def time_evolution_point(operating_point, delta1_point, delta2_point):\n match_freq = 10.0\n qubit_base = hybrid.SOSSHybrid(operating_point, match_freq)\n delta1_ref = qubit_base.delta1\n delta2_ref = qubit_base.delta2\n stsplitting_ref = qubit_base.stsplitting\n ed_ref = qubit_base.ed\n qubit = hybrid.HybridQubit(ed_ref,\n stsplitting_ref,\n delta1_point * delta1_ref,\n delta2_point * delta2_ref)\n trange, sigma_array, mass_chi_array = time_sweep(qubit)\n return trange, sigma_array, mass_chi_array","def arrival_time(self):\r\n return self.__arrival_time","def arrival_time(self):\r\n return self.__arrival_time","def calculate_travel_time_complex(distance_meters, accel_mps2):\n time = 0\n distance_progress = 0\n speed = 0\n halfway_point = distance_meters / 2\n while distance_progress < halfway_point:\n time = time + 1\n speed = speed + accel_mps2\n distance_progress = distance_progress + speed\n \"\"\"\n Output progress \n print \"{0} seconds | speed: {1} m/s | distance traveled: {2} m\".format(time, speed, distance_progress)\n \"\"\"\n time = time * 2\n return [time, speed]","def simulate(self) -> (int, int):\n sums = [0 for i in range(10)]\n iteration = 0\n time = 0\n cost = 0\n\n # initialize queues list\n queue = [None for _ in self.processors]\n while len(self.done_tasks) < len(self.task_graph.nodes):\n iteration += 1\n \n # apply tasks to queues (processors)\n for qi, proc in enumerate(self.processors):\n if len(self.application[proc.index]) > 0 and queue[qi] == None:\n task = self.application[proc.index].pop(0)\n queue[qi] = ProcessingFactory.ScheduledTask(self.parser, proc, task)\n logging.debug(\"queue: \", qi, \" -> \", task)\n cost += queue[qi].getCost()\n \n # calculates min time after which something will happen to move clock\n min_construction_time = min(queue, key=lambda x: x.getTime() if x else MAX).getTime()\n min_time = min_construction_time\n\n for queue_index, queue_el in enumerate(queue):\n # if queue empty, not interesting\n if not queue_el:\n continue\n parents = self.task_graph.find_parents(queue_el.task)\n logging.debug(queue_el.task, parents)\n\n # if node has no parents, can be built\n if not parents:\n queue_el.enable()\n queue_el.passTime(min_time)\n \n\n # all parents have to be constructed\n if parents and set(parents).intersection(set(self.done_tasks)) == set(parents) and len(self.done_tasks) > 0:\n logging.debug(\"transfer needed\")\n \n transfer_time = 0\n # Resources from all parents have to be on this node to start construction.\n # Performs transfer logic with additional time required for that.\n all_here = True\n for parent in parents:\n ind = self.done_tasks.index(parent)\n if self.location[ind] != queue_index and ind in self.done_tasks:\n all_here = False\n transfer_time += self.task_graph.get_weight(parent, queue_el.task) / self.transfer[self.location[ind]][queue_index].throughput\n logging.debug(\"transfering:\", self.location[ind], ind, \" -> \", queue_index, ind)\n self.location[ind] = queue_index\n \n # If item can be bult enables it in queue with one step delay (to avoid passing time of transmission)\n queue_el.enable(True)\n \n # If all items are already waiting on current node, construction can be started\n if all_here:\n logging.debug(\"all resources in place!\")\n queue_el.enable()\n \n # If there was transfer move time according to time required by transfer, else\n # move time according to minimal construction time\n if queue_el and transfer_time > 0:\n min_time = transfer_time\n elif queue_el:\n min_time = min_construction_time\n \n # Pass time for each element in queue\n for queue_el in queue:\n if queue_el:\n queue_el.passTime(min_time)\n\n time += min_time\n logging.info(\"TIME:\", time, min_time, iteration, self.done_tasks)\n\n # Memory of left times in round buffer to avoid looping for infinity for impossible graphs\n sums[iteration%len(sums)] = sum([x.getTime() for x in queue if x])\n \n # If construction time has passed, item is constructed\n for i, e in enumerate(queue):\n if queue[i] != None and queue[i].getTime() <= 0:\n # Done has task which were finished, location has nodes on which resources are currently stored\n self.done_tasks.append(queue[i].task)\n self.location.append(i)\n queue[i] = None\n\n logging.debug(sums)\n # Checks if all average value of sums is equal to last element, \n # if yes output MAX, MAX which means that it cannot be performed in finite time.\n if sum(sums)/len(sums) == sums[-1]:\n return MAX, MAX\n return time, cost","def output_tasks_launched_versus_time(self, output_directory):\r\n gnuplot_file = open(\"%s/task_launches_vs_time.gp\" % output_directory, \"w\")\r\n gnuplot_file.write(\"set terminal postscript color 'Helvetica' 12\\n\")\r\n gnuplot_file.write(\"set output '%s/task_launches_vs_time.ps'\\n\" % output_directory)\r\n gnuplot_file.write(\"set xlabel 'Time (ms)'\\n\")\r\n gnuplot_file.write(\"set ylabel 'Tasks Launched'\\n\")\r\n gnuplot_file.write(\"plot \")\r\n\r\n job_count = 0\r\n for id, request in self.__requests.items():\r\n results_filename = \"%s/%s_tasks_launched_vs_time\" % (output_directory, id)\r\n file = open(results_filename, \"w\")\r\n arrival_time, reservation_replies = request.get_scheduler_get_task_times()\r\n reservation_count = 0\r\n file.write(\"0\\t0\\n\")\r\n for reservation in reservation_replies:\r\n reservation_count += 1\r\n # Write the elapsed time since the request arrived.\r\n file.write(\"%s\\t%s\\n\" % (reservation - arrival_time, reservation_count))\r\n file.close()\r\n\r\n if job_count != 0:\r\n gnuplot_file.write(\",\\\\\\n\")\r\n gnuplot_file.write(\"'%s' using 1:2 lw 1 with lp\" % results_filename)\r\n job_count += 1\r\n if job_count >= 20:\r\n break\r\n gnuplot_file.close()","def compute_time_step(self, P_n):\n\n #Pointer reassignment for convenience\n method = self.solver_method\n dt = self.time_step\n theta = self.solver_theta\n T = self.T\n B = self.B\n Q = self.Q\n\n #If mixed, or explicit are specified, otherwise default is implicit\n #therefore it's not actually required to be placed in the input deck\n if method == 'mixed':\n A = ((1.0 - theta) * T + B / dt)\n b = (B / dt - theta * T).dot(P_n) + Q\n P_np1 = spsolve(A, b)\n elif method == 'explicit':\n P_np1 = P_n + 1 / B * dt * (Q - T.dot(P_n))\n else:\n A = T + B / dt\n b = (B / dt).dot(P_n) + Q\n P_np1 = spsolve(A, b)\n\n #Return solution vector from a single time-step\n return P_np1","def exptime(self):\n exptime = float(self.get('TRUITIME')) * int(self.get('COADDONE'))\n return exptime","def get_propagation_time(self):\n return 0.0 # self.get_distance_to_gateway() / (3 * pow(10,8))","def new_time(self, state, time, last_value, independent_sources):\n sum1 = np.matmul(state.__active_control[1],last_value) - state.__active_control[2]\n if (abs(sum1) > self.__absolute_error):\n sum2 = np.matmul(state.__active_control[1], np.matmul(self.__A, last_value) + np.matmul(self.__B, independent_sources))\n sum3 = np.matmul(state.__active_control[1], np.matmul(self.__A**2, last_value) + np.matmul(self.__A, \\\n np.matmul(self.__B, independent_sources)))\n return time + 1.0 / (sum3 / 2 / sum2 - sum2 / sum1)\n else:\n return -1","def _arrival_time(self):\n \n return self.mkt_time + timedelta(0, 0, self.latency)","def approximate_delivery_time(self):\n # this is set to seconds just of demonstration\n approx_delivery_time = random.randrange(300, 600)\n approx_time_to_main_station = approx_delivery_time / 2\n\n return {\n \"delivery_time\": approx_delivery_time,\n \"time_to_station\": approx_time_to_main_station,\n }","def start_and_service_times(self):\r\n return [(x.scheduler_launch_time, x.service_time()) for x in self.__tasks.values()\r\n if x.complete()]","def _omori_time(integ, c, p):\n if p == 1:\n return c*(exp(integ) - 1)\n else:\n return (integ*(1 - p) + c**(1 - p))**(1/(1 - p)) - c","def _generate_trajectory(self, current_pos, velocity, goal, dt, prediction_time):\n\n history = list(np.copy(self._reached_goals))\n\n\n\n\n out = []\n out.append(np.copy(current_pos))\n first_goal_idx = np.where(self._goals == goal)[0][0]\n selected_goal = goal\n reached_goal = False\n counter_in_goal = 0\n\n\n\n for _ in range(prediction_time):\n\n # Particle reached selected goal\n # This will continuously chose a next goal, if a particle already reached its predecessor goal\n\n if np.linalg.norm(current_pos - selected_goal) <= 0.1:\n reached_goal = True\n\n\n\n\n\n\n if counter_in_goal > self.curr_stay_duration_goals[self._is_human_in_range_of_goal(selected_goal)[1]] / dt:\n\n selected_goal_idx = np.where(self._goals == selected_goal)[0][0]\n\n if len(history) > 0:\n if not selected_goal_idx == history[-1]:\n history.append(selected_goal_idx)\n else:\n history.append(selected_goal_idx)\n #print \"history:\"\n #print history\n # Select next goal based on the pre-learned goal-change probabilities\n\n\n #print \"selected goal {}\".format(selected_goal_idx)\n probs,available_goals = self.compute_transition_probs_for_goal_from_history(history,selected_goal_idx)\n\n\n for p in probs:\n if p < self._belief_threshold:\n p = 0.0\n\n print \"probs sampling: \"\n print probs / np.sum(np.asarray(probs))\n selected_goal = self._goals[np.random.choice(available_goals, p=probs / np.sum(np.asarray(probs)))]\n\n counter_in_goal = 0.0\n\n #print(\"switching\")\n\n else:\n counter_in_goal += 1\n #print(\"incr counter\")\n\n\n\n\n\n if reached_goal:\n #print self.curr_stay_duration_goals\n #print self.curr_stay_duration_goals[ self._is_human_in_range_of_goal(selected_goal)[1] ]\n\n new_pos = self.transition_human(current_pos, velocity, selected_goal, dt)\n\n out.append(new_pos)\n current_pos = new_pos\n\n\n\n else:\n new_pos = self.transition_human(current_pos, velocity, selected_goal, dt)\n out.append(new_pos)\n current_pos = new_pos\n\n return np.asarray(out)","def getDeliveryTime(ori, dest):\n\n start_time = time.time()\n\n routingApi = herepy.RoutingApi(os.getenv(\"HERE_KEY\"))\n gm = GoogleMaps(os.getenv(\"GOOGLE_KEY\"))\n\n try:\n response = routingApi.truck_route(ori.coords[::-1], dest.coords[::-1], [herepy.RouteMode.truck, herepy.RouteMode.fastest]).as_dict()\n distance = response.get('response').get('route')[0].get('summary').get('distance') / 1000\n except herepy.error.HEREError:\n try:\n response = gm.distance_matrix(ori.coords[::-1], dest.coords[::-1], mode=\"driving\", departure_time=dt.datetime.now(), traffic_model=\"pessimistic\")\n distance = response.get('rows')[0].get('elements')[0].get('distance').get('value') / 1000\n except Exception as e:\n capture_exception(e)\n raise e\n\n if distance < 51:\n deltime = 6\n elif distance > 50 and distance < 701:\n deltime = 24\n elif distance > 700 and distance < 1400:\n deltime = 48\n else:\n deltime = 72\n\n print('--- Tiempo de ejecucion calcDeliveryTime: {} segundos ---'.format((time.time() - start_time)))\n\n return deltime, distance","def time_per_process():\n return int(random.normalvariate(PT_MEAN, PT_SIGMA))","def time_per_process():\n return int(random.normalvariate(PT_MEAN, PT_SIGMA))","def coalescence_time(star):\r\n\r\n assert isinstance(star, pd.Series), (\"Expected a Series object to be \"\r\n \"passed to coalescence_time.\")\r\n\r\n evo = star.evolution_age + star.rejuvenation_age\r\n _, _, h, _ = evolve_system(star.a0,\r\n star.e0,\r\n star.m1,\r\n star.m2,\r\n evotime=evo)\r\n\r\n return np.sum(h) / takahe.constants.SECONDS_PER_GYR","def a_to_P(M1, M2, a):\n mu = c.GGG * (M1 + M2) * c.Msun_to_g\n n = np.sqrt(mu/(a**3 * c.Rsun_to_cm**3))\n return 2.0*np.pi / n / c.day_to_sec","def compute_time_step():\n\n dt = Hydro.compute_time_step()\n\n return dt","def evaluateTime(self, *args):\n return _osgAnimation.Motion_evaluateTime(self, *args)","def prob2(N1, N2, P1, P2):\r\n #initialize function and its derivative\r\n f = lambda x: P1*((1+x)**N1-1)-P2*(1-(1+x)**-N2)\r\n Df = grad(f)\r\n #use Newton's method\r\n r,conv,iter = newton(f,.1,Df)\r\n return r","def get_c2c_time(job, pulse1, pulse2):\n analog_wf = job.simulated_analog_waveforms()\n element1 = pulse1[0]\n pulse_nb1 = pulse1[1]\n element2 = pulse2[0]\n pulse_nb2 = pulse2[1]\n\n time2 = (\n analog_wf[\"elements\"][element2][pulse_nb2][\"timestamp\"]\n + analog_wf[\"elements\"][element2][pulse_nb2][\"duration\"] / 2\n )\n time1 = (\n analog_wf[\"elements\"][element1][pulse_nb1][\"timestamp\"]\n + analog_wf[\"elements\"][element1][pulse_nb1][\"duration\"] / 2\n )\n\n return time2 - time1","def RunExactTimestep(self): \n if self.sim_t == 0:\n randoms = np.random.random(1000) \n self.randoms_log = np.log(randoms)*-1\n self.randoms = np.random.random(1000)\n self.count = 0 \n elif self.count == 1000:\n randoms = np.random.random(1000) \n self.randoms_log = np.log(randoms)*-1\n self.randoms = np.random.random(1000) \n self.count = 0 \n \n self.sim_tau = self.randoms_log[self.count]/float(self.sim_a_0) # reaction time generation\n self.sim_r2 = self.randoms[self.count] # Draw random number 2 [0-1]\n self.count +=1\n \n if (self.sim_t + self.sim_tau) < self.settings.endtime:\n self.sim_t += self.sim_tau # Time update\n self.reaction_index = 0\n sum_of_as = self.sim_a_mu[self.reaction_index]\n criteria = self.sim_r2*self.sim_a_0\n while sum_of_as < criteria: # Use r2 to determine which reaction will occur\n self.reaction_index += 1\t # Index\n sum_of_as += self.sim_a_mu[self.reaction_index] \n\n try:\n self.X_matrix += self.N_matrix_transpose[self.reaction_index]\n self.timestep += 1\n except MemoryError as ex:\n print(ex)\n sys.exit() \n else: \n self.sim_t = self.settings.endtime \n self.reaction_index = np.nan","def setupDistribution(tournamentsWon1):\n timesWon = np.sort(np.unique(tournamentsWon1))\n numberTimesWon = np.zeros_like(timesWon)\n for i in range (len(timesWon)):\n numberTimesWon[i] = count(tournamentsWon1, timesWon[i])\n return timesWon, numberTimesWon","def round_trip_time(self):\r\n return self.completion_time - self.launch_time","def calculate_travel_time_simple(distance_meters, accel_mps2):\n time = math.sqrt(4 * distance_meters / accel_mps2)\n speed = accel_mps2 * time * 0.5\n return [time, speed]","def timeWarp(destExposure, srcExposure, warpingControl):\n startTime = time.time()\n for nIter in range(1, MaxIter + 1):\n goodPix = afwMath.warpExposure(\n destExposure, srcExposure, warpingControl)\n endTime = time.time()\n if endTime - startTime > MaxTime:\n break\n\n return (endTime - startTime, nIter, goodPix)","def calculateWaitingTime(self, inputs):\n CollisionCounter.CollisionCounter.getInstance().waitingTimeCalculated(self.time)\n timeUntilDepature = self.getAtt('departure_time', inputs) - self.time\n remainingLoadingTime = self.calculateLoadingTime(inputs)\n # calculates first maximum possible waiting time\n sampleTime = int((timeUntilDepature - remainingLoadingTime) / self.participants)\n\n if sampleTime >= 1:\n # result is big enough for a standard treatment\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(sampleTime + 1)\n elif sampleTime < 1:\n # reslut is too small, special treatment necessary\n upperLimit = (10 * (1 - (math.exp(sampleTime - 1)))) + 1\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(max((min(upperLimit,\n timeUntilDepature)) + 1, 1))\n # decides whether charging is allowed during waiting time\n if not self.stayedConnected:\n self.stayConnected = True\n self.stayedConnected = True\n else:\n self.stayConnected = False\n self.stayedConnected = False","def update(self):\r\n \r\n self.time_to_next_fire = self.generate_fire_recurrence()\r\n return self.time_to_next_fire","def curr_policy_time_calc(self):\n for policy in self.policy_list:\n if policy == self.current_policy:\n policy.current_policy_time += 1","def determine_priority(robot1, robot2):\n L = 0.5*(robot1.length + robot2.length)\n # first case: robot 1 speeds up and robot 2 brakes:\n result1 = minimum_time_to_straight_line(1., L,\n robot2.amin, robot2.speed, robot2.pos,\n robot1.amax, robot1.speed, robot1.max_speed,\n robot1.pos)\n t1 = sum(t for t, _ in result1)\n # second case: robot 2 speeds up and robot 1 brakes:\n result2 = minimum_time_to_straight_line(1., L,\n robot1.amin, robot1.speed, robot1.pos,\n robot2.amax, robot2.speed, robot2.max_speed,\n robot2.pos)\n t2 = sum(t for t, _ in result2)\n return min((t1, 0), (t2, 1))[1]","def runtime(self):\n return self.stop_time - self.start_time","def calc_stay_prob(rollouts):\n states = rollouts.states\n actions = rollouts.actions\n rewards = rollouts.rewards\n\n num_test_episodes = states.shape[0]\n num_trials = states.shape[1]\n count_trial_stayed = 0.01 + np.zeros((2, 2, num_test_episodes)) # [common/uncommon, reward/unrewarded]\n count_trial_all = 0.01 + np.zeros((2, 2, num_test_episodes))\n for epi in range(num_test_episodes):\n for t in range(0, num_trials-2, 2):\n uncommon_transition = int(actions[epi, t] != states[epi, t+1]-1)\n count_trial_all[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += 1\n count_trial_stayed[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += \\\n int(actions[epi, t+2] == actions[epi, t])\n return np.divide(count_trial_stayed, count_trial_all), count_trial_stayed, count_trial_all","def p_transfer(a1, b1, a2, b2, m):\n assert a1 <= NCAR\n assert b1 <= NCAR\n assert a2 <= NCAR\n assert b2 <= NCAR\n\n if a1 < m or b1 < -m:\n return 0\n\n if a2 == 0:\n a2s = (-10, 1)\n elif a2 == NCAR:\n a2s = (NCAR, 10+NCAR)\n else:\n a2s = (a2, a2+1)\n\n if b2 == 0:\n b2s = (-10, 1)\n elif b2 == NCAR:\n b2s = (NCAR, 10+NCAR)\n else:\n b2s = (b2, b2+1)\n\n p = 0\n for a2 in range(*a2s):\n for b2 in range(*b2s):\n d1 = a2 - a1 - m\n d2 = b2 - b1 + m\n\n p += poisson_diff(d1, LAM_RENT1, LAM_RET1) * poisson_diff(d2, LAM_RENT2, LAM_RET2)\n\n return p","def relative_times(first_starts, focus_starts, motif_index):\n return np.asarray(focus_starts) - np.asarray(first_starts)[motif_index]","def estimated_time(self):\n self._update()\n if not self.running_mode:\n return 0 if self._is_finished() else float(\"nan\")\n elif self.running_mode == \"local\":\n start = self.processes[0].create_time()\n elif self.running_mode == \"grid\":\n start = self.job[\"start_time\"]\n if start == 0:\n # Queued, but not started\n return float(\"nan\")\n else:\n logger.warning(\"Invalid running_mode attribute\")\n return float(\"nan\")\n current = self.current_step()\n if current <= 0: # If not dumped yet or error\n return float('nan')\n else:\n elapsed = time() - start\n return elapsed * (self.total_steps / current - 1)","def _calculate_periodic_loop_sleep_time(self, time_for_process):\n if time_for_process > self.min_time_per_process_loop:\n sleep_time = 0\n else:\n sleep_time = self.min_time_per_process_loop - time_for_process\n\n return sleep_time","def Leapfrog2D(p1, v1, p2, v2, dt, energy, sigma):\n a = compute_acceleration(energy, sigma, p2-p1)\n pn1 = p1 + v1*dt + 0.5 * a * dt**2\n pn2 = p2 + v2*dt - 0.5 * a * dt**2\n a2 = compute_acceleration(energy, sigma, pn2-pn1)\n vn1 = v1 + 0.5 * (a+a2) * dt\n vn2 = v2 - 0.5 * (a+a2) * dt\n return pn1, vn1, pn2, vn2","def race(v1, v2, g):\n if v2 <= v1:\n return None\n\n time = math.floor(3600 * g / (v2-v1))\n hours = math.floor(time / 3600)\n minutes = math.floor((time - hours*3600) / 60)\n seconds = time - hours*3600 - minutes*60\n\n return [hours, minutes, seconds]","def calc_match_probability(obs, pred1):\n \n # Throw away any non-atom columns\n obs_reduced = obs.loc[:, self.pars[\"atom_set\"].\n intersection(obs.columns)]\n pred1_reduced = pred1.loc[self.pars[\"atom_set\"].\n intersection(pred1.index)]\n \n # Calculate shift differences for each observed spin system\n delta = obs_reduced - pred1_reduced\n \n # Make a note of NA positions in delta, and set them to zero \n # (this avoids warnings when using norm.cdf later)\n na_mask = delta.isna()\n delta[na_mask] = 0\n \n if self.pars[\"prob_method\"] == \"delta_correlation\":\n overall_prob = pd.Series(index=delta.index)\n overall_prob[:] = 1\n \n d_mean = pd.read_csv(\"../data/d_mean.csv\", header=None, \n index_col=0).loc[delta.columns,1]\n d_cov = (pd.read_csv(\"../data/d_cov.csv\", index_col=0).\n loc[delta.columns,delta.columns])\n \n mvn = multivariate_normal(d_mean, d_cov)\n \n overall_prob = mvn.logpdf(delta)\n \n # Penalise missing shifts, unless also missing in predictions\n overall_prob = (overall_prob + log10(default_prob) * \n (na_mask.sum(axis=1) - pred1_reduced.isna().sum()))\n \n else:\n prob = delta.copy()\n prob.iloc[:,:] = 1\n \n for c in delta.columns:\n if self.pars[\"prob_method\"] == \"cdf\":\n # Use the cdf to calculate the probability of a \n # delta *at least* as great as the actual one\n prob[c] = log10(2) + norm.logcdf(-1*abs(\n pd.to_numeric(delta[c])), scale=atom_sd[c]*sf)\n elif self.pars[\"prob_method\"] == \"pdf\":\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \n scale=atom_sd[c]*sf) \n elif shift_correlation:\n print(\"shift_correlation not yet implemented. Defaulting to pdf.\")\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \n scale=atom_sd[c]*sf)\n else:\n print(\"Method for calculating probability not recognised. Defaulting to pdf.\")\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \n scale=atom_sd[c]*sf)\n \n # In positions where data was missing, use default probability\n prob[na_mask] = log10(default_prob)\n \n # Calculate penalty for a HADAMAC mismatch\n if use_hadamac:\n # If the i-1 aa type of the predicted residue matches the \n # HADAMAC group of the observation, probability is 1.\n # Otherwise, probability defaults to 0.01\n prob[\"SS_classm1\"] = 0.01\n if type(pred1[\"Res_typem1\"])==str: # dummies have NaN\n prob.loc[obs[\"SS_classm1\"].str.find(\n pred1[\"Res_typem1\"])>=0, \"SS_classm1\"] = 1\n \n # Calculate overall probability of each row\n overall_prob = prob.sum(skipna=False, axis=1)\n \n return(overall_prob)","def event_timex_analysis(event1, event2):\n tagged = tag(event1.text)\n base_time = event1.get_best_time()\n\n if base_time is not None:\n dt, trusted = base_time.to_datetime() # Get the datetime representation of the reference's best_time\n grounded_times = ground(tagged, dt) # Ground any timex tags to that time\n new_dates = [] # holds new dates constructed from the grounded datetimes\n\n for time in grounded_times:\n new_date = Date()\n if trusted['year']:\n new_date.year = time.year\n\n if trusted['month']:\n new_date.month = time.month\n\n if trusted['day']:\n new_date.day = time.day\n\n if trusted['hour']:\n new_date.hour = time.hour\n\n if trusted['minute']:\n new_date.minute = time.minute\n\n new_dates.append(new_date)\n\n if len(new_dates) == 0: # Nothing interesting found.\n return\n\n new_dates = sorted(new_dates, lambda x: x.precision(), reverse=True)\n best_date = new_dates[0]\n\n other_best_date = event2.get_best_time()\n if other_best_date is not None:\n if best_date.precision() > other_best_date.precision():\n event2.set_best_time(best_date)\n else:\n event2.set_best_time(best_date)","def convergence_processor(self):\n while True:\n rexp = (yield)\n self.converged = True\n self.converged_time = int(rexp.group(2))","def next_state(self, decision):\n # TODO Static?\n if decision != 0:\n # Current index\n self.state.k += 1\n # current time\n delta_time, distance = get_travel_time(self.state.c_k, decision, self.state.t_k, mode=\"ggAPI\")\n self.state.t_k += delta_time\n\n # current location\n self.state.c_k = decision\n\n # remaining deliveries\n self.update_remaining_jobs(decision=decision)\n\n return delta_time, distance\n\n elif decision == 0: # wait\n # Current index: no modification\n\n # current time: wait\n self.state.t_k += datetime.timedelta(seconds=2*60)\n\n # current location: no modification\n\n # remaining deliveries\n self.update_remaining_jobs(decision=decision)\n\n return 2*60, 0","def next_change(self, t=None):\n if t is None:\n t = self.engine.get_now()\n t -= self.initTime\n\n p = float(self.period) / POINTS_PER_CYCLE\n t2 = math.floor(t / p) * p + p\n\n t2 += self.initTime\n\n return t2","def response_time(self, incorporate_skew=True):\r\n if self.__arrival_time == INVALID_TIME:\r\n self.__logger.debug(\"Request %s missing arrival time\" % self.__id)\r\n return -1\r\n completion_time = self.__arrival_time\r\n for task_id, task in self.__tasks.items():\r\n if task.completion_time == INVALID_TIME:\r\n self.__logger.debug((\"Task %s in request %s missing completion \"\r\n \"time\") % (task_id, self.__id))\r\n return INVALID_TIME_DELTA\r\n task_completion_time = task.completion_time\r\n if incorporate_skew:\r\n task_completion_time -= task.clock_skew\r\n # Here we compare two event times: the completion time, as\r\n # observed the the node monitor, minus the clock skew; and the\r\n # job arrival time, as observed by the scheduler. If the\r\n # adjusted completion time is before the arrival time, we know\r\n # we've made an error in calculating the clock skew.clock_skew\r\n if task_completion_time < self.__arrival_time:\r\n self.__logger.warn((\"Task %s in request %s has estimated \"\r\n \"completion time before arrival time, \"\r\n \"indicating inaccuracy in clock skew \"\r\n \"computation.\") % (task_id, self.__id))\r\n else:\r\n \tif task.scheduler_launch_time > task.node_monitor_launch_time:\r\n\t\t\t\t\t\t\t\tself.__logger.warn(\"Task %s suggests clock skew: \" % task_id)\r\n completion_time = max(completion_time, task_completion_time)\r\n\r\n if (completion_time - self.__arrival_time) > 2000:\r\n pass\r\n \"\"\"\r\n print \"TRUE: %s\" % (completion_time - self.__arrival_time)\r\n print self.network_delays()\r\n print self.service_times()\r\n print self.probing_time()\r\n print \"EST: %s\" % (max(self.service_times()) + max(self.network_delays()) + self.probing_time())\r\n \"\"\"\r\n return completion_time - self.__arrival_time","def method_compute_timestep(self):\n\n myg = self.cc_data.grid\n\n cfl = self.rp.get_param(\"driver.cfl\")\n\n u = self.cc_data.get_var(\"x-velocity\")\n v = self.cc_data.get_var(\"y-velocity\")\n\n # the timestep is min(dx/|u|, dy|v|)\n xtmp = ytmp = 1.e33\n if not abs(u).max() == 0:\n xtmp = myg.dx/abs(u.v()).max()\n if not abs(v).max() == 0:\n ytmp = myg.dy/abs(v.v()).max()\n\n dt = cfl*min(xtmp, ytmp)\n\n # We need an alternate timestep that accounts for buoyancy, to\n # handle the case where the velocity is initially zero.\n rho = self.cc_data.get_var(\"density\")\n rho0 = self.base[\"rho0\"]\n rhoprime = self.make_prime(rho, rho0)\n\n g = self.rp.get_param(\"lm-atmosphere.grav\")\n\n F_buoy = (abs(rhoprime*g).v()/rho.v()).max()\n\n dt_buoy = np.sqrt(2.0*myg.dx/F_buoy)\n\n self.dt = min(dt, dt_buoy)\n if self.verbose > 0:\n print(f\"timestep is {dt}\")","def readMovementVolumesAndTTs(self, movementFlowFileName, movementTimeFileName):\n\n inputStream1 = open(movementFlowFileName, 'r')\n inputStream2 = open(movementTimeFileName, 'r')\n\n numMovementsRead = 0\n\n for flowLine, timeLine in izip(inputStream1, inputStream2):\n \n numMovementsRead += 1 \n if numMovementsRead % 10000 == 0:\n print \"Num movements read %d\" % numMovementsRead\n\n flowFields = flowLine.strip().split()\n timeFields = timeLine.strip().split()\n\n nodeAid, nodeBid, nodeCid = flowFields[:3]\n\n if [nodeAid, nodeBid, nodeCid] != timeFields[:3]:\n raise SimError('The files %s and %s are not in sync. '\n 'Movement through %s from %s to %s in the first file is not '\n 'in the same line position in the second '\n 'file' % (movementFlowFileName,\n movementTimeFileName,\n nodeBid, nodeAid, nodeCid))\n\n try:\n edge = self.getEdge(nodeAid, nodeBid)\n movement = edge.getOutMovement(nodeCid)\n except GraphError, e:\n if nodeAid == nodeCid:\n continue\n\n logging.error(str(e))\n continue\n\n simFlows = imap(int, flowFields[3:])\n simTTs = imap(float, timeFields[3:])\n timePeriodStart = self.simStartTimeInMin\n \n for simFlow, simTT in izip(simFlows, simTTs):\n\n if simFlow == 0 and simTT > 0:\n raise SimError('Movement %s has zero flow in the '\n 'time period begining %d and a '\n 'positive travel time' % \n (movement.iid, timePeriodStart))\n elif simFlow > 0 and simTT == 0:\n simTT = 0.01\n message = ('Movement %s has positive flow in '\n 'the time period begining %d and a '\n 'zero travel time. Edge length is %f' % \n (movement.iid, timePeriodStart, edge.getLengthInFeet()))\n logging.error(message)\n #raise SimError('Movement %s has positive flow in '\n # 'the time period begining %d and a '\n # 'zero travel time' % \n # (movement.iid, timePeriodStart))\n \n elif simFlow == 0 and simTT == 0:\n #simTT = movement.getFreeFlowTTInMin()\n timePeriodStart += self.simTimeStepInMin\n if timePeriodStart >= self.simEndTimeInMin:\n break\n else:\n movement.setSimVolume(timePeriodStart, timePeriodStart + \n self.simTimeStepInMin, simFlow)\n\n movement.setSimTTInMin(timePeriodStart, timePeriodStart + \n self.simTimeStepInMin, simTT) #time is in minutes! \n \n timePeriodStart += self.simTimeStepInMin\n if timePeriodStart >= self.simEndTimeInMin:\n break\n\n inputStream1.close()\n inputStream2.close()","def poynting(t, dt):\r\n p1satbar, p2satbar, dp1satbar, dp2satbar = psat(t, dt)\r\n poy1 = math.exp(vl1 * (p - p1satbar)/(r * t))\r\n poy2 = math.exp(vl2 * (p - p2satbar)/(r * t))\t\r\n dpoy1 = math.exp(vl1 * (p - dp1satbar)/(r * dt))\r\n dpoy2 = math.exp(vl2 * (p - dp2satbar)/(r * dt))\r\n return poy1, poy2, dpoy1, dpoy2","def run_v1(self):\n start_time = dt.datetime.now()\n first_key_set = set(self.first)\n second_key_set = set(self.second)\n dict = {}\n\n for idx, key in enumerate(first_key_set):\n if(key in dict):\n dict[key] += (len(filter(lambda x:x==key, self.first)))\n else :\n dict[key] = int((len(filter(lambda x: x == key, self.first))))\n\n for idx, key in enumerate(second_key_set):\n if(key in dict):\n dict[key] += (len(filter(lambda x:x==key, self.second)))\n else :\n dict[key] = int((len(filter(lambda x: x == key, self.second))))\n\n result = max(dict.iterkeys(), key=(lambda key : dict[key]))\n print(\"inviting {0} people interest in {1}\".format(dict[result] , result))\n\n end_time = dt.datetime.now()\n print(\"time cost : {0}\".format(end_time - start_time))","def test_ns_ok2():\n env = NsSimPyEnvironment()\n env.process(some_process(env, env.to_time('10.1')))\n env.run()\n env.process(some_process(env, env.to_time('0.9')))\n env.run()\n assert env.now == 11","def part1() -> int:\n longest_sleeper = max(sleep_times, key=lambda g: len(sleep_times[g]))\n sleepiest_minute = max(\n sleep_times[longest_sleeper], key=sleep_times[longest_sleeper].count)\n\n return longest_sleeper * sleepiest_minute","def _collect_waiting_times(self):\n incoming_roads = [\"E2T\", \"N2T\", \"W2T\", \"S2T\"]\n car_list = traci.vehicle.getIDList()\n for car_id in car_list:\n wait_time = traci.vehicle.getAccumulatedWaitingTime(car_id)\n road_id = traci.vehicle.getRoadID(car_id) # get the road id where the car is located\n if road_id in incoming_roads: # consider only the waiting times of cars in incoming roads\n self._waiting_times[car_id] = wait_time\n else: # not in incoming road\n if car_id in self._waiting_times: # a car that was tracked has cleared the intersection\n del self._waiting_times[car_id] \n total_waiting_time = sum(self._waiting_times.values())\n return total_waiting_time","def _event_arrival_path(path, n, e_0=0):\n # if e_0 is None:\n # the entry time of the first event\n\n if n > 0:\n e = e_0 + path.tasks[0].in_event_model.delta_plus(n + 1)\n elif n < 0:\n e = e_0 - path.tasks[0].in_event_model.delta_min(-n + 1)\n else:\n e = 0 # same event, so the difference is 0\n\n return e","def _probe_wait_time(self):\n r = self.probe_cycle_time / float(len(self.servers)) #self.probe_cycle_time=5\n r = max(.25, r) # Cap it at four per second\n return r","def test_spring_forward(self):\n # Exact crossover time:\n # datetime.datetime(2011, 3, 13, 2, 0, 0, tzinfo=pytz.utc)\n # This test will use times on either side of it.\n\n # From the PST vantage point, the run time is 21.1 hours away:\n s1a, s1b = self.hours_to_job_at_datetime(\n 'tz_test_job', 2011, 3, 13, 1, 55, 0)\n\n # From the PDT vantage point, the run time is 20.9 hours away:\n s2a, s2b = self.hours_to_job_at_datetime(\n 'tz_test_job', 2011, 3, 13, 3, 05, 0)\n\n self._assert_range(s1b - s1a, 23.99, 24.01)\n self._assert_range(s2b - s2a, 23.99, 24.01)\n\n # So we lose an hour here. The 2 AM block does not exist.\n # If this were not a DST crossover, this difference would be\n # 1.2, not 0.2.\n self._assert_range(s1a - s2a, 0.19, 0.21)","def important_time(self):\n\t\twork_s = self.work_time().seconds\n\t\tbreak_s = self.break_time().seconds\n\t\tif self.status():\n\t\t\tremaining_time_s = tomato(work_s, break_s)\n\t\telse:\n\t\t\tremaining_time_s = potato(work_s, break_s)\n\n\t\timp_time = datetime.now() + timedelta(0, remaining_time_s)\n\t\treturn imp_time"],"string":"[\n \"def inter_arrival_times(self):\\n # this function returns arrival times between two subsequent tuples in ms\\n # task mean_inter_arrival_time std_inter_arrival_time\\n if self.inter_arrival_time is None:\\n if self.tuple_arrival is None:\\n self.tuple_arrivals()\\n self.inter_arrival_time = convert_throughput_to_inter_arr_times(self.tuple_arrival)\\n\\n return self.inter_arrival_time\",\n \"def receive_and_probing_time(self):\\r\\n latest_completion = 0\\r\\n for probe in self.__probes.values():\\r\\n\\t\\t \\t if probe.complete():\\r\\n\\t\\t\\t \\t\\t latest_completion = max(latest_completion, probe.completion_time)\\r\\n return latest_completion - self.__arrival_time\",\n \"def calculate_waiting_times(self):\\n\\n # let's count time in general units\\n wait_times = []\\n current = wait_time = self.processes[0].arrival_time\\n\\n for p in self.processes:\\n\\n # We won't actually wait :)\\n # if the arrival\\n wait_time = wait_time - p.arrival_time\\n\\n # The first process has wait time of zero\\n wait_times.append(wait_time)\\n wait_time = current + p.burst_time\\n\\n # Wait time for next process\\n current = wait_time\\n\\n return wait_times\",\n \"def compute_start_and_finish_times(a, n_t, current_time):\\n duration = n_t.getc()\\n speed = a.getv()\\n current_location = a.getz()\\n task_loc = n_t.getloc()\\n dist = np.sqrt((task_loc[0] - current_location[0]) ** 2 + (task_loc[1] - current_location[1]) ** 2)\\n travel_time = dist / speed\\n start_time = current_time + travel_time\\n finish_time = start_time + duration\\n return start_time, finish_time\",\n \"def probing_time(self):\\r\\n earliest_launch = (time.time() * 1000)**2\\r\\n latest_completion = 0\\r\\n for probe in self.__probes.values():\\r\\n if probe.complete():\\r\\n earliest_launch = min(earliest_launch, probe.launch_time)\\r\\n latest_completion = max(latest_completion, probe.completion_time)\\r\\n return latest_completion - earliest_launch\",\n \"def part_two():\\n tasks = {}\\n current_time = 0\\n while G.nodes():\\n # noinspection PyCallingNonCallable\\n candidate_next_tasks = [task for task in G.nodes()\\n if task not in tasks.keys() and G.in_degree(task) == 0]\\n if candidate_next_tasks and len(tasks) < 5:\\n next_task = sorted(candidate_next_tasks)[0]\\n tasks[next_task] = ord(next_task) - 4\\n else:\\n min_task_time = min(tasks.values())\\n current_time += min_task_time\\n completed_task = dict(zip(tasks.values(), tasks.keys()))[min_task_time]\\n tasks = {k: v - min_task_time for k, v in tasks.items() if k != completed_task}\\n G.remove_node(completed_task)\\n return current_time\",\n \"def particleCollisionTime(self, first_element, second_element):\\n\\n # Quantities required in following formula\\n r = self.measure.distance(first_element, second_element)\\n r2 = np.dot(r, r)\\n #v = relativeVelocity(first_element, second_element, self.vel)\\n v = self.measure.relativeVelocity(first_element, second_element)\\n v2 = np.dot(v, v)\\n b = np.dot(r, v)\\n d = 2*self.particle_radius\\n # We name 'inner_term' everything that will fall under the square root \\n inner_term = b*b - v2*(r2 - d*d)\\n \\n # We need to filter non-valid results\\n if (inner_term<0 or v2<=0 or first_element==second_element):\\n t = 'inf'\\n else:\\n # The following formula has been taken from Eq: 14.2.2 in\\n # 'The Art of Molecular Dynamics Simulations', D. Rapaport.\\n t = (-b - np.sqrt(inner_term))/v2\\n t = infIfNegative(t) # The collision ocurred in the past \\n return t\",\n \"def successive_poisson(tau1, tau2, size=1):\\n # Draw samples out of first exponential distribution: t1\\n t1 = np.random.exponential(tau1, size=1)\\n\\n # Draw samples out of second exponential distribution: t2\\n t2 = np.random.exponential(tau2, size=1)\\n\\n return t1 + t2\",\n \"def successive_poisson(tau1, tau2, size=1):\\n # Draw samples out of first exponential distribution: t1\\n t1 = np.random.exponential(tau1, size=size)\\n\\n # Draw samples out of second exponential distribution: t2\\n t2 = np.random.exponential(tau2, size=size)\\n\\n return t1 + t2\",\n \"def eta(self):\\n\\t\\tresult = []\\n\\t\\ttime = 0\\n\\t\\tposition = self\\n\\t\\tfor delay, star, order, num_ships in self.orders:\\n\\t\\t\\ttime += int(math.ceil(star.distance(position) / self.galaxy.fleet_speed))\\n\\t\\t\\tposition = star\\n\\t\\t\\tresult.append(time)\\n\\t\\t\\ttime += delay\\n\\t\\treturn result\",\n \"def ps_arrival_times(distance, depth, init_sec):\\n\\n # use taup to get the theoretical arrival times for P & S\\n TauPy_model = TauPyModel('iasp91')\\n tt = TauPy_model.get_travel_times(\\n distance_in_degree=0.001 * distance / 111.11, source_depth_in_km=depth)\\n tiemp = []\\n tiems = []\\n # from all possible P arrivals select the earliest one\\n for i2 in xrange(0, len(tt)):\\n if tt.__getitem__(i2).__dict__['name'] == 'P' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'p' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'Pdiff' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'PKiKP' or\\\\\\n tt.__getitem__(i2).__dict__['name'] == 'PKIKP' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'PP' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'Pb' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'Pn' or \\\\\\n tt.__getitem__(i2).__dict__['name'] == 'Pg':\\n tiem_p = tt.__getitem__(i2).__dict__['time']\\n tiemp.append(tiem_p)\\n arriv_p = np.floor(init_sec + min(tiemp))\\n\\n # from all possible S arrivals select the earliest one\\n for i3 in xrange(0, len(tt)):\\n if tt.__getitem__(i3).__dict__['name'] == 'S' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 's' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'Sdiff' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'SKiKS' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'SKIKS' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'SS' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'Sb' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'Sn' or \\\\\\n tt.__getitem__(i3).__dict__['name'] == 'Sg':\\n tiem_s = tt.__getitem__(i3).__dict__['time']\\n tiems.append(tiem_s)\\n arriv_s = np.floor(init_sec + min(tiems))\\n return arriv_p, arriv_s\",\n \"def time_diff_procs(numprocs):\\n conv_times = dict()\\n for proc in numprocs:\\n start_time = datetime.datetime.now()\\n convolve_spectra(spectrum_path, band, vsini, R, epsilon, fwhm_lim, plot, numprocs=proc)\\n end_time = datetime.datetime.now()\\n conv_times[proc] = end_time - start_time\\n return conv_times\",\n \"def successive_poisson(tau1, tau2, size=1):\\r\\n # Draw samples out of first exponential distribution: t1\\r\\n t1 = np.random.exponential(tau1,size=tau1)\\r\\n\\r\\n # Draw samples out of second exponential distribution: t2\\r\\n t2 = np.random.exponential(tau2,size=tau2)\\r\\n\\r\\n return t1 + t2\",\n \"def test_spring_forward_2(self):\\n # Exact crossover time:\\n # datetime.datetime(2011, 3, 13, 2, 0, 0, tzinfo=pytz.utc)\\n # This test will use times on either side of it.\\n\\n # From the PST vantage point, the run time is 0.6 hours away:\\n s1a, s1b = self.hours_to_job_at_datetime(\\n 'tz_config_test_job_spring_forward', 2011, 3, 13, 1, 55, 0)\\n\\n # This means the job at the nonexistent time 2:30 will be run at the\\n # \\\"new\\\" 3:30\\n self._assert_range(s1a, 0.59, 0.61)\\n\\n # From the PDT vantage point, the next run time is 23.4 hours away,\\n # because the job has already been run.\\n s2a, s2b = self.hours_to_job_at_datetime(\\n 'tz_config_test_job_spring_forward', 2011, 3, 13, 3, 05, 0)\\n\\n self._assert_range(s1b - s1a, 22.99, 23.01)\\n self._assert_range(s2b - s2a, 23.99, 24.01)\\n\\n # Since the second measurement is taken after the job runs, we check\\n # the 'second' run from the first measurement against the 'first' run\\n # of the first measurement.\\n # Like test_spring_forward, the times should differ by 0.2 hours.\\n self._assert_range(s1b - s2a, 0.19, 0.21)\",\n \"def test_poisson(self):\\n nt = 50\\n ns = 1000\\n num_giter = 5\\n net = self.poisson\\n\\n times = []\\n for i in range(ns):\\n arrv = net.sample (nt)\\n obs = arrv.subset (lambda a,e: a.is_last_in_queue(e), copy_evt)\\n gsmp = net.gibbs_resample (arrv, 0, num_giter)\\n resampled = gsmp[-1]\\n evts = resampled.events_of_task (2)\\n times.append (evts[0].d)\\n \\n exact_sample = [ numpy.random.gamma (shape=3, scale=0.5) for i in xrange (ns) ]\\n times.sort()\\n exact_sample.sort()\\n \\n print summarize(times)\\n print summarize(exact_sample)\\n \\n netutils.check_quantiles (self, exact_sample, times, ns)\",\n \"def on_time_step(self, event):\\n live_birth_rate = self.live_birth_rate.at[self.clock().year]\\n still_birth_rate = self.still_birth_rate.at[self.clock().year]\\n\\n step_size = event.step_size / pd.Timedelta(seconds=SECONDS_PER_YEAR)\\n\\n mean_births = (live_birth_rate + still_birth_rate) * step_size\\n # Assume births occur as a Poisson process\\n r = np.random.RandomState(seed=self.randomness.get_seed()) # does this always seed the PRNG with the same seed?\\n live_births = r.poisson(live_birth_rate * step_size)\\n stillbirths = r.poisson(still_birth_rate * step_size)\\n print(live_births, stillbirths)\\n simulants_to_add = live_births + stillbirths\\n if simulants_to_add > 0:\\n vital_status = ['alive'] * live_births + ['stillborn'] * stillbirths\\n\\n self.simulant_creator(simulants_to_add,\\n population_configuration={\\n 'age_start': 0,\\n 'age_end': 0,\\n 'alive': vital_status, # store this in the user_data dictionary\\n # for use in the on_initialize_simulants method\\n 'sim_state': 'time_step',\\n })\",\n \"def packet_arrival():\\r\\n return 1.0\",\n \"def get_ready_time(self):\\n\\n # historical data\\n data_pickup = self.data[self.data[\\\"ReadyTimePickup\\\"] != 0] # work on pickup data only\\n pickup_arrival_times = {\\n location: [\\n (datetime.datetime.strptime(str(int(row[\\\"ReadyTimePickup\\\"])), '%H%M').replace(year=1970,\\n month=1,\\n day=1,\\n tzinfo=gmt8)\\n - datetime.datetime.now().replace(\\n year=1970,\\n month=1,\\n day=1,\\n hour=0,\\n minute=0,\\n second=0,\\n microsecond=0,\\n tzinfo=gmt8)\\n ).total_seconds()\\n for _, row in data_pickup[data_pickup[\\\"PostalCode\\\"] == location].iterrows()\\n ]\\n for location in self.pc_pickup_jobs\\n }\\n\\n # mean\\n pickup_arrival_time_mean = {\\n location: mean_time(pickup_arrival_times[location])\\n for location in self.pc_pickup_jobs\\n }\\n # variance\\n pickup_arrival_time_var = {\\n location: np.std(pickup_arrival_times[location])/2\\n for location in self.pc_pickup_jobs\\n }\\n\\n return pickup_arrival_time_mean, pickup_arrival_time_var\",\n \"def M_D_1(arrival_time,max_time,service_time=1/90):\\n #conversion in seconds\\n max_seconds = max_time*60*60\\n sim_time = 0.0 # simulation time\\n t_1 = 0.0 # time for next event (arrival)\\n t_2 = max_seconds # time for next event (departure)\\n t_n = 0.0 #last event time--> tempo dell'ultimo avvenimento generico\\n t_b = 0.0 # last start of busy time--> tempo in cui la queue inizia ad essere non vuota per l'ultima volta\\n c = 0 # numero di servizi completati\\n queue_aircraft = [] # number of aircraft in the queue\\n aircraft = 0\\n arrival = [] # time of arrival\\n attesa = [] # attesa per gli aerei-->NON SICURO CHE SI CALCOLI COSI'\\n # simulation loop\\n while(sim_time < max_seconds):\\n if(t_1<t_2): #event1:arrival\\n sim_time = t_1\\n arrival.append(t_1)\\n aircraft += 1\\n queue_aircraft.append(aircraft)\\n t_n = sim_time\\n t_1 = sim_time + rm.expovariate(arrival_time)\\n if(aircraft==1):\\n t_b = sim_time\\n t_2 = sim_time + 1/service_time\\n else:\\n sim_time = t_2\\n aircraft = aircraft -1\\n queue_aircraft.append(aircraft)\\n t_n = sim_time\\n attesa.append( t_2 - arrival[c])\\n c+=1\\n if(aircraft>0):\\n t_2=sim_time + 1/service_time\\n else:\\n t_2 = max_seconds\\n\\n\\n\\n\\n return queue_aircraft,arrival,attesa\",\n \"def part2() -> int:\\n sleepiest_minute = None\\n sleeping_guard = None\\n sleep_frequency = 0\\n\\n for guard, times in sleep_times.items():\\n if len(times) == 0:\\n continue\\n _sleepiest_minute = max(\\n sleep_times[guard], key=sleep_times[guard].count)\\n _sleep_frequency = sleep_times[guard].count(_sleepiest_minute)\\n\\n if _sleep_frequency > sleep_frequency:\\n sleepiest_minute = _sleepiest_minute\\n sleeping_guard = guard\\n sleep_frequency = _sleep_frequency\\n\\n assert sleeping_guard is not None and sleepiest_minute is not None\\n return sleeping_guard * sleepiest_minute\",\n \"def _calcPlungerMoveTime(self, move_steps):\\n sd = self.sim_state\\n start_speed = sd['start_speed']\\n top_speed = sd['top_speed']\\n cutoff_speed = sd['cutoff_speed']\\n slope = sd['slope']\\n microstep = sd['microstep']\\n\\n slope *= 2500.0\\n if microstep:\\n move_steps = move_steps / 8.0\\n theo_top_speed = sqrt((4.0 * move_steps*slope) + start_speed ** 2.0)\\n # If theoretical top speed will not exceed cutoff speed\\n if theo_top_speed < cutoff_speed:\\n move_t = theo_top_speed - (start_speed/slope)\\n else:\\n theo_top_speed = sqrt(((2.0*move_steps*slope) +\\n ((start_speed**2.0+cutoff_speed**2.0)/2.0)))\\n # If theoretical top speed with exceed cutoff speed but not\\n # reach the set top speed\\n if cutoff_speed < theo_top_speed < top_speed:\\n move_t = ((1 / slope) * (2.0 * theo_top_speed - start_speed -\\n cutoff_speed))\\n # If start speed, top speed, and cutoff speed are all the same\\n elif start_speed == top_speed == cutoff_speed:\\n move_t = (2.0 * move_steps) / top_speed\\n # Otherwise, calculate time spent in each phase (start, constant,\\n # ramp down)\\n else:\\n ramp_up_halfsteps = ((top_speed ** 2.0 - start_speed ** 2.0) /\\n (2.0 * slope))\\n ramp_down_halfsteps = ((top_speed ** 2.0 - cutoff_speed ** 2.0) /\\n (2.0 * slope))\\n if (ramp_up_halfsteps + ramp_down_halfsteps) < (2.0 * top_speed):\\n ramp_up_t = (top_speed - start_speed) / slope\\n ramp_down_t = (top_speed - cutoff_speed) / slope\\n constant_halfsteps = (2.0 * move_steps - ramp_up_halfsteps -\\n ramp_down_halfsteps)\\n constant_t = constant_halfsteps / top_speed\\n move_t = ramp_up_t + ramp_down_t + constant_t\\n return move_t\",\n \"def expected_time_in_markov_state_ignoring_class_2_arrivals(\\n state, lambda_1, mu, num_of_servers, system_capacity\\n):\\n if state[1] == system_capacity:\\n return 1 / (min(state[1], num_of_servers) * mu)\\n return 1 / (min(state[1], num_of_servers) * mu + lambda_1)\",\n \"def prob_class_1_arrival(state, lambda_1, mu, num_of_servers):\\n return lambda_1 / (lambda_1 + (mu * min(state[1], num_of_servers)))\",\n \"def queue_times(self):\\r\\n return [task.scheduler_launch_time - self.__arrival_time\\r\\n for task in self.__tasks.values() if task.complete()]\",\n \"def elapseTime(self, gameState):\\n\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n allPossible = util.Counter()\\n\\n for oldPos in self.legalPositions:\\n actions = gameState.getLegalActions(agentIndex)\\n successorStates = [gameState.generateSuccessor(action) for action in actions]\\n newPosDist = {}\\n for state in successorStates:\\n position = state.getAgentPosition(agentIndex)\\n prob = 1.0/len(actions)\\n newPosDist[position] = prob\\n\\n for newPos, prob in newPosDist.items():\\n allPossible[newPos] += prob * self.beliefs[oldPos]\\n\\n allPossible.normalize()\\n self.beliefs = allPossible\",\n \"def time_step(self):\\n\\n rho_rel = np.abs(self.rho_dt / self.rho)\\n rho_rel_max = np.max(rho_rel)\\n e_rel = np.abs(self.e_dt / self.e)\\n e_rel_max = np.max(e_rel)\\n x_rel = np.abs(self.u / self.dx)\\n x_rel_max = np.max(x_rel)\\n y_rel = np.abs(self.w / self.dy)\\n y_rel_max = np.max(y_rel)\\n rel = [rho_rel_max, e_rel_max, x_rel_max, y_rel_max]\\n delta = np.max(np.abs(rel))\\n\\n if 0.1 <= delta <= 1e3:\\n self.dt = self.p / delta\\n else:\\n self.dt = self.p\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def duration(self):\\r\\n return self.t2 - self.t1\",\n \"def prob(E1, E2, t):\\n return 1 if E1 > E2 else np.exp((E1 - E2) / t)\",\n \"def example2():\\n arrive_time=rand_arr_time.rand_arr_time(6,100000,1000) # Get packet arrive time, with option 2, 100000 packets, expected in 1000 seconds.\\n return arrive_time\",\n \"def pcr_delta_time_ms(pcr_t1, pcr_t2, offset = 0):\\n return float(pcr_t2-pcr_t1)/90000.0 + offset\",\n \"def calculate_turnaround_times(self, wts=None):\\n trts = []\\n\\n if wts == None:\\n wts = self.calculate_waiting_times()\\n\\n for p in self.processes:\\n waiting_time = wts.pop(0)\\n trt = waiting_time + p.burst_time\\n trts.append(trt)\\n\\n return trts\",\n \"def response_time(self):\\r\\n if self.__arrival_time == INVALID_TIME:\\r\\n self.__logger.debug(\\\"Request %s missing arrival time\\\" % self.__id)\\r\\n return INVALID_TIME_DELTA\\r\\n completion_time = self.__arrival_time\\r\\n for task_id, task in self.__tasks.items():\\r\\n if task.completion_time == INVALID_TIME:\\r\\n self.__logger.debug((\\\"Task %s in request %s missing completion \\\"\\r\\n \\\"time\\\") % (task_id, self.__id))\\r\\n return INVALID_TIME_DELTA\\r\\n task_completion_time = task.adjusted_completion_time()\\r\\n #if task.scheduler_launch_time > task.node_monitor_launch_time:\\r\\n #self.__logger.warn((\\\"Task %s suggests clock skew: scheduler launch time %d, node \\\"\\r\\n # \\\"monitor launch time %d\\\") %\\r\\n\\r\\n #(task_id, task.scheduler_launch_time,\\r\\n # task.node_monitor_launch_time))\\r\\n completion_time = max(completion_time, task_completion_time)\\r\\n return completion_time - self.__arrival_time\",\n \"def part2(input):\\n ps = PlanetSystem(input)\\n c = ps.total_cycle_time()\\n return c\",\n \"def get_time_walking(self):\\n return self.time_step_to_enqueue - self.time_entered\",\n \"def stack_ps(ps1, ps2, keep_unique = False, fill_time = False, message = True):\\n # create deepcopies to avoid changing original instances\\n \\n ps1 = copy.deepcopy(ps1)\\n ps2 = copy.deepcopy(ps2)\\n \\n # create datetime information in PS instances\\n \\n try:\\n _ = getattr(ps1, \\\"datetime\\\")\\n except AttributeError:\\n ps1.createTimeDate()\\n \\n try: \\n _ = getattr(ps2, \\\"datetime\\\")\\n except AttributeError:\\n ps2.createTimeDate()\\n \\n # check time resolutions\\n res1 = (dt.datetime.strptime(ps1.datetime['data'][1], ps1.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])).seconds\\n res2 = (dt.datetime.strptime(ps2.datetime['data'][1], ps2.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])).seconds\\n \\n if abs(res1-res2) > 60:\\n if message:\\n print( (\\\"warning: resolutions differ %d seconds\\\")%(abs(res1-res2)) )\\n \\n # check if ps1 is \\\"older\\\" than ps2\\n \\n reversed_order = False\\n cut = None\\n \\n if dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units']) < dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']):\\n # ps2 starts after ps1 ends\\n timediff = (dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units'])).total_seconds()\\n elif dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units']) < dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']):\\n # ps1 starts after ps2 ends (user has inadvertently switched the order of the instances)\\n reversed_order = True\\n timediff = (dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units'])).total_seconds()\\n else:\\n # yikes! The particle sizer instances have overlapping data\\n # it is assumed that ps2 data replaces ps1 data starting \\n # from the overlapping time\\n cut, cutdate = tt.findNearestDate(ps1.datetime['data'], ps2.datetime['data'][0]) \\n fill_time = False\\n \\n #print(timediff, 1.5*res1)\\n # check if filling is required\\n if fill_time is True:\\n # check time difference\\n if reversed_order:\\n # ps1 starts after ps2 ends\\n if timediff > 1.5*res2:\\n # the time gap between two instances has to be\\n # larger than twice the normal resolution\\n numdates = int(np.ceil(timediff/res2))\\n base = dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])\\n date_list = [base - dt.timedelta(seconds=res2*x) for x in range(numdates)]\\n date_list = list(reversed(date_list[1:]))# because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\\n datetimelist = [dt.datetime.strftime(dl, ps2.datetime['units']) for dl in date_list]\\n ps2.datetime['data'] = np.append(ps2.datetime['data'], datetimelist)\\n timelist = [dt.datetime.strftime(dl, ps2.time['units']) for dl in date_list]\\n ps2.time['data'] = np.append(ps2.time['data'], timelist)\\n datelist = [dt.datetime.strftime(dl, ps2.date['units']) for dl in date_list]\\n ps2.date['data'] = np.append(ps2.date['data'], datelist)\\n else:\\n fill_time = False\\n else:\\n if timediff > 1.5*res1:\\n # the time gap between two instances has to be\\n # larger than twice the normal resolution\\n numdates = int(np.ceil(timediff/res1))\\n base = dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])\\n date_list = [base - dt.timedelta(seconds=res1*x) for x in range(numdates)]\\n date_list = list(reversed(date_list[1:])) # because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\\n datetimelist = [dt.datetime.strftime(dl, ps1.datetime['units']) for dl in date_list]\\n ps1.datetime['data'] = np.append(ps1.datetime['data'], datetimelist)\\n timelist = [dt.datetime.strftime(dl, ps1.time['units']) for dl in date_list]\\n ps1.time['data'] = np.append(ps1.time['data'], timelist)\\n datelist = [dt.datetime.strftime(dl, ps1.date['units']) for dl in date_list]\\n ps1.date['data'] = np.append(ps1.date['data'], datelist)\\n else:\\n fill_time = False\\n \\n if message:\\n print(\\\"reversed order:\\\", reversed_order)\\n # check which attributes are similar in both instances\\n if reversed_order:\\n # ps1 starts after ps2 ends\\n new_ps = copy.deepcopy(ps2)\\n for attribute in ps1.__dict__.keys():\\n if attribute in ps2.__dict__.keys():\\n afield = getattr(new_ps, attribute)\\n if attribute == 'diameter':\\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\\n \\n for var in new_ps.data['variables']:\\n if fill_time is True:\\n add = np.ma.zeros((ps2.data[var]['data'].shape[0],len(date_list))) \\n add[:] = np.nan\\n newdata = np.append(ps2.data[var]['data'],add,axis=1)\\n ps2.data[var]['data'] = newdata\\n \\n sh1 = ps1.data[var]['data'].shape\\n sh2 = ps2.data[var]['data'].shape\\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\\n new_field = np.ma.zeros(newfields)\\n new_field[:] = np.ma.masked\\n \\n new_field[st21:st22, 0:ps2.data[var]['data'][:,:cut].shape[1]] = ps2.data[var]['data'][:,:cut]\\n new_field[st11:st12, ps2.data[var]['data'][:,:cut].shape[1]:] = ps1.data[var]['data']\\n \\n new_ps.data[var]['data'] = new_field\\n \\n afield['data'] = diamlist\\n \\n elif attribute == 'data':\\n # data has been appended with diameters\\n pass\\n else:\\n try:\\n field_ps2 = getattr(ps2, attribute)\\n field_ps1 = getattr(ps1, attribute)\\n except TypeError:\\n if attribute == 'header':\\n pass\\n else:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n try:\\n data_ps2 = field_ps2['data']\\n data_ps1 = field_ps1['data']\\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\\n elif fill_time:\\n add = np.ma.zeros(len(date_list))\\n add[:] = np.nan\\n afield['data'] = np.append(np.append(data_ps2[:cut],add), data_ps1)\\n else:\\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\\n except:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n \\n else:\\n if keep_unique:\\n newattribute = getattr(ps1,attribute)\\n newattribute['time'] = ps1['datetime']['data']\\n setattr(new_ps, attribute, newattribute)\\n else:\\n pass\\n if keep_unique is False:\\n # get rid of attributes which were in ps2 but not in ps1\\n for attribute in ps2.__dict__.keys():\\n if attribute in ps1.__dict__.keys():\\n pass\\n else:\\n delattr(new_ps, attribute)\\n \\n \\n else:\\n # ps2 starts after ps1 ends\\n new_ps = copy.deepcopy(ps1)\\n for attribute in ps2.__dict__.keys():\\n if attribute in ps1.__dict__.keys():\\n afield = getattr(new_ps, attribute)\\n if attribute == 'diameter':\\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\\n for var in new_ps.data['variables']:\\n if fill_time is True:\\n add = np.ma.zeros((ps1.data[var]['data'].shape[0],len(date_list))) \\n add[:] = np.nan\\n newdata = np.append(ps1.data[var]['data'],add,axis=1)\\n ps1.data[var]['data'] = newdata\\n \\n sh1 = ps1.data[var]['data'].shape\\n sh2 = ps2.data[var]['data'].shape\\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\\n new_field = np.ma.zeros(newfields)\\n new_field[:] = np.ma.masked\\n \\n new_field[st11:st12, 0:ps1.data[var]['data'][:,:cut].shape[1]] = ps1.data[var]['data'][:,:cut]\\n new_field[st21:st22, ps1.data[var]['data'][:,:cut].shape[1]:] = ps2.data[var]['data']\\n \\n new_ps.data[var]['data'] = new_field\\n \\n afield['data'] = diamlist\\n \\n elif attribute == 'data':\\n # data has been appended with diameters\\n pass\\n else:\\n try:\\n field_ps2 = getattr(ps2, attribute)\\n field_ps1 = getattr(ps1, attribute)\\n except TypeError:\\n if attribute == 'header':\\n pass\\n else:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n try:\\n data_ps2 = field_ps2['data']\\n data_ps1 = field_ps1['data']\\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\\n elif fill_time:\\n add = np.ma.zeros(len(date_list))\\n add[:] = np.nan\\n afield['data'] = np.append(np.append(data_ps1[:cut],add), data_ps2)\\n else:\\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\\n except:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n \\n else:\\n if keep_unique:\\n newattribute = getattr(ps2,attribute)\\n newattribute['time'] = ps2['datetime']['data']\\n setattr(new_ps, attribute,newattribute)\\n else:\\n pass\\n if keep_unique is False:\\n # get rid of attributes which were in ps2 but not in ps1\\n for attribute in ps1.__dict__.keys():\\n if attribute in ps2.__dict__.keys():\\n pass\\n else:\\n delattr(new_ps, attribute)\\n \\n new_ps.sample['data'] = np.arange(1.0, len(new_ps.datetime['data'])+1)\\n new_ps.instrument_type = ps1.instrument_type.split('_')[0] + '_concatenated'\\n \\n if message:\\n print('filltime: ', fill_time)\\n \\n return new_ps\",\n \"def compute_trajectory():\\n pass\",\n \"def evaluate_time(self, pid, edge):\\n return self.get_process_speed(pid) * edge.get_time()\",\n \"def run_v2(self):\\n start_time = dt.datetime.now()\\n dic = {}\\n for key in self.first :\\n dic[key] = 0\\n dic[key] = 0\\n\\n for idx in range(0, len(self.first)) :\\n dic[self.first[idx]] = dic[self.first[idx]] + 1\\n dic[self.second[idx]] = dic[self.second[idx]] + 1\\n\\n ans = 0\\n\\n for key in dic.keys():\\n ans = max(ans , dic[key])\\n\\n print(ans)\\n\\n end_time = dt.datetime.now()\\n print(\\\"time cost : {0}\\\".format(end_time - start_time))\",\n \"def time_evolution_point(operating_point, delta1_point, delta2_point):\\n match_freq = 10.0\\n qubit_base = hybrid.SOSSHybrid(operating_point, match_freq)\\n delta1_ref = qubit_base.delta1\\n delta2_ref = qubit_base.delta2\\n stsplitting_ref = qubit_base.stsplitting\\n ed_ref = qubit_base.ed\\n qubit = hybrid.HybridQubit(ed_ref,\\n stsplitting_ref,\\n delta1_point * delta1_ref,\\n delta2_point * delta2_ref)\\n trange, sigma_array, mass_chi_array = time_sweep(qubit)\\n return trange, sigma_array, mass_chi_array\",\n \"def arrival_time(self):\\r\\n return self.__arrival_time\",\n \"def arrival_time(self):\\r\\n return self.__arrival_time\",\n \"def calculate_travel_time_complex(distance_meters, accel_mps2):\\n time = 0\\n distance_progress = 0\\n speed = 0\\n halfway_point = distance_meters / 2\\n while distance_progress < halfway_point:\\n time = time + 1\\n speed = speed + accel_mps2\\n distance_progress = distance_progress + speed\\n \\\"\\\"\\\"\\n Output progress \\n print \\\"{0} seconds | speed: {1} m/s | distance traveled: {2} m\\\".format(time, speed, distance_progress)\\n \\\"\\\"\\\"\\n time = time * 2\\n return [time, speed]\",\n \"def simulate(self) -> (int, int):\\n sums = [0 for i in range(10)]\\n iteration = 0\\n time = 0\\n cost = 0\\n\\n # initialize queues list\\n queue = [None for _ in self.processors]\\n while len(self.done_tasks) < len(self.task_graph.nodes):\\n iteration += 1\\n \\n # apply tasks to queues (processors)\\n for qi, proc in enumerate(self.processors):\\n if len(self.application[proc.index]) > 0 and queue[qi] == None:\\n task = self.application[proc.index].pop(0)\\n queue[qi] = ProcessingFactory.ScheduledTask(self.parser, proc, task)\\n logging.debug(\\\"queue: \\\", qi, \\\" -> \\\", task)\\n cost += queue[qi].getCost()\\n \\n # calculates min time after which something will happen to move clock\\n min_construction_time = min(queue, key=lambda x: x.getTime() if x else MAX).getTime()\\n min_time = min_construction_time\\n\\n for queue_index, queue_el in enumerate(queue):\\n # if queue empty, not interesting\\n if not queue_el:\\n continue\\n parents = self.task_graph.find_parents(queue_el.task)\\n logging.debug(queue_el.task, parents)\\n\\n # if node has no parents, can be built\\n if not parents:\\n queue_el.enable()\\n queue_el.passTime(min_time)\\n \\n\\n # all parents have to be constructed\\n if parents and set(parents).intersection(set(self.done_tasks)) == set(parents) and len(self.done_tasks) > 0:\\n logging.debug(\\\"transfer needed\\\")\\n \\n transfer_time = 0\\n # Resources from all parents have to be on this node to start construction.\\n # Performs transfer logic with additional time required for that.\\n all_here = True\\n for parent in parents:\\n ind = self.done_tasks.index(parent)\\n if self.location[ind] != queue_index and ind in self.done_tasks:\\n all_here = False\\n transfer_time += self.task_graph.get_weight(parent, queue_el.task) / self.transfer[self.location[ind]][queue_index].throughput\\n logging.debug(\\\"transfering:\\\", self.location[ind], ind, \\\" -> \\\", queue_index, ind)\\n self.location[ind] = queue_index\\n \\n # If item can be bult enables it in queue with one step delay (to avoid passing time of transmission)\\n queue_el.enable(True)\\n \\n # If all items are already waiting on current node, construction can be started\\n if all_here:\\n logging.debug(\\\"all resources in place!\\\")\\n queue_el.enable()\\n \\n # If there was transfer move time according to time required by transfer, else\\n # move time according to minimal construction time\\n if queue_el and transfer_time > 0:\\n min_time = transfer_time\\n elif queue_el:\\n min_time = min_construction_time\\n \\n # Pass time for each element in queue\\n for queue_el in queue:\\n if queue_el:\\n queue_el.passTime(min_time)\\n\\n time += min_time\\n logging.info(\\\"TIME:\\\", time, min_time, iteration, self.done_tasks)\\n\\n # Memory of left times in round buffer to avoid looping for infinity for impossible graphs\\n sums[iteration%len(sums)] = sum([x.getTime() for x in queue if x])\\n \\n # If construction time has passed, item is constructed\\n for i, e in enumerate(queue):\\n if queue[i] != None and queue[i].getTime() <= 0:\\n # Done has task which were finished, location has nodes on which resources are currently stored\\n self.done_tasks.append(queue[i].task)\\n self.location.append(i)\\n queue[i] = None\\n\\n logging.debug(sums)\\n # Checks if all average value of sums is equal to last element, \\n # if yes output MAX, MAX which means that it cannot be performed in finite time.\\n if sum(sums)/len(sums) == sums[-1]:\\n return MAX, MAX\\n return time, cost\",\n \"def output_tasks_launched_versus_time(self, output_directory):\\r\\n gnuplot_file = open(\\\"%s/task_launches_vs_time.gp\\\" % output_directory, \\\"w\\\")\\r\\n gnuplot_file.write(\\\"set terminal postscript color 'Helvetica' 12\\\\n\\\")\\r\\n gnuplot_file.write(\\\"set output '%s/task_launches_vs_time.ps'\\\\n\\\" % output_directory)\\r\\n gnuplot_file.write(\\\"set xlabel 'Time (ms)'\\\\n\\\")\\r\\n gnuplot_file.write(\\\"set ylabel 'Tasks Launched'\\\\n\\\")\\r\\n gnuplot_file.write(\\\"plot \\\")\\r\\n\\r\\n job_count = 0\\r\\n for id, request in self.__requests.items():\\r\\n results_filename = \\\"%s/%s_tasks_launched_vs_time\\\" % (output_directory, id)\\r\\n file = open(results_filename, \\\"w\\\")\\r\\n arrival_time, reservation_replies = request.get_scheduler_get_task_times()\\r\\n reservation_count = 0\\r\\n file.write(\\\"0\\\\t0\\\\n\\\")\\r\\n for reservation in reservation_replies:\\r\\n reservation_count += 1\\r\\n # Write the elapsed time since the request arrived.\\r\\n file.write(\\\"%s\\\\t%s\\\\n\\\" % (reservation - arrival_time, reservation_count))\\r\\n file.close()\\r\\n\\r\\n if job_count != 0:\\r\\n gnuplot_file.write(\\\",\\\\\\\\\\\\n\\\")\\r\\n gnuplot_file.write(\\\"'%s' using 1:2 lw 1 with lp\\\" % results_filename)\\r\\n job_count += 1\\r\\n if job_count >= 20:\\r\\n break\\r\\n gnuplot_file.close()\",\n \"def compute_time_step(self, P_n):\\n\\n #Pointer reassignment for convenience\\n method = self.solver_method\\n dt = self.time_step\\n theta = self.solver_theta\\n T = self.T\\n B = self.B\\n Q = self.Q\\n\\n #If mixed, or explicit are specified, otherwise default is implicit\\n #therefore it's not actually required to be placed in the input deck\\n if method == 'mixed':\\n A = ((1.0 - theta) * T + B / dt)\\n b = (B / dt - theta * T).dot(P_n) + Q\\n P_np1 = spsolve(A, b)\\n elif method == 'explicit':\\n P_np1 = P_n + 1 / B * dt * (Q - T.dot(P_n))\\n else:\\n A = T + B / dt\\n b = (B / dt).dot(P_n) + Q\\n P_np1 = spsolve(A, b)\\n\\n #Return solution vector from a single time-step\\n return P_np1\",\n \"def exptime(self):\\n exptime = float(self.get('TRUITIME')) * int(self.get('COADDONE'))\\n return exptime\",\n \"def get_propagation_time(self):\\n return 0.0 # self.get_distance_to_gateway() / (3 * pow(10,8))\",\n \"def new_time(self, state, time, last_value, independent_sources):\\n sum1 = np.matmul(state.__active_control[1],last_value) - state.__active_control[2]\\n if (abs(sum1) > self.__absolute_error):\\n sum2 = np.matmul(state.__active_control[1], np.matmul(self.__A, last_value) + np.matmul(self.__B, independent_sources))\\n sum3 = np.matmul(state.__active_control[1], np.matmul(self.__A**2, last_value) + np.matmul(self.__A, \\\\\\n np.matmul(self.__B, independent_sources)))\\n return time + 1.0 / (sum3 / 2 / sum2 - sum2 / sum1)\\n else:\\n return -1\",\n \"def _arrival_time(self):\\n \\n return self.mkt_time + timedelta(0, 0, self.latency)\",\n \"def approximate_delivery_time(self):\\n # this is set to seconds just of demonstration\\n approx_delivery_time = random.randrange(300, 600)\\n approx_time_to_main_station = approx_delivery_time / 2\\n\\n return {\\n \\\"delivery_time\\\": approx_delivery_time,\\n \\\"time_to_station\\\": approx_time_to_main_station,\\n }\",\n \"def start_and_service_times(self):\\r\\n return [(x.scheduler_launch_time, x.service_time()) for x in self.__tasks.values()\\r\\n if x.complete()]\",\n \"def _omori_time(integ, c, p):\\n if p == 1:\\n return c*(exp(integ) - 1)\\n else:\\n return (integ*(1 - p) + c**(1 - p))**(1/(1 - p)) - c\",\n \"def _generate_trajectory(self, current_pos, velocity, goal, dt, prediction_time):\\n\\n history = list(np.copy(self._reached_goals))\\n\\n\\n\\n\\n out = []\\n out.append(np.copy(current_pos))\\n first_goal_idx = np.where(self._goals == goal)[0][0]\\n selected_goal = goal\\n reached_goal = False\\n counter_in_goal = 0\\n\\n\\n\\n for _ in range(prediction_time):\\n\\n # Particle reached selected goal\\n # This will continuously chose a next goal, if a particle already reached its predecessor goal\\n\\n if np.linalg.norm(current_pos - selected_goal) <= 0.1:\\n reached_goal = True\\n\\n\\n\\n\\n\\n\\n if counter_in_goal > self.curr_stay_duration_goals[self._is_human_in_range_of_goal(selected_goal)[1]] / dt:\\n\\n selected_goal_idx = np.where(self._goals == selected_goal)[0][0]\\n\\n if len(history) > 0:\\n if not selected_goal_idx == history[-1]:\\n history.append(selected_goal_idx)\\n else:\\n history.append(selected_goal_idx)\\n #print \\\"history:\\\"\\n #print history\\n # Select next goal based on the pre-learned goal-change probabilities\\n\\n\\n #print \\\"selected goal {}\\\".format(selected_goal_idx)\\n probs,available_goals = self.compute_transition_probs_for_goal_from_history(history,selected_goal_idx)\\n\\n\\n for p in probs:\\n if p < self._belief_threshold:\\n p = 0.0\\n\\n print \\\"probs sampling: \\\"\\n print probs / np.sum(np.asarray(probs))\\n selected_goal = self._goals[np.random.choice(available_goals, p=probs / np.sum(np.asarray(probs)))]\\n\\n counter_in_goal = 0.0\\n\\n #print(\\\"switching\\\")\\n\\n else:\\n counter_in_goal += 1\\n #print(\\\"incr counter\\\")\\n\\n\\n\\n\\n\\n if reached_goal:\\n #print self.curr_stay_duration_goals\\n #print self.curr_stay_duration_goals[ self._is_human_in_range_of_goal(selected_goal)[1] ]\\n\\n new_pos = self.transition_human(current_pos, velocity, selected_goal, dt)\\n\\n out.append(new_pos)\\n current_pos = new_pos\\n\\n\\n\\n else:\\n new_pos = self.transition_human(current_pos, velocity, selected_goal, dt)\\n out.append(new_pos)\\n current_pos = new_pos\\n\\n return np.asarray(out)\",\n \"def getDeliveryTime(ori, dest):\\n\\n start_time = time.time()\\n\\n routingApi = herepy.RoutingApi(os.getenv(\\\"HERE_KEY\\\"))\\n gm = GoogleMaps(os.getenv(\\\"GOOGLE_KEY\\\"))\\n\\n try:\\n response = routingApi.truck_route(ori.coords[::-1], dest.coords[::-1], [herepy.RouteMode.truck, herepy.RouteMode.fastest]).as_dict()\\n distance = response.get('response').get('route')[0].get('summary').get('distance') / 1000\\n except herepy.error.HEREError:\\n try:\\n response = gm.distance_matrix(ori.coords[::-1], dest.coords[::-1], mode=\\\"driving\\\", departure_time=dt.datetime.now(), traffic_model=\\\"pessimistic\\\")\\n distance = response.get('rows')[0].get('elements')[0].get('distance').get('value') / 1000\\n except Exception as e:\\n capture_exception(e)\\n raise e\\n\\n if distance < 51:\\n deltime = 6\\n elif distance > 50 and distance < 701:\\n deltime = 24\\n elif distance > 700 and distance < 1400:\\n deltime = 48\\n else:\\n deltime = 72\\n\\n print('--- Tiempo de ejecucion calcDeliveryTime: {} segundos ---'.format((time.time() - start_time)))\\n\\n return deltime, distance\",\n \"def time_per_process():\\n return int(random.normalvariate(PT_MEAN, PT_SIGMA))\",\n \"def time_per_process():\\n return int(random.normalvariate(PT_MEAN, PT_SIGMA))\",\n \"def coalescence_time(star):\\r\\n\\r\\n assert isinstance(star, pd.Series), (\\\"Expected a Series object to be \\\"\\r\\n \\\"passed to coalescence_time.\\\")\\r\\n\\r\\n evo = star.evolution_age + star.rejuvenation_age\\r\\n _, _, h, _ = evolve_system(star.a0,\\r\\n star.e0,\\r\\n star.m1,\\r\\n star.m2,\\r\\n evotime=evo)\\r\\n\\r\\n return np.sum(h) / takahe.constants.SECONDS_PER_GYR\",\n \"def a_to_P(M1, M2, a):\\n mu = c.GGG * (M1 + M2) * c.Msun_to_g\\n n = np.sqrt(mu/(a**3 * c.Rsun_to_cm**3))\\n return 2.0*np.pi / n / c.day_to_sec\",\n \"def compute_time_step():\\n\\n dt = Hydro.compute_time_step()\\n\\n return dt\",\n \"def evaluateTime(self, *args):\\n return _osgAnimation.Motion_evaluateTime(self, *args)\",\n \"def prob2(N1, N2, P1, P2):\\r\\n #initialize function and its derivative\\r\\n f = lambda x: P1*((1+x)**N1-1)-P2*(1-(1+x)**-N2)\\r\\n Df = grad(f)\\r\\n #use Newton's method\\r\\n r,conv,iter = newton(f,.1,Df)\\r\\n return r\",\n \"def get_c2c_time(job, pulse1, pulse2):\\n analog_wf = job.simulated_analog_waveforms()\\n element1 = pulse1[0]\\n pulse_nb1 = pulse1[1]\\n element2 = pulse2[0]\\n pulse_nb2 = pulse2[1]\\n\\n time2 = (\\n analog_wf[\\\"elements\\\"][element2][pulse_nb2][\\\"timestamp\\\"]\\n + analog_wf[\\\"elements\\\"][element2][pulse_nb2][\\\"duration\\\"] / 2\\n )\\n time1 = (\\n analog_wf[\\\"elements\\\"][element1][pulse_nb1][\\\"timestamp\\\"]\\n + analog_wf[\\\"elements\\\"][element1][pulse_nb1][\\\"duration\\\"] / 2\\n )\\n\\n return time2 - time1\",\n \"def RunExactTimestep(self): \\n if self.sim_t == 0:\\n randoms = np.random.random(1000) \\n self.randoms_log = np.log(randoms)*-1\\n self.randoms = np.random.random(1000)\\n self.count = 0 \\n elif self.count == 1000:\\n randoms = np.random.random(1000) \\n self.randoms_log = np.log(randoms)*-1\\n self.randoms = np.random.random(1000) \\n self.count = 0 \\n \\n self.sim_tau = self.randoms_log[self.count]/float(self.sim_a_0) # reaction time generation\\n self.sim_r2 = self.randoms[self.count] # Draw random number 2 [0-1]\\n self.count +=1\\n \\n if (self.sim_t + self.sim_tau) < self.settings.endtime:\\n self.sim_t += self.sim_tau # Time update\\n self.reaction_index = 0\\n sum_of_as = self.sim_a_mu[self.reaction_index]\\n criteria = self.sim_r2*self.sim_a_0\\n while sum_of_as < criteria: # Use r2 to determine which reaction will occur\\n self.reaction_index += 1\\t # Index\\n sum_of_as += self.sim_a_mu[self.reaction_index] \\n\\n try:\\n self.X_matrix += self.N_matrix_transpose[self.reaction_index]\\n self.timestep += 1\\n except MemoryError as ex:\\n print(ex)\\n sys.exit() \\n else: \\n self.sim_t = self.settings.endtime \\n self.reaction_index = np.nan\",\n \"def setupDistribution(tournamentsWon1):\\n timesWon = np.sort(np.unique(tournamentsWon1))\\n numberTimesWon = np.zeros_like(timesWon)\\n for i in range (len(timesWon)):\\n numberTimesWon[i] = count(tournamentsWon1, timesWon[i])\\n return timesWon, numberTimesWon\",\n \"def round_trip_time(self):\\r\\n return self.completion_time - self.launch_time\",\n \"def calculate_travel_time_simple(distance_meters, accel_mps2):\\n time = math.sqrt(4 * distance_meters / accel_mps2)\\n speed = accel_mps2 * time * 0.5\\n return [time, speed]\",\n \"def timeWarp(destExposure, srcExposure, warpingControl):\\n startTime = time.time()\\n for nIter in range(1, MaxIter + 1):\\n goodPix = afwMath.warpExposure(\\n destExposure, srcExposure, warpingControl)\\n endTime = time.time()\\n if endTime - startTime > MaxTime:\\n break\\n\\n return (endTime - startTime, nIter, goodPix)\",\n \"def calculateWaitingTime(self, inputs):\\n CollisionCounter.CollisionCounter.getInstance().waitingTimeCalculated(self.time)\\n timeUntilDepature = self.getAtt('departure_time', inputs) - self.time\\n remainingLoadingTime = self.calculateLoadingTime(inputs)\\n # calculates first maximum possible waiting time\\n sampleTime = int((timeUntilDepature - remainingLoadingTime) / self.participants)\\n\\n if sampleTime >= 1:\\n # result is big enough for a standard treatment\\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(sampleTime + 1)\\n elif sampleTime < 1:\\n # reslut is too small, special treatment necessary\\n upperLimit = (10 * (1 - (math.exp(sampleTime - 1)))) + 1\\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(max((min(upperLimit,\\n timeUntilDepature)) + 1, 1))\\n # decides whether charging is allowed during waiting time\\n if not self.stayedConnected:\\n self.stayConnected = True\\n self.stayedConnected = True\\n else:\\n self.stayConnected = False\\n self.stayedConnected = False\",\n \"def update(self):\\r\\n \\r\\n self.time_to_next_fire = self.generate_fire_recurrence()\\r\\n return self.time_to_next_fire\",\n \"def curr_policy_time_calc(self):\\n for policy in self.policy_list:\\n if policy == self.current_policy:\\n policy.current_policy_time += 1\",\n \"def determine_priority(robot1, robot2):\\n L = 0.5*(robot1.length + robot2.length)\\n # first case: robot 1 speeds up and robot 2 brakes:\\n result1 = minimum_time_to_straight_line(1., L,\\n robot2.amin, robot2.speed, robot2.pos,\\n robot1.amax, robot1.speed, robot1.max_speed,\\n robot1.pos)\\n t1 = sum(t for t, _ in result1)\\n # second case: robot 2 speeds up and robot 1 brakes:\\n result2 = minimum_time_to_straight_line(1., L,\\n robot1.amin, robot1.speed, robot1.pos,\\n robot2.amax, robot2.speed, robot2.max_speed,\\n robot2.pos)\\n t2 = sum(t for t, _ in result2)\\n return min((t1, 0), (t2, 1))[1]\",\n \"def runtime(self):\\n return self.stop_time - self.start_time\",\n \"def calc_stay_prob(rollouts):\\n states = rollouts.states\\n actions = rollouts.actions\\n rewards = rollouts.rewards\\n\\n num_test_episodes = states.shape[0]\\n num_trials = states.shape[1]\\n count_trial_stayed = 0.01 + np.zeros((2, 2, num_test_episodes)) # [common/uncommon, reward/unrewarded]\\n count_trial_all = 0.01 + np.zeros((2, 2, num_test_episodes))\\n for epi in range(num_test_episodes):\\n for t in range(0, num_trials-2, 2):\\n uncommon_transition = int(actions[epi, t] != states[epi, t+1]-1)\\n count_trial_all[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += 1\\n count_trial_stayed[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += \\\\\\n int(actions[epi, t+2] == actions[epi, t])\\n return np.divide(count_trial_stayed, count_trial_all), count_trial_stayed, count_trial_all\",\n \"def p_transfer(a1, b1, a2, b2, m):\\n assert a1 <= NCAR\\n assert b1 <= NCAR\\n assert a2 <= NCAR\\n assert b2 <= NCAR\\n\\n if a1 < m or b1 < -m:\\n return 0\\n\\n if a2 == 0:\\n a2s = (-10, 1)\\n elif a2 == NCAR:\\n a2s = (NCAR, 10+NCAR)\\n else:\\n a2s = (a2, a2+1)\\n\\n if b2 == 0:\\n b2s = (-10, 1)\\n elif b2 == NCAR:\\n b2s = (NCAR, 10+NCAR)\\n else:\\n b2s = (b2, b2+1)\\n\\n p = 0\\n for a2 in range(*a2s):\\n for b2 in range(*b2s):\\n d1 = a2 - a1 - m\\n d2 = b2 - b1 + m\\n\\n p += poisson_diff(d1, LAM_RENT1, LAM_RET1) * poisson_diff(d2, LAM_RENT2, LAM_RET2)\\n\\n return p\",\n \"def relative_times(first_starts, focus_starts, motif_index):\\n return np.asarray(focus_starts) - np.asarray(first_starts)[motif_index]\",\n \"def estimated_time(self):\\n self._update()\\n if not self.running_mode:\\n return 0 if self._is_finished() else float(\\\"nan\\\")\\n elif self.running_mode == \\\"local\\\":\\n start = self.processes[0].create_time()\\n elif self.running_mode == \\\"grid\\\":\\n start = self.job[\\\"start_time\\\"]\\n if start == 0:\\n # Queued, but not started\\n return float(\\\"nan\\\")\\n else:\\n logger.warning(\\\"Invalid running_mode attribute\\\")\\n return float(\\\"nan\\\")\\n current = self.current_step()\\n if current <= 0: # If not dumped yet or error\\n return float('nan')\\n else:\\n elapsed = time() - start\\n return elapsed * (self.total_steps / current - 1)\",\n \"def _calculate_periodic_loop_sleep_time(self, time_for_process):\\n if time_for_process > self.min_time_per_process_loop:\\n sleep_time = 0\\n else:\\n sleep_time = self.min_time_per_process_loop - time_for_process\\n\\n return sleep_time\",\n \"def Leapfrog2D(p1, v1, p2, v2, dt, energy, sigma):\\n a = compute_acceleration(energy, sigma, p2-p1)\\n pn1 = p1 + v1*dt + 0.5 * a * dt**2\\n pn2 = p2 + v2*dt - 0.5 * a * dt**2\\n a2 = compute_acceleration(energy, sigma, pn2-pn1)\\n vn1 = v1 + 0.5 * (a+a2) * dt\\n vn2 = v2 - 0.5 * (a+a2) * dt\\n return pn1, vn1, pn2, vn2\",\n \"def race(v1, v2, g):\\n if v2 <= v1:\\n return None\\n\\n time = math.floor(3600 * g / (v2-v1))\\n hours = math.floor(time / 3600)\\n minutes = math.floor((time - hours*3600) / 60)\\n seconds = time - hours*3600 - minutes*60\\n\\n return [hours, minutes, seconds]\",\n \"def calc_match_probability(obs, pred1):\\n \\n # Throw away any non-atom columns\\n obs_reduced = obs.loc[:, self.pars[\\\"atom_set\\\"].\\n intersection(obs.columns)]\\n pred1_reduced = pred1.loc[self.pars[\\\"atom_set\\\"].\\n intersection(pred1.index)]\\n \\n # Calculate shift differences for each observed spin system\\n delta = obs_reduced - pred1_reduced\\n \\n # Make a note of NA positions in delta, and set them to zero \\n # (this avoids warnings when using norm.cdf later)\\n na_mask = delta.isna()\\n delta[na_mask] = 0\\n \\n if self.pars[\\\"prob_method\\\"] == \\\"delta_correlation\\\":\\n overall_prob = pd.Series(index=delta.index)\\n overall_prob[:] = 1\\n \\n d_mean = pd.read_csv(\\\"../data/d_mean.csv\\\", header=None, \\n index_col=0).loc[delta.columns,1]\\n d_cov = (pd.read_csv(\\\"../data/d_cov.csv\\\", index_col=0).\\n loc[delta.columns,delta.columns])\\n \\n mvn = multivariate_normal(d_mean, d_cov)\\n \\n overall_prob = mvn.logpdf(delta)\\n \\n # Penalise missing shifts, unless also missing in predictions\\n overall_prob = (overall_prob + log10(default_prob) * \\n (na_mask.sum(axis=1) - pred1_reduced.isna().sum()))\\n \\n else:\\n prob = delta.copy()\\n prob.iloc[:,:] = 1\\n \\n for c in delta.columns:\\n if self.pars[\\\"prob_method\\\"] == \\\"cdf\\\":\\n # Use the cdf to calculate the probability of a \\n # delta *at least* as great as the actual one\\n prob[c] = log10(2) + norm.logcdf(-1*abs(\\n pd.to_numeric(delta[c])), scale=atom_sd[c]*sf)\\n elif self.pars[\\\"prob_method\\\"] == \\\"pdf\\\":\\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \\n scale=atom_sd[c]*sf) \\n elif shift_correlation:\\n print(\\\"shift_correlation not yet implemented. Defaulting to pdf.\\\")\\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \\n scale=atom_sd[c]*sf)\\n else:\\n print(\\\"Method for calculating probability not recognised. Defaulting to pdf.\\\")\\n prob[c] = norm.logpdf(pd.to_numeric(delta[c]), \\n scale=atom_sd[c]*sf)\\n \\n # In positions where data was missing, use default probability\\n prob[na_mask] = log10(default_prob)\\n \\n # Calculate penalty for a HADAMAC mismatch\\n if use_hadamac:\\n # If the i-1 aa type of the predicted residue matches the \\n # HADAMAC group of the observation, probability is 1.\\n # Otherwise, probability defaults to 0.01\\n prob[\\\"SS_classm1\\\"] = 0.01\\n if type(pred1[\\\"Res_typem1\\\"])==str: # dummies have NaN\\n prob.loc[obs[\\\"SS_classm1\\\"].str.find(\\n pred1[\\\"Res_typem1\\\"])>=0, \\\"SS_classm1\\\"] = 1\\n \\n # Calculate overall probability of each row\\n overall_prob = prob.sum(skipna=False, axis=1)\\n \\n return(overall_prob)\",\n \"def event_timex_analysis(event1, event2):\\n tagged = tag(event1.text)\\n base_time = event1.get_best_time()\\n\\n if base_time is not None:\\n dt, trusted = base_time.to_datetime() # Get the datetime representation of the reference's best_time\\n grounded_times = ground(tagged, dt) # Ground any timex tags to that time\\n new_dates = [] # holds new dates constructed from the grounded datetimes\\n\\n for time in grounded_times:\\n new_date = Date()\\n if trusted['year']:\\n new_date.year = time.year\\n\\n if trusted['month']:\\n new_date.month = time.month\\n\\n if trusted['day']:\\n new_date.day = time.day\\n\\n if trusted['hour']:\\n new_date.hour = time.hour\\n\\n if trusted['minute']:\\n new_date.minute = time.minute\\n\\n new_dates.append(new_date)\\n\\n if len(new_dates) == 0: # Nothing interesting found.\\n return\\n\\n new_dates = sorted(new_dates, lambda x: x.precision(), reverse=True)\\n best_date = new_dates[0]\\n\\n other_best_date = event2.get_best_time()\\n if other_best_date is not None:\\n if best_date.precision() > other_best_date.precision():\\n event2.set_best_time(best_date)\\n else:\\n event2.set_best_time(best_date)\",\n \"def convergence_processor(self):\\n while True:\\n rexp = (yield)\\n self.converged = True\\n self.converged_time = int(rexp.group(2))\",\n \"def next_state(self, decision):\\n # TODO Static?\\n if decision != 0:\\n # Current index\\n self.state.k += 1\\n # current time\\n delta_time, distance = get_travel_time(self.state.c_k, decision, self.state.t_k, mode=\\\"ggAPI\\\")\\n self.state.t_k += delta_time\\n\\n # current location\\n self.state.c_k = decision\\n\\n # remaining deliveries\\n self.update_remaining_jobs(decision=decision)\\n\\n return delta_time, distance\\n\\n elif decision == 0: # wait\\n # Current index: no modification\\n\\n # current time: wait\\n self.state.t_k += datetime.timedelta(seconds=2*60)\\n\\n # current location: no modification\\n\\n # remaining deliveries\\n self.update_remaining_jobs(decision=decision)\\n\\n return 2*60, 0\",\n \"def next_change(self, t=None):\\n if t is None:\\n t = self.engine.get_now()\\n t -= self.initTime\\n\\n p = float(self.period) / POINTS_PER_CYCLE\\n t2 = math.floor(t / p) * p + p\\n\\n t2 += self.initTime\\n\\n return t2\",\n \"def response_time(self, incorporate_skew=True):\\r\\n if self.__arrival_time == INVALID_TIME:\\r\\n self.__logger.debug(\\\"Request %s missing arrival time\\\" % self.__id)\\r\\n return -1\\r\\n completion_time = self.__arrival_time\\r\\n for task_id, task in self.__tasks.items():\\r\\n if task.completion_time == INVALID_TIME:\\r\\n self.__logger.debug((\\\"Task %s in request %s missing completion \\\"\\r\\n \\\"time\\\") % (task_id, self.__id))\\r\\n return INVALID_TIME_DELTA\\r\\n task_completion_time = task.completion_time\\r\\n if incorporate_skew:\\r\\n task_completion_time -= task.clock_skew\\r\\n # Here we compare two event times: the completion time, as\\r\\n # observed the the node monitor, minus the clock skew; and the\\r\\n # job arrival time, as observed by the scheduler. If the\\r\\n # adjusted completion time is before the arrival time, we know\\r\\n # we've made an error in calculating the clock skew.clock_skew\\r\\n if task_completion_time < self.__arrival_time:\\r\\n self.__logger.warn((\\\"Task %s in request %s has estimated \\\"\\r\\n \\\"completion time before arrival time, \\\"\\r\\n \\\"indicating inaccuracy in clock skew \\\"\\r\\n \\\"computation.\\\") % (task_id, self.__id))\\r\\n else:\\r\\n \\tif task.scheduler_launch_time > task.node_monitor_launch_time:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tself.__logger.warn(\\\"Task %s suggests clock skew: \\\" % task_id)\\r\\n completion_time = max(completion_time, task_completion_time)\\r\\n\\r\\n if (completion_time - self.__arrival_time) > 2000:\\r\\n pass\\r\\n \\\"\\\"\\\"\\r\\n print \\\"TRUE: %s\\\" % (completion_time - self.__arrival_time)\\r\\n print self.network_delays()\\r\\n print self.service_times()\\r\\n print self.probing_time()\\r\\n print \\\"EST: %s\\\" % (max(self.service_times()) + max(self.network_delays()) + self.probing_time())\\r\\n \\\"\\\"\\\"\\r\\n return completion_time - self.__arrival_time\",\n \"def method_compute_timestep(self):\\n\\n myg = self.cc_data.grid\\n\\n cfl = self.rp.get_param(\\\"driver.cfl\\\")\\n\\n u = self.cc_data.get_var(\\\"x-velocity\\\")\\n v = self.cc_data.get_var(\\\"y-velocity\\\")\\n\\n # the timestep is min(dx/|u|, dy|v|)\\n xtmp = ytmp = 1.e33\\n if not abs(u).max() == 0:\\n xtmp = myg.dx/abs(u.v()).max()\\n if not abs(v).max() == 0:\\n ytmp = myg.dy/abs(v.v()).max()\\n\\n dt = cfl*min(xtmp, ytmp)\\n\\n # We need an alternate timestep that accounts for buoyancy, to\\n # handle the case where the velocity is initially zero.\\n rho = self.cc_data.get_var(\\\"density\\\")\\n rho0 = self.base[\\\"rho0\\\"]\\n rhoprime = self.make_prime(rho, rho0)\\n\\n g = self.rp.get_param(\\\"lm-atmosphere.grav\\\")\\n\\n F_buoy = (abs(rhoprime*g).v()/rho.v()).max()\\n\\n dt_buoy = np.sqrt(2.0*myg.dx/F_buoy)\\n\\n self.dt = min(dt, dt_buoy)\\n if self.verbose > 0:\\n print(f\\\"timestep is {dt}\\\")\",\n \"def readMovementVolumesAndTTs(self, movementFlowFileName, movementTimeFileName):\\n\\n inputStream1 = open(movementFlowFileName, 'r')\\n inputStream2 = open(movementTimeFileName, 'r')\\n\\n numMovementsRead = 0\\n\\n for flowLine, timeLine in izip(inputStream1, inputStream2):\\n \\n numMovementsRead += 1 \\n if numMovementsRead % 10000 == 0:\\n print \\\"Num movements read %d\\\" % numMovementsRead\\n\\n flowFields = flowLine.strip().split()\\n timeFields = timeLine.strip().split()\\n\\n nodeAid, nodeBid, nodeCid = flowFields[:3]\\n\\n if [nodeAid, nodeBid, nodeCid] != timeFields[:3]:\\n raise SimError('The files %s and %s are not in sync. '\\n 'Movement through %s from %s to %s in the first file is not '\\n 'in the same line position in the second '\\n 'file' % (movementFlowFileName,\\n movementTimeFileName,\\n nodeBid, nodeAid, nodeCid))\\n\\n try:\\n edge = self.getEdge(nodeAid, nodeBid)\\n movement = edge.getOutMovement(nodeCid)\\n except GraphError, e:\\n if nodeAid == nodeCid:\\n continue\\n\\n logging.error(str(e))\\n continue\\n\\n simFlows = imap(int, flowFields[3:])\\n simTTs = imap(float, timeFields[3:])\\n timePeriodStart = self.simStartTimeInMin\\n \\n for simFlow, simTT in izip(simFlows, simTTs):\\n\\n if simFlow == 0 and simTT > 0:\\n raise SimError('Movement %s has zero flow in the '\\n 'time period begining %d and a '\\n 'positive travel time' % \\n (movement.iid, timePeriodStart))\\n elif simFlow > 0 and simTT == 0:\\n simTT = 0.01\\n message = ('Movement %s has positive flow in '\\n 'the time period begining %d and a '\\n 'zero travel time. Edge length is %f' % \\n (movement.iid, timePeriodStart, edge.getLengthInFeet()))\\n logging.error(message)\\n #raise SimError('Movement %s has positive flow in '\\n # 'the time period begining %d and a '\\n # 'zero travel time' % \\n # (movement.iid, timePeriodStart))\\n \\n elif simFlow == 0 and simTT == 0:\\n #simTT = movement.getFreeFlowTTInMin()\\n timePeriodStart += self.simTimeStepInMin\\n if timePeriodStart >= self.simEndTimeInMin:\\n break\\n else:\\n movement.setSimVolume(timePeriodStart, timePeriodStart + \\n self.simTimeStepInMin, simFlow)\\n\\n movement.setSimTTInMin(timePeriodStart, timePeriodStart + \\n self.simTimeStepInMin, simTT) #time is in minutes! \\n \\n timePeriodStart += self.simTimeStepInMin\\n if timePeriodStart >= self.simEndTimeInMin:\\n break\\n\\n inputStream1.close()\\n inputStream2.close()\",\n \"def poynting(t, dt):\\r\\n p1satbar, p2satbar, dp1satbar, dp2satbar = psat(t, dt)\\r\\n poy1 = math.exp(vl1 * (p - p1satbar)/(r * t))\\r\\n poy2 = math.exp(vl2 * (p - p2satbar)/(r * t))\\t\\r\\n dpoy1 = math.exp(vl1 * (p - dp1satbar)/(r * dt))\\r\\n dpoy2 = math.exp(vl2 * (p - dp2satbar)/(r * dt))\\r\\n return poy1, poy2, dpoy1, dpoy2\",\n \"def run_v1(self):\\n start_time = dt.datetime.now()\\n first_key_set = set(self.first)\\n second_key_set = set(self.second)\\n dict = {}\\n\\n for idx, key in enumerate(first_key_set):\\n if(key in dict):\\n dict[key] += (len(filter(lambda x:x==key, self.first)))\\n else :\\n dict[key] = int((len(filter(lambda x: x == key, self.first))))\\n\\n for idx, key in enumerate(second_key_set):\\n if(key in dict):\\n dict[key] += (len(filter(lambda x:x==key, self.second)))\\n else :\\n dict[key] = int((len(filter(lambda x: x == key, self.second))))\\n\\n result = max(dict.iterkeys(), key=(lambda key : dict[key]))\\n print(\\\"inviting {0} people interest in {1}\\\".format(dict[result] , result))\\n\\n end_time = dt.datetime.now()\\n print(\\\"time cost : {0}\\\".format(end_time - start_time))\",\n \"def test_ns_ok2():\\n env = NsSimPyEnvironment()\\n env.process(some_process(env, env.to_time('10.1')))\\n env.run()\\n env.process(some_process(env, env.to_time('0.9')))\\n env.run()\\n assert env.now == 11\",\n \"def part1() -> int:\\n longest_sleeper = max(sleep_times, key=lambda g: len(sleep_times[g]))\\n sleepiest_minute = max(\\n sleep_times[longest_sleeper], key=sleep_times[longest_sleeper].count)\\n\\n return longest_sleeper * sleepiest_minute\",\n \"def _collect_waiting_times(self):\\n incoming_roads = [\\\"E2T\\\", \\\"N2T\\\", \\\"W2T\\\", \\\"S2T\\\"]\\n car_list = traci.vehicle.getIDList()\\n for car_id in car_list:\\n wait_time = traci.vehicle.getAccumulatedWaitingTime(car_id)\\n road_id = traci.vehicle.getRoadID(car_id) # get the road id where the car is located\\n if road_id in incoming_roads: # consider only the waiting times of cars in incoming roads\\n self._waiting_times[car_id] = wait_time\\n else: # not in incoming road\\n if car_id in self._waiting_times: # a car that was tracked has cleared the intersection\\n del self._waiting_times[car_id] \\n total_waiting_time = sum(self._waiting_times.values())\\n return total_waiting_time\",\n \"def _event_arrival_path(path, n, e_0=0):\\n # if e_0 is None:\\n # the entry time of the first event\\n\\n if n > 0:\\n e = e_0 + path.tasks[0].in_event_model.delta_plus(n + 1)\\n elif n < 0:\\n e = e_0 - path.tasks[0].in_event_model.delta_min(-n + 1)\\n else:\\n e = 0 # same event, so the difference is 0\\n\\n return e\",\n \"def _probe_wait_time(self):\\n r = self.probe_cycle_time / float(len(self.servers)) #self.probe_cycle_time=5\\n r = max(.25, r) # Cap it at four per second\\n return r\",\n \"def test_spring_forward(self):\\n # Exact crossover time:\\n # datetime.datetime(2011, 3, 13, 2, 0, 0, tzinfo=pytz.utc)\\n # This test will use times on either side of it.\\n\\n # From the PST vantage point, the run time is 21.1 hours away:\\n s1a, s1b = self.hours_to_job_at_datetime(\\n 'tz_test_job', 2011, 3, 13, 1, 55, 0)\\n\\n # From the PDT vantage point, the run time is 20.9 hours away:\\n s2a, s2b = self.hours_to_job_at_datetime(\\n 'tz_test_job', 2011, 3, 13, 3, 05, 0)\\n\\n self._assert_range(s1b - s1a, 23.99, 24.01)\\n self._assert_range(s2b - s2a, 23.99, 24.01)\\n\\n # So we lose an hour here. The 2 AM block does not exist.\\n # If this were not a DST crossover, this difference would be\\n # 1.2, not 0.2.\\n self._assert_range(s1a - s2a, 0.19, 0.21)\",\n \"def important_time(self):\\n\\t\\twork_s = self.work_time().seconds\\n\\t\\tbreak_s = self.break_time().seconds\\n\\t\\tif self.status():\\n\\t\\t\\tremaining_time_s = tomato(work_s, break_s)\\n\\t\\telse:\\n\\t\\t\\tremaining_time_s = potato(work_s, break_s)\\n\\n\\t\\timp_time = datetime.now() + timedelta(0, remaining_time_s)\\n\\t\\treturn imp_time\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6605994","0.64463836","0.5987773","0.5685406","0.5586563","0.5566638","0.55001855","0.54996055","0.5490612","0.5451096","0.5409021","0.53857464","0.5382504","0.53800315","0.52867806","0.5285191","0.52186906","0.51881415","0.5177398","0.51758695","0.5167033","0.51656914","0.5152677","0.5149017","0.5107466","0.5105477","0.5103524","0.5103524","0.5103524","0.50907177","0.50882673","0.5086305","0.5085711","0.50828135","0.5075001","0.50632256","0.50583386","0.50532615","0.50527734","0.50461316","0.5045706","0.5043942","0.5036206","0.5036206","0.5035485","0.50240785","0.50172174","0.5013024","0.50115633","0.5002602","0.49906686","0.49854344","0.49838054","0.49736658","0.49728885","0.4968886","0.49663287","0.49618655","0.49618655","0.49594358","0.49538228","0.49504706","0.493933","0.49372703","0.4928707","0.49255237","0.49143204","0.49088717","0.49072075","0.490409","0.4896696","0.48894766","0.487983","0.48789772","0.48768494","0.48642278","0.48579985","0.48449272","0.48441008","0.4843451","0.4841978","0.48396796","0.48393515","0.48376155","0.48288652","0.48269486","0.48240855","0.48200595","0.48175636","0.48052996","0.48030233","0.47981745","0.47978282","0.47960407","0.4791566","0.47914517","0.4789744","0.47866994","0.4780011"],"string":"[\n \"0.6605994\",\n \"0.64463836\",\n \"0.5987773\",\n \"0.5685406\",\n \"0.5586563\",\n \"0.5566638\",\n \"0.55001855\",\n \"0.54996055\",\n \"0.5490612\",\n \"0.5451096\",\n \"0.5409021\",\n \"0.53857464\",\n \"0.5382504\",\n \"0.53800315\",\n \"0.52867806\",\n \"0.5285191\",\n \"0.52186906\",\n \"0.51881415\",\n \"0.5177398\",\n \"0.51758695\",\n \"0.5167033\",\n \"0.51656914\",\n \"0.5152677\",\n \"0.5149017\",\n \"0.5107466\",\n \"0.5105477\",\n \"0.5103524\",\n \"0.5103524\",\n \"0.5103524\",\n \"0.50907177\",\n \"0.50882673\",\n \"0.5086305\",\n \"0.5085711\",\n \"0.50828135\",\n \"0.5075001\",\n \"0.50632256\",\n \"0.50583386\",\n \"0.50532615\",\n \"0.50527734\",\n \"0.50461316\",\n \"0.5045706\",\n \"0.5043942\",\n \"0.5036206\",\n \"0.5036206\",\n \"0.5035485\",\n \"0.50240785\",\n \"0.50172174\",\n \"0.5013024\",\n \"0.50115633\",\n \"0.5002602\",\n \"0.49906686\",\n \"0.49854344\",\n \"0.49838054\",\n \"0.49736658\",\n \"0.49728885\",\n \"0.4968886\",\n \"0.49663287\",\n \"0.49618655\",\n \"0.49618655\",\n \"0.49594358\",\n \"0.49538228\",\n \"0.49504706\",\n \"0.493933\",\n \"0.49372703\",\n \"0.4928707\",\n \"0.49255237\",\n \"0.49143204\",\n \"0.49088717\",\n \"0.49072075\",\n \"0.490409\",\n \"0.4896696\",\n \"0.48894766\",\n \"0.487983\",\n \"0.48789772\",\n \"0.48768494\",\n \"0.48642278\",\n \"0.48579985\",\n \"0.48449272\",\n \"0.48441008\",\n \"0.4843451\",\n \"0.4841978\",\n \"0.48396796\",\n \"0.48393515\",\n \"0.48376155\",\n \"0.48288652\",\n \"0.48269486\",\n \"0.48240855\",\n \"0.48200595\",\n \"0.48175636\",\n \"0.48052996\",\n \"0.48030233\",\n \"0.47981745\",\n \"0.47978282\",\n \"0.47960407\",\n \"0.4791566\",\n \"0.47914517\",\n \"0.4789744\",\n \"0.47866994\",\n \"0.4780011\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5467813"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":15098,"cells":{"query":{"kind":"string","value":"Callback when front server is connected."},"document":{"kind":"string","value":"def OnFrontConnected(self) -> None:\n self.gateway.write_log(\"行情服务器连接成功\")\n self.login()"},"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 on_connect(self):\n print('Client connected!')","def on_connect():\n print(\"User connected!\")","def connected(self):\n manager = self.manager()\n self.log().debug(\"Register [%s] callbacks\", self.name())\n\n manager.subscribeServerCallbacks(self, self.cfg().chatimg.servers or manager.SERVERS_ALL)","def on_connect(self, client, userdata, flags, rc):\n\n\t\tself.subscribe(\"system\")\n\t\tprint (\"[{}] Client connected\".format(\n\t\t\tint(time.time())\n\t\t))","def client_connected(data):\n print('a client connected')\n emit('queue_changed', queue.serialize())\n history = queue.instantiate_history()\n if len(history) > 0:\n song_data = history[-1]\n emit('mid_currently_playing', song_data)\n if cache.get('is_paused').decode('utf-8') == 'True':\n pause_time = int(cache.get('paused_time').decode('utf-8'))\n socketio.emit('paused', pause_time)","async def on_connected(self):\n self._connected = True","def on_connect(self):\n log.info(\"Stream connected\")","def on_connection_start(self) -> None:\r\n print(\r\n \"Connected with: {}:{}\\n\".format(\r\n self.connection_info[\"host\"], self.connection_info[\"port\"]\r\n )\r\n )","def on_connect( client, userdata, flag, rc ):\n if ( rc == 0 ):\n client.connected_flag = True\n logging.info( \"Connected to Broker! Returned code: %s\\n\" %rc )\n else:\n logging.info( \"Failed to connect. Returned code: %s\\n\" %rc )","def slot_client_connected(self, _sender, _data):\r\n self.check_connect_ready()","async def on_connect(self):\n pass","def on_connect():\n print('User connected!')\n return'connected'","def on_connect(client, userdata, flags, rc):\n print(\"Connected with with mqtt server: \" + str(rc))\n client.subscribe(\"clients/#\")","def on_connect():\n logger.info(f\"{request.sid} Connected\")","def on_server_start(self, server):\n pass","async def on_connect(self) -> None:","def on_server_start(self):\n raise NotImplementedError","def on_connect(client, userdata, flags, rc):\n if rc == 0:\n print(\"Connected to broker\")\n client.connected_flag = True\n else:\n print(\"Connection failed\")\n client.connected_flag = False","def callback_connect(self):\n pass","def callback_connect(self):\n pass","def callback_connect(self):\n pass","def on_connect(client, userdata, flags, return_code):\n\n if return_code != 0:\n print(\"Connected with result code: \", str(return_code))\n else:\n client.connected_flag=True\n client.publish(status_topic, \"Online\", retain=True)","def onConnect(self, request_or_response):","def on_connect(self, userdata, flags, rc):\n logging.info(f'Connected with Mosquitto Server: (code) {rc}')","def on_start(self):\n ProxyServerHandler.current.handler_ready(self)","def connect():\n logging.info('Client connected')","def on_connect(client):\n logging.info(\"Opened connection to %s\" % client.addrport() )\n\n state.set_client_list(client)\n state.initialize_client_state(client)\n client.send(\"\")\n client.send(\"Welcome to the %s Server, %s.\\n\" % (PRODUCT_NAME, client.addrport()) )\n client.send(\"Enter your user_id, or type \\\"enroll\\\" to create a new account: \")","def clientConnected(self):\n self.running = True\n for d in self._callWhenReady:\n d.callback(None)","def onConnect(self, response):\n\t\tif DEBUG:\n\t\t\tsys.stdout.write(\n\t\t\t\t\"Connection established. Response: '{r}'.\\n\".format(r=response)\n\t\t\t\t)\n\t\t# we need to tell the client-object that this is the connection\n\t\tself.factory.root.client = self","def on_session_started():\n #print(\"on_session_started\")","def on_session_started():\n #print(\"on_session_started\")","def on_connect(self, client, userdata, flags, rc):\n logging.info('Connection to MQTT Broker established with status {}'.format(rc))","def on_connected_cbk(_):\n cprint(\"\\nDevice connected.\", color=\"green\", flush=True)","def on_connected(connection):\n # Open a channel\n connection.channel(on_channel_open)","def on_connected(connection):\n # open a channel\n connection.channel(on_open_channel)","def _connected(self):\n self.isConnected.set()\n pub.sendMessage(\"meshtastic.connection.established\", interface=self)","async def on_connect(self, payload):\n # You can `raise` from here to reject the connection.\n print(\"New client connected!\")","def on_connection_init() -> None:\r\n print(\"\\nInitialize G-Earth connection\\n\")","def handle_connect(self):\n pass","def __connected(self):\n self.connected()\n if self.__callbacks.get(\"connected\"):\n cb = self.__callbacks[\"connected\"]()\n if cb is not None:\n cb()","def handle_connect(self):\n LOGGER.debug(\"server connected\")\n self.append_connect = False\n domain = MOLO_CONFIGS.get_config_object().get('domain', '')\n self.send_dict_pack(\n MoloSocketHelper.molo_auth(CLIENT_VERSION,\n MOLO_CLIENT_APP.hass_context,\n __short_version__, domain),)","def handle_connect(self):\n LOGGER.debug(\"server connected(%d)\", id(self))\n self.append_connect = False\n self.send_dict_pack(MoloSocketHelper.reg_proxy(self.client_id))","def on_connect(client, userdata, flags, rc_value):\n print(f\"Connected with result code : {rc_value}\")","def on_connect(client, userdata, flags, rc):\n if rc == 0:\n logging.info(\"Connected\")\n else:\n logging.warning(\"Connection issue - result code \"+str(rc))\n\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n client.subscribe(config.topic_set)\n publish_status(client) # update the status on connect","def server():","def server():","def on_connect(unused_client, unused_userdata, unused_flags, rc):\n #print('on_connect: ', mqtt.connack_string(rc))\n print(f\"on_connect: {error_str(rc)} ({mqtt.connack_string(rc)})\")\n print()\n\n global connected\n connected = True","async def on_ready():\n print(\"{} has joined the server\".format(client.user.name))","def ready(self, server):\n self.server = server\n self.title_lbl.config(\n text='ViewVC standalone server at\\n' + server.url)\n self.open_btn.config(state='normal')\n self.quit_btn.config(state='normal')","def on_connected(self, connection):\n\t\t# Open a channel\n\t\tconnection.channel(self.on_channel_open)","async def on_connect(self):\r\n self._try_shutdown_twitch()\r\n self.stream_thread = self.connect_thread()","def on_connect(mqtt_client, obj, flags, rc):\n print(\"Connected\")","def show_callback(self, client_data):\n pass","def server_activate(self):\n\t\tpass","def onOpen(self):\n\t\tif DEBUG:\n\t\t\tsys.stdout.write(\"Initiating Handshake...\\n\")\n\t\tself.sendMessage(struct.pack(\"!d\", VERSION), True)","def on_connect(connected_client, _, __, rc) -> None:\n logger.info(\"Connected client with result code \" + str(rc))\n # Subscribe in on_connect callback to automatically re-subscribe if the connection was lost\n # Subscribe to all arduino hexnumber actions\n # '+' means single level wildcard. '#' means multi level wildcard.\n # See http://www.hivemq.com/blog/mqtt-essentials-part-5-mqtt-topics-best-practices\n topic = str(constants.iRulezTopic) + \"/\" + str(constants.notificationTopic) + \"/\" + str(constants.mailTopic)\n logger.debug(\"Subscribing to \" + str(topic))\n connected_client.subscribe(str(topic))","def ready(self, component):\n self.fire(connect(self.host, self.port))","def on_connect(self, client, userdata, flags, rc):\n\n logger.info(f'Connected to {self.topic} with result code {rc}')\n # self.client.publish('Augmented/A.V.A.', str(rc)) # For return the connection situation to the subscriber device.\n if rc == 0:\n self.is_connected = True\n self.client.subscribe(self.topic)","def connection_callback(self, connected):\n self._connection_queue.put_nowait(connected)","def connected(self):\n d = self.configure()\n d.addCallback(self.startDiscovery)\n d.addErrback(self.reportError)","def _onconnect(self):\n\n pass","def handle_connect(self):\n #print \"Controller initiated on: %s:%s\" % (self.address, self.port)\n if not self.needs_migration:\n self.buffer.append(messages.of_hello)","def startedConnecting(self, connector):\n\n print(\"Connecting to the server...\") # client console notification","def connectionMade (self) :\r\n self.state = 'wait_hello'\r\n self.handshake_timeout = reactor.callLater(HANDSHAKE_TIMEOUT, self.err, \"handshake timeout expired\")\r\n self.log(\"connected\")","def on_connect(client, userdata, flags, rcdata):\n client.subscribe(\"diy/system/fire\", 1)\n client.subscribe(\"diy/system/panic\", 1)\n client.subscribe(\"diy/system/who\", 1)","def handle_connect(self):\n LOGGER.debug(\"local session connected(%d)\", id(self))\n self.append_connect = False","def _on_connection_success(self):\n if self.connect_handler:\n self.connect_handler()","def on_welcome(self, raw_msg, server, port, nickname, **kwargs):","def on_connect():\n articleList()\n #test_location()\n get_state_colors()\n ip = request.environ[\"HTTP_X_FORWARDED_FOR\"]\n loc = get_location(ip)\n push_stat_data(loc.state)\n return True","def onConnect(self, fetcher, connectionRespInfo): #$NON-NLS-1$\r","def _on_open(self):\n\n print(\"WebSocket successfully connected for \" + self.session_name + \"!\")\n self.web_socket_open = True\n self._send_login_request(sts_token, False)","def on_connect(client, userdata, flags, rc):\n if rc == 0:\n client.subscribe(topic_subscribe)\n print(\"connected OK with returned code=\", rc)\n else:\n print(\"Bad connection with returned code=\", rc)","def on_connect(self, mqtt_client, userdata, flags, rc):\n global connack\n logging.debug(\"DEBUG - Connected to broker\")\n connack = True","def connected_to_server(self, is_connected, msg):\n\n\t\tif is_connected:\n\t\t\tself.__logs.append(' CONNECTED')\n\n\n\n\t\telse:\n\t\t\tself.__logs.append(' CONNECTION FAILED : ' + msg)","def connected(self, host, port=None):\n print(f\"connected to {host}\")","def run(self):\n\t\t\n\t\tself.connect(self.config[\"server\"])","def on_connect(client, userdata, flags, rc):\n\t# subscribe to the LEDs topic when connected\n\tclient.subscribe(\"SNHU/IT697/leds\")","async def on_ready():\n print('We have logged in as {0.user}'.format(client))","def serverExplicitReady (self):\n self.server_ready.set()","def handleConnected(self):\n logging.info(\"%s %s\", self.address, \"connected\")\n self.logbook.connection_request = self.request\n self.logbook.clients_connected_count += 1","def serverAccept(self):\n self.handshake_deferred.callback(self.protocol_to_accept)\n logger.debug(\"WebSocket %s accepted by application\", self.reply_channel)","def on_connect(mqttc, mosq, obj, rc):\n print(\"Connected with result code:\"+str(rc))\n # subscribe for all devices of user\n mqttc.subscribe('+/devices/+/up')\n mqttc.subscribe('+/devices/+/events/down/sent')\n if rc != 0:\n sys.exit('Could not connect to server. \\n Result code: ' + str(rc))","def connect():","def cbMqtt_on_connect(client, userdata, flags, rc):\n if rc == 0:\n logger.debug('Connected to %s: %s', str(mqtt), userdata)\n setup_mqtt_filters()\n mqtt_publish_fan_state()\n else:\n logger.error('Connection to MQTT broker failed: %s (rc = %d)',\n userdata, rc)","def _on_connect(self, client, userdata, flags, rc):\n self.subscribe(self.topic)","def start_server():\n server = WebsocketServer(9001, host='0.0.0.0')\n server.set_fn_message_received(message_received)\n server.set_fn_client_left(client_left)\n print(\"Started\")\n server.run_forever()","def on_connect(self, client, userdata, flags, rc):\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n sleep(5) # quick delay\n self.client.subscribe(self.subControls)\n self.client.subscribe(self.subSettings)","def on_connect (client, userdata, flags, rc):\n print (\"Connect %s result is: %s\" % (host, rc))\n client.subscribe(\"some/message/to/publish\")\n #creator.createobjects()\n \n # With Paho, always subscribe at on_connect (if you want to\n # subscribe) to ensure you resubscribe if connection is\n # lost.\n # client.subscribe(\"some/topic\")\n\n if rc == 0:\n client.connected_flag = True\n print (\"Connection good!\")\n return\n \n print (\"Failed to connect to %s, error was, rc=%s\" % rc)\n # handle error here\n sys.exit (-1)","def _onConnect(self, client:mqtt.Client, userdata:Any, flags:dict, rc:int) -> None:\n\t\tself.messageHandler and self.messageHandler.logging(self, logging.DEBUG, f'MQTT: Connected with result code: {rc} ({mqtt.error_string(rc)})')\n\t\tif rc == 0:\n\t\t\tself.isConnected = True\n\t\t\tself.messageHandler and self.messageHandler.onConnect(self)\n\t\telse:\n\t\t\tself.isConnected = False\n\t\t\tif self.messageHandler:\n\t\t\t\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot connect to broker. Result code: {rc} ({mqtt.error_string(rc)})')\n\t\t\t\tself.messageHandler.onError(self, rc)","def connect_handler():\n print(\"---------------- CONNECTED ----------------\")\n\n user = User.query.get(get_jwt_identity())\n\n \"\"\"\n Save phone numbers with corresponding sid, so that server\n can push updates to clients that are connected\n \"\"\"\n redis.set(user.phone_num, request.sid)\n redis.set(request.sid, user.phone_num)\n\n payload = {\n \"phone_num\": user.phone_num,\n \"nickname\": user.nickname\n }\n emit(\"user_data\", json.dumps(payload))","def on_open(self, info):\n # When new client comes in, will add it to the clients list\n clients.add(self)","def connectionMade(self):\n print \"connection received from\", self.addr","def handle_connect(self):\r\n print \"http_evented::handle_connect\"\r\n self._connection_state = STATE_CONNECTED\r\n super(http_evented, self).handle_connect()\r\n call_if_not_none_and_callable(self._onConnected)","def connectionMade(self):\r\n HistoricRecvLine.connectionMade(self)\r\n self._factory = PBClientFactory()\r\n\r\n reactor.connectTCP(self._masterIP, self._console_port, self._factory) #@UndefinedVariable\r\n self.terminal.write(\"Username: \")","def on_connect(client, userdata, flags, rc):\n print(\"Connected with result code \"+str(rc))\n\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n client.subscribe(MQTT_PATH)","def on_client_connect(self, client):\r\n\t\tself.connection_logger.info('Received client connection from %s:%u' % (client.address, client.port))\r\n\t\tif (self.db_connection is False):\r\n\t\t\tclient.send('A critical database error has occurred. Please reconnect later.\\n')\r\n\t\t\tclient.socket_send()\r\n\t\t\tclient.deactivate()\r\n\t\t\tclient.sock.close()\r\n\t\t\treturn\r\n\t\tclient.send(self.welcome_message_data)\r\n\t\tself.pending_connection_list.append(client)\r\n\t\tself.post_client_connect.send(sender=client)","def _r_on_connection_established(self, protocol):\n print(\"Client connected\")\n self._current_protocol = protocol\n\n for d in self._waiting_for_connection:\n d.callback(True)\n self._waiting_for_connection = []","def client_connected(self, telnet_connection):","def on_connect():\n payload = dict(data='Connected')\n emit('log', payload, broadcast=True)"],"string":"[\n \"def on_connect(self):\\n print('Client connected!')\",\n \"def on_connect():\\n print(\\\"User connected!\\\")\",\n \"def connected(self):\\n manager = self.manager()\\n self.log().debug(\\\"Register [%s] callbacks\\\", self.name())\\n\\n manager.subscribeServerCallbacks(self, self.cfg().chatimg.servers or manager.SERVERS_ALL)\",\n \"def on_connect(self, client, userdata, flags, rc):\\n\\n\\t\\tself.subscribe(\\\"system\\\")\\n\\t\\tprint (\\\"[{}] Client connected\\\".format(\\n\\t\\t\\tint(time.time())\\n\\t\\t))\",\n \"def client_connected(data):\\n print('a client connected')\\n emit('queue_changed', queue.serialize())\\n history = queue.instantiate_history()\\n if len(history) > 0:\\n song_data = history[-1]\\n emit('mid_currently_playing', song_data)\\n if cache.get('is_paused').decode('utf-8') == 'True':\\n pause_time = int(cache.get('paused_time').decode('utf-8'))\\n socketio.emit('paused', pause_time)\",\n \"async def on_connected(self):\\n self._connected = True\",\n \"def on_connect(self):\\n log.info(\\\"Stream connected\\\")\",\n \"def on_connection_start(self) -> None:\\r\\n print(\\r\\n \\\"Connected with: {}:{}\\\\n\\\".format(\\r\\n self.connection_info[\\\"host\\\"], self.connection_info[\\\"port\\\"]\\r\\n )\\r\\n )\",\n \"def on_connect( client, userdata, flag, rc ):\\n if ( rc == 0 ):\\n client.connected_flag = True\\n logging.info( \\\"Connected to Broker! Returned code: %s\\\\n\\\" %rc )\\n else:\\n logging.info( \\\"Failed to connect. Returned code: %s\\\\n\\\" %rc )\",\n \"def slot_client_connected(self, _sender, _data):\\r\\n self.check_connect_ready()\",\n \"async def on_connect(self):\\n pass\",\n \"def on_connect():\\n print('User connected!')\\n return'connected'\",\n \"def on_connect(client, userdata, flags, rc):\\n print(\\\"Connected with with mqtt server: \\\" + str(rc))\\n client.subscribe(\\\"clients/#\\\")\",\n \"def on_connect():\\n logger.info(f\\\"{request.sid} Connected\\\")\",\n \"def on_server_start(self, server):\\n pass\",\n \"async def on_connect(self) -> None:\",\n \"def on_server_start(self):\\n raise NotImplementedError\",\n \"def on_connect(client, userdata, flags, rc):\\n if rc == 0:\\n print(\\\"Connected to broker\\\")\\n client.connected_flag = True\\n else:\\n print(\\\"Connection failed\\\")\\n client.connected_flag = False\",\n \"def callback_connect(self):\\n pass\",\n \"def callback_connect(self):\\n pass\",\n \"def callback_connect(self):\\n pass\",\n \"def on_connect(client, userdata, flags, return_code):\\n\\n if return_code != 0:\\n print(\\\"Connected with result code: \\\", str(return_code))\\n else:\\n client.connected_flag=True\\n client.publish(status_topic, \\\"Online\\\", retain=True)\",\n \"def onConnect(self, request_or_response):\",\n \"def on_connect(self, userdata, flags, rc):\\n logging.info(f'Connected with Mosquitto Server: (code) {rc}')\",\n \"def on_start(self):\\n ProxyServerHandler.current.handler_ready(self)\",\n \"def connect():\\n logging.info('Client connected')\",\n \"def on_connect(client):\\n logging.info(\\\"Opened connection to %s\\\" % client.addrport() )\\n\\n state.set_client_list(client)\\n state.initialize_client_state(client)\\n client.send(\\\"\\\")\\n client.send(\\\"Welcome to the %s Server, %s.\\\\n\\\" % (PRODUCT_NAME, client.addrport()) )\\n client.send(\\\"Enter your user_id, or type \\\\\\\"enroll\\\\\\\" to create a new account: \\\")\",\n \"def clientConnected(self):\\n self.running = True\\n for d in self._callWhenReady:\\n d.callback(None)\",\n \"def onConnect(self, response):\\n\\t\\tif DEBUG:\\n\\t\\t\\tsys.stdout.write(\\n\\t\\t\\t\\t\\\"Connection established. Response: '{r}'.\\\\n\\\".format(r=response)\\n\\t\\t\\t\\t)\\n\\t\\t# we need to tell the client-object that this is the connection\\n\\t\\tself.factory.root.client = self\",\n \"def on_session_started():\\n #print(\\\"on_session_started\\\")\",\n \"def on_session_started():\\n #print(\\\"on_session_started\\\")\",\n \"def on_connect(self, client, userdata, flags, rc):\\n logging.info('Connection to MQTT Broker established with status {}'.format(rc))\",\n \"def on_connected_cbk(_):\\n cprint(\\\"\\\\nDevice connected.\\\", color=\\\"green\\\", flush=True)\",\n \"def on_connected(connection):\\n # Open a channel\\n connection.channel(on_channel_open)\",\n \"def on_connected(connection):\\n # open a channel\\n connection.channel(on_open_channel)\",\n \"def _connected(self):\\n self.isConnected.set()\\n pub.sendMessage(\\\"meshtastic.connection.established\\\", interface=self)\",\n \"async def on_connect(self, payload):\\n # You can `raise` from here to reject the connection.\\n print(\\\"New client connected!\\\")\",\n \"def on_connection_init() -> None:\\r\\n print(\\\"\\\\nInitialize G-Earth connection\\\\n\\\")\",\n \"def handle_connect(self):\\n pass\",\n \"def __connected(self):\\n self.connected()\\n if self.__callbacks.get(\\\"connected\\\"):\\n cb = self.__callbacks[\\\"connected\\\"]()\\n if cb is not None:\\n cb()\",\n \"def handle_connect(self):\\n LOGGER.debug(\\\"server connected\\\")\\n self.append_connect = False\\n domain = MOLO_CONFIGS.get_config_object().get('domain', '')\\n self.send_dict_pack(\\n MoloSocketHelper.molo_auth(CLIENT_VERSION,\\n MOLO_CLIENT_APP.hass_context,\\n __short_version__, domain),)\",\n \"def handle_connect(self):\\n LOGGER.debug(\\\"server connected(%d)\\\", id(self))\\n self.append_connect = False\\n self.send_dict_pack(MoloSocketHelper.reg_proxy(self.client_id))\",\n \"def on_connect(client, userdata, flags, rc_value):\\n print(f\\\"Connected with result code : {rc_value}\\\")\",\n \"def on_connect(client, userdata, flags, rc):\\n if rc == 0:\\n logging.info(\\\"Connected\\\")\\n else:\\n logging.warning(\\\"Connection issue - result code \\\"+str(rc))\\n\\n # Subscribing in on_connect() means that if we lose the connection and\\n # reconnect then subscriptions will be renewed.\\n client.subscribe(config.topic_set)\\n publish_status(client) # update the status on connect\",\n \"def server():\",\n \"def server():\",\n \"def on_connect(unused_client, unused_userdata, unused_flags, rc):\\n #print('on_connect: ', mqtt.connack_string(rc))\\n print(f\\\"on_connect: {error_str(rc)} ({mqtt.connack_string(rc)})\\\")\\n print()\\n\\n global connected\\n connected = True\",\n \"async def on_ready():\\n print(\\\"{} has joined the server\\\".format(client.user.name))\",\n \"def ready(self, server):\\n self.server = server\\n self.title_lbl.config(\\n text='ViewVC standalone server at\\\\n' + server.url)\\n self.open_btn.config(state='normal')\\n self.quit_btn.config(state='normal')\",\n \"def on_connected(self, connection):\\n\\t\\t# Open a channel\\n\\t\\tconnection.channel(self.on_channel_open)\",\n \"async def on_connect(self):\\r\\n self._try_shutdown_twitch()\\r\\n self.stream_thread = self.connect_thread()\",\n \"def on_connect(mqtt_client, obj, flags, rc):\\n print(\\\"Connected\\\")\",\n \"def show_callback(self, client_data):\\n pass\",\n \"def server_activate(self):\\n\\t\\tpass\",\n \"def onOpen(self):\\n\\t\\tif DEBUG:\\n\\t\\t\\tsys.stdout.write(\\\"Initiating Handshake...\\\\n\\\")\\n\\t\\tself.sendMessage(struct.pack(\\\"!d\\\", VERSION), True)\",\n \"def on_connect(connected_client, _, __, rc) -> None:\\n logger.info(\\\"Connected client with result code \\\" + str(rc))\\n # Subscribe in on_connect callback to automatically re-subscribe if the connection was lost\\n # Subscribe to all arduino hexnumber actions\\n # '+' means single level wildcard. '#' means multi level wildcard.\\n # See http://www.hivemq.com/blog/mqtt-essentials-part-5-mqtt-topics-best-practices\\n topic = str(constants.iRulezTopic) + \\\"/\\\" + str(constants.notificationTopic) + \\\"/\\\" + str(constants.mailTopic)\\n logger.debug(\\\"Subscribing to \\\" + str(topic))\\n connected_client.subscribe(str(topic))\",\n \"def ready(self, component):\\n self.fire(connect(self.host, self.port))\",\n \"def on_connect(self, client, userdata, flags, rc):\\n\\n logger.info(f'Connected to {self.topic} with result code {rc}')\\n # self.client.publish('Augmented/A.V.A.', str(rc)) # For return the connection situation to the subscriber device.\\n if rc == 0:\\n self.is_connected = True\\n self.client.subscribe(self.topic)\",\n \"def connection_callback(self, connected):\\n self._connection_queue.put_nowait(connected)\",\n \"def connected(self):\\n d = self.configure()\\n d.addCallback(self.startDiscovery)\\n d.addErrback(self.reportError)\",\n \"def _onconnect(self):\\n\\n pass\",\n \"def handle_connect(self):\\n #print \\\"Controller initiated on: %s:%s\\\" % (self.address, self.port)\\n if not self.needs_migration:\\n self.buffer.append(messages.of_hello)\",\n \"def startedConnecting(self, connector):\\n\\n print(\\\"Connecting to the server...\\\") # client console notification\",\n \"def connectionMade (self) :\\r\\n self.state = 'wait_hello'\\r\\n self.handshake_timeout = reactor.callLater(HANDSHAKE_TIMEOUT, self.err, \\\"handshake timeout expired\\\")\\r\\n self.log(\\\"connected\\\")\",\n \"def on_connect(client, userdata, flags, rcdata):\\n client.subscribe(\\\"diy/system/fire\\\", 1)\\n client.subscribe(\\\"diy/system/panic\\\", 1)\\n client.subscribe(\\\"diy/system/who\\\", 1)\",\n \"def handle_connect(self):\\n LOGGER.debug(\\\"local session connected(%d)\\\", id(self))\\n self.append_connect = False\",\n \"def _on_connection_success(self):\\n if self.connect_handler:\\n self.connect_handler()\",\n \"def on_welcome(self, raw_msg, server, port, nickname, **kwargs):\",\n \"def on_connect():\\n articleList()\\n #test_location()\\n get_state_colors()\\n ip = request.environ[\\\"HTTP_X_FORWARDED_FOR\\\"]\\n loc = get_location(ip)\\n push_stat_data(loc.state)\\n return True\",\n \"def onConnect(self, fetcher, connectionRespInfo): #$NON-NLS-1$\\r\",\n \"def _on_open(self):\\n\\n print(\\\"WebSocket successfully connected for \\\" + self.session_name + \\\"!\\\")\\n self.web_socket_open = True\\n self._send_login_request(sts_token, False)\",\n \"def on_connect(client, userdata, flags, rc):\\n if rc == 0:\\n client.subscribe(topic_subscribe)\\n print(\\\"connected OK with returned code=\\\", rc)\\n else:\\n print(\\\"Bad connection with returned code=\\\", rc)\",\n \"def on_connect(self, mqtt_client, userdata, flags, rc):\\n global connack\\n logging.debug(\\\"DEBUG - Connected to broker\\\")\\n connack = True\",\n \"def connected_to_server(self, is_connected, msg):\\n\\n\\t\\tif is_connected:\\n\\t\\t\\tself.__logs.append(' CONNECTED')\\n\\n\\n\\n\\t\\telse:\\n\\t\\t\\tself.__logs.append(' CONNECTION FAILED : ' + msg)\",\n \"def connected(self, host, port=None):\\n print(f\\\"connected to {host}\\\")\",\n \"def run(self):\\n\\t\\t\\n\\t\\tself.connect(self.config[\\\"server\\\"])\",\n \"def on_connect(client, userdata, flags, rc):\\n\\t# subscribe to the LEDs topic when connected\\n\\tclient.subscribe(\\\"SNHU/IT697/leds\\\")\",\n \"async def on_ready():\\n print('We have logged in as {0.user}'.format(client))\",\n \"def serverExplicitReady (self):\\n self.server_ready.set()\",\n \"def handleConnected(self):\\n logging.info(\\\"%s %s\\\", self.address, \\\"connected\\\")\\n self.logbook.connection_request = self.request\\n self.logbook.clients_connected_count += 1\",\n \"def serverAccept(self):\\n self.handshake_deferred.callback(self.protocol_to_accept)\\n logger.debug(\\\"WebSocket %s accepted by application\\\", self.reply_channel)\",\n \"def on_connect(mqttc, mosq, obj, rc):\\n print(\\\"Connected with result code:\\\"+str(rc))\\n # subscribe for all devices of user\\n mqttc.subscribe('+/devices/+/up')\\n mqttc.subscribe('+/devices/+/events/down/sent')\\n if rc != 0:\\n sys.exit('Could not connect to server. \\\\n Result code: ' + str(rc))\",\n \"def connect():\",\n \"def cbMqtt_on_connect(client, userdata, flags, rc):\\n if rc == 0:\\n logger.debug('Connected to %s: %s', str(mqtt), userdata)\\n setup_mqtt_filters()\\n mqtt_publish_fan_state()\\n else:\\n logger.error('Connection to MQTT broker failed: %s (rc = %d)',\\n userdata, rc)\",\n \"def _on_connect(self, client, userdata, flags, rc):\\n self.subscribe(self.topic)\",\n \"def start_server():\\n server = WebsocketServer(9001, host='0.0.0.0')\\n server.set_fn_message_received(message_received)\\n server.set_fn_client_left(client_left)\\n print(\\\"Started\\\")\\n server.run_forever()\",\n \"def on_connect(self, client, userdata, flags, rc):\\n # Subscribing in on_connect() means that if we lose the connection and\\n # reconnect then subscriptions will be renewed.\\n sleep(5) # quick delay\\n self.client.subscribe(self.subControls)\\n self.client.subscribe(self.subSettings)\",\n \"def on_connect (client, userdata, flags, rc):\\n print (\\\"Connect %s result is: %s\\\" % (host, rc))\\n client.subscribe(\\\"some/message/to/publish\\\")\\n #creator.createobjects()\\n \\n # With Paho, always subscribe at on_connect (if you want to\\n # subscribe) to ensure you resubscribe if connection is\\n # lost.\\n # client.subscribe(\\\"some/topic\\\")\\n\\n if rc == 0:\\n client.connected_flag = True\\n print (\\\"Connection good!\\\")\\n return\\n \\n print (\\\"Failed to connect to %s, error was, rc=%s\\\" % rc)\\n # handle error here\\n sys.exit (-1)\",\n \"def _onConnect(self, client:mqtt.Client, userdata:Any, flags:dict, rc:int) -> None:\\n\\t\\tself.messageHandler and self.messageHandler.logging(self, logging.DEBUG, f'MQTT: Connected with result code: {rc} ({mqtt.error_string(rc)})')\\n\\t\\tif rc == 0:\\n\\t\\t\\tself.isConnected = True\\n\\t\\t\\tself.messageHandler and self.messageHandler.onConnect(self)\\n\\t\\telse:\\n\\t\\t\\tself.isConnected = False\\n\\t\\t\\tif self.messageHandler:\\n\\t\\t\\t\\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot connect to broker. Result code: {rc} ({mqtt.error_string(rc)})')\\n\\t\\t\\t\\tself.messageHandler.onError(self, rc)\",\n \"def connect_handler():\\n print(\\\"---------------- CONNECTED ----------------\\\")\\n\\n user = User.query.get(get_jwt_identity())\\n\\n \\\"\\\"\\\"\\n Save phone numbers with corresponding sid, so that server\\n can push updates to clients that are connected\\n \\\"\\\"\\\"\\n redis.set(user.phone_num, request.sid)\\n redis.set(request.sid, user.phone_num)\\n\\n payload = {\\n \\\"phone_num\\\": user.phone_num,\\n \\\"nickname\\\": user.nickname\\n }\\n emit(\\\"user_data\\\", json.dumps(payload))\",\n \"def on_open(self, info):\\n # When new client comes in, will add it to the clients list\\n clients.add(self)\",\n \"def connectionMade(self):\\n print \\\"connection received from\\\", self.addr\",\n \"def handle_connect(self):\\r\\n print \\\"http_evented::handle_connect\\\"\\r\\n self._connection_state = STATE_CONNECTED\\r\\n super(http_evented, self).handle_connect()\\r\\n call_if_not_none_and_callable(self._onConnected)\",\n \"def connectionMade(self):\\r\\n HistoricRecvLine.connectionMade(self)\\r\\n self._factory = PBClientFactory()\\r\\n\\r\\n reactor.connectTCP(self._masterIP, self._console_port, self._factory) #@UndefinedVariable\\r\\n self.terminal.write(\\\"Username: \\\")\",\n \"def on_connect(client, userdata, flags, rc):\\n print(\\\"Connected with result code \\\"+str(rc))\\n\\n # Subscribing in on_connect() means that if we lose the connection and\\n # reconnect then subscriptions will be renewed.\\n client.subscribe(MQTT_PATH)\",\n \"def on_client_connect(self, client):\\r\\n\\t\\tself.connection_logger.info('Received client connection from %s:%u' % (client.address, client.port))\\r\\n\\t\\tif (self.db_connection is False):\\r\\n\\t\\t\\tclient.send('A critical database error has occurred. Please reconnect later.\\\\n')\\r\\n\\t\\t\\tclient.socket_send()\\r\\n\\t\\t\\tclient.deactivate()\\r\\n\\t\\t\\tclient.sock.close()\\r\\n\\t\\t\\treturn\\r\\n\\t\\tclient.send(self.welcome_message_data)\\r\\n\\t\\tself.pending_connection_list.append(client)\\r\\n\\t\\tself.post_client_connect.send(sender=client)\",\n \"def _r_on_connection_established(self, protocol):\\n print(\\\"Client connected\\\")\\n self._current_protocol = protocol\\n\\n for d in self._waiting_for_connection:\\n d.callback(True)\\n self._waiting_for_connection = []\",\n \"def client_connected(self, telnet_connection):\",\n \"def on_connect():\\n payload = dict(data='Connected')\\n emit('log', payload, broadcast=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7463222","0.69432336","0.686189","0.6838079","0.67208254","0.66620857","0.66216904","0.658405","0.6554587","0.6540486","0.64954203","0.6479373","0.64791733","0.6423165","0.64106476","0.6404848","0.64022064","0.6392826","0.63813555","0.63813555","0.63813555","0.63579476","0.63478446","0.6328573","0.6324107","0.63224375","0.6271612","0.62385094","0.62299025","0.6214332","0.6214332","0.6208798","0.6179222","0.6176517","0.61755365","0.6168422","0.6141387","0.61324495","0.6128784","0.61241484","0.6108894","0.60991114","0.60965574","0.6094856","0.60882616","0.60882616","0.6081006","0.60768825","0.6068096","0.604971","0.6046883","0.60405767","0.6026062","0.60253024","0.60121006","0.60055536","0.60011613","0.5974724","0.59674597","0.5960009","0.5959955","0.5946753","0.5941335","0.59369904","0.59367055","0.5932343","0.5929499","0.59261894","0.5920728","0.5875633","0.5872686","0.5868267","0.58671147","0.58615613","0.58574086","0.5849961","0.5848662","0.58472186","0.5844967","0.58392996","0.5837808","0.5826651","0.582333","0.5812075","0.57983416","0.57926655","0.57878363","0.5786312","0.57811654","0.5765985","0.57624125","0.57603896","0.5760122","0.5756342","0.57557434","0.5752338","0.5751224","0.57440585","0.5742906"],"string":"[\n \"0.7463222\",\n \"0.69432336\",\n \"0.686189\",\n \"0.6838079\",\n \"0.67208254\",\n \"0.66620857\",\n \"0.66216904\",\n \"0.658405\",\n \"0.6554587\",\n \"0.6540486\",\n \"0.64954203\",\n \"0.6479373\",\n \"0.64791733\",\n \"0.6423165\",\n \"0.64106476\",\n \"0.6404848\",\n \"0.64022064\",\n \"0.6392826\",\n \"0.63813555\",\n \"0.63813555\",\n \"0.63813555\",\n \"0.63579476\",\n \"0.63478446\",\n \"0.6328573\",\n \"0.6324107\",\n \"0.63224375\",\n \"0.6271612\",\n \"0.62385094\",\n \"0.62299025\",\n \"0.6214332\",\n \"0.6214332\",\n \"0.6208798\",\n \"0.6179222\",\n \"0.6176517\",\n \"0.61755365\",\n \"0.6168422\",\n \"0.6141387\",\n \"0.61324495\",\n \"0.6128784\",\n \"0.61241484\",\n \"0.6108894\",\n \"0.60991114\",\n \"0.60965574\",\n \"0.6094856\",\n \"0.60882616\",\n \"0.60882616\",\n \"0.6081006\",\n \"0.60768825\",\n \"0.6068096\",\n \"0.604971\",\n \"0.6046883\",\n \"0.60405767\",\n \"0.6026062\",\n \"0.60253024\",\n \"0.60121006\",\n \"0.60055536\",\n \"0.60011613\",\n \"0.5974724\",\n \"0.59674597\",\n \"0.5960009\",\n \"0.5959955\",\n \"0.5946753\",\n \"0.5941335\",\n \"0.59369904\",\n \"0.59367055\",\n \"0.5932343\",\n \"0.5929499\",\n \"0.59261894\",\n \"0.5920728\",\n \"0.5875633\",\n \"0.5872686\",\n \"0.5868267\",\n \"0.58671147\",\n \"0.58615613\",\n \"0.58574086\",\n \"0.5849961\",\n \"0.5848662\",\n \"0.58472186\",\n \"0.5844967\",\n \"0.58392996\",\n \"0.5837808\",\n \"0.5826651\",\n \"0.582333\",\n \"0.5812075\",\n \"0.57983416\",\n \"0.57926655\",\n \"0.57878363\",\n \"0.5786312\",\n \"0.57811654\",\n \"0.5765985\",\n \"0.57624125\",\n \"0.57603896\",\n \"0.5760122\",\n \"0.5756342\",\n \"0.57557434\",\n \"0.5752338\",\n \"0.5751224\",\n \"0.57440585\",\n \"0.5742906\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74887276"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":15099,"cells":{"query":{"kind":"string","value":"Callback when front server is disconnected."},"document":{"kind":"string","value":"def OnFrontDisconnected(self, reason: int) -> None:\n self.login_status = False\n self.gateway.write_log(f\"行情服务器连接断开，原因{reason}\")"},"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 callback_disconnect():\n # if Networking.get_instance().is_host:\n logger.warning(\"It seems that client is not connected...\")\n Networking.get_instance().disconnect()\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))","def on_disconnect(self):\n print('Client disconnected!')","def on_disconnect():\n print(\"User disconnected!\")","def on_disconnect(self, client, userdata, rc):\n\t\tprint (\"[{}] Client disconnected\".format(\n\t\t\tint(time.time())\n\t\t))","def on_disconnect( client, userdata, rc ):\n logging.info( \"Disconnected from Broker. Returned code: %s\\n\" %rc )\n client.connected_flag = False\n client.disconnect_flag = True","def on_disconnect(unused_client, unused_userdata, rc):\n print(f\"on_disconnect: {error_str(rc)}\")\n print()\n\n global connected\n connected = False","def disconnect():\n logging.info('Client disconnected')","def _on_disconnect(self, *args, **kwargs):\n logger.info('Client disconnected %r', self.address)\n self.server.client_disconnected(self)\n # self.unregister()","def on_disconnect(unused_client, unused_userdata, rc):\n print('on_disconnect', error_str(rc))\n status_light.off()","def on_disconnect(self, client, userdata, msg):\n self.log.warning(\"Disconnected: \" + str(msg))\n self.connected = False\n self.dconn += 1","def clientConnectionLost(self, connector, reason):\n\n print(\"Disconnected from the server\") # client console notification\n reactor.callFromThread(reactor.stop) # reactor shutdown","def on_disconnect(self):\n # self.node_worker_ref.socketIO.disconnect()\n self.log.info('Disconnected from the server')","def on_disconnect():\n logger.info(f\"{request.sid} Disconnected\")","def on_disconnect(self, raw_msg, server, port, **kwargs):","def on_disconnect(client, userdata, rc):\n if rc != 0:\n print(\"Error: Mqtt server disconnection.\")","async def on_disconnect(self) -> None:","def on_disconnect():\n print('User disconnected!')\n return 'disconnected'","def on_disconnect(self):\n log.info(\"Stream disconnected\")","def _on_surface_disconnected(self):\n\n # Close the socket\n self._client_socket.close()\n\n # Inform that the connection has been closed\n print(\"Video stream from {} address closed successfully\".format(self._client_address))","async def on_disconnect(self, reconnecting):\n pass","def on_disconnect(self, mqtt_client, userdata, rc):\n logging.debug('DEBUG - Disconnected from broker')","async def on_disconnected(self):\n self._connected = False\n self._connectedToBroker = False","def handle_disconnected(self):\r\n if self.keepalive != None:\r\n self.keepalive.cancel()\r\n self.keepalive = None\r\n if self.password == \"guest\" and self.username.startswith(\"Guest\"):\r\n self.username = \"guest\"\r\n self._completed_models = False\r\n self._completed_tags = False\r\n if not self._manual_disconnect:\r\n print(\"Disconnected from MyFreeCams. Reconnecting in 30 seconds...\")\r\n self.loop.call_later(30, lambda: asyncio.ensure_future(self.connect(self._logged_in)))\r\n else:\r\n self.loop.stop()\r\n self._manual_disconnect = False\r\n self.emit(FCTYPE.CLIENT_DISCONNECTED)\r\n Model.All.reset()","def disconnected(self):\n pass","def disconnected(self):\n pass","def on_connection_closed(self):","def disconnect(self) -> None:\n ...","def disconnect(self):","def connectionLost(self, reason=None):\n\n # Log the disconnection\n MaverickServerProtocol._logger.debug(\"Client disconnected.\")","def _disconnected(self):\n self.isConnected.clear()\n pub.sendMessage(\"meshtastic.connection.lost\", interface=self)","def on_disconnect(client):\n logging.info(\"Lost connection to %s\" % client.addrport() )\n state.prune_client_state(client)\n state.prune_client_list(client)\n #broadcast('%s leaves the conversation.\\n' % client.addrport() )","def loseConnection(self):\n self.lost_connection.callback(self)\n return None","def on_disconnect(self, client, userdata, rc):\n if not self.stopplugin.isSet():\n self.log.error(u\"### Connection to MQTT broquer has been lost.\")\n self.log.error(u\"### Waiting for the connection to be back\") # loop_start() will automatically reconnect if connection be back.\n else:\n self.log.info(u\"==> MQTT client disconnected by plugin stop\")","def on_disconnect(unused_client, unused_userdata, rc):\n\tprint('on_disconnect', error_str(rc))\n\n\t# Since a disconnect occurred, the next loop iteration will wait with\n\t# exponential backoff.\n\tglobal should_backoff\n\tshould_backoff = True","def _on_mqtt_disconnect(\n self, client: mqtt.Client, userdata: str, rc: int\n ) -> None:\n self.log.debug(f\"Disconnect return code: {rc}\")\n self._connected = False\n if rc != 0:\n self.log.warning(\n f\"Unexpected disconnect on {self.host}:{self.port}!\"\n )","def disconnected(self):\n\n self.fire(connect(self.host, self.port))","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","def connectionLost(self, reason):\r\n _Protocol.remote_destroy(self)","def disconnect(self):\r\n\t\tself.ui.chatlist.clear()\r\n\t\t#self.top.quit()\r\n\t\tself.chat.disconnect(self.my_uri)\r\n\t\r\n\t\tprint('Disconnected')","def disconnect_handler():\n print(\"---------------- DISCONNECTED ----------------\")\n\n phone_num = redis.get(request.sid)\n redis.delete(phone_num)\n redis.delete(request.sid)","def disconnect(self):\n self.is_connected = False\n self.connection.disconnect(True)\n print('Disconnected from the Connected server')","def disconnect(self):\n\t\tself.client.disconnect()\n\t\tself.log.info(\"disconnected OBS Websocket _connection.\")","def on_disconnect(self, connection, event):\r\n\r\n print('[{}] Disconnected from {}' .format(event.type.upper(), event.source))\r\n print(\"{}\".format(event.arguments))\r\n\r\n res = self.cursor.execute(\"\"\"SELECT * FROM `IRC_servers` WHERE `Registred_users_userID` = %s AND `serverID` = %s;\"\"\", (self.userID, connection.serverID))\r\n if res != 0:\r\n result = self.cursor.fetchall()\r\n print(result)\r\n serverID_res = int(result[0][0])\r\n serverName_res = str(result[0][6])\r\n\r\n res = self.cursor.execute(\"\"\"UPDATE `IRC_servers` SET `isConnected` = %s WHERE `serverID` = %s;\"\"\", (0, serverID_res))\r\n\r\n print(\"RES: \",res)\r\n print(\"serverID = {}\".format(serverID_res))\r\n res = self.cursor.execute(\"\"\"INSERT INTO `IRC_other_messages` (IRC_servers_serverID,\r\n fromHostmask,\r\n messageBody,\r\n commandType,\r\n timeReceived)\r\n values (%s, %s, %s, %s, %s)\"\"\", (serverID_res, event.source,\r\n \"Disconnected from {0}...\".format(serverName_res), \"CLOUDCHAT_INFO\", datetime.datetime.utcnow()))\r\n self.db.commit()","def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\n if rc == mqtt.MQTT_ERR_SUCCESS:\n logging.info(\n f\"Successfully disconnected from the broker {self.config.broker}\"\n )\n sys.exit(0)\n else:\n logging.critical(\n f\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\"\n )\n sys.exit(1)","def cbMqtt_on_disconnect(client, userdata, rc):\n logger.warning('Disconnected from %s: %s (rc = %d)',\n str(mqtt), userdata, rc)","def device_disconnect(self):\n pass","def connectionLost(self, reason):\n\n # We need to remove this session from various dicts\n del self.factory.authorized_sessions[self.transport.sessionno]\n if self.transport.sessionno in self.factory.session_protocols:\n del self.factory.session_protocols[self.transport.sessionno]\n if self.transport.sessionno in self.factory.interested_events:\n del self.factory.interested_events[self.transport.sessionno]\n\n log.info(\"Deluge client disconnected: %s\", reason.value)","async def connection_lost(self):\n logging.info('connection dropped')","def on_disconnect(mqttc, obj, rc):\n if rc == 0:\n logger.debug(\"MQTT DISCONNECTED: rc: \" + str(rc))\n logger.debug(\"Disconnected Successfully from MQTT Broker\")","def callback_disconnect_client(self, connection_object):\n # Pops the client's connection object\n game = GameStateModel.instance()\n if game:\n players = [x for x in game.players if x.ip == connection_object.address[0]]\n if players:\n self.kick_client(connection_object.address[0])\n player_model = GameStateModel.instance().get_player_by_ip(connection_object.address[0])\n event = DisconnectEvent(player_model)\n Networking.get_instance().send_to_all_client(event)\n return super(MastermindServerUDP, self).callback_disconnect()","def shutdown_callback():\n pass","async def disconnect(self):","def _disconnect(self):\n self.socket.send_json({\"worker_id\": self.socket_id, \"message\": \"disconnect\"})\n self.socket.close()\n self.context.term()\n exit()","def on_close(self):\n self.clients.remove(self)","def on_close(self):\n APP.clients.remove(self)\n log(\"WebSocket closed. {0} child(s) connected\".\n format(len(APP.clients)))","def disconnect(self):\n # Nothing to do\n pass","def the_current_device_is_disconnected():\n assert web_app.disconnect_from_device()","def onDisconnect(self):\n if self.mode:\n self.onSlaveLost()\n else:\n self.onMasterLost()","def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def _disconnected(self, link_uri):\n print('Disconnected from %s' % link_uri)","def on_disconnected_cbk(_, reason: int):\n cprint(f\"\\nDevice disconnected because: {reason}.\", color=\"red\", flush=True)","def disconnect(self):\n r = requests.post(f'{self.SERVER_ADDR}/api/disconnect', headers={'Authorization': 'Token ' + self.token})\n r.raise_for_status()","def connectionLost(self, reason):\n self.finished.callback(self.display)","def disconnect(self):\n\n if self.connected:\n try:\n self.sock.shutdown(socket.SHUT_WR)\n self.sock.close()\n self.connected = False\n except SocketError as e:\n print(\"Server must have disconnected first!\")\n\n print(\"Disconnection successful!\")\n else:\n print(\"Nothing to disconnect!\")","def _disconnected(self, link_uri):\n print \"Disconnected from %s\" % link_uri","def onClose(self, wasClean, code=None, reason=None):\n self.sessionhandler.disconnect(self)\n if self.nextHeartbeatCall:\n self.nextHeartbeatCall.cancel()\n self.nextHeartbeatCall = None\n if wasClean:\n logger.log_info(f\"Discord connection closed ({code}) reason: {reason}\")\n else:\n logger.log_info(f\"Discord connection lost.\")","def disconnect(self):\n self.stop()\n self._send_command('exit')\n self.sock.close()\n self.disconnected = True","def handle_close(self):\n LOGGER.debug(\"server closed\")\n self.clear()\n data = {}\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\n self.close()\n\n # close all and restart\n asyncore.close_all()","def connectionLost(reason):","def _onDisconnect(self, client:mqtt.Client, userdata:Any, rc:int) -> None:\n\t\tself.messageHandler and self.messageHandler.logging(self, logging.DEBUG, f'MQTT: Disconnected with result code: {rc} ({mqtt.error_string(rc)})')\n\t\tself.subscribedTopics.clear()\n\t\tif rc == 0:\n\t\t\tself.isConnected = False\n\t\t\tself.messageHandler and\tself.messageHandler.onDisconnect(self)\n\t\telif rc == 7:\n\t\t\tself.isConnected = False\n\t\t\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot disconnect from broker. Result code: {rc} ({mqtt.error_string(rc)})')\n\t\t\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Did another client connected with the same ID ({self.clientID})?')\n\t\t\tself.messageHandler and\tself.messageHandler.onDisconnect(self)\n\t\telse:\n\t\t\tself.isConnected = False\n\t\t\tif self.messageHandler:\n\t\t\t\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot disconnect from broker. Result code: {rc} ({mqtt.error_string(rc)})')\n\t\t\t\tself.messageHandler.onDisconnect(self)\n\t\t\t\tself.messageHandler.onError(self, rc)","def connection_closed(self):\n self.atv = None\n self._start_connect_loop()\n self._update_state(disconnected=True)","def handle_disconnect(self,message,conn):\n if self.connections[message[\"ID\"]][\"host\"] == True: #if user is host, delete all connections\n print(\"HOST DISCONNECTING\")\n host = message[\"ID\"]\n session_location = self.connections[message[\"ID\"]][\"session_id\"]\n for key in self.sessions[session_location][\"USERS\"].keys():\n self.disconnect(self.connections[key][\"CONN\"],message[\"ID\"],f\"[HOST <{host}>:<{self.connections[host]['display_name']}>] Unexpectedly Disconnected\") #close each connection\n self.delete_connection_entry(key) #delete the connection from the connections dictionary\n self.delete_session(session_location) # delete the session\n self.delete_connection_entry(message[\"ID\"])\n self.disconnect(conn,message[\"ID\"], \"You Disconnected\")\n else:\n session_location = self.connections[message[\"ID\"]][\"session_id\"]\n self.delete_session_entry(session_location,message[\"ID\"])\n self.broadcast_to_session(session_location, \"USER_DISCONNECT\",f\"[USER <{message['ID']}>:<{self.connections[message['ID']]['display_name']}>] Disconnected\", exclude=[message[\"ID\"]])\n self.delete_connection_entry(message[\"ID\"])\n self.broadcast_to_session(session_location, \"USERS\", json.dumps(self.sessions[session_location][\"USERS\"]))\n self.disconnect(conn,message[\"ID\"],\"You Disconnected\")","def shutdown(self):\n if self.alive:\n libplasma.disconnect(self.conn)\n self.alive = False","def connectionLost(self,reason):\n pass","def on_disconnection(self, connection):\n\n self.root.connect_label.canvas.before.children[0].rgb = (1, 0, 0)\n self.root.connect_label.text = 'Disconnected'\n self.connection = None","def on_websocket_close(self) -> None:\n raise NotImplementedError() # pragma: no cover","async def on_disconnect():\n print(\"Bot has logged off\")","def connection_lost(self, exc):\n logger.info('The server closed the connection')\n self.loop.stop()","def notifyCNC2disconnected(self):\n self.notifyGUItask(\"CNC2_DISCONNECTED\")","def _disconnected(self, link_uri):\n logger.debug(\"Crazyflie disconnected from {}\".format(link_uri))\n self._clear_state()\n self._update_graphics()\n self._plot_3d.clear()\n self._basestation_geometry_dialog.close()\n self.is_lighthouse_deck_active = False\n self._is_connected = False\n self._update_ui()","def _disconnect(self):\n raise NotImplementedError(\"ERROR: Unimplemented function.\")","def _on_disconnection(self, *_):\n\n if self._proxy is not None:\n self._proxy.close()\n self._proxy = None","def __disconnected(self):\n self.__changeMotorState(NOTINITIALIZED)\n self.disconnected()\n if self.__callbacks.get(\"disconnected\"):\n cb = self.__callbacks[\"disconnected\"]()\n if cb is not None:\n cb()","def server_close(self):\n\t\tpass","def disconnect(self):\n\t\tself.ftp.quit()\n\t\tself.online = False\n\t\tErrore_connessione = True\n\t\tserver_ind = self.entry1.get_text()\n\t\tserverID = 0\n\t\tStato_server = \"Disconnessione effettuata\"\n\t\tOutwin_msg_FTP(Stato_server, Errore_connessione, server_ind, serverID)\n\t\tprint \"Server disconnesso\"","def _on_close(self):\n self.web_socket_open = False\n self.logged_in = False\n print(\"WebSocket Closed for \" + self.session_name)\n\n if not self.disconnected_by_user:\n print(\"Reconnect to the endpoint for \" + self.session_name + \" after 3 seconds... \")\n time.sleep(3)\n self.connect()","def _disconnected(self, link_uri):\n\t\tprint \"Disconnected from %s\" % link_uri\n\t\tself.is_connected = False\n\t\t#print results","def on_close(self):\n\n if self.id in self.funcserver.websocks:\n self.funcserver.websocks[self.id] = None\n ioloop = tornado.ioloop.IOLoop.instance()\n ioloop.add_callback(lambda: self.funcserver.websocks.pop(self.id, None))\n\n psession = self.funcserver.pysessions.get(self.pysession_id, None)\n if psession:\n psession[\"socks\"].remove(self.id)\n if not psession[\"socks\"]:\n del self.funcserver.pysessions[self.pysession_id]","def onClientDeath(self):\n\t\tDEBUG_MSG(\"Avatar[%i].onClientDeath:\" % self.id)\n\t\tself.destroySelf()","def\tconnectionLost(self, reason):\n\t\tprint \"[:)] Connectionlost con reason: \", reason, self.addr, self.port","def server_closed_connection(self):\n\n print(\"Game Over!\")\n if self._winner:\n print(\"Player {} wins!\".format(self._winner))\n else:\n print(\"Draw!\")","def disconnect_callback(self, excep):\r\n if excep is None:\r\n _LOGGER.debug(\"PyVisonic has caused an exception, no exception information is available\")\r\n else:\r\n _LOGGER.debug(\"PyVisonic has caused an exception %s\", str(excep))\r\n # General update trigger\r\n # 0 is a disconnect and (hopefully) reconnect from an exception (probably comms related)\r\n self.hass.bus.fire(self.visonic_event_name, {\"condition\": 0})\r\n sleep(5.0)\r\n _LOGGER.debug(\" ........... setting up reconnection\")\r\n self.panel_exception_counter = self.panel_exception_counter + 1\r\n asyncio.ensure_future(self.disconnect_callback_async(excep), loop=self.hass.loop)","def guiding_disconnect():\r\n try:\r\n app.guider.disconnect()\r\n return jsonify({\"status\": True})\r\n except Exception as e:\r\n return jsonify(\r\n {\"status\": False, \"error\": \"Failed disconnecting from guider: %s\" % e}\r\n )","def on_shutdown(self, server):\n pass","def destroy(self):\r\n\t\tsuper().destroy()\r\n\t\tif comm_server.is_running():\r\n\t\t\tcomm_server.remove_disconnection_handler(self._update_connection_num)\r\n\t\t\tcomm_server.stop_server()"],"string":"[\n \"def callback_disconnect():\\n # if Networking.get_instance().is_host:\\n logger.warning(\\\"It seems that client is not connected...\\\")\\n Networking.get_instance().disconnect()\\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))\",\n \"def on_disconnect(self):\\n print('Client disconnected!')\",\n \"def on_disconnect():\\n print(\\\"User disconnected!\\\")\",\n \"def on_disconnect(self, client, userdata, rc):\\n\\t\\tprint (\\\"[{}] Client disconnected\\\".format(\\n\\t\\t\\tint(time.time())\\n\\t\\t))\",\n \"def on_disconnect( client, userdata, rc ):\\n logging.info( \\\"Disconnected from Broker. Returned code: %s\\\\n\\\" %rc )\\n client.connected_flag = False\\n client.disconnect_flag = True\",\n \"def on_disconnect(unused_client, unused_userdata, rc):\\n print(f\\\"on_disconnect: {error_str(rc)}\\\")\\n print()\\n\\n global connected\\n connected = False\",\n \"def disconnect():\\n logging.info('Client disconnected')\",\n \"def _on_disconnect(self, *args, **kwargs):\\n logger.info('Client disconnected %r', self.address)\\n self.server.client_disconnected(self)\\n # self.unregister()\",\n \"def on_disconnect(unused_client, unused_userdata, rc):\\n print('on_disconnect', error_str(rc))\\n status_light.off()\",\n \"def on_disconnect(self, client, userdata, msg):\\n self.log.warning(\\\"Disconnected: \\\" + str(msg))\\n self.connected = False\\n self.dconn += 1\",\n \"def clientConnectionLost(self, connector, reason):\\n\\n print(\\\"Disconnected from the server\\\") # client console notification\\n reactor.callFromThread(reactor.stop) # reactor shutdown\",\n \"def on_disconnect(self):\\n # self.node_worker_ref.socketIO.disconnect()\\n self.log.info('Disconnected from the server')\",\n \"def on_disconnect():\\n logger.info(f\\\"{request.sid} Disconnected\\\")\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def on_disconnect(client, userdata, rc):\\n if rc != 0:\\n print(\\\"Error: Mqtt server disconnection.\\\")\",\n \"async def on_disconnect(self) -> None:\",\n \"def on_disconnect():\\n print('User disconnected!')\\n return 'disconnected'\",\n \"def on_disconnect(self):\\n log.info(\\\"Stream disconnected\\\")\",\n \"def _on_surface_disconnected(self):\\n\\n # Close the socket\\n self._client_socket.close()\\n\\n # Inform that the connection has been closed\\n print(\\\"Video stream from {} address closed successfully\\\".format(self._client_address))\",\n \"async def on_disconnect(self, reconnecting):\\n pass\",\n \"def on_disconnect(self, mqtt_client, userdata, rc):\\n logging.debug('DEBUG - Disconnected from broker')\",\n \"async def on_disconnected(self):\\n self._connected = False\\n self._connectedToBroker = False\",\n \"def handle_disconnected(self):\\r\\n if self.keepalive != None:\\r\\n self.keepalive.cancel()\\r\\n self.keepalive = None\\r\\n if self.password == \\\"guest\\\" and self.username.startswith(\\\"Guest\\\"):\\r\\n self.username = \\\"guest\\\"\\r\\n self._completed_models = False\\r\\n self._completed_tags = False\\r\\n if not self._manual_disconnect:\\r\\n print(\\\"Disconnected from MyFreeCams. Reconnecting in 30 seconds...\\\")\\r\\n self.loop.call_later(30, lambda: asyncio.ensure_future(self.connect(self._logged_in)))\\r\\n else:\\r\\n self.loop.stop()\\r\\n self._manual_disconnect = False\\r\\n self.emit(FCTYPE.CLIENT_DISCONNECTED)\\r\\n Model.All.reset()\",\n \"def disconnected(self):\\n pass\",\n \"def disconnected(self):\\n pass\",\n \"def on_connection_closed(self):\",\n \"def disconnect(self) -> None:\\n ...\",\n \"def disconnect(self):\",\n \"def connectionLost(self, reason=None):\\n\\n # Log the disconnection\\n MaverickServerProtocol._logger.debug(\\\"Client disconnected.\\\")\",\n \"def _disconnected(self):\\n self.isConnected.clear()\\n pub.sendMessage(\\\"meshtastic.connection.lost\\\", interface=self)\",\n \"def on_disconnect(client):\\n logging.info(\\\"Lost connection to %s\\\" % client.addrport() )\\n state.prune_client_state(client)\\n state.prune_client_list(client)\\n #broadcast('%s leaves the conversation.\\\\n' % client.addrport() )\",\n \"def loseConnection(self):\\n self.lost_connection.callback(self)\\n return None\",\n \"def on_disconnect(self, client, userdata, rc):\\n if not self.stopplugin.isSet():\\n self.log.error(u\\\"### Connection to MQTT broquer has been lost.\\\")\\n self.log.error(u\\\"### Waiting for the connection to be back\\\") # loop_start() will automatically reconnect if connection be back.\\n else:\\n self.log.info(u\\\"==> MQTT client disconnected by plugin stop\\\")\",\n \"def on_disconnect(unused_client, unused_userdata, rc):\\n\\tprint('on_disconnect', error_str(rc))\\n\\n\\t# Since a disconnect occurred, the next loop iteration will wait with\\n\\t# exponential backoff.\\n\\tglobal should_backoff\\n\\tshould_backoff = True\",\n \"def _on_mqtt_disconnect(\\n self, client: mqtt.Client, userdata: str, rc: int\\n ) -> None:\\n self.log.debug(f\\\"Disconnect return code: {rc}\\\")\\n self._connected = False\\n if rc != 0:\\n self.log.warning(\\n f\\\"Unexpected disconnect on {self.host}:{self.port}!\\\"\\n )\",\n \"def disconnected(self):\\n\\n self.fire(connect(self.host, self.port))\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def connectionLost(self, reason):\\r\\n _Protocol.remote_destroy(self)\",\n \"def disconnect(self):\\r\\n\\t\\tself.ui.chatlist.clear()\\r\\n\\t\\t#self.top.quit()\\r\\n\\t\\tself.chat.disconnect(self.my_uri)\\r\\n\\t\\r\\n\\t\\tprint('Disconnected')\",\n \"def disconnect_handler():\\n print(\\\"---------------- DISCONNECTED ----------------\\\")\\n\\n phone_num = redis.get(request.sid)\\n redis.delete(phone_num)\\n redis.delete(request.sid)\",\n \"def disconnect(self):\\n self.is_connected = False\\n self.connection.disconnect(True)\\n print('Disconnected from the Connected server')\",\n \"def disconnect(self):\\n\\t\\tself.client.disconnect()\\n\\t\\tself.log.info(\\\"disconnected OBS Websocket _connection.\\\")\",\n \"def on_disconnect(self, connection, event):\\r\\n\\r\\n print('[{}] Disconnected from {}' .format(event.type.upper(), event.source))\\r\\n print(\\\"{}\\\".format(event.arguments))\\r\\n\\r\\n res = self.cursor.execute(\\\"\\\"\\\"SELECT * FROM `IRC_servers` WHERE `Registred_users_userID` = %s AND `serverID` = %s;\\\"\\\"\\\", (self.userID, connection.serverID))\\r\\n if res != 0:\\r\\n result = self.cursor.fetchall()\\r\\n print(result)\\r\\n serverID_res = int(result[0][0])\\r\\n serverName_res = str(result[0][6])\\r\\n\\r\\n res = self.cursor.execute(\\\"\\\"\\\"UPDATE `IRC_servers` SET `isConnected` = %s WHERE `serverID` = %s;\\\"\\\"\\\", (0, serverID_res))\\r\\n\\r\\n print(\\\"RES: \\\",res)\\r\\n print(\\\"serverID = {}\\\".format(serverID_res))\\r\\n res = self.cursor.execute(\\\"\\\"\\\"INSERT INTO `IRC_other_messages` (IRC_servers_serverID,\\r\\n fromHostmask,\\r\\n messageBody,\\r\\n commandType,\\r\\n timeReceived)\\r\\n values (%s, %s, %s, %s, %s)\\\"\\\"\\\", (serverID_res, event.source,\\r\\n \\\"Disconnected from {0}...\\\".format(serverName_res), \\\"CLOUDCHAT_INFO\\\", datetime.datetime.utcnow()))\\r\\n self.db.commit()\",\n \"def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\\n if rc == mqtt.MQTT_ERR_SUCCESS:\\n logging.info(\\n f\\\"Successfully disconnected from the broker {self.config.broker}\\\"\\n )\\n sys.exit(0)\\n else:\\n logging.critical(\\n f\\\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\\\"\\n )\\n sys.exit(1)\",\n \"def cbMqtt_on_disconnect(client, userdata, rc):\\n logger.warning('Disconnected from %s: %s (rc = %d)',\\n str(mqtt), userdata, rc)\",\n \"def device_disconnect(self):\\n pass\",\n \"def connectionLost(self, reason):\\n\\n # We need to remove this session from various dicts\\n del self.factory.authorized_sessions[self.transport.sessionno]\\n if self.transport.sessionno in self.factory.session_protocols:\\n del self.factory.session_protocols[self.transport.sessionno]\\n if self.transport.sessionno in self.factory.interested_events:\\n del self.factory.interested_events[self.transport.sessionno]\\n\\n log.info(\\\"Deluge client disconnected: %s\\\", reason.value)\",\n \"async def connection_lost(self):\\n logging.info('connection dropped')\",\n \"def on_disconnect(mqttc, obj, rc):\\n if rc == 0:\\n logger.debug(\\\"MQTT DISCONNECTED: rc: \\\" + str(rc))\\n logger.debug(\\\"Disconnected Successfully from MQTT Broker\\\")\",\n \"def callback_disconnect_client(self, connection_object):\\n # Pops the client's connection object\\n game = GameStateModel.instance()\\n if game:\\n players = [x for x in game.players if x.ip == connection_object.address[0]]\\n if players:\\n self.kick_client(connection_object.address[0])\\n player_model = GameStateModel.instance().get_player_by_ip(connection_object.address[0])\\n event = DisconnectEvent(player_model)\\n Networking.get_instance().send_to_all_client(event)\\n return super(MastermindServerUDP, self).callback_disconnect()\",\n \"def shutdown_callback():\\n pass\",\n \"async def disconnect(self):\",\n \"def _disconnect(self):\\n self.socket.send_json({\\\"worker_id\\\": self.socket_id, \\\"message\\\": \\\"disconnect\\\"})\\n self.socket.close()\\n self.context.term()\\n exit()\",\n \"def on_close(self):\\n self.clients.remove(self)\",\n \"def on_close(self):\\n APP.clients.remove(self)\\n log(\\\"WebSocket closed. {0} child(s) connected\\\".\\n format(len(APP.clients)))\",\n \"def disconnect(self):\\n # Nothing to do\\n pass\",\n \"def the_current_device_is_disconnected():\\n assert web_app.disconnect_from_device()\",\n \"def onDisconnect(self):\\n if self.mode:\\n self.onSlaveLost()\\n else:\\n self.onMasterLost()\",\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def _disconnected(self, link_uri):\\n print('Disconnected from %s' % link_uri)\",\n \"def on_disconnected_cbk(_, reason: int):\\n cprint(f\\\"\\\\nDevice disconnected because: {reason}.\\\", color=\\\"red\\\", flush=True)\",\n \"def disconnect(self):\\n r = requests.post(f'{self.SERVER_ADDR}/api/disconnect', headers={'Authorization': 'Token ' + self.token})\\n r.raise_for_status()\",\n \"def connectionLost(self, reason):\\n self.finished.callback(self.display)\",\n \"def disconnect(self):\\n\\n if self.connected:\\n try:\\n self.sock.shutdown(socket.SHUT_WR)\\n self.sock.close()\\n self.connected = False\\n except SocketError as e:\\n print(\\\"Server must have disconnected first!\\\")\\n\\n print(\\\"Disconnection successful!\\\")\\n else:\\n print(\\\"Nothing to disconnect!\\\")\",\n \"def _disconnected(self, link_uri):\\n print \\\"Disconnected from %s\\\" % link_uri\",\n \"def onClose(self, wasClean, code=None, reason=None):\\n self.sessionhandler.disconnect(self)\\n if self.nextHeartbeatCall:\\n self.nextHeartbeatCall.cancel()\\n self.nextHeartbeatCall = None\\n if wasClean:\\n logger.log_info(f\\\"Discord connection closed ({code}) reason: {reason}\\\")\\n else:\\n logger.log_info(f\\\"Discord connection lost.\\\")\",\n \"def disconnect(self):\\n self.stop()\\n self._send_command('exit')\\n self.sock.close()\\n self.disconnected = True\",\n \"def handle_close(self):\\n LOGGER.debug(\\\"server closed\\\")\\n self.clear()\\n data = {}\\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\\n self.close()\\n\\n # close all and restart\\n asyncore.close_all()\",\n \"def connectionLost(reason):\",\n \"def _onDisconnect(self, client:mqtt.Client, userdata:Any, rc:int) -> None:\\n\\t\\tself.messageHandler and self.messageHandler.logging(self, logging.DEBUG, f'MQTT: Disconnected with result code: {rc} ({mqtt.error_string(rc)})')\\n\\t\\tself.subscribedTopics.clear()\\n\\t\\tif rc == 0:\\n\\t\\t\\tself.isConnected = False\\n\\t\\t\\tself.messageHandler and\\tself.messageHandler.onDisconnect(self)\\n\\t\\telif rc == 7:\\n\\t\\t\\tself.isConnected = False\\n\\t\\t\\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot disconnect from broker. Result code: {rc} ({mqtt.error_string(rc)})')\\n\\t\\t\\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Did another client connected with the same ID ({self.clientID})?')\\n\\t\\t\\tself.messageHandler and\\tself.messageHandler.onDisconnect(self)\\n\\t\\telse:\\n\\t\\t\\tself.isConnected = False\\n\\t\\t\\tif self.messageHandler:\\n\\t\\t\\t\\tself.messageHandler.logging(self, logging.ERROR, f'MQTT: Cannot disconnect from broker. Result code: {rc} ({mqtt.error_string(rc)})')\\n\\t\\t\\t\\tself.messageHandler.onDisconnect(self)\\n\\t\\t\\t\\tself.messageHandler.onError(self, rc)\",\n \"def connection_closed(self):\\n self.atv = None\\n self._start_connect_loop()\\n self._update_state(disconnected=True)\",\n \"def handle_disconnect(self,message,conn):\\n if self.connections[message[\\\"ID\\\"]][\\\"host\\\"] == True: #if user is host, delete all connections\\n print(\\\"HOST DISCONNECTING\\\")\\n host = message[\\\"ID\\\"]\\n session_location = self.connections[message[\\\"ID\\\"]][\\\"session_id\\\"]\\n for key in self.sessions[session_location][\\\"USERS\\\"].keys():\\n self.disconnect(self.connections[key][\\\"CONN\\\"],message[\\\"ID\\\"],f\\\"[HOST <{host}>:<{self.connections[host]['display_name']}>] Unexpectedly Disconnected\\\") #close each connection\\n self.delete_connection_entry(key) #delete the connection from the connections dictionary\\n self.delete_session(session_location) # delete the session\\n self.delete_connection_entry(message[\\\"ID\\\"])\\n self.disconnect(conn,message[\\\"ID\\\"], \\\"You Disconnected\\\")\\n else:\\n session_location = self.connections[message[\\\"ID\\\"]][\\\"session_id\\\"]\\n self.delete_session_entry(session_location,message[\\\"ID\\\"])\\n self.broadcast_to_session(session_location, \\\"USER_DISCONNECT\\\",f\\\"[USER <{message['ID']}>:<{self.connections[message['ID']]['display_name']}>] Disconnected\\\", exclude=[message[\\\"ID\\\"]])\\n self.delete_connection_entry(message[\\\"ID\\\"])\\n self.broadcast_to_session(session_location, \\\"USERS\\\", json.dumps(self.sessions[session_location][\\\"USERS\\\"]))\\n self.disconnect(conn,message[\\\"ID\\\"],\\\"You Disconnected\\\")\",\n \"def shutdown(self):\\n if self.alive:\\n libplasma.disconnect(self.conn)\\n self.alive = False\",\n \"def connectionLost(self,reason):\\n pass\",\n \"def on_disconnection(self, connection):\\n\\n self.root.connect_label.canvas.before.children[0].rgb = (1, 0, 0)\\n self.root.connect_label.text = 'Disconnected'\\n self.connection = None\",\n \"def on_websocket_close(self) -> None:\\n raise NotImplementedError() # pragma: no cover\",\n \"async def on_disconnect():\\n print(\\\"Bot has logged off\\\")\",\n \"def connection_lost(self, exc):\\n logger.info('The server closed the connection')\\n self.loop.stop()\",\n \"def notifyCNC2disconnected(self):\\n self.notifyGUItask(\\\"CNC2_DISCONNECTED\\\")\",\n \"def _disconnected(self, link_uri):\\n logger.debug(\\\"Crazyflie disconnected from {}\\\".format(link_uri))\\n self._clear_state()\\n self._update_graphics()\\n self._plot_3d.clear()\\n self._basestation_geometry_dialog.close()\\n self.is_lighthouse_deck_active = False\\n self._is_connected = False\\n self._update_ui()\",\n \"def _disconnect(self):\\n raise NotImplementedError(\\\"ERROR: Unimplemented function.\\\")\",\n \"def _on_disconnection(self, *_):\\n\\n if self._proxy is not None:\\n self._proxy.close()\\n self._proxy = None\",\n \"def __disconnected(self):\\n self.__changeMotorState(NOTINITIALIZED)\\n self.disconnected()\\n if self.__callbacks.get(\\\"disconnected\\\"):\\n cb = self.__callbacks[\\\"disconnected\\\"]()\\n if cb is not None:\\n cb()\",\n \"def server_close(self):\\n\\t\\tpass\",\n \"def disconnect(self):\\n\\t\\tself.ftp.quit()\\n\\t\\tself.online = False\\n\\t\\tErrore_connessione = True\\n\\t\\tserver_ind = self.entry1.get_text()\\n\\t\\tserverID = 0\\n\\t\\tStato_server = \\\"Disconnessione effettuata\\\"\\n\\t\\tOutwin_msg_FTP(Stato_server, Errore_connessione, server_ind, serverID)\\n\\t\\tprint \\\"Server disconnesso\\\"\",\n \"def _on_close(self):\\n self.web_socket_open = False\\n self.logged_in = False\\n print(\\\"WebSocket Closed for \\\" + self.session_name)\\n\\n if not self.disconnected_by_user:\\n print(\\\"Reconnect to the endpoint for \\\" + self.session_name + \\\" after 3 seconds... \\\")\\n time.sleep(3)\\n self.connect()\",\n \"def _disconnected(self, link_uri):\\n\\t\\tprint \\\"Disconnected from %s\\\" % link_uri\\n\\t\\tself.is_connected = False\\n\\t\\t#print results\",\n \"def on_close(self):\\n\\n if self.id in self.funcserver.websocks:\\n self.funcserver.websocks[self.id] = None\\n ioloop = tornado.ioloop.IOLoop.instance()\\n ioloop.add_callback(lambda: self.funcserver.websocks.pop(self.id, None))\\n\\n psession = self.funcserver.pysessions.get(self.pysession_id, None)\\n if psession:\\n psession[\\\"socks\\\"].remove(self.id)\\n if not psession[\\\"socks\\\"]:\\n del self.funcserver.pysessions[self.pysession_id]\",\n \"def onClientDeath(self):\\n\\t\\tDEBUG_MSG(\\\"Avatar[%i].onClientDeath:\\\" % self.id)\\n\\t\\tself.destroySelf()\",\n \"def\\tconnectionLost(self, reason):\\n\\t\\tprint \\\"[:)] Connectionlost con reason: \\\", reason, self.addr, self.port\",\n \"def server_closed_connection(self):\\n\\n print(\\\"Game Over!\\\")\\n if self._winner:\\n print(\\\"Player {} wins!\\\".format(self._winner))\\n else:\\n print(\\\"Draw!\\\")\",\n \"def disconnect_callback(self, excep):\\r\\n if excep is None:\\r\\n _LOGGER.debug(\\\"PyVisonic has caused an exception, no exception information is available\\\")\\r\\n else:\\r\\n _LOGGER.debug(\\\"PyVisonic has caused an exception %s\\\", str(excep))\\r\\n # General update trigger\\r\\n # 0 is a disconnect and (hopefully) reconnect from an exception (probably comms related)\\r\\n self.hass.bus.fire(self.visonic_event_name, {\\\"condition\\\": 0})\\r\\n sleep(5.0)\\r\\n _LOGGER.debug(\\\" ........... setting up reconnection\\\")\\r\\n self.panel_exception_counter = self.panel_exception_counter + 1\\r\\n asyncio.ensure_future(self.disconnect_callback_async(excep), loop=self.hass.loop)\",\n \"def guiding_disconnect():\\r\\n try:\\r\\n app.guider.disconnect()\\r\\n return jsonify({\\\"status\\\": True})\\r\\n except Exception as e:\\r\\n return jsonify(\\r\\n {\\\"status\\\": False, \\\"error\\\": \\\"Failed disconnecting from guider: %s\\\" % e}\\r\\n )\",\n \"def on_shutdown(self, server):\\n pass\",\n \"def destroy(self):\\r\\n\\t\\tsuper().destroy()\\r\\n\\t\\tif comm_server.is_running():\\r\\n\\t\\t\\tcomm_server.remove_disconnection_handler(self._update_connection_num)\\r\\n\\t\\t\\tcomm_server.stop_server()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79246867","0.78729045","0.742912","0.7390743","0.7358495","0.7335591","0.73057854","0.7276291","0.72380644","0.7230405","0.720272","0.71938705","0.7179511","0.7127314","0.71052814","0.7078196","0.706709","0.7030486","0.6985139","0.68786025","0.6869716","0.68351173","0.6828142","0.68252945","0.68252945","0.6771214","0.6728312","0.66678196","0.66562694","0.6645492","0.6589997","0.6581566","0.6575684","0.6551761","0.6538937","0.6526336","0.65254056","0.65254056","0.65254056","0.65254056","0.65254056","0.6523706","0.65048856","0.64833325","0.64669067","0.6445418","0.6444195","0.6442408","0.6401515","0.63882935","0.6368522","0.636778","0.63411206","0.634096","0.6337198","0.6335951","0.6330499","0.63300097","0.6317205","0.629813","0.62728703","0.62645775","0.6242834","0.62423474","0.62351596","0.62091833","0.6192625","0.6188959","0.61880577","0.6179101","0.6168461","0.61649644","0.61473465","0.6137611","0.6132806","0.6129122","0.61252296","0.6116899","0.6115446","0.61014855","0.6088057","0.60721785","0.60657406","0.6061965","0.60542285","0.60515946","0.604971","0.6046994","0.6041294","0.603448","0.60321945","0.6030767","0.6027545","0.60237926","0.6022623","0.6021333","0.60187876","0.6014094","0.60101175"],"string":"[\n \"0.79246867\",\n \"0.78729045\",\n \"0.742912\",\n \"0.7390743\",\n \"0.7358495\",\n \"0.7335591\",\n \"0.73057854\",\n \"0.7276291\",\n \"0.72380644\",\n \"0.7230405\",\n \"0.720272\",\n \"0.71938705\",\n \"0.7179511\",\n \"0.7127314\",\n \"0.71052814\",\n \"0.7078196\",\n \"0.706709\",\n \"0.7030486\",\n \"0.6985139\",\n \"0.68786025\",\n \"0.6869716\",\n \"0.68351173\",\n \"0.6828142\",\n \"0.68252945\",\n \"0.68252945\",\n \"0.6771214\",\n \"0.6728312\",\n \"0.66678196\",\n \"0.66562694\",\n \"0.6645492\",\n \"0.6589997\",\n \"0.6581566\",\n \"0.6575684\",\n \"0.6551761\",\n \"0.6538937\",\n \"0.6526336\",\n \"0.65254056\",\n \"0.65254056\",\n \"0.65254056\",\n \"0.65254056\",\n \"0.65254056\",\n \"0.6523706\",\n \"0.65048856\",\n \"0.64833325\",\n \"0.64669067\",\n \"0.6445418\",\n \"0.6444195\",\n \"0.6442408\",\n \"0.6401515\",\n \"0.63882935\",\n \"0.6368522\",\n \"0.636778\",\n \"0.63411206\",\n \"0.634096\",\n \"0.6337198\",\n \"0.6335951\",\n \"0.6330499\",\n \"0.63300097\",\n \"0.6317205\",\n \"0.629813\",\n \"0.62728703\",\n \"0.62645775\",\n \"0.6242834\",\n \"0.62423474\",\n \"0.62351596\",\n \"0.62091833\",\n \"0.6192625\",\n \"0.6188959\",\n \"0.61880577\",\n \"0.6179101\",\n \"0.6168461\",\n \"0.61649644\",\n \"0.61473465\",\n \"0.6137611\",\n \"0.6132806\",\n \"0.6129122\",\n \"0.61252296\",\n \"0.6116899\",\n \"0.6115446\",\n \"0.61014855\",\n \"0.6088057\",\n \"0.60721785\",\n \"0.60657406\",\n \"0.6061965\",\n \"0.60542285\",\n \"0.60515946\",\n \"0.604971\",\n \"0.6046994\",\n \"0.6041294\",\n \"0.603448\",\n \"0.60321945\",\n \"0.6030767\",\n \"0.6027545\",\n \"0.60237926\",\n \"0.6022623\",\n \"0.6021333\",\n \"0.60187876\",\n \"0.6014094\",\n \"0.60101175\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7072486"},"document_rank":{"kind":"string","value":"17"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":150,"numItemsPerPage":100,"numTotalItems":95772,"offset":15000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjA4NTM4MCwianRpIjoiYjdkNGVmYzktOTU0Ni00ZWIzLTlkNDItN2Y2NzUxYWQwNWZkIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwODg5ODAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.wT9L8dWg0HNA72y6U1t5nzgFkQncdSb7cCdEJ_qs3gFJq4rTjfoc7jPI53MmkjeHqMsr33RElbaXCJ_b5_hhDg","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 |
|---|---|---|---|---|---|---|
Test that Logger Temperature file with duplicate entry cannot be uploaded | def test_logger_temperature_upload_duplicate(self):
test_type_filename = 'server/tests/test_data_files/Test/Test_New_Logger_Type_Positive.csv'
test_temp_filename = 'server/tests/test_data_files/Test/temp_files/DUMMYID_2000_pgsql_Duplicate.txt'
with self.app.test_client() as client:
w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_upload_duplicate(client: FlaskClient):\n file = get_example_file(ExampleFileType.Png)\n response1 = util.upload_file(client, DEFAULT_USER, file)\n response2 = util.upload_file(client, DEFAULT_USER, file)\n\n assert response1.status == \"201 CREATED\"\n assert response2.status == \"200 OK\"\... | [
"0.67010015",
"0.638992",
"0.6376859",
"0.6261966",
"0.624463",
"0.62304544",
"0.62001187",
"0.6198904",
"0.6182989",
"0.61653274",
"0.6137007",
"0.6109847",
"0.6075311",
"0.60572684",
"0.6047644",
"0.60395145",
"0.60256505",
"0.6012684",
"0.5980237",
"0.595728",
"0.5917336",... | 0.7041636 | 0 |
Esta funcion resuelve una ecuacion de la forma ax^2+bx+c=0 | def raices_eq_cuadratica(a: float,b: float,c: float) -> Tuple[float, float]:
x1 = (-b + (b**2-4*a*c)**0.5) / (2*a)
x2 = (-b - (b ** 2 - 4 * a * c) ** 0.5) / (2 * a)
return x1, x2 # equivale a (x1, x2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex_inverse(c1,cr):",
"def wce(B):\n return eme*B",
"def TestFunc2(x):\r\n return 10*(-0.02*x[0] + 0.5*x[0]*x[0] + x[1])**2 \\\r\n + 128*(-0.02*x[0] + 0.5*x[0]*x[0] - x[1]/4) \\\r\n - (8e-5)*x[0]",
"def func_voce(eps,a,b0,c,b1):\n return a-b0*np.exp(-c*eps) + b1 *... | [
"0.640015",
"0.6392045",
"0.6250174",
"0.61922735",
"0.61771965",
"0.6088744",
"0.608832",
"0.6082301",
"0.60774565",
"0.6073722",
"0.6049641",
"0.6031505",
"0.60018027",
"0.59975994",
"0.5991092",
"0.59697044",
"0.59589124",
"0.59403247",
"0.5937569",
"0.59324086",
"0.591199... | 0.0 | -1 |
Computes the value of an entry by running its task. Requires that all the task's dependencies are already computed. | def compute(self, context):
# TODO There are a few cases here where we acccess private members on
# self.state; should we clean this up?
state = self.state
task = state.task
protocol = state.desc_metadata.protocol
assert state.is_initialized
assert not state.is... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute(self, task_key_logger):\n\n task = self.task\n\n dep_results = [\n dep_state.get_results_assuming_complete(task_key_logger)[\n dep_key.dnode.to_entity_name()\n ]\n for dep_state, dep_key in zip(self.dep_states, task.dep_keys)\n ]\n\n... | [
"0.63904375",
"0.59485996",
"0.57995343",
"0.5572648",
"0.5520435",
"0.55173826",
"0.54368013",
"0.53980225",
"0.53946394",
"0.53903",
"0.53770065",
"0.5371793",
"0.53237903",
"0.53095233",
"0.52732223",
"0.5251898",
"0.52199835",
"0.52119595",
"0.5201217",
"0.51943415",
"0.5... | 0.7053696 | 0 |
Indicates whether the task state's result is cached. | def is_cached(self):
if self.should_persist:
# If our value is persistable, it can be saved either on disk or in memory,
# but only the former counts as being officially "cached".
return self._result_value_hash is not None
else:
return self._result is not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_cached(self):\n return False",
"def is_cached(name, typ=\"pkl\"):\n return os.path.exists(cache_name(name, typ))",
"def _may_cache(self, request, response=None):\n # any successful request may be cached\n return ((HTTPStatus.OK <= response.status_code < HTTPStatus.BAD_REQUEST)\n ... | [
"0.74749434",
"0.68324715",
"0.68149173",
"0.6738329",
"0.6655201",
"0.66187906",
"0.65679663",
"0.6416865",
"0.6415875",
"0.6382452",
"0.6292968",
"0.62685287",
"0.62240446",
"0.6211946",
"0.6159335",
"0.61113095",
"0.60873556",
"0.60792947",
"0.6072414",
"0.6051632",
"0.604... | 0.79039913 | 0 |
Loads the hash of the persisted value for this task, if it exists. If the persisted value is available in the cache, this object's `is_cached` property will become True. Otherwise, nothing will happen. | def attempt_to_access_persistent_cached_value(self):
assert self.is_initialized
assert not self.is_cached
if not self.should_persist:
return
if not self._cache_accessor.can_load():
return
self._load_value_hash() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_value_hash(self):\n\n artifact = self._cache_accessor.load_artifact()\n if artifact is None or artifact.content_hash is None:\n raise AssertionError(\n oneline(\n f\"\"\"\n Failed to load cached value (hash) for descriptor\n ... | [
"0.72252256",
"0.63859934",
"0.5972734",
"0.5886148",
"0.5879905",
"0.5804419",
"0.574814",
"0.5677867",
"0.5613093",
"0.5568389",
"0.5543914",
"0.54435587",
"0.54258996",
"0.5423656",
"0.5395598",
"0.53953743",
"0.5376388",
"0.53689486",
"0.53612226",
"0.53522384",
"0.535086... | 0.71849906 | 1 |
Refreshes all state that depends on the persistent cache. This is useful if the external cache state might have changed since we last worked with this task. | def refresh_all_persistent_cache_state(self, context):
# If this task state is not initialized or not persisted, there's nothing to
# refresh.
if not self.is_initialized or not self.should_persist:
return
self.refresh_cache_accessor(context)
# If we haven't loaded ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flush_local_cache(self):\n self._local_cache = {}",
"def reload_cache(self):\n self.data = self.read_data_cache()",
"def flush_cache(self):\n if self.cache_modified:\n self.cache_manager.write(self.cache_file, self.cache)",
"def sync_after_remote_computation(self):\n\n ... | [
"0.6510259",
"0.63184017",
"0.6079995",
"0.6077812",
"0.60450286",
"0.5920667",
"0.5860059",
"0.58537775",
"0.5784254",
"0.5776172",
"0.5739506",
"0.57234544",
"0.5665114",
"0.56476337",
"0.564716",
"0.56465435",
"0.5620647",
"0.5612728",
"0.55823725",
"0.5579131",
"0.5574888... | 0.75692886 | 0 |
Syncs the task state by populating and reloading data in the current process after completing the task state in a subprocess. This is necessary because values populated in the task state are not communicated back from the subprocess. | def sync_after_remote_computation(self):
# If this state was never initialized, it doesn't have any out-of-date
# information, so there's no need to update anything.
if not self.is_initialized:
return
assert self.should_persist
# First, let's flush the stored entri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync_after_subprocess_completion(self):\n\n # First, let's flush the stored entries in cache accessors. This is to prevent cases\n # like current process contains an outdated stored entries.\n #\n # For example: In assisted mode, main process checks for versioning error which can\n ... | [
"0.679525",
"0.60613567",
"0.60286933",
"0.60181457",
"0.60056067",
"0.5974136",
"0.59708184",
"0.5753098",
"0.5696014",
"0.56820947",
"0.5621178",
"0.5607121",
"0.55749136",
"0.5534419",
"0.5492815",
"0.538063",
"0.53788686",
"0.53788686",
"0.53278726",
"0.5323719",
"0.53019... | 0.57953227 | 7 |
Initializes the cache acessor for this task state. This sets up state that allows us to read and write cache entries for this task's value. This includes some inmemory representations of exernal persistent resources (files or cloud blobs); calling this multiple times can be necessary in order to wipe this state and all... | def refresh_cache_accessor(self, context):
self._cache_accessor = context.core.persistent_cache.get_accessor(
task_key=self.task_key,
provenance=self._provenance,
)
if context.core.versioning_policy.check_for_bytecode_errors:
self._check_accessor_for_version_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_cache(self):\n if self.cacheable:\n self._instance._cache[self.name] = {}",
"def __init__(self, persistent=True):\n super().__init__()\n self.name_cache = {}\n self.run_cache = {}\n self.row_cache = {}\n self.persistent = persistent\n\n if self... | [
"0.71220165",
"0.65763086",
"0.65461636",
"0.6457462",
"0.64347655",
"0.63934815",
"0.62920505",
"0.62669885",
"0.61933905",
"0.60915667",
"0.6079506",
"0.60543674",
"0.6022922",
"0.60054994",
"0.599823",
"0.59843284",
"0.5959548",
"0.5947162",
"0.5927737",
"0.5907709",
"0.59... | 0.54607123 | 82 |
Checks for any versioning errors i.e., any cases where a task's function code was updated but its version annotation was not. | def _check_accessor_for_version_problems(self):
old_prov = self._cache_accessor.load_provenance()
if old_prov is None:
return
new_prov = self._cache_accessor.provenance
if old_prov.exactly_matches(new_prov):
return
if old_prov.nominally_matches(new_prov... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_missing_versioned_tags(module):\n version_table = getattr(module, '__version_table__', {})\n\n # Get all functions from the module.\n functions = inspect.getmembers(module, inspect.isfunction)\n functions_dict = dict(functions)\n function_names = list(functions_dict)\n\n # Get all c... | [
"0.69742507",
"0.61686295",
"0.6032363",
"0.59617966",
"0.58357054",
"0.5830695",
"0.5827717",
"0.5817524",
"0.58131546",
"0.5808405",
"0.58020407",
"0.57866913",
"0.57857144",
"0.57734126",
"0.57588166",
"0.56948197",
"0.5692589",
"0.56653947",
"0.56652933",
"0.56586355",
"0... | 0.6633973 | 1 |
Reads (from disk or cloud) and saves (in memory) this task's value hash. | def _load_value_hash(self):
artifact = self._cache_accessor.load_artifact()
if artifact is None or artifact.content_hash is None:
raise AssertionError(
oneline(
f"""
Failed to load cached value (hash) for descriptor
{self._... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_digest(self, task: \"TaskView\") -> dict:\n # XXX user definition should be able to exclude inputs from digest\n # XXX might throw AttributeError\n inputs = task._resolved_inputs # type: ignore\n\n # sensitive values are always redacted so no point in including them in the dig... | [
"0.61814857",
"0.5982808",
"0.5848447",
"0.5757408",
"0.5693065",
"0.5498911",
"0.54744655",
"0.5450672",
"0.5423203",
"0.54196274",
"0.5387745",
"0.53874344",
"0.53849286",
"0.5367149",
"0.5363407",
"0.53323734",
"0.5316272",
"0.52829957",
"0.52800924",
"0.5279477",
"0.52739... | 0.65436584 | 0 |
Returns copies of the provided TaskStates with any unnecessary state and ancestors "stripped" off; these copies can be safely transmitted to another process for computation. | def strip_states(self, states):
stripped_states_by_task_key = {}
def strip_state(original_state):
"""Returns a stripped copy of a TaskState."""
task_key = original_state.task_key
if task_key in stripped_states_by_task_key:
return stripped_states_by_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_state(original_state):\n\n task_key = original_state.task_key\n if task_key in stripped_states_by_task_key:\n return stripped_states_by_task_key[task_key]\n\n assert original_state in self.all_states\n assert original_state not in self.non_serial... | [
"0.7007315",
"0.6059291",
"0.57722616",
"0.5648813",
"0.54974157",
"0.54269755",
"0.53582656",
"0.53353125",
"0.52991706",
"0.52278084",
"0.5219097",
"0.5181115",
"0.5153454",
"0.5147667",
"0.5111569",
"0.51057416",
"0.5078182",
"0.5068822",
"0.5066051",
"0.49776638",
"0.4975... | 0.84246093 | 0 |
Returns a stripped copy of a TaskState. | def strip_state(original_state):
task_key = original_state.task_key
if task_key in stripped_states_by_task_key:
return stripped_states_by_task_key[task_key]
assert original_state in self.all_states
assert original_state not in self.non_serializable_state... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_states(self, states):\n\n stripped_states_by_task_key = {}\n\n def strip_state(original_state):\n \"\"\"Returns a stripped copy of a TaskState.\"\"\"\n\n task_key = original_state.task_key\n if task_key in stripped_states_by_task_key:\n return... | [
"0.74558896",
"0.59286696",
"0.5859667",
"0.5805591",
"0.57871336",
"0.5779528",
"0.5778583",
"0.5769516",
"0.5739012",
"0.57232314",
"0.56408906",
"0.5633798",
"0.5573608",
"0.55289465",
"0.5504658",
"0.5491149",
"0.547416",
"0.5440028",
"0.5384897",
"0.5383192",
"0.5378312"... | 0.7607578 | 0 |
View to return all information needed to display the cart, by converting what has been saved to the session into key variables. Protection in place in case a product, size or nic has been deleted while still in the cart, removing from the list before saving back to the cart session variable. | def cart_contents(request):
cart_items = []
total = 0
savings = 0
product_count = 0
points_available = 0
points_earned = 0
discount_applied = request.session.get('discount_applied')
cart = request.session.get('cart', {})
# Create a new dict so that items can be removed if needed
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detail(request):\n # del request.session['cart_id']\n # del request.session['total_in_cart']\n data = {}\n if (cart_id := request.session.get('cart_id', None)):\n cart = Cart.objects.get(pk=cart_id)\n data['products_in_cart'] = cart.cartitems.all()\n data['total_price'] = cart.... | [
"0.74897677",
"0.70164984",
"0.6997387",
"0.68789303",
"0.68103784",
"0.6678317",
"0.66267216",
"0.6622569",
"0.66087735",
"0.6606675",
"0.6523773",
"0.6521631",
"0.6521631",
"0.647599",
"0.647599",
"0.647599",
"0.64378947",
"0.6432149",
"0.63870263",
"0.63262737",
"0.6304301... | 0.76600933 | 0 |
Calculate the magnetic field from the WMM 2019. | def GeoMag(self, location, time=datetime.datetime.today(), location_format='geodetic', output_format='geodetic'):
# convert location to spherical coordinates
if location_format == 'geodetic':
lat, lon, alt = np.deg2rad(location[0]), np.deg2rad(location[1]), location[2] * 1e-3 # convert from... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magnetisation(field):\n norm_field = df.Field(field.mesh, dim=1, value=(field.norm.array != 0))\n volume = df.integral(norm_field * df.dV, direction='xyz')\n return df.integral(field * df.dV / volume, direction='xyz')",
"def magnetic_field(date: datetime.datetime, lat, lon, alt, output_format='carte... | [
"0.75418234",
"0.6628489",
"0.6579454",
"0.6525822",
"0.65067333",
"0.6360796",
"0.63534766",
"0.62209004",
"0.62075037",
"0.6105055",
"0.6103376",
"0.60536784",
"0.60512596",
"0.60364956",
"0.6007709",
"0.5975416",
"0.59612495",
"0.5917661",
"0.5905833",
"0.59036034",
"0.590... | 0.0 | -1 |
Outputs magnetic field given lat, lon, alt. | def magnetic_field(date: datetime.datetime, lat, lon, alt, output_format='cartesian'):
g = GeoMag()
return g.GeoMag(np.array([lat, lon, alt]), date, location_format='geodetic', output_format=output_format) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magnetization(h):\n if h.has_eh: raise\n if h.has_spin: \n mx = extract.mx(h.intra)\n my = extract.my(h.intra)\n mz = extract.mz(h.intra)\n else: raise\n np.savetxt(\"MAGNETIZATION_X.OUT\",np.matrix([h.geometry.x,h.geometry.y,mx]).T)\n np.savetxt(\"MAGNETIZATION_Y.OUT\",np.matrix([h.geometry.x,h.... | [
"0.6234416",
"0.6036381",
"0.56869453",
"0.5671633",
"0.5600399",
"0.55292517",
"0.55019677",
"0.544296",
"0.53621125",
"0.5302858",
"0.5279656",
"0.51781017",
"0.50963485",
"0.5032904",
"0.500878",
"0.49855933",
"0.49819586",
"0.49788126",
"0.49635312",
"0.49299306",
"0.4889... | 0.79384327 | 0 |
Calculate a checksum for num using the Luhn algorithm. | def luhn_checksum(num: str) -> str:
check = 0
for i, s in enumerate(reversed(num)):
sx = int(s)
if i % 2 == 0:
sx *= 2
if sx > 9:
sx -= 9
check += sx
return str(check * 9 % 10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checksum(n):\n\n # Compute the sum of the non-check digits.\n s = sum(luhn_digits(n * 10))\n\n # Multiply by 9.\n result = s * 9\n\n # The units digit is the check digit\n check_digit = result % 10\n\n m = int(str(n) + str(check_digit))\n assert(verify(m))\n\n return check_digit",
... | [
"0.8084448",
"0.80643237",
"0.78999513",
"0.7894059",
"0.7728619",
"0.75503194",
"0.7511611",
"0.7499676",
"0.74020904",
"0.71649796",
"0.7110696",
"0.6942625",
"0.6888765",
"0.68604416",
"0.6764696",
"0.6605598",
"0.65903616",
"0.6547519",
"0.64829504",
"0.64828515",
"0.6453... | 0.84606624 | 0 |
Romanize a given string. | def romanize(string: str, locale: t.Union[Locale, str]) -> str:
locale = validate_locale(locale)
if locale not in (Locale.RU, Locale.UK, Locale.KK):
raise ValueError(f"Romanization is not available for: {locale}")
table = _get_translation_table(locale)
return string.translate(table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fromRoman(s):\n pass",
"def toRoman(n):\n pass",
"def romanify(num):\n result = \"\"\n return result",
"def fromRoman(s):\n if not s:\n raise InvalidRomanNumeralError, 'Input can not be blank'\n if not romanNumeralPattern.search(s):\n raise InvalidRomanNumeralError, 'Invalid R... | [
"0.7063574",
"0.689551",
"0.6734362",
"0.64427215",
"0.63702637",
"0.6330791",
"0.6323522",
"0.6299757",
"0.6215566",
"0.6198708",
"0.61629105",
"0.61244214",
"0.6120783",
"0.61043954",
"0.60932904",
"0.60877186",
"0.6060893",
"0.5971338",
"0.5948232",
"0.5926629",
"0.5908616... | 0.7607582 | 0 |
Configure the logging system. If a logpath is provided, entries will also be written to that logfile. | def configure_logger(logpath, loglevel=logging.DEBUG):
handlers = [logging.StreamHandler()]
if logpath:
handlers.append(logging.FileHandler(logpath))
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%d-%m-%y %H:%M:%S', level=loglevel, handlers=han... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _configure_logging(self):\n self.log_level = Scaffold.LOG_LEVEL_MAP.get(self.log_level, ERROR)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n # assign the windmill instance logger\n #logging.basicConfig()\n self.log = logging.getLo... | [
"0.7340265",
"0.7313647",
"0.7286824",
"0.724973",
"0.7248175",
"0.7177587",
"0.7139101",
"0.70546126",
"0.70544195",
"0.7049995",
"0.70458865",
"0.70351976",
"0.70171684",
"0.70139897",
"0.7012062",
"0.7011974",
"0.69839966",
"0.69708276",
"0.69193125",
"0.68887985",
"0.6874... | 0.8009328 | 0 |
Evaluate `x` using locallyweighted regression parameters. Degree of polynomial used in loess is inferred from b. `x` is assumed to be a scalar. | def loc_eval(x, b):
loc_est = 0
for i in enumerate(b): loc_est+=i[1]*(x**i[0])
return(loc_est) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def local_linear_regression(x, y, basis_point_index, eps_scale=3, bandwidth=None, bandwidth_type='median'):\n # Calculate a bandwidth.\n if bandwidth is None:\n if bandwidth_type == 'median':\n bandwidth = np.power((np.median(squareform(pdist(x))) / eps_scale), 2)\n elif bandwidth_ty... | [
"0.6404346",
"0.62495077",
"0.6190865",
"0.6152914",
"0.60442674",
"0.5979035",
"0.5977707",
"0.5963519",
"0.59209937",
"0.58717275",
"0.58336616",
"0.582725",
"0.5811",
"0.577924",
"0.57749844",
"0.576471",
"0.5730046",
"0.57143253",
"0.56869996",
"0.5661605",
"0.56416345",
... | 0.5679762 | 19 |
Perform locallyweighted regression on xvals & yvals. | def loess(xvals, yvals, alpha, poly_degree=1):
# Sort dataset by xvals.
all_data = sorted(zip(xvals, yvals), key=lambda x: x[0])
xvals, yvals = zip(*all_data)
locsDF = pd.DataFrame(
columns=[
'loc','x','weights','v','y','raw_dists',
'scale_fac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def local_weight_regression(\r\n training_data_x: np.mat, training_data_y: np.mat, bandwidth: float\r\n) -> np.mat:\r\n m, n = np.shape(training_data_x)\r\n ypred = np.zeros(m)\r\n\r\n for i, item in enumerate(training_data_x):\r\n ypred[i] = item * local_weight(\r\n item, training_da... | [
"0.65673846",
"0.6203821",
"0.6122985",
"0.6087269",
"0.5884519",
"0.58712137",
"0.58013576",
"0.56775177",
"0.56697303",
"0.5631842",
"0.5581171",
"0.5570354",
"0.55586505",
"0.5543728",
"0.5536329",
"0.55168223",
"0.5516314",
"0.55150086",
"0.5470168",
"0.54659784",
"0.5465... | 0.58164173 | 6 |
Connect two nodes with a channel. Connects node a to node b using the given channel. | def connect(self, channel, a, b):
a.sender.channels.append(channel)
channel.receivers.append(b) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connectChannel(sock, chan):\n sock.send(\"JOIN {}\\r\\n\".format(chan).encode(\"utf-8\"))\n\n console.info(\"Successfully connected to {}\".format(chan))",
"def connect(self, node1, node2):\n self.neighbour1 = node1\n self.neighbour2 = node2",
"def connect_two_nodes(left_node: Node, rig... | [
"0.60193217",
"0.58925235",
"0.5827985",
"0.57909036",
"0.57899636",
"0.56818223",
"0.56715274",
"0.5666792",
"0.56497663",
"0.56090224",
"0.5600908",
"0.55008054",
"0.54504013",
"0.5447329",
"0.54283625",
"0.5424262",
"0.54172355",
"0.53959185",
"0.5352588",
"0.53389084",
"0... | 0.73055255 | 0 |
Get statistics collected during run. | def get_statistics(self):
return self.results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_run_stats(self):\n return self.run_stats",
"def statistics(self):\n return self.get_statistics()",
"def stats(self):\n pass",
"def get_stats(self):\n return self.stats",
"def get_stats(self):\n return self.manager.get_stats(self)",
"def getStats(self):\n\n ... | [
"0.8423611",
"0.7779856",
"0.77203375",
"0.77192026",
"0.76687366",
"0.7663487",
"0.75916755",
"0.75905645",
"0.75601757",
"0.73241776",
"0.7218625",
"0.71950877",
"0.71844864",
"0.7177086",
"0.7143594",
"0.7113807",
"0.710871",
"0.7092663",
"0.70923436",
"0.70543694",
"0.704... | 0.7649026 | 6 |
Create a time stamp | def format_time(self, record):
record.dbtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(record.created))
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_time_stamp() -> str:\n\n return datetime.datetime.now().strftime(\"%Y%m%d\")",
"def createTimeStamp_Time():\r\n\r\n return str(datetime.now().strftime(\"%H%M%S\"))",
"def _generate_timestamp():\n\t\treturn strftime(\"%Y%m%dT%H%M%S\")",
"def time_stamper() :\n\treturn datetime.now().strf... | [
"0.8233453",
"0.80581737",
"0.79409295",
"0.7885426",
"0.77232575",
"0.76477885",
"0.76388806",
"0.7607675",
"0.7506243",
"0.7500952",
"0.7491634",
"0.7403374",
"0.73812056",
"0.73719895",
"0.7358379",
"0.72903913",
"0.72595435",
"0.72434366",
"0.72373694",
"0.7206951",
"0.71... | 0.0 | -1 |
crawl objct and return the result | def crawl_start(crawl_obj):
res = None
if crawl_obj.type in ['user', 'song'] :
res = eval('crawl_' + crawl_obj.type)(crawl_obj)
elif crawl_obj.type in ['artist', 'album'] :
web_data = requests.get(crawl_obj.url, headers = cheat_headers)
soup = bs4.BeautifulSoup(web_data.text, '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _scrape(self):",
"def scrape(self):\n pass",
"def crawl(self, url):\n return None",
"def scrape(self):\n try:\n self.result = urlfetch.fetch(self.url)\n except DownloadError:\n self.result = urlfetch.fetch(self.url) \n if ((self.result.status_code == 200) and\n (s... | [
"0.71161443",
"0.66178185",
"0.6551923",
"0.60435236",
"0.6042226",
"0.6040767",
"0.59387845",
"0.5858021",
"0.5807145",
"0.58034575",
"0.5781588",
"0.5735748",
"0.5689979",
"0.568243",
"0.56674963",
"0.5655977",
"0.56543124",
"0.5643372",
"0.56385326",
"0.5625415",
"0.562377... | 0.6746438 | 1 |
Calculate frequency table of characters in text. | def histogram(text):
hist = {}
for char in text.lower():
if char.isalpha():
hist[char] = hist.get(char, 0) + 1
else:
hist['others'] = hist.get('others', 0) + 1
return hist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def letter_freq( text ):\n\tchars = string.ascii_uppercase\n\ttext = text.upper()\n\tresult = get_letter_dict()\n\ttotal = 0\n\tfor char in chars:\n\t\tcount = text.count(char)\n\t\tresult[char] = count\n\t\ttotal += count\n\tif total != 0:\n\t\tfor char in chars:\n\t\t\tresult[char] = (result[char]*10000 / total)... | [
"0.7648815",
"0.7515002",
"0.7477705",
"0.7281719",
"0.71724904",
"0.7077413",
"0.70214486",
"0.6931976",
"0.6922323",
"0.6906666",
"0.6856542",
"0.6832168",
"0.68194664",
"0.6801115",
"0.6779408",
"0.67449754",
"0.67339164",
"0.669856",
"0.66707134",
"0.6670571",
"0.6635781"... | 0.69385314 | 7 |
move and return the grid without a new random tile It won't affect the state of the game in the browser. | def execute_move(move, board):
if move == UP:
return game.merge_up(board)
elif move == DOWN:
return game.merge_down(board)
elif move == LEFT:
return game.merge_left(board)
elif move == RIGHT:
return game.merge_right(board)
else:
sys.exit("No valid move") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_tile(self):\n while True:\n random_row = random.randrange(self._grid_height)\n random_column = random.randrange(self._grid_width)\n if self._grid[random_row][random_column] == 0:\n self._grid[random_row][random_column] = random.choice([2] * 9 + [4])\n ... | [
"0.79467946",
"0.7700813",
"0.7627812",
"0.7521414",
"0.7489073",
"0.74867195",
"0.7348494",
"0.7314265",
"0.7249992",
"0.7211116",
"0.71880275",
"0.71707225",
"0.7168241",
"0.70822585",
"0.7079668",
"0.70515215",
"0.70515215",
"0.70206416",
"0.6987183",
"0.69860077",
"0.6921... | 0.0 | -1 |
Check if two boards are equal | def board_equals(board, newboard):
return (newboard == board).all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other) -> None:\n\t\tfor k, v in enumerate(self.board):\n\t\t\tif v != other.board[k]:\n\t\t\t\treturn False\n\t\treturn True",
"def __eq__(self, other):\n for row in range( self.n ):\n if self.board[row] != other.board[row]:\n return False\n return True",... | [
"0.7675659",
"0.75886476",
"0.75203764",
"0.7315694",
"0.70487416",
"0.6995405",
"0.697579",
"0.69640183",
"0.6950214",
"0.6934378",
"0.69289273",
"0.6896391",
"0.68638206",
"0.6810947",
"0.68063855",
"0.67953414",
"0.67835134",
"0.67559415",
"0.6735635",
"0.6714601",
"0.6604... | 0.77490914 | 2 |
Returns the learning rate scaled by schedule(s) that will be used for the next training step | def current_lr(
self, optimizer_states: types.OptimizerStates
) -> tp.Optional[jnp.ndarray]:
if self.lr_schedule is not None:
step = optimizer_states[-1].count
return self.lr_schedule(step) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def learning_rate_schedule(current_epoch,\n current_batch,\n batches_per_epoch,\n batch_size):\n del current_batch, batches_per_epoch # not used\n initial_learning_rate = common.BASE_LEARNING_RATE * batch_size / 128\n learning_rate =... | [
"0.79894197",
"0.7785588",
"0.77826655",
"0.76667386",
"0.76645464",
"0.7660921",
"0.74982613",
"0.74174136",
"0.74023986",
"0.7375447",
"0.7314137",
"0.7252626",
"0.7250394",
"0.72385687",
"0.7227012",
"0.7188356",
"0.717606",
"0.7163271",
"0.7159572",
"0.7149901",
"0.714215... | 0.0 | -1 |
get available directions for any number of dimensions | def get_directions(board_ndim):
directions = [
[[0 for _ in range(board_ndim)] for _ in range(2)]
for _ in range(board_ndim)
]
for ind in range(board_ndim):
directions[ind][0][ind] = 1
directions[ind][1][ind] = -1
return directions | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_directions():\n return [(1, 0), (0, 1), (-1, 0), (0, -1)]",
"def directions(self):\n return []",
"def directions(self):\n direction_list = []\n for direction in Maze.possible_directions:\n move = Maze.dirs_to_moves[direction]\n if (0 <= (self.location[0]+mo... | [
"0.6784351",
"0.6429041",
"0.6240504",
"0.6145204",
"0.5987847",
"0.59382296",
"0.59382296",
"0.59382296",
"0.59382296",
"0.59382296",
"0.59382296",
"0.59382296",
"0.588869",
"0.5781544",
"0.57415617",
"0.56616753",
"0.56433386",
"0.5630479",
"0.5609333",
"0.56029123",
"0.559... | 0.61947703 | 3 |
iterates over points on the board | def points_generator(self):
rows, cols = self.game.board.board_size
points = [Point(i, j) for i, j in product(range(rows), range(cols))]
for point in points:
yield point | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 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_... | [
"0.72778916",
"0.68592465",
"0.6857867",
"0.6820964",
"0.663074",
"0.6541899",
"0.64622074",
"0.6450496",
"0.6430873",
"0.6379434",
"0.6351412",
"0.6338828",
"0.62530154",
"0.6235255",
"0.622739",
"0.6218105",
"0.6214263",
"0.6176402",
"0.61315227",
"0.61090213",
"0.60718495"... | 0.6902485 | 1 |
calculate available actions for current board sizes | def get_available_actions(self):
actions = []
direction = [[1, 0], [0, 1]]
for dir_ in direction:
for point in self.points_generator():
dir_p = Point(*dir_)
new_point = point + dir_p
try:
_ = se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getAvailableActions(self, board):\r\n myPits = board.mySide(self.id)\r\n return [i for i in myPits if i > 0]",
"def _getAvailableActions(self, board):\r\n myPits = board.mySide(self.id)\r\n return [i for i in myPits if i > 0]",
"def get_cost_of_actions_helper(actions, board, st... | [
"0.68598026",
"0.68598026",
"0.6652122",
"0.66291237",
"0.6517024",
"0.650987",
"0.6499747",
"0.6499747",
"0.64463955",
"0.63347924",
"0.6303849",
"0.63017744",
"0.62943614",
"0.62659013",
"0.6257811",
"0.62543374",
"0.6197231",
"0.6195532",
"0.6195532",
"0.6195532",
"0.61796... | 0.57378316 | 39 |
r"""AlexNet model architecture from the | def alexnet(pretrained=False, **kwargs):
model = AlexNet(**kwargs)
if pretrained:
model_path = './alexnet.pth.tar'
pretrained_model = torch.load(model_path)
model.load_state_dict(pretrained_model['state_dict'])
return model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def alexnet(**kwargs):\n model = AlexNet(**kwargs)\n # if pretrained:\n # model.load_state_dict(model_zoo.load_url(model_urls['alexnet']))\n return model",
"def imagenet_alexnet(**kwargs):\r\n model = ImageNetAlexNet(**kwargs)\r\n return model",
"def load(self, name=\"\"):\n\n self... | [
"0.6519144",
"0.6468875",
"0.64450973",
"0.64397836",
"0.631246",
"0.6307277",
"0.6291954",
"0.6257867",
"0.62547463",
"0.6231065",
"0.61943966",
"0.614853",
"0.6140133",
"0.6133221",
"0.6133221",
"0.61192954",
"0.610197",
"0.60987943",
"0.6097791",
"0.6090801",
"0.6086538",
... | 0.61159253 | 16 |
Returns the minimum value of a and b ignoring any negative values. | def _get_min_positive_value(self, a, b):
if a < 0 and b >= 0:
return b
if a >= 0 and b < 0:
return a
return min(a, b) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mini(a, b):\n return min(a, b)",
"def _null_min(a, b):\n if a is None:\n return b\n if b is None:\n return a\n return min(a, b)",
"def minimum_inplace(a, b):",
"def minimum(lhs, rhs):\n return _make.minimum(lhs, rhs)",
"def smallest_diff(a, b):\n b.sort()\n smallest_diff = None\n\n... | [
"0.79426205",
"0.7518428",
"0.7121123",
"0.702984",
"0.6997932",
"0.69437057",
"0.68694836",
"0.68295264",
"0.6827028",
"0.6682797",
"0.66441596",
"0.6604545",
"0.6468406",
"0.64508706",
"0.64410925",
"0.64327365",
"0.63909256",
"0.6367581",
"0.6353903",
"0.63516575",
"0.6350... | 0.8785171 | 0 |
Converts image in PIL format to base64. | def img_to_base64(img):
with io.BytesIO() as output:
img.save(output, format="PNG")
img_string = base64.b64encode(output.getvalue())
return img_string.decode("utf-8") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def b64_image(self) -> bytes:\n buffer = BytesIO()\n self.image.save(buffer, \"PNG\") \n im_b64 = base64.b64encode(buffer.getvalue())\n im_b64 = b\"data:image/png;base64,\" + im_b64\n return im_b64",
"def image_to_base64str(image):\n file_bytes = image.file.read()\n bas... | [
"0.79094106",
"0.7860328",
"0.78388363",
"0.7716888",
"0.7690451",
"0.7282849",
"0.7243197",
"0.7226432",
"0.71624523",
"0.7135727",
"0.710332",
"0.7079983",
"0.7032868",
"0.70260394",
"0.7014201",
"0.70000696",
"0.6970189",
"0.69077027",
"0.6870043",
"0.6826366",
"0.6805358"... | 0.7843585 | 2 |
Creates a table with profile information. | def show_profile(self, aggregate=0):
s = ''
if aggregate == 0:
s += '%50s\t %7s \t %4s \t %4s \t %s \n' % ("call", "time", "#sol", "#call", "location")
s += '-' * 100 + '\n'
for tm, key in sorted((t, k) for k, t in self.timestats.items()):
term, locati... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_schema(self): \n q = (\"CREATE TABLE IF NOT EXISTS \" + \\\n \"profiles (username text, body text, epoch numeric)\",)\n for x in q: self.cursor.execute(x)\n self.conn.commit()",
"def create_table(self):\n pass",
"def create_table(self, repo, table, params):\n... | [
"0.75132346",
"0.7041197",
"0.6883866",
"0.68042874",
"0.667752",
"0.65733296",
"0.65512705",
"0.6545101",
"0.6514953",
"0.65127397",
"0.65013385",
"0.6498644",
"0.6493005",
"0.6491383",
"0.6481848",
"0.64752764",
"0.64375293",
"0.64184505",
"0.63713443",
"0.6357026",
"0.6349... | 0.0 | -1 |
Given input and target, it returns onehot encoding form | def convert_to_one_hot_labels(input, target, val=0):
tmp = input.new(target.size(0), target.max() + 1).fill_(-1)
tmp.scatter_(1, target.view(-1, 1), 1.0)
# for some activation functions, e.g. relu
if val == 0:
ret = (tmp + 1) / 2
# for some activation functions, e.g. tanh
if val == ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_one_hot_encoding(target_data):\n target_data = target_data.squeeze()\n n_class = len(np.unique(target_data))\n res = np.eye(n_class)[target_data.astype(int)]\n return res",
"def one_hot_encode(y, out_size):\n n = len(y)\n oh = np.zeros((n, out_size))\n oh[range(n), y] ... | [
"0.81693745",
"0.76902974",
"0.76337856",
"0.763148",
"0.75926006",
"0.75651914",
"0.75068235",
"0.7502144",
"0.7479327",
"0.74774253",
"0.74637264",
"0.741777",
"0.74170744",
"0.74142945",
"0.74105936",
"0.7311816",
"0.73084605",
"0.72973084",
"0.7291329",
"0.7266173",
"0.72... | 0.7634374 | 2 |
This function is provided by instructor to get cifar or mnist dataset using torchvision.dataset package | def load_data(one_hot_labels=False, normalize=False, flatten=True, data_dir=None, cifar=False, full=True, tiny=False,
val=0):
if data_dir is None:
data_dir = './data'
if cifar is not None and cifar:
print('* Using CIFAR')
cifar_train_set = datasets.CIFAR10(data_dir + '/ci... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_datasets(data):\n train_dataset, test_dataset = None, None\n data_dir = '../data'\n\n if data == 'fmnist':\n transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.2860], std=[0.3530])])\n train_dataset = datasets.FashionMNIST(data_dir, train=True, downl... | [
"0.76766676",
"0.75385934",
"0.7449148",
"0.7220129",
"0.72050947",
"0.7101542",
"0.69663155",
"0.69628024",
"0.69498754",
"0.6922246",
"0.6910327",
"0.68956625",
"0.6893303",
"0.6890772",
"0.684377",
"0.6840554",
"0.6813392",
"0.680247",
"0.67767084",
"0.67741853",
"0.673338... | 0.0 | -1 |
Retrieves all flashcards in ascending order (max 250 at a time) or using basic pagination returns `qty` flashcards occuring after `start`. | def retrieve_all_flashcards(start: int=0, qty:int=None):
qty = 250 if qty == None else qty
with sqlite3.connect(current_app.config['DB']) as db:
c = db.cursor()
c.execute("""
SELECT
id,
title,
description,
source,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cards(query_param):\n return _query_scryfall(query_param)",
"def get_all(self, start_at, limit, order=None):",
"def get_cards(shop, redas=None, skip=0, limit=40):\n connection = pymongo.MongoClient(MONGO_URL)\n db = connection[DB]\n\n selector = {'shops.' + shop: {'$exists': 1}}\n if red... | [
"0.5929701",
"0.56348145",
"0.5483227",
"0.52740085",
"0.5245021",
"0.5158718",
"0.51479316",
"0.5110173",
"0.5082926",
"0.5076762",
"0.5074356",
"0.5069807",
"0.50454044",
"0.5042",
"0.50204206",
"0.5005391",
"0.50021154",
"0.50005645",
"0.49998033",
"0.4996675",
"0.49888542... | 0.822305 | 0 |
Parse results filename in format 7, restricted to the ho basis special case, but allowing for natural orbitals built on this basis. | def parser(filename):
regex = re.compile(
# prolog
r"run(?P<run>\w+)"
##r"\-(?P<code_name>((mfdn)|(obscalc-ob))[^\-]*)"
r"\-(?P<descriptor>"
# descriptor contents
r"Z(?P<Z>\d+)\-N(?P<N>\d+)"
r"\-(?P<interaction>.+)\-(?P<coulomb>\d)"
r"\-(?P<truncation... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_results(self):\n for line in self.file_dic['output'].splitlines():\n if line.startswith(' * GAMESS VERSION = '):\n temp = line.split('=')[1]\n temp = temp.split('*')[0]\n self.version = temp.strip()\n\n if line[1... | [
"0.6099494",
"0.58512676",
"0.58459806",
"0.57934743",
"0.5758889",
"0.5653699",
"0.5639124",
"0.55673873",
"0.5560741",
"0.5470509",
"0.5454175",
"0.54439193",
"0.5409063",
"0.5407918",
"0.5395332",
"0.53548986",
"0.5347291",
"0.53218514",
"0.5285737",
"0.5282547",
"0.528069... | 0.5843057 | 3 |
rightpad a string with zeros to the given length | def _rzfill(string, to_len):
if len(string) > to_len:
raise ValueError("string is already longer than to_len")
return string + '0' * (to_len - len(string)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def r_pad(arg, length):\n if length <= len(arg):\n return arg\n else:\n return arg + \" \" * (length - len(arg))",
"def rightpad(field, length):\r\n field = str(field)\r\n field_length = len(field)\r\n if field_length>length:\r\n field = field[:length]\r\n if field_length<l... | [
"0.8048082",
"0.8042724",
"0.8026424",
"0.78363734",
"0.78341615",
"0.76930463",
"0.7581309",
"0.74894637",
"0.73814124",
"0.7381266",
"0.7312482",
"0.7301371",
"0.72033757",
"0.71582454",
"0.7100653",
"0.70349735",
"0.7018298",
"0.70045847",
"0.6971008",
"0.6954787",
"0.6918... | 0.8118524 | 0 |
simple base conversion using the RFC4648 base32 alphabet | def _base32_to_hex(base32):
ALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ234567'
x = 0
for digit in str(base32.upper().strip(' ')):
x = x * len(ALPHABET) + ALPHABET.index(digit)
return hex(x).lstrip('0x').rstrip('L').upper() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def baseEncode(number, base=36):\n if base == 10:\n return str(number)\n if not isinstance(number, int):\n raise TypeError('number must be an integer')\n alphabet='0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n if base > 62 or base <=1:\n print(\"base should be b... | [
"0.694653",
"0.6893029",
"0.68493384",
"0.6781506",
"0.6748476",
"0.6748476",
"0.6748476",
"0.6748476",
"0.673473",
"0.67151284",
"0.66982746",
"0.66775656",
"0.6672081",
"0.66119474",
"0.66039515",
"0.6559059",
"0.6518085",
"0.6504039",
"0.64883107",
"0.64749783",
"0.6462858... | 0.68664634 | 2 |
Confirms that programming should take place | def _program_confirm(self, slot, base32_key, require_button):
if int(slot) not in (1, 2):
return tkMessageBox.showerror("Error", "Please Choose a slot")
try:
_base32_to_hex(base32_key.replace(' ', ''))
except ValueError:
return tkMessageBox.showerror(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solveOneStep(self):\n ### Student code goes here\n return True",
"def entrance_exam(self):\n status = False\n tool = ProgrammingTool.create(self.PROGRAMMING_TOOL)\n if tool.connect(self.target_name):\n status = entrance_exam(tool, self.register_map)\n ... | [
"0.66171014",
"0.6451814",
"0.6279466",
"0.62419975",
"0.6218109",
"0.6213138",
"0.62026155",
"0.6154495",
"0.6097345",
"0.6082555",
"0.6067573",
"0.60469735",
"0.5939188",
"0.5937973",
"0.5904631",
"0.5901756",
"0.587484",
"0.58664036",
"0.5863362",
"0.5831017",
"0.5813148",... | 0.0 | -1 |
Once we get here, things get destructive | def _program_key(self, slot, base32_key, require_button):
try:
config = self.parent.yk.init_config()
except (AttributeError, yubico.yubikey_usb_hid.usb.USBError):
return tkMessageBox.showerror(
"Error",
"No YubiKey detected"
)
c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def finalise(self):",
"def cleanup(self):",
"def cleanup(self):",
"def cleanup(self):",
"def clean_up(self):\n\t\tpass",
"def _clean_up(self):",
"def cleanup(self):\r\n pass",
"def cleanup():",
"def cleanup(self):\r\n pass",
"def cleanup(self):\r\n pass",
"def cleanup (self):\n... | [
"0.7717137",
"0.76449364",
"0.76449364",
"0.76449364",
"0.7408459",
"0.73973453",
"0.7345347",
"0.7338911",
"0.7309913",
"0.7309913",
"0.7275345",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.72413766",
"0.... | 0.0 | -1 |
callback for the help>help pulldown | def _help_dialogue(self):
webbrowser.open('https://github.com/ldrumm/yubikey-totp-gui/wiki') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def help(self):",
"def help(self):",
"def Help(self, event):\n Help(self)",
"def help():\n \n pass",
"def help():",
"def cb_help( self, ):\r\n # this shows how to run stuff in the helper -- call thru queue, post to queue\r\n #help_file = self.parameters.help_file\r\n ... | [
"0.80186397",
"0.80186397",
"0.7731719",
"0.7697429",
"0.7684272",
"0.7594509",
"0.7521115",
"0.7521115",
"0.7504964",
"0.74518096",
"0.7404527",
"0.7398889",
"0.7305916",
"0.73051256",
"0.72600025",
"0.7231249",
"0.72197986",
"0.72022885",
"0.7184661",
"0.7184661",
"0.717605... | 0.6912986 | 39 |
callback for the help>about pulldown | def _about_dialogue(self):
webbrowser.open('https://github.com/ldrumm/yubikey-totp-gui') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _helpmenu_about():\n self.helpindex = Toplevel(self.master)\n self.helpindex.title(\"About\")\n self.helpindex.geometry(\"500x300\")\n self.helpindex.label()",
"def on_about(self, event):\n pass",
"def OnButtonAboutHelpButton(self, event):\r\n\t\twebbrowse... | [
"0.765356",
"0.7577206",
"0.74981856",
"0.7448139",
"0.7448139",
"0.7319513",
"0.72994196",
"0.72841066",
"0.7245511",
"0.7188178",
"0.71726924",
"0.71114415",
"0.70453995",
"0.70418257",
"0.7026243",
"0.7026243",
"0.7003405",
"0.69978666",
"0.69692993",
"0.69655603",
"0.6960... | 0.6496328 | 65 |
callback for the help>about pulldown | def _credits_dialogue(self):
credits_dialogue = _Credits(self)
self.root.wait_window(credits_dialogue.top) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _helpmenu_about():\n self.helpindex = Toplevel(self.master)\n self.helpindex.title(\"About\")\n self.helpindex.geometry(\"500x300\")\n self.helpindex.label()",
"def on_about(self, event):\n pass",
"def OnButtonAboutHelpButton(self, event):\r\n\t\twebbrowse... | [
"0.7653562",
"0.75776154",
"0.7498668",
"0.744767",
"0.744767",
"0.7320218",
"0.729864",
"0.72840995",
"0.7245795",
"0.71876717",
"0.7171748",
"0.71118313",
"0.70450485",
"0.70416677",
"0.70259655",
"0.70259655",
"0.70030093",
"0.6997717",
"0.6969709",
"0.69647825",
"0.696079... | 0.0 | -1 |
callback for the edit>Program YubiKey pulldown Opens a new configuration window, blocking until exit. | def _program_key(self):
prg_dialogue = _ProgrammingWindow(self)
self.root.wait_window(prg_dialogue.top) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_configuration(self,event):\n configDevFrame = Single_deviceconf(parent=self, ID=996)\n configDevFrame.Centre()\n configDevFrame.Show()\n configDevFrame.ShowModal()\n configDevFrame.Destroy()",
"def launch_configtool():\r\n from PyQt4 import QtGui\r\n from freesee... | [
"0.61349726",
"0.60930395",
"0.56927663",
"0.5672919",
"0.5658804",
"0.56028754",
"0.5590743",
"0.5581867",
"0.55574065",
"0.5545362",
"0.55315197",
"0.5530267",
"0.5511534",
"0.5434221",
"0.5433302",
"0.5420036",
"0.54124206",
"0.538076",
"0.5372468",
"0.5334774",
"0.5318711... | 0.5807202 | 2 |
Event handler for keypress events. | def keypress(self, event):
events = {
'1': lambda: self.slot.set(1),
'2': lambda: self.slot.set(2),
'6': lambda: self.digits.set(6),
'8': lambda: self.digits.set(8),
}
try:
events[event.keysym]()
except KeyError:
pas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _on_key_press(self, event):",
"def key_press_event(self, event):\n pass",
"def ev_KEYDOWN(self, event):",
"def ev_KEYUP(self, event):",
"def _on_key_release(self, event):",
"def ev_keydown(self, event: KeyDown) -> None:",
"def on_key_event(self, key):\n pass",
"def _handle_key_press... | [
"0.83731693",
"0.8224903",
"0.7860711",
"0.78414875",
"0.78065944",
"0.76585764",
"0.75980836",
"0.75778073",
"0.7518489",
"0.7457421",
"0.7428157",
"0.73604447",
"0.7314259",
"0.7306808",
"0.72179407",
"0.7211457",
"0.7195942",
"0.7187176",
"0.7184973",
"0.71583706",
"0.7148... | 0.7696556 | 5 |
Tries to detect a pluggedin YubiKey else alerts user | def detect_yubikey(self):
try:
self.yk = yubico.find_yubikey()
self.version.set("Version:%s" % self.yk.version())
self.serial.set("Serial:%s" % self.yk.serial())
except yubico.yubikey.YubiKeyError:
self.version.set("No YubiKey detected")
self.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detect_infrared():\n try:\n count = 0\n while True:\n if GPIO.input(PIN_NO) == True:\n count += 1\n print('[+] Detected ' + str(count))\n output_sound()\n send_message()\n time.sleep(2)\n except Exception ... | [
"0.5550443",
"0.54399776",
"0.5384098",
"0.5384029",
"0.535033",
"0.5321742",
"0.5321742",
"0.527354",
"0.5258836",
"0.52223456",
"0.5207884",
"0.5207635",
"0.5153576",
"0.5151934",
"0.5151934",
"0.5151934",
"0.5151934",
"0.5151934",
"0.5151934",
"0.5151934",
"0.5151934",
"... | 0.7784908 | 0 |
Create an OATH TOTP OTP and return it as a string (to disambiguate leading zeros). This is ripped straight out of yubico's commandline script, `yubikeytotp`. Credit due. | def _make_totp(self):
secret = struct.pack('> Q', int(time.time()) / DEFAULT_STEP).ljust(64, chr(0x0))
response = self.yk.challenge_response(secret, slot=self.slot.get())
# format with appropriate number of leading zeros
fmt = '%.' + str(self.digits.get()) + 'i'
totp_str = fmt % ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createOTP():\n\t code = []\n\t for i in range(6):\n\t\t code.append(random.randint(0,9))\n\t return \"\".join(str(code) for c in code)",
"def get_generate_root_otp():\n if vault_version_ge(\"1.10.0\"):\n test_otp = \"BMjzW3wAsEzINXCM05Wbas3u9zSl\"\n elif vault_version_ge(\"1.0.0\"):\n ... | [
"0.79286695",
"0.73375225",
"0.7077653",
"0.69100636",
"0.6877625",
"0.67218035",
"0.6552476",
"0.62827194",
"0.6226551",
"0.6221451",
"0.60806763",
"0.6037466",
"0.5912459",
"0.58949953",
"0.58866847",
"0.588235",
"0.5861816",
"0.5819198",
"0.58083165",
"0.57993615",
"0.5791... | 0.7274214 | 2 |
example action using the internationalization operator T and flash rendered by views/default/index.html or views/generic.html | def index():
#Have to create LOGIN
#serverxmpp.init()
return dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index(request, *args, **kwargs):\n return Response({\"message\":\"Nobody expects the spanish inquisition!\"})",
"def index():\n return dict(message=T('Hello World'))",
"def index():\n response.flash = \"Welcome to Myapp!\"\n return dict(message=T('Hello World'))",
"def index_en(request):\n ... | [
"0.6669253",
"0.6106264",
"0.60288846",
"0.58851135",
"0.5849926",
"0.5769206",
"0.57516533",
"0.5746587",
"0.56890476",
"0.56517714",
"0.5640681",
"0.5632036",
"0.5626597",
"0.5620084",
"0.5610544",
"0.55801964",
"0.5567253",
"0.5565134",
"0.5561535",
"0.5552105",
"0.5483386... | 0.0 | -1 |
Converts a twodimensional grid with onedimensional coordinate variables to an array where each grid value is associated with its coordinates. | def grid2triple(x, y, z, msg=None, meta=False):
# todo: Revisit for handling of "meta" argument
# Basic sanity checks
if z.ndim != 2:
raise DimensionError(
"ERROR grid2triple: `z` must be two dimensions !\n")
if isinstance(x, xr.DataArray):
x = x.values
if isinstance(y,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_numpy_array_2D(self):\n wx = []\n wy = []\n for wp in self.waypoints:\n wx.append(wp.location.x)\n wy.append(wp.location.y)\n return np.array([wx, wy])",
"def _getLatLonGrid(self, lats, lons):\n numLats, numLons = lats.shape\n\n #coordGrid = ... | [
"0.7030514",
"0.6824461",
"0.66414434",
"0.659834",
"0.6587743",
"0.65657645",
"0.6543118",
"0.6542288",
"0.6476765",
"0.6476106",
"0.6450794",
"0.62825483",
"0.62591165",
"0.62475705",
"0.62113106",
"0.6190677",
"0.61894315",
"0.6177104",
"0.61621207",
"0.6114646",
"0.610391... | 0.0 | -1 |
Display the Entry text value. | def save(self):
fname = self.dir_saving+str(self.folder)+'/colours.txt'
if not os.path.isfile(fname):
self.file_com = open(fname, 'w')
else:
print 'warning this person has an objects file in its dir, I will rewrite it.'
self.file_com = open(fname, 'w')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def displayText(self):\n if self.entryWidget.get().strip() == \"\":\n tkMessageBox.showerror(\"Tkinter Entry Widget\", \"Enter a text value\")\n else:\n self.file_com.write(self.entryWidget.get().strip()+'\\n')",
"def print_entry(text):\n print \"Text entered: \\n '%s'\" % t... | [
"0.76484877",
"0.6926207",
"0.6598252",
"0.6590535",
"0.65623057",
"0.6552865",
"0.6468197",
"0.64450836",
"0.64450836",
"0.64450836",
"0.64450836",
"0.64450836",
"0.6352432",
"0.6344944",
"0.6339035",
"0.6331184",
"0.6292854",
"0.62803704",
"0.6258875",
"0.62545305",
"0.6250... | 0.0 | -1 |
Display the Entry text value. | def displayText(self):
if self.entryWidget.get().strip() == "":
tkMessageBox.showerror("Tkinter Entry Widget", "Enter a text value")
else:
self.file_com.write(self.entryWidget.get().strip()+'\n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_entry(text):\n print \"Text entered: \\n '%s'\" % text",
"def value (self):\r\n return self.entry.get()",
"def display_entry(self, entry):\n border = '-' * 50\n print(border)\n print('Employee: {}'.format(entry.employee_name))\n print('Task Name: {}'.format(entry.... | [
"0.6926207",
"0.6598252",
"0.6590535",
"0.65623057",
"0.6552865",
"0.6468197",
"0.64450836",
"0.64450836",
"0.64450836",
"0.64450836",
"0.64450836",
"0.6352432",
"0.6344944",
"0.6339035",
"0.6331184",
"0.6292854",
"0.62803704",
"0.6258875",
"0.62545305",
"0.62507486",
"0.6148... | 0.76484877 | 0 |
Create and configure connexion app. | def create_app(env):
connexion_app = connexion.App(__name__, specification_dir='openapi/',
options={'swagger_url': '/swagger'})
app = connexion_app.app
env_config_class_map = {
'prod': 'config.Prod',
'testing': 'config.Testing',
'dev': 'config.Dev'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_app(self):\r\n self.app = Flask(__name__, instance_relative_config=True)\r\n\r\n # Init the secret key of the app -it is a must for flask to run\r\n self.app.config.from_mapping(\r\n SECRET_KEY='!ZNeverSayNever116Z!',\r\n MONGODB_SETTINGS= {'host': 'mongodb://l... | [
"0.74086744",
"0.71356195",
"0.71240467",
"0.7108809",
"0.7074317",
"0.7031925",
"0.70253813",
"0.7023434",
"0.7022537",
"0.7013904",
"0.70036703",
"0.69989914",
"0.69644254",
"0.6951114",
"0.69442946",
"0.693363",
"0.6925931",
"0.6923495",
"0.6898156",
"0.68892235",
"0.68874... | 0.7772478 | 0 |
Initialize the network. A variable size network with only fully connected layers and SELU activations after all but the last layer. | def __init__(self, sizes, final=None, batchnorm=False, dropout=0.0):
super(MLP, self).__init__()
self.layers = nn.ModuleList()
# If there is only one input dimension, everything is fine
if sizes[0] == 1:
self.layers.append(nn.Linear(sizes[0], sizes[1]))
# For multip... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialise_network(self):\n raise NotImplementedError",
"def initialize_network(self):\n self.sess = tf.InteractiveSession()\n sys.stderr.write(\"------\\n\")\n self.model.create_model()\n self._initialize_trainer()\n self.sess.run(tf.initialize_all_variables())\n ... | [
"0.72223186",
"0.7183338",
"0.7149344",
"0.70532566",
"0.68841815",
"0.685267",
"0.6812993",
"0.67817575",
"0.67806387",
"0.6703794",
"0.6695307",
"0.6632749",
"0.65558106",
"0.65533847",
"0.655",
"0.651694",
"0.6497365",
"0.646424",
"0.64635956",
"0.64514226",
"0.64335364",
... | 0.0 | -1 |
Train a network on data with Adam. | def train(net, x, y, loss_func=nn.MSELoss(), epochs=50, batchsize=64,
**kwargs):
opt = torch.optim.Adam(net.parameters(), **kwargs)
n_samples = x.size(0)
for epoch in tqdm(range(epochs), desc='epochs'):
# Shuffle training data
p = torch.randperm(n_samples).long()
xp = x[p]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _train(self, dataset):\n net = buildNetwork(\n dataset.params_length,\n self._default_hidden_layers,\n 1 # a binary classifier only requires one output layer\n )\n ds = SupervisedDataSet(dataset)\n trainer = BackpropTrainer(net, ds)\n trainer... | [
"0.7142611",
"0.70283115",
"0.6980313",
"0.6946319",
"0.6933361",
"0.6874179",
"0.68680966",
"0.6864175",
"0.6826276",
"0.6806006",
"0.6795536",
"0.6792484",
"0.6745369",
"0.6721245",
"0.6689094",
"0.6658409",
"0.66518384",
"0.6641448",
"0.66338944",
"0.66302085",
"0.6597788"... | 0.0 | -1 |
Right Call/Contact Center Monitoring written in Python | def main(transcribe_bucket_name, mp3_bucket_name):
s3 = boto3.resource('s3')
for bucket in s3.buckets.all():
if bucket.name == transcribe_bucket_name:
for key in bucket.objects.all():
if key.key.endswith('.json'):
r = {}
# Get referenc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verifyActionCenterRts():\n pass",
"def process_calls():\n try:\n sdplus_api = API(os.environ['SDPLUS_ADMIN'], 'http://sdplus/sdpapi/')\n if not sdplus_api:\n raise KeyError\n except KeyError:\n print('Windows environment varible for \"SDPLUS_ADMIN\" (the API key for s... | [
"0.5548838",
"0.5275075",
"0.52695197",
"0.52552813",
"0.5224158",
"0.5212015",
"0.52037525",
"0.519015",
"0.516678",
"0.5134479",
"0.5118978",
"0.5107609",
"0.51021135",
"0.5101119",
"0.5096831",
"0.5074387",
"0.5034377",
"0.49731573",
"0.49478325",
"0.49477607",
"0.4943996"... | 0.0 | -1 |
Handle the command line. | def parse_args(arglist):
help = dedent("""
Currently this can start with ROIs defined as a surface label on
fsaverage, labels defined on each subject's native surface, ROIs
defined on the high-res volume in Freesurfer space, or a statistical
volume from a subject-level analysis.
You can always ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n args = parse_args()\n process_args(args)",
"def main(args):\n cli = CLI()\n # Check arguments\n cli.parse_arguments(args)",
"def cli():\n config, auth, execute_now = read_command_line_arguments()\n main(config, auth, execute_now)",
"def main():\n\n try:\n args.han... | [
"0.74971783",
"0.71494687",
"0.71189487",
"0.6985615",
"0.6935053",
"0.6935053",
"0.6898837",
"0.68239295",
"0.68239295",
"0.67813313",
"0.67738324",
"0.67629313",
"0.67573655",
"0.67573655",
"0.67573655",
"0.67573655",
"0.67573655",
"0.67573655",
"0.67573655",
"0.67573655",
... | 0.0 | -1 |
Verify that the proper recommendation class is used (in this case MinimizeIngredients) | def test_minimize_recommendation(self, mocker):
all_prop = mocker.patch(
'saana_lib.recommendation.MinimizeIngredients.all',
new_callable=mocker.PropertyMock,
return_value={"onion": 0, "flax": 2}
)
_ = MinimizeRecommendation(patient_id()).as_list()
all... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_set_scored_recommendations_post(self):\n pass",
"def test_recommender(self):\n\n self._build_sample_graph()\n\n # set skill sa score to 1.0 and skill sb score to 0.5\n measure_sa = competency.SuccessRateCompetencyMeasure.load(\n self.user_id, self.sa.id)\n m... | [
"0.595414",
"0.58778185",
"0.5792868",
"0.5701177",
"0.569256",
"0.56817424",
"0.55536056",
"0.5547504",
"0.5527662",
"0.551033",
"0.5493342",
"0.54928374",
"0.54863137",
"0.54805166",
"0.54494816",
"0.54334074",
"0.54104257",
"0.5401707",
"0.54013485",
"0.5377127",
"0.536532... | 0.56267285 | 6 |
Verify the content of the list being returned | def test_prioritize_recommendation(self, mocker):
mocker.patch(
'saana_lib.recommendation.PrioritizeIngredients.all',
new_callable=mocker.PropertyMock,
return_value={"onion": 0, "flax": 2}
)
get_ingr_mock = mocker.patch(
'saana_lib.recommendation.R... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_list(check):",
"def test_list(self):\n self.assertEqual(1, len(self.response.data))",
"def test_get_list(self):\n pass",
"def test_list(self):\n pass",
"def test_list(self):\n pass",
"def testList(self):\n response = requests.get(url=self.url)\n hea... | [
"0.7559709",
"0.7377934",
"0.72857034",
"0.70505047",
"0.70505047",
"0.69433063",
"0.6929412",
"0.6905284",
"0.6885966",
"0.68247575",
"0.67836255",
"0.67389417",
"0.67317593",
"0.6656165",
"0.6630503",
"0.66243565",
"0.66194046",
"0.6611757",
"0.6585201",
"0.65682197",
"0.65... | 0.0 | -1 |
This test verifies both, that the correct property is called, and check the content values being returned | def test_avoid_recommendation_values(self, mocker, datetime_mock):
all_prop = mocker.patch(
'saana_lib.patient.AvoidIngredients.all',
new_callable=mocker.PropertyMock,
return_value={"onion", "flax"}
)
get_ingr_mock = mocker.patch(
'saana_lib.recomm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_properties_get(self):\n pass",
"def test_getContent(self):\n self.assertEquals(\n self.successResultOf(self.testObject.getContent()), 'somecontent')",
"def test_instance_of_property_methods_are_displayed(self):\n self.assertContains(self.response, \"<td>a_property</td>\... | [
"0.7652187",
"0.7556218",
"0.7029322",
"0.6970644",
"0.67729867",
"0.6761271",
"0.6690784",
"0.66681176",
"0.66422534",
"0.66307294",
"0.66168284",
"0.659183",
"0.6556387",
"0.6556158",
"0.6489865",
"0.6480734",
"0.64655095",
"0.6452075",
"0.6446517",
"0.64337194",
"0.6402229... | 0.0 | -1 |
Do not return anything, modify nums inplace instead. first, use a variable to represent the value of original num and also define a variable to store the possible upper num. | def nextPermutation(self, nums) -> None:
def helper(a, i):
while i > 0:
for j in range(i - 1, a - 1, - 1):
if nums[j] < nums[i]:
nums[j], nums[i] = nums[i], nums[j]
nums[j + 1:] = reversed(nums[j + 1:])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nextPermutation(self, nums: List[int]) -> None:\n '''\n 思路：下一个最大的数字，这个思想其实总是不明确，今天总结一下\n 原数组\n 1 2 7 4 3 1\n 下一个排列是\n 1 3 1 2 4 7\n 如何得到的？\n 观察原数组可以看出，如果从末尾往前看，数字逐渐变大，到了2时才减小，然后再从后往前找到第一个比2大的数字，3，然后交换2和3，再把此时3后面的数字转换一下即可\n\n '''\n\n # 交换元素\n... | [
"0.60333633",
"0.5858728",
"0.58256394",
"0.5781123",
"0.5750352",
"0.5716651",
"0.571249",
"0.5701299",
"0.56818914",
"0.56051517",
"0.5545243",
"0.5529484",
"0.55239177",
"0.55072266",
"0.54970944",
"0.549113",
"0.547449",
"0.5471782",
"0.5469615",
"0.54593915",
"0.5451816"... | 0.5503453 | 14 |
Do not return anything, modify nums inplace instead. | def nextPermutation(self, nums: List[int]) -> None:
n = len(nums)
if n > 1:
q = 0
for i in range(n - 2, -1, -1):
if nums[i] < nums[i + 1]:
nums[i], nums[i + 1] = nums[i + 1], nums[i]
break
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fn(i):\n if i == len(nums): ans.append(nums.copy())\n for j in range(i, len(nums)): \n nums[i], nums[j] = nums[j], nums[i]\n fn(i+1)\n nums[i], nums[j] = nums[j], nums[i]",
"def double_nums(num_list):",
"def remove_dups(nums):\r\n nums[:... | [
"0.70469916",
"0.67161703",
"0.66934896",
"0.6586775",
"0.6501143",
"0.6482345",
"0.6442288",
"0.6407945",
"0.6376896",
"0.6372343",
"0.63671577",
"0.6365932",
"0.63512594",
"0.6328759",
"0.6298402",
"0.62855035",
"0.62671727",
"0.62472045",
"0.62221444",
"0.6193869",
"0.6188... | 0.0 | -1 |
Trains the policy on given training data. | def train(self, X, y, domain, **kwargs):
# type: (ndarray, List[int], Domain, **Any) -> None
self.memorise(X, y, domain) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, training_data):\n pass",
"def train(self, trainData):\n pass",
"def train(self, training_data, cfg, **kwargs):\n pass",
"def train (self, data, options={}):\n dir_name = list(data.keys())[0]\n relations = list(data.values())[0]\n if \"relation\" in opti... | [
"0.72467244",
"0.6953124",
"0.6876966",
"0.68157506",
"0.671062",
"0.66017306",
"0.64371353",
"0.6391807",
"0.631205",
"0.61942077",
"0.618187",
"0.6178154",
"0.6176209",
"0.6142582",
"0.61402553",
"0.61247325",
"0.6084632",
"0.6078516",
"0.60584986",
"0.6033105",
"0.6020605"... | 0.0 | -1 |
Predicts the next action the bot should take after seeing x. This should be overwritten by more advanced policies to use ML to predict the action. Returns the index of the next action | def predict_action_probabilities(self, tracker, domain):
# type: (DialogueStateTracker, Domain) -> (float, Optional[int])
x = self.featurize(tracker, domain)
logger.debug('Current tracker state [\n\t{}]'.format(
"\n\t".join(["{}".format(e) for e in self.featurizer.decode_features(x, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(self, x):\n self.eval()\n self.forward(x)\n predicted = np.argmax(self.inputs[-1], axis=1)\n return predicted",
"def predict_step(self, x):\n\n input_x = self.session.graph.get_operation_by_name(\"input_x\").outputs[0]\n predictions_op = self.session.graph.ge... | [
"0.6715775",
"0.66838646",
"0.6628312",
"0.66075236",
"0.657193",
"0.6548782",
"0.6548782",
"0.6548782",
"0.6548782",
"0.6533695",
"0.64771754",
"0.64747757",
"0.6376165",
"0.635955",
"0.63558674",
"0.6354287",
"0.635243",
"0.63053787",
"0.62928694",
"0.6230337",
"0.6215346",... | 0.62475294 | 19 |
Adds the commit to the commits array if it doesn't already exist, and returns the commit's index in the array. | def add_commit(self, commit):
sha1 = commit.hex
if sha1 in self._commits:
return self._commits[sha1]
title, separator, body = commit.message.partition("\n")
commit = {
'explored': False,
'sha1': sha1,
'name': GitUtils.abbreviate_sha1(sha1),... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_commit(self, commit_id):\n commit = next((\n commit for commit in self.repo.iter_commits()\n if commit.hexsha == commit_id\n ), None)\n if commit is None:\n raise Exception(f'Commit {commit_id} not found!')\n return commit",
"def commit_id(self... | [
"0.6170759",
"0.593805",
"0.5836253",
"0.56688523",
"0.5563604",
"0.5461908",
"0.5457891",
"0.5429918",
"0.5420427",
"0.5400795",
"0.5369754",
"0.5346436",
"0.5341644",
"0.532622",
"0.53205234",
"0.5295877",
"0.5276312",
"0.52377796",
"0.5224023",
"0.52174157",
"0.5216296",
... | 0.64689815 | 0 |
Uniquely abbreviates the given SHA1. | def abbreviate_sha1(cls, sha1):
# For now we invoke git-rev-parse(1), but hopefully eventually
# we will be able to do this via pygit2.
cmd = ['git', 'rev-parse', '--short', sha1]
# cls.logger.debug(" ".join(cmd))
out = subprocess.check_output(cmd).strip()
# cls.logger.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uniquely_shorten(string, length):\n\n if len(string) <= length and not (len(string) == length and\n string.startswith(SHORTENED_PREFIX)):\n return string\n\n h = hashlib.sha256()\n h.update(\"%s \" % length)\n h.update(string)\n hash_text = h.hexdigest... | [
"0.6464314",
"0.6410942",
"0.6409734",
"0.6156034",
"0.6136031",
"0.60622424",
"0.6015629",
"0.58965665",
"0.58919525",
"0.58904374",
"0.58792883",
"0.5815525",
"0.5790694",
"0.5786428",
"0.5748069",
"0.5668363",
"0.5649841",
"0.5627461",
"0.56117934",
"0.5600008",
"0.5596956... | 0.7369677 | 0 |
Returns a humanreadable representation of the given SHA1. | def describe(cls, sha1):
# For now we invoke git-describe(1), but eventually we will be
# able to do this via pygit2, since libgit2 already provides
# an API for this:
# https://github.com/libgit2/pygit2/pull/459#issuecomment-68866929
# https://github.com/libgit2/libgit2/pul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sha1(self) -> str:\n return self.data.sha1",
"def SHA1(self) -> _n_0_t_3[_n_0_t_9]:",
"def abbreviate_sha1(cls, sha1):\n\n # For now we invoke git-rev-parse(1), but hopefully eventually\n # we will be able to do this via pygit2.\n cmd = ['git', 'rev-parse', '--short', sha1]\n ... | [
"0.75539047",
"0.7134043",
"0.71139914",
"0.70256186",
"0.6916353",
"0.64446837",
"0.6435449",
"0.6391325",
"0.6265224",
"0.6232268",
"0.62071484",
"0.61809236",
"0.61722136",
"0.61514837",
"0.6138544",
"0.6127918",
"0.610665",
"0.6103424",
"0.6097129",
"0.6020157",
"0.596937... | 0.6566882 | 5 |
Returns all refs pointing to the given SHA1. | def refs_to(cls, sha1, repo):
matching = []
for refname in repo.listall_references():
symref = repo.lookup_reference(refname)
dref = symref.resolve()
oid = dref.target
commit = repo.get(oid)
if commit.hex == sha1:
matching.appen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_refs(self, for_push: bool) -> List[Tuple[str, str]]:\n try:\n loc = posixpath.join(self._path, \"refs\")\n res = self._connection.files_list_folder(loc, recursive=True)\n files = res.entries\n while res.has_more:\n res = self._connection.fil... | [
"0.62746805",
"0.6179788",
"0.6128106",
"0.5900156",
"0.5898155",
"0.5856927",
"0.57434326",
"0.57430595",
"0.56420195",
"0.5639147",
"0.55719423",
"0.55558074",
"0.5515448",
"0.5467317",
"0.5440578",
"0.5412632",
"0.5385987",
"0.5375013",
"0.5368968",
"0.53635335",
"0.535599... | 0.7806024 | 0 |
Find all dependencies of the given revision, recursively traversing the dependency tree if requested. | def find_dependencies(self, dependent_rev, recurse=None):
if recurse is None:
recurse = self.options.recurse
try:
dependent = self.get_commit(dependent_rev)
except InvalidCommitish as e:
abort(e.message())
self.todo.append(dependent)
self.tod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dependencies(self, revision: Dict) -> List[Dict]:\n dependency_ids = revision['auxiliary']['phabricator:depends-on']\n revisions = self.get_revisions(phids=dependency_ids)\n result = []\n for r in revisions:\n result.append(r)\n sub = self.get_dependencies(... | [
"0.73712236",
"0.6699492",
"0.6546439",
"0.6005795",
"0.5999744",
"0.55470836",
"0.55263996",
"0.54850143",
"0.54307467",
"0.5410811",
"0.5379994",
"0.5371398",
"0.53526044",
"0.5317734",
"0.529328",
"0.5279827",
"0.5243155",
"0.52289486",
"0.5216687",
"0.5189272",
"0.5168941... | 0.67338073 | 1 |
Find all dependencies of the given revision caused by the given parent commit. This will be called multiple times for merge commits which have multiple parents. | def find_dependencies_with_parent(self, dependent, parent):
self.logger.debug(" Finding dependencies of %s via parent %s" %
(dependent.hex[:8], parent.hex[:8]))
diff = self.repo.diff(parent, dependent,
context_lines=self.options.context_lines)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dependencies(self, revision: Dict) -> List[Dict]:\n dependency_ids = revision['auxiliary']['phabricator:depends-on']\n revisions = self.get_revisions(phids=dependency_ids)\n result = []\n for r in revisions:\n result.append(r)\n sub = self.get_dependencies(... | [
"0.65092677",
"0.6238572",
"0.6223792",
"0.5911293",
"0.5791384",
"0.56604487",
"0.56449336",
"0.5590153",
"0.55886996",
"0.5547283",
"0.55453926",
"0.5428988",
"0.53039974",
"0.5298665",
"0.52890277",
"0.5216612",
"0.51291275",
"0.51100653",
"0.510938",
"0.5103599",
"0.50664... | 0.6811225 | 0 |
Run git blame on the parts of the hunk which exist in the older commit in the diff. The commits generated by git blame are the commits which the newer commit in the diff depends on, because without the lines from those commits, the hunk would not apply correctly. | def blame_hunk(self, dependent, parent, path, hunk):
first_line_num = hunk.old_start
line_range_before = "-%d,%d" % (hunk.old_start, hunk.old_lines)
line_range_after = "+%d,%d" % (hunk.new_start, hunk.new_lines)
self.logger.debug(" Blaming hunk %s @ %s" %
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_blame(blameoutput):\n lines = blameoutput.split('\\n')\n i = 0\n commits = {}\n\n while i < len(lines):\n # Read a commit line and parse it.\n line = lines[i]\n i += 1\n if not line.strip():\n continue\n commitline = line.split()\n commithash = commitline[0]\n lineno_then = ... | [
"0.6053283",
"0.6032853",
"0.57691056",
"0.5739654",
"0.5558605",
"0.54753315",
"0.54249126",
"0.53682005",
"0.5311587",
"0.52980185",
"0.5267507",
"0.52285916",
"0.5224669",
"0.5176965",
"0.5174912",
"0.51497334",
"0.51410127",
"0.5134518",
"0.5132892",
"0.5132451",
"0.51252... | 0.7433148 | 0 |
Navigate to the tree or blob object pointed to by the given target path for the given commit. This is necessary because each git tree only contains entries for the directory it refers to, not recursively for all subdirectories. | def tree_lookup(self, target_path, commit):
segments = target_path.split("/")
tree_or_blob = commit.tree
path = ''
while segments:
dirent = segments.pop(0)
if isinstance(tree_or_blob, pygit2.Tree):
if dirent in tree_or_blob:
tre... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def traverse_tree(tree, thisFolder, path, submission):\n\n # Get files directly underneath this folder.\n blobs = tree.blobs\n thisFolderName = tree.name\n\n # Add this folder to the path.\n path = os.path.join(path, thisFolderName)\n print(path)\n\n for blob in blobs:\n filepath = os.p... | [
"0.5894933",
"0.55579513",
"0.5489392",
"0.52907765",
"0.528364",
"0.5157724",
"0.5122504",
"0.51111794",
"0.50728655",
"0.4995954",
"0.49644312",
"0.486692",
"0.4840334",
"0.48367405",
"0.48296806",
"0.4813238",
"0.47609875",
"0.47581476",
"0.472949",
"0.4682747",
"0.4671142... | 0.7929258 | 0 |
Multiplies a chain of matrices (2d ndarrays) All matrices must have dimensions compatible with matrix multiplication. This function actually calls the dot() method of the objects passed in as arguments. It thus handles any object that provides a dot() method that accepts 2D ndarrays. We also try to call dot_left(), in ... | def mmul(*args):
#if not out is None and (args.count == 2 and out in args or args[-1] is out):
# raise
res = args[0]
for x in args[1:]:
try:
res = x.dot_left(res)
except:
res = res.dot(x)
#Since, for some reason, the method version of dot() does ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrix_dot(*args):\n rval = args[0]\n for a in args[1:]:\n rval = tm.dot(rval, a)\n return rval",
"def matrix_dot(*args):\r\n rval = args[0]\r\n for a in args[1:]:\r\n rval = theano.tensor.dot(rval, a)\r\n return rval",
"def _mul(*args):\n\treturn functools.reduce(numpy.dot,... | [
"0.6464176",
"0.64443016",
"0.64034724",
"0.6394492",
"0.6346506",
"0.63325864",
"0.63120514",
"0.62841994",
"0.6244109",
"0.6220131",
"0.62185293",
"0.61864954",
"0.6177195",
"0.61623335",
"0.61421657",
"0.6108951",
"0.60923254",
"0.60776734",
"0.6048294",
"0.60069627",
"0.5... | 0.0 | -1 |
Calculates the scalar product for the ancilla, expecting the arguments in matrix form. Equivalent to trace(dot(H(a), b)) | def adot(a, b):
return sp.inner(a.ravel().conj(), b.ravel()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trace_dot(a, b):\r\n return np.sum(a * b)",
"def _mul(*args):\n\treturn functools.reduce(numpy.dot, args)",
"def matmul(a, b):\n raise NotImplementedError",
"def matrix_dot(*args):\r\n rval = args[0]\r\n for a in args[1:]:\r\n rval = theano.tensor.dot(rval, a)\r\n return rval",
... | [
"0.6860679",
"0.6431614",
"0.6235833",
"0.61052155",
"0.60941714",
"0.60521656",
"0.6050547",
"0.60273427",
"0.6012209",
"0.59840417",
"0.59786654",
"0.5951647",
"0.5924648",
"0.5924604",
"0.59234643",
"0.5921385",
"0.58611935",
"0.58481747",
"0.58075315",
"0.5792245",
"0.577... | 0.0 | -1 |
Calculates the scalar product for the ancilla, expecting the arguments in matrix form. Equivalent to trace(dot(a, b)) | def adot_noconj(a, b):
return sp.inner(a.T.ravel(), b.ravel()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trace_dot(a, b):\r\n return np.sum(a * b)",
"def _mul(*args):\n\treturn functools.reduce(numpy.dot, args)",
"def matrix_dot(*args):\r\n rval = args[0]\r\n for a in args[1:]:\r\n rval = theano.tensor.dot(rval, a)\r\n return rval",
"def dot_product(a,b):\n return sum(pairwise_mult(a,b... | [
"0.7579812",
"0.7087431",
"0.69432503",
"0.6658414",
"0.66481644",
"0.6605811",
"0.6450666",
"0.636946",
"0.6345714",
"0.6341091",
"0.62828994",
"0.62696874",
"0.6254947",
"0.6234855",
"0.62276524",
"0.62057537",
"0.620463",
"0.6201436",
"0.6200419",
"0.61829895",
"0.61677045... | 0.0 | -1 |
Matrix conjugate transpose (adjoint). This is just a shortcut for performing this operation on normal ndarrays. | def H(m, out=None):
if out is None:
return m.T.conj()
else:
out = sp.conjugate(m.T, out)
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transjugate(A):\n return conjugate(transpose(A))",
"def conjugate(self) -> JaggedArray:\n return self._unitary_op(np.conjugate)",
"def conjugate(x):\n if len(list(x.size())) == 2:\n z = torch.zeros(2, x.size()[1], dtype=torch.double, device=x.device)\n z[0] = x[0]\n z[1] =... | [
"0.70892566",
"0.69537246",
"0.6742829",
"0.65784675",
"0.65714926",
"0.6512788",
"0.645505",
"0.6390343",
"0.6361083",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
"0.6300163",
... | 0.51606166 | 73 |
Randomizes a complex matrix in place. | def randomize_cmplx(x, a=-0.5, b=0.5, aj=-0.5, bj=0.5):
x[:] = (((b - a) * sp.random.ranf(x.shape) + a)
+ 1.j * ((bj - aj) * sp.random.ranf(x.shape) + aj))
return x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex_randn(shape):\n return np.random.randn(*shape) + 1j * np.random.randn(*shape)",
"def generate_random_matrix(dim):\n\n A = np.complex128(np.random.random([dim, dim]))\n A_adjoint = A.conj().T\n\n P = A @ A_adjoint\n P += np.identity(len(P))\n\n P_inverse = np.linalg.inv(P)\n\n ret... | [
"0.7110527",
"0.6787168",
"0.62844926",
"0.6281481",
"0.5847896",
"0.58324844",
"0.578896",
"0.57016677",
"0.56990147",
"0.56330025",
"0.55559",
"0.553823",
"0.5475703",
"0.5463606",
"0.54351777",
"0.5429479",
"0.5408215",
"0.5368029",
"0.5358951",
"0.5351161",
"0.5349964",
... | 0.55754113 | 10 |
Return the matrix square root of a hermitian or symmetric matrix Uses scipy.linalg.eigh() to diagonalize the input efficiently. | def sqrtmh(A, ret_evd=False, evd=None):
if not evd is None:
(ev, EV) = evd
else:
ev, EV = la.eigh(A) #uses LAPACK ***EVR
ev = sp.sqrt(ev) #we don't require positive (semi) definiteness, so we need the scipy sqrt here
#Carry out multiplication with the diagonal matrix of eigenva... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trans_hellinger(m):\n m = asmatrix(m)\n row_sums = sum(m, axis=1)\n result = sqrt(m / row_sums)\n return result",
"def _symmetric_matrix_square_root(mat, eps=1e-10):\n # Unlike numpy, tensorflow's return order is (s, u, v)\n s, u, v = linalg_ops.svd(mat)\n # sqrt is unstable around 0, just use... | [
"0.62265146",
"0.6111763",
"0.60754913",
"0.6025185",
"0.5924092",
"0.57053465",
"0.56531864",
"0.56375873",
"0.56003076",
"0.5589603",
"0.5574254",
"0.55350095",
"0.5526857",
"0.55079013",
"0.5506939",
"0.5501033",
"0.54983324",
"0.5481793",
"0.5479065",
"0.5479065",
"0.5477... | 0.66119474 | 0 |
Return the matrix square root of a hermitian or symmetric positive definite matrix Uses a Cholesky decomposition, followed by a QR decomposition, and then Nwewton iteration to obtain a polar form UH, with H Hermitian p.d. and | def sqrtmpo(A, out=None):
R = la.cholesky(A)
R = la.qr(R, overwrite_a=True, mode='r')
#FIXME: NOTFINISHED
assert False
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Rouwenhorst(rho, sig_z, num):\n import numpy as np\n p = (1+rho)/2.0\n q = (1+rho)/2.0\n psi = ((num-1)**0.5)*sig_z\n \n z = np.linspace(-psi, psi, num)\n \n T = np.array([[p, 1-p], [1-q, q]])\n \n if num == 2:\n return [z, T]\n elif num > 2:\n for i in range(3, n... | [
"0.6257454",
"0.61807615",
"0.6158862",
"0.60760975",
"0.59674543",
"0.59514034",
"0.5946448",
"0.5878034",
"0.5824738",
"0.5823747",
"0.57836276",
"0.5773169",
"0.57698154",
"0.57347274",
"0.57316196",
"0.5720248",
"0.5710236",
"0.5703914",
"0.5693833",
"0.5690064",
"0.56817... | 0.0 | -1 |
Compute the inverse of a triangular matrix Uses the corresponding LAPACK routine. | def invtr(A, overwrite=False, lower=False):
trtri, = la.lapack.get_lapack_funcs(('trtri',), (A,))
inv_A, info = trtri(A, lower=lower, overwrite_c=overwrite)
if info > 0:
raise sp.LinAlgError("%d-th diagonal element of the matrix is zero" % info)
if info < 0:
raise ValueErro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse(self):\n self.check_square()\n\n\n N = self.rows\n\n inverse = make_matrix(N, N)\n\n # Solve on a per-column basis using Ax = b formalism\n for j in range(N):\n b = make_matrix(N, 1)\n b[j, 0] = 1\n\n x = self.solve_linear_system(b)\n\... | [
"0.74353004",
"0.7380224",
"0.7320343",
"0.7228165",
"0.7201508",
"0.7059015",
"0.69849044",
"0.6935947",
"0.6908033",
"0.6890019",
"0.6872973",
"0.687151",
"0.6863323",
"0.6860544",
"0.6846122",
"0.68341386",
"0.68189293",
"0.68002075",
"0.674202",
"0.6693195",
"0.6651067",
... | 0.68603367 | 14 |
Efficient inversion of positive definite matrices using Cholesky decomposition. NOT YET WORKING | def invpo(A, out=None, lower=False):
t = la.cholesky(A, lower=lower)
print sp.allclose(sp.dot(H(t), t), A)
#a, lower = la.cho_factor(A, lower=lower) #no.. we need a clean answer, it seems
potri, = la.lapack.get_lapack_funcs(('potri',), (A,))
inv_A, info = potri(t, lower=lower, overwri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Cholesky_Derivs(InvV,m_points):\n #get some of the dimensions from the inputs\n m = InvV.n\n d = InvV.d\n\n #initialise the required array\n dM_dL = np.zeros([m,d,d,d,d])\n \n #firstly loop through each map/component\n for j in range(m):\n #now want to loop over each of the Chole... | [
"0.70008886",
"0.6660287",
"0.6586627",
"0.6476201",
"0.636285",
"0.63269204",
"0.62721646",
"0.6265825",
"0.6253143",
"0.6099577",
"0.60407466",
"0.60191023",
"0.5977014",
"0.59703875",
"0.5964205",
"0.5907188",
"0.58999556",
"0.5897118",
"0.5881195",
"0.58727616",
"0.586985... | 0.52888644 | 99 |
Implements the BiCGSTAB method for isomorphic operations. The BiCGSTAB method is used to solve linear equations Ax = b. Should the vectors x, b be isomorphic to some other objects, say matrices x' and b', with corresponding opeator A' (for example, via the ChoiJamiolkowski isomorphism), the method can similarly be carr... | def bicgstab_iso(A, x, b, MVop, VVop, max_itr=500, atol=1E-14, rtol=1E-14):
r_prv = b - MVop(A, x)
r0 = r_prv.copy()
rho_prv = 1
alpha = 1
omega_prv = 1
v_prv = sp.zeros_like(x)
p_prv = sp.zeros_like(x)
for i in xrange(max_itr):
rho = sp.trace(sp.dot(r0, r_prv... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_bicgstab(matvec: Callable,\n b: Any,\n ridge: Optional[float] = None,\n **kwargs) -> Any:\n if ridge is not None:\n matvec = _make_ridge_matvec(matvec, ridge=ridge)\n return jax.scipy.sparse.linalg.bicgstab(matvec, b, **kwargs)[0]",
"def Matrix... | [
"0.6539457",
"0.6492956",
"0.59676135",
"0.5878155",
"0.58677775",
"0.570458",
"0.5635726",
"0.5610093",
"0.5430438",
"0.5374911",
"0.5323662",
"0.5261384",
"0.5253508",
"0.52354103",
"0.5204675",
"0.51971155",
"0.51891965",
"0.5166504",
"0.513837",
"0.51158535",
"0.51150143"... | 0.5772146 | 5 |
Create a category for that party. | def create_category(party_id: PartyID, title: str) -> TourneyCategory:
party = DbParty.query.get(party_id)
if party is None:
raise ValueError('Unknown party ID "{}"'.format(party_id))
category = TourneyCategory(party.id, title)
party.tourney_categories.append(category)
db.session.commit()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_category():\n category = Category(name='testcategory', description=\"\", fee=DEFAULT_FEE)\n category.save()\n return category",
"def create_new_wallet_category(self):\n wallet_category_vals = self._build_new_wallet_category_vals()\n return self.env['wallet.category'].create(wall... | [
"0.6914065",
"0.67545795",
"0.6537486",
"0.65007126",
"0.6460365",
"0.62982243",
"0.6271935",
"0.6229216",
"0.6228962",
"0.6087871",
"0.5959873",
"0.59403336",
"0.5935428",
"0.5925022",
"0.5839894",
"0.58252794",
"0.58077806",
"0.57977724",
"0.5763211",
"0.5750037",
"0.573666... | 0.76143277 | 0 |
Move a category upwards by one position. | def move_category_up(category: TourneyCategory) -> None:
category_list = category.party.tourney_categories
if category.position == 1:
raise ValueError('Category already is at the top.')
popped_category = category_list.pop(category.position - 1)
category_list.insert(popped_category.position - 2... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_category_down(category: TourneyCategory) -> None:\n category_list = category.party.tourney_categories\n\n if category.position == len(category_list):\n raise ValueError('Category already is at the bottom.')\n\n popped_category = category_list.pop(category.position - 1)\n category_list.i... | [
"0.7810339",
"0.71507776",
"0.70979744",
"0.7040622",
"0.70031214",
"0.700075",
"0.6951428",
"0.6938323",
"0.6885611",
"0.6773141",
"0.67571497",
"0.6626251",
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"0.65745205",
"0.6459161",
"0.64488965",
"0.6434402",
"0.63261694",
"0.6297202",
"0.6264247",
"0.622065... | 0.8217802 | 0 |
Move a category downwards by one position. | def move_category_down(category: TourneyCategory) -> None:
category_list = category.party.tourney_categories
if category.position == len(category_list):
raise ValueError('Category already is at the bottom.')
popped_category = category_list.pop(category.position - 1)
category_list.insert(popped... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_category_up(category: TourneyCategory) -> None:\n category_list = category.party.tourney_categories\n\n if category.position == 1:\n raise ValueError('Category already is at the top.')\n\n popped_category = category_list.pop(category.position - 1)\n category_list.insert(popped_category.... | [
"0.75041527",
"0.6625059",
"0.65953684",
"0.6541835",
"0.65137774",
"0.6502901",
"0.64917004",
"0.6411511",
"0.63754165",
"0.63680816",
"0.63534594",
"0.634604",
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"0.6307063",
"0.6192082",
"0.6185564",
"0.6164168",
"0.6129639",
"0.60469884",
"0.6000192",
"0.600018... | 0.80289805 | 0 |
Return the category with that id, or `None` if not found. | def find_category(category_id: TourneyCategoryID) -> Optional[TourneyCategory]:
return TourneyCategory.query.get(category_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_category(id):\n\n category = get_db().execute('SELECT * FROM categories WHERE id = ?', (id, )).fetchone()\n\n if not category:\n abort(404, \"category id {0} does not exist.\".format(id))\n\n return category",
"async def get_category(cls, session: AsyncSession, id: int) -> Optional[Categ... | [
"0.8413986",
"0.82415974",
"0.7956866",
"0.7917487",
"0.78595597",
"0.73798203",
"0.73384565",
"0.72064275",
"0.71930254",
"0.6999618",
"0.67847186",
"0.6735789",
"0.6639301",
"0.65112734",
"0.650825",
"0.6481206",
"0.6460801",
"0.6449573",
"0.63940597",
"0.63577557",
"0.6324... | 0.7904236 | 4 |
Return the categories for this party. | def get_categories_for_party(party_id: PartyID) -> Sequence[TourneyCategory]:
return TourneyCategory.query \
.filter_by(party_id=party_id) \
.order_by(TourneyCategory.position) \
.all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def categories(self):\n\t\treturn self._categories",
"def categories(self):\n return self._data[\"categories\"]",
"def categories(self):\n return self.__categories",
"def categories(self):\n return self._categories",
"def getCategories(self):\r\n return self.categories",
"def ... | [
"0.7996981",
"0.7990833",
"0.79048777",
"0.78893447",
"0.7860822",
"0.78507435",
"0.7831972",
"0.78282094",
"0.7793402",
"0.7686937",
"0.7667401",
"0.76551235",
"0.7599832",
"0.7522415",
"0.7482043",
"0.7397668",
"0.73637855",
"0.73436093",
"0.7312128",
"0.72793543",
"0.72627... | 0.66241163 | 54 |
Set up an interceptor so all grpc calls will have the apikey added on the header, in order to authenticate. | def set_interceptor(self, apikey):
self.header_interceptor = \
interceptor.header_adder_interceptor(
'lc-api-key', apikey
)
try:
self.intercept_channel = grpc.intercept_channel(
self.channel, self.header_interceptor)
except ValueError as e:
raise Exception("Attempted to connect on termninated cli... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def authenticate(self, api_key):\n self.headers['x-rapidapi-key'] = api_key",
"def __init__(self, api_key):\n self._api_key = api_key\n self.headers = {\n \"hibp-api-key\": api_key,\n \"user-agent\": \"haveibeenpywned.py\",\n }\n \"\"\"Dict of additional h... | [
"0.6686443",
"0.63640404",
"0.59382933",
"0.5867886",
"0.5780181",
"0.5739476",
"0.57209116",
"0.57013947",
"0.56940454",
"0.569231",
"0.56693566",
"0.56687033",
"0.5631621",
"0.56137",
"0.5556618",
"0.55513924",
"0.5533995",
"0.5528315",
"0.55266106",
"0.5516029",
"0.5476392... | 0.79679054 | 0 |
The function add key to registry under 'REG_PATH' | def persistent(name, value):
try:
_winreg.CreateKey(_winreg.HKEY_CURRENT_USER, REG_PATH)
registry_key = _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, REG_PATH, 0,
_winreg.KEY_WRITE)
_winreg.SetValueEx(registry_key, name, 0, _winreg.REG_SZ, value)
_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, key_path=''):\n super(WinRegistryKey, self).__init__()\n self._key_path = key_paths.JoinKeyPath([key_path])",
"def WriteRegValue(hiveKey, key, name, data, typeId=win32con.REG_SZ):\n try:\n keyHandle = win32api.RegOpenKeyEx(hiveKey, key, 0, win32con.KEY_ALL_ACCESS)\n win3... | [
"0.67100567",
"0.6677133",
"0.6297388",
"0.62430936",
"0.6082676",
"0.6067533",
"0.6060717",
"0.600192",
"0.5982344",
"0.5917521",
"0.59152794",
"0.59032106",
"0.5898263",
"0.5894519",
"0.58920854",
"0.5866869",
"0.5840783",
"0.5834587",
"0.58304256",
"0.5813845",
"0.57618916... | 0.59483933 | 9 |
The function sends data to server, and prints msgs from other clients. | def main():
try:
# persistent('infected', os.path.abspath(__file__)) # DON'T REMOVE
# rename me to client.py
# os.rename(__file__, os.getcwd() + "/client1.py")
# print os.path.abspath(__file__)
global my_socket
my_socket = socket.socket(socket.AF_INET, socket.SOCK_S... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n # send data to client\n currProcessID = os.getpid()\n print \"PID: %s sending echo message to server: %s\" % (currProcessID,\n ECHO_MSG)\n sentDataLen = self.sock.send(ECHO_MSG)\n print \"Sent: %d chara... | [
"0.7056971",
"0.7019039",
"0.68477523",
"0.6810195",
"0.6644971",
"0.6635883",
"0.66154855",
"0.66057706",
"0.65976816",
"0.65917546",
"0.6570916",
"0.6568058",
"0.6564347",
"0.654368",
"0.6525813",
"0.6509941",
"0.6500165",
"0.6489766",
"0.64577127",
"0.6444351",
"0.64402354... | 0.6016732 | 72 |
Writes results to a file | def write_to_file(file_name, bds, parameters=None, success=None, result=None, add_head=False, overwrite=False):
if overwrite:
f = open(file_name, "w")
else:
f = open(file_name, "a")
# adds header for the results file
if add_head:
head_str = "Iteration"
params = {}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_result(self, file_name):\n f = file(file_name, \"w\")\n f.write(self.m_result)\n f.close()",
"def write_results_to_disk(self, result_path,results):\n with open(result_path+\"/results.txt\",\"w+\") as out:\n\n for query_num in results:\n for doc_num ... | [
"0.77442235",
"0.77054715",
"0.7367398",
"0.7360259",
"0.73494154",
"0.73246366",
"0.7308568",
"0.72313744",
"0.7201102",
"0.71957",
"0.7158928",
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"0.7059509",
"0.7049528",
"0.69777733",
"0.69493216",
"0.688521",
"0.6858873",
... | 0.6562216 | 50 |
returning all elements names from ``ImmunizationRecommendation`` according specification, with preserving original sequence order. | def elements_sequence(cls):
return [
"id",
"meta",
"implicitRules",
"language",
"text",
"contained",
"extension",
"modifierExtension",
"identifier",
"patient",
"date",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def itemnames():\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\n return(g)",
"def elemen... | [
"0.56782556",
"0.56028837",
"0.5595492",
"0.5593947",
"0.55300343",
"0.5521369",
"0.55187535",
"0.55180925",
"0.5463728",
"0.54546034",
"0.54065096",
"0.538096",
"0.538058",
"0.5376563",
"0.5376563",
"0.53717583",
"0.5366831",
"0.53619134",
"0.5345186",
"0.53447956",
"0.53373... | 0.56116945 | 1 |
returning all elements names from ``ImmunizationRecommendationRecommendation`` according specification, with preserving original sequence order. | def elements_sequence(cls):
return [
"id",
"extension",
"modifierExtension",
"vaccineCode",
"targetDisease",
"contraindicatedVaccineCode",
"forecastStatus",
"forecastReason",
"dateCriterion",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def itemnames():\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\n return(g)",
"def get_na... | [
"0.55996305",
"0.5533983",
"0.5532082",
"0.5531071",
"0.551983",
"0.55109936",
"0.5510303",
"0.5445824",
"0.54457086",
"0.54309005",
"0.54169387",
"0.5382732",
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"0.5324852",
"0.5321274",
"0.5321274",
"0.53174865",
"0.53172743",
"0.53057075",
"0.52920... | 0.5134457 | 46 |
returning all elements names from ``ImmunizationRecommendationRecommendationDateCriterion`` according specification, with preserving original sequence order. | def elements_sequence(cls):
return ["id", "extension", "modifierExtension", "code", "value"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_date_pred():\r\n \r\n date_now = dt.datetime.now()\r\n date_pred = [date_now - dt.timedelta(days=1)+dt.timedelta(days=i) for i in range(8)]\r\n month_pred = [item.month for item in date_pred]\r\n day_pred = [item.day for item in date_pred]\r\n \r\n return date_pred,month_pred,day_pred"... | [
"0.5164994",
"0.49957657",
"0.49790373",
"0.49350888",
"0.489302",
"0.4817644",
"0.47879577",
"0.4780576",
"0.4754007",
"0.4715894",
"0.46958432",
"0.46928817",
"0.46879193",
"0.46861127",
"0.46854797",
"0.4663666",
"0.46342486",
"0.4606411",
"0.45946944",
"0.4579715",
"0.457... | 0.0 | -1 |
default cal index from 19000101 | def __init__(self, code, start_date="1900-01-01", end_date="2020-01-01"):
base = Base()
self.datas = base.getData(
code=code, start_date=start_date, end_date=end_date)
self._index = 0
self.period = 14 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setDefaultIndex():\r\n return 0",
"def dayofyear(self) -> Index:\n warnings.warn(\n \"`dayofyear` will return int32 index instead of int 64 index in 4.0.0.\",\n FutureWarning,\n )\n return Index(self.to_series().dt.dayofyear)",
"def day(self) -> Index:\n ... | [
"0.625253",
"0.6234539",
"0.61408883",
"0.5848705",
"0.58286357",
"0.57312787",
"0.57171273",
"0.5658349",
"0.556045",
"0.5558227",
"0.555326",
"0.55059904",
"0.54892826",
"0.54743004",
"0.540458",
"0.5399076",
"0.53972256",
"0.53660876",
"0.53427213",
"0.5341637",
"0.5278321... | 0.0 | -1 |
Compute time for arrival of 2 successive Poisson processes. | def successive_poisson(tau1, tau2, size=1):
# Draw samples out of first exponential distribution: t1
t1 = np.random.exponential(tau1, size)
# Draw samples out of second exponential distribution: t2
t2 = np.random.exponential(tau2, size)
return t1 + t2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inter_arrival_times(self):\n # this function returns arrival times between two subsequent tuples in ms\n # task mean_inter_arrival_time std_inter_arrival_time\n if self.inter_arrival_time is None:\n if self.tuple_arrival is None:\n self.tuple_arrivals()\n ... | [
"0.6605994",
"0.64463836",
"0.5987773",
"0.5685406",
"0.5586563",
"0.5566638",
"0.55001855",
"0.54996055",
"0.5490612",
"0.5451096",
"0.5409021",
"0.53857464",
"0.5382504",
"0.53800315",
"0.52867806",
"0.5285191",
"0.52186906",
"0.51881415",
"0.5177398",
"0.51758695",
"0.5167... | 0.5467813 | 10 |
Callback when front server is connected. | def OnFrontConnected(self) -> None:
self.gateway.write_log("行情服务器连接成功")
self.login() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_connect(self):\n print('Client connected!')",
"def on_connect():\n print(\"User connected!\")",
"def connected(self):\n manager = self.manager()\n self.log().debug(\"Register [%s] callbacks\", self.name())\n\n manager.subscribeServerCallbacks(self, self.cfg().chatimg.serve... | [
"0.7463222",
"0.69432336",
"0.686189",
"0.6838079",
"0.67208254",
"0.66620857",
"0.66216904",
"0.658405",
"0.6554587",
"0.6540486",
"0.64954203",
"0.6479373",
"0.64791733",
"0.6423165",
"0.64106476",
"0.6404848",
"0.64022064",
"0.6392826",
"0.63813555",
"0.63813555",
"0.63813... | 0.74887276 | 1 |
Callback when front server is disconnected. | def OnFrontDisconnected(self, reason: int) -> None:
self.login_status = False
self.gateway.write_log(f"行情服务器连接断开，原因{reason}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def callback_disconnect():\n # if Networking.get_instance().is_host:\n logger.warning(\"It seems that client is not connected...\")\n Networking.get_instance().disconnect()\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))",
"def on_disconnect(self):\n ... | [
"0.79246867",
"0.78729045",
"0.742912",
"0.7390743",
"0.7358495",
"0.7335591",
"0.73057854",
"0.7276291",
"0.72380644",
"0.7230405",
"0.720272",
"0.71938705",
"0.7179511",
"0.7127314",
"0.71052814",
"0.7078196",
"0.706709",
"0.7030486",
"0.6985139",
"0.68786025",
"0.6869716",... | 0.7072486 | 17 |
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